A novel nomogram and risk classification system predicting radiation pneumonitis in patients with esophageal cancer receiving radiotherapy

Journal Pre-proof A novel nomogram and risk classification system predicting radiation pneumonitis in patients with esophageal cancer receiving radiotherapy Lu Wang, MD, Shuai Liang, MS, Chengqiang Li, MS, Xindong Sun, MS, Linlin Pang, BS, Xue Meng, MD, Jinming Yu, MD PII:

S0360-3016(19)33659-4

DOI:

https://doi.org/10.1016/j.ijrobp.2019.08.024

Reference:

ROB 25899

To appear in:

International Journal of Radiation Oncology • Biology • Physics

Received Date: 3 May 2019 Revised Date:

12 August 2019

Accepted Date: 15 August 2019

Please cite this article as: Wang L, Liang S, Li C, Sun X, Pang L, Meng X, Yu J, A novel nomogram and risk classification system predicting radiation pneumonitis in patients with esophageal cancer receiving radiotherapy, International Journal of Radiation Oncology • Biology • Physics (2019), doi: https://doi.org/10.1016/j.ijrobp.2019.08.024. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Elsevier Inc. All rights reserved.

A novel nomogram and risk classification system predicting radiation pneumonitis in patients with esophageal cancer receiving radiotherapy

Short title: a novel nomogram predicting RP for EC Authors: Lu Wang MD1,2, Shuai Liang MS2, Chengqiang Li MS3, Xindong Sun MS2, Linlin Pang BS2, Xue Meng MD2*, Jinming Yu MD1,2* 1

Department of Radiation Oncology, School of Medicine, Shandong University,

Jinan, China. 2

Department of Radiation Oncology, Shandong Cancer Hospital and Institute,

Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China. 3

Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.

*Corresponding authors (also responsible for statistical analyses): Xue Meng, Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jiyan Road 440, Jinan 250117, Shandong, People’s Republic of China; Tel +86 531 67626143, Email: [email protected]. Jinming Yu, Department of Radiation Oncology, School of Medicine, Shandong University, Wenhua West Road 44, Jinan 250012, Shandong, People’s Republic of China; Department of Radiation Oncology, Shandong Cancer Hospital and Institute,

Shandong First Medical University and Shandong Academy of Medical Sciences, Jiyan Road 440, Jinan 250117, Shandong, People’s Republic of China; Tel +86 531 67626142, Email: [email protected]. Conflict of interest: None. Funding: None. Acknowledgements: The authors wish to thank patients for supporting our work and thank editors as well as reviewers for reading the manuscript.

1

Abstract

2

Purpose: We initially aimed to ascertain the application value of inflammatory

3

indexes in predicting severe acute radiation pneumonitis (SARP). Furthermore, a

4

novel nomogram and risk classification system integrating clinicopathological,

5

dosimetric and biological parameters were built to provide individualized risk

6

assessment and accurate prediction of SARP in patients with esophageal cancer (EC)

7

who received radiotherapy (RT).

8

Methods and Materials: All data were retrospectively collected from 416 EC

9

patients in two participating institutes. A novel nomogram was constructed that

10

forecasted SARP based on logistic regression analyses. The concordance index

11

(C-index), calibration curves and decision curve analyses (DCA) were used by both

12

internal and external validation to demonstrate its discriminatory and predictive

13

capacity. Moreover, a corresponding risk classification system was generated by

14

recursive partitioning analysis (RPA).

15

Results: The Subjective Global Assessment score (SGA), pulmonary fibrosis score

16

(PFS), planning target volume/total lungs volume (PTV/LV), mean lung dose (MLD)

17

and ratio of change regarding systemic immune inflammation index at 4-week (∆4w

18

SII) in the course of treatment were independent predictors of SARP and finally

19

incorporated into our nomogram. The C-index of nomogram for SARP prediction was

20

0.852, which showed superior discriminatory power (range, 0.604-0.712). Calibration

21

curves indicated favorable consistency between the nomogram prediction and the

22

actual outcomes. DCA exhibited satisfactory clinical utility. A risk classification 1

1

system was established to perfectly divide patients into three different risk groups

2

which were low-risk group (6.1%, score 0–158), intermediate-risk group (37.3%,

3

score 159–280), and high-risk group (78.9%, score>280).

4

Conclusions: SGA, PFS, PTV/LV, MLD and ∆4w SII were potential valuable

5

markers in predicting SARP. The developed nomogram and corresponding risk

6

classification system with superior prediction ability for SARP could assist in patients

7

counseling and guide to make treatment decisions.

8 9 10

Key words: esophageal cancer, radiation pneumonitis, nomogram, risk classification system

11 12 13 14 15 16 17 18 19 20 21 22 2

1

Introduction

2

Esophageal cancer (EC) has ranked as the seventh most frequently diagnosed

3

cancer and the sixth leading cause of cancer-related death in 2018 worldwide (1).

4

Radiotherapy (RT) provides the backbone of definitive and neoadjuvant therapy for

5

EC. Not to be ignored, radiation pneumonitis (RP) as a common and potentially

6

dose-limiting toxicity of thoracic irradiation usually occurs during the six months

7

after RT (2), which can not only cause chronic respiratory insufficiency and seriously

8

influence patients’ quality of life, but also lead to poor prognosis and even death (3, 4).

9

Therefore, it is imperative for EC patients undergoing RT to identify this toxicity at

10

the earliest possible time. More importantly, the accurate prediction of RP is essential

11

to facilitate individualized radiation dosing and lead to maximized therapeutic gain.

12

Emerging studies have made significant advances in comprehending the biologic

13

mechanisms of RP. One widely recognized hypothesis indicated that RP was an

14

inflammatory response to radiation injury (2, 5-8), and associated with elevated levels

15

of systemic pro-inflammatory cytokines, chemokines and bronchioalveolar immune

16

cells (6, 7, 9-12). Based on the existing evidence, we assumed that the representative

17

inflammatory status markers such as systemic immune inflammation index (SII),

18

platelet-lymphocyte

19

neutrophils-lymphocyte ratio (NLR), which were routinely measured in clinical and

20

linked with prognosis of EC (13), might have particular relationship with RP.

21

Additionally, considering the inflammatory status is not static, dynamic analysis may

22

be more informative. However, to the best of our knowledge, the application value of

ratio

(PLR),

lymphocyte-monocyte

3

ratio

(LMR)

and

1

these dynamically changing inflammation-based indexes as predictive biomarkers of

2

RP has not been evaluated yet.

3

Prior evidence demonstrated a series of clinical and dosimetric factors could predict

4

the onset and severity of RP, which were performance status, smoking history,

5

nutrition status, pre-existing lung diseases, concurrent chemotherapy and pulmonary

6

function tests (PFTs) as well as mean lung dose (MLD), percentage of lung volume

7

receiving ≥20 Gy and ≥30 Gy (V20 and V30) and planning target volume to total lung

8

volume ratio (PTV/LV) (3, 14-22). But the consensus on the comparative importance

9

of these related predictors were still unavailable at present. Furthermore, we

10

regrettably found that the majority of published dosimetric data on RP was originated

11

from patients with lung cancer (LC). Due to the specific RT planning between LC and

12

EC, the predictive factors for RP with regard to dosimetry could not be immediately

13

applied from pulmonary to esophageal tumors. But only few studies explored the

14

dosimetric risk factors of RP for EC patients. Of note, the limited predictive capacity

15

of single predictor and the existing substantial heterogeneity among different cases

16

should been perceived and paid much more attention in clinical practice.

17

Consequently, in order to individually and precisely discern RP, an accurate predictive

18

model incorporating multiple types of factors with superior clinical utility was

19

urgently needed. While, no current study focused on the issue in EC.

20

Therefore, the initial aim of this study was to investigate whether PLR, NLR, LMR

21

and SII could be potential markers to aid in prediction of RP. We also attempted to

22

identify the unique DVH metrics for EC patients with RP and furthermore defined the 4

1

optimal thresholds. More importantly, a comprehensive nomogram, which was a

2

convenient applicable predictive model integrating clinicopathological, dosimetric

3

and biological parameters, was built for individualized risk assessment and precise

4

prediction of SARP. A risk classification system was further established to

5

differentiate patients into different risk groups of RP to guide clinical treatment

6

strategies.

7 8 9

Methods and Materials Patients

10

This was a retrospective study. Between June 2012 and June 2018, a total of 416

11

EC patients who received thoracic RT in two independent institutes were

12

retrospectively reviewed. The inclusion criteria were: (1) newly diagnosed and

13

pathologically confirmed EC; (2) receipt of ≥50Gy RT with chemotherapy for a

14

curative aim; (3) availability of clinicopathological, dosimetric and laboratory data.

15

The exclusion criteria included: (1) previous history of thoracic RT; (2) lack of image

16

diagnosis when RP occurred; (3) lost to follow-up when patients could not be

17

contacted by the end of follow-up at June 2019; (4) having acute infectious disease or

18

autoimmune disease. All eligible patients were divided into either the primary or

19

validation cohort. In detail, the primary cohort consisted of 208 patients from institute

20

A, and the validation cohort comprised an independent series of 208 patients from

21

institute B. This study was approved by both Ethics Committee of institute A and B.

22

Informed consent for being part of this study and approval for data collection was 5

1

obtained from each patient.

2

Collection and definition of parameters

3

The clinicopathological information, imaging data and laboratory test results were

4

all acquired from institutes’ medical records. Specifically, clinicopathological

5

parameters included age, gender, Eastern Cooperative Oncology Group performance

6

status (ECOG PS), smoking index, diabetes history, Subjective Global Assessment

7

score (SGA), pulmonary fibrosis score (PFS) and emphysema score (PES), baseline

8

PFTs, pathological diagnosis, tumor location and length, TNM stage, RT technique,

9

radiation dose, elective nodal irradiation (ENI) and chemotherapy. The SGA was used

10

to evaluate patients’ nutritional status at the end of RT (18). The PFS and PES were

11

assessed based on computer tomography (CT) pre-RT by the criteria of Kazerooni et

12

al. (23) and Satoh et al. (24), respectively. In terms of PFTs, the commonly used

13

objective measure such as percent predicted value of forced vital capacity (FVC%),

14

percent predicted value of forced expiratory volume at 1 second (FEV1%) and

15

diffusion capacity of lung for carbon monoxide (DLCO) were collected from baseline

16

reports pre-RT. Staging classification of EC was reviewed according to 8th edition.

17

Inflammation-based parameters which were PLR, NLR, LMR and SII were obtained

18

at 4 different time points: within 1 week before RT (baseline), at 2-week (2w), 4-week

19

(4w) and 6-week (6w) during RT. The PLR, NLR, LMR and SII were calculated as

20

follows: PLR = P/L; NLR = N/L; LMR = L/M; SII = P × N/L (neutrophil count (N),

21

lymphocyte count (L), platelet count (P) and monocyte count (M)). Additionally,

22

DVH parameters were extracted from Varian treatment planning system. The PTV/LV 6

1

needed simple calculation on the basis of DVH analyses and was defined as the ratio

2

of planning target volume to total lungs volume (19).

3

Radiotherapy

4

Patients

were

treated

with

3-dimensional

conformal

RT

(3D-CRT),

5

intensity-modulated RT (IMRT) or helical tomotherapy (TOMO). The delineation of

6

target volumes and organs at risk (OARs) referred to the Radiotherapy and Oncology

7

Group (RTOG) guidelines. All RT plans were generated in the Eclipse system (Varian

8

Medical Systems, Palo Alto, CA, Version 13.5.35) or the TomoTherapy® Planning

9

Station (Accuray, Sunnyvale, CA, Hi-Art, Version 5.1.3), and delivered with 6 MV

10

photons beams. The prescribed doses of RT were 50Gy-66Gy at 1.8Gy-2Gy per

11

fraction once daily and five fractions per week. Plans were normalized to 95% of the

12

PTV received 100% of the prescribed dose. The dose constraints were defined for

13

OARs as follows: maximum point dose of spinal cord<45Gy; total lungs: V5<60%,

14

V20<30%, V30<20%, MLD<20Gy; heart: V30<40%, V40<30%.

15

Chemotherapy

16

All patients received concurrent or sequential chemotherapy based on

17

individualized treatment strategy. The chemotherapy regimens contained cisplatin

18

with fluorouracil (PF regimen) or cisplatin with paclitaxel (TP regimen), which were

19

applied widespreadly in clinical settings. The median chemotherapy cycles were 4

20

(range, 2∼6 cycles). The doses and adjustments of chemotherapy regimens followed

21

the guidelines of Chinese Society of Clinical Oncology (CSCO) and National

22

Comprehensive Cancer Network (NCCN) for EC. 7

1

Evaluation of RP

2

Patients were evaluated weekly during RT, and routinely followed up at 1 month

3

after completion of the initial treatment and then every 3 months for the first 2 year,

4

every 6 months thereafter until death. RP was diagnosed based on clinical symptoms,

5

physical examination, laboratory test and chest imaging. It was graded in accordance

6

with the Common Terminology Criteria for Adverse Events, version 4.03 (CTCAE

7

v4.03). The endpoint of this study was severe acute radiation pneumonitis (SARP)

8

defined as equal to or greater than grade 3 RP occurring within 3 months post-RT.

9

Construction and validation of nomogram and risk classification system

10

The nomogram and risk classification system were developed as follows. In the

11

primary cohort, univariate logistic regression analysis was firstly used to identify each

12

factor’s power in predicting SARP. Next, only parameters with P<0.05 could be

13

further included in multivariate logistic regression analysis. Notably, Spearman rank

14

correlation analyses were performed before multivariate analysis to avoid

15

multicollinearity between different parameters. Based on the regression coefficient of

16

each factor in multivariate analysis, a visually predictive nomogram was finally built.

17

Afterwards, the internal and external validation were conducted in the primary and

18

validation cohort, respectively, using concordance index (C-index), calibration curve

19

(1000 bootstrap resamples) and decision curve analysis (DCA). The C-index was

20

utilized to demonstrate the predictive accuracy as well as estimate the discrimination

21

ability of each factor and the nomogram. Calibration curve was generated to

22

determine whether the predicted and observed probabilities for SARP were in 8

1

concordance. DCA was performed to evaluate the clinical usefulness of the

2

nomogram. Additionally, a risk classification system for SARP was generated by

3

recursive partitioning analysis (RPA) according to the calculated total score of each

4

patient by using the nomogram.

5

Statistical analysis

6

The characteristics of patients were calculated as proportion for categorical

7

variables or mean±standard deviation for continuous variables. The differences

8

between variables were evaluated by Pearson Chi-square test or Student’s t test. The

9

optimal cut-off values of parameters were calculated using receiver operating

10

characteristic (ROC) curves. At present analysis of PFTs, less than 10% of data was

11

unavailable and further confirmed to be missing completely at random. In order to

12

ensure statistical power and avoid bias to a great extent, the missing data was properly

13

handled by pairwise deletion method referring to previous studies (25-27). All data

14

were computed using the Statistical Package for Social Science program (SPSS for

15

Windows, version 17.0, SPSS Inc., Chicago, IL) and R software (version 3.5.3). A

16

value of two-sided P< 0.05 was considered statistically significant.

17 18

Results

19

Patient characteristics and incidence of SARP

20

The detailed characteristics of enrolled population were listed in Table 1. No

21

significant differences were observed between the primary cohort and the validation

22

cohort (all P>0.05), except for RT techniques (P<0.001). It was worth noting that all 9

1

clinicopathological, dosimetric and laboratory parameters were complete but PFTs

2

indexes. Among the entire population, PFTs data was attainable for 396 patients

3

(396/416, 95.2%). Specifically, FVC%, FEV1% and DLCO were routinely monitored

4

and could be available for 188 cases (188/208, 90.4%) in the primary cohort. As for

5

the validation cohort, FVC% and FEV1% could be collected for all patients, but

6

DLCO was not obtained due to unconventional examination of lung diffusion capacity.

7

Totally, the incidence of SARP was 20.2% (42/208) and 18.8% (39/208) in the

8

primary and validation cohort, respectively (P=0.710). The median interval from the

9

completion of RT to the appearance of SARP was 52 days (range, 15–86 days).

10 11

The dynamic change of inflammatory parameter during RT and its correlation with SARP

12

The trends of inflammation-based indexes which were PLR, NLR, LMR and SII

13

during RT were plotted with respect to time in reference to RT start. The results were

14

clearly shown in Supplementary Figure 1. To further investigate the levels of PLR,

15

NLR, LMR and SII for patients with or without SARP, the entire group was divided

16

into two subgroups (SARP group and without SARP group). We found that PLR,

17

NLR and SII measurements gradually elevated over time during RT for either the

18

entire group or the two subgroups. Further analyses evidently indicated that patients

19

with SARP had a higher level of PLR, NLR and SII than those without SARP.

20

Whereas, the converse trend was exhibited in terms of LMR, which progressively

21

reduced during RT for the entire group and the two subgroups. In contrast to without

22

SARP group, SARP group had lower level of LMR. 10

1

The relationships between inflammatory indexes and SARP were reported in

2

Supplementary Table 1. We observed that although higher levels were shown in SARP

3

group, the values of PLR at baseline, 2w, 4w and 6w during RT as well as the ratio of

4

change at 2w (∆2w), 4w (∆4w) and 6w (∆6w) were no statistically significant

5

between SARP group and without SARP group (all P>0.05). Similarly, neither the

6

baseline, 2w, 4w and 6w LMR nor ∆2w, ∆4w and ∆6w LMR were significantly

7

different for the two subgroups (all P>0.05), despite lower level of LMR was

8

displayed in SARP group. Of note, we surprisedly found that the sharply increased 4w

9

(11.16±0.92 vs. 17.32±1.89, P=0.003), 6w (11.80±1.02 vs. 17.74±1.91, P=0.009) and

10

∆4w (4.24±0.45 vs. 7.07±0.96, P=0.002) NLR were extremely related to higher risk

11

of SARP. With regard to the baseline, 2w, ∆2w and ∆6w NLR, no obvious differences

12

were investigated between SARP group and without SARP group (all P>0.05). As for

13

SII, patients with SARP had much higher levels at 4w (1479.37±92.33 vs.

14

2086.05±212.97, P=0.005), 6w (1634.94±115.14 vs. 2322.93±238.41, P=0.003) and

15

∆4w (2.42±0.21 vs. 4.03±0.53, P=0.001) than those without SARP. Besides, the

16

baseline, 2w, ∆2w and ∆6w SII showed similar results between SARP group and

17

without SARP group (all P>0.05).

18

Univariate and multivariate analyses

19

The results of univariate analysis were reported in Table 2. The potential factors

20

which could predict SARP were as follows: SGA, PFS on baseline CT, PTV/LV, V5,

21

MLD, 4w, 6w and ∆4w NLR as well as 4w, 6w and ∆4w SII. Spearman’s analyses

22

were displayed in Supplementary Table 2 and demonstrated weak correlations 11

1

between these factors (r range, -1.014-0.496; all P<0.05). As shown in Table 3,

2

multivariate analysis indicated that SGA (OR: 6.790, 95%CI: 2.384-19.335, P<0.001;

3

OR: 8.354, 95%CI: 1.905-36.639, P=0.005), PFS (OR: 7.746, 95%CI: 1.998-30.033,

4

P=0.003), PTV/LV (OR: 3.837, 95%CI: 1.220-12.065, P=0.021), MLD (OR: 5.359,

5

95%CI: 1.664-17.263, P=0.005) and ∆4w SII (OR: 4.201, 95%CI: 1.299–13.586,

6

P=0.017) were independent predictors of SARP. Therefore, these factors were

7

eventually utilized to build our nomogram.

8

Construction and validation of nomogram

9

The graphic form of nomogram was shown in Figure 1. According to internal

10

validation, the C-index of our nomogram was 0.852 (95%CI: 0.792–0.910), which

11

was much higher than any other predictors (SGA: 0.712, 95%CI: 0.621–0.803; PFS:

12

0.604, 95%CI: 0.500–0.708; PTV/LV: 0.637, 95%CI: 0.549–0.724; MLD: 0.637,

13

95%CI: 0.546–0.729; ∆4w SII: 0.676, 95%CI: 0.589–0.763), and proved superior

14

discrimination ability of the model (Figure 2a). The calibration curve showed

15

favorable consistency between the predicted SARP and the actual observation (Figure

16

2b). DCA exhibited satisfactory positive net benefits of the nomogram at the threshold

17

probabilities (Figure 2c). More importantly, our results could be further supported by

18

external validation. In detail, the C-index of the nomogram for predicting SARP was

19

0.879 (95%CI: 0.820-0.937) (Figure 2d), which supported it was really an excellent

20

model with outstanding predictive capacity. The calibration curve confirmed that our

21

nomogram was well calibrated (Figure 2e). DCA demonstrated the agreeable potential

22

clinical effect of our prediction model (Figure 2f). 12

1

Risk classification system

2

A novel risk classification system for SARP was displayed in Figure 3. All patients

3

were classified into three risk groups which were the low-risk group (score 0–158),

4

intermediate-risk group (score 159–280) and high-risk group (score>280). The

5

incidence of SARP in the low-, intermediate-, and high-risk group was 6.1% (16/263),

6

37.3% (50/134) and 78.9% (15/19), respectively (P<0.001). Further analyses plenty

7

demonstrated that the incidence risk of SARP in different groups could be effectively

8

differentiated by our risk classification system.

9 10

Discussion

11

In this study, we initially demonstrated a robust relationship between inflammatory

12

indexes and SARP, and furthermore correlated clinicopathological and DVH

13

parameters with the development of SARP in EC. More importantly, for the first time,

14

a visual nomogram and risk classification system, which could be readily understood

15

by patients and physicians, were developed and validated to provide more

16

individualized and accurate prediction of SARP for EC patients treated with RT.

17

Numerous studies reported that several serum inflammatory biomarkers including

18

C-reactive protein (CRP), lactate dehydrogenase (LDH), transforming growth factor

19

(TGF-β), tumor necrosis factor (TNF-α) and interleukins (ILs) were all associated

20

with RP (28, 29). Unfortunately, owing to debatable conclusions and unconventional

21

detection, they were still not widely used in clinical settings. However, the

22

inflammatory indexes such as PLR, NLR, LMR and SII in our study, as simply 13

1

determined by neutrophils, lymphocytes, platelets and monocytes, could be routinely

2

and easily measured in the course of treatment. Our results showed that patients with

3

elevated levels of 4w, 6w and ∆4w NLR and SII were more prone to develop SARP.

4

According to further analysis, only ∆4w SII was an independent risk factor in

5

predicting RP with the most superior predictive ability than any other inflammatory

6

indexes. Therefore, ∆4w SII was selected to build nomogram. In a similar study, Lee

7

et al. (30) found that the NLR at radiological RP was a useful biomarker for

8

predicting symptomatic RP after definitive concurrent chemoradiotherapy in LC

9

patients. However, apart from NLR, PLR, LMR and SII were not paid more attention

10

in their study. Based on our findings, SII rather than NLR appeared to have more

11

superior discrimination ability to monitor RP. Consequently, further studies will need

12

to verify which inflammation index was an optimal indicator to predict RP. In addition,

13

we observed that PLR, NLR and SII increased but LMR decreased in the course of RT,

14

demonstrating progress in systemic inflammatory response. Compared with baseline

15

values, 4w, 6w and ∆4w NLR and SII during RT were found to be excellent in

16

discerning SARP, which supported the dynamic evaluation of inflammatory status

17

was essential to more precisely forecast the occurrence of RP.

18

Several clinical features have emerged as important risk factors to contribute to RP

19

progression (18, 19, 30-37). Our nomogram utilized clinically significant variables

20

which were SGA and PFS that have been demonstrated the positive relationship with

21

SARP based on our study. Overall, a vast majority of patients with EC frequently

22

experienced esophagitis in the course of RT, which resulted in long-lasting decrease of 14

1

intake and even chronic malnutrition. Nevertheless, several studies revealed that

2

malnutrition was associated with the severity of pneumonia (38-42), and even had a

3

negative effect on patients’ survival (43) and quality of life (44). Unfortunately, the

4

relationship between nutrition status and RP has not been sufficiently concerned in

5

cancer patients, especially EC. Our findings undoubtedly provided reliable evidence,

6

and demonstrated that SGA, a widely used nutrition screening tool created by Detsky

7

et al. in 1987 (39), was an independent prognostic factor of RP (P<0.001). As a result,

8

in order to avoid the appearance of RP to the greatest extent, it was indispensable to

9

attentively supervise the nutrition status of patients with EC during RT. For some

10

cases of malnutrition, enteral and parenteral nutrition should be opportunely offered

11

as needed.

12

Interestingly, the interstitial lung diseases (ILDs) such as pulmonary fibrosis (PF)

13

and emphysema (PE) were frequently observed on CT pre-RT. Ueki et al. (45) and Li

14

et al. (31) both reported that pre-existing radiological ILD was a significant risk factor

15

for RP in patients with LC after RT. As consensus guideline suggested, severe ILDs

16

were regarded as a relative contraindication for thoracic RT. However, previous

17

studies were all performed in LC, and the relationship between pre-existing ILDs and

18

RP in EC was still not clear. In this study, one of our vital findings demonstrated the

19

positive correlation between PF and RP in patients with EC, indicating that the

20

pre-existing radiological PF could contribute to increase risk of ≥grade 3 RP. Notably,

21

our results raised the important pointers that patients with thoracic tumor who had

22

pre-existing radiological ILD should be closely surveillance during RT to reduce the 15

1

incidence risk of RP. In addition, the association of RP with PE was still debated at

2

present. We found that the pre-existing radiological PE did not increase the risk of RP.

3

Similar conclusions were confirmed by Tsujino et al. (7) and Kasymjanova et al. (32).

4

However, some other studies reported that patients with severe PE had lower risk of

5

RP than those with no underlying lung disease (46, 47). This could be interpreted as

6

severe emphysema caused a decrease in the volume of the parenchyma, the total dose

7

absorbed by the lung reduced, which might lead to the low incidence of RP. Anyway,

8

based on existing evidences, PE was not an absolute contraindication for thoracic RT,

9

and patients with PE could be offered RT when clinically necessary.

10

As was known to all, PFTs have been described in previous studies for their ability

11

to predict lung toxicity from thoracic surgery, chemotherapy and radiotherapy (20-22).

12

Consequently, the most commonly used objective measurements, for example FVC%,

13

FEV1% and DLCO, were evaluated at present analyses. In contrast to conventional

14

view, our results supported that baseline FVC%, FEV1% and DLCO pre-RT were not

15

correlated with SARP. This was consistent with previous published articles to some

16

extent (48-50). Whereas, in other studies conducted by Torre-Bouscoulet et al. (20),

17

Mehta et al. (21) and Lind et al. (22), they investigated that baseline PFTs prior to RT

18

could predict the development of RP. The conflict results might be attributed to

19

limited number of studied populations, incomplete assessment of PFTs, different

20

chemotherapy regimen and RT techniques as well as inconsistent evaluation criteria of

21

RP. Therefore, more prospective, well-designed randomized controlled trials with

22

larger sample size are needed to demonstrate the role of PFTs in predicting RP. 16

1

Besides, a prospective study was conducted in 52 patients with non-small cell lung

2

cancer treated with concurrent chemoradiotherapy (CCRT) to evaluate PFTs changes

3

before and after treatment (51). It found that the most significant lung function

4

abnormalities were observed at 12 weeks post-CCRT. This had vital clinical

5

implication, suggesting clinician to perform longitudinal monitoring and follow-up

6

through PFTs in patients treated with RT. However, the longitudinal evaluation of

7

PFTs post-RT was not obtained in our study because of a retrospective review. In

8

consideration of limited information from PFTs at someone time point, more studies

9

are necessary to confirm the association between PFTs changes and RP.

10

Additionally, DVH metrics have been extensively observed and reported to be

11

correlated with RP. Tonison et al. (4) reported limiting the V20 to 23% or lower could

12

keep the risk for grade 2 or higher RP below 10% in EC patients treated with

13

chemoradiotherapy. Cho et al. (52) explored the dosimetric predictors for RP

14

following neoadjuvant chemoradiotherapy and surgery for EC, and indicated that

15

MLD but not V5, V10 and V20 was the most related parameter in predicting RP.

16

Another similar study suggested MLD ≥ 12 Gy and V30 ≥ 13% were significantly

17

correlated with an increased risk of RP (29). In a way, our results were consistent with

18

previous data. We found that compared to those with MLD ≥ 12Gy, patients with

19

MLD < 12Gy had lower risk of SARP. However, no relationship was observed

20

between V20 or V30 and RP in this study. Multivariate analysis reported that MLD

21

was an independent dosimetric indicator for RP, and has the most superior ability in

22

predicting SARP rather than V5, V20 and V30. Notably, after the introduction of 17

1

IMRT and TOMO into clinical practice, a potential negative impact of the low dose

2

lung DVH parameters on RP, particularly V5 has been paid more attention (53, 54).

3

Several studies recommended the lung V5 should be limited to <60% or <65%. But

4

the threshold value of V5 for the development of SARP was 57% in our study, which

5

was smaller than previous studies. Moreover, no significant differences were observed

6

in terms of V5 for 3D-CRT, IMRT and TOMO planning. Multivariate analysis showed

7

that V5 did not play a vital role in the development of SARP. Therefore, more

8

researches are needed to demonstrate our results not accidental, and define the

9

optimal cutoffs of dosimetric criteria for more accurate prediction of RP. Additionally,

10

PTV/LV as a novel independent predictor for SARP was also included in our

11

nomogram. It should be emphasized that the assessment of the extent of primary

12

tumor and lungs volume was essential to monitor the onset and severity of RP.

13

Although limited evidence has demonstrated that PTV/LV was significantly

14

associated with developing RP in LC (19, 55, 56), no relevant research was conducted

15

in EC. According to our findings, PTV/LV which was independent from other

16

dosimetric factors should be regarded as a novel and exceptional indicator of SARP

17

for EC. Its incorporation into our nomogram could facilitate the development in risk

18

stratification and provide more comprehensive treatment decision. Notably, PTV/LV

19

was simple to obtain on the basis of DVH analysis and could easily put to clinical

20

application. In a way, our results suggested that when optimizing RT plans for EC,

21

PTV/LV might need to be routinely evaluated and constrained.

22

It was known that a combining multifactor prediction model could contribute to 18

1

improve the prediction of RP. Nevertheless, several predictive models have been

2

tentatively established in previous studies, they were still not utilized in clinical

3

currently due to the unsatisfied ability to identify RP, and totally ignored

4

heterogeneity between different patients. However, in our study, several significant

5

variables including SGA, PFS, PTV/LV, MLD and ∆4w SII were integrated into a

6

nomogram to give an accurately individualized risk assessment of SARP for each

7

patient. The C-index of the nomogram was 0.852 and 0. 879 in the primary and

8

validation cohort, respectively, which demonstrated more superior capacity in

9

predicting RP than previous models. Furthermore, a novel risk classification system

10

was established to effectively classify the whole EC cohort into different RP risk

11

groups. Our robust analyses indicated that these developed tools showed the

12

outstanding discriminating power to forecast the occurrence of RP. Particularly, the

13

incorporated variables could be easily measured by any physician, enhancing its

14

practical utility. Although our study had many strengths, several limitations should be

15

addressed here. Firstly, the sample size was small, thus large cohort are needed to

16

further develop and validate nomogram in terms of predicting RP. Additionally,

17

because of a retrospective study, selective bias might exist in our study. Secondly, the

18

used RT techniques were somewhat different due to Institute B not yet carrying out

19

TOMO service for EC cases. But even so, given a solid based analysis, RT techniques

20

were not associated with RP, and had no impact on our results in this study. Thirdly, at

21

present, we only integrated the most vital and commonly used parameters in clinical,

22

but other valuable variables involving genomic information and radiomics might offer 19

1

additional information to promote predictive accuracy of RP. Therefore, the relevant

2

research is underway in our institute, and the results will be reported soon.

3 4

Conclusions

5

A positive correlation between ∆4w SII and SARP was observed in EC for the first

6

time. Our results suggested that clinician should closely monitor the dynamic change

7

of SII during RT to comprehensively evaluate systemic inflammatory status and

8

timely discern RP at the earliest possible time. More importantly, SGA, PFS, PTV/LV,

9

MLD and ∆4w SII were demonstrated to be significant risk factors for SARP.

10

Furthermore, using these factors, we firstly built and validated a novel nomogram and

11

corresponding risk classification system to predict SARP for EC patients, which had a

12

high degree of accuracy based on two independent cohorts from different institutes.

13

These easy-to-operated tools could assist in patients counseling, and help clinicians

14

individually identify patients at increased risk of SARP to guide personalized

15

treatment and surveillance decisions. Once our work will be further confirmed in

16

future, we encourage the nomogram and risk classification system should be widely

17

used in clinical.

18 19

References

20

1.

Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018:

21

GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers

22

in 185 countries. CA Cancer J Clin 2018;68:394-424. 20

1

2.

2 3

Bledsoe TJ, Nath SK, Decker RH. Radiation Pneumonitis. Clin Chest Med 2017;38:201-208.

3.

Palma DA, Senan S, Tsujino K, et al. Predicting radiation pneumonitis after

4

chemoradiation therapy for lung cancer: an international individual patient

5

data meta-analysis. Int J Radiat Oncol Biol Phys 2013;85:444-450.

6

4.

Tonison JJ, Fischer SG, Viehrig M, et al. Radiation pneumonitis after

7

intensity-modulated radiotherapy for esophageal cancer: institutional data and

8

a systematic review. Sci Rep 2019;9:2255.

9

5.

Morgan GW, Breit SN. Radiation and the lung: a reevaluation of the

10

mechanisms mediating pulmonary injury. Int J Radiat Oncol Biol Phys

11

1995;31:361-369.

12

6.

Roberts CM, Foulcher E, Zaunders JJ, et al. Radiation pneumonitis: a possible

13

lymphocyte-mediated

14

1993;118:696-700.

15

7.

hypersensitivity

reaction.

Ann

Intern

Med

Stenmark MH, Cai XW, Shedden K, et al. Combining physical and biologic

16

parameters to predict radiation-induced lung toxicity in patients with

17

non-small-cell lung cancer treated with definitive radiation therapy. Int J

18

Radiat Oncol Biol Phys 2012;84:e217-222.

19

8.

20 21 22

Jain V, Berman AT. Radiation Pneumonitis: Old Problem, New Tricks. Cancers (Basel) 2018;10. pii: E222.

9.

Arpin D, Perol D, Blay JY, et al. Early variations of circulating interleukin-6 and interleukin-10 levels during thoracic radiotherapy are predictive for 21

1 2

radiation pneumonitis. J Clin Oncol 2005;23:8748-8756. 10.

3 4

Trott KR, Herrmann T, Kasper M. Target cells in radiation pneumopathy. Int J Radiat Oncol Biol Phys 2004;58:463-469.

11.

Tsoutsou PG, Koukourakis MI. Radiation pneumonitis and fibrosis:

5

mechanisms underlying its pathogenesis and implications for future research.

6

Int J Radiat Oncol Biol Phys 2006;66:1281-1293.

7

12.

Nakayama Y, Makino S, Fukuda Y, et al. Activation of lavage lymphocytes in

8

lung injuries caused by radiotherapy for lung cancer. Int J Radiat Oncol Biol

9

Phys 1996;34:459-467.

10

13.

Wang L, Wang C, Wang J, et al. A novel systemic immune-inflammation index

11

predicts survival and quality of life of patients after curative resection for

12

esophageal

13

2017;143:2077-2086.

14

14.

squamous

cell

carcinoma.

J

Cancer

Res

Clin

Oncol

Glick D, Lyen S, Kandel S, et al. Impact of pretreatment interstitial lung

15

disease on radiation pneumonitis and survival in patients treated with lung

16

stereotactic

17

2018;19:e219-e226.

18

15.

body

radiation

therapy

(SBRT).

Clin

Lung

Cancer

Hayashi K, Yamamoto N, Karube M, et al. Prognostic analysis of radiation

19

pneumonitis: carbon-ion radiotherapy in patients with locally advanced lung

20

cancer. Radiat Oncol 2017;12:91.

21 22

16.

Asakura H, Hashimoto T, Zenda S, et al. Analysis of dose-volume histogram parameters

for

radiation

pneumonitis 22

after

definitive

concurrent

1 2

chemoradiotherapy for esophageal cancer. Radiother Oncol 2010;95:240-244. 17.

Nomura M, Kodaira T, Furutani K, et al. Predictive factors for radiation

3

pneumonitis in oesophageal cancer patients treated with chemoradiotherapy

4

without prophylactic nodal irradiation. Br J Radiol 2012;85:813-818.

5

18.

Ma L, Ye W, Li Q, et al. Subjective Global Assessment (SGA) score could be a

6

predictive factor for radiation pneumonitis in lung cancer patients with normal

7

pulmonary function treated by intensity-modulated radiation therapy and

8

concurrent chemotherapy. Clin Lung Cancer 2018;19:e211-e217.

9

19.

Ueyama T, Arimura T, Takumi K, et al. Risk factors for radiation pneumonitis

10

after stereotactic radiation therapy for lung tumours: clinical usefulness of the

11

planning target volume to total lung volume ratio. Br J Radiol

12

2018;91:20170453.

13

20.

Torre-Bouscoulet L, Munoz-Montano WR, Martinez-Briseno D, et al.

14

Abnormal

15

radiation-induced pneumonitis in advanced non-small cell lung Cancer. Respir

16

Res 2018;19:72.

17

21.

pulmonary

function

tests

predict

the

development

of

Mehta V. Radiation pneumonitis and pulmonary fibrosis in non-small-cell lung

18

cancer: pulmonary function, prediction, and prevention. Int J Radiat Oncol

19

Biol Phys 2005;63:5-24.

20

22.

Lind PA, Marks LB, Hollis D, et al. Receiver operating characteristic curves to

21

assess predictors of radiation-induced symptomatic lung injury. Int J Radiat

22

Oncol Biol Phys 2002;54:340-347. 23

1

23.

Kazerooni EA, Martinez FJ, Flint A, et al. Thin-section CT obtained at 10-mm

2

increments versus limited three-level thin-section CT for idiopathic pulmonary

3

fibrosis: correlation with pathologic scoring. AJR Am J Roentgenol

4

1997;169:977-983.

5

24.

6 7

Satoh K, Kobayashi T, Misao T, et al. CT assessment of subtypes of pulmonary emphysema in smokers. Chest 2001;120:725-729.

25.

8

Eekhout I, de Boer RM, Twisk JW, et al. Missing data: a systematic review of how they are reported and handled. Epidemiology 2012;23:729-732.

9

26.

Sainani KL. Dealing with missing data. PM R 2015;7:990-994.

10

27.

Baraldi AN, Enders CK. An introduction to modern missing data analyses. J

11 12

Sch Psychol 2010;48:5-37. 28.

13 14

Kong FM and Wang S. Nondosimetric risk factors for radiation-induced lung toxicity. Semin Radiat Oncol 2015;25:100-109.

29.

Hartsell WF, Scott CB, Dundas GS, et al. Can serum markers be used to

15

predict acute and late toxicity in patients with lung cancer? Analysis of RTOG

16

91-03. Am J Clin Oncol 2007;30:368-376.

17

30.

Lee YH, Choi HS, Jeong H, et al. Neutrophil-lymphocyte ratio and a

18

dosimetric factor for predicting symptomatic radiation pneumonitis in

19

non-small-cell lung cancer patients treated with concurrent chemoradiotherapy.

20

Clin Respir J 2018;12:1264-1273.

21 22

31.

Li F, Zhou Z, Wu A, et al. Preexisting radiological interstitial lung abnormalities are a risk factor for severe radiation pneumonitis in patients with 24

1

small-cell lung cancer after thoracic radiation therapy. Radiat Oncol

2

2018;13:82.

3

32.

Kasymjanova G, Jagoe RT, Pepe C, et al. Does the presence of emphysema

4

increase the risk of radiation pneumonitis in lung cancer patients? Curr Oncol

5

2018;25:e610-e614.

6

33.

Niska JR, Schild SE, Rule WG, et al. Fatal radiation pneumonitis in patients

7

with

8

2018;19:e417-e420.

9

34.

subclinical

interstitial

lung

disease.

Clin

Lung

Cancer

Xu L, Jiang J, Li Y, et al. Genetic variants of SP-D confer susceptibility to

10

radiation pneumonitis in lung cancer patients undergoing thoracic radiation

11

therapy. Cancer Med 2019.

12

35.

Zhao Y, Chen L, Zhang S, et al. Predictive factors for acute radiation

13

pneumonitis in postoperative intensity modulated radiation therapy and

14

volumetric modulated arc therapy of esophageal cancer. Thorac Cancer

15

2015;6:49-57.

16

36.

Yin M, Liao Z, Liu Z, et al. Genetic variants of the nonhomologous end

17

joining gene LIG4 and severe radiation pneumonitis in nonsmall cell lung

18

cancer patients treated with definitive radiotherapy. Cancer 2012;118:528-535.

19

37.

Takeda A, Kunieda E, Ohashi T, et al. Severe COPD is correlated with mild

20

radiation pneumonitis following stereotactic body radiotherapy. Chest

21

2012;141:858-866.

22

38.

Akuzawa N, Naito H. Nutritional parameters affecting severity of pneumonia 25

1

and length of hospital stay in patients with pneumococcal pneumonia: a

2

retrospective cross-sectional study. BMC Pulm Med 2015;15:149.

3

39.

Lee JC, Williams GW, Kozar RA, et al. Multitargeted Feeding Strategies

4

Improve Nutrition Outcome and Are Associated With Reduced Pneumonia in a

5

Level 1 Trauma Intensive Care Unit. JPEN J Parenter Enteral Nutr

6

2018;42:529-537.

7

40.

Feijo PM, Rodrigues VD, Viana MS, et al. Effects of omega-3 supplementation

8

on the nutritional status, immune, and inflammatory profiles of gastric cancer

9

patients: A randomized controlled trial. Nutrition 2019;61:125-131.

10

41.

Alifano M, Mansuet-Lupo A, Lococo F, et al. Systemic inflammation,

11

nutritional status and tumor immune microenvironment determine outcome of

12

resected non-small cell lung cancer. PLoS One 2014;9:e106914.

13

42.

14 15

Batatinha HAP, Biondo LA, Lira FS, et al. Nutrients, immune system, and exercise: Where will it take us? Nutrition 2019;61:151-156.

43.

Pan YP, Hsu TY, Lin JY, et al. Prognostic significance of low body mass index

16

and betel-quid use in the 5-year survival rates of esophageal squamous cell

17

carcinoma patients. Nutr Cancer 2018;70:1315-1321.

18

44.

Hanna L, Huggins CE, Furness K, et al. Effect of early and intensive nutrition

19

care, delivered via telephone or mobile application, on quality of life in people

20

with upper gastrointestinal cancer: study protocol of a randomised controlled

21

trial. BMC Cancer 2018;18:707.

22

45.

Ueki N, Matsuo Y, Togashi Y, et al. Impact of pretreatment interstitial lung 26

1

disease on radiation pneumonitis and survival after stereotactic body radiation

2

therapy for lung cancer. J Thorac Oncol 2015;10:116-125.

3

46.

Kimura T, Matsuura K, Murakami Y, et al. CT appearance of radiation injury of

4

the lung and clinical symptoms after stereotactic body radiation therapy

5

(SBRT) for lung cancers: are patients with pulmonary emphysema also

6

candidates for SBRT for lung cancers? Int J Radiat Oncol Biol Phys

7

2006;66:483-491.

8

47.

9

have a low risk of radiation pneumonitis following stereotactic body

10 11

Ishijima M, Nakayama H, Itonaga T, et al. Patients with severe emphysema

radiotherapy. Br J Radiol 2015;88:20140596. 48.

Wang J, Cao J, Yuan S, et al. Poor baseline pulmonary function may not

12

increase the risk of radiation-induced lung toxicity. Int J Radiat Oncol Biol

13

Phys 2013;85:798-804.

14

49.

Luna JM, Chao HH, Diffenderfer ES, et al. Predicting radiation pneumonitis in

15

locally advanced stage II-III non-small cell lung cancer using machine

16

learning. Radiother Oncol 2019;133:106-112.

17

50.

Kong FM, Hayman JA, Griffith KA, et al. Final toxicity results of a

18

radiation-dose escalation study in patients with non-small-cell lung cancer

19

(NSCLC): predictors for radiation pneumonitis and fibrosis. Int J Radiat

20

Oncol Biol Phys 2006;65:1075-1086.

21 22

51.

Torre-Bouscoulet L, Arroyo-Hernandez M, Martinez-Briseno D, et al. Longitudinal evaluation of lung function in patients with advanced non-small 27

1

cell lung cancer treated with concurrent chemoradiation therapy. Int J Radiat

2

Oncol Biol Phys 2018;101:910-918.

3

52.

Cho WK, Oh D, Kim HK, et al. Dosimetric predictors for postoperative

4

pulmonary complications in esophageal cancer following neoadjuvant

5

chemoradiotherapy and surgery. Radiother Oncol 2019;133:87-92.

6

53.

Jo IY, Kay CS, Kim JY, et al. Significance of low-dose radiation distribution in

7

development of radiation pneumonitis after helical-tomotherapy-based

8

hypofractionated radiotherapy for pulmonary metastases. J Radiat Res

9

2014;55:105-112.

10

54.

Song CH, Pyo H, Moon SH, et al. Treatment-related pneumonitis and acute

11

esophagitis in non-small-cell lung cancer patients treated with chemotherapy

12

and helical tomotherapy. Int J Radiat Oncol Biol Phys 2010;78:651-658.

13

55.

Baker R, Han G, Sarangkasiri S, et al. Clinical and dosimetric predictors of

14

radiation pneumonitis in a large series of patients treated with stereotactic

15

body radiation therapy to the lung. Int J Radiat Oncol Biol Phys

16

2013;85:190-195.

17

56.

Dang J, Li G, Ma L, et al. Predictors of grade ≥ 2 and grade ≥ 3 radiation

18

pneumonitis in patients with locally advanced non-small cell lung cancer

19

treated

20

2013;52:1175-1180.

with

three-dimensional

conformal

21 22

Figure captions 28

radiotherapy.

Acta

Oncol

1

Figure 1. Nomogram predicting the development of SARP.

2

Figure 2. (a) C-index of SGA, PFS, PTV/LV, MLD, ∆4w SII and the prediction

3

model in the primary cohort. (b) Calibration curves of the nomogram predicting

4

SARP in the primary cohort. (c) Decision curves of SGA, PFS, PTV/LV, MLD, ∆4w

5

SII and the prediction model predicting SARP in the primary cohort. (d) C-index of

6

SGA, PFS, PTV/LV, MLD, ∆4w SII and the prediction model in the validation cohort.

7

(e) Calibration curves of the nomogram predicting SARP in the validation cohort. (f)

8

Decision curves of SGA, PFS, PTV/LV, MLD, ∆4w SII and the prediction model

9

predicting SARP in the validation cohort.

10

Figure 3. RPA-generated risk classification system predicting SARP.

11 12

Summary

13

Radiation pneumonitis (RP) as a complication of radiotherapy (RT) is an urgent

14

clinical problem. We firstly constructed a novel nomogram and corresponding risk

15

classification system integrating clinicopathological, dosimetric and biological

16

information to individually and precisely predict RP in patients with esophageal

17

cancer who received RT. These easy-to-operated tools could assist in patients

18

counseling and guide to make treatment decisions.

29

Table 1. Baseline characteristics of all patients

Characteristics Age (years) ≤60 >60 Gender Male Female ECOG PS 0-1 2 Smoking (pack-year) <20 ≥20 Diabetes Yes No SGA A B C PFS on baseline CT 0-1 2-3 PES on baseline CT 0-2 3-4 Histology SCC AC Location Cervical Upper Middle Lower Length<3cm TNM stage IIA/IIB IIIA/IIIB

Total No. (%); N=416

Primary cohort No. (%); N=208

Validation cohort No. (%); N=208

174(41.83) 242(58.17)

81(38.94) 127(61.06)

93(44.71) 115(55.29)

P value 0.233

0.096 326(78.37) 90(21.63)

156(75.00) 52(25.00)

170(81.73) 38(18.27) 0.708

337(81.01) 79(18.99)

170(81.73) 38(18.27)

167(80.29) 41(19.71) 0.202

221(53.12) 195(46.88)

104(50.00) 104(50.00)

117(56.25) 91(43.75) 0.295

51(12.26) 365(87.74)

22(10.58) 186(89.42)

29(13.94) 179(86.06) 0.506

254(61.06) 129(31.01) 33(7.93)

132(63.46) 59(28.37) 17(8.17)

122(58.65) 70(33.65) 16(7.70) 0.128

340(81.73) 76(18.27)

164(78.85) 44(21.15)

176(84.62) 32(15.38) 0.093

385(92.55) 31(7.45)

188(90.38) 20(9.62)

197(94.71) 11(5.29) 0.216

399(95.91) 17(4.09)

197(94.71) 11(5.29)

202(97.12) 6(2.88) 0.252

33(7.93) 134(32.21) 164(39.43) 85(20.43) 61(14.67)

14(6.73) 75(36.06) 82(39.42) 37(17.79) 28(13.47)

19(9.13) 59(28.37) 82(39.42) 48(23.08) 33(15.87)

45(10.82) 371(89.18)

22(10.58) 186(89.42)

23(11.06) 185(88.94)

0.488 0.875

Radiation technique <0.001 3D-CRT 135(32.45) 53(25.48) 82(39.42) IMRT 230(55.29) 104(50.00) 126(60.58) TOMO 51(12.26) 51(24.52) 0(0) Radiation dose (Gy) 0.228 50-60 301(72.36) 145(69.71) 156(75.00) >60 115(27.64) 63(30.29) 52(25.00) ENI 0.127 Yes 355(85.34) 183(87.98) 172(82.69) No 61(14.66) 25(12.02) 36(17.31) Chemoradiotherapy 0.106 Concurrent 318(76.44) 166(79.81) 152(73.08) Sequential 98(23.56) 42(20.19) 56(26.92) Chemotherapy 0.507 PF regimen 112(26.92) 59(28.37) 53(25.48) TP regimen 304(73.08) 149(71.63) 155(74.52) PFTs FVC (%) 98.84±15.82 99.16±17.35 98.42±13.61 0.721 FEV1 (%) 93.84±17.28 93.79±19.12 93.90±14.60 0.963 DLCO — 21.81±4.33 — — (ml/mmHg/min) PTV/LV 0.14±0.07 0.14±0.08 0.14±0.07 0.949 V5 (%) 58.11±19.30 57.86±19.81 58.36±18.82 0.793 V20 (%) 21.27±6.63 20.91±6.63 21.63±6.63 0.264 V30 (%) 10.57±4.45 10.70±4.47 10.43±4.43 0.540 MLD (cGy) 1222.49±350.64 1223.69±361.70 1221.29±340.08 0.944 Abbreviation: ECOG PS: Eastern Cooperative Oncology Group performance status; SGA: Subjective Global Assessment score; PFS: pulmonary fibrosis score; PEF: pulmonary emphysema score; CT: computed tomography; SCC: squamous cell carcinoma; AC: adenocarcinoma; 3D-CRT: 3-dimensional conformal radiotherapy; IMRT: intensity-modulated radiotherapy; TOMO: helical tomotherapy; ENI: elective nodal irradiation; PF regimen: cisplatin+fluorouracil; TP regimen: paclitaxel+cisplatin; PFTs: pulmonary function tests; FVC: percent predicted value of forced vital capacity, FEV1: percent predicted value of forced expiratory volume at 1 second; DLCO: diffusion capacity of lung for carbon monoxide; PTV/LV: planning target volume/total lungs volume; Vx: percentage of total lungs volume receiving x Gy; MLD: mean dose of total lungs.

Table 2. Univariate analysis of clinicopathological, dosimetric and inflammatory parameters in predicting SARP Parameters Clinicopathological parameters Age (≤60 vs. >60; years) Gender (male vs. female) ECOG PS (0-1 vs. 2) Smoking (<20 vs. ≥20; pack-year) Diabetes (Yes vs. No) SGA A B C PFS on baseline CT (0-1 vs. 2-3) PES on baseline CT (0-2 vs. 3-4) Histology (SCC vs. AC) Location Cervical Upper Middle Lower Length<3cm TNM stage (IIA/IIB vs. IIIA/IIIB) Radiation technique 3D-CRT IMRT TOMO Radiation dose (50-60 vs. >60; Gy) ENI (Yes vs. No) Chemoradiotherapy (concurrent vs. sequential) Chemotherapy (PF regimen vs. TP regimen) PFTs FVC (<96% vs. ≥96%) FEV1 (<92% vs. ≥92%) DLCO (<18 vs. ≥18; ml/mmHg/min) Dosimetric parameters PTV/LV (<0.11 vs. ≥0.11) V5 (<57% vs. ≥57%) V20 (<22% vs. ≥22%)

P value

OR

95%CI

0.199 0.079 0.057 0.730 0.422

0.639 0.435 2.154 1.127 0.595

0.323-1.265 0.172-1.101 0.978-4.744 0.572-2.219 0.168-2.115

<0.001 <0.001 <0.001 <0.001 0.574 0.550

Ref. 5.059 8.137 6.190 1.360 1.519

2.314-11.059 2.678-24.720 2.367-16.190 0.465-3.982 0.385-5.993

0.315 0.116 0.150 0.161 0.406 0.804

Ref. 1.960 2.089 2.600 1.606 1.155

0.846-4.543 0.766-5.700 0.683-9.900 0.525-4.908 0.369-3.615

0.943 0.844 0.732 0.917 0.579

Ref. 1.088 1.182 1.040 0.727

0.470-2.516 0.453-3.084 0.499-2.165 0.235-2.244

0.823

1.099

0.479-2.520

0.726

1.146

0.533-2.464

0.526 0.590 0.315

0.758 1.279 1.939

0.322-1.786 0.523-3.126 0.533-7.049

0.002 0.025 0.215

3.925 2.256 1.541

1.648-9.344 1.109-4.591 0.778-3.051

V30 (<14% vs. ≥14%) 0.243 1.563 0.739-3.307 MLD (<12 vs. ≥12; Gy) 0.002 3.185 1.525-6.651 Inflammatory parameters PLR baseline (<160 vs. ≥160) 0.404 1.349 0.667-2.727 2w (<300 vs. ≥300) 0.231 1.533 0.762-3.084 4w (<200 vs. ≥200) 0.228 0.590 0.250-1.392 6w (<250 vs. ≥250) 0.081 1.955 0.920-4.157 ∆2w (<0.8 vs. ≥0.8) 0.145 1.660 0.840-3.281 ∆4w (<1.5 vs. ≥1.5) 0.336 0.711 0.356-1.423 ∆6w (<1.0 vs. ≥1.0) 0.126 1.778 0.851-3.717 NLR baseline (<3.0 vs. ≥3.0) 0.796 1.102 0.528-2.299 2w (<3.6 vs. ≥3.6) 0.323 1.452 0.693-3.044 4w (<7.0 vs. ≥7.0) <0.001 4.354 1.902-9.965 6w (<12.0 vs. ≥12.0) 0.005 2.689 1.342-5.388 ∆2w (<1.6 vs. ≥1.6) 0.231 1.533 0.762-3.084 ∆4w (<6.0 vs. ≥6.0) <0.001 4.481 2.161-9.290 ∆6w (<4.0 vs. ≥4.0) 0.211 1.556 0.779-3.108 LMR baseline (<3.5 vs. ≥3.5) 0.989 0.995 0.499-1.984 2w (<2.5 vs. ≥2.5) 0.284 0.661 0.310-1.410 4w (<1.5 vs. ≥1.5) 0.441 0.752 0.364-1.554 6w (<0.7 vs. ≥0.7) 0.709 1.177 0.501-2.765 ∆2w (<-0.6 vs. ≥-0.6) 0.180 0.627 0.318-1.240 ∆4w (<-0.6 vs. ≥-0.6) 0.166 0.600 0.291-1.236 ∆6w (<-0.8 vs. ≥-0.8) 0.726 1.146 0.533-2.464 SII baseline (<350 vs. ≥350) 0.587 0.821 0.403-1.672 2w (<1300 vs. ≥1300) 0.085 1.952 0.911-4.182 4w (<1600 vs. ≥1600) 0.002 3.049 1.514-6.141 6w (<2200 vs. ≥2200) 0.025 2.326 1.110-4.874 ∆2w (<-0.3 vs. ≥-0.3) 0.096 2.868 0.831-9.900 ∆4w (<1.5 vs. ≥1.5) <0.001 4.787 2.154-10.637 ∆6w (<5.0 vs. ≥5.0) 0.055 2.118 0.983-4.561 Abbreviation: SARP: severe acute radiation pneumonitis; OR: odds ratio; CI: confidence interval; ECOG PS: Eastern Cooperative Oncology Group performance status; SGA: Subjective Global Assessment score; PFS: pulmonary fibrosis score; PEF: pulmonary emphysema score; CT: computed tomography; SCC: squamous cell carcinoma; AC: adenocarcinoma; 3D-CRT: 3-dimensional conformal radiotherapy; IMRT: intensity-modulated radiotherapy; TOMO: helical tomotherapy; ENI: elective nodal irradiation; PF regimen: cisplatin+fluorouracil; TP regimen: paclitaxel+cisplatin; PFTs: pulmonary function tests; FVC: percent predicted value of forced vital capacity, FEV1: percent predicted value of forced expiratory volume at 1 second; DLCO:

diffusion capacity of lung for carbon monoxide; PTV/LV: planning target volume/total lungs volume; Vx: percentage of total lungs volume receiving x Gy; MLD: mean dose of total lungs; PLR: platelet-lymphocyte ratio; NLR: neutrophils-lymphocyte ratio; LMR: lymphocyte-monocyte ratio; SII: systemic immune-inflammation index; 2w: 2 weeks; 4w: 4 weeks; 6w: 6 weeks; ∆2w: the ratio of change at 2 weeks; ∆4w: the ratio of change at 4 weeks; ∆6w: the ratio of change at 6 weeks.

Table

3.

Multivariate

analysis

of

clinicopathological,

dosimetric

and

inflammatory parameters in predicting SARP Parameters P value OR 95%CI SGA <0.001 Ref. A 2.384-19.335 <0.001 6.790 B 8.354 1.905-36.639 C 0.005 1.998-30.033 7.746 PFS on baseline CT (0-1 vs. 2-3) 0.003 1.220-12.065 0.021 3.837 PTV/LV (<0.11 vs. ≥0.11) 0.625 0.218-1.788 0.381 V5 (<57% vs. ≥57%) 5.359 1.664-17.263 0.005 MLD (<12 vs. ≥12; Gy) NLR 0.878-9.747 0.080 2.926 4w (<7.0 vs. ≥7.0) 0.698-4.679 1.807 0.223 6w (<12.0 vs. ≥12.0) 0.360-3.683 1.151 ∆4w (<6.0 vs. ≥6.0) 0.812 SII 0.436-3.473 1.230 4w (<1600 vs. ≥1600) 0.696 0.668-5.977 1.998 0.216 6w (<2200 vs. ≥2200) 1.299-13.586 4.201 0.017 ∆4w (<1.5 vs. ≥1.5) Abbreviation: SARP: severe acute radiation pneumonitis; OR: odds ratio; CI: confidence interval; SGA: Subjective Global Assessment score; PFS: pulmonary fibrosis score; CT: computed tomography; PTV/LV: planning target volume/total lungs volume; V5: percentage of total lungs volume receiving 5Gy; MLD: mean dose of total lungs; NLR: neutrophils-lymphocyte ratio; SII: systemic immune-inflammation index; 4w: 4 weeks; 6w: 6 weeks; ∆4w: the ratio of change at 4 weeks.

Figure 1. Nomogram predicting the development of SARP. Abbreviation: SGA: Subjective Global Assessment; PFS: pulmonary fibrosis score; PTV/LV: planning target volume/total lungs volume; MLD: mean dose of total lungs; Δ4w SII: the ratio of change of systemic immune-inflammation index at 4 weeks during radiotherapy; SARP: severe acute radiation pneumonitis.

(a)

*

*

*

*

1.0

* 0.852

C-index

0.8 0.712 0.604

0.637 0.637

0.676

0.6 0.4 0.2

(b)

SI no I m og ra m

Δ4 w

M LD

PT V/ LV

PF S

SG A

0.0

(c)

(d)

*

*

*

*

1.0

0.642 0.662

0.879

0.665 0.680

0.6 0.4 0.2

SI no I m og ra m

Δ4 w

M LD

PT V/ LV

PF S

0.0 SG A

C-index

0.8 0.704

*

(e)

(f)

Figure 2. (a) C-index of SGA, PFS, PTV/LV, MLD, Δ4w SII and the prediction model in the primary cohort. (b) Calibration curves of the nomogram predicting SARP in the primary cohort. (c) Decision curves of SGA, PFS, PTV/LV, MLD, Δ4w SII and the prediction model predicting SARP in the primary cohort. (d) C-index of SGA, PFS, PTV/LV, MLD, Δ4w SII and the prediction model in the validation cohort. (e)

Calibration curves of the nomogram predicting SARP in the validation cohort. (f) Decision curves of SGA, PFS, PTV/LV, MLD, Δ4w SII and the prediction model predicting SARP in the validation cohort. Abbreviation: *: P<0.01; C-index: concordance index; SGA: Subjective Global Assessment score; PFS: pulmonary fibrosis score; PTV/LV: planning target volume/total lungs volume; MLD: mean dose of total lungs; Δ4w SII: the ratio of change of systemic immune-inflammation index at 4 weeks during radiotherapy.

all patients N=416

score≤158 N=263 SARP incidence: 6.1%

score>158 N=153

score≤280 N=134 SARP incidence: 37.3%

Low-risk group

Intermediate-risk group

score>280 N=19 SARP incidence: 78.9%

High-risk group

Figure 3. RPA-generated risk classification system predicting SARP. Abbreviation: RPA: recursive partitioning analysis; SARP: severe acute radiation pneumonitis.