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Rectal Cancer Outcome Study Based on Multiple NRG Oncology Clinical Trials with Random Survival Forests
S168
International Journal of Radiation Oncology Biology Physics
Author Disclosure: F. Palorini: None. A. Cicchetti: None. T. Rancati: None. C. Cozzarini: None. B. Avuzzi: None. D. Cante: None. C. Degli Esposti: None. E. Garibaldi: None. C. Iotti: None. F. Munoz: None. V. Vavassori: None. R. Valdagni: Advisory Board; Amgen Dompe`, Bayer SpA. C. Fiorino: None.
R.J. Lee,8 B.A. Erickson,9 M. Augspurger,10 E.R. Sigurdson,11 G. Lasio,6 A. Dekker,2 and Y. Xiao1; 1University of Pennsylvania, Philadelphia, PA, 2 Maastro Clinic, Maastricht, Netherlands, 3Fudan University Shanghai Cancer Center, Shanghai, China, 4King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia, 5University Hospitals Seidman Cancer Center, Cleveland, OH, 6University of Maryland, Baltimore, MD, 7 Thomas Jefferson University Hospital, Philadelphia, PA, 8Intermountain Medical Center, Murray, UT, 9Medical College of Wisconsin Department of Radiation Oncology and Clement J Zablocki VA Medical Center, Milwaukee, WI, 10Baptist Cancer Center, Jacksonville, FL, 11Fox Chase Cancer Center, Philadelphia, PA
360 Deep Convolutional Neural Networks With Transfer Learning for Rectum Toxicity Prediction in Combined Brachytherapy and External Beam Radiation Therapy for Cervical Cancer X. Zhen,1 J. Chen,2 Z. Zhong,3 B.A. Hrycushko,4 K. Albuquerque,1 L. Zhou,2 S.B. Jiang,4 and X. Gu4; 1Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, 2Department of Biomedical Engineering, Southern Medical University, Guangzhou, China, 3Department of Computer Science, Wayne State University, Detroit, MI, 4University of Texas Southwestern Medical Center, Dallas, TX Purpose/Objective(s): Neural networks have been popularly employed in image analysis due to their powerful capabilities in extracting complex features to build robust prediction models, which traditional methods are insufficient to deal with. This work aims to reveal rectum dose-toxicity relationship in cervical cancer treated with combined high-dose-rate brachytherapy (BT) and external beam radiotherapy (EBRT) by employing 1) an accurate deformable dose accumulation model and 2) a deep convolutional neural network (CNN) with transfer learning. Materials/Methods: A VGG-16 layers CNN network is first pre-trained on the large scale natural image database ImageNet to build a rectum toxicity prediction model. To fine-tune the pre-trained VGG-16 CNN network, fractional doses are converted to equivalent dose in 2-Gy fractions (EQD2) and deformably summed by a previously developed topography-preserved point-matching deformable image registration (TOP-DIR) algorithm. The accumulative EBRT+BT EQD2 doses on the rectum surface are unfolded to obtain the rectum surface dose maps (RSDM) to fine-tune the pretrained VGG-16 network. The gradient-weighted class activation maps (Grad-CAM) which highlight the discriminative regions on the RSDM are generated along with the CNN prediction model for analysis. The advantage of the proposed prediction model over dose volume metrics, i.e. D0.1cc, D1cc and D2cc, for toxicity prediction is evaluated. The D0.1/1/ 2ccvalues are extracted from the accumulative EBRT+BT EQD2 dose by the “worst-case scenario” (WS) addition method and logistic regression (LR) is performed to calculate the toxicity probability. Data from 42 cervical cancer patients treated with EBRT followed by BT were retrospectively collected for evaluation. Twelve patients complained about hematochezia were further examined with colonoscopy and scored for toxicity according to RTOG criteria. The other 30 non-toxicity patients were used as the reference group. A leave-one-out method was used to evaluate the prediction performance for both the proposed model and LR. Results: Moderate prediction results were obtained by LR using D0.1/1/2cc values with an accuracy of 59.5%, sensitivity of 52.8%, specificity of 60.0%, and AUC of 58.3. In contrast, with all layers fine-tuned in VGG-16, a satisfactory prediction performance was achieved with a total accuracy of 88.1%, sensitivity of 75%, specificity of 93.3%, and AUC of 0.96. Conclusion: The results of this work have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning. The proposed prediction model can serve as a practical tool for rectum dose and induced toxicity analysis for cervical cancer radiotherapy. Author Disclosure: X. Zhen: None. J. Chen: None. Z. Zhong: None. B.A. Hrycushko: None. K. Albuquerque: None. L. Zhou: None. S.B. Jiang: Patent/License Fees/Copyright; Sun Nuclear. X. Gu: None.
361 Rectal Cancer Outcome Study Based on Multiple NRG Oncology Clinical Trials with Random Survival Forests M. Huang,1 J. van Soest,2 H. Zhong,1 E. Ben-Josef,1 H. Geng,1 J. Wang,3 Z. Zhang,3 M. Mohiuddin,4 N.J. Meropol,5 M. Garofalo,6 P.R. Anne,7
Purpose/Objective(s): To use the machine learning Random Forest (RF) model and novel Random Survival Forest (RSF) model, to predict overall survival, local recurrence, and distant metastases for patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy followed by surgery, using data from RTOG 0822, 0247, and 0012. Materials/Methods: The 79 patients from RTOG 0822, 146 patients from RTOG 0247, and 106 patients from RTOG 0012 were included in the study. Data with missing clinical information or duplication were eliminated. Locally advanced rectal cancer patients were treated with neoadjuvant chemoradiotherapy and suggested total mesorectal excision. The following prognosis and clinical data were collected and applied for the RF and RSF method for survival analysis: gender, age, treatment arm, clinical tumor staging (cT, cN, and cM), pathological staging (pT, pN), tumor distance (TD) from the anorectal verge (<5 cm, 5-10 cm, >10 cm), adjuvant chemotherapy (chemo) status (Y/N) and type, radiation dose and treatment duration. Overall survival (OS), distant metastases (DS), and local recurrence (LR) were the outcomes for prediction. Features were ranked correspondingly for OS, LR and DM prediction. RSF incorporates RF and can rank features, and predict survival function over time. To pursue RSF for event-specific variable outcomes, the prediction result was five-fold cross-validated with RF. Results: The RF analysis stabilized at 10,000 randomized decision trees, with concordance index (C-index) of 0.75, 0.80, and 0.70 for OS, LR, and DM using RTOG 0822 data. A five-fold cross-validation was performed on the 0822 patients; the “out of bag” validation training set error was 0.30, corresponding to a C-index of 0.70. The important features for OS (high to low) are pT, TD, pN, cN, cT, adjuvant (adj) chemo, dose, cM, chemo type, surgery procedure, age. For LR, importance lists: TD, cN, adj. chemo, cT, cM, dose, age, pT, sex, surg. procedure, chemo type, pN; for DM are pN, cN, TD, pT, postsurgery complications, cM, adj chemo, dose, cT, sex, age. The RSF model also calculates survival prediction over time, 96.4%, 95.5%, 90.3%, 82.5%, 75.0% at 1-, 2-, 3-, 4-, 5-year survival rate, and aligns with the Kaplan-Meier curve. For trial 0247 and trial 0012 cohorts, OS prediction generated AUC Z 0.66 for RTOG 0012 and AUC Z 0.65 for RTOG 0247. However, the LR and DM would not be predicted in RTOG 0822 due to the uncollected P-stage information. Conclusion: RF and RSF survival analysis can predict for OS, LR, and DM outcome of locally advanced rectal cancer patients. RF/RSF method features automatic data mining. More clinical trial data for further validations of the model are needed for robustness and reliability. RF/RSF also provides an alternative machine learning method for decision support of precision medicine. Author Disclosure: M. Huang: None. J. van Soest: None. H. Zhong: None. E. Ben-Josef: NCI Board of Scientific Counselors, NCI Board of Scientific Counselors. H. Geng: None. J. Wang: None. Z. Zhang: Independent Contractor; Varian Medical System. M. Mohiuddin: None. N.J. Meropol: Patent/License Fees/Copyright; Patent/License Fees; ASCO. M. Garofalo: None. P.R. Anne’: None. R. Lee: None. B.A. Erickson: Employee; ProHealth Care, Waukesha, WI. Help to organize the annual or bi-annual Gyn Brachytherapy school -2015; American Brachytherapy Society. M. Augspurger: None. E.R. Sigurdson: None. G. Lasio: None. A. Dekker: Research Grant; Varian Medical Systems. Honoraria; Varian Medical Systems. Consultant; Varian Medical Systems. Y. Xiao: None.
International Journal of Radiation Oncology Biology Physics
Author Disclosure: F. Palorini: None. A. Cicchetti: None. T. Rancati: None. C. Cozzarini: None. B. Avuzzi: None. D. Cante: None. C. Degli Esposti: None. E. Garibaldi: None. C. Iotti: None. F. Munoz: None. V. Vavassori: None. R. Valdagni: Advisory Board; Amgen Dompe`, Bayer SpA. C. Fiorino: None.
R.J. Lee,8 B.A. Erickson,9 M. Augspurger,10 E.R. Sigurdson,11 G. Lasio,6 A. Dekker,2 and Y. Xiao1; 1University of Pennsylvania, Philadelphia, PA, 2 Maastro Clinic, Maastricht, Netherlands, 3Fudan University Shanghai Cancer Center, Shanghai, China, 4King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia, 5University Hospitals Seidman Cancer Center, Cleveland, OH, 6University of Maryland, Baltimore, MD, 7 Thomas Jefferson University Hospital, Philadelphia, PA, 8Intermountain Medical Center, Murray, UT, 9Medical College of Wisconsin Department of Radiation Oncology and Clement J Zablocki VA Medical Center, Milwaukee, WI, 10Baptist Cancer Center, Jacksonville, FL, 11Fox Chase Cancer Center, Philadelphia, PA
360 Deep Convolutional Neural Networks With Transfer Learning for Rectum Toxicity Prediction in Combined Brachytherapy and External Beam Radiation Therapy for Cervical Cancer X. Zhen,1 J. Chen,2 Z. Zhong,3 B.A. Hrycushko,4 K. Albuquerque,1 L. Zhou,2 S.B. Jiang,4 and X. Gu4; 1Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, 2Department of Biomedical Engineering, Southern Medical University, Guangzhou, China, 3Department of Computer Science, Wayne State University, Detroit, MI, 4University of Texas Southwestern Medical Center, Dallas, TX Purpose/Objective(s): Neural networks have been popularly employed in image analysis due to their powerful capabilities in extracting complex features to build robust prediction models, which traditional methods are insufficient to deal with. This work aims to reveal rectum dose-toxicity relationship in cervical cancer treated with combined high-dose-rate brachytherapy (BT) and external beam radiotherapy (EBRT) by employing 1) an accurate deformable dose accumulation model and 2) a deep convolutional neural network (CNN) with transfer learning. Materials/Methods: A VGG-16 layers CNN network is first pre-trained on the large scale natural image database ImageNet to build a rectum toxicity prediction model. To fine-tune the pre-trained VGG-16 CNN network, fractional doses are converted to equivalent dose in 2-Gy fractions (EQD2) and deformably summed by a previously developed topography-preserved point-matching deformable image registration (TOP-DIR) algorithm. The accumulative EBRT+BT EQD2 doses on the rectum surface are unfolded to obtain the rectum surface dose maps (RSDM) to fine-tune the pretrained VGG-16 network. The gradient-weighted class activation maps (Grad-CAM) which highlight the discriminative regions on the RSDM are generated along with the CNN prediction model for analysis. The advantage of the proposed prediction model over dose volume metrics, i.e. D0.1cc, D1cc and D2cc, for toxicity prediction is evaluated. The D0.1/1/ 2ccvalues are extracted from the accumulative EBRT+BT EQD2 dose by the “worst-case scenario” (WS) addition method and logistic regression (LR) is performed to calculate the toxicity probability. Data from 42 cervical cancer patients treated with EBRT followed by BT were retrospectively collected for evaluation. Twelve patients complained about hematochezia were further examined with colonoscopy and scored for toxicity according to RTOG criteria. The other 30 non-toxicity patients were used as the reference group. A leave-one-out method was used to evaluate the prediction performance for both the proposed model and LR. Results: Moderate prediction results were obtained by LR using D0.1/1/2cc values with an accuracy of 59.5%, sensitivity of 52.8%, specificity of 60.0%, and AUC of 58.3. In contrast, with all layers fine-tuned in VGG-16, a satisfactory prediction performance was achieved with a total accuracy of 88.1%, sensitivity of 75%, specificity of 93.3%, and AUC of 0.96. Conclusion: The results of this work have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning. The proposed prediction model can serve as a practical tool for rectum dose and induced toxicity analysis for cervical cancer radiotherapy. Author Disclosure: X. Zhen: None. J. Chen: None. Z. Zhong: None. B.A. Hrycushko: None. K. Albuquerque: None. L. Zhou: None. S.B. Jiang: Patent/License Fees/Copyright; Sun Nuclear. X. Gu: None.
361 Rectal Cancer Outcome Study Based on Multiple NRG Oncology Clinical Trials with Random Survival Forests M. Huang,1 J. van Soest,2 H. Zhong,1 E. Ben-Josef,1 H. Geng,1 J. Wang,3 Z. Zhang,3 M. Mohiuddin,4 N.J. Meropol,5 M. Garofalo,6 P.R. Anne,7
Purpose/Objective(s): To use the machine learning Random Forest (RF) model and novel Random Survival Forest (RSF) model, to predict overall survival, local recurrence, and distant metastases for patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy followed by surgery, using data from RTOG 0822, 0247, and 0012. Materials/Methods: The 79 patients from RTOG 0822, 146 patients from RTOG 0247, and 106 patients from RTOG 0012 were included in the study. Data with missing clinical information or duplication were eliminated. Locally advanced rectal cancer patients were treated with neoadjuvant chemoradiotherapy and suggested total mesorectal excision. The following prognosis and clinical data were collected and applied for the RF and RSF method for survival analysis: gender, age, treatment arm, clinical tumor staging (cT, cN, and cM), pathological staging (pT, pN), tumor distance (TD) from the anorectal verge (<5 cm, 5-10 cm, >10 cm), adjuvant chemotherapy (chemo) status (Y/N) and type, radiation dose and treatment duration. Overall survival (OS), distant metastases (DS), and local recurrence (LR) were the outcomes for prediction. Features were ranked correspondingly for OS, LR and DM prediction. RSF incorporates RF and can rank features, and predict survival function over time. To pursue RSF for event-specific variable outcomes, the prediction result was five-fold cross-validated with RF. Results: The RF analysis stabilized at 10,000 randomized decision trees, with concordance index (C-index) of 0.75, 0.80, and 0.70 for OS, LR, and DM using RTOG 0822 data. A five-fold cross-validation was performed on the 0822 patients; the “out of bag” validation training set error was 0.30, corresponding to a C-index of 0.70. The important features for OS (high to low) are pT, TD, pN, cN, cT, adjuvant (adj) chemo, dose, cM, chemo type, surgery procedure, age. For LR, importance lists: TD, cN, adj. chemo, cT, cM, dose, age, pT, sex, surg. procedure, chemo type, pN; for DM are pN, cN, TD, pT, postsurgery complications, cM, adj chemo, dose, cT, sex, age. The RSF model also calculates survival prediction over time, 96.4%, 95.5%, 90.3%, 82.5%, 75.0% at 1-, 2-, 3-, 4-, 5-year survival rate, and aligns with the Kaplan-Meier curve. For trial 0247 and trial 0012 cohorts, OS prediction generated AUC Z 0.66 for RTOG 0012 and AUC Z 0.65 for RTOG 0247. However, the LR and DM would not be predicted in RTOG 0822 due to the uncollected P-stage information. Conclusion: RF and RSF survival analysis can predict for OS, LR, and DM outcome of locally advanced rectal cancer patients. RF/RSF method features automatic data mining. More clinical trial data for further validations of the model are needed for robustness and reliability. RF/RSF also provides an alternative machine learning method for decision support of precision medicine. Author Disclosure: M. Huang: None. J. van Soest: None. H. Zhong: None. E. Ben-Josef: NCI Board of Scientific Counselors, NCI Board of Scientific Counselors. H. Geng: None. J. Wang: None. Z. Zhang: Independent Contractor; Varian Medical System. M. Mohiuddin: None. N.J. Meropol: Patent/License Fees/Copyright; Patent/License Fees; ASCO. M. Garofalo: None. P.R. Anne’: None. R. Lee: None. B.A. Erickson: Employee; ProHealth Care, Waukesha, WI. Help to organize the annual or bi-annual Gyn Brachytherapy school -2015; American Brachytherapy Society. M. Augspurger: None. E.R. Sigurdson: None. G. Lasio: None. A. Dekker: Research Grant; Varian Medical Systems. Honoraria; Varian Medical Systems. Consultant; Varian Medical Systems. Y. Xiao: None.