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The challenge of predicting changes in pulmonary function tests after thoracic irradiation
Int. J. Radiation Oncology Biol. Phys., Vol. 55, No. 5, pp. 1164 –1165, 2003 Copyright © 2003 Elsevier Science Inc. Printed in the USA. All rights reserved 0360-3016/03/$–see front matter
doi:10.1016/S0360-3016(02)04289-X
EDITORIAL
THE CHALLENGE OF PREDICTING CHANGES IN PULMONARY FUNCTION TESTS AFTER THORACIC IRRADIATION LAWRENCE B. MARKS, M.D.,*
AND
JOOS V. LEBESQUE, M.D., PH.D.†
*Department of Radiation Oncology, Duke University Medical Center, Durham, NC; †Department of Radiation Oncology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
In this issue of the International Journal of Radiation Oncology, Biology, Physics, Allen et al. from the University of Michigan consider methods of predicting radiation-induced changes in whole lung function. They report that they were unable to identify a relationship between three-dimensional dosimetric parameters (e.g., the mean lung dose [MLD], or the percent of lung volume receiving ⬎20 Gy [V20]) and reductions of pulmonary function tests (PFTs) in 43 patients receiving radiotherapy (RT) for lung cancer (1). They conclude that additional work needs to be done to develop better methods of predicting RT-induced changes in PFTs. We agree. Their negative result is likely primarily the result of the tumor type studied, plus several methodologic limitations. Prospective studies at the Netherlands Cancer Institute (NKI) and Duke University Medical Center have also attempted to relate 3D dosimetric parameters to changes in quantitative pulmonary function tests (2, 3). The NKI group reported, for 81 patients with breast cancer and Hodgkin’s lymphoma, a good correlation between the mean lung dose and reductions of the diffusion capacity (TL,CO) and forced expiratory volume in 1 second (FEV1) (r ⫽ 0.58 and 0.74, respectively) at 3 months postradiotherapy (2). For TL,CO, the correlation improved after excluding patients that received chemotherapy. At Duke, only weak correlations were found (r ⫽ 0.2– 0.4), but the study group also included patients with lung cancer (3). Predicting changes in PFTs after RT for lung cancer is complicated, because there are many confounding factors. Often, tumor-related reductions in PFTs are present at baseline (4 – 8). Thus, RT-induced tumor shrinkage might actually lead to an improvement in PFTs. The post-RT PFTs, therefore, reflect both improvements in function resulting from tumor shrinkage and declines in function because of damage to normal lung. It seems logical, therefore, that the correlation would be improved if patients without central lung tumors (that frequently cause adjacent hypoperfusion
or atelectasis) are excluded. In the Duke study, the correlation coefficient between 3-D dosimetric parameters and declines in PFTs was improved when patients with “central tumors and adjacent hypoperfusion” were excluded (r ⬃ 0.3– 0.9) (3). In the study from Allen et al., their correlations did not improve when the 6 patients with pre-RT “atelectasis involving at least a lung lobe” were excluded. Twenty-four of their 43 evaluated patients had Stage III disease and, presumably, had central mediastinal disease. A large fraction of these patients would be expected to have some tumorinduced reduction in lung function, such as reduced perfusion (5, 8 –10), but very few of these were excluded from their subset analysis. The absence of atelectasis of an entire lobe with routine radiography does not exclude significant tumor-induced physiologic changes within the lung. Single photon emission computer tomography (SPECT) perfusion scans are more sensitive in assessing such regional functional heterogeneities than is computed tomography (10, 11). In the series from the NKI involving patients with breast cancer and lymphoma, there were good correlations between 3-D dosimetric parameters and declines in PFTs (2). This is consistent with the concept that tumor-induced functional changes make prediction of PFTs a particularly challenging problem in patients with lung cancer. Moreover, the exacerbation of coexisting pulmonary disease in lung cancer patients undoubtedly complicates prediction of PFTs. Interestingly, several surgical series demonstrated generally excellent correlations between declines in PFTs and the estimated percent of lung resected (r ⬃ 0.6 to 0.9) (12–16, review see 17). This supports the concept that the sum of the regional injuries can be used to predict changes in whole organ function in parallel-architecture type organs such as the lung. In the Duke series, the correlation between the dosimetric parameters and the subsequent decline in PFTs was strongest in patients with a larger number of post-RT PFTs (i.e.,
Reprint requests to: Lawrence B. Marks, M.D., Department of Radiation Oncology, Duke University Medical Center, DUMC, Durham, NC 27710. Tel: (919) 668-5640; Fax: (919) 684-3953; E-mail: [email protected]
Supported in part by Grant CA69579, awarded by the NIH. Received Oct 18, 2002. Accepted for publication Oct 21, 2002. 1164
Predicting changes in pulmonary function tests
longer follow-up; r ⫽ 0.4 – 0.6) (3). In the study by Allen et al., the minimum and median follow-up were 3 and 6 months, respectively. They may observe improved correlations with increased follow-up. Although PFTs provide an objective measurement of global pulmonary function, there are some serious limitations of this methodology. The reproducibility of PFTs is generally considered to be ⬇ 5–10%, but is likely worse in patients with coexistent pulmonary disease (18). Furthermore, smoking habits and chemotherapy regimens may also affect reductions of PFTs. Additional work should be directed toward the evaluation of new approaches for measurement of global lung function (19, 20). Three-dimensional RT planning software provides the tools
●
1165
to study the relationship between complex dose distributions and changes in organ function. The article by Allen et al. is an important contribution to this area of study and highlights some of the limitations of our current approaches. Additional work is clearly needed to better understand and, therefore, develop models to improve prediction of RT-induced changes in lung function, especially for patients with lung cancer. Preliminary results from an NKI study in lung cancer patients indicate that weighing of dosimetric parameters with baseline perfusion information might contribute to a better prediction of PFTs after radiotherapy (21). Such refinements in our predictive models are essential for us to logically apply rapidly improving treatment planning and delivery systems to the care of our patients.
REFERENCES 1. De Jaeger K, Seppenwoolde Y, Boersma LJ, et al. Pulmonary function following high-dose radiotherapy of non–small-cell lung cancer. Int J Radiat Oncol Biol Phys 2003;55:1331– 1340. 2. Theuws JCM, Kwa SLS, Wagenaar AC, et al. Prediction of overall pulmonary function loss in relation to the 3-D dose distribution, for patients with breast cancer and malignant lymphoma. Radiother Oncol 1998;49:233–243. 3. Fan M, Marks LB, Hollis D, et al. Can we predict radiationinduced changes in pulmonary function based on the sum of predicted regional dysfunction? J Clin Oncol 2001;19:543– 550. 4. Curran WJ, Moldofsky PJ, Solin LJ. Observations on the predictive value of perfusion lung scans on post-irradiation pulmonary function among 210 patients with bronchogenic carcinoma. Int J Radiat Oncol Biol Phys 1992;24:31–36. 5. Choi NC, Kanarek DJ, Kazemi H. Physiologic changes in pulmonary function after thoracic radiotherapy for patients with lung cancer and role of regional pulmonary function studies in predicting post-radiotherapy pulmonary function before radiotherapy. Cancer Treatments Symp 1985;2:119– 130. 6. Abratt RP, Willcox PA, Smith JA. Lung cancer in patients with borderline lung functions—zonal lung perfusion scans at presentation and lung function after high dose irradiation. Radiother Oncol 1990;19:317–322. 7. Marks LB, Hollis D, Clough R, et al. The role of lung perfusion imaging in predicting the direction of radiationinduced changes in pulmonary function tests. Cancer 2000; 88:2135–2141. 8. Fazio F, Pratt TA, McKenzie CG, et al. Improvement in regional ventilation and perfusion after radiotherapy for unresectable carcinoma of the bronchus. Am J Roentgenol 1979; 133:199–200. 9. Seppenwoolde Y, Muller SH, Theuws JC, et al. Radiation dose-effect relations and local recovery in perfusion for patients with non-small cell lung cancer. Int J Radiat Oncol Biol Phys 2000;47:681–690. 10. Marks LB, Spencer DP, Sherouse GW, et al. The role of 3-dimensional functional lung imaging in radiation treatment
11. 12.
13. 14.
15.
16. 17. 18. 19.
20. 21.
planning: The functional DVH. Int J Radiat Oncol Biol Phys 1995;33:65–75. Levinson B, Marks LB, Munley MT, et al. Regional dose response to pulmonary irradiation using a manual method. Radiother Oncol 1998;48:53–60. Julius AJ, de Jong D, van Deutekom H, et al. The value of 99mTC macroaggregated albumin lung perfusion scanning in the prediction of postpneumonectomy function and pulmonary artery pressure. Scan J Thor Cardiovasc Surg 1987;21:81–85. Cordiner A, de Carlo F, de Gennaro R, et al. Prediction of postoperative pulmonary function following thoracic surgery for bronchial carcinoma. Angiology 1991;42:985–989. Pierce RJ, Copland JM, Sharpe K, et al. Preoperative risk evaluation for lung cancer resection: Predicted postoperative product as a predictor of surgical mortality. Am J Respir Crit Care Med 1994;150:947–955. Bolliger CT, Wyser C, Roser H, et al. Lung scanning and exercise testing for the prediction of postoperative performance in lung resection candidates at increased risk for complications. Chest 1995;108:341–348. Giordano A, Calcagni ML, Meduri G, et al. Perfusion lung scintigraphy for the prediction of postlobectomy residual pulmonary function. Chest 1997;111:1542–1547. Fan M, Marks LB, Lind P, et al. Relating radiation-induced regional lung injury to changes in pulmonary function tests. Int J Radiat Oncol Biol Phys 2001;51:311–317. Quanjer PH, Anderson LH, Tammeling GJ. Clinical respiratory physiology. Bull Eur Physiopathol Respir 1983;19:11– 21. Mu¨ ller CJ, Schwaiblmair M, Scheidler J, et al. Pulmonary diffusion capacity: assessment with oxygen-enhanced lung MR imaging—preliminary findings. Radiology 2002;222:499– 506. Enright PL, Sherrill DL. Reference equations for the sixminute walk in healthy adults. Am J Respir Crit Care Med 1998;158:1384–1387. De Jaeger K, Seppenwoolde Y, Goedbloed C, et al. Impact of tumor regression on functional recovery after high-dose radiotherapy of non-small cell lung cancer. Int J Radiat Oncol Biol Phys 2001;51:88.
doi:10.1016/S0360-3016(02)04289-X
EDITORIAL
THE CHALLENGE OF PREDICTING CHANGES IN PULMONARY FUNCTION TESTS AFTER THORACIC IRRADIATION LAWRENCE B. MARKS, M.D.,*
AND
JOOS V. LEBESQUE, M.D., PH.D.†
*Department of Radiation Oncology, Duke University Medical Center, Durham, NC; †Department of Radiation Oncology, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
In this issue of the International Journal of Radiation Oncology, Biology, Physics, Allen et al. from the University of Michigan consider methods of predicting radiation-induced changes in whole lung function. They report that they were unable to identify a relationship between three-dimensional dosimetric parameters (e.g., the mean lung dose [MLD], or the percent of lung volume receiving ⬎20 Gy [V20]) and reductions of pulmonary function tests (PFTs) in 43 patients receiving radiotherapy (RT) for lung cancer (1). They conclude that additional work needs to be done to develop better methods of predicting RT-induced changes in PFTs. We agree. Their negative result is likely primarily the result of the tumor type studied, plus several methodologic limitations. Prospective studies at the Netherlands Cancer Institute (NKI) and Duke University Medical Center have also attempted to relate 3D dosimetric parameters to changes in quantitative pulmonary function tests (2, 3). The NKI group reported, for 81 patients with breast cancer and Hodgkin’s lymphoma, a good correlation between the mean lung dose and reductions of the diffusion capacity (TL,CO) and forced expiratory volume in 1 second (FEV1) (r ⫽ 0.58 and 0.74, respectively) at 3 months postradiotherapy (2). For TL,CO, the correlation improved after excluding patients that received chemotherapy. At Duke, only weak correlations were found (r ⫽ 0.2– 0.4), but the study group also included patients with lung cancer (3). Predicting changes in PFTs after RT for lung cancer is complicated, because there are many confounding factors. Often, tumor-related reductions in PFTs are present at baseline (4 – 8). Thus, RT-induced tumor shrinkage might actually lead to an improvement in PFTs. The post-RT PFTs, therefore, reflect both improvements in function resulting from tumor shrinkage and declines in function because of damage to normal lung. It seems logical, therefore, that the correlation would be improved if patients without central lung tumors (that frequently cause adjacent hypoperfusion
or atelectasis) are excluded. In the Duke study, the correlation coefficient between 3-D dosimetric parameters and declines in PFTs was improved when patients with “central tumors and adjacent hypoperfusion” were excluded (r ⬃ 0.3– 0.9) (3). In the study from Allen et al., their correlations did not improve when the 6 patients with pre-RT “atelectasis involving at least a lung lobe” were excluded. Twenty-four of their 43 evaluated patients had Stage III disease and, presumably, had central mediastinal disease. A large fraction of these patients would be expected to have some tumorinduced reduction in lung function, such as reduced perfusion (5, 8 –10), but very few of these were excluded from their subset analysis. The absence of atelectasis of an entire lobe with routine radiography does not exclude significant tumor-induced physiologic changes within the lung. Single photon emission computer tomography (SPECT) perfusion scans are more sensitive in assessing such regional functional heterogeneities than is computed tomography (10, 11). In the series from the NKI involving patients with breast cancer and lymphoma, there were good correlations between 3-D dosimetric parameters and declines in PFTs (2). This is consistent with the concept that tumor-induced functional changes make prediction of PFTs a particularly challenging problem in patients with lung cancer. Moreover, the exacerbation of coexisting pulmonary disease in lung cancer patients undoubtedly complicates prediction of PFTs. Interestingly, several surgical series demonstrated generally excellent correlations between declines in PFTs and the estimated percent of lung resected (r ⬃ 0.6 to 0.9) (12–16, review see 17). This supports the concept that the sum of the regional injuries can be used to predict changes in whole organ function in parallel-architecture type organs such as the lung. In the Duke series, the correlation between the dosimetric parameters and the subsequent decline in PFTs was strongest in patients with a larger number of post-RT PFTs (i.e.,
Reprint requests to: Lawrence B. Marks, M.D., Department of Radiation Oncology, Duke University Medical Center, DUMC, Durham, NC 27710. Tel: (919) 668-5640; Fax: (919) 684-3953; E-mail: [email protected]
Supported in part by Grant CA69579, awarded by the NIH. Received Oct 18, 2002. Accepted for publication Oct 21, 2002. 1164
Predicting changes in pulmonary function tests
longer follow-up; r ⫽ 0.4 – 0.6) (3). In the study by Allen et al., the minimum and median follow-up were 3 and 6 months, respectively. They may observe improved correlations with increased follow-up. Although PFTs provide an objective measurement of global pulmonary function, there are some serious limitations of this methodology. The reproducibility of PFTs is generally considered to be ⬇ 5–10%, but is likely worse in patients with coexistent pulmonary disease (18). Furthermore, smoking habits and chemotherapy regimens may also affect reductions of PFTs. Additional work should be directed toward the evaluation of new approaches for measurement of global lung function (19, 20). Three-dimensional RT planning software provides the tools
●
1165
to study the relationship between complex dose distributions and changes in organ function. The article by Allen et al. is an important contribution to this area of study and highlights some of the limitations of our current approaches. Additional work is clearly needed to better understand and, therefore, develop models to improve prediction of RT-induced changes in lung function, especially for patients with lung cancer. Preliminary results from an NKI study in lung cancer patients indicate that weighing of dosimetric parameters with baseline perfusion information might contribute to a better prediction of PFTs after radiotherapy (21). Such refinements in our predictive models are essential for us to logically apply rapidly improving treatment planning and delivery systems to the care of our patients.
REFERENCES 1. De Jaeger K, Seppenwoolde Y, Boersma LJ, et al. Pulmonary function following high-dose radiotherapy of non–small-cell lung cancer. Int J Radiat Oncol Biol Phys 2003;55:1331– 1340. 2. Theuws JCM, Kwa SLS, Wagenaar AC, et al. Prediction of overall pulmonary function loss in relation to the 3-D dose distribution, for patients with breast cancer and malignant lymphoma. Radiother Oncol 1998;49:233–243. 3. Fan M, Marks LB, Hollis D, et al. Can we predict radiationinduced changes in pulmonary function based on the sum of predicted regional dysfunction? J Clin Oncol 2001;19:543– 550. 4. Curran WJ, Moldofsky PJ, Solin LJ. Observations on the predictive value of perfusion lung scans on post-irradiation pulmonary function among 210 patients with bronchogenic carcinoma. Int J Radiat Oncol Biol Phys 1992;24:31–36. 5. Choi NC, Kanarek DJ, Kazemi H. Physiologic changes in pulmonary function after thoracic radiotherapy for patients with lung cancer and role of regional pulmonary function studies in predicting post-radiotherapy pulmonary function before radiotherapy. Cancer Treatments Symp 1985;2:119– 130. 6. Abratt RP, Willcox PA, Smith JA. Lung cancer in patients with borderline lung functions—zonal lung perfusion scans at presentation and lung function after high dose irradiation. Radiother Oncol 1990;19:317–322. 7. Marks LB, Hollis D, Clough R, et al. The role of lung perfusion imaging in predicting the direction of radiationinduced changes in pulmonary function tests. Cancer 2000; 88:2135–2141. 8. Fazio F, Pratt TA, McKenzie CG, et al. Improvement in regional ventilation and perfusion after radiotherapy for unresectable carcinoma of the bronchus. Am J Roentgenol 1979; 133:199–200. 9. Seppenwoolde Y, Muller SH, Theuws JC, et al. Radiation dose-effect relations and local recovery in perfusion for patients with non-small cell lung cancer. Int J Radiat Oncol Biol Phys 2000;47:681–690. 10. Marks LB, Spencer DP, Sherouse GW, et al. The role of 3-dimensional functional lung imaging in radiation treatment
11. 12.
13. 14.
15.
16. 17. 18. 19.
20. 21.
planning: The functional DVH. Int J Radiat Oncol Biol Phys 1995;33:65–75. Levinson B, Marks LB, Munley MT, et al. Regional dose response to pulmonary irradiation using a manual method. Radiother Oncol 1998;48:53–60. Julius AJ, de Jong D, van Deutekom H, et al. The value of 99mTC macroaggregated albumin lung perfusion scanning in the prediction of postpneumonectomy function and pulmonary artery pressure. Scan J Thor Cardiovasc Surg 1987;21:81–85. Cordiner A, de Carlo F, de Gennaro R, et al. Prediction of postoperative pulmonary function following thoracic surgery for bronchial carcinoma. Angiology 1991;42:985–989. Pierce RJ, Copland JM, Sharpe K, et al. Preoperative risk evaluation for lung cancer resection: Predicted postoperative product as a predictor of surgical mortality. Am J Respir Crit Care Med 1994;150:947–955. Bolliger CT, Wyser C, Roser H, et al. Lung scanning and exercise testing for the prediction of postoperative performance in lung resection candidates at increased risk for complications. Chest 1995;108:341–348. Giordano A, Calcagni ML, Meduri G, et al. Perfusion lung scintigraphy for the prediction of postlobectomy residual pulmonary function. Chest 1997;111:1542–1547. Fan M, Marks LB, Lind P, et al. Relating radiation-induced regional lung injury to changes in pulmonary function tests. Int J Radiat Oncol Biol Phys 2001;51:311–317. Quanjer PH, Anderson LH, Tammeling GJ. Clinical respiratory physiology. Bull Eur Physiopathol Respir 1983;19:11– 21. Mu¨ ller CJ, Schwaiblmair M, Scheidler J, et al. Pulmonary diffusion capacity: assessment with oxygen-enhanced lung MR imaging—preliminary findings. Radiology 2002;222:499– 506. Enright PL, Sherrill DL. Reference equations for the sixminute walk in healthy adults. Am J Respir Crit Care Med 1998;158:1384–1387. De Jaeger K, Seppenwoolde Y, Goedbloed C, et al. Impact of tumor regression on functional recovery after high-dose radiotherapy of non-small cell lung cancer. Int J Radiat Oncol Biol Phys 2001;51:88.