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A new approach for modeling patient overall radiosensitivity and predicting multiple toxicity endpoints for breast cancer patients

(2018) ACTA ONCOLOGICA. 57(5). p.604-612
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Abstract
Introduction: Evaluation of patient characteristics inducing toxicity in breast radiotherapy, using simultaneous modeling of multiple endpoints. Methods and Materials: In 269 early-stage breast cancer patients treated with whole-breast irradiation (WBI) after breast-conserving surgery, toxicity was scored, based on five dichotomized endpoints. Five logistic regression models were fitted, one for each endpoint and the effect sizes of all variables were estimated using maximum likelihood (MLE). The MLEs are improved with James-Stein estimates (JSEs). The method combines all the MLEs, obtained for the same variable but from different endpoints. Misclassification errors were computed using MLE- and JSE-based prediction models. For associations, p-values from the sum of squares of MLEs were compared with p-values from the Standardized Total Average Toxicity (STAT) Score. Results: With JSEs, 19 highest ranked variables were predictive of the five different endpoints. Important variables increasing radiation-induced toxicity were chemotherapy, age, SATB2 rs2881208 SNP and nodal irradiation. Treatment position (prone position) was most protective and ranked eighth. Overall, the misclassification errors were 45% and 34% for the MLE- and JSE-based models, respectively. p-Values from the sum of squares of MLEs and p-values from STAT score led to very similar conclusions, except for the variables nodal irradiation and treatment position, for which STAT p-values suggested an association with radiosensitivity, whereas p-values from the sum of squares indicated no association. Breast volume was ranked as the most significant variable in both strategies. Discussion: The James-Stein estimator was used for selecting variables that are predictive for multiple toxicity endpoints. With this estimator, 19 variables were predictive for all toxicities of which four were significantly associated with overall radiosensitivity. JSEs led to almost 25% reduction in the misclassification error rate compared to conventional MLEs. Finally, patient characteristics that are associated with radiosensitivity were identified without explicitly quantifying radiosensitivity.
Keywords
INTENSITY-MODULATED RADIOTHERAPY, CONSERVATIVE TREATMENT, CONSERVING SURGERY, RADIATION-THERAPY, TISSUE, TRIAL, POLYMORPHISMS, IRRADIATION, WOMEN, RISK

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MLA
Mbah, Chamberlain et al. “A New Approach for Modeling Patient Overall Radiosensitivity and Predicting Multiple Toxicity Endpoints for Breast Cancer Patients.” ACTA ONCOLOGICA 57.5 (2018): 604–612. Print.
APA
Mbah, C., De Ruyck, K., De Schrijver, S., De Sutter, C., Schiettecatte, K., Monten, C., Paelinck, L., et al. (2018). A new approach for modeling patient overall radiosensitivity and predicting multiple toxicity endpoints for breast cancer patients. ACTA ONCOLOGICA, 57(5), 604–612.
Chicago author-date
Mbah, Chamberlain, Kim De Ruyck, Silke De Schrijver, Charlotte De Sutter, Kimberly Schiettecatte, Christel Monten, Leen Paelinck, et al. 2018. “A New Approach for Modeling Patient Overall Radiosensitivity and Predicting Multiple Toxicity Endpoints for Breast Cancer Patients.” Acta Oncologica 57 (5): 604–612.
Chicago author-date (all authors)
Mbah, Chamberlain, Kim De Ruyck, Silke De Schrijver, Charlotte De Sutter, Kimberly Schiettecatte, Christel Monten, Leen Paelinck, Wilfried De Neve, Hubert Thierens, Catharine West, Gustavo Amorim, Olivier Thas, and Liv Veldeman. 2018. “A New Approach for Modeling Patient Overall Radiosensitivity and Predicting Multiple Toxicity Endpoints for Breast Cancer Patients.” Acta Oncologica 57 (5): 604–612.
Vancouver
1.
Mbah C, De Ruyck K, De Schrijver S, De Sutter C, Schiettecatte K, Monten C, et al. A new approach for modeling patient overall radiosensitivity and predicting multiple toxicity endpoints for breast cancer patients. ACTA ONCOLOGICA. 2018;57(5):604–12.
IEEE
[1]
C. Mbah et al., “A new approach for modeling patient overall radiosensitivity and predicting multiple toxicity endpoints for breast cancer patients,” ACTA ONCOLOGICA, vol. 57, no. 5, pp. 604–612, 2018.
@article{8611185,
  abstract     = {Introduction: Evaluation of patient characteristics inducing toxicity in breast radiotherapy, using simultaneous modeling of multiple endpoints.
Methods and Materials: In 269 early-stage breast cancer patients treated with whole-breast irradiation (WBI) after breast-conserving surgery, toxicity was scored, based on five dichotomized endpoints. Five logistic regression models were fitted, one for each endpoint and the effect sizes of all variables were estimated using maximum likelihood (MLE). The MLEs are improved with James-Stein estimates (JSEs). The method combines all the MLEs, obtained for the same variable but from different endpoints. Misclassification errors were computed using MLE- and JSE-based prediction models. For associations, p-values from the sum of squares of MLEs were compared with p-values from the Standardized Total Average Toxicity (STAT) Score.
Results: With JSEs, 19 highest ranked variables were predictive of the five different endpoints. Important variables increasing radiation-induced toxicity were chemotherapy, age, SATB2 rs2881208 SNP and nodal irradiation. Treatment position (prone position) was most protective and ranked eighth. Overall, the misclassification errors were 45% and 34% for the MLE- and JSE-based models, respectively. p-Values from the sum of squares of MLEs and p-values from STAT score led to very similar conclusions, except for the variables nodal irradiation and treatment position, for which STAT p-values suggested an association with radiosensitivity, whereas p-values from the sum of squares indicated no association. Breast volume was ranked as the most significant variable in both strategies.
Discussion: The James-Stein estimator was used for selecting variables that are predictive for multiple toxicity endpoints. With this estimator, 19 variables were predictive for all toxicities of which four were significantly associated with overall radiosensitivity. JSEs led to almost 25% reduction in the misclassification error rate compared to conventional MLEs. Finally, patient characteristics that are associated with radiosensitivity were identified without explicitly quantifying radiosensitivity.},
  author       = {Mbah, Chamberlain and De Ruyck, Kim and De Schrijver, Silke and De Sutter, Charlotte and Schiettecatte, Kimberly and Monten, Christel and Paelinck, Leen and De Neve, Wilfried and Thierens, Hubert and West, Catharine and Amorim, Gustavo and Thas, Olivier and Veldeman, Liv},
  issn         = {0284-186X},
  journal      = {ACTA ONCOLOGICA},
  keywords     = {INTENSITY-MODULATED RADIOTHERAPY,CONSERVATIVE TREATMENT,CONSERVING SURGERY,RADIATION-THERAPY,TISSUE,TRIAL,POLYMORPHISMS,IRRADIATION,WOMEN,RISK},
  language     = {eng},
  number       = {5},
  pages        = {604--612},
  title        = {A new approach for modeling patient overall radiosensitivity and predicting multiple toxicity endpoints for breast cancer patients},
  url          = {http://dx.doi.org/10.1080/0284186x.2017.1417633},
  volume       = {57},
  year         = {2018},
}

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