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

  • External authors:
  • Chamberlain Mbah
  • Kim De Ruyck
  • Silke De Schrijver
  • Charlotte De Sutter
  • Kimberly Schiettecatte
  • Chris Monten
  • Leen Paelinck
  • Wilfried De Neve
  • Hubert Thierens
  • Gustavo Amorim
  • Olivier Thas
  • Liv Veldeman

Standard

A new approach for modeling patient overall radiosensitivity and predicting multiple toxicity endpoints for breast cancer patients. / Mbah, Chamberlain; De Ruyck, Kim; De Schrijver, Silke; De Sutter, Charlotte; Schiettecatte, Kimberly; Monten, Chris; Paelinck, Leen; De Neve, Wilfried; Thierens, Hubert; West, Catharine; Amorim, Gustavo; Thas, Olivier; Veldeman, Liv.

In: Acta Oncologica , Vol. 57, No. 5, 2018, p. 604-612.

Research output: Contribution to journalArticle

Harvard

Mbah, C, De Ruyck, K, De Schrijver, S, De Sutter, C, Schiettecatte, K, Monten, C, Paelinck, L, De Neve, W, Thierens, H, West, C, Amorim, G, Thas, O & Veldeman, L 2018, '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. https://doi.org/10.1080/0284186X.2017.1417633

APA

Mbah, C., De Ruyck, K., De Schrijver, S., De Sutter, C., Schiettecatte, K., Monten, C., ... Veldeman, L. (2018). A new approach for modeling patient overall radiosensitivity and predicting multiple toxicity endpoints for breast cancer patients. Acta Oncologica , 57(5), 604-612. https://doi.org/10.1080/0284186X.2017.1417633

Vancouver

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):604-612. https://doi.org/10.1080/0284186X.2017.1417633

Author

Mbah, Chamberlain ; De Ruyck, Kim ; De Schrijver, Silke ; De Sutter, Charlotte ; Schiettecatte, Kimberly ; Monten, Chris ; Paelinck, Leen ; De Neve, Wilfried ; Thierens, Hubert ; West, Catharine ; Amorim, Gustavo ; Thas, Olivier ; Veldeman, Liv. / A new approach for modeling patient overall radiosensitivity and predicting multiple toxicity endpoints for breast cancer patients. In: Acta Oncologica . 2018 ; Vol. 57, No. 5. pp. 604-612.

Bibtex

@article{c346743e42e54461a1c518fb56d7a37d,
title = "A new approach for modeling patient overall radiosensitivity and predicting multiple toxicity endpoints for breast cancer patients",
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 = "Chamberlain Mbah and {De Ruyck}, Kim and {De Schrijver}, Silke and {De Sutter}, Charlotte and Kimberly Schiettecatte and Chris Monten and Leen Paelinck and {De Neve}, Wilfried and Hubert Thierens and Catharine West and Gustavo Amorim and Olivier Thas and Liv Veldeman",
year = "2018",
doi = "10.1080/0284186X.2017.1417633",
language = "English",
volume = "57",
pages = "604--612",
journal = "Acta Oncologica",
issn = "0284-186X",
publisher = "Taylor & Francis",
number = "5",

}

RIS

TY - JOUR

T1 - A new approach for modeling patient overall radiosensitivity and predicting multiple toxicity endpoints for breast cancer patients

AU - Mbah, Chamberlain

AU - De Ruyck, Kim

AU - De Schrijver, Silke

AU - De Sutter, Charlotte

AU - Schiettecatte, Kimberly

AU - Monten, Chris

AU - Paelinck, Leen

AU - De Neve, Wilfried

AU - Thierens, Hubert

AU - West, Catharine

AU - Amorim, Gustavo

AU - Thas, Olivier

AU - Veldeman, Liv

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85041124890&partnerID=8YFLogxK

U2 - 10.1080/0284186X.2017.1417633

DO - 10.1080/0284186X.2017.1417633

M3 - Article

VL - 57

SP - 604

EP - 612

JO - Acta Oncologica

JF - Acta Oncologica

SN - 0284-186X

IS - 5

ER -