PURPOSE: To study the impact of clinical predisposing factors on rectal normal tissue complication probability modeling using the updated results of the Dutch prostate dose-escalation trial. METHODS AND MATERIALS: Toxicity data of 512 patients (conformally treated to 68 Gy [n = 284] and 78 Gy [n = 228]) with complete follow-up at 3 years after radiotherapy were studied. Scored end points were rectal bleeding, high stool frequency, and fecal incontinence. Two traditional dose-based models (Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) and a logistic model were fitted using a maximum likelihood approach. Furthermore, these model fits were improved by including the most significant clinical factors. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminating ability of all fits. RESULTS: Including clinical factors significantly increased the predictive power of the models for all end points. In the optimal LKB, RS, and logistic models for rectal bleeding and fecal incontinence, the first significant (p = 0.011-0.013) clinical factor was "previous abdominal surgery." As second significant (p = 0.012-0.016) factor, "cardiac history" was included in all three rectal bleeding fits, whereas including "diabetes" was significant (p = 0.039-0.048) in fecal incontinence modeling but only in the LKB and logistic models. High stool frequency fits only benefitted significantly (p = 0.003-0.006) from the inclusion of the baseline toxicity score. For all models rectal bleeding fits had the highest AUC (0.77) where it was 0.63 and 0.68 for high stool frequency and fecal incontinence, respectively. LKB and logistic model fits resulted in similar values for the volume parameter. The steepness parameter was somewhat higher in the logistic model, also resulting in a slightly lower D(50). Anal wall DVHs were used for fecal incontinence, whereas anorectal wall dose best described the other two endpoints. CONCLUSIONS: Comparable prediction models were obtained with LKB, RS, and logistic NTCP models. Including clinical factors improved the predictive power of all models significantly.
RCT Entities:
PURPOSE: To study the impact of clinical predisposing factors on rectal normal tissue complication probability modeling using the updated results of the Dutch prostate dose-escalation trial. METHODS AND MATERIALS: Toxicity data of 512 patients (conformally treated to 68 Gy [n = 284] and 78 Gy [n = 228]) with complete follow-up at 3 years after radiotherapy were studied. Scored end points were rectal bleeding, high stool frequency, and fecal incontinence. Two traditional dose-based models (Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) and a logistic model were fitted using a maximum likelihood approach. Furthermore, these model fits were improved by including the most significant clinical factors. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminating ability of all fits. RESULTS: Including clinical factors significantly increased the predictive power of the models for all end points. In the optimal LKB, RS, and logistic models for rectal bleeding and fecal incontinence, the first significant (p = 0.011-0.013) clinical factor was "previous abdominal surgery." As second significant (p = 0.012-0.016) factor, "cardiac history" was included in all three rectal bleeding fits, whereas including "diabetes" was significant (p = 0.039-0.048) in fecal incontinence modeling but only in the LKB and logistic models. High stool frequency fits only benefitted significantly (p = 0.003-0.006) from the inclusion of the baseline toxicity score. For all models rectal bleeding fits had the highest AUC (0.77) where it was 0.63 and 0.68 for high stool frequency and fecal incontinence, respectively. LKB and logistic model fits resulted in similar values for the volume parameter. The steepness parameter was somewhat higher in the logistic model, also resulting in a slightly lower D(50). Anal wall DVHs were used for fecal incontinence, whereas anorectal wall dose best described the other two endpoints. CONCLUSIONS: Comparable prediction models were obtained with LKB, RS, and logistic NTCP models. Including clinical factors improved the predictive power of all models significantly.
Authors: Issam El Naqa; Sarah L Kerns; James Coates; Yi Luo; Corey Speers; Catharine M L West; Barry S Rosenstein; Randall K Ten Haken Journal: Phys Med Biol Date: 2017-08-01 Impact factor: 3.609
Authors: Sarah L Kerns; Suman Kundu; Jung Hun Oh; Sandeep K Singhal; Michelle Janelsins; Lois B Travis; Joseph O Deasy; A Cecile J E Janssens; Harry Ostrer; Matthew Parliament; Nawaid Usmani; Barry S Rosenstein Journal: Semin Radiat Oncol Date: 2015-05-15 Impact factor: 5.934
Authors: Isabelle R Miousse; Laura E Ewing; Charles M Skinner; Rupak Pathak; Sarita Garg; Kristy R Kutanzi; Stepan Melnyk; Martin Hauer-Jensen; Igor Koturbash Journal: Am J Physiol Gastrointest Liver Physiol Date: 2020-01-21 Impact factor: 4.052
Authors: Maria Thor; Caroline E Olsson; Jung Hun Oh; Stine E Petersen; David Alsadius; Lise Bentzen; Niclas Pettersson; Ludvig P Muren; Ann-Charlotte Waldenström; Morten Høyer; Gunnar Steineck; Joseph O Deasy Journal: Acta Oncol Date: 2015-09-04 Impact factor: 4.089
Authors: Issam El Naqa; Adam Johansson; Dawn Owen; Kyle Cuneo; Yue Cao; Martha Matuszak; Latifa Bazzi; Theodore S Lawrence; Randall K Ten Haken Journal: Int J Radiat Oncol Biol Phys Date: 2018-02-01 Impact factor: 7.038
Authors: Douglas H Brand; Sarah C Brüningk; Anna Wilkins; Katie Fernandez; Olivia Naismith; Annie Gao; Isabel Syndikus; David P Dearnaley; Alison C Tree; Nicholas van As; Emma Hall; Sarah Gulliford Journal: Int J Radiat Oncol Biol Phys Date: 2021-01-04 Impact factor: 7.038
Authors: Paul Sargos; Mame Daro Faye; Manon Bacci; Stéphane Supiot; Igor Latorzeff; David Azria; Tamim M Niazi; Te Vuong; Véronique Vendrely; Renaud de Crevoisier Journal: Front Oncol Date: 2021-06-16 Impact factor: 6.244