The Different Dose-Volume Effects of Normal Tissue Complication Probability Using LASSO for Acute Small-Bowel Toxicity during Radiotherapy in Gynecological Patients with or without Prior Abdominal Surgery

Abstract

Purpose. To develop normal tissue complication probability (NTCP) model with least absolute shrinkage and selection operator (LASSO) to analyze dose-volume effects that influence the incidence of acute diarrhea among gynecological patients with/without prior abdominal surgery. Methods and Materials. Ninety-five patients receiving gynecologic radiotherapy (RT) were enrolled. The endpoint was defined as the grade 2+ acute diarrhea toxicity during treatment. We obtained the range of small-bowel volume in V4 Gy to V40 Gy of dose. Results. The number of patients experiencing grade 2+ acute diarrhea toxicity was 23/61 (38%) in the group without abdominal surgery (group 0) and 17/34 (50%) patients with abdominal surgery (group 1). The most significant predictor was found for the logistic NTCP model with V16 Gy as the cutoff dose for group 0 and V40 Gy for group 1. Logistic regression NTCP model parameters were TV10 ≈ 290 cc for V16 Gy and TV10 ≈ 75 cc for V40 Gy, respectively. Conclusion. To keep the incidence of grade 2+ acute small-bowel toxicity below 10%, we suggest that small-bowel volume above the prescription dose (V16 Gy) should be held to <290 cc for patients without abdominal surgery, and the prescription dose (V40 Gy) should be maintained <75 cc for patients with abdominal surgery.

Visceral adipose ratio as a novel predictor for acute bowel toxicity in patients receiving pelvic radiation: Preliminary dosimetric analysis and clinical validation

“…Supporting this assertion, a recent multi‐institutional series of 56 patients treated with preoperative radiation for retroperitoneal sarcoma showed low rates of grade 3 or greater acute gastrointestinal toxicity despite a median bowel bag V45 of 575 cc and 82% of treatment plans exceeding a V45 of 195 cc; the authors did not observe a difference in acute toxicity among patients with V45 greater or less than 195 cc . Our results may also help to explain to the lack of consistency observed among institutional studies correlating radiation dose and volume relationships with toxicity …”

“…19 Our results may also help to explain to the lack of consistency observed among institutional studies correlating radiation dose and volume relationships with toxicity. 1,18,20,21 Results of the recently published RTOG 1203 clinical trial have demonstrated a significant decrease in acute radiation toxicity as assessed by the bowel domain of Expanded Prostate Cancer Index Composite (EPIC) with the use of IMRT versus 3D conformal radiation among patients undergoing adjuvant external beam radiation for endometrial and cervical cancers. 3 These findings, as well as smaller institutional series, demonstrate that highly conformal radiation techniques which emphasize small bowel sparing can significantly reduce acute and late toxicity associated with pelvic radiation among gynaecologic cancer patients.…”

Dosimetry and Gastrointestinal Toxicity Relationships in a Phase II Trial of Pelvic Lymph Node Radiotherapy in Advanced Localised Prostate Cancer

“…Interestingly, derived constraints were similar to the ones we propose, although a whole intestinal cavity contouring method was used [32]. Other studies also showed similar results [33], [34]. Robust dosimetric relationships with late toxicity have proved more elusive, but, to our knowledge, we report the largest prospectively collected patient series to date [35], [36].…”

Developing Multivariable Normal Tissue Complication Probability Model to Predict the Incidence of Symptomatic Radiation Pneumonitis among Breast Cancer Patients

“…Data were fitted to a multivariate logistic NTCP regression model, and the method has been described in previous studies [ 15 , 16 , 27 , 28 ]. For each patient, predictive values were calculated for each set of risk factors based on the multivariate logistic regression coefficients according to the following formula: where n is the number of risk factors in the built model, variable x i represents the different risk factors and β i is the corresponding regression coefficient.…”