Patient Specific Characteristics Are an Important Factor That Determines the Risk of Acute Grade ≥ 2 Rectal Toxicity in Patients Treated for Prostate Cancer with IMRT and Daily Image Guidance Based on Implanted Gold Markers

Abstract

AimTo model acute rectal toxicity in Intensity Modulated Radiation Therapy (IMRT) for prostate cancer using dosimetry and patient specific characteristics.MethodsA database of 79 prostate cancer patients treated with image guided IMRT was used to fit parameters of Lyman-Kutcher-Burman (LKB) and logistic regression Normal Tissue Complications Probability (NTCP) models to acute grade ≥ 2 rectal toxicities. We used a univariate regression model to find the dosimetric index which was most correlated with toxicity and a multivariate logistic regression model with machine learning algorithm to integrate dosimetry with patient specific characteristics. We used Receiver Operating Characteristics (ROC) analysis and the area under the ROC curve (AUC) to quantify the predictive power of models.ResultsSixteen patients (20.3%) developed acute grade≥2 rectal toxicity. Our best estimate (95% confidence interval) of LKB model parameters for acute rectal toxicity are exponent n=0.13 (0.1–0.16), slope m=0.09 (0.08–0.11), and threshold dose TD50=56.8 (53.7–59.9) Gy. The best dosimetric indices in the univariate logistic regression NTCP model were D25% and V50Gy. The best AUC of dosimetry only modeling was 0.67 (0.54, 0.8). In the multivariate logistic regression two patient specific variables were particularly strongly correlated with acute rectal toxicity, the use of statin drugs and PSA level prior to IMRT, while two additional variables, age and diabetes were weakly correlated. The AUC of the logistic regression NTCP model improved to 0.88 (0.8, 0.96) when patient specific characteristics were included. In a group of 79 patients, 40 took Statins and 39 did not. Among patients who took statins, (4/40)=10% developed acute grade ≥2 rectal toxicity, compared to (12/39)=30.8% who did not take statins (p=0.03). The average and standard deviation of PSA distribution for patients with acute rectal toxicity was PSAtox = 5.77 ± 2.27 and it was PSAnotox = 9.5 ± 7.8 for the remainder (p=0.01).ConclusionsPatient specific characteristics strongly influence the likelihood of acute grade ≥ 2 rectal toxicity in radiation therapy for prostate cancer.

Statins Protect Against Acute RT-related Rectal Toxicity in Patients with Prostate Cancer: An Observational Prospective Study

“…A low acute rectal toxicity rate, prevalently grade 1, was observed. Other than the fractionation schedule (24,25), the toxicity potential depends on many variables, such as dosimetric parameters (10,11) and concomitant treatments such as hormone therapy (15)(16)(17) and medications for other conditions (9,(19)(20)(21). In our patient series, no significant correlation emerged between acute rectal toxicity and dosimetric variables, which may have depended on our adherence to dose constraints that were adjusted for our hypofractionation schedule (V38 <60%, V57 <40% and V66.5 <25%).…”

“…RT-related toxicity might be reduced by some specific classes of drugs, such as statins and calcium channel blockers (9,(19)(20)(21). The statin-related drop in rectal cancer toxicity might be due to their anti-fibrotic, anti-inflammatory and anti-thrombotic effects, which were demonstrated in vitro and in vivo (19).…”

“…In acute rectal toxicity, the role of dosimetric variables is quite well understood and a solid set of dose-volume constraints and logistic curves estimating the associated risk of rectal injury are readily available (10,11). The same consensus does not apply to the impact of clinical variables such as age (7,(12)(13)(14), and androgen deprivation (15)(16)(17)(18), while concomitant medication with 3-hydroxy-methylglutaryl coenzyme-A reductase inhibitors (statins) or antihypertensive drugs seems to have a protective effect (9,(19)(20)(21).…”

Modeling of Acute Rectal Toxicity to Compare Two Patient Positioning Methods for Prostate Cancer Radiotherapy: Can Toxicity Modeling be Used for Quality Assurance?

“…An analysis which was discussed in this work can be relatively easily extended to a multivariate analysis which includes patient specific characteristics. We have shown in a prior work [32] that patient specific characteristics are not only important for full characterization of the toxicity risk but may even be a dominant predictor of toxicity in some cases. A commercial implementation of the present work could lead to the development of an “expert system” which quantifies a risk for toxicity for patients who are treated within a particular institution, using treatment protocols which are specific to this institution.…”

Strategies for prediction and mitigation of radiation-induced liver toxicity

“…Methodologically, random forests and neural networks are the most commonly selected approaches for toxicity prediction [ 112 , 116 , 117 , 119 , 121 – 123 ]. Computational models to predict drug-induced liver toxicity have been previously reported, but to our knowledge, there is lack of evidence on RILD prediction using machine-learning methods [ 124 , 125 ].…”