Machine learning for prediction of dose-volume histograms of organs-at-risk in prostate cancer from simple structure volume parameters

Saheli Saha; Debasmita Banerjee; Rishi Ram; Gowtham Reddy; Debashree Guha; Arnab Sarkar; Bapi Dutta; Moses ArunSingh S; Suman Chakraborty; Indranil Mallick

arXiv · 2411.05378

Machine learning for prediction of dose-volume histograms of organs-at-risk in prostate cancer from simple structure volume parameters

Saheli Saha·Debasmita Banerjee·Rishi Ram·Gowtham Reddy·Debashree Guha·Arnab Sarkar·Bapi Dutta·Moses ArunSingh S·Suman Chakraborty·Indranil Mallick

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Abstract

Dose prediction is an area of ongoing research that facilitates radiotherapy planning. Most commercial models utilise imaging data and intense computing resources. This study aimed to predict the dose-volume of rectum and bladder from volumes of target, at-risk structure organs and their overlap regions using machine learning. Dose-volume information of 94 patients with prostate cancer planned for 6000cGy in 20 fractions was exported from the treatment planning system as text files and mined to create a training dataset. Several statistical modelling, machine learning methods, and a new fuzzy rule-based prediction (FRBP) model were explored and validated on an independent dataset of 39 patients. The median absolute error was 2.0%-3.7% for bladder and 1.7-2.4% for rectum in the 4000-6420cGy range. For 5300cGy, 5600cGy and 6000cGy, the median difference was less than 2.5% for rectum and 3.8% for bladder. The FRBP model produced errors of 1.2%, 1.3%, 0.9% and 1.6%, 1.2%, 0.1% for the rectum and bladder respectively at these dose levels. These findings indicate feasibility of obtaining accurate predictions of the clinically important dose-volume parameters for rectum and bladder using just the volumes of these structures.

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Saheli Saha, Debasmita Banerjee, Rishi Ram, Gowtham Reddy, Debashree Guha, Arnab Sarkar, Bapi Dutta, Moses ArunSingh S, Suman Chakraborty, Indranil Mallick. 2024-11-08. Machine learning for prediction of dose-volume histograms of organs-at-risk in prostate cancer from simple structure volume parameters. https://arxiv.org/abs/2411.05378

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