1. Machine learning for prediction of dose-volume histograms of organs-at-risk in prostate cancer from simple structure volume parameters
- Author
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Saha, Saheli, Banerjee, Debasmita, Ram, Rishi, Reddy, Gowtham, Guha, Debashree, Sarkar, Arnab, Dutta, Bapi, S, Moses ArunSingh, Chakraborty, Suman, and Mallick, Indranil
- Subjects
Computer Science - Machine Learning - 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.
- Published
- 2024