Summary of Machine Learning For Prediction Of Dose-volume Histograms Of Organs-at-risk in Prostate Cancer From Simple Structure Volume Parameters, by Saheli Saha et al.
Machine learning for prediction of dose-volume histograms of organs-at-risk in prostate cancer from simple structure volume parameters
by Saheli Saha, Debasmita Banerjee, Rishi Ram, Gowtham Reddy, Debashree Guha, Arnab Sarkar, Bapi Dutta, Moses ArunSingh S, Suman Chakraborty, Indranil Mallick
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning educators can learn about a new approach to predicting dose-volume information for radiotherapy planning in this study. The researchers aimed to develop machine learning models that use volume data from target and at-risk organs, as well as overlap regions, to predict rectum and bladder doses. They used text files of patient data from a treatment planning system to create a training dataset, which they validated on an independent set of patients. The results show that the fuzzy rule-based prediction (FRBP) model produced accurate predictions for dose-volume parameters, with median absolute errors ranging from 1.2% to 3.7%. This study demonstrates the feasibility of using machine learning models to predict clinically important dose-volume parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make sure radiation treatment plans are safe and effective. The goal was to create a computer model that can predict how much radiation will affect certain parts of the body, like the rectum and bladder. To do this, they used information from special files that doctors use to plan treatments. They tested their model on a group of patients with prostate cancer and found that it worked really well, producing accurate predictions within 1-4% of the actual values. This is important because accurate dose-volume predictions can help doctors make better treatment plans for patients. |
Keywords
* Artificial intelligence * Machine learning
