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

• URL: http://arxiv.org/abs/2411.05378v1
• Date: Fri, 08 Nov 2024 07:19:49 GMT
• Title: Machine learning for prediction of dose-volume histograms of organs-at-risk in prostate cancer from simple structure volume parameters
• Authors: Saheli Saha, Debasmita Banerjee, Rishi Ram, Gowtham Reddy, Debashree Guha, Arnab Sarkar, Bapi Dutta, Moses ArunSingh S, Suman Chakraborty, Indranil Mallick,
• Abstract summary: 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. Several statistical modelling, machine learning methods, and a new fuzzy rule-based prediction model were explored and validated.
• Score: 3.08257604678684
• License:
• 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.

Related papers

• Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray [86.38767955626179]
  Deep-learning algorithm to predict coronary artery calcium (CAC) score was developed on 460 chest x-ray. The diagnostic accuracy of the AICAC model assessed by the area under the curve (AUC) was the primary outcome.
  arXiv  Detail & Related papers  (2024-03-27T16:56:14Z)
• Pulmonologists-Level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach [2.545682175108217]
  Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. In recent years, machine learning has demonstrated considerable potential in healthcare by facilitating the detection of various diseases. We developed an ML model based on dynamic ensemble selection (DES) for LC detection.
  arXiv  Detail & Related papers  (2024-02-14T22:00:57Z)
• A new methodology to predict the oncotype scores based on clinico-pathological data with similar tumor profiles [0.0]
  The Oncotype DX (ODX) test is a commercially available molecular test for breast cancer. The aim of this study is to propose a novel methodology to assist physicians in their decision-making.
  arXiv  Detail & Related papers  (2023-03-13T10:08:13Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Machine Learning-Assisted Recurrence Prediction for Early-Stage Non-Small-Cell Lung Cancer Patients [10.127130900852405]
  Stratifying cancer patients according to risk of relapse can personalize their care. In this work, we provide an answer to how to utilize machine learning to estimate probability of relapse in early-stage non-small-cell lung cancer patients.
  arXiv  Detail & Related papers  (2022-11-17T19:34:16Z)
• Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
  We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents. We generate an automatic tumor boundary detector for the rare disease of glioblastoma. We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
  arXiv  Detail & Related papers  (2022-04-22T17:27:00Z)
• Deep Learning to Quantify Pulmonary Edema in Chest Radiographs [7.121765928263759]
  We developed a machine learning model to classify the severity grades of pulmonary edema on chest radiographs. Deep learning models were trained on a large chest radiograph dataset.
  arXiv  Detail & Related papers  (2020-08-13T15:45:44Z)
• Using Deep Learning to Predict Beam-Tunable Pareto Optimal Dose Distribution for Intensity Modulated Radiation Therapy [0.5735035463793008]
  We implement and compare two deep learning networks that predict with two different beam configuration modalities. We studied and compared two different models, Model I and Model II. Our deep learning models predicted voxel-level dose distributions that precisely matched the ground truth dose distributions.
  arXiv  Detail & Related papers  (2020-06-19T17:15:45Z)
• Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan [49.209225484926634]
  We propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification. Our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time.
  arXiv  Detail & Related papers  (2020-05-07T12:16:37Z)
• Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT [48.785596536318884]
  The proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States.
  arXiv  Detail & Related papers  (2020-04-02T21:49:14Z)
• Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes [64.21642241351857]
  We curated and analyzed a chest computed tomography (CT) data set of 36,316 volumes from 19,993 unique patients. We developed a rule-based method for automatically extracting abnormality labels from free-text radiology reports. We also developed a model for multi-organ, multi-disease classification of chest CT volumes.
  arXiv  Detail & Related papers  (2020-02-12T00:59:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.