{"title":"评估MRI解剖在机器学习预测模型中评估水凝胶间隔剂对前列腺癌患者的益处","authors":"Madison Bush , Scott Jones , Catriona Hargrave","doi":"10.1016/j.tipsro.2025.100305","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><div>Hydrogel spacers (HS) are designed to minimise the radiation doses to the rectum in prostate cancer radiation therapy (RT) by creating a physical gap between the rectum and the target treatment volume inclusive of the prostate and seminal vesicles (SV). This study aims to determine the feasibility of incorporating diagnostic MRI (dMRI) information in statistical machine learning (SML) models developed with planning CT (pCT) anatomy for dose and rectal toxicity prediction. The SML models aim to support HS insertion decision-making prior to RT planning procedures.</div></div><div><h3>Methods</h3><div>Regions of interest (ROIs) were retrospectively contoured on the pCT and registered dMRI scans for 20 patients. ROI Dice and Hausdorff distance (HD) comparison metrics were calculated. The ROI and patient clinical risk factors (CRFs) variables were inputted into three SML models and then pCT and dMRI-based dose and toxicity model performance compared through confusion matrices, AUC curves, accuracy performance metric results and observed patient outcomes.</div></div><div><h3>Results</h3><div>Average Dice values comparing dMRI and pCT ROIs were 0.81, 0.47 and 0.71 for the prostate, SV, and rectum respectively. Average Hausdorff distances were 2.15, 2.75 and 2.75 mm for the prostate, SV, and rectum respectively. The average accuracy metric across all models was 0.83 when using dMRI ROIs and 0.85 when using pCT ROIs.</div></div><div><h3>Conclusion</h3><div>Differences between pCT and dMRI anatomical ROI variables did not impact SML model performance in this study, demonstrating the feasibility of using dMRI images. Due to the limited sample size further training of the predictive models including dMRI anatomy is recommended.</div></div>","PeriodicalId":36328,"journal":{"name":"Technical Innovations and Patient Support in Radiation Oncology","volume":"34 ","pages":"Article 100305"},"PeriodicalIF":0.0000,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluation of MRI anatomy in machine learning predictive models to assess hydrogel spacer benefit for prostate cancer patients\",\"authors\":\"Madison Bush , Scott Jones , Catriona Hargrave\",\"doi\":\"10.1016/j.tipsro.2025.100305\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Introduction</h3><div>Hydrogel spacers (HS) are designed to minimise the radiation doses to the rectum in prostate cancer radiation therapy (RT) by creating a physical gap between the rectum and the target treatment volume inclusive of the prostate and seminal vesicles (SV). This study aims to determine the feasibility of incorporating diagnostic MRI (dMRI) information in statistical machine learning (SML) models developed with planning CT (pCT) anatomy for dose and rectal toxicity prediction. The SML models aim to support HS insertion decision-making prior to RT planning procedures.</div></div><div><h3>Methods</h3><div>Regions of interest (ROIs) were retrospectively contoured on the pCT and registered dMRI scans for 20 patients. ROI Dice and Hausdorff distance (HD) comparison metrics were calculated. The ROI and patient clinical risk factors (CRFs) variables were inputted into three SML models and then pCT and dMRI-based dose and toxicity model performance compared through confusion matrices, AUC curves, accuracy performance metric results and observed patient outcomes.</div></div><div><h3>Results</h3><div>Average Dice values comparing dMRI and pCT ROIs were 0.81, 0.47 and 0.71 for the prostate, SV, and rectum respectively. Average Hausdorff distances were 2.15, 2.75 and 2.75 mm for the prostate, SV, and rectum respectively. The average accuracy metric across all models was 0.83 when using dMRI ROIs and 0.85 when using pCT ROIs.</div></div><div><h3>Conclusion</h3><div>Differences between pCT and dMRI anatomical ROI variables did not impact SML model performance in this study, demonstrating the feasibility of using dMRI images. Due to the limited sample size further training of the predictive models including dMRI anatomy is recommended.</div></div>\",\"PeriodicalId\":36328,\"journal\":{\"name\":\"Technical Innovations and Patient Support in Radiation Oncology\",\"volume\":\"34 \",\"pages\":\"Article 100305\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Technical Innovations and Patient Support in Radiation Oncology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S240563242500006X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Nursing\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technical Innovations and Patient Support in Radiation Oncology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S240563242500006X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Nursing","Score":null,"Total":0}
Evaluation of MRI anatomy in machine learning predictive models to assess hydrogel spacer benefit for prostate cancer patients
Introduction
Hydrogel spacers (HS) are designed to minimise the radiation doses to the rectum in prostate cancer radiation therapy (RT) by creating a physical gap between the rectum and the target treatment volume inclusive of the prostate and seminal vesicles (SV). This study aims to determine the feasibility of incorporating diagnostic MRI (dMRI) information in statistical machine learning (SML) models developed with planning CT (pCT) anatomy for dose and rectal toxicity prediction. The SML models aim to support HS insertion decision-making prior to RT planning procedures.
Methods
Regions of interest (ROIs) were retrospectively contoured on the pCT and registered dMRI scans for 20 patients. ROI Dice and Hausdorff distance (HD) comparison metrics were calculated. The ROI and patient clinical risk factors (CRFs) variables were inputted into three SML models and then pCT and dMRI-based dose and toxicity model performance compared through confusion matrices, AUC curves, accuracy performance metric results and observed patient outcomes.
Results
Average Dice values comparing dMRI and pCT ROIs were 0.81, 0.47 and 0.71 for the prostate, SV, and rectum respectively. Average Hausdorff distances were 2.15, 2.75 and 2.75 mm for the prostate, SV, and rectum respectively. The average accuracy metric across all models was 0.83 when using dMRI ROIs and 0.85 when using pCT ROIs.
Conclusion
Differences between pCT and dMRI anatomical ROI variables did not impact SML model performance in this study, demonstrating the feasibility of using dMRI images. Due to the limited sample size further training of the predictive models including dMRI anatomy is recommended.