Objective: The purpose of this study was to develop a model using dose volume histogram (DVH) and dosiomic features to predict the risk of radiation pneumonitis (RP) in the treatment of esophageal cancer with radiation therapy and to compare the performance of DVH and dosiomic features after adjustment for the effect of fractionation by correcting the dose to the equivalent dose in 2 Gy (EQD2).
Materials And Methods: DVH features and dosiomic features were extracted from the 3D dose distribution of 101 esophageal cancer patients. The features were extracted with and without correction to EQD2. A predictive model was trained to predict RP grade ≥ 1 by logistic regression with L1 norm regularization. The models were then evaluated by the areas under the receiver operating characteristic curves (AUCs).
Result: The AUCs of both DVH-based models with and without correction of the dose to EQD2 were 0.66 and 0.66, respectively. Both dosiomic-based models with correction of the dose to EQD2 (AUC = 0.70) and without correction of the dose to EQD2 (AUC = 0.71) showed significant improvement in performance when compared to both DVH-based models. There were no significant differences in the performance of the model by correcting the dose to EQD2.
Conclusion: Dosiomic features can improve the performance of the predictive model for RP compared with that obtained with the DVH-based model.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8591796 | PMC |
| http://dx.doi.org/10.1186/s13014-021-01950-y | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Added Value of Biological Effective Dose in Dosiomics-Based Modelling of Late Rectal Bleeding in Prostate Cancer.
Cancers (Basel)
December 2024
Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands.
Background/objectives: Extracting spatial features (texture analysis) from dose distributions (dosiomics) for outcome prediction is a rapidly evolving field in radiotherapy. To account for fraction size differences, the biological effective dose (BED) is often calculated. We evaluated the impact and added value of the BED in the dosiomics prediction modelling of grade ≥ 2 late rectal bleeding (LRB) probability within 5 years after treatment in three parts.
View Article and Find Full Text PDFPredicting grade II-IV bone marrow suppression in patients with cervical cancer based on radiomics and dosiomics.
Front Oncol
November 2024
Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Objective: The objective of this study is to develop a machine learning model integrating clinical characteristics with radiomics and dosiomics data, aiming to assess their predictive utility in anticipating grade 2 or higher BMS occurrences in cervical cancer patients undergoing radiotherapy.
Methods: A retrospective analysis was conducted on the clinical data, planning CT images, and radiotherapy planning documents of 106 cervical cancer patients who underwent radiotherapy at our hospital. The patients were randomly divided into training set and test set in an 8:2 ratio.
A multi-center, multi-organ, multi-omic prediction model for treatment-induced severe oral mucositis in nasopharyngeal carcinoma.
Radiol Med
November 2024
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Room Y910, 9/F, Block Y, Lee Shau Kee Building, Hung Hom, Kowloon, Hong Kong, China.
Article Synopsis
- Oral mucositis (OM) is a significant complication for nasopharyngeal carcinoma (NPC) patients undergoing radiotherapy, impacting their quality of life, and this study aimed to create a prediction model for severe OM.
- The research involved 464 NPC patients, with a focus on developing a multi-omic model that incorporated clinical, radiomic, and dosiomic features and comparing its effectiveness to a conventional model.
- The multi-omic approach demonstrated better predictive accuracy for severe OM, highlighting the potential for personalized treatment strategies in managing this debilitating side effect in NPC patients.
A deep learning-informed interpretation of why and when dose metrics outside the PTV can affect the risk of distant metastasis in SBRT NSCLC patients.
Radiat Oncol
September 2024
Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Purpose: Recent papers suggested a correlation between the risk of distant metastasis (DM) and dose outside the PTV, though conclusions in different publications conflicted. This study resolves these conflicts and provides a compelling explanation of prognostic factors.
Materials And Methods: A dataset of 478 NSCLC patients treated with SBRT (IMRT or VMAT) was analyzed.
Utilizing radiomics and dosiomics with AI for precision prediction of radiation dermatitis in breast cancer patients.
BMC Cancer
August 2024
Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Jiangong RdSanmin Dist., No.415, Kaohsiung, 80778, Taiwan, ROC.
Purpose: This study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT).
Materials And Methods: This study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!