Purpose: Dose-volume histograms (DVHs) may be very useful tools for estimating probability of normal tissue complications (NTCP), but there is not yet an agreed upon method for their analysis. This study introduces a statistical method of aggregating and analyzing primary data from DVHs and associated outcomes. It explores the dose-volume relationship for NTCP of the rectum, using long-term data on rectal wall bleeding following prostatic irradiation.
Methods And Materials: Previously published data were reviewed and updated on 41 patients with Stages T3 and T4 prostatic carcinoma treated with photons followed by perineal proton boost, including dose-volume histograms (DVHs) of each patient's anterior rectal wall and data on the occurrence of postirradiation rectal bleeding (minimum FU > 4 years). Logistic regression was used to test whether some individual combination of dose and volume irradiated might best separate the DVHs into categories of high or low risk for rectal bleeding. Further analysis explored whether a group of such dose-volume combinations might be superior in predicting complication risk. These results were compared with results of the "critical volume model," a mathematical model based on assumptions of underlying radiobiological interactions.
Results: Ten of the 128 tested dose-volume combinations proved to be "statistically significant combinations" (SSCs) distinguishing between bleeders (14 out of 41) and nonbleeders (27 out of 41), ranging contiguously between 60 CGE (Cobalt Gray Equivalent) to 70% of the anterior rectal wall and 75 CGE to 30%. Calculated odds ratios for each SSC were not significantly different across the individual SSCs; however, analysis combining SSCs allowed segregation of DVHs into three risk groups: low, moderate, and high. Estimates of probabilities of normal tissue complications (NTCPs) based on these risk groups correlated strongly with observed data (p = 0.003) and with biomathematical model-generated NTCPs.
Conclusions: There is a dose-volume relationship for rectal mucosal bleeding in the region between 60 and 75 CGE; therefore, efforts to spare rectal wall volume using improved treatment planning and delivery techniques are important. Stratifying dose-volume histograms (DVHs) into risk groups, as done in this study, represents a useful means of analyzing empirical data as a function of hetereogeneous dose distributions. Modeling efforts may extend these results to more heterogeneous treatment techniques. Such analysis of DVH data may allow practicing clinicians to better assess the risk of various treatments, fields, or doses, when caring for an individual patient.
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http://dx.doi.org/10.1016/s0360-3016(96)00366-5 | DOI Listing |
Cureus
December 2024
Physics and Engineering, London Regional Cancer Program, London, CAN.
Introduction: Radiation may unintentionally injure myocardial tissue, potentially leading to radiation-induced cardiac disease (RICD), with the net benefit of non-small cell lung cancer (NSCLC) radiotherapy (RT) due to the proximity of the lung and heart. RTOG-0617 showed a greater reduction in overall survival (OS) comparing higher doses to standard radiation doses in NSCLC RT. VHeart has been reported as an OS predictor in the first- and fifth-year follow-ups.
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
Institute of Information Technology, Vietnam Academy of Science and Technology, Hoang Quoc Viet, Hanoi 10072, Vietnam.
: Cancer is a highly lethal disease with a significantly high mortality rate. One of the most commonly used methods for treatment is radiation therapy. However, cancer treatment using radiotherapy is a time-consuming process that requires significant manual work from planners and doctors.
View Article and Find Full Text PDFFront Oncol
January 2025
Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
Purpose: Recent deep-learning based synthetic computed tomography (sCT) generation using magnetic resonance (MR) images have shown promising results. However, generating sCT for the abdominal region poses challenges due to the patient motion, including respiration and peristalsis. To address these challenges, this study investigated an unsupervised learning approach using a transformer-based cycle-GAN with structure-preserving loss for abdominal cancer patients.
View Article and Find Full Text PDFPhys Med
January 2025
Medical Physics Dept IRCCS San Raffaele Scientific Institution Milano Italy.
Purpose: To train and validate KB prediction models by merging a large multi-institutional cohort of whole breast irradiation (WBI) plans using tangential fields.
Methods: Ten institutions (INST1-INST10, 1481 patients) developed their KB-institutional models for left/right WBI (ten models for right and eight models for left). The transferability of models among centers was assessed based on the overlap of the geometric Principal Component (PC1) of each model when applied to other institutions and/or on the presence of significantly different optimization policies.
Phys Med Biol
January 2025
Joint Department of Physics, Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
This study aims to develop and evaluate a fast and robust deep learning-based auto-segmentation approach for organs at risk in MRI-guided radiotherapy of pancreatic cancer to overcome the problems of time-intensive manual contouring in online adaptive workflows. The research focuses on implementing novel data augmentation techniques to address the challenges posed by limited datasets.This study was conducted in two phases.
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