ESTRO 2025 - Abstract Book

S3710

Physics - Radiomics, functional and biological imaging and outcome prediction

ESTRO 2025

4

Digital Poster Improving DVH based analysis of clinical outcomes using modern statistical techniques. A systematic answer to multiple comparisons concerns Mirek Fatyga 1 , Jiuyun Hu 2 , Jing Li 3 1 Radiation Oncology, Mayo Clinic Arizona, Phoenix, USA. 2 School of Computing & Augmented Intelligence, Arizona State University, Tempe, USA. 3 H. Milton Stewart School of Indistrial and Systems Engineering, Georgia Institute of Technology, Atlanta, USA Purpose/Objective: Historically, DVH constraints were found through sparse sampling of DVH indices to find the index most predictive for clinical toxicity. This approach can lead to inconsistent results among studies and to multiple comparison concerns. We aim to solve both problems by examining a full array of DVH indices using statistical methods that account for strong correlations among DVH indices and incorporate radiobiological knowledge constraints. Material/Methods: We extracted a dense array of V%_D indices from Eclipse treatment planning system using ESAPI interface, with V%_D corresponding to the volume fraction irradiated to dose D, or higher. We performed a scan of V%_D index space. We used Fused Lasso as the base integrative model to compensate for correlations among DVH indices because it applies a penalty on the difference between DVH variables with adjacent dose. The base model was augmented with additional constraints based on radiobiological considerations: the positivity constraint () which assumes that any tissue irradiation cannot reduce the risk of toxicity, and monotonicity constraint ( which assumes that higher dose to a fixed volume fraction cannot be associated with a lower risk of toxicity. We called the hybrid model KC-Lasso (Knowledge Constrained Lasso) and applied it to two clinical examples: grade 2 acute rectal toxicity in conventionally fractionated RT for 79 prostate cancer patients (77.4 Gy + MR based boost to 81-83Gy) and cardiac toxicity in conventionally fractionated RT for 119 locally advanced Non-small Cell Lung Cancer (NSCLC) patients (Median prescribed dose 62Gy). We further examined alternative data driven models to determine the importance of knowledge constraints. Results: Index scan revealed three distinct regions: predictive, transition, and non-predictive. KC-Lasso detected two distinct dose thresholds for grade 2 rectal toxicity, at 35Gy and 78Gy. A single threshold of 51Gy was detected for reduced overall survival due to cardiac irradiation in NSCLC patients. An examination of KC-Lasso models at varying step size suggested that a single index in the predictive region can be used to establish a treatment planning constraint while full model can be used for confirmatory, final plan evaluation. Alternative models which lack knowledge constraints show patterns of negative and isolated coefficients which are difficult to interpret and are not likely to be generalizable. Conclusion: A systematic approach to the analysis of correlations between DVH constraints and toxicity can lead to greater consistency of results among different studies, better understanding of true dose thresholds and results which are more generalizable.

Keywords: rectal toxicity cardiac toxicity