Chen, Xuguang

98 Background: Prior authorization (PA) purports to control costs and ensure adherence to evidence-based guidelines but negatively impacts cancer care. Radiation oncology (RO) represents a minority of overall cancer spending yet bears a disproportionately high PA burden. Limited research exists on the effects of PA on patient outcomes and administrative workload within RO clinics. Methods: From 9/2024 to 10/2024, an online survey was open to U.S. members of the American Society for Radiation Oncology assessing experiences with PA including disposition (approval/denial/appeal including peer-to-peer [P2P] review) and perceived impact on patient care and administrative workload. Outcomes included treatment delays, changes from recommended treatment, and patient event including hospitalization, ED visits, permanent disability, or death. Descriptive statistics evaluated responses. Results: Of 736 responding radiation oncologists (16% response rate), 83% reported a rise in PA over the past three years, with 58% noting the burden has increased greatly. In the past year, 50% reported that more than half of their cases required PA, with a median approval rate of 75% and median overturn rate of 82% for denials. For more than half of their cases, nearly half of physicians (43%) noted that P2P was a futile effort to overturn PA denials due to insurance policies. Most (75%) reported that PA resulted in their practice needing to reallocate staff; 91% reported worsening burnout due to PA. P2P reviews were particularly burdensome, averaging 1.65 hours per week. A third (30%) reported that PA requirements had caused serious complications for their patients, including 7% who reported PA had contributed to patient death. Almost all (91%) reported PA-related treatment delays for some of their patients, with 58% indicating average delays of ≥5 days. Most (81%) reported that at times they had to use radiation techniques they considered less optimal due to PA, including 58% who were forced to deviate from national guidelines. The most common reasons for not appealing a PA denial were anticipated delays (73%), perceived futility based on prior PA experiences (62%), and administrative burden (45%). Conclusions: The administrative and clinical burdens associated with PA have significant implications for patient care, physician well-being, and healthcare resource allocation in RO. Treatment delays, compromised therapies, and adverse patient outcomes are commonly reported and underscore the need for PA reform to improve patient outcomes, reduce physician burnout, and improve efficiency.

We introduce tumor connectomics, a novel MRI-based complex graph theory framework that describes the intricate network of relationships within the tumor and surrounding tissue, and combine this with multiparametric radiomics (mpRad) in a machine-learning approach to distinguish radiation necrosis (RN) from true progression (TP). Pathologically confirmed cases of RN vs. TP in brain metastases treated with SRS were included from a single institution. The region of interest was manually segmented as the single largest diameter of the T1 post-contrast (T1C) lesion plus the corresponding area of T2 FLAIR hyperintensity. There were 40 mpRad features and 6 connectomics features extracted, as well as 5 clinical and treatment factors. We developed an Integrated Radiomics Informatics System (IRIS) based on an Isomap support vector machine (IsoSVM) model to distinguish TP from RN using leave-one-out cross-validation. Class imbalance was resolved with differential misclassification weighting during model training using the IRIS. In total, 135 lesions in 110 patients were analyzed, including 43 cases (31.9%) of pathologically proven RN and 92 cases (68.1%) of TP. The top-performing connectomics features were three centrality measures of degree, betweenness, and eigenvector centralities. Combining these with the 10 top-performing mpRad features, an optimized IsoSVM model was able to produce a sensitivity of 0.87, specificity of 0.84, AUC-ROC of 0.89 (95% CI: 0.82–0.94), and AUC-PR of 0.94 (95% CI: 0.87–0.97).