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The radiation oncology alternative payment model (RO-APM) was developed by the Center for Medicare and Medicaid Innovation, a part of the Centers for Medicare & Medicaid Services, as a vehicle to optimize value for patients undergoing radiation therapy. By shifting reimbursement away from fee-for-service and toward a prospective bundled payment system, the RO-APM is intended to bend the cost curve in radiation oncology while preserving or even enhancing outcomes. As with prior large-scale policy initiatives, the nature and magnitude of the RO-APM's impact on care delivery will vary substantially depending on a host of local factors, including practice setting. Urban academic centers play a key role in radiation oncology by spearheading innovation, managing the most complicated cases, training the next generation of radiation oncologists, and often caring for vulnerable patient populations. Thus, to protect patients' access to this high-quality cancer care, it will be crucial to characterize the RO-APM's projected impact on large urban academic institutions before its implementation, including possible unintended adverse consequences. Here, we provide an overview of this seismic change in radiation oncology reimbursement and discuss its unique potential implications for large urban academic institutions as a means to facilitate necessary preparations and inform future revisions to the model.

This study, set at the epicenter of the initial U.S. COVID-19 outbreak, shows that nearly 1/5 of cancer patients seeking radiotherapy during the initial surge experienced a COVID-related care change. Notably, advanced age, curative intent, and rectal cancer were predictors of experiencing a care change. Taken together with long-term outcomes data and changes implemented by other groups, we hope this study will help inform the development of treatment guidelines for this and potential future pandemics., Purpose To define how the coronavirus disease 2019 (COVID-19) pandemic impacted the role, timing, and delivery of radiotherapy in a high-prevalence region at the height of the initial U.S. outbreak. Methods We performed a retrospective review of all patients seen at three radiation oncology departments within the “XXXX” Health system in “XXXX” during the initial COVID-19 surge. Primary end points were to define and quantify COVID-related, radiation-specific care changes, and to identify predictive factors of experiencing COVID-related care changes. Results 545 cancer patients were seen during the study period, 99 of whom (18.1%) experienced >1 COVID-related care change. Radiation treatment (RT) delays were most common, accounting for 51.5% of all care changes. Physician-directed delays accounted for 41.2% of RT delays, while patient fears, COVID testing, and access barriers were responsible for 27.5%, 17.6%, and 13.7%, respectively. Patient age (p=0.040), intent of treatment (p=0.047), and cancer type (p

Although there are considerable data on the use of mathematical modeling to describe tumor growth and response to therapy, previous approaches are often not of the form that can be easily applied to clinical data to generate testable predictions in individual patients. Thus, there is a clear need to develop and apply clinically relevant oncologic models that are amenable to available patient data and yet retain the most salient features of response prediction. In this study we show how a biomechanical model of tumor growth can be initialized and constrained by serial patient-specific magnetic resonance imaging data, obtained at two time points early in the course of therapy (before initiation and following one cycle of therapy), to predict the response for individual patients with breast cancer undergoing neoadjuvant therapy. Using our mechanics coupled modeling approach, we are able to predict, after the first cycle of therapy, breast cancer patients that would eventually achieve a complete pathologic response and those who would not, with receiver operating characteristic area under the curve (AUC) of 0.87, sensitivity of 92%, and specificity of 84%. Our approach significantly outperformed the AUCs achieved by standard (i.e., not mechanically coupled) reaction–diffusion predictive modeling (0.75), simple analysis of the tumor cellularity estimated from imaging data (0.73), and the Response Evaluation Criteria in Solid Tumors (0.71). Thus, we show the potential for mathematical model prediction for use as a prognostic indicator of response to therapy. The work indicates the considerable promise of image-driven biophysical modeling for predictive frameworks within therapeutic applications. Cancer Res; 75(22); 4697–707. ©2015 AACR.

To dynamically detect and characterizeInvestigational gamma scintillation sensors were topically applied to patients with locally advanced breast cancer scheduled to undergo limited whole-body FDG-PET as part of an ongoing clinical study. Relative to the affected breast, sensors were placed on the contralateral injection arm and ipsilateral control arm during the resting uptake phase prior to each patient's PET scan. Time-activity curves (TACs) from the sensors were integrated at varying intervals (0-10, 0-20, 0-30, 0-40, and 30-40 min) post-FDG and the resulting areas under the curve (AUCs) were compared to SUVs obtained from PET.In cases of infiltration, observed in three sensor recordings (30 %), the injection arm TAC shape varied depending on the extent and severity of infiltration. In two of these cases, TAC characteristics suggested the infiltration was partially resolving prior to image acquisition, although it was still apparent on subsequent PET. Areas under the TAC 0-10 and 0-20 min post-FDG were significantly different in infiltrated versus non-infiltrated cases (Mann-Whitney, p 0.05). When normalized to control, all TAC integration intervals from the injection arm were significantly correlated with SUVTopical sensors applied near the injection site provide dynamic information from the time of FDG administration through the uptake period and may be useful in detecting infiltrations regardless of PET image field of view. This dynamic information may also complement the static PET image to better characterize the true extent of infiltrations.

Background Planning and delivery of IMRT for locally advanced head and neck cancer (LAHNC) can be performed using sequential boost or simultaneous integrated boost (SIB). Whether these techniques differ in treatment-related outcomes including survival and acute and late toxicities remain largely unexplored. Methods We performed a single institutional retrospective matched cohort analysis on patients with LAHNC treated with definitive chemoradiotherapy to 69.3 Gy in 33 fractions. Treatment was delivered via sequential boost (n = 68) or SIB (n = 141). Contours, plan evaluation, and toxicity assessment were performed by a single experienced physician. Toxicities were graded weekly during treatment and at 3-month follow up intervals. Recurrence-free survival, disease-free survival, and overall survival were estimated via Kaplan-Meier statistical method. Results At 4 years, the estimated overall survival was 69.3% in the sequential boost cohort and 76.8% in the SIB cohort (p = 0.13). Disease-free survival was 63 and 69% respectively (p = 0.27). There were no significant differences in local, regional or distant recurrence-free survival. There were no significant differences in weight loss (p = 0.291), gastrostomy tube placement (p = 0.494), or duration of gastrostomy tube dependence (p = 0.465). Rates of acute grade 3 or 4 dysphagia (82% vs 55%) and dermatitis (78% vs 58%) were significantly higher in the SIB group (p