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PurposeSecond malignancies (SMs) after radiation therapy are rare but serious sequelae of treatment. This study investigates whether radiation therapy use is associated with changes in baseline SM risk.Methods and materialsWe extracted all patients with cancer, with or without SM, in the Surveillance, Epidemiology, and End Results database from 1973 to 2014. Cumulative incidence of SM for patients stratified by radiation therapy status was calculated using a competing risk model, both for the entire cohort and for subgroups based on the primary tumor's anatomic location.ResultsWe identified 2,872,063 patients with cancer, including 761,289 patients who received radiation therapy and 2,110,774 who did not. The SM rate at 20 years for patients receiving radiation therapy versus no radiation therapy was 21.4% versus 18.8%. The relative risk for SM associated with radiation therapy for the overall group was 1.138 at 20 years. The relative risks for SM associated with radiation therapy to malignancies arising from central nervous system and orbits, head and neck, thorax, abdomen, and pelvis at 20 years were 0.704, 1.011, 0.559, 0.646, and 1.106 for men and 0.792, 1.298, 1.265, 0.780, and 0.988 for women, respectively.ConclusionsThe association between SM and radiation therapy varies with both sex and disease anatomic location, with the largest increase in SM seen in females irradiated to the head and neck region. Overall, the absolute change in SM rates associated with radiation therapy remains small, with differences in various clinical contexts.

BackgroundPatients with gastrointestinal cancers and brain metastases (BM) represent a unique and heterogeneous population. Our group previously published the Diagnosis-Specific Graded Prognostic Assessment (DS-GPA) for patients with GI cancers (GI-GPA) (1985-2007, n = 209). The purpose of this study is to update the GI-GPA based on a larger contemporary database.MethodsAn IRB-approved consortium database analysis was performed using a multi-institutional (18), multi-national (3) cohort of 792 patients with gastrointestinal (GI) cancers, with newly-diagnosed BM diagnosed between 1/1/2006 and 12/31/2017. Survival was measured from date of first treatment for BM. Multiple Cox regression was used to select and weight prognostic factors in proportion to their hazard ratios. These factors were incorporated into the updated GI-GPA.ResultsMedian survival (MS) varied widely by primary site and other prognostic factors. Four significant factors (KPS, age, extracranial metastases and number of BM) were used to formulate the updated GI-GPA. Overall MS for this cohort remains poor; 8 months. MS by GPA was 3, 7, 11 and 17 months for GPA 0-1, 1.5-2, 2.5-3.0 and 3.5-4.0, respectively. >30% present in the worst prognostic group (GI-GPA of ≤1.0).ConclusionsBrain metastases are not uncommon in GI cancer patients and MS varies widely among them. This updated GI-GPA index improves our ability to estimate survival for these patients and will be useful for therapy selection, end-of-life decision-making and stratification for future clinical trials. A user-friendly, free, on-line app to calculate the GPA score and estimate survival for an individual patient is available at brainmetgpa.com.

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.

BackgroundBrain metastases are a common complication of renal cell carcinoma (RCC). Our group previously published the Renal Graded Prognostic Assessment (GPA) tool. In our prior RCC study (n = 286, 1985-2005), we found marked heterogeneity and variation in outcomes. In our recent update in a larger, more contemporary cohort, we identified additional significant prognostic factors. The purpose of this study is to update the original Renal-GPA based on the newly identified prognostic factors.MethodsA multi-institutional retrospective institutional review board-approved database of 711 RCC patients with new brain metastases diagnosed from January 1, 2006 to December 31, 2015 was created. Clinical parameters and treatment were correlated with survival. A revised Renal GPA index was designed by weighting the most significant factors in proportion to their hazard ratios and assigning scores such that the patients with the best and worst prognoses would have a GPA of 4.0 and 0.0, respectively.ResultsThe 4 most significant factors were Karnofsky performance status, number of brain metastases, extracranial metastases, and hemoglobin. The overall median survival was 12 months. Median survival for GPA groups 0-1.0, 1.5-2.0, 2.5-3, and 3.5-4.0 (% n = 25, 27, 30 and 17) was 4, 12, 17, and 35 months, respectively.ConclusionThe updated Renal GPA is a user-friendly tool that will help clinicians and patients better understand prognosis, individualize clinical decision making and treatment selection, provide a means to compare retrospective literature, and provide more robust stratification of future clinical trials in this heterogeneous population. To simplify use of this tool in daily practice, a free online application is available at brainmetgpa.com.

PurposeTo identify prognostic factors, define evolving patterns of care, and the effect of targeted therapies in a larger contemporary cohort of renal cell carcinoma (RCC) patients with new brain metastases (BM).Methods and materialsA multi-institutional retrospective institutional review board-approved database of 711 RCC patients with new BM diagnosed from January 1, 2006, to December 31, 2015, was created. Clinical parameters and treatment were correlated with median survival and time from primary diagnosis to BM. Multivariable analyses were performed.ResultsThe median survival for the prior/present cohorts was 9.6/12 months, respectively (P

PurposeLeaders in the oncology community are sounding a clarion call to promote "value" in cancer care decisions. Value in cancer care considers the clinical effectiveness, along with the costs, when selecting a treatment. To discuss possible solutions to the current obstacles to achieving value in the use of advanced technologies in oncology, the National Cancer Policy Forum of the National Academies of Sciences, Engineering, and Medicine held a workshop, "Appropriate Use of Advanced Technologies for Radiation Therapy and Surgery in Oncology" in July 2015. The present report summarizes the discussions related to radiation oncology.Methods and materialsThe workshop convened stakeholders, including oncologists, researchers, payers, policymakers, and patients. Speakers presented on key themes, including the rationale for a value discussion on advanced technology use in radiation oncology, the generation of scientific evidence for value of advanced radiation technologies, the effect of both scientific evidence and "marketplace" (or economic) factors on the adoption of technologies, and newer approaches to improving value in the practice of radiation oncology. The presentations were followed by a panel discussion with dialogue among the stakeholders.ResultsChallenges to generating evidence for the value of advanced technologies include obtaining contemporary, prospective, randomized, and representative comparative effectiveness data. Proposed solutions include the use of prospective registry data; integrating radiation oncology treatment, outcomes, and quality benchmark data; and encouraging insurance coverage with evidence development. Challenges to improving value in practice include the slow adoption of higher value and the de-adoption of lower value treatments. The proposed solutions focused on engaging stakeholders in iterative, collaborative, and evidence-based efforts to define value and promote change in radiation oncology practice. Recent examples of ongoing or successful responses to the discussed challenges were provided.ConclusionsDiscussions of "value" have increased as a priority in the radiation oncology community. Practitioners in the radiation oncology community can play a critical role in promoting a value-oriented framework to approach radiation oncology treatment.