Assessing the Training and Research Environment for Genomics, Bioinformatics, and Immunology in Radiation Oncology

A Call for Reform: Variability and Insufficiency in Radiation Oncology Resident Didactics-a Brief Report and National Survey of Program Directors

An informal needs assessment and lack of a national standardized curriculum suggest that there is tremendous variability in the formal teaching of radiation oncology resident throughout the USA. The goal of this study was to characterize formal radiation oncology resident education, in order to identify knowledge gaps and areas for improvement. We developed a 14-item survey consisting of the following domains: program characteristics, teaching faculty, formal teaching time, instructional approaches for formal teaching, curricular topics, and satisfaction with didactics. All 91 accredited US-based radiation oncology program directors received an invitation to complete the survey anonymously by email. Twenty-four (26% response rate) program directors responded. Programs used a variety of instructional methods; all programs reported using lecture-based teaching and only a minority using simulation (38%) or flipped classroom techniques (17%). Other than PowerPoint, the most common electronic resource utilized was quizzing/polling (67%), webinar (33%), and econtour.org (13%). The lack of a national, standardized, radiation oncology residency didactic curriculum promotes variability and insufficiency in resident training. Themes for improvement were diversity in didactic topics, incorporation of evidence-based teaching practices, increased faculty involvement, and sharing of resources across programs. Development of a national curriculum and increased electronic resource sharing may help address some of these areas of improvement.

Digital remote monitoring plus usual care versus usual care in patients treated with oral anticancer agents: the randomized phase 3 CAPRI trial

Strategies that individualize the care of cancer patients receiving oral anticancer agents offer opportunities to improve treatment adherence and patient care. However, the impact of digital remote monitoring systems in this setting has not been evaluated. Here, we report the results of a phase 3 trial (CAPRI, NCT02828462) to assess the impact of a nurse navigator-led program on treatment delivery for patients with metastatic cancer. Patients receiving approved oral anticancer agents were randomized (1:1) to an intervention combining a nurse navigator-led follow-up system and a web portal-smartphone application on top of usual care, or to usual symptom monitoring at the discretion of the treating oncologist, for a duration of 6 months. The primary objective included optimization of the treatment dose. Secondary objectives were grade ≥3 toxicities, patient experience, rates and duration of hospitalization, response and survival, and quality of life. In 559 evaluable patients the relative dose intensity was higher in the experimental arm (93.4% versus 89.4%, P = 0.04). The intervention improved the patient experience (Patient Assessment of Chronic Illness Care score, 2.94 versus 2.67, P = 0.01), reduced the days of hospitalization (2.82 versus 4.44 days, P = 0.02), and decreased treatment-related grade ≥3 toxicities (27.6% versus 36.9%, P = 0.02). These findings show that patient-centered care through remote monitoring of symptoms and treatment may improve patient outcomes and experience.

National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation

PURPOSE

Artificial intelligence (AI) is about to touch every aspect of radiation therapy, from consultation to treatment planning, quality assurance, therapy delivery, and outcomes modeling. There is an urgent need to train radiation oncologists and medical physicists in data science to help shepherd AI solutions into clinical practice. Poorly trained personnel may do more harm than good when attempting to apply rapidly developing and complex technologies. As the amount of AI research expands in our field, the radiation oncology community needs to discuss how to educate future generations in this area.

METHODS AND MATERIALS

The National Cancer Institute (NCI) Workshop on AI in Radiation Oncology (Shady Grove, MD, April 4-5, 2019) was the first of 2 data science workshops in radiation oncology hosted by the NCI in 2019. During this workshop, the Training and Education Working Group was formed by volunteers among the invited attendees. Its members represent radiation oncology, medical physics, radiology, computer science, industry, and the NCI.

RESULTS

In this perspective article written by members of the Training and Education Working Group, we provide and discuss action points relevant for future trainees interested in radiation oncology AI: (1) creating AI awareness and responsible conduct; (2) implementing a practical didactic curriculum; (3) creating a publicly available database of training resources; and (4) accelerating learning and funding opportunities.

CONCLUSION

Together, these action points can facilitate the translation of AI into clinical practice.