Toward automation of initial chart check for photon/electron EBRT: the clinical implementation of new AAPM task group reports and automation techniques

Assessing initial plan check efficacy using TG 275 failure modes and incident reporting

Plan checks are important components of a robust quality assurance (QA) program. Recently, the American Association of Physicists in Medicine (AAPM) published two reports concerning plan and chart checking, Task Group (TG) 275 and Medical Physics Practice Guideline (MPPG) 11.A. The purpose of the current study was to crosswalk initial plan check failure modes revealed in TG 275 against our institutional QA program and local incident reporting data. Ten physicists reviewed 46 high‐risk failure modes reported in Table S1.A.i of the TG 275 report. The committee identified steps in our planning process which sufficiently checked each failure mode. Failure modes that were not covered were noted for follow‐up. A multidisciplinary committee reviewed the narratives of 1599 locally‐reported incidents in our Radiation Oncology Incident Learning System (ROILS) database and categorized each into the high‐risk TG 275 failure modes. We found that over half of the 46 high‐risk failure modes, six of which were top‐ten failure modes, were covered in part by daily contouring peer‐review rounds, upstream of the traditional initial plan check. Five failure modes were not adequately covered, three of which concerned pregnancy, pacemakers, and prior dose. Of the 1599 incidents analyzed, 710 were germane to the initial plan check, 23.4% of which concerned missing pregnancy attestations. Most, however, were caught prior to CT simulation (98.8%). Physics review and initial plan check were the least efficacious checks, with error detection rates of 31.8% and 31.3%, respectively, for some failure modes. Our QA process that includes daily contouring rounds resulted in increased upstream error detection. This work has led to several initiatives in the department, including increased automation and enhancement of several policies and procedures. With TG 275 and MPPG 11.A as a guide, we strongly recommend that departments consider an internal chart checking policy and procedure review.

Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond

In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.