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Moving Forward in the Next Decade: Radiation Oncology Sciences for Patient-Centered Cancer Care.

ABSTRACT: In a time of rapid advances in science and technology, the opportunities for radiation oncology are undergoing transformational change. The linkage between and understanding of the physical dose and induced biological perturbations are opening entirely new areas of application. The ability to define anatomic extent of disease and the elucidation of the biology of metastases has brought a key role for radiation oncology for treating metastatic disease. That radiation can stimulate and suppress subpopulations of the immune response makes radiation a key participant in cancer immunotherapy. Targeted radiopharmaceutical therapy delivers radiation systemically with radionuclides and carrier molecules selected for their physical, chemical, and biochemical properties. Radiation oncology usage of "big data" and machine learning and artificial intelligence adds the opportunity to markedly change the workflow for clinical practice while physically targeting and adapting radiation fields in real time. Future precision targeting requires multidimensional understanding of the imaging, underlying biology, and anatomical relationship among tissues for radiation as spatial and temporal "focused biology." Other means of energy delivery are available as are agents that can be activated by radiation with increasing ability to target treatments. With broad applicability of radiation in cancer treatment, radiation therapy is a necessity for effective cancer care, opening a career path for global health serving the medically underserved in geographically isolated populations as a substantial societal contribution addressing health disparities. Understanding risk and mitigation of radiation injury make it an important discipline for and beyond cancer care including energy policy, space exploration, national security, and global partnerships.

SUBMITTER: Coleman CN

PROVIDER: S-EPMC8328099 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportanceArtificial intelligence (AI) large language models (LLMs) demonstrate potential in simulating human-like dialogue. Their efficacy in accurate patient-clinician communication within radiation oncology has yet to be explored.ObjectiveTo determine an LLM's quality of responses to radiation oncology patient care questions using both domain-specific expertise and domain-agnostic metrics.Design, setting, and participantsThis cross-sectional study retrieved questions and answers from websites (accessed February 1 to March 20, 2023) affiliated with the National Cancer Institute and the Radiological Society of North America. These questions were used as queries for an AI LLM, ChatGPT version 3.5 (accessed February 20 to April 20, 2023), to prompt LLM-generated responses. Three radiation oncologists and 3 radiation physicists ranked the LLM-generated responses for relative factual correctness, relative completeness, and relative conciseness compared with online expert answers. Statistical analysis was performed from July to October 2023.Main outcomes and measuresThe LLM's responses were ranked by experts using domain-specific metrics such as relative correctness, conciseness, completeness, and potential harm compared with online expert answers on a 5-point Likert scale. Domain-agnostic metrics encompassing cosine similarity scores, readability scores, word count, lexicon, and syllable counts were computed as independent quality checks for LLM-generated responses.ResultsOf the 115 radiation oncology questions retrieved from 4 professional society websites, the LLM performed the same or better in 108 responses (94%) for relative correctness, 89 responses (77%) for completeness, and 105 responses (91%) for conciseness compared with expert answers. Only 2 LLM responses were ranked as having potential harm. The mean (SD) readability consensus score for expert answers was 10.63 (3.17) vs 13.64 (2.22) for LLM answers (P < .001), indicating 10th grade and college reading levels, respectively. The mean (SD) number of syllables was 327.35 (277.15) for expert vs 376.21 (107.89) for LLM answers (P = .07), the mean (SD) word count was 226.33 (191.92) for expert vs 246.26 (69.36) for LLM answers (P = .27), and the mean (SD) lexicon score was 200.15 (171.28) for expert vs 219.10 (61.59) for LLM answers (P = .24).Conclusions and relevanceIn this cross-sectional study, the LLM generated accurate, comprehensive, and concise responses with minimal risk of harm, using language similar to human experts but at a higher reading level. These findings suggest the LLM's potential, with some retraining, as a valuable resource for patient queries in radiation oncology and other medical fields.

Project description:BackgroundThe COVID-19 pandemic has transformed cancer care with the rapid expansion of telemedicine, but given the limited use of telemedicine in oncology, concerns have been raised about the quality of care being delivered. We assessed the patient experience with telemedicine in routine radiation oncology practice to determine satisfaction, quality of care, and opportunities for optimization.Patients and methodsPatients seen within a multistate comprehensive cancer center for prepandemic office visits and intrapandemic telemedicine visits in December 2019 through June 2020 who completed patient experience questionnaires were evaluated. Patient satisfaction between office and telemedicine consultations were compared, patient visit-type preferences were assessed, and factors associated with an office visit preference were determined.ResultsIn total, 1,077 patients were assessed (office visit, n=726; telemedicine, n=351). The telemedicine-consult survey response rate was 40%. No significant differences were seen in satisfaction scores between office and telemedicine consultations, including the appointment experience versus expectation, quality of physician's explanation, and level of physician concern and friendliness. Among telemedicine survey respondents, 45% and 34% preferred telemedicine and office visits, respectively, and 21% had no preference for their visit type. Most respondents found their confidence in their physician (90%), understanding of the treatment plan (88%), and confidence in their treatment (87%) to be better or no different than with an office visit. Patients with better performance status and who were married/partnered were more likely to prefer in-person office visit consultations (odds ratio [OR], 1.04 [95% CI, 1.00-1.08]; P=.047, and 2.41 [95% CI, 1.14-5.47]; P=.009, respectively). Patients with telephone-only encounters were more likely to report better treatment plan understanding with an office visit (OR, 2.25; 95% CI, 1.00-4.77; P=.04).ConclusionsThis study is the first to assess telemedicine in routine radiation oncology practice, and found high patient satisfaction and confidence in their care. Optimization of telemedicine in oncology should be a priority, specifically access to audiovisual capabilities that can improve patient-oncologist communication.

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Moving Forward in the Next Decade: Radiation Oncology Sciences for Patient-Centered Cancer Care.

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