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The 150 most important questions in cancer research and clinical oncology series: questions 94-101 : Edited by Cancer Communications.

ABSTRACT: Since the beginning of 2017, Cancer Communications (former title: Chinese Journal of Cancer) has published a series of important questions regarding cancer research and clinical oncology, to provide an enhanced stimulus for cancer research, and to accelerate collaborations between institutions and investigators. In this edition, the following 8 valuable questions are presented. Question 94. The origin of tumors: time for a new paradigm? Question 95. How can we accelerate the identification of biomarkers for the early detection of pancreatic ductal adenocarcinoma? Question 96. Can we improve the treatment outcomes of metastatic pancreatic ductal adenocarcinoma through precision medicine guided by a combination of the genetic and proteomic information of the tumor? Question 97. What are the parameters that determine a competent immune system that gives a complete response to cancers after immune induction? Question 98. Is high local concentration of metformin essential for its anti-cancer activity? Question 99. How can we monitor the emergence of cancer cells anywhere in the body through plasma testing? Question 100. Can phytochemicals be more specific and efficient at targeting P-glycoproteins to overcome multi-drug resistance in cancer cells? Question 101. Is cell migration a selectable trait in the natural evolution of carcinoma?

SUBMITTER: Cancer Communications

PROVIDER: S-EPMC6257962 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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The 150 most important questions in cancer research and clinical oncology series: questions 94-101 : Edited by Cancer Communications.

Cancer communications (London, England) 20181126 1

Since the beginning of 2017, Cancer Communications (former title: Chinese Journal of Cancer) has published a series of important questions regarding cancer research and clinical oncology, to provide an enhanced stimulus for cancer research, and to accelerate collaborations between institutions and investigators. In this edition, the following 8 valuable questions are presented. Question 94. The origin of tumors: time for a new paradigm? Question 95. How can we accelerate the identification of bi ...[more]

PMID: 30477575

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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.

Dataset Information

The 150 most important questions in cancer research and clinical oncology series: questions 94-101 : Edited by Cancer Communications.

Publications

The 150 most important questions in cancer research and clinical oncology series: questions 94-101 : Edited by Cancer Communications.

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