Language：English   Publishing type：Research paper (scientific journal)  

BACKGROUND: Programmed death ligand 1 (PD-L1) serves as a crucial biomarker for predicting the efficacy of immune checkpoint inhibitors in patients with non-small cell lung cancer (NSCLC). This study aimed to identify the most suitable first-line treatment regimen for patients with PD-L1 expression <1% (PD-L1-negative) NSCLC by comparing nivolumab plus ipilimumab (NI), NI combined with chemotherapy (NICT), and pembrolizumab and chemotherapy (PCT). METHODS: We analyzed data from 141 patients with PD-L1-negative NSCLC treated with NI, NICT, or PCT at 14 Japanese institutions between December 2020 and November 2022. Propensity score analysis was employed to minimize selection bias, and Kaplan-Meier analysis and Cox proportional hazards regression were used to evaluate progression-free survival (PFS) and overall survival (OS). RESULTS: Neither NI nor NICT demonstrated superior PFS or OS than PCT. Subgroup analyses revealed no significant differences between treatment groups across age, histological subtypes, or clinical features. Results from propensity score matching and inverse probability of treatment weighting were consistent with those observed in the overall cohort. Moreover, safety profiles showed that PCT was associated with the lowest rates of treatment discontinuation and immune-related adverse events requiring systemic corticosteroid therapy. CONCLUSIONS: In patients with PD-L1-negative NSCLC, the efficacy of NI and NICT was not superior to that of PCT. Thus, we concluded that PCT could be a favorable treatment option for this patient population.

DOI： 10.1093/jjco/hyaf073

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

This pilot study is the first phase of a broader project aimed at developing an explainable artificial intelligence (AI) tool to support the ethical evaluation of Japanese-language clinical research documents. The tool is explicitly not intended to assist document drafting. We assessed the baseline performance of generative AI-Generative Pre-trained Transformer (GPT)-4 and GPT-4o-in analyzing clinical research protocols and informed consent forms (ICFs). The goal was to determine whether these models could accurately and consistently extract ethically relevant information, including the research objectives and background, research design, and participant-related risks and benefits. First, we compared the performance of GPT-4 and GPT-4o using custom agents developed via OpenAI's Custom GPT functionality (hereafter "GPTs"). Then, using GPT-4o alone, we compared outputs generated by GPTs optimized with customized Japanese prompts to those generated by standard prompts. GPT-4o achieved 80% agreement in extracting research objectives and background and 100% in extracting research design, while both models demonstrated high reproducibility across ten trials. GPTs with customized prompts produced more accurate and consistent outputs than standard prompts. This study suggests the potential utility of generative AI in pre-institutional review board (IRB) review tasks; it also provides foundational data for future validation and standardization efforts involving retrieval-augmented generation and fine-tuning. Importantly, this tool is intended not to automate ethical review but rather to support IRB decision-making. Limitations include the absence of gold standard reference data, reliance on a single evaluator, lack of convergence and inter-rater reliability analysis, and the inability of AI to substitute for in-person elements such as site visits.

DOI： 10.1371/journal.pdig.0000695

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Language：English   Publishing type：Research paper (scientific journal)  

INTRODUCTION: The aim of the study was to examine whether disinfection of bacillus Calmette-Guerin-containing urine with etaprocohol® (ethanol 76.9-81.4 vol % and isopropanol as an additive) is safer than disinfection with sodium hypochlorite. METHOD: In prospective research, safety and efficacy was analyzed in 5 patients in the etaprocohol® disinfection group and 5 patients in the sodium hypochlorite disinfection group. The primary endpoint was the temperature change after disinfection and the secondary endpoint was the unpleasantness of the odor caused by disinfection. Additionally, concentration of gas produced was also examined. Sensory tests were taken from staff who performed urine disinfection and the odor generated by disinfection was evaluated. As a safety protocol, post-BCG-treated urine is cultured to verify the negativity for mycobacteria. RESULTS: Mycobacteria were disinfected in all cases. The temperature rise following disinfection was significantly higher in the sodium hypochlorite group. The sensory test outcomes were significantly worse in the group disinfected with sodium hypochlorite. The concentration of gas generated immediately after disinfection in both groups reached the maximum value and declined quickly. CONCLUSIONS: Disinfection of bacillus Calmette-Guerin-containing urine with etaprocohol® was safer than disinfection with sodium hypochlorite, and an equivalent disinfection effect was achieved.

DOI： 10.1159/000538758

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Language：English   Publishing type：Research paper (scientific journal)  

Medical interviews are expected to undergo a major transformation through the use of artificial intelligence. However, artificial intelligence-based systems that support medical interviews are not yet widespread in Japan, and their usefulness is unclear. A randomized, controlled trial to determine the usefulness of a commercial medical interview support system using a question flow chart-type application based on a Bayesian model was conducted. Ten resident physicians were allocated to two groups with or without information from an artificial intelligence-based support system. The rate of correct diagnoses, amount of time to complete the interviews, and number of questions they asked were compared between the two groups. Two trials were conducted on different dates, with a total of 20 resident physicians participating. Data for 192 differential diagnoses were obtained. There was a significant difference in the rate of correct diagnosis between the two groups for two cases and for overall cases (0.561 vs. 0.393; p = 0.02). There was a significant difference in the time required between the two groups for overall cases (370 s (352-387) vs. 390 s (373-406), p = 0.04). Artificial intelligence-assisted medical interviews helped resident physicians make more accurate diagnoses and reduced consultation time. The widespread use of artificial intelligence systems in clinical settings could contribute to improving the quality of medical care.

DOI： 10.3390/ijerph20126176

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Authorship：Lead author,　Corresponding author  

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Event date： 2025.12

Language：Japanese   Presentation type：Symposium, workshop panel (public)  

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Language：Japanese   Presentation type：Poster presentation  

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Presentation type：Poster presentation  

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Presentation type：Poster presentation  

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Language：Japanese   Presentation type：Poster presentation  

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