TY - JOUR
T1 - SEETrials
T2 - Leveraging large language models for safety and efficacy extraction in oncology clinical trials
AU - Lee, Kyeryoung
AU - Paek, Hunki
AU - Huang, Liang Chin
AU - Hilton, C. Beau
AU - Datta, Surabhi
AU - Higashi, Josh
AU - Ofoegbu, Nneka
AU - Wang, Jingqi
AU - Rubinstein, Samuel M.
AU - Cowan, Andrew J.
AU - Kwok, Mary
AU - Warner, Jeremy L.
AU - Xu, Hua
AU - Wang, Xiaoyan
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/1
Y1 - 2024/1
N2 - Background: Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making. Methods: We collected clinical trial abstracts from key conferences and PubMed (2012–2023). The SEETrials system was developed with three modules: preprocessing, prompt engineering with knowledge ingestion, and postprocessing. We evaluated the system's performance qualitatively and quantitatively and assessed its generalizability across different cancer types— multiple myeloma (MM), breast, lung, lymphoma, and leukemia. Furthermore, the efficacy and safety of innovative therapies, including CAR-T, bispecific antibodies, and antibody-drug conjugates (ADC), in MM were analyzed across a large scale of clinical trial studies. Results: SEETrials achieved high precision (0.964), recall (sensitivity) (0.988), and F1 score (0.974) across 70 data elements present in the MM trial studies Generalizability tests on four additional cancers yielded precision, recall, and F1 scores within the 0.979–0.992 range. Variation in the distribution of safety and efficacy-related entities was observed across diverse therapies, with certain adverse events more common in specific treatments. Comparative performance analysis using overall response rate (ORR) and complete response (CR) highlighted differences among therapies: CAR-T (ORR: 88 %, 95 % CI: 84–92 %; CR: 95 %, 95 % CI: 53–66 %), bispecific antibodies (ORR: 64 %, 95 % CI: 55–73 %; CR: 27 %, 95 % CI: 16–37 %), and ADC (ORR: 51 %, 95 % CI: 37–65 %; CR: 26 %, 95 % CI: 1–51 %). Notable study heterogeneity was identified (>75 % I2 heterogeneity index scores) across several outcome entities analyzed within therapy subgroups. Conclusion: SEETrials demonstrated highly accurate data extraction and versatility across different therapeutics and various cancer domains. Its automated processing of large datasets facilitates nuanced data comparisons, promoting the swift and effective dissemination of clinical insights.
AB - Background: Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making. Methods: We collected clinical trial abstracts from key conferences and PubMed (2012–2023). The SEETrials system was developed with three modules: preprocessing, prompt engineering with knowledge ingestion, and postprocessing. We evaluated the system's performance qualitatively and quantitatively and assessed its generalizability across different cancer types— multiple myeloma (MM), breast, lung, lymphoma, and leukemia. Furthermore, the efficacy and safety of innovative therapies, including CAR-T, bispecific antibodies, and antibody-drug conjugates (ADC), in MM were analyzed across a large scale of clinical trial studies. Results: SEETrials achieved high precision (0.964), recall (sensitivity) (0.988), and F1 score (0.974) across 70 data elements present in the MM trial studies Generalizability tests on four additional cancers yielded precision, recall, and F1 scores within the 0.979–0.992 range. Variation in the distribution of safety and efficacy-related entities was observed across diverse therapies, with certain adverse events more common in specific treatments. Comparative performance analysis using overall response rate (ORR) and complete response (CR) highlighted differences among therapies: CAR-T (ORR: 88 %, 95 % CI: 84–92 %; CR: 95 %, 95 % CI: 53–66 %), bispecific antibodies (ORR: 64 %, 95 % CI: 55–73 %; CR: 27 %, 95 % CI: 16–37 %), and ADC (ORR: 51 %, 95 % CI: 37–65 %; CR: 26 %, 95 % CI: 1–51 %). Notable study heterogeneity was identified (>75 % I2 heterogeneity index scores) across several outcome entities analyzed within therapy subgroups. Conclusion: SEETrials demonstrated highly accurate data extraction and versatility across different therapeutics and various cancer domains. Its automated processing of large datasets facilitates nuanced data comparisons, promoting the swift and effective dissemination of clinical insights.
KW - Automated safety and efficacy extraction
KW - Conference abstracts
KW - GPT-4
KW - Large language models
KW - Large scale analysis
KW - Oncology clinical trial
UR - http://www.scopus.com/inward/record.url?scp=85206266780&partnerID=8YFLogxK
U2 - 10.1016/j.imu.2024.101589
DO - 10.1016/j.imu.2024.101589
M3 - Article
AN - SCOPUS:85206266780
SN - 2352-9148
VL - 50
JO - Informatics in Medicine Unlocked
JF - Informatics in Medicine Unlocked
M1 - 101589
ER -