Multi-agent LLMs for Decision Support in Cervical Cancer During Pregnancy
Multi-agent Large Language Models for Multidisciplinary Decision Support in Cervical Cancer During Pregnancy
1 other identifier
interventional
150
0 countries
N/A
Brief Summary
The aim of this study is to develop an AI-assisted decision-making system based on multi-agent large language models and to evaluate its effectiveness and accuracy in the diagnosis and treatment of cervical cancer during pregnancy.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable
Started Jan 2026
Shorter than P25 for not_applicable
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
December 18, 2025
CompletedStudy Start
First participant enrolled
January 1, 2026
CompletedFirst Posted
Study publicly available on registry
January 6, 2026
CompletedPrimary Completion
Last participant's last visit for primary outcome
June 30, 2026
ExpectedStudy Completion
Last participant's last visit for all outcomes
October 30, 2026
January 6, 2026
January 1, 2026
6 months
December 18, 2025
January 4, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Accuracy of the MDT decision
scores, from 0 to 100, accuracy of the MDT decision according to evaluation indicators of each discipline
immediately after the intervention
Secondary Outcomes (1)
Consuming time
immediately after the intervention
Study Arms (2)
Arm1: multi-disciplinary agents group
EXPERIMENTALgenerate diagnosis and treatment opinions from multi-disciplinary agents
Arm2: real MDT group/ junior doctors group/junior doctors after referring to agent results group
PLACEBO COMPARATORgenerate diagnosis and treatment opinions from real MDT group/ junior doctors group/junior doctors after referring to agent results group
Interventions
generate diagnosis and treatment opinions for each case from a real MDT team inclduing senior physicians from relevant departments, including gynecologic oncology, pediatrics, obstetrics, medical oncology and radiation oncology.
generate diagnosis and treatment opinions for each case from junior doctor who are residents from relevant departments, including gynecologic oncology, pediatrics, obstetrics, medical oncology and radiation oncology.
generate diagnosis and treatment opinions for each case from junior doctor who are residents from relevant departments, including gynecologic oncology, pediatrics, obstetrics, medical oncology and radiation oncology after referring to the results from MDT agent .
generate diagnosis and treatment opinions for each case from multi-disciplinary agents
Eligibility Criteria
You may qualify if:
- Pathologically confirmed diagnosis of cervical cancer.
- Confirmed intrauterine pregnancy status via ultrasound.
- Patients receiving initial treatment.
- Agreement to participate in the study with signed informed consent.
You may not qualify if:
- Previous treatment received for cervical cancer during pregnancy.
- Pathological pregnancy states (e.g., ectopic pregnancy).
- Inability or unwillingness to provide signed informed consent.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Related Publications (10)
Li R, Wang X, Berlowitz D, Mez J, Lin H, Yu H. CARE-AD: a multi-agent large language model framework for Alzheimer's disease prediction using longitudinal clinical notes. NPJ Digit Med. 2025 Aug 24;8(1):541. doi: 10.1038/s41746-025-01940-4.
PMID: 40849361RESULTMeyer R, Hamilton KM, Truong MD, Wright KN, Siedhoff MT, Brezinov Y, Levin G. ChatGPT compared with Google Search and healthcare institution as sources of postoperative patient instructions after gynecological surgery. BJOG. 2024 Jul;131(8):1154-1156. doi: 10.1111/1471-0528.17746. Epub 2024 Jan 4. No abstract available.
PMID: 38177090RESULTPatel JM, Hermann CE, Growdon WB, Aviki E, Stasenko M. ChatGPT accurately performs genetic counseling for gynecologic cancers. Gynecol Oncol. 2024 Apr;183:115-119. doi: 10.1016/j.ygyno.2024.04.006. Epub 2024 Apr 26.
PMID: 38676973RESULTHermann CE, Patel JM, Boyd L, Growdon WB, Aviki E, Stasenko M. Let's chat about cervical cancer: Assessing the accuracy of ChatGPT responses to cervical cancer questions. Gynecol Oncol. 2023 Dec;179:164-168. doi: 10.1016/j.ygyno.2023.11.008. Epub 2023 Nov 21.
PMID: 37988948RESULTGarg P, Mohanty A, Ramisetty S, Kulkarni P, Horne D, Pisick E, Salgia R, Singhal SS. Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers. Biochim Biophys Acta Rev Cancer. 2023 Nov;1878(6):189026. doi: 10.1016/j.bbcan.2023.189026. Epub 2023 Nov 20.
PMID: 37980945RESULTBedi S, Jain SS, Shah NH. Evaluating the clinical benefits of LLMs. Nat Med. 2024 Sep;30(9):2409-2410. doi: 10.1038/s41591-024-03181-6. No abstract available.
PMID: 39060659RESULTMacchia G, Ferrandina G, Patarnello S, Autorino R, Masciocchi C, Pisapia V, Calvani C, Iacomini C, Cesario A, Boldrini L, Gui B, Rufini V, Gambacorta MA, Scambia G, Valentini V. Multidisciplinary Tumor Board Smart Virtual Assistant in Locally Advanced Cervical Cancer: A Proof of Concept. Front Oncol. 2022 Jan 3;11:797454. doi: 10.3389/fonc.2021.797454. eCollection 2021.
PMID: 35047408RESULTAmant F, Berveiller P, Boere IA, Cardonick E, Fruscio R, Fumagalli M, Halaska MJ, Hasenburg A, Johansson ALV, Lambertini M, Lok CAR, Maggen C, Morice P, Peccatori F, Poortmans P, Van Calsteren K, Vandenbroucke T, van Gerwen M, van den Heuvel-Eibrink M, Zagouri F, Zapardiel I. Gynecologic cancers in pregnancy: guidelines based on a third international consensus meeting. Ann Oncol. 2019 Oct 1;30(10):1601-1612. doi: 10.1093/annonc/mdz228.
PMID: 31435648RESULTPeccatori FA, Azim HA Jr, Orecchia R, Hoekstra HJ, Pavlidis N, Kesic V, Pentheroudakis G; ESMO Guidelines Working Group. Cancer, pregnancy and fertility: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2013 Oct;24 Suppl 6:vi160-70. doi: 10.1093/annonc/mdt199. Epub 2013 Jun 27. No abstract available.
PMID: 23813932RESULTHalaska MJ, Drochytek V, Shmakov RG, Amant F. Fertility sparing treatment in cervical cancer management in pregnancy. Best Pract Res Clin Obstet Gynaecol. 2021 Sep;75:101-112. doi: 10.1016/j.bpobgyn.2021.03.014. Epub 2021 Apr 22.
PMID: 33992541RESULT
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Keqin Hua, Doctor
Gynecology and obstetrics hospital of fudan university
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- DOUBLE
- Who Masked
- PARTICIPANT, OUTCOMES ASSESSOR
- Purpose
- SUPPORTIVE CARE
- Intervention Model
- PARALLEL
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Doctor, Principal Investigator, Clinical Professor
Study Record Dates
First Submitted
December 18, 2025
First Posted
January 6, 2026
Study Start
January 1, 2026
Primary Completion (Estimated)
June 30, 2026
Study Completion (Estimated)
October 30, 2026
Last Updated
January 6, 2026
Record last verified: 2026-01
Data Sharing
- IPD Sharing
- Will not share