Concordance of a decision algorithm and multidisciplinary team meetings for patients with liver cancer—a study protocol for a randomized controlled trial

Trials

Study Protocol

Timo A. Auer¹  Robert Oehring²  Johann Pratschke²  Simon Moosburner²  Axel Winter²  Christian Benzing²  Sharlyn S. T. Ng²  Can Kamali²  Nikitha Ramasetti²  Max-Magnus Maurer²  Felix Krenzien³  Roland Roller⁴  Philippe Thomas⁴  Yuxuan Chen⁴ 

[1] Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany;Department of Radiology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany;Department of Surgery, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany;Department of Surgery, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany;Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany;German Research Center for Artificial Intelligence (DFKI), Berlin, Germany;

关键词: Decision support systems, Clinical;    Tumor board;    Multidisciplinary team meeting;    Carcinoma, Hepatocellular;    Cholangiocarcinoma;    Artificial intelligence;    Machine learning;    Natural language processing;   

DOI  :  10.1186/s13063-023-07610-8

 received in 2023-06-27, accepted in 2023-08-28,  发布年份 2023

来源: Springer

【 摘 要 】

IntroductionMultidisciplinary team meetings (MDMs), also known as tumor conferences, are a cornerstone of cancer treatments. However, barriers such as incomplete patient information or logistical challenges can postpone tumor board decisions and delay patient treatment, potentially affecting clinical outcomes. Therapeutic Assistance and Decision algorithms for hepatobiliary tumor Boards (ADBoard) aims to reduce this delay by providing automated data extraction and high-quality, evidence-based treatment recommendations.Methods and analysisWith the help of natural language processing, relevant patient information will be automatically extracted from electronic medical records and used to complete a classic tumor conference protocol. A machine learning model is trained on retrospective MDM data and clinical guidelines to recommend treatment options for patients in our inclusion criteria. Study participants will be randomized to either MDM with ADBoard (Arm A: MDM-AB) or conventional MDM (Arm B: MDM-C). The concordance of recommendations of both groups will be compared using interrater reliability. We hypothesize that the therapy recommendations of ADBoard would be in high agreement with those of the MDM-C, with a Cohen’s kappa value of ≥ 0.75. Furthermore, our secondary hypotheses state that the completeness of patient information presented in MDM is higher when using ADBoard than without, and the explainability of tumor board protocols in MDM-AB is higher compared to MDM-C as measured by the System Causability Scale.DiscussionThe implementation of ADBoard aims to improve the quality and completeness of the data required for MDM decision-making and to propose therapeutic recommendations that consider current medical evidence and guidelines in a transparent and reproducible manner.Ethics and disseminationThe project was approved by the Ethics Committee of the Charité – Universitätsmedizin Berlin.Registration detailsThe study was registered on ClinicalTrials.gov (trial identifying number: NCT05681949; https://clinicaltrials.gov/study/NCT05681949) on 12 January 2023.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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Fig. 1