A human-interpretable machine learning pipeline based on ultrasound to support leiomyosarcoma diagnosis.

Autor: Lombardi A; Department of Electrical and Information Engineering (DEI), Politecnico di Bari, Bari, Italy. Electronic address: angela.lombardi@poliba.it., Arezzo F; Gynecologic Oncology Unit, Interdisciplinar Department of Medicine, IRCCS Istituto Tumori 'Giovanni Paolo II', Bari, Italy., Di Sciascio E; Department of Electrical and Information Engineering (DEI), Politecnico di Bari, Bari, Italy., Ardito C; Department of Engineering, LUM 'Giuseppe Degennaro' University, Casamassima, Bari, Italy., Mongelli M; Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari 'Aldo Moro', Bari, Italy., Di Lillo N; Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari 'Aldo Moro', Bari, Italy., Fascilla FD; Obstetrics and Gynecology Unit, Di Venere Hospital, Bari, Italy., Silvestris E; Gynecologic Oncology Unit, Interdisciplinar Department of Medicine, IRCCS Istituto Tumori 'Giovanni Paolo II', Bari, Italy., Kardhashi A; Gynecologic Oncology Unit, Interdisciplinar Department of Medicine, IRCCS Istituto Tumori 'Giovanni Paolo II', Bari, Italy., Putino C; Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari 'Aldo Moro', Bari, Italy., Cazzolla A; Gynecologic Oncology Unit, Interdisciplinar Department of Medicine, IRCCS Istituto Tumori 'Giovanni Paolo II', Bari, Italy., Loizzi V; Gynecologic Oncology Unit, Interdisciplinar Department of Medicine, IRCCS Istituto Tumori 'Giovanni Paolo II', Bari, Italy; Interdisciplinar Department of Medicine, University of Bari 'Aldo Moro', Bari, Italy., Cazzato G; Section of Pathology, Department of Emergency and Organ Transplantation (DETO), University of Bari 'Aldo Moro', Bari, Italy., Cormio G; Gynecologic Oncology Unit, Interdisciplinar Department of Medicine, IRCCS Istituto Tumori 'Giovanni Paolo II', Bari, Italy; Interdisciplinar Department of Medicine, University of Bari 'Aldo Moro', Bari, Italy., Di Noia T; Department of Electrical and Information Engineering (DEI), Politecnico di Bari, Bari, Italy.
Jazyk: angličtina
Zdroj: Artificial intelligence in medicine [Artif Intell Med] 2023 Dec; Vol. 146, pp. 102697. Date of Electronic Publication: 2023 Nov 03.
DOI: 10.1016/j.artmed.2023.102697
Abstrakt: The preoperative evaluation of myometrial tumors is essential to avoid delayed treatment and to establish the appropriate surgical approach. Specifically, the differential diagnosis of leiomyosarcoma (LMS) is particularly challenging due to the overlapping of clinical, laboratory and ultrasound features between fibroids and LMS. In this work, we present a human-interpretable machine learning (ML) pipeline to support the preoperative differential diagnosis of LMS from leiomyomas, based on both clinical data and gynecological ultrasound assessment of 68 patients (8 with LMS diagnosis). The pipeline provides the following novel contributions: (i) end-users have been involved both in the definition of the ML tasks and in the evaluation of the overall approach; (ii) clinical specialists get a full understanding of both the decision-making mechanisms of the ML algorithms and the impact of the features on each automatic decision. Moreover, the proposed pipeline addresses some of the problems concerning both the imbalance of the two classes by analyzing and selecting the best combination of the synthetic oversampling strategy of the minority class and the classification algorithm among different choices, and the explainability of the features at global and local levels. The results show very high performance of the best strategy (AUC = 0.99, F1 = 0.87) and the strong and stable impact of two ultrasound-based features (i.e., tumor borders and consistency of the lesions). Furthermore, the SHAP algorithm was exploited to quantify the impact of the features at the local level and a specific module was developed to provide a template-based natural language (NL) translation of the explanations for enhancing their interpretability and fostering the use of ML in the clinical setting.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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