Uncovering Symptom-Lesion Associations Through Machine Learning

Santana RS, Roberto Santana, Renato Silva Santana, Renato Máximo Sátiro, Sátiro R, Renato Sátiro, Henrique Mendonca Abrão, Henrique M Abrão, Abrão HM, Tainá Pezzin Rocha, Rocha TP, Taina Pezzin Rocha, Fernanda Milani, Franco Milani, Milani F, Caldeira BT, Bruna T Caldeira, Andres MP, Marina Paula Andres, Maurício Simões Abrão, Abrão MS, Mauricio Simoes Abrão
Journal of minimally invasive gynecology · 2025 · vol. 32(11) , pp. 980–986 · doi:10.1016/j.jmig.2025.08.005 · PMID:40783172 · W4413094597
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⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

This study employed machine learning to identify associations between symptoms and lesions in a biomedical dataset.

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Abstract

OBJECTIVE: to evaluate the association between symptoms and the site of endometriosis lesions using machine learning analysis. DESIGN: retrospective study. SETTING: Two tertiary hospitals. PARTICIPANTS: A total of 726 patients undergoing laparoscopic treatment for endometriosis with histological confirmation between 2009 and 2019 and at least one endometriosis-related symptom. INTERVENTIONS: Clinical data was retrieved from pre-operative consultations and after laparoscopic surgery for endometriosis in an online database. Data was analyzed using machine learning, specifically multiple correspondence analysis (MCA), to evaluate associations between symptoms and lesion location. RESULTS: MCA revealed three major dimensions explaining 56.9% of the variance. The arrangement of categories in the factorial plane (Dim.1 × Dim.2 × Dim. 3) indicated the presence of three distinct profiles. The first group profile predominantly presents severe pain symptoms, including dysmenorrhea, dyspareunia, and acyclic pelvic pain associated with retrocervical and rectosigmoid lesions. The second group profile is characterized by the presence of infertility, associated with lower pain intensity and ovarian, round ligament, tubal, and pararectal lesions. The third group profile includes patients with mild-moderate pain symptoms, not related to any location of endometriosis. CONCLUSION: MCA identified distinct clinical profiles in endometriosis patients. Symptom patterns were associated with specific anatomical sites of lesions, suggesting that symptom profiling may assist in predicting disease location and guiding surgical planning. While these associations are probabilistic and cannot replace clinical imaging or individualized judgment, they provide a structured framework that can complement diagnostic reasoning in specialized settings.

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Condition tags

dysmenorrheadyspareuniaendometriosischronic_pelvic_pain

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (26)

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