Diagnostic Accuracy of Machine-Learning Models on Predicting Chemo-Brain in Breast Cancer Survivors Previously Treated with Chemotherapy: A Meta-Analysis

Adina Turcu-Stiolica, Maria Bogdan, Elena Adriana Dumitrescu, Daniela Luminita Zob (), Victor Gheorman, Madalina Aldea, Venera Cristina Dinescu, Mihaela-Simona Subtirelu (), Dana-Lucia Stanculeanu, Daniel Sur and Cristian Virgil Lungulescu
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Adina Turcu-Stiolica: Department of Pharmacoeconomics, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
Maria Bogdan: Department of Pharmacology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
Elena Adriana Dumitrescu: Department of Oncology, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania
Daniela Luminita Zob: Institute of Oncology, Prof Dr. Alexandru Trestioreanu, Soseaua Fundeni, 022328 Bucharest, Romania
Victor Gheorman: Department of Psychiatry, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
Madalina Aldea: Department of Psychiatry, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
Venera Cristina Dinescu: Department of Health Promotion and Occupational Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
Mihaela-Simona Subtirelu: Department of Pharmacoeconomics, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
Dana-Lucia Stanculeanu: Department of Oncology, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania
Daniel Sur: 11th Department of Medical Oncology, University of Medicine and Pharmacy “Iuliu Hatieganu”, 400125 Cluj-Napoca, Romania
Cristian Virgil Lungulescu: Department of Oncology, University of Medicine and Pharmacy Craiova, 200349 Craiova, Romania

IJERPH, 2022, vol. 19, issue 24, 1-14

Abstract: We performed a meta-analysis of chemo-brain diagnostic, pooling sensitivities, and specificities in order to assess the accuracy of a machine-learning (ML) algorithm in breast cancer survivors previously treated with chemotherapy. We searched PubMed, Web of Science, and Scopus for eligible articles before 30 September 2022. We identified three eligible studies from which we extracted seven ML algorithms. For our data, the χ 2 tests demonstrated the homogeneity of the sensitivity’s models (χ 2 = 7.6987, df = 6, p -value = 0.261) and the specificities of the ML models (χ 2 = 3.0151, df = 6, p -value = 0.807). The pooled area under the curve (AUC) for the overall ML models in this study was 0.914 (95%CI: 0.891–0.939) and partial AUC (restricted to observed false positive rates and normalized) was 0.844 (95%CI: 0.80–0.889). Additionally, the pooled sensitivity and pooled specificity values were 0.81 (95% CI: 0.75–0.86) and 0.82 (95% CI: 0.76–0.86), respectively. From all included ML models, support vector machine demonstrated the best test performance. ML models represent a promising, reliable modality for chemo-brain prediction in breast cancer survivors previously treated with chemotherapy, demonstrating high accuracy.

Keywords: chemo-brain; machine learning; diagnostic accuracy; breast cancer; chemotherapy (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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