article · Artificial Intelligence Review
A systematic review examining a decade of research evaluated the use of data mining and machine learning in gynaecologic oncology, analysing 181 eligible primary studies. Research in this field has expanded notably since 2019, with over sixty per cent of the recent work focusing on cervical neoplasms. These investigations predominantly present empirical solutions derived from patient cohorts, with medical records representing the most common data source. Methodologically, neural networks serve as the primary architecture, with most applications targeted at patient classification and clinical diagnosis. While the compiled evidence affirms the potential clinical utility of data mining and machine learning tools, substantial challenges remain. Current studies frequently rely on single-institution datasets, resulting in limited interoperability. To establish dependable tools, future development requires cross-cohort generalisability to prevent reporting bias tied to locally trained algorithms.
Gynaecologic cancers present complex clinical variations across global populations, creating an urgent need for precise diagnostic tools. Machine learning offers powerful techniques to analyse medical records and assist clinical decision-making. However, algorithms trained solely within single institutions risk poor real-world performance, making broad multi-cohort validation vital before these methods can reliably support patient care across diverse healthcare settings.
The review highlights applications in clinical diagnostic support and patient classification, aimed primarily at healthcare practitioners and oncologists. The underlying technologies remain at an early to applied research stage, as most tools are empirical models trained on single-centre cohorts. Commercial adoption and clinical deployment will require software developers to resolve substantial interoperability limitations and validate their algorithms across multiple institutions to prove broader generalisability.
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Abstract Gynecologic (GYN) malignancies are gaining new and much-needed attention, perpetually fueling literature. Intra-/inter-tumor heterogeneity and “frightened” global distribution by race, ethnicity, and human development index, are pivotal clues to such ubiquitous interest. To advance “precision medicine” and downplay the heavy burden, data mining (DM) is timely in clinical GYN oncology. No consolidated work has been conducted to examine the depth and breadth of DM applicability as an adjunct to GYN oncology, emphasizing machine learning (ML)-based schemes. This systematic literature review (SLR) synthesizes evidence to fill knowledge gaps, flaws, and limitations. We report this SLR in compliance with Kitchenham and Charters’ guidelines. Defined research questions and PICO crafted a search string across five libraries: PubMed, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar—over the past decade. Of the 3499 potential records, 181 primary studies were eligible for in-depth analysis. A spike (60.53%) corollary to cervical neoplasms is denoted onward 2019, predominantly featuring empirical solution proposals drawn from cohorts. Medical records led (23.77%, 53 art.). DM-ML in use is primarily built on neural networks (127 art.), appoint classification (73.19%, 172 art.) and diagnoses (42%, 111 art.), all devoted to assessment. Summarized evidence is sufficient to guide and support the clinical utility of DM schemes in GYN oncology. Gaps persist, inculpating the interoperability of single-institute scrutiny. Cross-cohort generalizability is needed to establish evidence while avoiding outcome reporting bias to locally, site-specific trained models. This SLR is exempt from ethics approval as it entails published articles.
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DOI: 10.1007/s10462-023-10666-2
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