Exploring data mining and machine learning in gynecologic oncology

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.