Ovarian Cancer Data Analysis using Deep Learning: A Systematic Review
  from the Perspectives of Key Features of Data Analysis and AI Assurance
Muta Tah Hira Mohammad A. Razzaque Mosharraf Sarker
Background and objectives: By extracting this information, Machine or Deep
Learning (ML/DL)-based autonomous data analysis tools can assist clinicians and
cancer researchers in discovering patterns and relationships from complex data
sets. Many DL-based analyses on ovarian cancer (OC) data have recently been
published. These analyses are highly diverse in various aspects of cancer
(e.g., subdomain(s) and cancer type they address) and data analysis features.
However, a comprehensive understanding of these analyses in terms of these
features and AI assurance (AIA) is currently lacking. This systematic review
aims to fill this gap by examining the existing literature and identifying
important aspects of OC data analysis using DL, explicitly focusing on the key
features and AI assurance perspectives. Methods: The PRISMA framework was used
to conduct comprehensive searches in three journal databases. Only studies
published between 2015 and 2023 in peer-reviewed journals were included in the
analysis. Results: In the review, a total of 96 DL-driven analyses were
examined. The findings reveal several important insights regarding DL-driven
ovarian cancer data analysis: - Most studies 71% (68 out of 96) focused on
detection and diagnosis, while no study addressed the prediction and prevention
of OC. - The analyses were predominantly based on samples from a non-diverse
population (75% (72/96 studies)), limited to a geographic location or country.
- Only a small proportion of studies (only 33% (32/96)) performed integrated
analyses, most of which used homogeneous data (clinical or omics). - Notably, a
mere 8.3% (8/96) of the studies validated their models using external and
diverse data sets, highlighting the need for enhanced model validation, and -
The inclusion of AIA in cancer data analysis is in a very early stage; only
2.1% (2/96) explicitly addressed AIA through explainability.