# Ovarian Cancer Data Analysis using Deep Learning: A Systematic Review from the Perspectives of Key Features of Data Analysis and AI Assurance

arXiv: https://arxiv.org/abs/2311.11932 · HTML: https://mlsanity.com/paper/2311.11932/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 176 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [A Survey on AI Assurance](https://mlsanity.com/paper/2111.07505/) (2021)
- [Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification](https://mlsanity.com/paper/1908.06278/) (2019)
- [Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research](https://mlsanity.com/paper/1909.00311/) (2019)
- [Unsupervised anomaly detection in digital pathology using GANs](https://mlsanity.com/paper/2103.08945/) (2021)
- [Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation](https://mlsanity.com/paper/2203.15015/) (2022)
- [Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach](https://mlsanity.com/paper/2009.14260/) (2020)
- [EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological Images](https://mlsanity.com/paper/2210.05258/) (2022)
- [Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer](https://mlsanity.com/paper/2205.09285/) (2022)

## Abstract (arXiv)

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.