Deep Learning in Gynecologic Cancer Diagnosis:  Current Advances, Challenges, and Future Directions

Venue: IEEE_org_ICIST_2024_Conference
Published: 2024-08-13 14:55:44

Abstract: Gynecologic cancers remain a significant health challenge globally, particularly in low-resource settings where diagnostic consistency and expertise are often limited. Traditional diagnostic methods based on human image analysis can be inconsistent and prone to errors. Recent advancements in deep learning (DL) offer promising solutions for automating image analysis, providing more objective and
 accurate diagnostic outcomes. This review synthesizes current advances, identifies challenges, and explores future directions in the application of DL for gynecologic cancer diagnosis using various imaging modalities. A thorough literature review following PRISMA-2 guidelines examined studies employing DL to diagnose various gynecologic cancers through imaging modalities like MRI, CT scans, Pap
 smears, and colposcopy. Data extraction and quality assessment were conducted using the QUADAS-2 tool, and diagnostic performance was evaluated through pooled sensitivity, specificity, SROC curves, and AUC metrics using R software.
From 48 studies reviewed, 24 met the inclusion criteria for the meta-analysis. The studies employed various DL models, predominantly ResNet, VGGNet, and UNet, across different imaging modalities. DL models demonstrated superior sensitivity (89.40%) but slightly lower specificity (87.6%) compared to traditional machine learning (ML) methods, which exhibited 68.1% sensitivity and 94.1% specificity.
 The AUC for DL models was 0.88, underscoring their high diagnostic accuracy.
Challenges such as study heterogeneity and methodological biases were identified, underscoring the importance of standardized protocols. Despite these obstacles, DL holds significant promise in gynecologic cancer diagnosis, particularly in resource constrained settings. Addressing these challenges can enhance the clinical utility of DL and contribute to improved patient outcomes.

Main paper: 
 introduction: 
 Gynecologic cancers, including cervical, ovarian, and endometrial cancers, pose significant health risks to women globally, especially in low-resource settings [11, 2, 3] . Traditional diagnostic methods relying on human interpretation are inconsistent and error-prone [2, 3, 7] . Recent advances in DL offer automated, consistent, and precise diagnostic capabilities using medical images [5, 10, 1, 12, 8, 4, 6, 13] . This paper reviews recent DL applications in gynecologic cancer diagnosis, discusses their performance compared to conventional methods, and highlights future research directions aimed at overcoming current challenges.Submitted to 38th Conference on Neural Information Processing Systems (NeurIPS 2024). Do not distribute.A systematic review and meta-analysis following PRISMA-2 [9] guidelines were conducted. The 34 protocol was registered in PROSPERO under registration number 356104. We searched databases 35 including PubMed, Embase, and Scopus for articles published between January 2018 and December 36 2022, focusing on DL applications for diagnosing several gynecologic cancers. Articles were screened 37 and assessed using QUADAS-2 [14] for eligibility and quality. Data on diagnostic performance 38 metrics, specifically sensitivity and specificity, were extracted and analyzed using the "meta" and 39 "mada" packages. Pooled sensitivity, specificity, summary receiver operating characteristic (SROC) 40 curves, and area under the curve (AUC) metrics were calculated using R software.  
 
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Reviews: review: title: title: accept
main_review: main_review: This paper synthesizes current advances, identifies challenges, and explores future directions in the application of DL for gynecologic cancer diagnosis using various imaging modalities. The theory is correct and can be accepted after responding the following comments.
(1) There are many typos and grammar errors. The authors should have a native English speaker or software packages to perform the editing check.
(2)What is the contribution of the paper? It should be highlighted both in the introduction and in the content.
(3)The conclusion suggests adding a section on the prospects for future research.
title: title: This paper investigated employing DL to diagnose 9 various gynecologic cancers through imaging modalities like MRI, CT scans, Pap 10 smears, and colposcopy. The topic of this paper is interesting.
main_review: main_review: Comments to the Author
This paper investigated employing DL to diagnose 9 various gynecologic cancers through imaging modalities like MRI, CT scans, Pap 10 smears, and colposcopy. The topic of this paper is interesting. Below is a list of comments that should be taken into account further when revising the paper.
1.	In the introduction section, the order of references should be from small to large, which will make the overall structure of the article more beautiful.
2.	In the methodology section, the adopted method should be explained to make readers more aware of why it is used and its benefits.
3.	The paper should provide a detailed description of the innovative points to enable readers to quickly understand the article. Meanwhile, please elaborate on the future plans.
title: title: This article is acceptable after detailed revisions
main_review: main_review: This review synthesizes current advances, identifies challenges, and explores future directions in the application of DL for gynecologic cancer diagnosis using various imaging modalities. The obtained result is valuable and can be accepted if the following problems can be clarified. 
(1) In the introduction, the shortages of those relevant studies are suggested to be further summarized. 
(2) There exist several spelling and grammar errors. Please check carefully and further polish
 (3) In the Methods and Experimental results, more analysis can be added to better explain the main contents of this paper.
 (4) The future work is missing in the Conclusion. 
(5) The references should be updated and their format standardized for enhanced consistency and accuracy.

Comments: title: This article is acceptable after detailed revisions
comment: This review synthesizes current advances, identifies challenges, and explores future directions in the application of DL for gynecologic cancer diagnosis using various imaging modalities. The obtained result is valuable and can be accepted if the following problems can be clarified.
(1)	In the introduction, the shortages of those relevant studies are suggested to be further summarized.
(2)	There exist several spelling and grammar errors. Please check carefully and further polish
(3)	In the Methods and Experimental results, more analysis can be added to better explain the main contents of this paper.
(4)	The future work is missing in the Conclusion.
(5)	The references should be updated and their format standardized for enhanced consistency and accuracy.