Vision Transformer-Based Deep Learning for Histologic Classification of Endometrial Cancer

Belangrijkste concepten

EndoNet, a vision transformer-based deep learning model, shows promise in accurately classifying low- and high-grade endometrial cancer from histologic images.

Samenvatting

Authors: Manu Goyal, Laura J. Tafe, James X. Feng, Kristen E. Muller, Liesbeth Hondelink, Jessica L. Bentz, Saeed Hassanpour
Funding: Supported by US National Library of Medicine and US National Cancer Institute
Pages: 16
Figures: 3
Tables: 4
Corresponding Author: Manu Goyal
Abstract: Introduces EndoNet using convolutional neural networks and vision transformer for histologic classification of endometrial cancer.
Introduction: Discusses the importance of precise histologic evaluation and molecular classification in effective patient management.
Materials and Methods: Details datasets, data annotation, model development, and evaluation metrics.
Results: Compares the performance of Fully Supervised CNN and EndoNet on internal and external test sets.
Visualization: Shows attention maps of EndoNet in classifying low- and high-grade endometrial cancer.
Discussion and Future Directions: Acknowledges limitations, proposes future improvements, and outlines plans for clinical deployment.

Statistieken

The model achieved a weighted average F1-score of 0.91 and an AUC of 0.95 on the internal test.
On the external test, the model achieved an F1 score of 0.86 and an AUC of 0.86.

Citaten

"EndoNet has the potential to support pathologists without the need for manual annotations in classifying the grades of gynecologic pathology tumors."
"The model exhibited an increased focus or 'attention' towards regions that heavily overlap with endometrial cancer tissues independently segmented by expert pathologists."

Belangrijkste Inzichten Gedestilleerd Uit

Vision Transformer-Based Deep Learning for Histologic Classification of Endometrial Cancer

by Manu Goyal,L... om arxiv.org 03-28-2024

https://arxiv.org/pdf/2312.08479.pdf

Diepere vragen

How can EndoNet be integrated into clinical practice to enhance diagnostic accuracy and patient care?

EndoNet can be integrated into clinical practice to enhance diagnostic accuracy and patient care by serving as a valuable tool for pathologists in grading endometrial cancer. Its ability to classify low- and high-grade endometrial cancers based on histologic features from whole-slide images can provide pathologists with a second opinion, reducing interobserver variability and improving diagnostic accuracy. By automating the classification process, EndoNet can expedite decision-making, leading to quicker treatment plans and more personalized patient care. Additionally, the model's attention maps can offer insights into the regions of interest within the images, aiding pathologists in understanding the basis for the classification decisions.

How can the challenges might arise in deploying EndoNet in real-world settings, and how can they be addressed?

Challenges that may arise in deploying EndoNet in real-world settings include the need for validation in diverse patient populations, addressing data variability in histologic slides from different institutions, and ensuring regulatory compliance and data privacy. To address these challenges, it is essential to conduct prospective clinical trials to validate the model's performance across various patient demographics and institutions. Collaborating with multiple healthcare facilities to gather diverse datasets can help in training the model on a broader range of cases, improving its generalizability. Implementing robust data governance protocols and ensuring compliance with regulatory standards such as HIPAA can address data privacy concerns and ensure ethical use of patient data.

How can the incorporation of additional genetic and clinical information improve the predictive accuracy of EndoNet?

Incorporating additional genetic and clinical information into EndoNet can significantly enhance its predictive accuracy by providing a more comprehensive understanding of the disease. By integrating genetic data such as mutations in genes like POLE, MMR, and p53, along with clinical information like patient demographics and treatment history, the model can make more informed predictions about disease progression and treatment outcomes. This multi-modal approach can help in risk stratification, prognosis prediction, and personalized treatment planning for patients with endometrial cancer. By considering a holistic view of the patient's genetic and clinical profile, EndoNet can offer more accurate and tailored recommendations, ultimately improving patient outcomes.