Enhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer
Jihwan Park, Mi Jung Rho, Mi Hyoung Moon
Abstract
Radical surgery is the primary treatment for early-stage resectable lung cancer, yet recurrence after curative surgery is not uncommon. Identifying patients at high risk of recurrence using preoperative computed tomography (CT) images could enable more aggressive surgical approaches.
Introduction
Just a century ago, lung cancer was merely considered a reportable disease. Today, it is a leading cause of mortality worldwide, affecting both developed and developing countries, including the United States.
Material and Methods
In this retrospective study, we targeted patients with primary non-small cell lung cancers who underwent curative pulmonary resections from January 1, 2010, to December 31, 2018.
Result
From the total of 120 epochs of model training, we selected the epoch that performed best based on its weight values.
Discussion
The primary goal of the deep learning models was to support physicians in clinical decision-making by offering cancer site profiles and predicting recurrence probabilities. Initially, our findings comprised solely the maximum confidence levels of regions of interest (ROIs) derived from various prediction regions and classifications.
Conclusion
The MRCNN model serves to generate multiple regions of interest and classification scores, contributing significantly to postoperative patient management for lung cancer.
Citation: Park J, Rho MJ, Moon MH (2024) Enhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer. PLoS ONE 19(7): e0300442. https://doi.org/10.1371/journal.pone.0300442
Editor: Xiaohui Zhang, University of Illinois Urbana-Champaign, UNITED STATES OF AMERICA.
Received: December 14, 2023; Accepted: February 27, 2024; Published: July 12, 2024
Copyright: © 2024 Park et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Owing to strict security regulations at our institution, researchers interested in accessing the data from this study are required to contact the Data Review Board of Seoul St. Mary's Hospital. For inquiries, please use the following contact details: telephone number +82-02-2258-5808, email liveinstar@cmcnu.or.kr, with Saesbyeol Yang as the designated point of contact.
Funding: The authors wish to acknowledge the financial support of the Catholic Medical Center Research Foundation made in the program year of 2020 in the form of a grant to MHM [5-2020-B0001-00262]. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: P and R, who are married, participated in the project as co-participants from D University. No other authors have any conflicts of interest to disclose.
Source: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0300442#abstract0
