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Enhancing Lung Cancer Survival Prediction: 3D CNN Analysis of CT Images Using Novel GTV1-SliceNum Feature and PEN-BCE Loss Function.
- Source :
-
Diagnostics (2075-4418) . Jun2024, Vol. 14 Issue 12, p1309. 23p. - Publication Year :
- 2024
-
Abstract
- Lung cancer is a prevalent malignancy associated with a high mortality rate, with a 5-year relative survival rate of 23%. Traditional survival analysis methods, reliant on clinician judgment, may lack accuracy due to their subjective nature. Consequently, there is growing interest in leveraging AI-based systems for survival analysis using clinical data and medical imaging. The purpose of this study is to improve survival classification for lung cancer patients by utilizing a 3D-CNN architecture (ResNet-34) applied to CT images from the NSCLC-Radiomics dataset. Through comprehensive ablation studies, we evaluate the effectiveness of different features and methodologies in classification performance. Key contributions include the introduction of a novel feature (GTV1-SliceNum), the proposal of a novel loss function (PEN-BCE) accounting for false negatives and false positives, and the showcasing of their efficacy in classification. Experimental work demonstrates results surpassing those of the existing literature, achieving a classification accuracy of 0.7434 and an ROC-AUC of 0.7768. The conclusions of this research indicate that the AI-driven approach significantly improves survival prediction for lung cancer patients, highlighting its potential for enhancing personalized treatment strategies and prognostic modeling. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20754418
- Volume :
- 14
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Diagnostics (2075-4418)
- Publication Type :
- Academic Journal
- Accession number :
- 178160475
- Full Text :
- https://doi.org/10.3390/diagnostics14121309