Integrating radiomic and 3D autoencoder-based features for Non-Small Cell Lung Cancer survival analysis.

Journal: Computer methods and programs in biomedicine

PMID: 39551025

Abstract

BACKGROUND AND OBJECTIVES: The aim of this study is to develop a radiomic and deep learning-based signature for survival analysis of patients with Non-Small Cell Lung Cancer.

Authors

Meri Ferretti

Cardio Tech-Lab, Centro Cardiologico Monzino IRCCS, Via Carlo Parea 4 20138 Milan, Italy. Electronic address: meri.ferretti@mail.polimi.it.
Valentina D A Corino

Department of Electronics, Information and Bioengineering (DEIB), Politecnico Di Milano, Via Golgi 39, 20133, Milan, Italy.

Keywords

Aged Carcinoma, Non-Small-Cell Lung Deep Learning Female Humans Imaging, Three-Dimensional Lung Neoplasms Male Middle Aged Proportional Hazards Models Radiomics Survival Analysis

External Resources

View on PubMed Access via DOI PubMed (39551025)

Integrating radiomic and 3D autoencoder-based features for Non-Small Cell Lung Cancer survival analysis.

Abstract

Authors

Keywords

External Resources

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