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

These preliminary results show that combining information carried by AE, radiomic and clinical domain shows potential for improving the prediction of overall survival in NSCLC patients.

Authors

Mário Ferretti (ORCID: https://orcid.org/0000-0002-5298-2486)
Valentina Corino (ORCID: https://orcid.org/0000-0003-1825-0422)

Institutions

Centro Cardiologico Monzino (IT)

Publication Details

Journal: Computer Methods and Programs in Biomedicine
Published: 2024-11-08
DOI: https://doi.org/10.1016/j.cmpb.2024.108496
Citations: 9
Primary Topic: Radiomics and Machine Learning in Medical Imaging
Type: article
Field-Weighted Citation Impact: 3.64

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article

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

Mário Ferretti, Valentina Corino

Nov 8, 2024

9 citations

Computer Methods and Programs in Biomedicine

Radiomics and Machine Learning in Medical Imaging

3.64

article

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

Mário Ferretti, Valentina Corino

November 8, 2024article en

9 citations

Abstract

These preliminary results show that combining information carried by AE, radiomic and clinical domain shows potential for improving the prediction of overall survival in NSCLC patients.

Computer Methods and Programs in BiomedicineVol. 258

Centro Cardiologico Monzino (IT)

Good health and well-being

Openalex Percentile: Top 6%

Radiomics and Machine Learning in Medical Imaging

3.64

DOI Open Access PDF OpenAlex

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Integrating radiomic and 3D autoencoder-based features for Non-Small Cell Lung Cancer survival analysis — Mário Ferretti, Valentina Corino · Computer Methods and Programs in Biomedicine (2024) | TGRS Research Map | TGRS