Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach

To the best of our knowledge, the DNetPRO algorithm was applied for the first time to medical image analysis and proved suitable for the selection of radiomic features with the highest predictive power, a crucial step in a radiomic pipeline. Our results confirmed the efficiency of radiomic features in predicting LRRC, with CT features outperforming PET features in identifying the characteristic texture of LRRC. The combination of both yielded no performance improvement.

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Publication Details

Journal
Heliyon
Published
2024-12-20
DOI
https://doi.org/10.1016/j.heliyon.2024.e41404
Citations
3
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.21
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article

Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach

Arrigo Cattabriga, Francesca Coppola, Maria Adriana Cocozza, Emilia Fortunati et al.
3 citations
Heliyon
Radiomics and Machine Learning in Medical Imaging
1.21
article

Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach

Arrigo Cattabriga, Francesca Coppola, Maria Adriana Cocozza, Emilia Fortunati, Dajana Cuicchi, Paolo Castellucci, Daniel Remondini, Gastone Castellani, Stefano Fanti, Alberta Cappelli, Nico Curti, Riccardo Biondi, Lorenzo Faggioni, Andrea Farolfi, S. Dalmonte, Gilberto Poggioli
article en
3 citations

Abstract

To the best of our knowledge, the DNetPRO algorithm was applied for the first time to medical image analysis and proved suitable for the selection of radiomic features with the highest predictive power, a crucial step in a radiomic pipeline. Our results confirmed the efficiency of radiomic features in predicting LRRC, with CT features outperforming PET features in identifying the characteristic texture of LRRC. The combination of both yielded no performance improvement.

HeliyonVol. 11(1)
University of Pisa (IT), Azienda USL di Bologna (IT), Institute of Neurological Sciences (IT), Ospedale per gli Infermi (IT), Orthopedic Institute (US), University of Bologna (IT)
Good health and well-being
Openalex Percentile: Top 19%
Radiomics and Machine Learning in Medical Imaging
1.21
DOIOpen Access PDFOpenAlex
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Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach — Arrigo Cattabriga, Francesca Coppola, et al. · Heliyon (2024) | TGRS Research Map | TGRS