Purpose To assess a radiomic machine learning (ML) model in classifying solid adrenal lesions (ALs) without fat signal drop on chemical shift (CS) as benign or malignant. Method 55 indeterminate ALs (21 lipid poor adenomas, 15 benign pheocromocytomas, 1 oncocytoma, 12 metastases, 6 primary tumors) showing no fat signal drop on CS were retrospectively included. Manual 3D segmentation on T2-weighted and CS images was performed for subsequent radiomic feature extraction. After feature stability testing and an 80–20% train-test split, the train set was balanced via oversampling. Following a multi-step feature selection, an Extra Trees model was tuned with 5-fold stratified cross-validation in the train set and then tested on the hold-out test set. Results A total of 3396 features were extracted from each AL, of which 133 resulted unstable while none had low variance (< 0.01). Highly correlated (r > 0.8) features were also excluded, leaving 440 parameters. Among these, Support Vector Machine 5-fold stratified cross-validated recursive feature elimination selected a subset of 6 features. ML obtained a cross-validation accuracy of 0.94 on the train and 0.91 on the test sets. Precision, recall and F1 score were respectively 0.92, 0.91 and 0.91. Conclusions Our MRI handcrafted radiomics and ML pipeline proved useful to characterize benign and malignant solid indeterminate adrenal lesions.

Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions / Stanzione, Arnaldo; Cuocolo, Renato; Verde, Francesco; Galatola, Roberta; Romeo, Valeria; Mainenti Pier, Paolo; Aprea, Giovanni; Guadagno, Elia; DEL BASSO DE CARO, Marialaura; Maurea, Simone. - In: MAGNETIC RESONANCE IMAGING. - ISSN 0730-725X. - (2021). [10.1016/j.mri.2021.03.009]

Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions

Arnaldo, Stanzione;Renato, Cuocolo;Francesco, Verde;Roberta, Galatola;Valeria, Romeo
;
Giovanni, Aprea;Elia, Guadagno;Marialaura, Del Basso De Caro;Simone, Maurea
2021

Abstract

Purpose To assess a radiomic machine learning (ML) model in classifying solid adrenal lesions (ALs) without fat signal drop on chemical shift (CS) as benign or malignant. Method 55 indeterminate ALs (21 lipid poor adenomas, 15 benign pheocromocytomas, 1 oncocytoma, 12 metastases, 6 primary tumors) showing no fat signal drop on CS were retrospectively included. Manual 3D segmentation on T2-weighted and CS images was performed for subsequent radiomic feature extraction. After feature stability testing and an 80–20% train-test split, the train set was balanced via oversampling. Following a multi-step feature selection, an Extra Trees model was tuned with 5-fold stratified cross-validation in the train set and then tested on the hold-out test set. Results A total of 3396 features were extracted from each AL, of which 133 resulted unstable while none had low variance (< 0.01). Highly correlated (r > 0.8) features were also excluded, leaving 440 parameters. Among these, Support Vector Machine 5-fold stratified cross-validated recursive feature elimination selected a subset of 6 features. ML obtained a cross-validation accuracy of 0.94 on the train and 0.91 on the test sets. Precision, recall and F1 score were respectively 0.92, 0.91 and 0.91. Conclusions Our MRI handcrafted radiomics and ML pipeline proved useful to characterize benign and malignant solid indeterminate adrenal lesions.
2021
Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions / Stanzione, Arnaldo; Cuocolo, Renato; Verde, Francesco; Galatola, Roberta; Romeo, Valeria; Mainenti Pier, Paolo; Aprea, Giovanni; Guadagno, Elia; DEL BASSO DE CARO, Marialaura; Maurea, Simone. - In: MAGNETIC RESONANCE IMAGING. - ISSN 0730-725X. - (2021). [10.1016/j.mri.2021.03.009]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/847138
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