Radiomics-based prediction of two-year clinical outcome in locally advanced cervical cancer patients undergoing neoadjuvant chemoradiotherapy

Purpose The aim of this study is to determine if radiomics features extracted from staging magnetic resonance (MR) images could predict 2-year long-term clinical outcome in patients with locally advanced cervical cancer (LACC) after neoadjuvant chemoradiotherapy (NACRT). Materials and methods We ret...

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Bibliographic Details
Published in:Radiologia medica 2022-05, Vol.127 (5), p.498-506
Main Authors: Autorino, Rosa, Gui, Benedetta, Panza, Giulia, Boldrini, Luca, Cusumano, Davide, Russo, Luca, Nardangeli, Alessia, Persiani, Salvatore, Campitelli, Maura, Ferrandina, Gabriella, Macchia, Gabriella, Valentini, Vincenzo, Gambacorta, Maria Antonietta, Manfredi, Riccardo
Format: Article
Language:English
Subjects:
Cancer
Cervical cancer
Chemotherapy
Correlation coefficients
Diagnostic Imaging in Oncology
Diagnostic Radiology
Feature extraction
Imaging
Interventional Radiology
Magnetic resonance imaging
Medical imaging
Medicine
Medicine & Public Health
Neuroradiology
Performance prediction
Radiation therapy
Radiology
Radiomics
Regression models
Training
Ultrasound
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Summary:Purpose The aim of this study is to determine if radiomics features extracted from staging magnetic resonance (MR) images could predict 2-year long-term clinical outcome in patients with locally advanced cervical cancer (LACC) after neoadjuvant chemoradiotherapy (NACRT). Materials and methods We retrospectively enrolled patients with LACC diagnosis who underwent NACRT followed by radical surgery in two different institutions. Radiomics features were extracted from pre-treatment 1.5 T T2w MR images. The predictive performance of each feature was quantified in terms of Wilcoxon–Mann–Whitney test. Among the significant features, Pearson correlation coefficient (PCC) was calculated to quantify the correlation among the different predictors. A logistic regression model was calculated considering the two most significant features at the univariate analysis showing the lowest PCC value. The predictive performance of the model created was quantified out using the area under the receiver operating characteristic curve (AUC). Results A total of 175 patients were retrospectively enrolled (142 for the training cohort and 33 for the validation one). 1896 radiomic feature were extracted, 91 of which showed significance ( p  
ISSN:1826-6983
0033-8362
1826-6983
DOI:10.1007/s11547-022-01482-9