Pretreatment mri radiomics based response prediction model in locally advanced cervical cancer

Maria Antonietta Gambacorta, Maria Gabriella Ferrandina, Riccardo Manfredi, Vincenzo Valentini, Benedetta Gui, Rosa Autorino, Jacopo Lenkowicz, Davide Cusumano, Luca Russo, Salvatore Persiani, Luca Boldrini, Nicola Dinapoli, Gabriella Macchia, Giuseppina Sallustio

Risultato della ricerca: Contributo in rivistaArticolo in rivista

Abstract

The aim of this study was to create a radiomics model for Locally Advanced Cervical Cancer (LACC) patients to predict pathological complete response (pCR) after neoadjuvant chemora-diotherapy (NACRT) analysing T2-weighted 1.5 T magnetic resonance imaging (MRI) acquired before treatment start. Patients with LACC and an International Federation of Gynecology and Obstetrics stage from IB2 to IVA at diagnosis were retrospectively enrolled for this study. All patients underwent NACRT, followed by radical surgery; pCR—assessed on surgical specimen—was defined as absence of any residual tumour. Finally, 1889 features were extracted from MR images; features showing statistical significance in predicting pCR at the univariate analysis were selected following an iterative method, which was ad-hoc developed for this study. Based on this method, 15 different classifiers were trained considering the most significant features selected. Model selection was carried out using the area under the receiver operating characteristic curve (AUC) as target metrics. One hundred eighty-three patients from two institutions were analysed. The model, showing the highest performance with an AUC of 0.80, was the random forest method initialised with default parameters. Radiomics appeared to be a reliable tool in pCR prediction for LACC patients undergoing NACRT, supporting the identification of patient risk groups, which paves treatment pathways tailored according to the predicted outcome.
Lingua originaleEnglish
pagine (da-a)631-631
Numero di pagine1
RivistaDiagnostics
Volume11
DOI
Stato di pubblicazionePubblicato - 2021

Keywords

  • Cervical cancer
  • MRI
  • Pathological response
  • Prediction model
  • Radiomics

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