Preoperative Tumor Texture Analysis on MRI for High-Risk Disease Prediction in Endometrial Cancer: A Hypothesis-Generating Study

Maura Miccò, Benedetta Gui, Luca Russo, Luca Boldrini, Jacopo Lenkowicz, Stefania Cicogna, Francesco Cosentino, Gennaro Restaino, Giacomo Avesani, Camilla Panico, Francesca Moro, Francesca Ciccarone, Gabriella Macchia, Vincenzo Valentini, Giovanni Scambia, Riccardo Manfredi, Francesco Fanfani

Risultato della ricerca: Contributo in rivistaArticolo in rivista

Abstract

Objective: To develop and validate magnetic resonance (MR) imaging-based radiomics models for high-risk endometrial cancer (EC) prediction preoperatively, to be able to estimate deep myometrial invasion (DMI) and lymphovascular space invasion (LVSI), and to discriminate between low-risk and other categories of risk as proposed by ESGO/ESTRO/ESP (European Society of Gynaecological Oncology-European Society for Radiotherapy & Oncology and European Society of Pathology) guidelines. Methods: This retrospective study included 96 women with EC who underwent 1.5-T MR imaging before surgical staging between April 2009 and May 2019 in two referral centers divided into training (T = 73) and validation cohorts (V = 23). Radiomics features were extracted using the MODDICOM library with manual delineation of whole-tumor volume on MR images (axial T2-weighted). Diagnostic performances of radiomic models were evaluated by area under the receiver operating characteristic (ROC) curve in training (AUCT) and validation (AUCV) cohorts by using a subset of the most relevant texture features tested individually in univariate analysis using Wilcoxon-Mann-Whitney. Results: A total of 228 radiomics features were extracted and ultimately limited to 38 for DMI, 29 for LVSI, and 15 for risk-classes prediction for logistic radiomic modeling. Whole-tumor radiomic models yielded an AUCT/AUCV of 0.85/0.68 in DMI estimation, 0.92/0.81 in LVSI prediction, and 0.84/0.76 for differentiating low-risk vs other risk classes (intermediate/high-intermediate/high). Conclusion: MRI-based radiomics has great potential in developing advanced prognostication in EC.
Lingua originaleEnglish
pagine (da-a)1854-N/A
RivistaJournal of Personalized Medicine
Volume12
DOI
Stato di pubblicazionePubblicato - 2022

Keywords

  • endometrial cancer
  • magnetic resonance imaging
  • radiomics

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