Literature DB >> 33970517

Multiparametric Magnetic Resonance Imaging-Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension With Prostate Cancer.

Honglin Bai1,2, Wei Xia2, Xuefu Ji3, Dong He4, Xingyu Zhao1,2, Jie Bao5, Jian Zhou6, Xuedong Wei4, Yuhua Huang4, Qiong Li6, Xin Gao2,7.   

Abstract

BACKGROUND: Preoperative prediction of extracapsular extension (ECE) of prostate cancer (PCa) is important to guide clinical decision-making and improve patient prognosis.
PURPOSE: To investigate the value of multiparametric magnetic resonance imaging (mpMRI)-based peritumoral radiomics for preoperative prediction of the presence of ECE. STUDY TYPE: Retrospective. POPULATION: Two hundred eighty-four patients with PCa from two centers (center 1: 226 patients; center 2: 58 patients). Cases from center 1 were randomly divided into training (158 patients) and internal validation (68 patients) sets. Cases from center 2 were assigned to the external validation set. FIELD STRENGTH/SEQUENCE: A 3.0 T MRI scanners (three vendors). Sequence: Pelvic T2-weighted turbo/fast spin echo sequence and diffusion weighted echo planar imaging sequence. ASSESSMENT: The peritumoral region (PTR) was obtained by 3-12 mm (half of the tumor length) 3D dilatation of the intratumoral region (ITR). Single-MRI radiomics signatures, mpMRI radiomics signatures, and integrated models, which combined clinical characteristics with the radiomics signatures were built. The discrimination ability was assessed by area under the receiver operating characteristic curve (AUC) in the internal and external validation sets. STATISTICAL TESTS: Fisher's exact test, Mann-Whitney U-test, DeLong test.
RESULTS: The PTR radiomics signatures demonstrated significantly better performance than the corresponding ITR radiomics signatures (AUC: 0.674 vs. 0.554, P < 0.05 on T2-weighted, 0.652 vs. 0.546, P < 0.05 on apparent diffusion coefficient, 0.682 vs. 0.556 on mpMRI in the external validation set). The integrated models combining the PTR radiomics signature with clinical characteristics performed better than corresponding radiomics signatures in the internal validation set (eg. AUC: 0.718 vs. 0.671, P < 0.05 on mpMRI) but performed similar in the external validation set (eg. AUC: 0.684, vs. 0.682, P = 0.45 on mpMRI). DATA
CONCLUSION: The peritumoral radiomics can better predict the presence of ECE preoperatively compared with the intratumoral radiomics and may have better generalization than clinical characteristics. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: 2.
© 2021 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  extracapsular extension; mpMRI; peritumoral region; prostate cancer; radiomics

Year:  2021        PMID: 33970517     DOI: 10.1002/jmri.27678

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  3 in total

Review 1.  More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis-A Systematic Review.

Authors:  Teodora Telecan; Iulia Andras; Nicolae Crisan; Lorin Giurgiu; Emanuel Darius Căta; Cosmin Caraiani; Andrei Lebovici; Bianca Boca; Zoltan Balint; Laura Diosan; Monica Lupsor-Platon
Journal:  J Pers Med       Date:  2022-06-16

Review 2.  Artificial Intelligence-based Radiomics in the Era of Immuno-oncology.

Authors:  Cyra Y Kang; Samantha E Duarte; Hye Sung Kim; Eugene Kim; Jonghanne Park; Alice Daeun Lee; Yeseul Kim; Leeseul Kim; Sukjoo Cho; Yoojin Oh; Gahyun Gim; Inae Park; Dongyup Lee; Mohamed Abazeed; Yury S Velichko; Young Kwang Chae
Journal:  Oncologist       Date:  2022-06-08       Impact factor: 5.837

3.  Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer.

Authors:  Xuhui Fan; Ni Xie; Jingwen Chen; Tiewen Li; Rong Cao; Hongwei Yu; Meijuan He; Zilin Wang; Yihui Wang; Hao Liu; Han Wang; Xiaorui Yin
Journal:  Front Oncol       Date:  2022-02-07       Impact factor: 6.244

  3 in total

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