MRI Based Radiomics Approach With Deep Learning for Prediction of Vessel Invasion in Early-Stage Cervical Cancer

This article aims to build deep learning-based radiomic methods in differentiating vessel invasion from non-vessel invasion in cervical cancer with multi-parametric MRI data. A set of 1,070 dynamic T1 contrast-enhanced (DCE-T1) and 986 T2 weighted imaging (T2WI) MRI images from 167 early-stage cervical cancer patients (January 2014 - August 2018) were used to train and validate deep learning models. Predictive performances were evaluated using receiver operating characteristic (ROC) curve and confusion matrix analysis, with the DCE-T1 showing more discriminative results than T2WI MRI. By adopting an attention ensemble learning strategy that integrates both MRI sequences, the highest average area was obtained under the ROC curve (AUC) of 0.911 (Sensitivity <inline-formula><tex-math notation="LaTeX">$= 0.881$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>881</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="jiang-ieq1-2963867.gif"/></alternatives></inline-formula> and Specificity <inline-formula><tex-math notation="LaTeX">$= 0.752$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>752</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="jiang-ieq2-2963867.gif"/></alternatives></inline-formula>). The superior performances in this article, when compared to existing radiomic methods, indicate that a wealth of deep learning-based radiomics could be developed to aid radiologists in preoperatively predicting vessel invasion in cervical cancer patients.

• Publication year

  2020

• Venue

  IEEE/ACM Transactions on Computational Biology & Bioinformatics

• Publication date

  2020-01-03

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.1109/TCBB.2019.2963867PMID 31905143
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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