Rationale and Objectives
To evaluate the performance of a machine learning method to differentiate malignant
from benign soft tissue tumors based on textural features on multiparametric magnetic
resonance imaging (mpMRI).
Materials and Methods
We enrolled 163 patients with soft tissue tumors whose diagnosis was pathologically
proven (71 malignant, 92 benign). All patients underwent mpMRI. Twelve histographic
and textural parameters were assessed on T1-weighted imaging (T1WI), T2-weighted imaging,
diffusion-weighted imaging, apparent diffusion coefficient maps, and contrast-enhanced
T1WI imaging. We compared mean signals of all sequences from the malignant and benign
tumors using Welch's t-test. Prediction models were developed via a machine learning
technique (support vector machine) using textural features of each sequence, clinical
information (sex + age + tumor size), and the combined model incorporating all features.
Areas under the receiver operating characteristic curves (AUCs) of these models were
calculated using fivefold cross validation.
Results
The diagnostic ability of clinical information model (AUC 0.85) was not inferior to
the model with textural features of each sequence (AUC 0.79–0.84). The combined model
showed the highest diagnostic ability (AUC 0.89). The AUC of the combined model (0.89)
was comparable to those of two board-certified radiologists (0.89 and 0.87).
Conclusions
Machine learning methods based on textural features on mpMRI and clinical information
offer adequate diagnostic performance to differentiate between malignant and benign
soft tissue tumors.
Key Words
Abbreviations:
mpMRI (multiparametric magnetic resonance imaging), T1WI (T1-weighted imaging), T2WI (T2-weighted imaging), DWI (diffusion-weighted imaging), ADC (apparent diffusion coefficient), CE-T1WI (contrast-enhanced T1WI imaging), SVM (support vector machine), AUC (areas under the receiver operating characteristic curve), ITA (image texture analysis), ROC (receiver operating characteristic)To read this article in full you will need to make a payment
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Article Info
Publication History
Published online: June 17, 2022
Accepted:
April 9,
2022
Received in revised form:
April 1,
2022
Received:
August 23,
2021
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 Published by Elsevier Inc. on behalf of The Association of University Radiologists.
