Radiomic Analysis of HTR-DCE MR Sequences Improves Diagnostic Performance Compared to BI-RADS Analysis of Breast MR Lesions

Overview

Journal Eur Radiol

Specialty Radiology

Date 2021 Jan 6

PMID 33404696

Citations 8

Authors

Saskia Vande Perre

Loic Duron

Audrey Milon

Asma Bekhouche

Daniel Balvay

Francois H Cornelis

Laure Fournier

Isabelle Thomassin-Naggara

Affiliations

Soon will be listed here.

Abstract

Purpose: To assess the diagnostic performance of radiomic analysis using high temporal resolution (HTR)-dynamic contrast enhancement (DCE) MR sequences compared to BI-RADS analysis to distinguish benign from malignant breast lesions.

Materials And Methods: We retrospectively analyzed data from consecutive women who underwent breast MRI including HTR-DCE MR sequencing for abnormal enhancing lesions and who had subsequent pathological analysis at our tertiary center. Semi-quantitative enhancement parameters and textural features were extracted. Temporal change across each phase of textural features in HTR-DCE MR sequences was calculated and called "kinetic textural parameters." Statistical analysis by LASSO logistic regression and cross validation was performed to build a model. The diagnostic performance of the radiomic model was compared to the results of BI-RADS MR score analysis.

Results: We included 117 women with a mean age of 54 years (28-88). Of the 174 lesions analyzed, 75 were benign and 99 malignant. Seven semi-quantitative enhancement parameters and 57 textural features were extracted. Regression analysis selected 15 significant variables in a radiomic model (called "malignant probability score") which displayed an AUC = 0.876 (sensitivity = 0.98, specificity = 0.52, accuracy = 0.78). The performance of the malignant probability score to distinguish benign from malignant breast lesions (AUC = 0.876, 95%CI 0.825-0.925) was significantly better than that of BI-RADS analysis (AUC = 0.831, 95%CI 0.769-0.892). The radiomic model significantly reduced false positives (42%) with the same number of missed cancers (n = 2).

Conclusion: A radiomic model including kinetic textural features extracted from an HTR-DCE MR sequence improves diagnostic performance over BI-RADS analysis.

Key Points: • Radiomic analysis using HTR-DCE is of better diagnostic performance (AUC = 0.876) than conventional breast MRI reading with BI-RADS (AUC = 0.831) (p < 0.001). • A radiomic malignant probability score under 19.5% gives a negative predictive value of 100% while a malignant probability score over 81% gives a positive predictive value of 100%. • Kinetic textural features extracted from HTR-DCE-MRI have a major role to play in distinguishing benign from malignant breast lesions.

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