Whole-lesion histogram and texture analyses of breast lesions on inline quantitative DCE mapping with CAIPIRINHA-Dixon-TWIST-VIBE

Purpose: To investigate the diagnostic capability of whole-lesion (WL) histogram and texture analysis of dynamic contrast-enhanced (DCE) MRI inline-generated quantitative parametric maps using CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) to differentiate malignant from benign breast lesions and breast cancer subtypes.

Materials and methods: From February 2018 to November 2018, DCE MRI using CDTV was performed on 211 patients. The inline-generated parametric maps included K^trans, k_ep, V_e, and IAUGC₆₀. Histogram and texture features were extracted from the above parametric maps respectively based on a WL analysis. Student's t tests, one-way ANOVAs, Mann-Whitney U tests, Jonckheere-Terpstra tests, and ROC curves were used for statistical analysis.

Results: Compared with benign breast lesions, malignant breast lesions showed significantly higher K^trans_{_median, 5th percentile, entropy, and diff-entropy}, IAUGC_{60_median, 5th percentile, entropy, and diff-entropy}, k_{ep_mean, median, 5th percentile, entropy, and diff-entropy}, and V_{e_95th percentile, diff-variance, and contrast}, and significantly lower k_{ep_skewness} and V_{e_SD, entropy, diff-entropy, and skewness} (all p ≤ 0.011). The combination of all the extracted parameters yielded an AUC of 0.85 (sensitivity 76%, specificity 86%). k_{ep_contrast} showed a significant difference among different subtypes of breast cancer (p = 0.006). k_{ep_skewness} showed a significant difference between lymph node-positive and lymph node-negative breast cancer (p = 0.007). The IAGC_{60_5th percentile} had an AUC of 0.71 (sensitivity 50%, specificity 91%) for differentiating between high- and low-proliferation groups of breast cancer.

Conclusions: The WL histogram and texture analyses of CDTV-DCE-derived parameters may give additional information for further evaluation of breast cancer.

Key points: • Inline DCE mapping with CDTV is effective and time-saving. • WL histogram and texture-extracted features could distinguish breast cancer from benign lesions accurately. • k_{ep_contrast}, k_{ep_skewness}, and IAUGC_{60_5th percentile} could predict breast cancer subtypes, lymph node metastasis, and proliferation abilities, respectively.