Texture Analysis of Dynamic Contrast-Enhanced MRI in Evaluating Pathologic Complete Response (pCR)‎ of Mass-Like Breast Cancer after Neoadjuvant Therapy

Abstract EN

Objectives.

MRI is the standard imaging method in evaluating treatment response of breast cancer after neoadjuvant therapy (NAT), while identification of pathologic complete response (pCR) remains challenging.

Texture analysis (TA) on post-NAT dynamic contrast-enhanced (DCE) MRI was explored to assess the existence of pCR in mass-like cancer.

Materials and Methods.

A primary cohort of 112 consecutive patients (40 pCR and 72 non-pCR) with mass-like breast cancers who received preoperative NAT were retrospectively enrolled.

On post-NAT MRI, volumes of the residual-enhanced areas and TA first-order features (19 for each sequence) of the corresponding areas were achieved for both early- and late-phase DCE using an in-house radiomics software.

Groups were divided according to the operational pathology.

Receiver operating characteristic curves and binary logistic regression analysis were used to select features and achieve a predicting formula.

Overall diagnostic abilities were compared between TA and radiologists’ subjective judgments.

Validation was performed on a time-independent cohort of 39 consecutive patients.

Results.

TA features with high consistency (Cronbach’s alpha >0.9) between 2 observers showed significant differences between pCR and non-pCR groups.

Logistic regression using features selected by ROC curves generated a synthesized formula containing 3 variables (volume of residual enhancement, entropy, and robust mean absolute deviation from early-phase) to yield AUC = 0.81, higher than that of using radiologists’ subjective judgment (AUC = 0.72), and entropy was an independent risk factor (P<0.001).

Accuracy and sensitivity for identifying pCR were 83.93% and 70.00%.

AUC of the validation cohort was 0.80.

Conclusions.

TA may help to improve the diagnostic ability of post-NAT MRI in identifying pCR in mass-like breast cancer.

Entropy, as a first-order feature to depict residual tumor heterogeneity, is an important factor.