Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

Abstract

Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy.

Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method.

Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC=0.85±0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96±0.03 (p<0.01).

Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.

FIG. 1.

An example showing two matched breast MRI slices acquired from before (a) and after (b) neoadjuvant chemotherapy of one patient assigned in the NR group. A matched malignant tumor is marked by a red arrow in two image slices.

FIG. 2.

Illustration of chest wall detection and breast region segmentation. In this figure, the detected middle point A between the left and right breasts and two fitted oblique lines (B and C) are marked in (a), and the final segmented breast region, which is used to compute the BPE features, is shown in (b).

FIG. 3.

An example of two breast tumors including (a) a tumor without necrotic area and (b) a tumor with necrotic area inside.

FIG. 4.

The result of the detection process for tumor, enhanced, and dead area shown as image slices: (a) tumor detection, (b) enhanced area detection, and (c) necrotic area detection with their corresponding binary images (d)–(f), respectively.

FIG. 5.

Two ROC curves generated using the classification scores of the ANN-based classifier (solid curve) and feature fusion method (dashed curve).