Computer‐aided breast MR image feature analysis for prediction of tumor response to chemotherapy. Issue 11 (19th October 2015)

• Record Type: Journal Article
• Authors: Aghaei, Faranak; Tan, Maxine; Hollingsworth, Alan B.; Qian, Wei; Liu, Hong; Zheng, Bin
• Is Part Of: Medical physics. Volume 42:Issue 11(2015)
• Page Start: 6520
• Page End: 6528
• Publication Date: 2015-10-19
• Subjects: biomedical MRI -- cancer -- data acquisition -- feature extraction -- image classification -- image enhancement -- image fusion -- image segmentation -- medical image processing -- neural nets -- sensitivity analysis -- tumours; Clinical applications -- Cancer -- Segmentation -- Edge enhancement; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- Data acquisition and logging -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image; dynamic contrast‐enhanced breast magnetic resonance imaging -- tumor response to neoadjuvant chemotherapy -- quantitative image feature analysis -- assessment of breast cancer prognosis; Cancer -- Medical magnetic resonance imaging -- Chemotherapy -- Image analysis -- Medical image segmentation -- Medical image contrast -- Testing procedures -- Machine learning; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4933198
• Languages: English
• ISSNs: 0094-2405

Abstract

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.