Evaluating pathologic response of breast cancer to neoadjuvant chemotherapy with computer-extracted features from contrast-enhanced ultrasound videos. (July 2017)

• Record Type: Journal Article
• Authors: Zhang, Qi; Yuan, Congcong; Dai, Wei; Tang, Lei; Shi, Jun; Li, Zuoyong; Chen, Man
• Is Part Of: Physica medica. Volume 39(2017)
• Page Start: 156
• Page End: 163
• Publication Date: 2017-07
• Subjects: Contrast-enhanced ultrasound -- Neoadjuvant chemotherapy -- Breast cancer -- Pathologic response; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2017.06.023
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Extracted perfusion and texture features with computer assistance from CEUS videos. Compared features between pre- and post-chemotherapy in pathologic responders. Intratumoral contrast enhancement decreases and becomes more heterogeneous after NAC. Features achieve 0.946 AUC for discriminating between pre- and post-NAC cancers. Computer-extracted CEUS features may be valuable for tumor response evaluation. Abstract: Purpose: To extract quantitative perfusion and texture features with computer assistance from contrast-enhanced ultrasound (CEUS) videos of breast cancer before and after neoadjuvant chemotherapy (NAC), and to evaluate pathologic response to NAC with these features. Methods: Forty-two CEUS videos with 140, 484 images were acquired from 21 breast cancer patients pre- and post-NAC. Time-intensity curve (TIC) features were calculated including the difference between area under TIC within a tumor and that within a computer-detected reference region (AUT_T-R). Four texture features were extracted including Homogeneity and Contrast. All patients were identified as pathologic responders by Miller and Payne criteria. The features between pre- and post-treatment in these responders were statistically compared, and the discrimination between pre- and post-treatment cancers was assessed with a receiver operating characteristic (ROC) curve. Results: Compared with the pre-treatment cancers, the post-treatment cancers had significantly lower Homogeneity ( p < 0.001) and AUT_T-R ( p = 0.014), as well as higher Contrast ( p < 0.001), indicating the intratumoral contrast enhancement decreased and became more heterogeneous after NAC in responders. The combination of Homogeneity and AUT_T-R achieved an accuracy of 90.5% and area under ROC curve of 0.946 for discrimination between pre- and post-chemotherapy cancers without cross validation. The accuracy still reached as high as 85.7% under leave-one-out cross validation. Conclusions: The computer-extracted CEUS features show reduced and more heterogeneous neovascularization of cancer after NAC. The features achieve high accuracy for discriminating between pre- and post-chemotherapy cancers in responders and thus are potentially valuable for tumor response evaluation in clinical practice.