Dynamic contrast‐enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer

Abstract

The aim of this study was to investigate the potential of texture analysis, applied to dynamic contrast-enhanced MRI (DCE-MRI), to predict the clinical and pathological response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) before NAC is started. Fifty-eight patients with LABC were classified on the basis of their clinical response according to the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines after four cycles of NAC, and according to their pathological response after surgery. T1 -weighted DCE-MRI with a temporal resolution of 1 min was acquired on a 3-T Siemens Trio scanner using a dedicated four-channel breast coil before the onset of treatment. Each lesion was segmented semi-automatically using the 2-min post-contrast subtracted image. Sixteen texture features were obtained at each non-subtracted post-contrast time point using a gray level co-occurrence matrix. Appropriate statistical analyses were performed and false discovery rate-based q values were reported to correct for multiple comparisons. Statistically significant results were found at 1-3 min post-contrast for various texture features for the prediction of both the clinical and pathological response. In particular, eight texture features were found to be statistically significant at 2 min post-contrast, the most significant feature yielding an area under the curve (AUC) of 0.77 for response prediction for stable disease versus complete responders after four cycles of NAC. In addition, four texture features were found to be significant at the same time point, with an AUC of 0.69 for response prediction using the most significant feature for classification based on the pathological response. Our results suggest that texture analysis could provide clinicians with additional information to increase the accuracy of prediction of an individual response before NAC is started.

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound

“…Other modalities have relied on similar mean-value and texture analyses including ultrasound spectroscopy, diffuse optical spectroscopy and MRI methods for therapy response monitoring43444546. In patients with triple negative disease pre-treatment, MRI-based kinetic maps have demonstrated positive results for chemotherapy response prediction in “triple-negative” breast tumours.…”

“…Results also strongly suggest that pre-treatment tumour heterogeneity can influence drug efficacy44. Texture analyses of dynamic contrast-enhanced MRI images have also been used4546 to predict neoadjuvant chemotherapy response. Specifically, results have indicated significant differences in GLCM-texture features between chemotherapy responding and non-responders patients from pre-treatment data46.…”

Predicting breast cancer response to neoadjuvant chemotherapy using pretreatment diffuse optical spectroscopic texture analysis

“…In comparison to other studies, texture analysis of MRI (Ahmed et al , 2013; Golden et al , 2013; Teruel et al , 2014), ultrasound spectroscopy (Sadeghi-Naini et al , 2014), and DOS (Sadeghi-Naini et al , 2015) images have been used to assess and monitor chemotherapy response in breast tumours during the course of treatment. Textural analysis of pretreatment MRI-based kinetic maps have indicated positive results for predicting chemotherapy response in ‘triple-negative’ breast tumours (Golden et al , 2013).…”

“…Those results also strongly suggest that pretreatment tumour heterogeneity can influence drug resistance (Golden et al , 2013). Other similar studies have examined texture features of dynamic contrast-enhanced MRI images to predict NAC response (Ahmed et al , 2013; Teruel et al , 2014). Results have indicated significant differences in GLCM texture features between responders and non-responders at pretreatment (Ahmed et al , 2013) and have reported an increase in textural heterogeneity caused by necrotic tumour areas (Ahmed et al , 2013).…”

Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging

“…While Kuo et al 28 were the first to explore specific hepatocellular carcinoma imaging phenotypes that correlated with doxorubicin drug response in 2007 (28), more recently, a study in women who completed treatment for locally advanced breast cancer suggested that texture analysis of DCE MRI can enable us to predict response to neoadjuvant chemotherapy before its initiation (29). Understanding the spatial and temporal phenotypic, physiological, and genetic heterogeneity of most solid tumours has led to the realisation that most chemotherapy responses are not durable and that targeted therapies are necessary to improve outcomes (6, 30).…”