Features from Computerized Texture Analysis of Breast Cancers at Pretreatment MR Imaging Are Associated with Response to Neoadjuvant Chemotherapy

Abstract

Purpose To evaluate whether features from texture analysis of breast cancers were associated with pathologic complete response (pCR) after neoadjuvant chemotherapy and to explore the association between texture features and tumor subtypes at pretreatment magnetic resonance (MR) imaging. Materials and Methods Institutional review board approval was obtained. This retrospective study included 85 patients with 85 breast cancers who underwent breast MR imaging before neoadjuvant chemotherapy between April 10, 2008, and March 12, 2015. Two-dimensional texture analysis was performed by using software at T2-weighted MR imaging and contrast material-enhanced T1-weighted MR imaging. Quantitative parameters were compared between patients with pCR and those with non-pCR and between patients with triple-negative breast cancer and those with non-triple-negative cancer. Multiple logistic regression analysis was used to determine independent parameters. Results Eighteen tumors (22%) were triple-negative breast cancers. pCR was achieved in 30 of the 85 tumors (35%). At univariate analysis, mean pixel intensity with spatial scaling factor (SSF) of 2 and 4 on T2-weighted images and kurtosis on contrast-enhanced T1-weighted images showed a significant difference between triple-negative breast cancer and non-triple-negative breast cancer (P = .009, .003, and .001, respectively). Kurtosis (SSF, 2) on T2-weighted images showed a significant difference between pCR and non-pCR (P = .015). At multiple logistic regression, kurtosis on T2-weighted images was independently associated with pCR in non-triple-negative breast cancer (P = .033). A multivariate model incorporating T2-weighted and contrast-enhanced T1-weighted kurtosis showed good performance for the identification of triple-negative breast cancer (area under the receiver operating characteristic curve, 0.834). Conclusion At pretreatment MR imaging, kurtosis appears to be associated with pCR to neoadjuvant chemotherapy in non-triple-negative breast cancer and may be a promising biomarker for the identification of triple-negative breast cancer. RSNA, 2017.

Resectable pancreatic ductal adenocarcinoma: association between preoperative CT texture features and metastatic nodal involvement

“…Although this is a standard approach used in many previous studies, it may be less representative of the entire tumor volume than 3D analysis. 2D approach is sill widely used because of its operability and practicality [12,26]. Third, inter-observer variability was not available in this study.…”

Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences

“…Quantitative radiomic features extracted from breast dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) have been shown to be associated with genetic expression profiles in breast cancer . Radiomic breast MRI features have also been shown to be predictive of the response of neoadjuvant chemotherapy, recurrence risk, and recurrence‐free survival for breast cancer patients …”

“…9,10 Radiomic breast MRI features have also been shown to be predictive of the response of neoadjuvant chemotherapy, recurrence risk, and recurrence-free survival for breast cancer patients. [11][12][13][14][15] Typical breast MRI protocols include multiple different sequences, including T 1 -weighted imaging (T 1 WI), T 2weighted imaging (T 2 WI), and diffusion-weighted imaging (DWI), as well as often DCE. The DCE sequence may reflect angiogenesis of tumor through the pattern of enhancement, 16 and has been extensively investigated in many radiomic studies.…”

Texture analysis can be used to improve skin sonography quantification capacities

“…A matriz foi então usada para extrair parâmetros estatísticos, como correlação, média, raiz quadrada média [14]. A TA tem sido utilizad com sucesso no passado para a análise de imagens radiográficas do osso trabecular [15], imagens de ressonância magnética [16], [17], mas não para ultrassonografias até o momento. O objetivo deste trabalho foi explorar ferramentas TA para quantificar e comparar as alterações na epiderme, derme, hipoderme na pele intacta, hidratada e eritema e oferecer uma estratégia potencial para obter parâmetros semi-quantificáveis de imagens de ultrassonografia.…”

“…The matrix is then used to extract statistical parameters, such as correlation, mean, root mean square [14]. TA has been successfully employed in the past for the analysis of Xray images of trabecular bone [15], magnetic resonance images [16], [17], but not for sonograms as yet. Our aim was to explore TA tools to quantify and compare changes in the epidermis, dermis, hypodermis in intact, hydrated skin and erythema and offer a potential strategy to obtain semi-quantifiable parameters from ultrasonography images.…”