Predicting Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer

Abstract

Purpose The aim of this study was to develop a combined statistical model using both clinicopathological factors and texture parameters from 18F-FDG PET/CT to predict responses to neoadjuvant chemotherapy in patients with breast cancer. Materials and Methods A total of 435 patients with breast cancer were retrospectively enrolled. Clinical and pathological data were obtained from electronic medical records. Texture parameters were extracted from pretreatment FDG PET/CT images. The end point was pathological complete response, defined as the absence of residual disease or the presence of residual ductal carcinoma in situ without residual lymph node metastasis. Multivariable logistic regression modeling was performed using clinicopathological factors and texture parameters as covariates. Results In the multivariable logistic regression model, various factors and parameters, including HER2, histological grade or Ki-67, gradient skewness, gradient kurtosis, contrast, difference variance, angular second moment, and inverse difference moment, were selected as significant prognostic variables. The predictive power of the multivariable logistic regression model incorporating both clinicopathological factors and texture parameters was significantly higher than that of a model with only clinicopathological factors (P = 0.0067). In subgroup analysis, texture parameters, including gradient skewness and gradient kurtosis, were selected as independent prognostic factors in the HER2-negative group. Conclusions A combined statistical model was successfully generated using both clinicopathological factors and texture parameters to predict the response to neoadjuvant chemotherapy. Results suggest that addition of texture parameters from FDG PET/CT can provide more information regarding treatment response prediction compared with clinicopathological factors alone.

Radiomic Features of 18F-FDG PET in Hodgkin Lymphoma Are Predictive of Outcomes

“…Radiomics analysis of noninvasive imaging is a widely used approach to quantify intratumor heterogeneity [ 34 ]. Previous studies have shown that textural features can effectively predict treatment response and patient survival for various types of cancer [ 30 , 35 , 36 ]. Our results indicated that SUV kurtosis and LZHGE GLZLM might improve the risk stratification in HL patients.…”

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

“…We evaluated 20 publications on breast cancer [59,[188][189][190][191][192][193][194][195][196][197][198][199][200][201][202][203][204][205][206], all of them using FDG as the radiotracer. The average number of patients was 118 (median = 81, range, 34-435).…”

“…In Moscoso et al [203], the authors showed that PET textural features are correlated with immunohistochemical factors and the immunohistochemical subtype of breast cancer using images acquired using a dedicated breast PET scanner. Finally, Lee et al [188] developed a statistical model combining clinicopathological factors and texture parameters from PET and CT images to predict individual responses to neoadjuvant chemotherapy.…”

Progress and Future Trends in PET/CT and PET/MRI Molecular Imaging Approaches for Breast Cancer

“…Non-invasive image-derived biomarkers can also be generated from PET radiomics according to the intensity of the pixels, their associated parameters, and their positions (138). Based on the clinical application of PET-based techniques, several studies have focused on the clinical and technical feasibility of applying PET radiomics to diagnosis (139,140), staging (141)(142)(143), pathological characterization (17,18), NAC response (18,(144)(145)(146), and outcome prediction (147,148) in breast cancer. However, the difficulty in detecting the edges of breast lesions using CT limits the PET/CT in identifying the breast tumor and the alignment of the imaging modalities (141).…”