Prognostic Value of Axillary Lymph Node Texture Parameters Measured by Pretreatment 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Locally Advanced Breast Cancer with Neoadjuvant Chemotherapy

Abstract

Background: This study investigated the prognostic value of axillary lymph node (ALN) heterogeneity texture features through 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in patients with locally advanced breast cancer (LABC). Methods: We retrospectively analyzed 158 LABC patients with FDG-avid, pathology-proven, metastatic ALN who underwent neoadjuvant chemotherapy (NAC) and curative surgery. Tumor and ALN texture parameters were extracted from pretreatment 18F-FDG PET/CT using Chang-Gung Image Texture Analysis software. The least absolute shrinkage and selection operator regression was performed to select the most significant predictive texture parameters. The predictive impact of texture parameters was evaluated for both progression-free survival and pathologic NAC response. Results: The median follow-up period of 36.8 months and progression of disease (PD) was observed in 36 patients. In the univariate analysis, ALN textures (minimum standardized uptake value (SUV) (p = 0.026), SUV skewness (p = 0.038), SUV bias-corrected Kurtosis (p = 0.034), total lesion glycolysis (p = 0.011)), tumor textures (low-intensity size zone emphasis (p = 0.045), minimum SUV (p = 0.047), and homogeneity (p = 0.041)) were significant texture predictors. On the Cox regression analysis, ALN SUV skewness was an independent texture predictor of PD (p = 0.016, hazard ratio 2.3, 95% confidence interval 1.16–4.58). Conclusions: ALN texture feature from pretreatment 18F-FDG PET/CT is useful for the prediction of LABC progression.

Radiomics Analysis of 18F-FDG PET/CT for Prognosis Prediction in Patients with Stage III Non-Small Cell Lung Cancer Undergoing Neoadjuvant Chemoradiation Therapy Followed by Surgery

“…Since high-dimensional feature data with a quite large number of candidate predictors are evaluated, it is necessary to select relevant features to establish radiomics [ 28 ]. In this study, LASSO regression was used to select the radiomic features in order to minimize the influence of overfitting, as previously studied [ 29 , 30 , 31 , 32 ]. Our results are in agreement with those of earlier studies reporting that higher LASSO scores can be associated with poorer survival rates and distinguish patients into low- and high-risk groups with significant differences in survival.…”