Clinical Significance of Peritumoral Adipose Tissue PET/CT Imaging Features for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer

Abstract

We investigated whether textural parameters of peritumoral breast adipose tissue (AT) based on F-18 fluorodeoxyglucose (FDG) PET/CT could predict axillary lymph node metastasis in patients with breast cancer. A total of 326 breast cancer patients with preoperative FDG PET/CT were retrospectively enrolled. PET/CT images were visually assessed and the maximum FDG uptake of axillary lymph nodes (LN SUVmax) was measured. From peritumoral breast AT, 38 textural features of PET imaging were extracted. The diagnostic ability of PET based on visual analysis, LN SUVmax, and textural features of peritumoral breast AT for predicting axillary lymph node metastasis were assessed using the area under the receiver operating characteristic curve (AUC) values. Among the 38 peritumoral breast AT textural features, grey-level co-occurrence matrix (GLCM) entropy showed the highest AUC value (0.830) for predicting axillary lymph node metastasis. The value of GLCM entropy was higher than that of visual analysis (0.739; p < 0.05) and the AUC value was comparable to that of LN SUVmax (0.793; p > 0.05). In the subgroup analysis of patients with negative findings on visual analysis, GLCM entropy still showed a high diagnostic ability (AUC: 0.759) in predicting lymph node metastasis. The findings suggest a potential diagnostic role of PET/CT imaging features of peritumoral breast AT in predicting axillary lymph node metastasis in patients with breast cancer.

Predicting the Recurrence of Gastric Cancer Using the Textural Features of Perigastric Adipose Tissue on [18F]FDG PET/CT

“…In addition to detecting malignant lesions, since [ 18 F]FDG PET/CT can be used to evaluate glucose metabolism in an organ, several studies have demonstrated the clinical use of [ 18 F]FDG PET/CT in estimating the host response to cancers [ 15 , 16 ]. In previous studies, PET imaging parameters of AT were significantly associated with the presence of lymph node metastases and clinical outcomes in diverse cancers, suggesting that PET imaging parameters of AT could be used as surrogate markers of inflammatory response in AT [ 16 , 17 , 18 ]. Since gastric cancer cells also have a substantial interrelationship with peritumoral AT, [ 18 F]FDG PET imaging features of peritumoral AT could reflect this interaction and have a significant association with cancer progression in patients with gastric cancer.…”

“…Recently, radiomic analysis, which extracts a large number of quantitative features from diagnostic imaging examinations through imaging processing methods, such as textural analysis, has been shown to provide deep and valuable insights into malignant diseases [ 19 ]. In previous studies with malignant diseases, first-order features based on standard uptake value (SUV) histograms, which measured diverse parameters related with SUV distribution, and second-order grey-level co-occurrence matrix (GLCM) features, which showed the SUV intensity level distribution in a neighborhood, have been generally used as radiomic features of PET images [ 17 , 20 ]. These first-order and second-order features have demonstrated superior values in detecting metastasis and predicting prognosis than conventional [ 18 F]FDG PET/CT parameters such as the maximum SUV [ 17 , 21 , 22 ].…”

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

“…We screened 32 publications on breast cancer [ 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 , 207 , 208 , 209 , 210 , 211 , 212 , 213 , 214 , 215 , 216 , 217 , 218 , 219 , 220 , 221 ], all employing 18F-FDG. The average number of enrolled patients was 126.8 (range 35–435), 18/32 (56.3%) studies including more than 100 patients; 3 studies (9.3%) were based on prospectively acquired data and 6/32 used an internal independent validation cohort (18.8%).…”

Relationship of FDG PET/CT Textural Features with the Tumor Microenvironment and Recurrence Risks in Patients with Advanced Gastric Cancers

“…In contrast, GLCM contrast and dissimilarity measure local variations, and GLCM entropy measures the randomness of the SUV distribution of voxel pairs in an image. High values of these features represent a heterogeneous SUV distribution in the image, indicating increased metabolic heterogeneity [ 32 , 33 ]. On the other hand, the GLCM correlation indicates how the SUV of one voxel correlates well with the SUV of its neighborhood voxels over the whole image, which reflects different aspects of voxel intensity distribution from other GLCM features [ 32 ].…”