The ability of pre-treatment F-18 FDG PET/CT metabolic parameters for predicting axillary lymph node and distant metastasis and overall survival:

Comparison of [18F] FDG PET/CT and [18F]FDG PET/MRI in the Detection of Distant Metastases in Breast Cancer: A Meta-Analysis

PURPOSE

This meta-analysis aims to assess and compare the diagnostic effectiveness of [18F] FDG PET/CT and [18F] FDG PET/MRI for distant metastases in breast cancer patients.

METHODS

A comprehensive search of the PubMed and Embase databases was performed to identify relevant articles until September 22, 2023. Studies were eligible to be included if they assessed the diagnostic performance of [18F] FDG PET/CT and/or [18F] FDG PET/MRI in detecting distant metastases of breast cancer patients. The DerSimonian and Laird method was used to assess sensitivity and specificity, and then transformed through the Freeman-Tukey double arcsine transformation.

RESULTS

29 articles consisting of 3779 patients were finally included in this study. The overall sensitivity of [18F] FDG PET/CT in diagnosing distant metastases of breast cancer was 0.96 (95% CI: 0.93-0.98), and the overall specificity was 0.95 (95% CI: 0.92-0.97). The overall sensitivity of [18F] FDG PET/MRI was 1.00 (95% CI: 0.97-1.00), and the specificity was 0.97 (95% CI: 0.94-1.00). The results suggested that [18F] FDG PET/CT and [18F] FDG PET/MRI appears to have similar sensitivity (P = .16) and specificity (P = .30) in diagnosing distant metastases of breast cancer.

CONCLUSIONS

The results of our meta-analysis indicated that [18F] FDG PET/CT and [18F] FDG PET/MRI in diagnosing distant metastases of breast cancer appear to have similar sensitivity and specificity. Patients who have access to only one of these modalities will not have the accuracy of their staging compromised. In clinical practice, both of these imaging techniques have their respective strengths and limitations, and physicians should take these into account when making the most suitable choice for patients.

Relationship between total lesion glycolysis of primary lesions based on 18F-FDG PET/CT and lymph node metastasis in gastric adenocarcinoma: a cross-sectional preliminary study

OBJECTIVES

We explored the relationship between lymph node metastasis (LNM) and total lesion glycolysis (TLG) of primary lesions determined by 18fluoro-2-deoxyglucose PET/computed tomography (18F-FDG PET/CT) in patients with gastric adenocarcinoma, and evaluated the independent effect of this association.

METHODS

This retrospective study included 106 gastric adenocarcinoma patients who were examined by preoperative 18F-FDG PET/CT imaging between April 2016 and April 2020. We measured TLG of primary gastric lesions and evaluated its association with LNM. Multivariate logistic regression and a two-piece-wise linear regression were performed to evaluate the relationship between TLG of primary lesions and LNM.

RESULTS

Of the 106 patients, 75 cases (71%) had LNM and 31 cases (29%) did not have LNM. Univariate analyses revealed that a per-SD increase in TLG was independently associated with LNM [odds ratio (OR) = 2.37; 95% confidence interval (CI), 1.42-3.98; P = 0.0010]. After full adjustment of confounding factors, multivariate analyses exhibited that TLG of primary lesions was still significantly associated with LNM (OR per-SD: 2.20; 95% CI, 1.16-4.19; P = 0.0164). Generalized additive model indicated a nonlinear relationship and saturation effect between TLG of primary lesions and LNM. When TLG of primary lesions was <23.2, TLG was significantly correlated with LNM (OR = 1.26; 95% CI, 1.07-1.48; P = 0.0053), whereas when TLG of primary lesions was ≥ 23.2, the probability of LNM was greater than 60%, gradually reached saturation effect, as high as 80% or more.

CONCLUSIONS

In this preliminary study, there were saturation and segmentation effects between TLG of primary lesions determined by preoperative 18F-FDG PET/CT and LNM. When TLG of primary lesions was ≥ 23.2, the probability of LNM was greater than 60%, gradually reached saturation effect, as high as 80% or more. TLG of primary lesions is helpful in the preoperative diagnosis of LNM in patients with gastric adenocarcinoma.

Identification of glycolysis genes signature for predicting prognosis in malignant pleural mesothelioma by bioinformatics and machine learning

BACKGROUND

Glycolysis-related genes as prognostic markers in malignant pleural mesothelioma (MPM) is still unclear. We hope to explore the relationship between glycolytic pathway genes and MPM prognosis by constructing prognostic risk models through bioinformatics and machine learning.

METHODS

The authors screened the dataset GSE51024 from the GEO database for Gene set enrichment analysis (GSEA), and performed differentially expressed genes (DEGs) of glycolytic pathway gene sets. Then, Cox regression analysis was used to identify prognosis-associated glycolytic genes and establish a risk model. Further, the validity of the risk model was evaluated using the dataset GSE67487 in GEO database, and finally, a specimen classification model was constructed by support vector machine (SVM) and random forest (RF) to further screen prognostic genes.

RESULTS

By DEGs, five glycolysis-related pathway gene sets (17 glycolytic genes) were identified to be highly expressed in MPM tumor tissues. Also 11 genes associated with MPM prognosis were identified in TCGA-MPM patients, and 6 (COL5A1, ALDH2, KIF20A, ADH1B, SDC1, VCAN) of them were included by Multi-factor COX analysis to construct a prognostic risk model for MPM patients, with Area under the ROC curve (AUC) was 0.830. Further, dataset GSE67487 also confirmed the validity of the risk model, with a significant difference in overall survival (OS) between the low-risk and high-risk groups (P < 0.05). The final machine learning screened the five prognostic genes with the highest risk of MPM, in order of importance, were ALDH2, KIF20A, COL5A1, ADH1B and SDC1.

CONCLUSIONS

A risk model based on six glycolytic genes (ALDH2, KIF20A, COL5A1, ADH1B, SDC1, VCAN) can effectively predict the prognosis of MPM patients.

Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

Background

The use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately.

Purpose

The aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer.

Methods

We retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC).

Results

The radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, p=0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, p=0.005) for predicting ALNM in breast cancer.

Conclusion

The radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis.

Correlation between semiquantitative and volumetric 18F-FDG PET/computed tomography parameters and Ki-67 expression in breast cancer

OBJECTIVES

To evaluate the relationship between semiquantitative and volumetric parameters on 18F-FDG PET/computed tomography (CT), including maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), tumor to liver ratio (TLR) and tumor to mediastinum ratio (TMR) with the level of Ki-67 expression in breast cancer.

PATIENT AND METHODS

We retrospectively reviewed 105 female patients with newly diagnosed breast cancer who underwent baseline 18F-FDG PET/CT and had immunohistochemical staining to determine the level of Ki-67 expression. The following PET parameters were measured (SUVmax, SUVmean, MTV, TLG, TLR and TMR) and correlated with level of Ki-67 expression.

RESULTS

Significant moderate positive correlations were found between the PET parameters (primary SUVmax, SUVmean, TLG, TLR and TMR) and level of Ki-67 expression. The primary SUVmax had the highest correlation coefficient (r = 0.461) followed by TMR (r = 0.455) and P value of <0.001 for both. In ROC analysis, primary SUVmax had the largest area under the curve (0.806, P = 0.0001), with sensitivity of 76.5 % and specificity of 75% for prediction of high Ki-67 level. In univariate analysis, all PET parameters, patient age, tumor grade, molecular subtype, estrogen receptor and progesterone receptor status were significantly associated with Ki-67 level. In multivariate regression analysis, only tumor grade [odds ratio (OR) = 20.460, 95% confidence interval (CI): 11.360-29.559, P = <0.0001], molecular subtype (OR = -21.894, 95% CI: -37.921 to -5.866, P = 0.008), SUVmax (OR = 2.299, 95% CI: 0.703-3.895, P = 0.005) and TLR (OR = -4.908, 95% CI: -9.476 to -0.340, P = 0.035) were found to be the strongest independent predictor factors for the level of Ki-67 expression and hence proliferative activity of malignant cells in breast cancer.

CONCLUSION

The semiquantitative parameters and volumetric 18F-FDG PET/CT parameter, that is, TLG correlated well with proliferation marker Ki-67 in breast cancer. 18F-FDG PET/CT imaging can be used as a useful noninvasive diagnostic tool in imaging cellular proliferation and hence may substitute for in vitro testing of molecular markers in the diagnoses and staging of breast cancer.