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Background The tumor microenvironment represents an abnormal niche containing numerous factors, such as T cells, dendritic cells (DCs), regulatory T cells (Tregs), and indoleamine 2,3‐dioxygenase (IDO), involved in maintaining immune homeostasis and tolerance. All these factors may influence the choice of therapy and the clinical outcomes. Methods Flow cytometry was performed to identify CD4+/CD8 + T cells and DCs, and immunohistochemistry was used to evaluate IDO and Forkhead Box P3 (Foxp3) expression; these experiments were performed in order to explore the clinical and prognostic significance of CD4/CD8 + T cells, DCs, Tregs, and IDO expression in gastric carcinoma. Results Smaller tumor size was correlated with higher expression levels of peripheral CD4 + T cells (P = .003) and CD8 + T cells (P = .002), and lower IDO expression (P = .044) in tumors. Well‐differentiated gastric carcinomas displayed higher peripheral (P = .029) and tumor‐infiltrating CD4 + T cell (P = .009) populations and a higher tumor‐infiltrating DC1/DC2 ratio (P = .048). Gastric cancer in the early T stages exhibited higher populations of peripheral DC2s (P = .044) and a higher tumor‐infiltrating DC1/DC2 ratio (P = .012). Gastric cancer at the N0 stage had lower tumor‐infiltrating DC2s (P = .032) and a higher DC1/DC2 ratio (P = .037). IDO expression was positively correlated with tumor‐infiltrating Foxp3 + Tregs (P < .001) as well as DC2s (P < .001), whereas it was negatively correlated with the tumor‐infiltrating CD4/CD8 + T cell ratio (P = .023). Tumor‐infiltrating Foxp3 + Treg was positively correlated with tumor‐infiltrating DC2s (r2 = 0.772; P < .001). At T, N, and TNM stages, the expression levels of peripheral DC2s, tumor‐infiltrating DC1/DC2 ratios, Foxp3 + Tregs, and IDO were significantly correlated with prognosis (P < .05). The T stage and peripheral DC2s were significant risk factors for overall survival. Conclusion Immunocompetent cells and humoral immune factors, including DC2s, CD4+/CD8 + T cells, Foxp3 + Tregs, and IDO, interact with each other to compose a complex community of tumor immune microenvironment, ultimately affecting tumor progression and survival of gastric cancer. The tumor microenvironment represents an abnormal niche containing numerous factors, such as T cells, DCs, Tregs, and IDO, involved in maintaining immune homeostasis and tolerance. We confirmed that the expression levels of peripheral and tumor‐infiltrating DC2s, CD4+/CD8 + T cells, Foxp3 + Tregs, and IDO were correlated with each other as well as with clinicopathological characters and gastric cancer prognosis.

Background Predicting the response to neoadjuvant chemoimmunotherapy in patients with resectable non-small cell lung cancer (NSCLC) facilitates clinical treatment decisions. Our study aimed to establish a machine learning model that accurately predicts the pathological complete response (pCR) using 18 F-FDG PET radiomics features. Methods We retrospectively included 210 patients with NSCLC who completed neoadjuvant chemoimmunotherapy and subsequently underwent surgery with pathological results, categorising them into a training set of 147 patients and a test set of 63 patients. Radiomic features were extracted from the primary tumour and lymph nodes. Using 10-fold cross-validation with the least absolute shrinkage and selection operator method, we identified the most impactful radiomic features. The clinical features were screened using univariate and multivariate analyses. Machine learning models were developed using the random forest method, leading to the establishment of one clinical feature model, one primary tumour radiomics model, and two fusion radiomics models. The performance of these models was evaluated based on the area under the curve (AUC). Results In the training set, the three radiomic models showed comparable AUC values, ranging from 0.901 to 0.925. The clinical model underperformed, with an AUC of 0.677. In the test set, the Fusion_LN1LN2 model achieved the highest AUC (0.823), closely followed by the Fusion_Lnall model with an AUC of 0.729. The primary tumour model achieved a moderate AUC of 0.666, whereas the clinical model had the lowest AUC at 0.631. Additionally, the Fusion_LN1LN2 model demonstrated positive net reclassification improvement and integrated discrimination improvement values compared with the other models, and we employed the SHapley Additive exPlanations methodology to interpret the results of our optimal model. Conclusions Our fusion radiomics model, based on 18 F-FDG-PET, will assist clinicians in predicting pCR before neoadjuvant chemoimmunotherapy for patients with resectable NSCLC.

Background This study aims to develop habitat radiomic models to predict overall survival (OS) for hepatocellular carcinoma (HCC), based on the characterization of the intratumoral heterogeneity reflected in 18 F-FDG PET/CT images. Methods A total of 137 HCC patients from two institutions were retrospectively included. First, intratumoral habitats were achieved by a two-step unsupervised clustering process based on k-means clustering. Second, a total of 4032 radiomic features were extracted based on each habitat, including 2016 PET-based and 2016 CT-based radiomic features. Then, after feature selection, the stacking ensemble learning approach which combined six machine learning classifiers as the first-level learners with Cox proportional hazards regression as the second-level learner, was employed to build multiple radiomic models. Finally, the optimal model was selected based on the calculation of the C-index, and a combined model integrating with a clinical model was also constructed to identify the potentially complementary effect. Results Three spatially distinct habitats were identified in the two cohorts. Among a total of 30 stacking ensemble learning models established based on different combinations of 5 types of segmented volumes of interest (VOIs) with 6 types of classifiers, the MLP-Cox-habitat-2 model was selected as the optimal radiomic model with a C-index of 0.702 in the external validation cohort. Furthermore, the combined model integrating the optimal radiomic model with the clinical model achieved an improved C-index of 0.747. Consistently, the combined model outperformed the other models for OS prediction, with a time-dependent AUC of 0.835, 0.828, and 0.800 in the 1-year, 2-year, and 3-year OS, respectively. Conclusion 18 F-FDG PET/CT-based habitat radiomics outperformed traditional radiomics in OS prediction for HCC, with a further improved predictive power by integrating with the clinical model. The optimal combined habitat model was potentially promising in guiding individualized treatment for HCC. Trial registration This study was a retrospective study, so it was free from registration.

Background Radiomic models combining intratumoral with peritumoral features are potentially beneficial to enhance the predictive performance. This study aimed to identify the optimal 18 F-FDG PET/CT-derived radiomic models for prediction of prognosis in hepatocellular carcinoma (HCC). Methods A total of 135 HCC patients from two institutions were retrospectively included. Four peritumoral regions were defined by dilating tumor region with thicknesses of 2 mm, 4 mm, 6 mm, and 8 mm, respectively. Based on segmentation of intratumoral, peritumoral and integrated volume of interest (VOI), corresponding radiomic features were extracted respectively. After feature selection, a total of 15 intratumoral radiomic models were constructed based on five ensemble learning algorithms and radiomic features from three image modalities. Then, the optimal combination of ensemble learning algorithms and image modality in the intratumoral models was selected to develop subsequent peritumoral radiomic models and integrated radiomic models. Finally, a nomogram was developed incorporating the optimal radiomic model with clinical independent predictors to achieve an intuitive representation of the prediction model. Results Among the intratumoral radiomic models, the one which combined PET/CT-based radiomic features with SVM classifier outperformed other models. With the addition of peritumoral information, the integrated model based on an integration of intratumoral and 2 mm-peritumoral VOI, was finally approved as the optimal radiomic model with a mean AUC of 0.831 in the internal validation, and a highest AUC of 0.839 (95%CI:0.718–0.960) in the external test. Furthermore, a nomogram incorporating the optimal radiomic model with HBV infection and TNM status, was able to predict the prognosis for HCC with an AUC of 0.889 (95%CI: 0.799–0.979). Conclusions The integrated intratumoral and peritumoral radiomic model, especially for a 2 mm peritumoral region, was verified as the optimal radiomic model to predict the overall survival of HCC. Furthermore, combination of integrated radiomic model with significant clinical parameter contributed to further enhance the prediction efficacy. Trial registration This study was a retrospective study, so it was free from registration.

Purpose According to the updated classification system, human epidermal growth factor receptor 2 (HER2) expression statuses are divided into the following three groups: HER2-over-expression, HER2-low-expression, and HER2-zero-expression. HER2-negative expression was reclassified into HER2-low-expression and HER2-zero-expression. This study aimed to identify three different HER2 expression statuses for breast cancer (BC) patients using PET/CT radiomics and clinicopathological characteristics. Methods and materials A total of 315 BC patients who met the inclusion and exclusion criteria from two institutions were retrospectively included. The patients in institution 1 were divided into the training set and the independent validation set according to the ratio of 7:3, and institution 2 was used as the external validation set. According to the results of pathological examination, all BC patients were divided into HER2-over-expression, HER2-low-expression, and HER2-zero-expression. First, PET/CT radiomic features and clinicopathological features based on each patient were extracted and collected. Second, multiple methods were used to perform feature screening and feature selection. Then, four machine learning classifiers, including logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), were constructed to identify HER2-over-expression vs. others, HER2-low-expression vs. others, and HER2-zero-expression vs. others. The receiver operator characteristic (ROC) curve was plotted to measure the model’s predictive power. Results According to the feature screening process, 8, 10, and 2 radiomics features and 2 clinicopathological features were finally selected to construct three prediction models (HER2-over-expression vs. others, HER2-low-expression vs. others, and HER2-zero-expression vs. others). For HER2-over-expression vs. others, the RF model outperformed other models with an AUC value of 0.843 (95%CI: 0.774–0.897), 0.785 (95%CI: 0.665–0.877), and 0.788 (95%CI: 0.708–0.868) in the training set, independent validation set, and external validation set. Concerning HER2-low-expression vs. others, the outperformance of the LR model over other models was identified with an AUC value of 0.783 (95%CI: 0.708–0.846), 0.756 (95%CI: 0.634–0.854), and 0.779 (95%CI: 0.698–0.860) in the training set, independent validation set, and external validation set. Whereas, the KNN model was confirmed as the optimal model to distinguish HER2-zero-expression from others, with an AUC value of 0.929 (95%CI: 0.890–0.958), 0.847 (95%CI: 0.764–0.910), and 0.835 (95%CI: 0.762–0.908) in the training set, independent validation set, and external validation set. Conclusion Combined PET/CT radiomic models integrating with clinicopathological characteristics are non-invasively predictive of different HER2 statuses of BC patients.

The morphology of benign and malignant solitary pulmonary lesions sometimes overlaps, making the differentiation difficult. This research aimed to create a radiomics-based prediction model using dual-phase 18 F-fluorodeoxyglucose positron emission tomography-computed tomography ( 18 F-FDG PET/CT) for noninvasive classification of these lesions. A total of 132 patients with solitary pulmonary lesions were included. CT, routine PET (PET 1 ), delayed PET (PET 2 ) and clinical data were acquired. Five combinations of radiomic features (CT, CT + PET 1 , CT + PET 2 , CT + PET 1  + PET 2 , CT+(PET 2 -PET 1 )/PET 1 ) were analyzed. Feature selection used eight methods, and the top ten ranked features were retained based on their weight coefficients. Seven classifiers were used to construct models. The receiver operating characteristic (ROC) curves of the five optimal radiomics models for solitary pulmonary lesions were compared. The optimal CT+(PET 2 -PET 1 )/PET 1 model achieved the highest AUC of 0.898 (95% CI: 0.828–0.968), compared to the optimal CT (0.828, 95% confidence interval [CI]: 0.754–0.902), CT + PET 1 (0.858, 95% CI: 0.785–0.931), CT + PET 2 (0.867, 95% CI: 0.796–0.938), and CT + PET 1  + PET 2 (0.868, 95% CI: 0.798–0.939) models. Based on dual-phase 18 F-FDG PET/CT for radiomic analysis, the optimal CT+(PET 2 -PET 1 )/PET 1 model demonstrated promising diagnostic efficacy and can be a clinical diagnostic tool to distinguish between benign and malignant solitary pulmonary lesions.

From a metabolic perspective, cancer may be considered as a metabolic disease characterized by reprogrammed glycolytic metabolism. The aim of the present study was to investigate CD147-mediated glucose metabolic regulation in hepatocellular carcinoma (HCC) and its contribution to altered immune responses in the tumor microenvironment. Several HCC cell lines and corresponding nude mice xenografts models differing in CD147 expressions were established to directly investigate the role of CD147 in the reprogramming of glucose metabolism, and to determine the underlying molecular mechanisms. Immunohistochemistry (IHC) analyses and flow cytometry were used to identify the relationship between reprogrammed glycolysis and immunosuppression in HCC. Upregulated CD147 expressions were found to be associated with enhanced expressions of GLUT1, MCT1 in HCC tumorous tissues. CD147 promoted the glycolytic metabolism in HCC cell lines in vitro via the PI3K/Akt/mTOR signaling pathway. A positive correlation existed between a profile of immunosuppressive lymphocytes infiltration and CD147 expression in HCC tissues. Accumulation of FOXP3-expressing regulatory T cells was induced under a stimulation with lactate in vitro. In conclusion, CD147 promoted glycolytic metabolism in HCC via the PI3K/Akt/mTOR signaling pathway, and was related to immunosuppression in HCC.

Background: To develop and validate a radiomics model based on 18F-FDG PET/CT images to preoperatively predict occult axillary lymph node (ALN) metastases in patients with invasive ductal breast cancer (IDC) with clinically node-negative (cN0); Methods: A total of 180 patients (mean age, 55 years; range, 31–82 years) with pathologically proven IDC and a preoperative 18F-FDG PET/CT scan from January 2013 to January 2021 were included in this retrospective study. According to the intraoperative pathological results of ALN, we divided patients into the true-negative group and ALN occult metastasis group. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, t-tests, and LASSO were used to screen the feature, and the random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and k-nearest neighbor (KNN) were used to build the prediction models. The best-performing model was further tested by the permutation test; Results: Among the four models, RF had the best prediction results, the AUC range of RF was 0.661–0.929 (mean AUC, 0.817), and the accuracy range was 65.3–93.9% (mean accuracy, 81.2%). The p-values of the permutation tests for the RF model with maximum and minimum accuracy were less than 0.01; Conclusions: The developed RF model was able to predict occult ALN metastases in IDC patients based on preoperative 18F-FDG PET/CT radiomic features.

Background To assess the value of an 18 F-FDG-positron emission tomography/computed tomography (PET/CT)-based machine learning model for distinguishing between adrenal benign nodules (ABNs) and adrenal metastases (AMs) in patients with indeterminate adrenal nodules and extra-adrenal malignancies. Methods A total of 303 patients who underwent 18 F-FDG-PET/CT with indeterminate adrenal nodules and extra-adrenal malignancies from March 2015 to June 2021 were included in this retrospective study (training dataset ( n  = 182): AMs ( n  = 97), ABNs ( n  = 85); testing dataset ( n  = 121): AMs ( n  = 68), ABNs ( n  = 55)). The clinical and PET/CT imaging features of the two groups were analyzed. The predictive model and simplified scoring system for distinguishing between AMs and ABNs were built based on clinical and PET/CT risk factors using multivariable logistic regression in the training cohort. The performances of the predictive model and simplified scoring system in both the training and testing cohorts were evaluated by the areas under the receiver operating characteristic curves (AUCs) and calibration curves. The comparison of AUCs was evaluated by the DeLong test. Results The predictive model included four risk factors: sex, the ratio of the maximum standardized uptake value (SUVmax) of adrenal lesions to the mean liver standardized uptake value, the value on unenhanced CT (CTU), and the clinical stage of extra-adrenal malignancies. The model achieved an AUC of 0.936 with a specificity, sensitivity and accuracy of 0.918, 0.835, and 0.874 in the training dataset, respectively, while it yielded an AUC of 0.931 with a specificity, sensitivity, and accuracy of 1.00, 0.735, and 0.851 in the testing dataset, respectively. The simplified scoring system had comparable diagnostic value to the predictive model in both the training (AUC 0.938, sensitivity: 0.825, specificity 0.953, accuracy 0.885; P  = 0.5733) and testing (AUC 0.931, sensitivity 0.735, specificity 1.000, accuracy 0.851; P  = 1.00) datasets. Conclusions Our study showed the potential ability of a machine learning model and a simplified scoring system based on clinical and 18F-FDG-PET/CT imaging features to predict AMs in patients with indeterminate adrenal nodules and extra-adrenal malignancies. The simplified scoring system is simple, convenient, and easy to popularize.

Background: The prevalence of chronic total occlusion (CTO) lesions is as high as 30% in patients undergoing coronary angiography (CAG). Some CTO patients do not undergo revascularization due to procedural complexity and high risks. This study aimed to investigate the value of cadmium-zinc-telluride (CZT) SPECT dynamic myocardial perfusion imaging (MPI) for risk stratification and prognosis assessment in patients with coronary CTO. Methods: This study retrospectively included 62 patients who underwent CZT SPECT dynamic MPI examination and were diagnosed with CTO by angiography. The primary endpoint was major adverse cardiovascular events (MACEs), defined as cardiovascular death, non-fatal myocardial infarction, non-fatal stroke, hospitalization for heart failure, late coronary revascularization, or hospitalization for unstable angina. Results: Over a median follow-up of 17 months (IQR 11–23), 15 MACEs occurred. The stress myocardial blood flow (sMBF) and coronary flow reserve (CFR) in the CTO territory were significantly lower in the MACEs group compared to the non-MACEs group (all p < 0.05). Receiver operating characteristic analysis determined the optimal cut-off values for predicting MACEs as sMBF < 0.75 (sensitivity 78.7%, specificity 73.3%, AUC = 0.74, p < 0.05) and CFR < 1.39 (sensitivity 70.2%, specificity 80.0%, AUC = 0.75, p < 0.01). Kaplan–Meier survival analysis showed that patients with impaired sMBF (p < 0.001) or impaired CFR (p < 0.01), defined by these cut-off values, had significantly worse clinical outcomes. Conclusions: The results of this study indicate that sMBF and CFR obtained from CZT SPECT dynamic MPI provide valuable prognostic prediction for patients with coronary CTO lesions, offering critical evidence for identifying high-risk patients requiring active intervention.