Jingwu Li

This study aimed to develop and validate a noninvasive predictive model integrating conventional metabolic parameters and radiomic features derived from ¹⁸ F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) for the preoperative assessment of proliferation marker Ki-67 expression in patients with breast cancer (BC). This retrospective study adhered to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, Extended for Artificial Intelligence guidelines and included 119 patients with BC who underwent preoperative ¹⁸ F-fluorodeoxyglucose PET/CT. Patients were randomly assigned to a training cohort (n = 95) and an independent testing cohort (n = 24) at a 4:1 ratio. Radiomics scores (Radscores) were constructed separately for CT and PET using logistic regression models weighted by features selected via least absolute shrinkage and selection operator regression. Five machine learning algorithms – logistic regression, support vector machine (SVM), random forest, extreme gradient boosting, and k-nearest neighbors – were employed to build combined models integrating significant clinical, imaging, and serum tumor marker features with CT-Radscore and PET-Radscore. Model performance was evaluated in the testing set using the area under the receiver operating characteristic curve (AUC), precision-recall curves, and decision curve analysis. The DeLong test was used to compare AUCs between the optimal combined model and individual predictors. Model interpretability was enhanced using SHapley Additive exPlanations to quantify feature contributions at both global and case-specific levels. In the testing set, CT-Radscore and PET-Radscore achieved AUCs of 0.785 (95% confidence interval [CI]: 0.592, 0.977) and 0.806 (95% CI: 0.625, 0.986), respectively. The SVM-based combined model incorporating maximum standardized uptake value, total lesion glycolysis, CT-Radscore, and PET-Radscore demonstrated superior performance, yielding an AUC of 0.847 (95% CI: 0.684, 1.000), which outperformed all single-variable predictors. Precision-recall curves confirmed its robust discriminative ability, while decision curve analysis showed that the SVM model provided greater net clinical benefit across a wide range of threshold probabilities compared with any individual predictor. SHapley Additive exPlanations analysis identified PET-Radscore as the most influential feature, followed by CT-Radscore, maximum standardized uptake value, and total lesion glycolysis. A combined SVM model integrating PET/CT-derived metabolic parameters and radiomic features showed preliminary ability to noninvasively predict high proliferation marker Ki-67 expression in BC. This supports the potential utility of multimodal radiomics in preoperative assessment of tumor proliferative status, pending further validation.

Purpose: To evaluate the potential of an interpretable radiomics model based on ¹⁸ F-FDG PET/CT for predicting adrenal metastases (AMs) in cancer patients with indeterminate adrenal nodules. Materials and methods: A total of 177 patients with extra-adrenal malignancies and indeterminate adrenal nodules (74 metastases; 103 benign lesions) were included and randomly assigned to training and testing sets in a 7:3 ratio. Radiomics features were extracted separately from the CT and PET components of PET/CT examinations. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression (LR) were used to identify independent predictive radiomics factors. Based on these features, single-modality CT, PET, and combined PET/CT radiomics models were constructed using four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), LR, and Decision Tree (DT). The best-performing algorithm for each modality determined through cross-validation was selected to establish the final models. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). DeLong test was used to compare the AUCs between models. Internal validation of the best-performing radiomics model was conducted by bootstrapping to assess potential optimism. Shapley Additive Explanations (SHAP) was utilized to interpret the best-performing radiomics model. Results: The optimal algorithms identified were LR for the CT model and SVM for both the PET and integrated PET/CT models. In the testing set, the AUC values were 0.811 (95% CI: 0.694–0.928) for the CT model and 0.879 (95%CI: 0.789– 0.970) for the PET model. The combined PET/CT model integrating both CT and PET radiomics features achieved an AUC of 0.915 (95%CI: 0.834–0.997), which was significantly higher than that of the CT model alone (p < 0.05). DCA confirmed superior clinical utility of the combined PET/CT model across most threshold probabilities compared to the single-modality models. Bootstrap-corrected internal validation showed an optimism-corrected AUC of 0.919 (95% CI: 0.884-0.964), with minimal observed optimism (0.003, 95% CI: -0.002-0.007). SHAP analysis showed that a texture feature derived from the gray level size zone matrix of PET images was the most significant predictor of AMs. Conclusions: The interpretable radiomics model based on combined PET/CT data provides a non-invasive tool for predicting AMs in cancer patients with indeterminate adrenal nodules. By integrating features from both modalities, this approach significantly improves diagnostic performance and holds strong potential to support personalized treatment.

This retrospective study aims to evaluate the effectiveness of a simplified scoring model utilizing contrast-enhanced computed tomography (CECT) in distinguishing low-risk thymomas (LRTs) from thymic cysts in patients with anterior mediastinal hyper-attenuating nodules. A total of 32 patients of LRTs and 40 patients of hyper-attenuating thymic cysts who underwent chest biphasic CECT preoperatively from January 2015 to December 2022 were included. The traditional CT imaging features and clinical features of each patient were analyzed. A predictive model was built by multivariable logistic regression, and subsequently, a simplified scoring model was developed according to the regression coefficients of each risk factor of LRTs. The performance of risk factors and models were assessed by receiver operating characteristic curve, decision curve analysis, and DeLong test. Compared to hyper-attenuating thymic cysts, LRTs tended to be located off-midline, higher CT values in the venous phase, and moderate to severe enhancement (all P < .001). Based on the above risk factors of LTRs, the predictive model achieved an area under the receiver operating characteristic curve of 0.938. While the simplified scoring model demonstrated comparable diagnostic ability (area under the receiver operating characteristic curve = 0.936, P = .42), with ideal sensitivity (0.719), accuracy (0.861), and specificity (0.975). Decision curve analysis indicated this scoring model provided a higher clinical net benefit. Biphasic CECT had a strong diagnostic capability in differentiating LRTs from hyper-attenuating thymic cysts. The diagnostic scoring model is straightforward and convenient, making it easy to popularize.

Colorectal cancer (CRC) is a highly aggressive form of cancer that poses a significant threat to public health worldwide. The progression of this disease is primarily driven by metabolic changes, especially the disruption of lipid metabolism. Cancerous CRC cells proliferate uncontrollably and invade surrounding tissues due to abnormal modifications in fatty acid synthesis, lipid uptake, storage, and β-oxidation. These metabolic shifts are influenced by key oncogenic signaling pathways, such as the PI3K/AKT/mTOR pathway and the MYC transcriptional network, which also enhance interactions with the tumor microenvironment's stromal elements. Recent research suggests that targeting lipid metabolism through pharmacological means could offer substantial clinical benefits. This article provides a comprehensive analysis of the molecular mechanisms involved in the reprogramming of lipid metabolism in CRC development and assesses its potential for innovative therapeutic approaches.

The interaction between intratumoral microbiome and the tumor microenvironment (TME) has furthered our understanding of tumor ecology. Yet, the implications of their interaction for lung cancer management remain unclear. In the current work, we collected host transcriptome samples and matched intratumoral microbiome samples, as well as detailed clinical metadata from The Cancer Genome Atlas (TCGA) of 478 patients with lung adenocarcinoma (LUAD). Utilizing the multiomics integration approach, we comprehensively investigated the crosstalk between the TME and intratumoral microbiome in patients with LUAD. First, we developed a prognostic model based on the TME signatures (TMEindex) that clearly distinguished clinical, survival, and response to immunotherapy of patients with LUAD. Additionally, we found profound differences in intratumoral microbiota signatures, including alpha- and beta-diversity, among patients with different survival risks based on the TME signatures. In depth, we detected that genera Luteibacter and Chryseobacterium were strongly negatively and positively associated with patients’ survival risk, respectively, suggesting their opposing roles in cancer progression. Moreover, we developed a model that fused intratumoral microbial abundance information with TME signatures, called intratumoral microbiome-modified TMEindex (IMTMEindex), leading in predicting patient overall survival at 1-, 3-, and 5-years. Future clinical profiling of the specific intratumoral microbes in the TME could improve prognosis, inform immunotherapy, and facilitate the development of novel therapeutics for LUAD.

Purpose: To develop and validate a logistic regression (LR) model to improve the diagnostic performance of chest CT in distinguishing small (≤3 cm in long diameter on CT) thymomas from other asymptomatic small anterior mediastinal nodules (SAMNs).Materials and methods: A total of 231 patients (94 thymomas and 137 other SAMNs) with surgically resected asymptomatic SAMNs underwenting plain CT and biphasic enhanced CT from January 2013 to December 2023 were included and randomly allocated into training and internal testing sets at a 7:3 ratio. Clinical and CT features were analyzed, and a predictive model was developed based on independent risk features for small thymomas using multivariate LR in the training set. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to compare the performance of the model and individual risk factors in the internal testing set. An additional prospective testing set (10 thymomas and 13 other SAMNs) was collected from the same institution between 2023 and 2024. The model’s performance was evaluated by area under the curve (AUC) and compared with the results of three radiologists using the DeLong test.Results: The LR model incorporating four CT independent risk features (lesion location, attenuation pattern, CT values in the venous phase [CTV], and enhancement degree) achieved an AUC of 0.887 for small thymomas prediction. This performance was superior to CTV alone (AUC = 0.849, P = 0.118) and significantly higher than other individual risk factors in the internal testing set (P < 0.05). DCA confirmed the model’s enhanced clinical utility across most threshold probabilities. In the prospective test set, the LR showed an AUC of 0.908 (95% CI: 0.765-1.00), comparable to the senior radiologist’s performance (AUC = 0.912 [95% CI: 0.765-1.00], P = 0.961), higher than the intermediate radiologist’s performance (AUC = 0.762 [95% CI: 0.554-0.969], P = 0.094), and significantly better than the junior radiologist’s performance (AUC = 0.700 [95% CI: 0.463-0.937], P = 0.044).Conclusions: The CT-based LR model demonstrated well diagnostic performance comparable to that of senior radiologists in differentiating small thymomas from other asymptomatic SAMNs. CTV played a leading role in the model.

Background: Metastasis-associated in colon cancer-1 (MACC1) is a novel oncogene involved in the growth and metastasis of tumors, which is overexpression in various tumors. MACC1 could promote the growth, invasion, and metastasis of colorectal cancer (CRC) by activating the HGF/MET signaling pathway in vivo . It has been confirmed that MACC1 mainly promotes the Warburg effect in gastric cancer cells through the PI3 K/AKT signaling pathway. Objective: Here, we mainly investigated the association between MACC1 and the glycolysis process in CRC cells. Methods: The expression of MACC1 in CRC and its relationship with the patient's survival were analyzed by TCGA database. We used different concentrations of glucose medium to culture HT-29 and HCT-116 with or without siMACC1. Cell Counting Kit-8 assay was used to detect cell proliferation. The content of lactic acid and glucose in cells was examined by enzyme-linked immunosorbent assay, and extracellular acidification rate was also determined with Seahorse XFe96 Extracellular Flux Analyzer. Western blot was used to detect the protein expressions related to glycolysis. Immunofluorescence was conducted to observe the expression and distribution of GLUT4. Results: In this study, we observed that MACC1 was highly expressed in CRC and negatively correlated with the survival of patients. The expression of glycolysis related enzymes was significantly increased under the stimulation of different concentrations of glucose in HT-29 and HCT-116 cells. However, MACC1 knockdown could significantly reduce high glucose-induced the expressions of glycolysis-related enzymes. Besides, MACC1 knockdown could decrease the content of glucose and lactate, and inhibit glycolytic function in HT-29 and HCT-116 cells. Moreover, the expression of GLUT4 was significantly decreased in the two cell lines treated by 4.5 g/L or 9.0 g/L glucose with MACC1 knockdown. Additionally, MACC1 knockdown could inhibit high glucose-induced membrane translocation of GLUT4. Conclusions: MACC1 knockdown can attenuate glucose metabolism by inhibiting the membrane translocation of GLUT4 in CRC cells.

Various components of the traditional medicine Eleutherococcus senticosus (E. senticosus) exhibit anti-cancer effects, but the specific components and mechanisms of ferroptosis in gastric cancer (GC) have not been clarified. This study aims to identify how E. senticosus induces ferroptosis in GC through network pharmacology, experimental validation, and integrated transcriptomics and metabolomics analysis, using molecular docking and kinetic simulations to predict the core active components of E. senticosus. Multiple active ingredients of E. senticosus were found to influence ferroptosis in GC cells via various pathways. First, E. senticosus significantly decreased glutathione levels and increased malondialdehyde and unsaturated fatty acid synthesis precursors in GC cells. Transcriptomic and metabolomic analyses revealed significant alterations in genes and metabolites related to the ferroptosis pathway in GC cells. EGFR (epidermal growth factor receptor), TFRC (transferrin receptor), ACSL4 (long-chain acyl-CoA synthetase 4), GPX4 (glutathione peroxidase 4), and FTH1 (ferritin heavy chain 1) were identified as core target genes showing the highest correlation. Next, the results of cellular validation experiments showed that the treatment of E. senticosus significantly upregulated the genes that promote ferroptosis and downregulated inhibitory genes. Molecular docking and dynamics simulations identified syringin, ciwujianoside I, ursolic acid, ciwujianoside B, and quercetin as active components of E. senticosus acting on these core targets. In summary, E. senticosus may act on EGFR, TFRC, ACSL4, GPX4, and FTH1 through syringin, ciwujianoside I, ursolic acid, ciwujianoside B, and quercetin, respectively, modulating their expression to disrupt GC cell homeostasis and promote ferroptosis.