Pancreatic cancer exhibits extensive metabolic reprogramming that supports rapid progression and therapeutic resistance, making metabolic vulnerabilities attractive targets for intervention. Here, a tumor microenvironment-responsive nanoreactor (Pht@HMnO2-HA) is designed to induce disulfidptosis in pancreatic cancer through coordinated metabolic interference and redox catalysis. Following CD44-mediated tumor targeting and cellular internalization, the nanoreactor's responsive self-optimization within the tumor microenvironment enables localized release of phloretin, suppressing glucose uptake and pentose phosphate pathway activity and thereby limiting intracellular reducing-power generation. In parallel, the nanoreactor consumes intracellular glutathione and amplifies oxidative stress via MnO2-mediated redox reactions, thereby depleting antioxidant defenses. Together, these processes impose reducing-power deprivation and disrupt redox homeostasis, leading to cystine accumulation, disulfide stress, actin cytoskeleton collapse, and disulfidptosis in pancreatic cancer cells. Moreover, degradation-associated Mn2 + release provides activatable T1-weighted MRI contrast, enabling noninvasive visualization of intratumoral nanoreactor activation and therapeutic progression. Collectively, this work establishes a theranostic nanoreactor that exploits coupled metabolic and redox vulnerabilities to induce disulfidptosis, offering a mechanistically grounded strategy for precision therapy in pancreatic cancer.

BACKGROUND:Epstein-Barr Virus-Positive Inflammatory Follicular Dendritic Cell Sarcoma (EBV+ IFDCS) is a rare low-grade malignant neoplasm. Preoperative diagnosis of EBV+ IFDCS is challenging because it lacks specific clinical or laboratory manifestations as well as distinctive imaging features. This report presents a case of hepatic EBV+ IFDCS and reviews the relevant literature. The study summarized the imaging characteristics of this condition and provided insights to support preoperative imaging evaluation. CASE PRESENTATION:A 54-year-old asymptomatic woman was found to have a hepatic mass during a routine health screening and subsequently underwent surgical resection. Preoperative computed tomography and magnetic resonance imaging identified a lesion in the left hepatic lobe. The mass demonstrated marked but heterogeneous enhancement in the arterial phase, followed by decreased enhancement in the portal venous and delayed phases. The diagnosis of hepatic EBV+ IFDCS was confirmed by histopathological examination. CONCLUSION:Because the imaging findings of hepatic EBV+ IFDCS are variable, preoperative diagnosis remains challenging. This report aims to improve recognition of the imaging characteristics of this disease.

Background:Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast and enables multi-parametric assessment of tumor biology. Longitudinal relaxation time (T1) mapping has emerged as a quantitative method capable of measuring the intrinsic T1 value of tissues, reflecting microscopic structural and compositional changes in the tumor microenvironment. This study aimed to evaluate the utility of magnetic resonance T1 mapping, alone and in combination with diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), in differentiating histologic subtypes and assessing tumor differentiation in non-small cell lung cancer (NSCLC). Methods:A total of 76 patients with pathologically confirmed NSCLC [48 adenocarcinoma (AD), 28 squamous cell carcinoma (SCC)] were prospectively enrolled. Patients were further stratified into poorly differentiated (n=32) and moderately/highly differentiated (n=44) groups. All underwent conventional MRI, DWI, DCE-MRI, and native/post-contrast T1 mapping. Quantitative parameters included apparent diffusion coefficient (ADC), Ktrans, Kep, Ve, T1pre, T1post, absolute T1 reduction (T1d), and percentage T1 reduction (T1d%). For parameters showing statistically significant differences between groups, receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic performance. The area under the curve (AUC), optimal cutoff values, sensitivity, specificity, and Youden index were calculated. Results:The agreement between the two readers was reasonably good with intraclass coefficient (ICC) values of 0.938 for T1pre, 0.922 for T1post, and 0.814 for ADC. AD demonstrated significantly higher ADC values (1,159.01 vs. 1,041.75)×10-6 mm2/s and lower T1pre (1,440 vs. 1,576.83) ms, T1post (549.07 vs. 607.44) ms, and T1d (890.93 vs. 969.39) ms values compared with SCC (P<0.05). The four-parameter model (ADC + T1pre + T1post + T1d) achieved the highest performance for differentiating AD from SCC (AUC =0.805, with 75% sensitivity and 79.2% specificity). Poorly differentiated tumors showed significantly lower ADC (985.69 vs. 1,210.44)×10-6 mm2/s and higher T1pre (1,553.4 vs. 1,444.61) ms values than moderately/highly differentiated tumors (P<0.05), with the combination of ADC + T1pre yielding the best diagnostic accuracy (AUC =0.866, with 77.3% sensitivity and 84.4% specificity). No DCE parameters showed significant differences between groups (All P>0.05). Conclusions:Multi-parametric MRI centered on T1 mapping, particularly when combined with ADC, provides a reproducible and non-invasive tool for subtyping and grading NSCLC, underscoring its potential as a clinically useful imaging biomarker.

To evaluate the value of integrating habitat radiomics features and deep learning features for predicting occult lymph node metastasis (OLNM) in pancreatic ductal adenocarcinoma (PDAC). Data from 212 eligible PDAC patients across two institutions were analyzed. Cohorts were allocated as follows: training (n = 115), internal validation (n = 50), and external validation (n = 47). Habitat subregion partitioning of the tumor volume of interest (VOI) from portal venous phase computed tomography images was performed using a K-means clustering algorithm, and radiomics features were subsequently extracted. A 2.5D deep learning model based on ResNet18 was used to extract features from the whole VOI. After feature selection, models based on single-feature types and a fusion model integrating habitat radiomics features and deep learning features were developed. Model performance was assessed using receiver operating characteristic curves, decision curve analysis (DCA), and calibration curves. Model interpretability was evaluated via SHapley Additive exPlanations (SHAP). Relative to single-feature-based models, the fusion model achieved superior predictive performance with an area under the curve (AUC) of 0.832 (95

BackgroundCoronary artery calcium score (CACS) quantifies calcification to assess coronary artery disease (CAD), but it provides insufficient warning for low-attenuation non-calcified plaques. This study proposes and validates an automated pipeline that combines deep learning and radiomics for efficient detection of non-calcified plaques in the left anterior descending artery (LAD) and right coronary artery (RCA) using non-contrast CACS.MethodsPatients undergoing coronary CT angiography for suspected CAD from two medical sites were retrospectively enrolled and categorized into lesion and control groups. LAD and RCA vessels on CACS images from the development set were manually annotated to train deep learning-based segmentation models for automated coronary segmentation and subsequent pericoronary adipose tissue (PCAT) extraction. Radiomics models were built for LAD and RCA using three regions of interest—coronary artery, PCAT, and their combination—based on the training set. Model performance was evaluated across all datasets using receiver operating characteristic analyses, and DeLong tests were applied for pairwise comparisons.ResultsThe SegResNet models achieved optimal performance in coronary segmentation. Radiomics models for predicting non-calcified plaques demonstrated moderate to good vessel-level diagnostic performance, with areas under the curve (AUCs) ranging from 0.700 to 0.855 across datasets, encompassing separate LAD and RCA models and all ROI strategies. The coronary artery and combined-region models generally outperformed or matched the PCAT model, with comparable AUCs between them in most settings.ConclusionsThe automated pipeline enables efficient detection of non-calcified coronary plaques in CACS, with combined-region models showing promise for future use. The approach may facilitate further research and support the clinical translation of chest CT for large-scale CAD screening.

The integration-segregation theory proposes that early facilitation and later inhibition (i.e. inhibition of return [IOR]) in exogenous attention arises from the competition between cue-target event integration and segregation. Although widely supported behaviorally, the theory lacked direct neural evidence. Here, we used event-related functional magnetic resonance imaging (fMRI) in human participants with an optimized cue-target paradigm to test this account. Cued targets elicited stronger activation in the frontoparietal attention networks, including the bilateral frontal eye field (FEF), intraparietal sulcus (IPS), right temporoparietal junction (TPJ), and left dorsal anterior cingulate cortex (dACC), consistent with the notion of attentional demand of reactivating the cue-initiated representations for integration. In contrast, uncued targets engaged the medial temporal cortex, particularly the bilateral parahippocampal gyrus (PHG) and superior temporal gyrus (STG), reflecting the segregation processes associated with new object-file creation and novelty encoding. These dissociable activations provide the first direct neuroimaging evidence for the integration-segregation theory. Moreover, we observed neural interactions between IOR and cognitive conflict, suggesting a potential modulation of conflict processing by attentional orienting. Taken together, these findings provide new insights into exogenous attention by clarifying the neural underpinnings of integration and segregation and uncovering the interaction between spatial orienting and conflict processing.

Background:In lung cancer, preoperative prediction of visceral pleural invasion (VPI) is helpful for choosing the best treatment plan and improving the prognosis of patients. This study aimed to investigate the usefulness of computed tomography (CT) features in predicting VPI in clinical stage IA peripheral lung adenocarcinoma (LUAD) with pleural contact. Methods:This study divided the type of contact between tumor and pleura into indirect and direct contacts. This study retrospectively analyzed patients with clinical stage IA peripheral LUAD in three hospitals and enrolled 485 patients. The CT features of lesions were analyzed to predict VPI, including relative pleural features, tumor signs, and characteristics between the tumor and pleura. Univariate and multivariate logistic regression analyses were used to select the best combination of variables to predict VPI, and the prediction models were developed. Results:The multivariate logistic regression analysis identified solid component size, pleural tag type, and vascular convergence sign to be independent risk factors for VPI in indirect pleural contact type. The area under curve (AUC) values of the model for predicting VPI in the training, internal validation, and external validation sets were 0.887, 0.799, and 0.862, respectively. Solid component size and pleural indentation sign were identified as independent risk factors for predicting VPI in direct pleural contact type. The AUC values of the model for predicting VPI in the training, internal validation, and external validation sets were 0.903, 0.848, and 0.842, respectively. Conclusions:CT predictors associated with VPI differ based on the type of contact with the pleura. The multivariate logistic regression models utilizing CT features demonstrates acceptable diagnostic accuracy in predicting VPI in clinical stage IA LUAD with pleural contact.

The role of radiomics and abdominal fat analysis in the survival prediction of pancreatic ductal adenocarcinoma (PDAC) has attracted attention. This study aims to develop a preoperative model for predicting early recurrence (ER) in patients pathologically confirmed PDAC, combining radiomic and abdominal fat analysis. A total of 177 patients (Hospital A) were retrospectively analyzed and allocated to the training cohort (n = 124) and internal validation cohort (n = 53). Another 71 patients (Hospital B) group formed the geographic external validation cohort. The threshold of ER was set at 6 months after surgery, and the primary endpoint was to determine the best model to predict ER of PDAC patients. A radiomics model for predicting ER was constructed by the least absolute shrinkage and selection operator Cox regression. Univariate and multivariate Cox regression analyses were used to build a combined model based on radiomics, fat quantitation, and clinical features. The combined model’s performance was assessed using the Harrell concordance index (C-index). Based on the nomogram score, patients were stratified into high-risk and low-risk groups, and survival analysis of different risk groups was performed using the Kaplan-Meier (KM) method. All patients were divided into four subgroups according to recurrence patterns: local recurrence subgroup, distant recurrence subgroup, “local + distant” recurrence subgroup, and “multiple” recurrence subgroup. The predictive efficacy of the combined model was calculated in different subgroups. Radiomics scores (P < 0.001), CA19-9 (P = 0.009), and visceral to subcutaneous fat volume ratio(P = 0.009) were selected for the combined model. Compared to clinical and radiomics models, the combined model exhibited the best prediction performance. C indexes of the training cohort, internal validation cohort, and external validation cohort were 0.778 (0.711,0.845), 0.746 (0.632,0.860), and 0.712 (0.612,0.812) respectively, showing the improvement over the clinical model (without radiomics and fat quantitation features) in the internal validation and external validation sets (DeLong test: P = 0.027, P = 0.079). KM analysis showed significant differences between risk groups (all P < 0.05). The combined model also achieved robust performance in different subgroups of recurrence patterns. The combined model effectively predicted the probability of ER in PDAC patients and may provide an emerging tool to preoperatively guide personalized treatment. Not applicable

BACKGROUND AND OBJECTIVES:The incidence and mortality rates of breast cancer continue to pose significant challenges. Neoadjuvant chemotherapy is now established as a standardized treatment for locally advanced breast cancer. Notably, a subset of breast cancer patients may attain pathological complete remission (pCR) through neoadjuvant chemotherapy (NAC). The potential to avoid surgery exists if accurate preoperative recognition of complete pathological remission is achieved. Therefore, our research is dedicated to determine the potential of the combination of contrast-enhanced magnetic resonance imaging (CE-MRI) method with the serum level of extracellular domain of human epidermal growth factor receptor-2 (Her-2neu ECD) in the evaluation of the efficacy of neoadjuvant chemotherapy in breast cancer patients. METHODS:Sixty-six patients with breast cancer who received NAC in our hospital from September 2019 to July 2022 were enrolled retrospectively, and were divided into the pathological complete remission group and non-pathological complete remission (n-pCR) group based on pathological results. All patients underwent 6 to 8 cycles of NAC. Lesions were measured using CE-MRI and apparent diffusion coefficient (ADC) maps before and after NAC. Serum levels of Her-2neu ECD were measured by chemiluminescence before and after NAC. The change in tumor volume, maximum diameter and ADC values before and after NAC were calculated. Two logistic prediction model were established based on the independent predictors, and the performance of the models for predicting pCR of NAC were compared. RESULTS:The pCR group and n-pCR group were included 30 patients (average age, 48 years) and 36 patients (average age, 48 years), respectively. Hormone receptor status (odds ratio [OR], 4.47 [95% CI: 1.40, 14.32]; p = 0.012), human epidermal growth factor receptor-2 status (OR, 0.15 [95% CI: 0.05, 0.49]; p < 0.01), tumor volume change rate (ΔTV%) during NAC (OR, 1.12 [95% CI: 1.06, 1.26]; p < 0.001), and changes in serum Her-2neu ECD levels during NAC (OR, 1.14 [95% CI: 1.05, 1.24]; p < 0.001) were independently associated with the odds of achieving pCR. The model that combined ΔTV% and ΔHer-2neu ECD showed a relatively higher performance (AUC = 0.914, [95%CI: 0.850, 0.978]) than the model included ΔTV% and Her-2 receptor (AUC = 0.894, [95%CI: 0.819, 0.970]). CONCLUSION:The model that combined MRI indicators and serum Her-2neu ECD levels showed a good performance for predicting pCR to NAC in patients with breast cancer.

Objective:This study aimed to develop MRI-based radiomics machine learning models for predicting adverse pathological prognostic features in prostate cancer and to explore the feasibility of integrating radiomics with clinical characteristics to improve preoperative risk stratification, addressing the limitations of conventional clinical models. Methods:A retrospective cohort of 137 prostate cancer patients between January 2021 and April 2023 with preoperative MRI and postoperative pathology data was divided into adverse-feature-positive (n=85) and negative (n=52) groups. Regions of interest (ROIs) were delineated on ADC and T2WI sequences, and 31 radiomics features were extracted using PyRadiomics. LASSO regression selected optimal features, followed by model construction via five algorithms (logistic regression, decision tree, random forest, SVM, AdaBoost). Clinical models incorporated three variables: biopsy Gleason grade, total PSA, and prostate volume. The best-performing radiomics model was combined with clinical features to build a hybrid model. Model performance was evaluated by AUC, sensitivity, specificity, accuracy, calibration curves, and decision curve analysis (DCA). Results:Patients were randomly split into training (n=95) and validation (n=42) cohorts. The random forest model using ADC-T2WI combined features achieved the highest AUC (0.832; 95% CI: 0.706-0.958) in the validation set, outperforming the clinical model (AUC=0.772). The hybrid model demonstrated superior performance (AUC=0.909; 95% CI: 0.822-0.995), with sensitivity=0.813, specificity=0.885, and accuracy=0.857. Calibration and DCA confirmed its robust clinical utility (p<0.01 vs. single models). Conclusions:The biparametric MRI radiomics-random forest model effectively predicts adverse pathological features in prostate cancer. Integration with clinical characteristics further enhances predictive accuracy, offering a non-invasive tool for preoperative risk stratification and personalized treatment planning.

Background:To explore the value of MRI radiomics-based machine learning models for predicting the pathological grade of pancreatic cancer preoperatively. Methods:125 patients with pathologically confirmed pancreatic cancer who underwent preoperative MRI were retrospectively enrolled. The primary cohort was randomized in an 8:2 ratio into a training cohort (n = 100) and a validation cohort (n = 25). 1316 radiomics features were extracted from contrast-enhanced T1WI arterial phase (AP) or portal venous phase (PVP) images, respectively. After feature reduction and filtering, the best features were selected to construct machine learning models (K-nearest neighbor, KNN; support vector machine, SVM; logistic regression, LR; random forest, RF). Finally, the performance of these models was evaluated using the receiver operating characteristic curve (ROC). Results:There were no statistical differences in clinical characteristics between the low-grade and high-grade cohorts (P > 0.05). The best radiomics features selected from the AP, PVP and AP+PVP images were 6, 6 and 10, respectively. Among the four models, the LR machine learning model achieved the best predictive performance. The distribution of the Radscore values was clinically significant between the low-grade and high-grade groups both in the training cohort (median, 0.26 vs 0.99; P < 0.001) and validation cohort (median, 0.63 vs 1.48; P = 0.011). LR model of AP+PVP performed the best with AUC value of 0.81 (95 % CI: 0.72-0.91) for the training cohort and 0.82 (95 % CI: 0.62-1.00) for the validation cohort. Conclusions:MRI radiomics-based machine learning model is a potential non-invasive method to predict the pathological grade of pancreatic cancer.

Objective:: In this study, a radiomics model was created based on High-Resolution Computed Tomography (HRCT) images to noninvasively predict whether the sub-centimeter pure Ground Glass Nodule (pGGN) is benign or malignant. Methods:: A total of 235 patients (251 sub-centimeter pGGNs) who underwent preoperative HRCT scans and had postoperative pathology results were retrospectively evaluated. The nodules were randomized in a 7:3 ratio to the training (n=175) and the validation cohort (n=76). The volume of interest was delineated in the thin-slice lung window, from which 1316 radiomics features were extracted. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select the radiomics features. Univariate and multivariable logistic regression were used to evaluate the independent risk variables. The performance was assessed by obtaining Receiver Operating Characteristic (ROC) curves for the clinical, radiomics, and combined models, and then the Decision Curve Analysis (DCA) assessed the clinical applicability of each model. Results:: Sex, volume, shape, and intensity mean were chosen by univariate analysis to establish the clinical model. Two radiomics features were retained by LASSO regression to build the radiomics model. In the training cohort, the Area Under the Curve (AUC) of the radiomics (AUC=0.844) and combined model (AUC=0.871) was higher than the clinical model (AUC=0.773). In evaluating whether or not the sub-centimeter pGGN is benign, the DCA demonstrated that the radiomics and combined model had a greater overall net benefit than the clinical model. Conclusion:: The radiomics model may be useful in predicting the benign and malignant sub-centimeter pGGN before surgery.

To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC). A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs − (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model’s efficacy. The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035). Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies. The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making.

Purpose: The clinical, pathological, gene expression, and prognosis of invasive mucinous adenocarcinoma (IMA) differ from those of invasive non-mucinous adenocarcinoma (INMA), but it is not easy to distinguish these two. This study aims to explore the value of combining CT-based radiomics features with clinic-radiological characteristics for preoperative diagnosis of solitary-type IMA and to establish an optimal diagnostic model. Methods: In this retrospective study, a total of 220 patients were enrolled and randomly assigned to a training cohort (n = 154; 73 IMA and 81 INMA) and a testing cohort (n = 66; 31 IMA and 35 INMA). Radiomics features and clinic-radiological characteristics were extracted from plain CT images. The radiomics models for predicting solitary-type IMA were developed by three classifiers: linear discriminant analysis (LDA), logistic regression-least absolute shrinkage and selection operator (LR-LASSO), and support vector machine (SVM). The combined model was constructed by integrating radiomics and clinic-radiological features with the best performing classifier. Receiver operating characteristic (ROC) curves were used to evaluate models' performance, and the area under the curve (AUC) were compared by the DeLong test. Decision curve analysis (DCA) was conducted to assess the clinical utility. Results: Regarding CT characteristics, tumor lung interface, and pleural retraction were the independent risk factors of solitary-type IMA. The radiomics model using the SVM classifier outperformed the other two classifiers in the testing cohort, with an AUC of 0.776 (95% CI: 0.664-0.888). The combined model incorporating radiomics features and clinic-radiological factors was the optimal model, with AUCs of 0.843 (95% CI: 0.781-0.906) and 0.836 (95% CI: 0.732-0.940) in the training and testing cohorts, respectively. Conclusion: The combined model showed good ability in predicting solitary-type IMA and can provide a non-invasive and efficient approach to clinical decision-making.

Background: Preoperative accurate judgment of the degree of invasiveness in subpleural ground-glass lung adenocarcinoma (LUAD) with a consolidation-to-tumor ratio (CTR) <= 50% is very important for the choice of surgical timing and planning. This study aims to investigate the performance of intratumoral and peritumoral radiomics combined with computed tomography (CT) features for predicting the invasiveness of LUAD presenting as a subpleural ground-glass nodule (GGN) with a CTR <= 50%. Methods: A total of 247 patients with LUAD from our hospital were randomly divided into two groups, i.e., the training cohort (n=173) and the internal validation cohort (n=74) (7:3 ratio). Furthermore, 47 patients from three other hospitals were collected as the external validation cohort. In the training cohort, the differences in clinical-radiological features were compared using univariate and multivariate analyses. The gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV5, GPTV10, and GPTV15) radiomics models were constructed based on intratumoral and peritumoral (5, 10, and 15 mm) radiomics features. Additionally, the radscore of the best radiomics model and clinical risk factors were used to construct a combined model and the predictive efficacy of the model was evaluated in the validation cohorts. Finally, the receiver operating characteristics (ROC) curve and area under the curve (AUC) value were used to evaluate the discriminative ability of the model. Results: Tumor size and CTR were independent risk factors for predicting the invasiveness of LUAD. The GPTV10 model outperformed the other radiomics models, with AUC values of 0.910, 0.870, and 0.887 in the three cohorts. The AUC values of the combined model were 0.912, 0.874, and 0.892. Conclusions: A nomogram based on GPTV10-radscore, tumor size, and CTR exhibited high predictive efficiency for predicting the invasiveness of LUAD.

To investigate the prognostic performance of radiomics analysis of lesion-specific pericoronary adipose tissue (PCAT) for major adverse cardiovascular events (MACE) with the guidance of CT derived fractional flow reserve (CT-FFR) in coronary artery disease (CAD). The study retrospectively analyzed 608 CAD patients who underwent coronary CT angiography. Lesion-specific PCAT was determined by the lowest CT-FFR value and 1691 radiomic features were extracted. MACE included cardiovascular death, nonfatal myocardial infarction, unplanned revascularization and hospitalization for unstable angina. Four models were generated, incorporating traditional risk factors (clinical model), radiomics score (Rad-score, radiomics model), traditional risk factors and Rad-score (clinical radiomics model) and all together (combined model). The model performances were evaluated and compared with Harrell concordance index (C-index), area under curve (AUC) of the receiver operator characteristic. Lesion-specific Rad-score was associated with MACE (adjusted HR = 1.330, p = 0.009). The combined model yielded the highest C-index of 0.718, which was higher than clinical model (C-index = 0.639), radiomics model (C-index = 0.653) and clinical radiomics model (C-index = 0.698) (all p < 0.05). The clinical radiomics model had significant higher C-index than clinical model (p = 0.030). There were no significant differences in C-index between clinical or clinical radiomics model and radiomics model (p values were 0.796 and 0.147 respectively). The AUC increased from 0.674 for clinical model to 0.721 for radiomics model, 0.759 for clinical radiomics model and 0.773 for combined model. Radiomics analysis of lesion-specific PCAT is useful in predicting MACE. Combination of lesion-specific Rad-score and CT-FFR shows incremental value over traditional risk factors.

Background:The mutation status of epidermal growth factor receptor (EGFR) in lung adenocarcinoma is significantly associated with postoperative progression-free survival. Computed tomography (CT)-based radiomics analysis may have potential value in predicting EGFR mutation status. This study aims to explore the predictive capacity of radiomics analysis for EGFR mutation status in lung adenocarcinomas presenting as ground-glass nodules (GGNs). Methods:We included 199 GGNs confirmed by histopathology from 2016 to 2020. The clinical factors and radiographic characteristics were counted and evaluated. All GGNs were manually delineated and the radiomics features were extracted, using the least absolute shrinkage and selection operator for feature selection. Then the radiographic, radiomics, and combined nomogram model were constructed respectively, and compared with each other. Decision curve analysis (DCA) was used to assess the clinical usefulness of the models, while receiver operating characteristic curves and calibration curves were used to evaluate their predictive performance. Results:Univariate analysis revealed five variables that were significantly different between the EGFR mutant and wild-type groups. Fifteen radiomics features were significantly associated with EGFR mutations. Among the three models, both the radiomics [area under the curve (AUC) =0.818] and the nomogram (AUC =0.820) had good discriminatory ability in predicting EGFR mutation status and performed consistently in the validation cohort (AUC =0.805, and 0.833, respectively), with higher predictive performance than the radiographic model. The DCA showed that when it comes to EGFR mutation status prediction, the nomogram and the radiomics model showed better overall net benefit than the radiographic model. Conclusions:For preoperatively predicting the status of EGFR mutation in lung adenocarcinomas manifesting as GGNs, the CT-based radiomics analysis will be valuable.

Background Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma.Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV5, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model.Results The GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets.Conclusions The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.

目的 探讨妇科癌肉瘤(CS)的影像特征及其临床价值.方法 回顾性分析经病理确诊的42 例女性原发性生殖系统癌肉瘤患者的影像资料,观察并评估病灶的位置、形态、大小、数目、边缘以及强化方式,分析邻近器官受侵及转移情况,总结特征并与病理相对照.结果 15 例卵巢癌肉瘤病灶呈囊实性巨大肿块,多类圆形或椭圆形,瘤体最大径为5.3～24.2 cm;27 例子宫癌肉瘤病灶多呈类圆形、椭圆形、类三角形,Ⅰ型瘤体最大径 3.5～11.8cm,Ⅱ型子宫内膜厚度为1.3～2.8 cm.妇科CS密度、信号混杂,多边界欠清或不清,增强扫描多轻中度强化,可见包膜及迂曲增粗血管影,周围可见侵犯征象,腹盆腔可见淋巴结转移及少至大量腹腔积液.结论 妇科CS 在影像表现上具有一定的特征,影像检查可以评估病变的位置、大小、数目、边缘、与邻近结构的关系、是否存在转移等,为临床诊治提供重要依据.