BackgroundTesticular embryonal rhabdomyosarcoma (ERMS) is a rare type of testicular neoplasm. This study aimed to conduct a differential diagnosis between testicular seminoma and testicular ERMS based on enhanced CT features.MethodsIn this retrospective study, 15 cases of intratesticular ERMS that were pathologically confirmed and 30 cases of testicular seminoma were collected from December 2012 to May 2024. The general information of the patients was collected, and CT images were retrieved, reviewed, and reanalyzed. CT parameters were measured (the length and short diameter of each lesion, as well as the CT attenuation in the non-enhanced phase, arterial phase, and venous phase), as well as laboratory indicators such as alpha-fetoprotein (AFP), lactate dehydrogenase (LDH), and β-human chorionic gonadotropin (β-HCG). Statistical analysis for quantitative data was conducted using an independent sample t-test or a non-parametric test, while the chi-square test was used for qualitative parameters. Further, a receiver operating characteristic (ROC) curve analysis was conducted to test the diagnostic value for differential diagnosis. The existence of postoperative disease progression (recurrence or metastasis) in the two groups was compared using the chi-square test.ResultsA total of 45 patients were involved in the study (15 intratesticular ERMS and 30 testicular seminoma). There were differences in age, CT parameters, and laboratory indicators between rhabdomyosarcoma and seminoma (all p < 0.05). Similarly, the parallel relationship between the lesion and the ipsilateral inguinal region, as well as the vascular ball sign and other signs, also showed statistically significant differences (all p < 0.05). Further single and multi-factor analyses revealed that the parallel relationship between the lesion and the ipsilateral inguinal region, as well as the vascular ball sign, showed statistically significant differences (p < 0.05). The area under the ROC curve (AUC) of the model by logistic regression reached 0.961 [95% confidence interval (CI), 0.857–0.996], with a sensitivity of 86.7% and a specificity of 96.7%. Furthermore, the probability of postoperative disease progression of testicular ERMS is significantly higher than that of testicular seminoma (66.67% vs. 3.3%). Among the 15 ERMS patients, 10 cases (66.7%) had postoperative disease progression, including five cases of recurrence and five cases of distant metastasis. The median time to progression was 7 months (range 2–16.5 months).ConclusionsThe existence of a parallel relationship between the lesion and the ipsilateral inguinal region, as well as the vascular ball sign, is helpful in differentiating testicular ERMS from testicular seminoma.

ObjectivesTo investigate the application of amide proton transfer (APT)-weighted MRI, T1 mapping in evaluating the preoperative high-risk histopathologic phenotypes of rectal adenocarcinoma and their correlation with Ki-67 expression.Materials and MethodsRetrospective collection of 178 confirmed cases of rectal adenocarcinoma from two centers (center 1: 97 cases, center 2: 81 cases). High-resolution T2WI, APT, T1 mapping, diffusion-weighted imaging (DWI), and Ki-67 staining were performed in all patients. The measured parameters included APT signal intensity (APT SI), T1 relaxation time before (native T1) and after (post-contrast T1) enhancement, and apparent diffusion coefficient (ADC). The receiver operating characteristic (ROC) curve was used to evaluate diagnostic efficiency, and Spearman correlation analysis was used to evaluate the correlation between parameters with Ki-67, respectively.ResultsAPT SI values were significantly different between the mucinous adenocarcinoma (MC) group and the common adenocarcinoma (AC) group in two centers (center 1: [2.64 ± 0.33%] vs. [2.22 ± 0.78%], P<0.05), (center 2: [3.27 ± 0.80%] vs. [2.59 ± 0.77%], P<0.05). In the AC group, APT SI, native T1 and ADC values were significantly different between T1–2 and T3–4 groups (center 1: [2.58 ± 0.69%] vs. [1.61 ± 0.49%], [1540 ± 150 ms] vs. [1360 ± 130 ms], [0.85 ± 0.15×10-3 mm2/s] vs. [0.99± 0.15×10-3 mm2/s], respectively, all P<0.05), the results were consistent with the findings of center 2. APT SI and native T1 values in the lymph node metastasis group were higher than those in the non-metastatic group (center 1: [2.49 ± 0.77%] vs. [2.07 ± 0.74%], [1540 ± 170 ms] vs. [1430 ± 160 ms], respectively, all P<0.05), the result were consistent with the findings of center 2. APT SI were statistically significant in evaluating lymphovascular invasion (LVI) and extramural vascular invasion (EMVI) in two centers (P<0.05). Ki-67 expression was correlated with APT SI (mild to medium), ADC (mild) and native T1 (mild to high) in two centers, respectively (P<0.05), but there was no correlation between post-contrast T1 and Ki-67 (P>0.05).ConclusionAPT and T1 mapping can be used to evaluate the preoperative pathological classification, TN staging, and structural invasion of rectal adenocarcinoma, which has the potential to become an imaging marker for the evaluation of high-risk histopathologic phenotypes and Ki-67 expression of rectal adenocarcinoma.

Objective:Our study aimed to explore the potential of deep learning (DL) radiomics features from CT images of primary gastric cancer (GC) in predicting gastric cancer liver metastasis (GCLM) by establishing and verifying a prediction model based on clinical factors, classical radiomics and DL features. Methods:We retrospectively analyzed 1001 pathologically confirmed GC patients from June 2014 to May 2024, divided into non-LM (n=689) and LM groups (n=312). CT-based classic radiomics and DL features were extracted and screened to construct a DL-radiomics score. This score, along with statistically significant clinical factors, was used to build a fused model which visualized as a nomogram. The model's predictive performance, calibration, and clinical utility were assessed and compared against a clinical model. Additionally, the DL-radiomics score's role in distinguishing between synchronous and metachronous GCLM was evaluated. Results:The fused model showed good predictive performance [AUC: 0.796 (95% CI: 0.766-0.826) in training cohort and 0.787 (95% CI: 0.741-0.834) in test cohort], outperforming the clinical model, radiomics score and DL score (P<0.05). In addition, the decision curve confirmed that the model provided the largest clinical net benefit compared with all other models in the relevant threshold. DL-radiomics score showed moderate predictive performance in distinguishing between synchronous GCLM and metachronous GCLM, with an AUC of 0.665 (95% CI, 0.613-0.718). Conclusion:The CT-based fused model has demonstrated significant value in predicting the occurrence of GCLM, and can provide a reference for the personalized follow-up and treatment of patients.

Atherosclerosis is a leading cause of cardiovascular disorders such as coronary heart disease, heart failure, and stroke. Ferroptosis, a novel type of cell death, plays an important role in atherosclerosis progression, especially macrophage ferroptosis. Paclitaxel (PTX) is an antiproliferative drug that has been developed in recent decades and is helpful in the treatment of atherosclerosis. However, the mechanism underlying the anti-atherosclerotic effects of PTX via inhibition of ferroptosis in macrophages has not been determined. This study aimed to investigate the effect of PTX on ferroptosis in macrophages and determine the underlying mechanism of action in atherosclerosis. In ApoE-/- mice, an atherosclerosis model was established by feeding a high-fat diet (HFD) for 12 weeks. Then, ApoE-/- mice were treated with PTX and Atorvastatin (positive) for 8 weeks. The effects of PTX on atherosclerosis pathology were evaluated using Oil Red O, Masson's trichrome, and hematoxylin-eosin (HE) staining. To confirm whether PTX inhibited macrophage ferroptosis through the Sirt1/Nrf2/GPX4 pathway, immunofluorescence co-staining was performed on GPX4 and CD68 (a macrophage marker). Western blotting was used to determine the protein expression levels of the Sirt1/Nrf2/GPX4 pathway and iron metabolism-related markers. The extent of lipid peroxidation and iron concentration was examined. In addition, we further investigated the potential mechanism of PTX by constructing an atherosclerosis model of RAW 264.7 cells induced by ox-low-density lipoprotein (LDL) in vitro. The mechanism through which PTX improves AS was further verified using Sirt1 inhibitors and the knockdown of Sirt1. In vivo studies have shown that PTX significantly improves atherosclerosis progression, which is characterized by reduced lipid deposition, cholesterol crystallization, and increased collagen. Further studies in vivo have shown that PTX inhibits macrophage ferroptosis by activating the Sirt1/Nrf2/GPX4 pathway, thereby effectively improving atherosclerosis. In vitro experiments revealed that PTX decreases reactive oxygen species (ROS) levels and lipid accumulation in ox-LDL-induced RAW 264.7 cells. Consistent with in vivo studies, PTX not only changed the iron content and iron metabolism-related markers in ox-LDL-induced RAW 264.7 but also activated the Sirt1/Nrf2/GPX4 pathway. Additionally, the use of Sirt1 inhibitors and the knockdown of Sirt1 identified the potential of PTX to inhibit macrophage ferroptosis by activating the Sirt1/Nrf2/GPX4 pathway. These findings suggest that PTX reduces atherosclerosis by suppressing macrophage ferroptosis and improving lipid metabolism through the activation of the Sirt1/Nrf2/GPX4 pathway and offers new perspectives on the possible application of PTX as a powerful atherosclerosis medication option.

Intrahepatic cholangiocarcinoma (iCCA) and other subtypes of primary liver cancer (PLC) have overlapping clinical manifestations and radiological characteristics. The objective of this study was to evaluate the efficacy of deep learning (DL) radiomics analysis, performed using computed tomography (CT) and magnetic resonance imaging (MRI), in diagnosing iCCA within PLC. 178 pathologically confirmed PLC patients (training cohort: test cohort = 124: 54) who underwent both CT and MRI examinations was enrolled. Univariate and multivariate analysis was used to identify the significant factors of radiological findings for diagnosing iCCA. DL radiomics analysis was applied to CT and MRI images, respectively. We constructed and evaluated six distinct models: CT DL radiomics (DLRSCT), CT radiological (RCT), CT DL radiomics-radiological (DLRRCT), MRI DL radiomics (DLRSMRI), MRI radiological (RMRI) and MRI DL radiomics-radiological (DLRRMRI). To further explore the diagnostic and predictive value of a cross-modal approach, we developed a fused model that combined DLRRCT and DLRRMRI. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were employed to compare the performance of different models. MRI-based models demonstrated a superior predictive performance than CT-based models in test cohort (AUCs of MRI vs. CT: DLRR, 0.923 vs. 0.880, P = 0.521; DLRS, 0.875 vs. 0.867, P = 0.922; R, 0.859 vs. 0.840, P = 0.808). The CT-MRI cross-modal model yielded the highest AUC of 0.994 and 0.937 in training and test cohorts, respectively. CT- and MRI-based DL radiomics analyses exhibited good performance in diagnosing iCCA, and the CT-MRI cross-modal model may have significant clinical implications on detection of liver malignancies.

RATIONALE AND OBJECTIVES:Accurate risk stratification in human epidermal growth factor receptor 2-positive surgically resectable advanced gastric cancer (HER2-p SRAGC) can strengthen monitoring for high-risk patients, allowing timely HER2-specific treatment and potentially improving prognosis. Therefore, we aimed to develop a CT radiomics model for predicting outcomes in HER2-p SRAGC and compare it with the 8th edition TNM staging system. MATERIALS AND METHODS:621 HER2-p SRAGC patients who received either radical gastrectomy or radical gastrectomy after neoadjuvant therapy or chemotherapy were retrospectively enrolled in two hospitals and assigned to a training (n=330), an internal validation (n=143) and an external validation (n=148) cohorts. A radiomics model incorporating Radscore and clinical scores was constructed. Model performance was assessed by Kaplan-Meier estimator, Log-rank test, and Harrell's C-index. RESULTS:The radiomics model was correlated with the overall survival (OS) across all cohorts, with C-indexes of 0.711[95% confidence interval (CI): 0.666-0.756; p<0.001; training], 0.669 (95%CI: 0.585-0.753; p=0.009; internal validation) and 0.693 (95%CI: 0.597-0.789; p=0.015; external validation). In all study cohorts, the radiomics model successfully stratified patients into high-risk and low-risk groups, and outweighed individual scores, pathological staging (pTNM), and clinical staging (cTNM) but was inferior to post-neoadjuvant therapy staging (ypTNM). Additionally, the radiomics model had added value to the prognostic efficacy of pTNM and was unaffected by patient age and gender. CONCLUSION:The radiomics model delivers individualized prognosis prediction of HER2-p SRAGC, surpassing clinical scores, and both pTNM and cTNM in forecasting OS. It confers incremental benefit to pTNM and exhibits certain universality across patient types. TRIAL REGISTRATION:Retrospectively registered.

Purpose:This study aimed to investigate the usefulness of CT-based deep learning radiomics analysis (DLRA) for preoperatively differentiating Lauren classification in gastric cancer (GC) patients and explore the tumor microenvironment. Methods:578 patients were recruited from January 2015 to June 2024, and divided into the training cohort (n = 311), the internal validation cohort (n = 132), and the external validation cohort (n = 135). Clinical characteristics were collected. Radiomics features were extracted from CT images at arterial phase (AP) and venous phase (VP). A radiomics nomogram incorporating radiomics signature and clinical information was built for distinguishing Lauren classification, and its discrimination, calibration, and clinical usefulness were evaluated. RNA sequencing data from The Cancer Imaging Archive database were used to perform transcriptomics analysis. Results:The nomogram incorporating the two radiomics signatures and clinical characteristics exhibited good discrimination of Lauren classification on all cohorts [overall C-indexes 0.815 (95 % CI: 0.739-0.869) in the training cohort, 0.785 (95 % CI: 0.702-0.834) in the internal validation cohort, 0.756 (95 % CI: 0.685-0.816) in the external validation cohort]. It outperformed the clinical model in predictive ability. The calibration and decision curve substantiated the model's excellent fitness and clinical applicability. Further, transcriptomics analysis showed that the differentially expressed genes of different Lauren types were mainly enriched in pathways related to cell contraction and migration, and the infiltration degree of various immune cells was also significantly different. Conclusions:DLRA effectively differentiated Lauren classification in GC, and our analysis of transcriptomic data across different Lauren subtypes revealed the heterogeneity within the GC microenvironment.

Purpose Preoperative prediction of the Lauren classification in gastric cancer (GC) has important clinical significance for improving the prognostic system and guiding personalized treatment. This study investigated the usefulness of deep learning radiomics analysis (DLRA) for preoperatively differentiating Lauren classification in patients with GC, using computed tomography (CT) images. Methods A total of 329 patients pathologically diagnosed with GC were recruited from August 2012 and December 2020. Patients (n = 262) recruited from August 2012 to July 2019 were randomly allocated into training cohort (n = 184) and internal validation cohort (n = 78), and patients recruited from August 2019 to December 2020 were included in external validation cohort (n = 67). Information on clinical characteristics were collected. Radiomics features were extracted from CT images at arterial phase (AP) and venous phase (VP). A radiomics nomogram incorporating the radiomics signature and clinical information was built for distinguishing Lauren classification, and its discrimination, calibration, and clinical usefulness were evaluated. Moreover, we also constructed a clinical model using the clinical factors only for baseline comparison. Results The nomogram incorporating the two radiomics signatures and clinical characteristics exhibited good discrimination of Lauren classification on all cohorts [overall C-indexes 0.771 (95% CI: 0.709–0.833) in the training cohort, 0.757 (95% CI: 0.698–0.807) in the internal validation cohort, 0.725 (95% CI: 0.655–0.793) in the external validation cohort]. Compared with the conventional clinical model, the deep learning hybrid radiomics nomogram (DHRN) exhibits enhanced predictive ability. Further, the calibration curve and decision curve substantiated the excellent fitness and clinical applicability of the model. Conclusions DLRA exhibited good performance in distinguishing Lauren classification in GC. In personalized treatment of GC, this preoperative nomogram could provide baseline information for optimizing the quality of clinical decision-making and therapeutic strategies.

The purpose of the article is to determine whether differentiation and enhanced CT features can preoperatively predict microvascular/nerve invasion in locally advanced gastric cancer. Retrospective analysis of the CT and pathological data of 325 patients with locally advanced gastric cancer confirmed by pathology in our hospital from July 2011 to August 2023. The patient's age, gender, tumor location, T stage, N stage, TNM stage, differentiation, Lauren classification, as well as tumor thickness, tumor longest diameter, plain CT value, arterial CT value, venous CT value, arterial phase enhancement rate, and venous phase enhancement rate were assessed. This study included a total of 325 patients with locally advanced gastric cancer and 189 patients (58.15%) with microvascular/nerve invasion. The results of the univariate analysis showed that gender, location, T stage, N stage, TNM stage, differentiation, Lauren classification, tumor thickness, and longest diameter of the tumor were associated with microvascular/nerve invasion (P < .05). Multivariate analysis suggested that TNM stage and differentiation were independent risk factors for microvascular/nerve invasion. The receiver operating characteristic analysis showed that the diagnostic efficacy of the combined parameter of TNM stage and differentiation was better than that of the single parameter, in which area under the curve, sensitivity, and specificity were 0.819 (95%CI: 0.770-0.867), 66.7%, and 83.8%, respectively. Differentiation and enhanced CT are helpful in predicting whether microvascular/nerve invasion occurs in locally advanced gastric cancer before operation, especially the combined parameters of TNM stage and differentiation.

BackgroundUrachal tumors are rare in clinical practice, among which urachal adenocarcinoma is the most common. In this study, we report a rare case of urachal perivascular epithelioid cell tumor to improve our understanding of the disease.Case presentationA 26-year-old male patient was hospitalized for lower abdominal pain. The US showed a hypoechoic mass measuring 26mm × 18mm in the superior aspect of the bladder. MRI showed an irregular mass located anterior to the bladder roof, near the midline. The tumor exhibited hypointense on T1WI and heterogeneous hyperintense on T2WI. Additionally, contrast-enhanced T1-weighted imaging revealed obvious ring enhancement of the tumor. The patient underwent surgical resection of the urachal tumor, with subsequent pathological examination revealing a diagnosis of urachal PEComa. Following surgery, the patient underwent regular follow-up assessments, with no evidence of recurrence or metastasis observed after three and a half years.ConclusionsUrachal PEComa is a rare mesenchymal tumor that presents challenges in diagnosis through imaging and clinical symptoms. Definitive diagnosis relies on pathological and immunohistochemical analysis. Due to the rarity of urachal PEComa, prognosis assessment necessitates long-term follow-up and evaluation of more cases.

Gastrointestinal stromal tumors (GISTs) predominantly develop in the stomach. While nomogram offer tremendous therapeutic promise, there is yet no ideal nomogram comparison customized specifically for handling categorical data and model selection related gastric GISTs. (1) We selected 5463 patients with gastric GISTs from the SEER Research Plus database spanning from 2000 to 2020; (2) We proposed an advanced missing data imputation algorithm specifically designed for categorical variables; (3) We constructed five Cox nomogram models, each employing distinct methods for the selection and modeling of categorical variables, including Cox (Two-Stage), Lasso-Cox, Ridge-Cox, Elastic Net-Cox, and Cox With Lasso; (4) We conducted a comprehensive comparison of both overall survival (OS) and cancer-specific survival (CSS) tasks at six different time points; (5) To ensure robustness, we performed 50 randomized splits for each task, maintaining a 7:3 ratio between the training and test cohorts with no discernible statistical differences. Among the five models, the Cox (Two-Stage) nomogram contains the fewest features. Notably, at Near-term, Mid-term, and Long-term intervals, the Cox (Two-Stage) model attains the highest Area Under the Curve (AUC), top-1 ratio, and top-3 ratio in both OS and CSS tasks. For the prediction of survival in patients with gastric GISTs, the Cox (Two-Stage) nomogram stands as a simple, stable, and accurate predictive model with substantial promise for clinical application. To enhance the clinical utility and accessibility of our findings, we have deployed the nomogram model online, allowing healthcare professionals and researchers worldwide to access and utilize this predictive tool.

BACKGROUND:The non-exercise estimated cardiorespiratory fitness (eCRF) has been recognized as an important predictor of mortality among the general population. This study sought to evaluate the relationship between eCRF and mortality from all causes, cardiovascular disease (CVD), and cancer in hypertensive adults. METHODS:We included 27,437 adults with hypertension from the National Health and Nutrition Examination Survey (NHANES) III and 10 NHANES cycles from 1999 to 2018. Multivariate Cox proportional hazard models were used to assess the hazard ratios and 95% confidence intervals (CIs) of eCRF for mortality. RESULTS:A total of 8,023 deaths were recorded throughout a median 8.6-year follow-up, including 2,338 from CVD, and 1,761 from cancer. The eCRF with per 1 metabolic equivalent increase was linked to decreased risk of all-cause (adjusted HR 0.78, 95% CI: 0.75-0.81) and CVD mortality (adjusted HR 0.79, 95% CI: 0.74-0.84), rather than cancer mortality (adjusted HR 0.94, 95% CI: 0.86-1.03). Moreover, a stronger protective effect of eCRF was observed for females (HR 0.66 (95% CI: 0.62-0.72) versus HR 0.78 (95% CI: 0.73-0.83), Pinteraction < 0.001 for all-cause mortality; HR 0.70 (95% CI: 0.61-0.80;) versus HR 0.82 (95% CI: 0.73-0.92), Pinteraction = 0.026 for CVD mortality) compared with males. Findings did not significantly differ in subgroup analyses and sensitivity analyses. CONCLUSIONS:Among adults with hypertension, eCRF was inversely related to all-cause and CVD mortality, but not cancer mortality. A significant interaction effect existed between sex and eCRF. Further studies are needed to verify this association in different populations.

Abstract Background Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC. Methods This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017–2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018–2021) and another from center 2 (n = 43, 2020–2021), were utilized to assess the signature’s association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data. Results Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression. Conclusion This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC’s MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications.

BackgroundAs a device for percutaneous coronary intervention, drug-coated balloon (DCB) is widely used to treat in-stent restenosis. However, data regarding the use of DCB in treating de novo saphenous vein graft (SVG) lesions are limited. This study aimed to explore the outcomes of using the DCB in the treatment of de novo SVG lesions of coronary heart disease (CHD).MethodsThis retrospective and observational study analyzed CHD patients with de novo SVG lesions treated with DCB or the new-generation drug-eluting stent (DES) between January 2018 and December 2020. Restenosis was the primary endpoint, whereas target lesion revascularization (TLR), major adverse cardiac events, restenosis, cardiac death, target vessel revascularization, and myocardial infarction were the secondary outcomes.ResultsWe enrolled 31 and 23 patients treated with DCB and DES, respectively. The baseline clinical data, lesion characteristics, and procedural characteristics were similar between the two groups. Twenty-eight (90.3%) patients in the DCB group and 21 (91.3%) in the DES group completed follow-up angiography after 1 year. The quantitative coronary angiography measurements at angiographic follow-up showing late lumen loss were −0.07 ± 0.95 mm for the DCB group and 0.86 ± 0.71 mm for the DES group (P = 0.039), and the rates of restenosis were 13.3% and 21.7% for the DCB and DES groups, respectively (P = 0.470). No significant differences were observed in the rates of MACE (16.7% vs. 26.1%, P = 0.402) and TLR (13.3% vs. 4.3%, P = 0.374) during clinical follow-up.ConclusionOur findings suggest that when pre-dilatation was successful, DCB might be safe and effective in treating de novo SVG lesions.

Background:The differentiation status of gastric cancer is related to clinical stage, treatment and prognosis. It is expected to establish a radiomic model based on the combination of gastric cancer and spleen to predict the differentiation degree of gastric cancer. Thus, we aim to determine whether radiomic spleen features can be used to distinguish advanced gastric cancer with varying states of differentiation.Materials and methods:January 2019 to January 2021, we retrospectively analyzed 147 patients with advanced gastric cancer confirmed by pathology. The clinical data were reviewed and analyzed. Three radiomics predictive models were built from radiomics features based on gastric cancer (GC), spleen (SP) and combination of two organ position (GC+SP) images. Then, three Radscores (GC, SP and GC+SP) were obtained. A nomogram was developed to predict differentiation statue by incorporating GC+SP Radscore and clinical risk factors. The area under the curve (AUC) of operating characteristics (ROC) and calibration curves were assessed to evaluate the differential performance of radiomic models based on gastric cancer and spleen for advanced gastric cancer with different states of differentiation (poorly differentiated group and non- poorly differentiated group).Results:There were 147 patients evaluated (mean age, 60 years ± 11SD, 111 men). Univariate and multivariate logistic analysis identified three clinical features (age, cTNM stage and CT attenuation of spleen arterial phase) were independent risk factors for the degree of differentiation of GC (p =0.004,0.000,0.020, respectively). The clinical radiomics (namely, GC+SP+Clin) model showed powerful prognostic ability in the training and test cohorts with AUCs of 0.97 and 0.91, respectively. The established model has the best clinical benefit in diagnosing GC differentiation.Conclusion:By combining radiomic features (GC and spleen) with clinical risk factors, we develop a radiomic nomogram to predict differentiation status in patients with AGC, which can be used to guide treatment decisions.

BackgroundData on drug-coated balloons (DCB) for de novo coronary chronic total occlusion (CTO) are limited. We aimed to investigate the long-term outcomes of substitution of drug-eluting stents (DES) by DCB.MethodsWe compared the outcomes of less DES strategy (DCB alone or combined with DES) and DES-only strategy in treating de novo coronary CTO in this prospective, observational, multicenter study. The primary endpoints were major adverse cardiovascular events (MACE), target vessel revascularization, myocardial infarction, and death during 3-year follow-up. The secondary endpoints were late lumen loss (LLL) and restenosis until 1-year after operation.ResultsOf the 591 eligible patients consecutively enrolled between January 2015 and December 2019, 281 (290 lesions) were treated with DCB (DCB-only or combined with DES) and 310 (319 lesions) with DES only. In the DCB group, 147 (50.7%) lesions were treated using DCB-only, and the bailout stenting rate was relatively low (3.1%). The average stent length per lesion in the DCB group was significantly shorter compared with the DES-only group (21.5 ± 25.5 mm vs. 54.5 ± 26.0 mm, p < 0.001). A total of 112 patients in the DCB group and 71 patients in the DES-only group (38.6% vs. 22.3%, p < 0.001) completed angiographic follow-up until 1-year, and LLL was much less in the DCB group (−0.08 ± 0.65 mm vs. 0.35 ± 0.62 mm, p < 0.001). There were no significant differences in restenosis occurrence between the two groups (20.5% vs. 19.7%, p > 0.999). The Kaplan–Meier estimates of MACE at 3-year (11.8% vs. 12.0%, log-rank p = 0.688) was similar between the groups.ConclusionPercutaneous coronary intervention with DCB is a potential “stent-less” therapy for de novo CTO lesions with satisfactory long-term clinical results compared to the DES-only approach.

Effective identification of T1a stage cancer is crucial for planning endoscopic resection for early gastric cancers. The present study aimed to determine the diagnostic value of the double-track sign in patients with T1a gastric cancer using computed tomography (CT) imaging. A total of 152 patients diagnosed with pathologically proven T1a gastric cancer at The First Affiliated Hospital of Zhengzhou University (Zhengzhou, China) between July 2011 and August 2021 were retrospectively reviewed. The control group consisted of 2,926 patients with gastritis. Clinical data, including patient characteristics and preoperative CT imaging findings with gastric morphological features, were reviewed and analyzed. Out of 51 patients with T1a gastric cancer finally included, 31 (60.8%) exhibited local double-track enhancement changes of the stomach, referred to as the 'double-track sign', on CT images. In addition, four patients (7.8%) had well-enhanced mucosal thickening of the gastric wall. Of the 2,926 control subjects, none had any double-track sign and six patients (0.2%) had local gastric wall thickening with abnormally strengthened enhancement. In conclusion, a double-track sign on CT images is beneficial in the diagnostic differentiation of T1a gastric cancer.

目的:探讨药物包被球囊(DCB)治疗冠状动脉真性分叉病变的疗效及安全性.方法:回顾性纳入于我院接受介入治疗的真性分叉病变患者,根据手术策略将其分为药物洗脱支架组(DES组,158例)和DCB组(98例).主要观察终点为2年靶病变血运重建,次要观察终点为主要不良心血管事件(包括心源性死亡、靶血管血运重建、靶血管心肌梗死和支架内血栓).结果:DCB组靶病变血运重建和主要不良心血管事件发生率均显著低于DES 组(3.1％vs.11.4％,log-rank P=0.019;5.1％vs.13.9％,log-rank P=0.029).两组均无支架内血栓和靶血管心肌梗死发生.结论:DCB治疗冠状动脉真性分叉病变疗效明确,安全可行.

Background To develop and externally validate a conventional CT-based radiomics model for identifying HER2-positive status in gastric cancer (GC). Methods 950 GC patients who underwent pretreatment CT were retrospectively enrolled and assigned into a training cohort ( n = 388, conventional CT), an internal validation cohort ( n = 325, conventional CT) and an external validation cohort ( n = 237, dual-energy CT, DECT). Radiomics features were extracted from venous phase images to construct the “Radscore”. On the basis of univariate and multivariate analyses, a conventional CT-based radiomics model was built in the training cohort, combining significant clinical-laboratory characteristics and Radscore. The model was assessed and validated regarding its diagnostic effectiveness and clinical practicability using AUC and decision curve analysis, respectively. Results Location, clinical TNM staging, CEA, CA199, and Radscore were independent predictors of HER2 status (all p < 0.05). Integrating these five indicators, the proposed model exerted a favorable diagnostic performance with AUCs of 0.732 (95%CI 0.683–0.781), 0.703 (95%CI 0.624–0.783), and 0.711 (95%CI 0.625–0.798) observed for the training, internal validation, and external validation cohorts, respectively. Meanwhile, the model would offer more net benefits than the default simple schemes and its performance was not affected by the age, gender, location, immunohistochemistry results, and type of tissue for confirmation (all p > 0.05). Conclusions The conventional CT-based radiomics model had a good diagnostic performance of HER2 positivity in GC and the potential to generalize to DECT, which is beneficial to simplify clinical workflow and help clinicians initially identify potential candidates who might benefit from HER2-targeted therapy.

患者 男,71岁.咳嗽、少量咯血1月余,1～3次/日.体格检查:甲状腺右侧叶触及4.1 cm×5.0 cm质硬结节,压痛,随吞咽上下移动.实验室检查:白细胞10.00×109/L,中性粒细胞7.28×109/L.超声检查:甲状腺右侧叶体积增大,形态失常,内见多个实性、囊实性结节,较大者约5.6 cm×4.6 cm×3.8 cm,凸向包膜,边缘模糊,内见簇状强回声(图1);CDFI显示结节内Ⅱ级、边缘型血流信号.右侧颈部Ⅱ、Ⅲ、Ⅳ、Ⅴ区和左颈部Ⅳ、ⅤB、Ⅴ区多个淋巴结肿大,其结构紊乱,呈不均质低回声,CDFI显示淋巴结内Ⅰ～Ⅲ级血流信号.