Evaluation of image quality on low contrast media with deep learning image reconstruction algorithm in prospective ECG-triggering coronary CT angiography.

To assess the impact of low-dose contrast media (CM) injection protocol with deep learning image reconstruction (DLIR) algorithm on image quality in coronary CT angiography (CCTA). In this prospective study, patients underwent CCTA were prospectively and randomly assigned to three groups with different contrast volume protocols (at 320mgI/mL concentration and constant flow rate of 5ml/s). After pairing basic information, 210 patients were enrolled in this study: Group A, 0.7mL/kg (n = 70); Group B, 0.6mL/kg (n = 70); Group C, 0.5mL/kg (n = 70). All patients were examined via a prospective ECG-triggered scan protocol within one heartbeat. A high level DLIR (DLIR-H) algorithm was used for image reconstruction with a thickness and interval of 0.625mm. The CT values of ascending aorta (AA), descending aorta (DA), three main coronary arteries, pulmonary artery (PA), and superior vena cava (SVC) were measured and analyzed for objective assessment. Two radiologists assessed the image quality and diagnostic confidence using a 5-point Likert scale. The CM doses were 46.81 ± 6.41mL, 41.96 ± 7.51mL and 34.65 ± 5.38mL for Group A, B and C, respectively. The objective assessments on AA, DA and the three main coronary arteries and the overall subjective scoring showed no significant difference among the three groups (all p > 0.05). The subjective assessment proved that excellent CCTA images can be obtained from the three different contrast media protocols. There were no significant differences in intracoronary attenuation values between the higher HR subgroup and the lower HR subgroup among three groups. CCTA reconstructed with DLIR could be realized with adequate enhancement in coronary arteries, excellent image quality and diagnostic confidence at low contrast dose of a 0.5mL/kg. The use of lower tube voltages may further reduce the contrast dose requirement.

Preoperative Risk Stratification for Gastric Cancer: The Establishment of Dual-Energy CT-Based Radiomics Using Prospective Datasets at Two Centers.

RATIONALE AND OBJECTIVES: To evaluate the performance of dual-energy CT (DECT)-based radiomics models for identifying high-risk histopathologic phenotypes-serosal invasion (pT4a), lymph node metastasis (LNM), lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer. MATERIAL AND METHODS: This prospective bi-center study recruited histologically confirmed gastric adenocarcinoma patients who underwent triple-phase enhanced DECT before gastrectomy between January 2021 and July 2023. Radiomics features were extracted from polychromatic/monochromatic (40 keV, 100 keV)/iodine images at arterial/venous/delay phase, respectively. Predictive features were selected in the training dataset using logistic regression classifier, and trained models were applied to the external validation dataset. Performances of clinical models, conventional contrast enhanced CT (CECT) models and DECT models were evaluated using areas under the receiver operating characteristic curve (AUCs). RESULTS: In total, 503 patients were recruited: 396 at training dataset (60.1 ± 10.8 years, 110 females, 286 males) and 107 at validation dataset (61.4 ± 9.5 years, 29 females, 78 males). DECT models dichotomizing pT4a, LNM, LVI, and PNI achieved AUCs of 0.891, 0.817, 0.834, and 0.889, respectively, in the validation dataset, similar with the CECT models. In the training dataset, compared to the CECT model, the DECT model provided increased performance for identifying pT4a, LNM, LVI (all P＜0.05), and similar performance for stratifying PNI (P = 0.104). The DECT models was associated with patient disease-free survival (all P＜0.05). CONCLUSION: DECT radiomics can stratify patients preoperatively according to high-risk histopathologic phenotypes for gastric cancer and are associated with patient disease-free survival in the training dataset.

Evaluating nomogram models for predicting survival outcomes in gastric gastrointestinal stromal tumors with SEER database analysis.

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.

Intratumoral metabolic heterogeneity by 18F-FDG PET/CT to predict prognosis for patients with thymic epithelial tumors.

BACKGROUND: The aim of the present study was to evaluate the impact of intratumoral metabolic heterogeneity and quantitative 18F-FDG PET/CT imaging parameters in predicting patient outcomes in thymic epithelial tumors (TETs). METHODS: This retrospective study included 100 patients diagnosed with TETs who underwent pretreatment 18F-FDG PET/CT. The maximum and mean standardized uptake values (SUVmax and SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) on PET/CT were measured. Heterogeneity index-1 (HI-1; standard deviation [SD] divided by SUVmean) and heterogeneity index-2 (HI-2; linear regression slopes of the MTV according with different SUV thresholds), were evaluated as heterogeneity indices. Associations between these parameters and patient survival outcomes were analyzed. RESULTS: The univariate analysis showed that Masaoka stage, TNM stage, WHO classification, SUVmax, SUVmean, TLG, and HI-1 were significant prognostic factors for progression-free survival (PFS), while MTV, HI-2, age, gender, presence of myasthenia gravis, and maximum tumor diameter were not. Subsequently, multivariate analyses showed that HI-1 (p < 0.001) and TNM stage (p = 0.002) were independent prognostic factors for PFS. For the overall survival analysis, TNM stage, WHO classification, SUVmax, and HI-1 were significant prognostic factors in the univariate analysis, while TNM stage remained an independent prognostic factor in multivariate analyses (p = 0.024). The Kaplan Meier survival analyses showed worse prognoses for patients with TNM stages III and IV and HI-1 ≥ 0.16 compared to those with stages I and II and HI-1 < 0.16 (log-rank p < 0.001). CONCLUSION: HI-1 and TNM stage were independent prognostic factors for progression-free survival in TETs. HI-1 generated from baseline 18F-FDG PET/CT might be promising to identify patients with poor prognosis.

Feasibility Analysis of Individualized Low Flow Rate Abdominal Contrast-Enhanced Computed Tomography in Chemotherapy Patients: Dual-Source Computed Tomography With Low Tube Voltage.

PURPOSE: The aim of the study is to investigate the feasibility of using dual-source computed tomography (CT) combined with low flow rate and low tube voltage for postchemotherapy image assessment in cancer patients. METHODS: Ninety patients undergoing contrast-enhanced CT scans of the upper abdomen were prospectively enrolled and randomly assigned to groups A, B, and C (n = 30 each). In group A, patients underwent scans at 120 kVp with 448 mgI/kg. Patients in group B underwent scans at 100 kVp with 336 mgI/kg. Patient in group C underwent scans at 70 kVp with of 224 mgI/kg. Quantitative measurements including the CT number, standard deviation of CT number, signal-to-noise ratio, contrast-to-noise ratio, subjective reader scores, and the volume and flow rate of contrast agent were evaluated for each group. RESULTS: There was no statistically significant difference in the subjective image scores within the three groups except for the kidney (all P > 0.05). Group C showed significantly higher CT values, lower noise levels, and higher signal-to-noise ratio and contrast-to-noise ratio values in the majority of the regions of interest compared to the other groups (P < 0.05). In group C, the contrast agent dose was decreased by 46% compared to group A (79.48 ± 12.24 vs 42.7 ± 8.6, P < 0.01), and the contrast agent injection rate was reduced by 22% (2.7 ± 0.41 vs 2.1 ± 0.4, P < 0.01). CONCLUSIONS: The use of 70 kVp tube voltage combined with low iodine flow rates prove to be a more effective approach in solving the challenge of compromised blood vessels in postchemotherapy tumor patients, without reducing image quality and diagnostic confidence.

A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer: a multi-cohort study.

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.

Case report: Urachal perivascular epithelioid cell tumor.

Background: Urachal 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 presentation: A 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. Conclusions: Urachal 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.

Evaluating the consistency in different methods for measuring left atrium diameters.

BACKGROUND: The morphological information of the pulmonary vein (PV) and left atrium (LA) is of immense clinical importance for effective atrial fibrillation ablation. The aim of this study is to examine the consistency in different LA diameter measurement techniques. METHODS: Retrospective imaging data from 87 patients diagnosed with PV computed tomography angiography were included. The patients consisted of 50 males and 37 females, with an average age of (60.74 ± 8.70) years. Two physicians independently measured the anteroposterior diameter, long diameter, and transverse diameter of the LA using six different methods. Additionally, we recorded the post-processing time of the images. Physician 1 conducted measurements twice with a one-month interval between the measurements to assess intra-rater reliability. Using the intraclass correlation coefficient (ICC), the consistency of each LA diameter measurement by the two physicians was evaluated. We compared the differences in the LA diameter and the time consumed for measurements using different methods. This was done by employing the rank sum test of a randomized block design (Friedman M test) and the q test for pairwise comparisons among multiple relevant samples. RESULTS: (1) The consistency of the measured LA diameter by the two physicians was strong or very strong. (2) There were statistical differences in the anteroposterior diameter, long diameter, and transverse diameter of LA assessed using different methods (χ2 = 222.28, 32.74, 293.83, P < 0.001). (3) Different methods for measuring the diameters of LA required different amounts of time (χ2 = 333.10, P < 0.001). CONCLUSION: The results of left atrium (LA) diameter measurements conducted by different physicians were found to be reliable. However, the LA diameters obtained through various techniques exhibited variations. It was observed that measuring LA long diameters using only the VR (volume rendering) picture was the most clinically applicable method.

An Exploratory Pilot Study on the Application of Radiofrequency Ablation for Atrial Fibrillation Guided by Computed Tomography-Based 3D Printing Technology.

Radiofrequency ablation (RFA) is the treatment of choice for atrial fibrillation (AF). Additionally, the utilization of 3D printing for cardiac models offers an in-depth insight into cardiac anatomy and cardiovascular diseases. The study aims to evaluate the clinical utility and outcomes of RFA following in vitro visualization of the left atrium (LA) and pulmonary vein (PV) structures via 3D printing (3DP). Between November 2017 and April 2021, patients who underwent RFA at the First Affiliated Hospital of Xinxiang Medical University were consecutively enrolled and randomly allocated into two groups: the 3DP group and the control group, in a 1:1 ratio. Computed tomography angiography (CTA) was employed to capture the morphology and diameter of the LA and PV, which facilitated the construction of a 3D entity model. Additionally, surgical procedures were simulated using the 3D model. Parameters such as the duration of the procedure, complications, and rates of RFA recurrence were meticulously documented. Statistical analysis was performed using the t-test or Mann-Whitney U test to evaluate the differences between the groups, with a P-value of less than 0.05 considered statistically significant. In this study, a total of 122 patients were included, with 53 allocated to the 3DP group and 69 to the control group. The analysis of the morphological measurements of the LA and PV taken from the workstation or direct entity measurement showed no significant difference between the two groups (P > 0.05). However, patients in the 3DP group experienced significantly shorter RFA times (97.03 ± 28.39 compared to 120.51 ± 44.76 min, t = 3.05, P = 0.003), reduced duration of radiation exposure (2.55 [interquartile range 2.01, 3.24] versus 3.20 [2.28, 3.91] min, Z = 3.23, P < 0.001), and shorter modeling times (7.68 ± 1.03 compared to 8.89 ± 1.45 min, t = 5.38, P < 0.001). 3DP technology has the potential to enhance standard RFA practices by reducing the time required for intraoperative interventions and exposure to radiation.

Multiscale Static Compressive Damage Characteristics of Kiwifruit Based on the Finite Element Method.

In the handling or processing process, fruits are easily crushed by external loads. This type of damage in fruit often leads to the internal pulp browning and rotting, with the severity largely dependent on the fruit tissue's geometric and mechanical properties. In kiwifruits, with their thin skin and dark-colored flesh, it is particularly challenging to observe and analyze the damage caused by extrusion through traditional experimental methods. The objective of this research is to construct a multi-scale finite element model encompassing the skin, flesh, and core by measuring the geometric and mechanical properties of kiwifruit, to assess and predict the damage characteristics under compression, and to verify the accuracy of the finite element model through experiments. The results indicated that kiwifruits demonstrated different compressive strengths in different directions during compression. The compressive strength in the axial direction was higher than that in the radial direction, and there was little difference between the long and short radial directions. The flesh tissue is the most vulnerable to mechanical damage under external compression, followed by the core. At strain levels below 5%, there was no noticeable damage in the axial or radial directions of the kiwifruit. However, when strain exceeded 5%, damage began to manifest in some of the flesh tissue. To maintain fruit quality during storage and transportation, the stacking height should not exceed 77 fruits in the axial direction, 48 in the long direction, and 53 in the short direction. The finite element analysis showed that the established model can effectively simulate and predict the internal damage behavior of kiwifruits under compression loads, which is helpful for a deeper understanding of the mechanical properties of fruits and provides a theoretical basis and technical guidance for minimizing mechanical damage during fruit handling.

Predicting microvascular invasion in small (≤ 5 cm) hepatocellular carcinomas using radiomics-based peritumoral analysis.

OBJECTIVE: We assessed the predictive capacity of computed tomography (CT)-enhanced radiomics models in determining microvascular invasion (MVI) for isolated hepatocellular carcinoma (HCC) ≤ 5 cm within peritumoral margins of 5 and 10 mm. METHODS: Radiomics software was used for feature extraction. We used the least absolute shrinkage and selection operator (LASSO) algorithm to establish an effective model to predict patients' preoperative MVI status. RESULTS: The area under the curve (AUC) values in the validation sets for the 5- and 10-mm radiomics models concerning arterial tumors were 0.759 and 0.637, respectively. In the portal vein phase, they were 0.626 and 0.693, respectively. Additionally, the combined radiomics model for arterial tumors and the peritumoral 5-mm margin had an AUC value of 0.820. The decision curve showed that the combined tumor and peritumoral radiomics model exhibited a somewhat superior benefit compared to the traditional model, while the fusion model demonstrated an even greater advantage, indicating its significant potential in clinical application. CONCLUSION: The 5-mm peritumoral arterial model had superior accuracy and sensitivity in predicting MVI. Moreover, the combined tumor and peritumoral radiomics model outperformed both the individual tumor and peritumoral radiomics models. The most effective combination was the arterial phase tumor and peritumor 5-mm margin combination. Using a fusion model that integrates tumor and peritumoral radiomics and clinical data can aid in the preoperative diagnosis of the MVI of isolated HCC ≤ 5 cm, indicating considerable practical value. CRITICAL RELEVANCE STATEMENT: The radiomics model including a 5-mm peritumoral expansion is a promising noninvasive biomarker for preoperatively predicting microvascular invasion in patients diagnosed with a solitary HCC ≤ 5 cm. KEY POINTS: • Radiomics features extracted at a 5-mm distance from the tumor could better predict hepatocellular carcinoma microvascular invasion. • Peritumoral radiomics can be used to capture tumor heterogeneity and predict microvascular invasion. • This radiomics model stands as a promising noninvasive biomarker for preoperatively predicting MVI in individuals.

Differences in Vertebral Morphology and bone Mineral Density between Grade 1 Vertebral Fracture and Non-Fractured Participants in the Chinese Population.

PURPOSE: To investigate the difference in vertebral morphology and bone mineral density (BMD) between grade 1 VFs and non-fractured participants in the Chinese population to shed light on the clinical significance of grade 1 VFs from various perspectives. METHODS: This retrospective cohort study included patients who received a chest low-dose computed tomography (LDCT) scan for health examination and visited the First Affiliated Hospital of Zhengzhou University, Henan, China, from October 2019 to August 2022. Data were analyzed from March 2023 to July 2023. The main outcome of this study was the difference in morphological parameters and BMD between grade 1 VFs and non-fractured participants. The prevalence of grade 1 VFs in China populations was calculated. The difference in BMD of three fracture types in the Grade 1 group was also evaluated. RESULTS: A total of 3652 participants (1799 males, 54.85 ± 9.02 years, range, 40-92 years; 1853 females, 56.00 ± 9.08 years, range, 40-93 years) were included. The prevalence of grade 2 and 3 increase with age. The prevalence of grade 1 VFs gradually increases ≤ 50y to 60-69y group, but there is a decrease in the ≥ 70 years male group (6.6%) and a rise in the female group (25.5%). There was no significant statistical difference observed in vertebral shape indices (VSI) and BMD between the Grade 1 group and the no-fractured group aged < 50 years old except the wedge index in male. The biconcavity index did not differ between the non-fractured group and the Grade 1 group in men aged 50-59 years, whereas a significant statistical difference was observed in women. Additionally, the results of BMD were consistent with these findings. For the 40-59 years age group, there were significant differences between the compression deformity group and the other groups. CONCLUSIONS: The grade 1 group had higher VSI and lower BMD than the non-fractured group, suggesting an association between the Grade 1 group and osteoporosis in individuals aged over 50 for women and over 60 for men. Different fracture types have significant variations in BMD among middle-aged people. The prevalence of grade 1 VFs exhibits an age-related increase in both genders, with opposite trends observed between older males and females. We suggested VSI can aid physicians in the diagnosis of grade 1 VFs.

Hydralazine represses Fpn ubiquitination to rescue injured neurons via competitive binding to UBA52.

A major impedance to neuronal regeneration after peripheral nerve injury (PNI) is the activation of various programmed cell death mechanisms in the dorsal root ganglion. Ferroptosis is a form of programmed cell death distinguished by imbalance in iron and thiol metabolism, leading to lethal lipid peroxidation. However, the molecular mechanisms of ferroptosis in the context of PNI and nerve regeneration remain unclear. Ferroportin (Fpn), the only known mammalian nonheme iron export protein, plays a pivotal part in inhibiting ferroptosis by maintaining intracellular iron homeostasis. Here, we explored in vitro and in vivo the involvement of Fpn in neuronal ferroptosis. We first delineated that reactive oxygen species at the injury site induces neuronal ferroptosis by increasing intracellular iron via accelerated UBA52-driven ubiquitination and degradation of Fpn, and stimulation of lipid peroxidation. Early administration of the potent arterial vasodilator, hydralazine (HYD), decreases the ubiquitination of Fpn after PNI by binding to UBA52, leading to suppression of neuronal cell death and significant acceleration of axon regeneration and motor function recovery. HYD targeting of ferroptosis is a promising strategy for clinical management of PNI.

Full-Spectrum White-Light Emission from Triple Self-Trapped Excitons in Hybrid Mixed-Metal Halides.

Low-dimensional metal halides with broadband emissions are expected to serve as downconversion luminescent materials for solid-state lighting (SSL). However, efficiently generating full-spectrum white-light emission with a high color-rendering index (CRI) in single-phase emitters remains a challenge. Here, we report a novel zero-dimensional (0D) hybrid mixed-metal halide (TPA)2CuAgI4 (TPA = tetrapropylammonium), in which individual [CuAgI4]2- dimers are completely isolated and surrounded by the organic cations TPA+. Cu+ and Ag+ share the same crystallographic site in [CuAgI4]2- dimers with the same statistical probability. Upon photoexcitation, single crystals exhibit a full-spectrum white-light emission with a full width at half-maximum (fwhm) of up to 314 nm and a high quantum efficiency of 46.8%. Detailed photophysical studies and theoretical calculations reveal that the ultra-broadband emission of (TPA)2CuAgI4 originates from the radiative recombination of red-, green-, and blue-emitting self-trapped excitons in [CuAgI4]2- dimers. In addition, (TPA)2CuAgI4 nanocrystals were successfully synthesized and exhibited optical properties similar to those of single-crystal counterparts. Finally, a prototype ultraviolet (UV)-pumped white-light-emitting diode (WLED) and a composite thin film employing this new white-light emitter produces a well-distributed full-spectrum white light with a high CRI of 91.4 and a warm correlated color temperature (CCT) of 4135 K, indicating the potential application of this white-light emitter in SSL. These results provide a new perspective for designing superior single-phase white-light emitters.

Deep Learning Radiomics Analysis of CT Imaging for Differentiating Between Crohn's Disease and Intestinal Tuberculosis.

This study aimed to develop and evaluate a CT-based deep learning radiomics model for differentiating between Crohn's disease (CD) and intestinal tuberculosis (ITB). A total of 330 patients with pathologically confirmed as CD or ITB from the First Affiliated Hospital of Zhengzhou University were divided into the validation dataset one (CD: 167; ITB: 57) and validation dataset two (CD: 78; ITB: 28). Based on the validation dataset one, the synthetic minority oversampling technique (SMOTE) was adopted to create balanced dataset as training data for feature selection and model construction. The handcrafted and deep learning (DL) radiomics features were extracted from the arterial and venous phases images, respectively. The interobserver consistency analysis, Spearman's correlation, univariate analysis, and the least absolute shrinkage and selection operator (LASSO) regression were used to select features. Based on extracted multi-phase radiomics features, six logistic regression models were finally constructed. The diagnostic performances of different models were compared using ROC analysis and Delong test. The arterial-venous combined deep learning radiomics model for differentiating between CD and ITB showed a high prediction quality with AUCs of 0.885, 0.877, and 0.800 in SMOTE dataset, validation dataset one, and validation dataset two, respectively. Moreover, the deep learning radiomics model outperformed the handcrafted radiomics model in same phase images. In validation dataset one, the Delong test results indicated that there was a significant difference in the AUC of the arterial models (p = 0.037), while not in venous and arterial-venous combined models (p = 0.398 and p = 0.265) as comparing deep learning radiomics models and handcrafted radiomics models. In our study, the arterial-venous combined model based on deep learning radiomics analysis exhibited good performance in differentiating between CD and ITB.

Deep learning model based on multi-lesion and time series CT images for predicting the benefits from anti-HER2 targeted therapy in stage IV gastric cancer.

OBJECTIVE: To develop and validate a deep learning model based on multi-lesion and time series CT images in predicting overall survival (OS) in patients with stage IV gastric cancer (GC) receiving anti-HER2 targeted therapy. METHODS: A total of 207 patients were enrolled in this multicenter study, with 137 patients for retrospective training and internal validation, 33 patients for prospective validation, and 37 patients for external validation. All patients received anti-HER2 targeted therapy and underwent pre- and post-treatment CT scans (baseline and at least one follow-up). The proposed deep learning model evaluated the multiple lesions in time series CT images to predict risk probabilities. We further evaluated and validated the risk score of the nomogram combining a two-follow-up lesion-based deep learning model (LDLM-2F), tumor markers, and clinical information for predicting the benefits from treatment (Nomo-LDLM-2F). RESULTS: In the internal validation and prospective cohorts, the one-year AUCs for Nomo-LDLM-2F using the time series medical images and tumor markers were 0.894 (0.728-1.000) and 0.809 (0.561-1.000), respectively. In the external validation cohort, the one-year AUC of Nomo-LDLM-2F without tumor markers was 0.771 (0.510-1.000). Patients with a low Nomo-LDLM-2F score derived survival benefits from anti-HER2 targeted therapy significantly compared to those with a high Nomo-LDLM-2F score (all p < 0.05). CONCLUSION: The Nomo-LDLM-2F score derived from multi-lesion and time series CT images holds promise for the effective readout of OS probability in patients with HER2-positive stage IV GC receiving anti-HER2 therapy. CRITICAL RELEVANCE STATEMENT: The deep learning model using baseline and early follow-up CT images aims to predict OS in patients with stage IV gastric cancer receiving anti-HER2 targeted therapy. This model highlights the spatiotemporal heterogeneity of stage IV GC, assisting clinicians in the early evaluation of the efficacy of anti-HER2 therapy. KEY POINTS: • Multi-lesion and time series model revealed the spatiotemporal heterogeneity in anti-HER2 therapy. • The Nomo-LDLM-2F score was a valuable prognostic marker for anti-HER2 therapy. • CT-based deep learning model incorporating time-series tumor markers improved performance.

Intratumoral and peritumoral radiomics predict pathological response after neoadjuvant chemotherapy against advanced gastric cancer.

BACKGROUND: To investigate whether intratumoral and peritumoral radiomics may predict pathological responses after neoadjuvant chemotherapy against advanced gastric cancer. METHODS: Clinical, pathological, and CT data from 231 patients with advanced gastric cancer who underwent neoadjuvant chemotherapy at our hospital between July 2014 and February 2022 were retrospectively collected. Patients were randomly divided into a training group (n = 161) and a validation group (n = 70). The support vector machine classifier was used to establish radiomics models. A clinical model was established based on the selected clinical indicators. Finally, the radiomics and clinical models were combined to generate a radiomics-clinical model. ROC analyses were used to evaluate the prediction efficiency for each model. Calibration curves and decision curves were used to evaluate the optimal model. RESULTS: A total of 91 cases were recorded with good response and 140 with poor response. The radiomics model demonstrated that the AUC was higher in the combined model than in the intratumoral and peritumoral models (training group: 0.949, 0.943, and 0.846, respectively; validation group: 0.815, 0.778, and 0.701, respectively). Age, Borrmann classification, and Lauren classification were used to construct the clinical model. Among the radiomics-clinical models, the combined-clinical model showed the highest AUC (training group: 0.960; validation group: 0.843), which significantly improved prediction efficiency. CONCLUSION: The peritumoral model provided additional value in the evaluation of pathological response after neoadjuvant chemotherapy against advanced gastric cancer, and the combined-clinical model showed the highest predictive efficiency. CRITICAL RELEVANCE STATEMENT: Intratumoral and peritumoral radiomics can noninvasively predict the pathological response against advanced gastric cancer after neoadjuvant chemotherapy to guide early treatment decision and provide individual treatment for patients. KEY POINTS: 1. Radiomics can predict pathological responses after neoadjuvant chemotherapy against advanced gastric cancer. 2. Peritumoral radiomics has additional predictive value. 3. Radiomics-clinical models can guide early treatment decisions and improve patient prognosis.

Protective effect of naringin against radiation-induced heart disease in rats via Sirt1/NF-κB signaling pathway and endoplasmic reticulum stress.

This study was designed to explore the protective effect and mechanism of naringin (NG) on radiation-induced heart disease (RIHD) in rats. Rats were divided into four x-ray (XR) irradiation groups with different absorbed doses (0/10/15/20 Gy), or into three groups (control, XR, and XR + NG groups). Subsequently, the ultrasonic diagnostic apparatus was adopted to assess and compare the left ventricular ejection fraction (LVEF), left ventricular fractional shortening (LVFS), left ventricular internal diameter at end diastole (LVIDd), and left ventricular internal diameter at end systole (LVIDs) in rats. Hematoxylin-eosin (H&E) staining and Masson staining were applied to detect the pathological damage and fibrosis of heart tissue. Western blot was used to measure the expression levels of myocardial fibrosis-related proteins, endoplasmic reticulum stress-related proteins, and Sirt1 (silent information regulator 1)/NF-κB (nuclear factor kappa-B) signaling pathway-related proteins in cardiac tissues. Additionally, enzyme-linked immunosorbent assay was utilized to detect the activities of pro-inflammatory cytokines, malondialdehyde (MDA), superoxide dismutase (SOD), and catalase (CAT) in cardiac tissue. The results showed that NG treatment significantly attenuated the 20 Gy XR-induced decline of LVEF and LVFS and the elevation of LVIDs. Cardiac tissue damage and fibrosis caused by 20 Gy XR were significant improved after NG treatment. Meanwhile, in rats irradiated by XR, marked downregulation was identified in the expressions of fibrosis-related proteins (Col I, collagen type I; α-SMA, α-smooth muscle actin; and TGF-ß1, transforming growth factor-beta 1) and endoplasmic reticulum stress-related proteins (GRP78, glucose regulatory protein 78; CHOP, C/EBP homologous protein; ATF6, activating transcription factor 6; and caspase 12) after NG treatment. Moreover, NG treatment also inhibited the production of pro-inflammatory cytokines [interleukin-6, interleukin-1ß, and monocyte chemoattractant protein-1 (MCP-1)], reduced the expression of MDA, and promoted the activities of SOD and CAT. Also, NG treatment promoted Sirt1 expression and inhibited p65 phosphorylation. Collectively, XR irradiation induced cardiac injury in rats in a dose-dependent manner. NG could improve the cardiac injury induced by XR irradiation by inhibiting endoplasmic reticulum stress and activating Sirt1/NF-κB signaling pathway.

Radiomics using CT images for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma: a multi-centric study.

OBJECTIVES: To develop a CT-based radiomics model for preoperative prediction of lymph node (LN) metastasis in perihilar cholangiocarcinoma (pCCA). METHODS: The study enrolled consecutive pCCA patients from three independent Chinese medical centers. The Boruta algorithm was applied to build the radiomics signature for the primary tumor and LN. The k-means algorithm was employed to cluster the selected LNs based on the radiomics signature LN. Support vector machines were used to construct the prediction models. The diagnostic efficiency was measured by the area under the receiver operating characteristic curve (AUC). The optimal model was evaluated in terms of calibration, clinical usefulness, and prognostic value. RESULTS: A total of 214 patients were included in the study (mean age: 61.6 years ± 9.4; 130 male). The selected LNs were classified into two clusters, which were significantly correlated with LN metastasis in all cohorts (p < 0.001). The model incorporated the clinical risk factors, radiomics signature primary tumor, and the LN cluster obtained the best discrimination, with AUC values of 0.981 (95% CI: 0.962-1), 0.896 (95% CI: 0.810-0.982), and 0.865 (95% CI: 0.768-0.961) in the training, internal validation, and external validation cohorts, respectively. High-risk patients predicted by the optimal model had shorter overall survival than low-risk patients (median, 13.7 vs. 27.3 months, p < 0.001). CONCLUSIONS: The study proposed a radiomics model with good performance to predict LN metastasis in pCCA. As a noninvasive preoperative prediction tool, this model may help in patient risk stratification and personalized treatment. CLINICAL RELEVANCE STATEMENT: A CT-based radiomics model accurately predicts lymph node metastasis in perihilar cholangiocarcinoma patients. This noninvasive preoperative tool can aid in patient risk stratification and personalized treatment, potentially improving patient outcomes. KEY POINTS: • The radiomics model based on contrast-enhanced CT is a useful tool for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma. • Radiomics features extracted from lymph nodes show great potential for predicting lymph node metastasis. • The study is the first to identify a lymph node phenotype with a high probability of metastasis based on radiomics.