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To compare the accuracies of quantitative computed tomography (CT) parameters and semiquantitative visual score in evaluating clinical classification of severity of coronavirus disease (COVID-19). We retrospectively enrolled 187 patients with COVID-19 treated at Tongji Hospital of Tongji Medical College from February 15, 2020, to February 29, 2020. Demographic data, imaging characteristics, and clinical data were collected, and based on the clinical classification of severity, patients were divided into groups 1 (mild) and 2 (severe/critical). A semiquantitative visual score was used to estimate the lesion extent. A three-dimensional slicer was used to precisely quantify the volume and CT value of the lung and lesions. Correlation coefficients of the quantitative CT parameters, semiquantitative visual score, and clinical classification were calculated using Spearman's correlation. A receiver operating characteristic curve was used to compare the accuracies of quantitative and semi-quantitative methods. There were 59 patients in group 1 and 128 patients in group 2. The mean age and sex distribution of the two groups were not significantly different. The lesions were primarily located in the subpleural area. Compared to group 1, group 2 had larger values for all volume-dependent parameters ( < 0.001). The percentage of lesions had the strongest correlation with disease severity with a correlation coefficient of 0.495. In comparison, the correlation coefficient of semiquantitative score was 0.349. To classify the severity of COVID-19, area under the curve of the percentage of lesions was the highest (0.807; 95% confidence interval, 0.744-0.861: < 0.001) and that of the quantitative CT parameters was significantly higher than that of the semiquantitative visual score ( = 0.001). The classification accuracy of quantitative CT parameters was significantly superior to that of semiquantitative visual score in terms of evaluating the severity of COVID-19.

Background Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization. Methods Retrospective preoperative MRI scans from 115 patients diagnosed with testicular disease through pathology were obtained. A total of 1781 radiomics features were extracted from each lesion on the T2-weighted images. Intraclass and interclass correlation coefficients were used to evaluate the intra-observer and interobserver agreements for each radiomics feature. We developed an AutoML method based on the tree-based pipeline optimization tool (TPOT) algorithm to construct a discriminant model. The best pipeline was determined through 100 repeated operations using a 5-fold cross-validation algorithm in TPOT. The model was evaluated for accuracy, sensitivity, and specificity using the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve. Shapley Additive exPlanations were used to illustrate the optimization results. Results Utilizing the TPOT method, 100 diagnostic models were developed to identify testicular lesions. The best model was determined based on the highest AUC in the training cohort. The prediction model yielded AUC values of 0.989 (95% confidence interval [CI]: 0.985–0.993) and 0.909 (95% CI: 0.893–0.923) in the training and testing cohorts, respectively. Conclusions AutoML, based on the TPOT algorithm, holds potential as a noninvasive method for effectively discriminating between benign and malignant testicular lesions.

Purpose To quantify the agreement of prostate cancer radiomic features within a reader, between readers, and across scanners for T2‐weighted imaging (T2WI), diffusion‐weighted imaging (DWI; b = 1500 s/mm2), and apparent diffusion coefficient (ADC). Materials and Methods Seventeen men with biopsy‐proven prostate cancer underwent 3.0 T magnetic resonance imaging on two platforms. Two radiologists contoured the dominant lesion. Reader 1 repeated the segmentation approximately 4 weeks later on the same examination. For each sequence and prespecified comparison, feature‐wise agreement across patients was quantified using the concordance correlation coefficient (CCC). CCCs were summarized as median (interquartile range) and categorized as poor (<  0.40), moderate (0.40–0.69), good (0.70–0.89), or excellent (≥  0.90). Results Within a given scanner, prostate cancer radiomic features showed consistently high agreement for both Reader 1 repeat segmentation and Reader 1 versus Reader 2, with most features in the good and excellent ranges across T2WI, DWI, and ADC. In contrast, the cross‐scanner agreement was low, even for the same reader and session, and the majority of features were categorized as poor. Sequence‐wise, as shown in Figure 2, adjusted T2WI outperformed DWI and ADC for cross‐scanner comparisons, yet still fell short of within‐scanner performance. Conclusion Prostate cancer radiomic features demonstrated good reproducibility on a single scanner but poor cross‐scanner reproducibility. For future radiomic research, researchers should incorporate scanner type into model analyses and perform data harmonization before integrating data from different manufacturers.

Even though deep learning is fascinated in fields of coronary vessel segmentation in X-ray angiography and achieves prominent progresses, most of those models probably bring high false and missed detections due to indistinct contrast between coronary vessels and background, especially for tiny sub-branches. Image improvement technique is able to better such contrast, while boosting extraneous information, e.g., other tissues with similar intensities and noise. If incorporating features derived from original and enhanced images, the segmentation performance is improved because those images comprise complementary information from different contrasts. Accordingly, inspired from advantages of contrast improvement and encoding-decoding architecture, a dual multi-scale feature aggregation network (named DFA-Net) is introduced for coronary vessel segmentation in digital subtraction angiography (DSA). DFA-Net integrates the contrast improvement using exponent transformation into a semantic segmentation network that individually accepts original and enhanced images as inputs. Through parameter sharing, multi-scale complementary features are aggregated from different contrasts, which strengthens leaning capabilities of networks, and thus achieves an efficient segmentation. Meanwhile, a risk cross-entropy loss is enforced on the segmentation, for availably decreasing false negatives, which is incorporated with Dice loss for joint optimization of the proposed strategy during training. Experimental results demonstrate that DFA-Net can not only work more robustly and effectively for DSA images under diverse conditions, but also achieve better performance, in comparison with state-of-the-art methods. Consequently, DFA-Net has high fidelity and structure similarity to the reference, providing a way for early diagnosis of cardiovascular diseases.

Background Accurate preoperative T and TNM staging of clear cell renal cell carcinoma (ccRCC) is crucial for diagnosis and treatment, but these assessments often depend on subjective radiologist judgment, leading to interobserver variability. This study aims to design and validate two CT-based deep learning models and evaluate their clinical utility for the preoperative T and TNM staging of ccRCC. Methods Data from 1,148 ccRCC patients across five medical centers were retrospectively collected. Specifically, data from two centers were merged and randomly divided into a training set (80%) and a testing (20%) set. Data from two additional centers comprised external validation set 1, and data from the remaining independent center comprised external validation set 2. Two 3D deep learning models based on a Transformer-ResNet (TR-Net) architecture were developed to predict T staging (T1, T2, T3 + T4) and TNM staging (I, II, III, IV) using corticomedullary phase CT images. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to generate heatmaps for improved model interpretability, and a human-machine collaboration experiment was conducted to evaluate clinical utility. Models’ performance was evaluated using micro-average AUC (micro-AUC), macro-average AUC (macro-AUC), and accuracy (ACC). Results Across the two external validation sets, the T staging model achieved micro-AUCs of 0.939 and 0.954, macro-AUCs of 0.857 and 0.894, and ACCs of 0.843 and 0.869, while the TNM staging model achieved micro-AUCs of 0.935 and 0.924, macro-AUCs of 0.817 and 0.888, and ACCs of 0.856 and 0.807. While the models demonstrated acceptable overall performance in preoperative ccRCC staging, performance was moderate for advanced subclasses (T3 + T4 AUC: 0.769 and 0.795; TNM III AUC: 0.669 and 0.801). Grad-CAM heatmaps highlighted key tumor regions, improving interpretability. The human-machine collaboration demonstrated improved diagnostic accuracy with model assistance. Conclusion The CT-based 3D TR-Net models showed acceptable overall performance with moderate results in advanced subclasses in preoperative ccRCC staging, with interpretable outputs and collaborative benefits, making them potentially useful decision-support tools.

Peripherally inserted central catheters (PICCs) are essential for long-term infusion in vulnerable pediatric patients. Optimal tip placement in the lower third of the superior vena cava or at the cavoatrial junction is critical to prevent serious complications. Verifying correct tip position in infants and toddlers is challenging because of very small anatomic target zones, non-standard radiograph acquisition, interference from other devices, low contrast, and high risk of catheter migration. Existing automated segmentation methods, mostly developed for adults, perform poorly on pediatric images. We retrospectively collected 1184 PICC patients from three medical centers, including 280 pediatric cases (210 neonates, 46 infants, 24 toddlers), with appropriate ethical approval. We introduce TopNet, a topology-preserving embedded network designed for automated PICC segmentation in pediatric patients. TopNet maintains catheter continuity and enables precise tip localization under difficult conditions. Quantitative and qualitative evaluations show superior segmentation and tip localization on both internal and external validation.

Aim To investigate the prognostic significance of histone acetylation (HAc) regulators in esophageal cancer (EC) and develop a transcriptome-based HAc_score reflecting epigenetic and immunogenomic states. Methods Expression and mutation from EC were analyzed to identify prognostic HAc regulators via univariable Cox models. Consensus clustering defined HAc-related expression patterns. Differentially expressed genes (DEGs) among clusters were functionally enriched. A principal component-based HAc_score was constructed from prognostic DEGs and tested for associations with overall survival, tumor mutational burden (TMB), immunophenoscore (IPS), and immune cell infiltration. Results Three HAc-related expression patterns showed distinct biological and immune features. From shared DEGs, 19 prognostic genes defined two molecular subtypes and served as the basis for the HAc_score. Higher HAc_score was associated with better overall survival, particularly in early-stage disease. HAc_score correlated inversely with TMB and positively with IPS components, suggesting a transcriptionally active, immunogenic phenotype despite lower mutation burden. Combining HAc_score with TMB improved risk stratification. Conclusion HAc_score quantifies HAc–linked transcriptional states in EC and reflects tumor–immune interactions. It stratifies survival risk and complements TMB, supporting its potential use as a prognostic biomarker and integrative epigenetic–immune signature.

PurposeRefractory lower urinary tract symptoms (LUTS) coexisting with lumbar disc hernia (LDH) have been shown to resolve following LDH surgery, implying that LDH causes these LUTS. The purpose of this study was to report outcomes in patients with refractory LUTS and LDH following non-surgical treatment targeting LDH.MethodsA retrospective cohort study was conducted using outpatient data collected at Tongji Hospital, China, between 2016 and 2018. This study included 131 adult patients with refractory LUTS and LDH. Patients were stratified into two groups. Group A underwent non-surgical treatment for LDH plus pharmacological treatment for LUTS. Group B underwent only pharmacological treatment for LUTS. The International Prostate Symptom Score (IPSS), the IPSS quality of life (QoL) score, and uroflowmetry were used to evaluate outcomes.ResultsIn group A, following treatment, the maximum flow rate (Qmax) increased by 3.92 ml/s (p < 0.001), the IPSS reduced by 5.99 points (p < 0.001), and the QoL score decreased by 1.51 points (p < 0.001). In group B, the Qmax increased by 0.09 ml/s (p = 0.833), the IPSS reduced by 0.72 points (p = 0.163), and the QoL score decreased by 0.07 points (p = 0.784).ConclusionsLUTS can be relieved by a combination of pharmacological treatment for LUTS and non-surgical treatment for LDH in some refractory LUTS patients with LDH. MRI is recommended for these patients.

The two-compartment intravoxel incoherent motion (IVIM) theory assumes that the transverse relaxation time is the same in both compartments. However, blood and tissue have different T2 values, and echo time (TE) may thus have an effect on the quantitative parameters of IVIM. The purpose of this study was to investigate the effects of TE on IVIM-DWI-derived parameters of the prostate. In total, 17 healthy volunteers underwent two repeat examinations. IVIM-DWI data were scanned 6 times with variable TE values of 60, 70, 80, 90, 100, and 120 ms. The ADC of a mono-exponential model and the D, D*, and f parameters of the IVIM model were calculated separately for each TE. Repeat measures were assessed by calculating the coefficient of variation and Bland-Altman limits of agreement for each parameter. Spearman’s rho test was used to analyse relationships between IVIM indices and TE. Our results showed that TE had an effect on IVIM quantification, which should be kept constant in the examination protocol at each individual institution. Alternatively, an extended IVIM could be used to eliminate the effect of the TE value on the quantitative parameters of IVIM. This may be helpful for guiding clinical research, especially for longitudinal studies.

Background Anoikis resistance is a hallmark characteristic of oncogenic transformation, which is crucial for tumor progression and metastasis. The aim of this study was to identify and validate a novel anoikis‐related prognostic model for prostate cancer (PCa). Methods We collected a gene expression profile, single nucleotide polymorphism mutation and copy number variation (CNV) data of 495 PCa patients from the TCGA database and 140 PCa samples from the MSKCC dataset. We extracted 434 anoikis‐related genes and unsupervised consensus cluster analysis was used to identify molecular subtypes. The immune infiltration, molecular function, and genome alteration of subtypes were evaluated. A risk signature was developed using Cox regression analysis and validated with the MSKCC dataset. We also identify potential drugs for high‐risk group patients. Results Two subtypes were identified. C1 exhibited a higher level of CNV amplification, immune score, stromal score, aneuploidy score, homologous recombination deficiency, intratumor heterogeneity, single‐nucleotide variant neoantigens, and tumor mutational burden compared to C2. C2 showed a better survival outcome and had a high level of gamma delta T cell and activated B cell infiltration. The risk signature consisting of four genes (HELLS, ZWINT, ABCC5, and TPSB2) was developed (area under the curve = 0.780) and was found to be an independent prognostic factor for overall survival in PCa patients. Four CTRP‐derived and four PRISM‐derived compounds were identified for high‐risk patients. Conclusions The anoikis‐related prognostic model developed in this study could be a useful tool for clinical decision‐making. This study may provide a new perspective for the treatment of anoikis‐related PCa. Our study has identified a novel anoikis‐related signature consisting of four genes, which demonstrated significant prognostic value for PCa patients. Our findings suggest that this signature could serve as a valuable tool for predicting patient outcomes and guiding personalized treatment strategies for PCa. The identification of immune cells and drug sensitivity information could also provide potential targets for developing novel immunotherapies and personalized treatments for PCa patients.