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Objectives: To investigate microvascular alterations in the Glisson system of biliary atresia (BA) patients after Kasai portoenterostomy (KP) using three-dimensional (3D) virtual histopathology based on X-ray phase-contrast CT (PCCT). Methods: Liver explants from BA patients were imaged using PCCT, and 32 subjects were included and divided into two groups: KP (n = 16) and non-KP (n = 16). Combined with histological analysis and 3D visualization technology, 3D virtual histopathological assessment of the biliary, arterial, and portal venous systems was performed. According to loop volume ratio, 3D spatial density, relative surface area, tortuosity, and other parameters, pathological changes of microvasculature in the Glisson system were investigated. Results: In the non-KP group, bile ducts mostly manifested as radial multifurcated hyperplasia and twisted into loops. In the KP group, the bile duct hyperplasia was less, and the loop volume ratio of bile ducts decreased by 13.89%. Simultaneously, the arterial and portal venous systems presented adaptive alterations in response to degrees of bile duct hyperplasia. Compared with the non-KP group, the 3D spatial density of arteries in the KP group decreased by 3.53%, and the relative surface area decreased from 0.088 ± 0.035 to 0.039 ± 0.015 (p <.01). Deformed portal branches gradually recovered after KP, with a 2.93% increase in 3D spatial density and a decrease in tortuosity from 1.17 ± 0.06 to 1.14 ± 0.04 (p <.01) compared to the non-KP group. Conclusion: 3D virtual histopathology via PCCT clearly reveals the microvascular structures in the Glisson system of BA patients and provides key insights into the morphological mechanism of microvascular adaptation induced by biliary tract dredging after KP in BA disease. Key Points: • 3D virtual histopathology via X-ray phase-contrast computed tomography clearly presented the morphological structures and pathological changes of microvasculature in the Glisson system of biliary atresia patients. • The morphological alterations of microvasculature in the Glisson system followed the competitive occupancy mechanism in the process of biliary atresia. [ABSTRACT FROM AUTHOR]

Background: This study aims to develop and validate an artificial intelligence (AI)-aided Prostate Imaging Reporting and Data System (PI-RADSAI) for prostate cancer (PCa) diagnosis based on MRI. Methods: The deidentified MRI data of 1540 biopsy-naïve patients were collected from four centres. PI-RADSAI is a two-stage, human-in-the-loop AI capable of emulating the diagnostic acumen of subspecialists for PCa on MRI. The first stage uses a UNet-Seg model to detect and segment biopsy-candidate prostate lesions, whereas the second stage leverages UNet-Seg segmentation is trained specifically with subspecialist' knowledge-guided 3D-Resnet to achieve an automatic AI-aided diagnosis for PCa. Results: In the independent test set, UNet-Seg identified 87.2% (628/720) of target lesions, with a Dice score of 44.9% (range, 22.8–60.2%) in segmenting lesion contours. In the ablation experiment, the model trained with the data from three centres was superior (kappa coefficient, 0.716 vs. 0.531) to that trained with single-centre data. In the internal and external tests, the triple-centre PI-RADSAI model achieved an overall agreement of 58.4% (188/322) and 60.1% (92/153) with a referential subspecialist in scoring target lesions; when one-point margin of error was permissible, the agreement rose to 91.3% (294/322) and 97.3% (149/153), respectively. In the paired test, PI-RADSAI outperformed 5/11 (45.5%) and matched the performance of 3/11 (27.3%) general radiologists in achieving a clinically significant PCa diagnosis (area under the curve, internal test, 0.801 vs. 0.770, p < 0.01; external test, 0.833 vs. 0.867, p = 0.309). Conclusions: Our closed-loop PI-RADSAI outperforms or matches the performance of more than 70% of general readers in the MRI assessment of PCa. This system might provide an alternative to radiologists and offer diagnostic benefits to clinical practice, especially where subspecialist expertise is unavailable. [ABSTRACT FROM AUTHOR]

Background: To develop and test a Prostate Imaging Stratification Risk (PRISK) tool for precisely assessing the International Society of Urological Pathology Gleason grade (ISUP-GG) of prostate cancer (PCa). Methods: This study included 1442 patients with prostate biopsy from two centres (training, n = 672; internal test, n = 231 and external test, n = 539). PRISK is designed to classify ISUP-GG 0 (benign), ISUP-GG 1, ISUP-GG 2, ISUP-GG 3 and ISUP GG 4/5. Clinical indicators and high-throughput MRI features of PCa were integrated and modelled with hybrid stacked-ensemble learning algorithms. Results: PRISK achieved a macro area-under-curve of 0.783, 0.798 and 0.762 for the classification of ISUP-GGs in training, internal and external test data. Permitting error ±1 in grading ISUP-GGs, the overall accuracy of PRISK is nearly comparable to invasive biopsy (train: 85.1% vs 88.7%; internal test: 85.1% vs 90.4%; external test: 90.4% vs 94.2%). PSA ≥ 20 ng/ml (odds ratio [OR], 1.58; p = 0.001) and PRISK ≥ GG 3 (OR, 1.45; p = 0.005) were two independent predictors of biochemical recurrence (BCR)-free survival, with a C-index of 0.76 (95% CI, 0.73–0.79) for BCR-free survival prediction. Conclusions: PRISK might offer a potential alternative to non-invasively assess ISUP-GG of PCa. [ABSTRACT FROM AUTHOR]

The era of big data in healthcare is here, and this era will significantly improve medicine and especially oncology. However, traditional machine learning algorithms need to be promoted to solve such large-scale realworld problems due to a large amount of data that needs to be analyzed and the difficulty in solving problems with nonconvex nonlinear settings. We aim to minimize the composite of a smooth nonlinear function and a block-separable nonconvex function on a large number of block variables with inequality constraints. We propose a novel parallel first-order optimization method, called asynchronous block coordinate descent with time perturbation (ATP), which adopts a time perturbation technique that escapes from saddle points and sub-optimal local points. The details of the proposed method are presented with analyses of convergence and iteration complexity properties. Experiments conducted on real-world machine learning problems validate the efficacy of our proposed method. The experimental results demonstrate that time perturbation enables ATP to escape from saddle points and sub-optimal points, providing a promising way to handle nonconvex optimization problems with inequality constraints employing asynchronous block coordinate descent. The asynchronous parallel implementation on shared memory multi-core platforms indicates that the proposed algorithm, ATP, has strong scalability. [ABSTRACT FROM AUTHOR]

Objective: Tongue squamous cell carcinoma (TSCC) is significantly more malignant than other type of oral squamous cell carcinoma (OSCC). In this study, we aimed to identify specific global gene expression signatures of TSCC to investigate the more invasive behavior of the deeply infiltrating cancer. Methods: Using RNA-seq technology, we detected gene expression of 20 TSCCs, 20 matched paratumor tissues, and 10 healthy normal mucosa tissues. Enrichment analysis of gene ontology (GO) and pathway was conducted using online tools DAVID for the dysregulated genes. Additionally, we performed the quantitative real-time RT-PCR (qRT-PCR) to validate the findings of RNA-Seq in 10 samples of TSCC, matched paratumor, and normal mucosa, respectively. Results: We detected 252 differentially expressed genes (DEGs) between TSCC and matched paratumor tissue, including 117 up-regulated and 135 down-regulated genes. For comparison between TSCC and normal mucosa, 234 DEGS were identified, consisting of 67 up-regulated and 167 down-regulated genes. For both two comparisons, GO categories of muscle contraction (GO: 0006936), epidermis development (GO: 0008544), epithelial cell differentiation (GO: 0030855), and keratinization (GO: 0031424) were commonly enriched. Altered gene expression affected some cancer-related pathways, such as tight junction. The qRT-PCR validation showed that gene expression patterns of FOLR1, NKX3-1, TFF3, PIGR, NEFL, MMP13, and HMGA2 were fully in concordance with RNA-Seq results. Conclusion: Findings in this study demonstrated the genetic and molecular alterations associated with TSCC, providing new clues for understanding the molecular mechanisms of TSCC pathogenesis. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the most common but serious cancers in the world. Both the X-ray repair cross-complementing group 1 ( XRCC1) gene and the human multidrug resistance 1 ( MDR1) gene are important candidate genes influencing the susceptibility to various diseases, including lung cancer. This study aimed to assess the correlation of genetic polymorphisms in XRCC1 and MDR1 with the susceptibility to lung cancer. In this study, a total of 320 lung cancer patients and 346 cancer-free controls in Chinese population were enrolled in this study. Data about the clinical characteristics and related risk factors of lung cancer were collected by questionnaires. The single-nucleotide polymorphisms (SNPs) of XRCC1 and MDR1 genes were genotyped by created restriction site-polymerase chain reaction method. Our data showed that the risk for lung cancer increased significantly among the variant Arg194Trp (C > T, rs1799782) and Arg399Gln (G > A, rs25487) of XRCC1, but there are no significant differences in the allelic and genotypic frequencies of c.1564A > T and c.3073A > C of MDR1 between lung cancer patients and cancer-free controls. In conclusion, these preliminary results suggest that the C > T, rs1799782 and C > T, rs25487 of XRCC1 genetic variants might be used as molecular markers for detecting lung cancer susceptibility. [ABSTRACT FROM AUTHOR]

By using a low-cost and facile hydrothermal method, a peculiar claw-like ZnO nanorod array is successfully synthesized. The hydrothermal growth is done in an aqueous solution with equimolar zinc acetate (ZAc, Zn(CHCOO)·2HO) and hexamethylenetetramine (HMTA, CHN). The obtained ZnO nanorod array is characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The results indicate that the nanorods are high-quality monocrystals. The photoluminescence (PL) spectrum is performed to investigate the optical properties of this product. [ABSTRACT FROM AUTHOR]

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