Obliteration of portal venules contributes to portal hypertension in biliary cirrhosis.

The effects of the obliteration of portal venules (OPV) in cirrhotic portal hypertension are poorly understood. To investigate its contribution to portal hypertension in biliary cirrhosis and its underlying mechanism, we evaluated OPV using two-dimensional (2D) histopathology in liver explants from patients with biliary atresia (BA, n = 63), primary biliary cholangitis (PBC, n = 18), and hepatitis B-related cirrhosis (Hep-B-cirrhosis, n = 35). Then, three-dimensional (3D) OPV was measured by X-ray phase-contrast CT in two parallel models in rats following bile duct ligation (BDL) or carbon tetrachloride (CCl4) administration, representing biliary cirrhosis and post-necrotic cirrhosis, respectively. The portal pressure was also measured in the two models. Finally, the effects of proliferative bile ducts on OPV were investigated. We found that OPV was significantly more frequent in patients with biliary cirrhosis, including BA (78.57 ± 16.45%) and PBC (60.00 ± 17.15%), than that in Hep-B-cirrhotic patients (29.43 ± 14.94%, p < 0.001). OPV occurred earlier, evidenced by the paired liver biopsy at a Kasai procedure (KP), and was irreversible even after a successful KP in the patients with BA. OPV was also significantly more frequent in the BDL models than in the CCl4 models, as shown by 2D and 3D quantitative analysis. Portal pressure was significantly higher in the BDL model than that in the CCl4 model. With the proliferation of bile ducts, portal venules were compressed and irreversibly occluded, contributing to the earlier and higher portal pressure in biliary cirrhosis. OPV, as a pre-sinusoidal component, plays a key role in the pathogenesis of portal hypertension in biliary cirrhosis. The proliferated bile ducts and ductules gradually take up the 'territory' originally attributed to portal venules and compress the portal venules, which may lead to OPV in biliary cirrhosis. © 2024 The Pathological Society of Great Britain and Ireland.

Circ_PABPC1 promotes the malignancy of gastric cancer through interacting with ILK to activate NF-κB pathway.

BACKGROUND: Gastric cancer (GC) is a common cancer type with both high incidence and mortality. Recent studies have revealed an important role of circRNA in the development of GC. However, more experiments are needed to reveal the precise molecular mechanisms of circRNA in GC development. METHODS: Bioinformatics analysis was conducted to predict the potential role of circ_PABPC1 in GC and the target proteins of circ_PABPC1. Quantitative RT-PCR, Western blot and immunohistochemistry assays were conducted to detect the levels of circ_PABPC1, NF-κB p65, NF-κB p65 (Ser536) and ILK. MTT, Edu staining, cell scratch-wound and trans-well assays were carried out to detect cell proliferation, migration and invasion. The interaction between ILK and circ_PABPC1 was confirmed by RNA immunoprecipitation (RIP), RNA pull-down and fluorescence in situ hybridization assays. Genetically modified GC cells were injected into mice to evaluate the tumor growth performance. RESULTS: This study found that the high expression of circ_PABPC1 was associated with a poor prognosis of GC. The up-regulation of circ_PABPC1 promoted the proliferation, migration and invasion of GC cells. Circ_PABPC1 bound to ILK protein, thereby preventing the degradation of ILK. ILK mediated the effect of circ_PABPC1 on GC cells through activating NF-κB. CONCLUSION: circ_PABPC1 promotes the malignancy of GC cells through binding to ILK to activate NF-κB signaling pathway.

circ_PPAPDC1A promotes Osimertinib resistance by sponging the miR-30a-3p/ IGF1R pathway in non-small cell lung cancer (NSCLC).

BACKGROUND: Recent evidence has demonstrated that abnormal expression and regulation of circular RNA (circRNAs) are involved in the occurrence and development of a variety of tumors. The aim of this study was to investigate the effects of circ_PPAPDC1A in Osimertinib resistance in NSCLC. METHODS: Human circRNAs microarray analysis was conducted to identify differentially expressed (DE) circRNAs in Osimertinib-acquired resistance tissues of NSCLC. The effect of circ_PPAPDC1A on cell proliferation, invasion, migration, and apoptosis was assessed in both in vitro and in vivo. Dual-luciferase reporter assay, RT-qPCR, Western-blot, and rescue assay were employed to confirm the interaction between circ_PPAPDC1A/miR-30a-3p/IGF1R axis. RESULTS: The results revealed that circ_PPAPDC1A was significantly upregulated in Osimertinib acquired resistance tissues of NSCLC. circ_PPAPDC1A reduced the sensitivity of PC9 and HCC827 cells to Osimertinib and promoted cell proliferation, invasion, migration, while inhibiting apoptosis in Osimertinib-resistant PC9/OR and HCC829/OR cells, both in vitro and in vivo. Silencing circ_PPAPDC1A partially reversed Osimertinib resistance. Additionally, circ_PPAPDC1A acted as a competing endogenous RNA (ceRNA) by targeting miR-30a-3p, and Insulin-like Growth Factor 1 Receptor (IGF1R) was identified as a functional gene for miR-30a-3p in NSCLC. Furthermore, the results confirmed that circ_PPAPDC1A/miR-30a-3p/IGF1R axis plays a role in activating the PI3K/AKT/mTOR signaling pathway in NSCLC with Osimertinib resistance. CONCLUSIONS: Therefore, for the first time we identified that circ_PPAPDC1A was significantly upregulated and exerts an oncogenic role in NSCLC with Osimertinib resistance by sponging miR-30a-3p to active IGF1R/PI3K/AKT/mTOR pathway. circ_PPAPDC1A may serve as a novel diagnostic biomarker and therapeutic target for NSCLC patients with Osimertinib resistance.

Radiomics analysis of dual-layer spectral-detector CT-derived iodine maps for predicting tumor deposits in colorectal cancer.

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). MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSION: 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. CLINICAL RELEVANCE STATEMENT: 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. KEY POINTS: 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.

Stent-specific fat attenuation index is associated with target vessel revascularization after PCI.

OBJECTIVES: To explore the clinical relevance of stent-specific perivascular fat attenuation index (FAI) in patients with stent implantation. METHODS: A total of 162 consecutive patients who underwent coronary computed tomography angiography (CCTA) following stent implantation were retrospectively included. The stent-specific FAI at 2 cm adjacent to the stent edge was calculated. The endpoints were defined as target vessel revascularization (TVR) on the stented vessel after CCTA and readmission times due to chest pain after stent implantation. Binary logistic regression analysis for TVR and ordinal regression models were conducted to identify readmission times (0, 1, and ≥ 2) with generalized estimating equations on a per-stent basis. RESULTS: On a per-stent basis, 9 stents (4.5%) experienced TVR after PCI at a median 30 months' follow-up duration. Stent-specific FAI differed significantly among subgroups of patients with stent implantation and different readmission times (p = 0.002); patients with at least one readmission had higher stent-specific FAI than those without readmission (p < 0.001). Bifurcated stents (odds ratio [OR]: 11.192, p = 0.001) and stent-specific FAI (OR: 1.189, p = 0.04) were independently associated with TVR. With no readmission as a reference, stent-specific FAI (OR: 0.984, p = 0.007) was an independent predictor for hospital readmission times ≥ 2 (p = 0.003). CONCLUSION: Non-invasive stent-specific FAI derived from CCTA was found to be associated with TVR, which was a promising imaging marker for functional assessment in patients who underwent stent implantation. CLINICAL RELEVANCE STATEMENT: Noninvasive fat attenuation index adjacent to the stents edge derived from CCTA, an imaging marker reflecting the presence of inflammation acting on the neointimal tissue at the sites of coronary stenting, might be relevant clinically with target vessel revascularization. KEY POINTS: • Non-invasive stent-specific FAI derived from CCTA was associated with TVR (OR: 1.189 [95% CI: 1.007-1.043], p = 0.04) in patients who underwent stent implantation. • Stent-specific FAI significantly differed among a subgroup of patients with chest pain after stent implantation and with different readmission times (p = 0.002); the patients with at least one readmission had higher stent-specific FAI than those without readmission (p < 0.001). • Non-invasive stent-specific FAI derived from CCTA could be used as an imaging maker for the functional assessment of patients following stent implantation.

Radiomics model based on dual-energy CT can determine the source of thrombus in strokes with middle cerebral artery occlusion.

PURPOSE: To develop thrombus radiomics models based on dual-energy CT (DECT) for predicting etiologic cause of stroke. METHODS: We retrospectively enrolled patients with occlusion of the middle cerebral artery who underwent computed tomography (NCCT) and DECT angiography (DECTA). 70 keV virtual monoenergetic images (simulate conventional 120kVp CTA images) and iodine overlay maps (IOM) were reconstructed for analysis. Five logistic regression radiomics models for predicting cardioembolism (CE) were built based on the features extracted from NCCT, CTA and IOM images. From these, the best one was selected to integrate with clinical information for further construction of the combined model. The performance of the different models was evaluated and compared using ROC curve analysis, clinical decision curves (DCA), calibration curves and Delong test. RESULTS: Among all the radiomic models, model NCCT+IOM performed the best, with AUC = 0.95 significantly higher than model NCCT, model CTA, model IOM and model NCCT+CTA in the training set (AUC = 0.88, 0.78, 0.90,0.87, respectively, P < 0.05), and AUC = 0.92 in the testing set, significantly higher than model CTA (AUC = 0.71, P < 0.05). Smoking and NIHSS score were independent predictors of CE (P < 0.05). The combined model performed similarly to the model NCCT+IOM, with no statistically significant difference in AUC either in the training or test sets. (0.96 vs. 0.95; 0.94 vs. 0.92, both P > 0.05). CONCLUSION: Radiomics models constructed based on NCCT and IOM images can effectively determine the source of thrombus in stroke without relying on clinical information.

Prognostic value of native T1 and extracellular volume in patients with immunoglubin light-chain amyloidosis.

BACKGROUND: Cardiac involvement in patients with immunoglubin light-chain amyloidosis (AL) is a major determinant of treatment choice and prognosis, and early identification of high-risk patients can initiate intensive treatment strategies to achieve better survival. This study aimed to investigate the prognostic value of native T1 and ECV in patients with AL-cardiac amyloidosis (CA). METHODS: A total of 38 patients (mean age 59 ± 11 years) with AL diagnosed histopathologically from July 2017 to October 2021 were collected consecutively. All patients were performed 3.0-T cardiac magnetic resonance (CMR) including cine, T1 mapping, and late gadolinium enhancement (LGE). Pre- and post-contrast T1 mapping images were transferred to a dedicated research software package (CVI42 v5.11.3) to create parametric T1 and ECV values. In addition, clinical and laboratory data of all patients were collected, and patients or their family members were regularly followed up by telephone every 3 months. The starting point of follow-up was the time of definitive pathological diagnosis, and the main endpoint was all-cause death. Kaplan-Meier analysis and Cox proportional risk model were used to evaluate the association between native T1 and ECV and death in patients with CA. RESULTS: After a median follow-up of 27 (16, 37) months, 12 patients with CA died. Kaplan-Meier analysis showed that elevated native T1 and ECV were closely associated with poor prognosis in patients with CA. The survival rate of patients with ECV > 44% and native T1 > 1389ms were significantly lower than that of patients with ECV ≤ 44% and native T1 ≤ 1389ms (Log-rank P < 0.001), and was not associated with the presence of LGE. After adjusting for clinical risk factors and CMR measurements in a stepwise multivariate Cox regression model, ECV [risk ratio (HR):1.37, 95%CI: 1.09-1.73, P = 0.008] and native T1 (HR:1.01, 95%CI: 1.00-1.02, P = 0.037) remained independent predictors of all-cause mortality in patients with CA. CONCLUSIONS: Both native T1 and ECV were independently prognostic for mortality in patients with CA, and can be used as important indicators for clinical prognosis assessment of AL.

Radiomics Analysis to Predict Lymphovascular Invasion of Gastric Cancer Based on Iodine-Based Material Decomposition Images and Virtual Monoenergetic Images.

OBJECTIVE: This study aimed to investigate the utility of virtual monoenergetic images (VMIs) and iodine-based material decomposition images (IMDIs) in the assessment of lymphovascular invasion (LVI) in gastric cancer (GC) patients. METHODS: A total of 103 GC patients who underwent dual-energy spectral computed tomography preoperatively were enrolled. The LVI status was confirmed by pathological analysis. The radiomics features obtained from the 70 keV VMI and IMDI were used to build radiomics models. Independent clinical factors for LVI were identified and used to build the clinical model. Then, combined models were constructed by fusing clinical factors and radiomics signatures. The predictive performance of these models was evaluated. RESULTS: The computed tomography-reported N stage was an independent predictor of LVI, and the areas under the curve (AUCs) of the clinical model in the training group and testing group were 0.750 and 0.765, respectively. The radiomics models using the VMI signature and IMDI signature and combining these 2 signatures outperformed the clinical model, with AUCs of 0.835, 0.855, and 0.924 in the training set and 0.838, 0.825, and 0.899 in the testing set, respectively. The model combined with the computed tomography-reported N stage and the 2 radiomics signatures achieved the best performance in the training (AUC, 0.925) and testing (AUC, 0.961) sets, with a good degree of calibration and clinical utility for LVI prediction. CONCLUSIONS: The preoperative assessment of LVI in GC is improved by radiomics features based on VMI and IMDI. The combination of clinical, VMI-, and IMDI-based radiomics features effectively predicts LVI and provides support for clinical treatment decisions.

Radiomics analysis of lesion-specific pericoronary adipose tissue to predict major adverse cardiovascular events in coronary artery disease.

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

Effects on brain structural and functional in deaf children after aerobic exercise training: a pilot cluster randomized controlled study.

Purpose: To examine effects of aerobic exercise interventions on brain via the structural Magnetic Resonance Imaging (MRI), as well as functional change during working memory (WM) task using fMRI in deaf children.Method: The study applied a cluster randomized controlled design. Twelve deaf children in the intervention group were required to complete an eleven-week aerobic exercise intervention, while other twelve age and gender matched deaf children in the control group were required to keep their normal daily life. Task fMRI images of each participant were acquired in the baseline and post intervention period. The surface-based morphometry (SBM) analysis and functional activation analysis were employed to probe the effects of 11-week aerobic exercise on cerebral structural and functional in deaf children, respectively.Results: The 11-week aerobic exercise intervention did not change brain structure in deaf children. However, behavior performance (reaction time and mean accuracy rate) presented significant improvements after the 11-week aerobic exercise intervention. Compared to the control group, the intervention group showed decreased reaction time in the 2-back (p < 0.001) and 2-0 back (p < 0.001), and increased mean accuracy rate during 2-back (p = 0.034). Furthermore, enhanced brain activations in the left supplementary motor cortex (p < 0.05, FDR-corrected) and left paracentral lobule (p < 0.05, FDR-corrected) were observed in the intervention group.Conclusion: 11-week aerobic exercise intervention may not be able to modulate brain structure in deaf children, but may have significantly positive effects on behavior performance and brain functional activation during WM task.

Dynamics and concordance alterations of regional brain function indices in vestibular migraine: a resting-state fMRI study.

BACKGROUND: Prior MRI studies on vestibular migraine (VM) have revealed abnormalities in static regional intrinsic brain activity (iBA) and dynamic functional connectivity between brain regions or networks. However, the temporal variation and concordance of regional iBA measures remain to be explored. METHODS: 57 VM patients during the interictal period were compared to 88 healthy controls (HC) in this resting-state functional magnetic resonance imaging (fMRI) study. The dynamics and concordance of regional iBA indices, including amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo), were examined by utilizing sliding time-window analysis. Partial correlation analyses were performed between clinical parameters and resting-state fMRI indices in brain areas showing significant group differences. RESULTS: The VM group showed increased ALFF and ReHo dynamics, as well as increased temporal concordance between ALFF and ReHo in the bilateral paracentral lobule and supplementary motor area relative to the HC group. We also found decreased ReHo dynamics in the right temporal pole, and decreased ALFF dynamics in the right cerebellum posterior lobe, bilateral angular gyrus and middle occipital gyrus (MOG) in the VM group compared with the HC group. Moreover, a positive correlation was observed between ALFF dynamics in the left MOG and vertigo disease duration across all VM patients. CONCLUSION: Temporal dynamics and concordance of regional iBA indices were altered in the motor cortex, cerebellum, occipital and temporoparietal cortex, which may contribute to disrupted multisensory processing and vestibular control in patients with VM. ALFF dynamics in the left MOG may be useful biomarker for evaluating vertigo burden in this disorder.

circ_rac GTPase-Activating Protein 1 Facilitates Stemness and Metastasis of Non-Small Cell Lung Cancer via Polypyrimidine Tract-Binding Protein 1 Recruitment to Promote Sirtuin-3-Mediated Replication Timing Regulatory Factor 1 Deacetylation.

Circular RNAs have been identified as diagnostic and therapeutic targets for various tumors. The expression of circ_rac GTPase-activating protein 1 (circRACGAP1) is reported to drive the development of non-small cell lung cancer (NSCLC). This study further explored the potential mechanism of circRACGAP1-mediated development of NSCLC. The circRACGAP1 level was detected by quantitative RT-PCR. Sphere formation, CD133-positive cell percentage, and expression of octamer-binding transcription factor 4, Sox2, Nanog, and CD133 were detected to evaluate stemness of NSCLC. Migration and invasion were determined using wound healing and transwell assays. Protein expression was measured using Western blotting. The molecular mechanism was evaluated using RNA pull-down, RNA immunoprecipitation, and coimmunoprecipitation assays. In vivo tumor growth and metastasis were determined in nude mice. circRACGAP1 was highly expressed in NSCLC and was associated with stemness marker Sox2 expression. The stemness, metastasis, and epithelial mesenchymal transformation were repressed in circRACGAP1-depleted NSCLC cells. Mechanistically, circRACGAP1 recruited RNA-binding protein polypyrimidine tract-binding protein 1 to enhance the stability and expression of sirtuin-3 (SIRT3), which subsequently led to replication timing regulatory factor 1 (RIF1) deacetylation and activation of the Wnt/ß-catenin pathway. circRACGAP1 overexpression counteracted SIRT3 or RIF1 knockdown-mediated inhibition in stemness and metastasis of NSCLC cells. The in vivo tumor growth and metastasis were repressed by circRACGAP1 depletion. Patients with NSCLC with a higher serum exosomal circRACGAP1 level had a lower overall survival rate. In conclusion, circRACGAP1 facilitated stemness and metastasis of NSCLC cells through the recruitment of polypyrimidine tract-binding protein 1 to promote SIRT3-mediated RIF1 deacetylation. Our results uncover a novel regulatory mechanism of circRACGAP1 in NSCLC and identify circRACGAP1 as a promising therapeutic target.

PI-RADSAI: introducing a new human-in-the-loop AI model for prostate cancer diagnosis based on MRI.

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.

High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade.

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.

Predicting clinically significant prostate cancer with a deep learning approach: a multicentre retrospective study.

PURPOSE: This study aimed to develop deep learning (DL) models based on multicentre biparametric magnetic resonance imaging (bpMRI) for the diagnosis of clinically significant prostate cancer (csPCa) and compare the performance of these models with that of the Prostate Imaging and Reporting and Data System (PI-RADS) assessment by expert radiologists based on multiparametric MRI (mpMRI). METHODS: We included 1861 consecutive male patients who underwent radical prostatectomy or biopsy at seven hospitals with mpMRI. These patients were divided into the training (1216 patients in three hospitals) and external validation cohorts (645 patients in four hospitals). PI-RADS assessment was performed by expert radiologists. We developed DL models for the classification between benign and malignant lesions (DL-BM) and that between csPCa and non-csPCa (DL-CS). An integrated model combining PI-RADS and the DL-CS model, abbreviated as PIDL-CS, was developed. The performances of the DL models and PIDL-CS were compared with that of PI-RADS. RESULTS: In each external validation cohort, the area under the receiver operating characteristic curve (AUC) values of the DL-BM and DL-CS models were not significantly different from that of PI-RADS (P > 0.05), whereas the AUC of PIDL-CS was superior to that of PI-RADS (P < 0.05), except for one external validation cohort (P > 0.05). The specificity of PIDL-CS for the detection of csPCa was much higher than that of PI-RADS (P < 0.05). CONCLUSION: Our proposed DL models can be a potential non-invasive auxiliary tool for predicting csPCa. Furthermore, PIDL-CS greatly increased the specificity of csPCa detection compared with PI-RADS assessment by expert radiologists, greatly reducing unnecessary biopsies and helping radiologists achieve a precise diagnosis of csPCa.

Preoperative Diagnosis of Dual-Phenotype Hepatocellular Carcinoma Using Enhanced MRI Radiomics Models.

BACKGROUND: Dual-phenotype hepatocellular carcinoma (DPHCC) is highly aggressive and difficult to distinguish from hepatocellular carcinoma (HCC). PURPOSE: To develop and validate clinical and radiomics models based on contrast-enhanced MRI for the preoperative diagnosis of DPHCC. STUDY TYPE: Retrospective. POPULATION: A total of 87 patients with DPHCC and 92 patients with non-DPHCC randomly divided into a training cohort (n = 125: 64 non-DPHCC; 61 DPHCC) and a validation cohort (n = 54: 28 non-DPHCC; 26 DPHCC). FIELD STRENGTH/SEQUENCE: A 3.0 T; dynamic contrast-enhanced MRI with time-resolved T1-weighted imaging sequence. ASSESSMENT: In the clinical model, the maximum tumor diameter and hepatitis B virus (HBV) were independent risk factors of DPHCC. In the radiomics model, a total of 1781 radiomics features were extracted from tumor volumes of interest (VOIs) in the arterial phase (AP) and portal venous phase (PP) images. For feature reduction and selection, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were used. Clinical, AP, PP, and combined radiomics models were established using machine learning algorithms (support vector machine [SVM], logistic regression [LR], and logistic regression-least absolute shrinkage and selection operator [LR-LASSO]) and their discriminatory efficacy assessed and compared. STATISTICAL TESTS: The independent sample t test, Mann-Whitney U test, Chi-square test, regression analysis, receiver operating characteristic curve (ROC) analysis, Pearson correlation analysis, the Delong test. A P value < 0.05 was considered statistically significant. RESULTS: In the validation cohort, the combined radiomics model (area under the curve [AUC] = 0.908, 95% confidence interval [CI]: 0.831-0.985) showed the highest diagnostic performance. The AUCs of the PP (AUC = 0.879, 95% CI: 0.779-0.979) and combined radiomics models were significantly higher than that of clinical model (AUC = 0.685, 95% CI: 0.526-0.844). There were no significant differences in AUC between AP or PP radiomics model and combined radiomics model (P = 0.286, 0.180 and 0.543). CONCLUSION: MRI radiomics models may be useful for discriminating DPHCC from non-DPHCC before surgery. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Assessment of Cerebral White Matter Involvement in Amyotrophic Lateral Sclerosis Patients With Disease Progression and Cognitive Impairment by Fixel-Based Analysis and Neurite Orientation Dispersion and Density Imaging.

BACKGROUND: Previous studies using emerging diffusion MRI techniques have revealed damage to the white matter (WM) microstructure in amyotrophic lateral sclerosis (ALS), particularly the influence of crossed fibers, but there is a lack of subgroup analyses. PURPOSE: To detect WM microstructural changes in ALS patients using fixel-based analysis (FBA) and neurite orientation dispersion and density imaging (NODDI) MRI. STUDY TYPE: Prospective. POPULATION: Thirty-six ALS patients (aged 60.50 ± 9.5 years) and 25 healthy controls (HCs) (aged 58.90 ± 8.1 years). FIELD STRENGTH/SEQUENCE: 3 T; NODDI and FBA (b-values = 0, 1000, and 2500 seconds/mm2 ). ASSESSMENT: Subgroups were performed according to progression rate and cognition, including fast and slow progression (FP/SP), ALS with and without cognitive impairment (ALS-ci/ALS-nci). Fiber density (FD), fiber-bundle cross-section (FC), combined fiber density and cross-section (FDC), neurite density index (NDI), orientation dispersion index (ODI), isotropic volume fraction (ISO), and fractional anisotropy (FA) were calculated and their correlation with clinical variables examined. STATISTICAL TESTING: Chi-square test, Mann-Whitney U test, two-sample t test, partial correlation analysis, and false discovery rate (FDR) corrected. A P-value <0.05 was considered significant. RESULTS: ALS patients had lower FD and FDC values predominantly in the corticospinal tract (CST) and corpus callosum (CC) regions, as well as lower NDI value in the CC, radial crown, and internal capsule compared to HCs. Subgroup analysis based on progression rate and cognitive function showed significant differences in FBA results. The FC in the right CST region was significantly lower in the FP than SP, and the FD in the CC region was significantly lower in the ALS-ci than ALS-nci. Furthermore, a negative correlation was found between the mean FC value and the rate of progression in ALS patients (r = -0.408). DATA CONCLUSION: FBA is a powerful tool for detecting complex cerebral WM microstructural damage for evaluating ALS cognition and disease progression.

The Use of Diffusion Tensor Imaging in the Identification of Acute Rejection and Chronic Allograft Nephropathy After Renal Transplantation.

BACKGROUND: Identifying the cause of renal allograft dysfunction is important for the clinical management of kidney transplant recipients. PURPOSE: To evaluate the diagnostic efficiency of diffusion tensor imaging (DTI) for identifying allografts with acute rejection (AR) and chronic allograft nephropathy (CAN). STUDY TYPE: Prospective. SUBJECTS: Seventy-seven renal transplant patients (aged 42.5 ± 9.5 years), including 29 patients with well-functioning stable allografts (Control group), 25 patients diagnosed with acute rejection (AR group), and 23 patients diagnosed with chronic allograft nephropathy (CAN group). FIELD STRENGTH/SEQUENCE: 1.5 T/T2-weighted imaging and DTI. ASSESSMENT: The serum creatinine, proteinuria, pathologic results, and fractional anisotropy (FA) values were obtained and compared among the three groups. STATISTICAL TEST: One-way analysis of variance; correlation analysis; independent-sample t-test; intraclass correlation coefficients and receiver operating characteristic curves. Statistical significance was set to a P-value <0.05. RESULTS: The AR and CAN groups presented with significantly elevated serum creatinine as compared with the Control group (191.8 ± 181.0 and 163.1 ± 115.8 µmol/L vs. 82.3 ± 20.9 µmol/L). FA decreased in AR group (cortical/medullary: 0.13 ± 0.02/0.31 ± 0.07) and CAN group (cortical/medullary: 0.11 ± 0.02/0.27 ± 0.06), compared with the Control group (cortical/medullary: 0.15 ± 0.02/0.35 ± 0.05). Cortical FA in the AR group was higher than in the CAN group. The area under the curve (AUC) for identifying AR from normal allografts was 0.756 and 0.744 by cortical FA and medullary FA, respectively. The AUC of cortical FA and medullary FA for differentiating CAN from normal allografts was 0.907 and 0.830, respectively. The AUC of cortical FA and medullary FA for distinguishing AR and CAN from normal allografts was 0.828 and 0.785, respectively. Cortical FA was able to distinguish between AR and CAN with an AUC of 0.728. DATA CONCLUSION: DTI was able to detect patients with dysfunctional allografts. Cortical FA can further distinguish between AR and CAN. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin-Transformer Model and Biparametric MRI: A Multicenter Retrospective Study.

BACKGROUND: Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision-making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP presence. PURPOSE: To develop deep learning models for detecting AP presence, and to compare the performance of these models with those of a clinical model (CM) and radiologists' interpretation (RI). STUDY TYPE: Retrospective. POPULATION: Totally, 616 men from six institutions who underwent radical prostatectomy, were divided into a training cohort (508 patients from five institutions) and an external validation cohort (108 patients from one institution). FIELD STRENGTH/SEQUENCES: T2-weighted imaging with a turbo spin echo sequence and diffusion-weighted imaging with a single-shot echo plane-imaging sequence at 3.0 T. ASSESSMENT: The reference standard for AP was histopathological extracapsular extension, seminal vesicle invasion, or positive surgical margins. A deep learning model based on the Swin-Transformer network (TransNet) was developed for detecting AP. An integrated model was also developed, which combined TransNet signature with clinical characteristics (TransCL). The clinical characteristics included biopsy Gleason grade group, Prostate Imaging Reporting and Data System scores, prostate-specific antigen, ADC value, and the lesion maximum cross-sectional diameter. STATISTICAL TESTS: Model and radiologists' performance were assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The Delong test was used to evaluate difference in AUC. P < 0.05 was considered significant. RESULTS: The AUC of TransCL for detecting AP presence was 0.813 (95% CI, 0.726-0.882), which was higher than that of TransNet (0.791 [95% CI, 0.702-0.863], P = 0.429), and significantly higher than those of CM (0.749 [95% CI, 0.656-0.827]) and RI (0.664 [95% CI, 0.566-0.752]). DATA CONCLUSION: TransNet and TransCL have potential to aid in detecting the presence of AP and some single adverse pathologic features. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 4.

Insight into microvascular adaptive alterations in the Glisson system of biliary atresia after Kasai portoenterostomy using X-ray phase-contrast CT.

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.