Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications.

Background: Image-based assessment of prostate cancer (PCa) is increasingly emphasized in the diagnostic workflow for selecting biopsy targets and possibly predicting clinically significant prostate cancer (csPCa). Assessment is based on Prostate Imaging-Reporting and Data System (PI-RADS) which is largely dependent on T2-weighted image (T2WI) and diffusion weighted image (DWI). This study aims to determine whether deep learning reconstruction (DLR) can improve the image quality of DWI and affect the assessment of PI-RADS ≥4 in patients with PCa. Methods: In this retrospective study, 3.0T post-biopsy prostate magnetic resonance imaging (MRI) of 70 patients with PCa in Korea University Ansan Hospital from November 2021 to July 2022 was reconstructed with and without using DLR. Four DWI image sets were made: (I) conventional DWI (CDWI): DWI with acceleration factor 2 and conventional parallel imaging reconstruction, (II) DL1: DWI with acceleration factor 2 using DLR, (III) DL2: DWI with acceleration factor 3 using DLR, and (IV) DL3: DWI with acceleration factor 3 and half average b-value using DLR. Apparent diffusion coefficient (ADC) value, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured by one reviewer, while two reviewers independently assessed overall image quality, noise, and lesion conspicuity using a four-point visual scoring system from each DWI image set. Two reviewers also performed PI-RADSv2.1 scoring on lesions suspected of malignancy. Results: A total of 70 patients (mean age, 70.8±9.7 years) were analyzed. The image acquisition time was 4:46 min for CDWI and DL1, 3:40 min for DL2, and 2:00 min for DL3. DL1 and DL2 images resulted in better lesion conspicuity compared to CDWI images assessed by both readers (P<0.05). DLR resulted in a significant increase in SNR, from 38.4±14.7 in CDWI to 56.9±21.0 in DL1. CNR increased from 25.1±11.5 in CDWI to 43.1±17.8 in DL1 (P<0.001). PI-RADS v2.1 scoring for PCa lesions was more agreeable with the DL1 reconstruction method than with CDWI (κ value CDWI, DL1; 0.40, 0.61, respectively). A statistically significant number of lesions were upgraded from PI-RADS <4 in CDWI image to PI-RADS ≥4 in DL1 images for both readers (P<0.05). Most of the PI-RADS upgraded lesions were from higher than unfavorable intermediate-risk groups according to the 2023 National Comprehensive Cancer Network guidelines with statistically significant difference of marginal probability in DL1 and DL2 for both readers (P<0.05). Conclusions: DLR in DWI for PCa can provide options for improving image quality with a significant impact on PI-RADS evaluation or about a 23% reduction in acquisition time without compromising image quality.

Development and multicenter validation of deep convolutional neural network-based detection of colorectal cancer on abdominal CT.

OBJECTIVES: This study aims to develop computer-aided detection (CAD) for colorectal cancer (CRC) using abdominal CT based on a deep convolutional neural network. METHODS: This retrospective study included consecutive patients with colorectal adenocarcinoma who underwent abdominal CT before CRC resection surgery (training set = 379, test set = 103). We customized the 3D U-Net of nnU-Net (CUNET) for CRC detection, which was trained with fivefold cross-validation using annotated CT images. CUNET was validated using datasets covering various clinical situations and institutions: an internal test set (n = 103), internal patients with CRC first determined by CT (n = 54) and asymptomatic CRC (n = 51), and an external validation set from two institutions (n = 60). During each validation, data from the healthy population were added (internal = 60; external = 130). CUNET was compared with other deep CNNs: residual U-Net and EfficientDet. The CAD performances were evaluated using per-CRC sensitivity (true positive/all CRCs), free-response receiver operating characteristic (FROC), and jackknife alternative FROC (JAFROC) curves. RESULTS: CUNET showed a higher maximum per-CRC sensitivity than residual U-Net and EfficientDet (internal test set 91.3% vs. 61.2%, and 64.1%). The per-CRC sensitivity of CUNET at false-positive rates of 3.0 was as follows: internal CRC determined by CT, 89.3%; internal asymptomatic CRC, 87.3%; and external validation, 89.6%. CUNET detected 69.2% (9/13) of CRCs missed by radiologists and 89.7% (252/281) of CRCs from all validation sets. CONCLUSIONS: CUNET can detect CRC on abdominal CT in patients with various clinical situations and from external institutions. KEY POINTS: • Customized 3D U-Net of nnU-Net (CUNET) can be applied to the opportunistic detection of colorectal cancer (CRC) in abdominal CT, helping radiologists detect unexpected CRC. • CUNET showed the best performance at false-positive rates ≥ 3.0, and 30.1% of false-positives were in the colorectum. CUNET detected 69.2% (9/13) of CRCs missed by radiologists and 87.3% (48/55) of asymptomatic CRCs. • CUNET detected CRCs in multiple validation sets composed of varying clinical situations and from different institutions, and CUNET detected 89.7% (252/281) of CRCs from all validation sets.

Transjugular approach: comparison with conventional endovascular treatment of native arteriovenous fistulas.

OBJECTIVES: The aim of this study was to compare the outcomes of the transjugular approach with those of the conventional approach for endovascular treatment of arteriovenous fistulas (AVFs). METHODS: Between May 2015 and July 2019, 112 patients with endovascular treatment of dysfunctional or immature AVFs were included and divided into the transjugular (n = 46) and conventional (n = 66) groups. Electronic medical records and angiography of the patients were retrospectively reviewed to assess technical and clinical success rates, time to first fistulography, total procedure time, primary and secondary patency, and complications in both groups. RESULTS: There were no significant differences in technical success rate (87.0% vs 97.0%; P = .062), clinical success rate (80.4% vs 90.9%; P = .109), or total procedure time (60.2 vs 57.9 min; P = .670) between the groups. Cox proportional hazards models showed that the cumulative primary patency was significantly higher in the transjugular group than in the conventional group (P = .041; 6-month patency rates, 93.8% vs 91.5%). Also, a statistically significant difference was found between the cumulative secondary patency of the groups (P = .014; 6-month patency rates, 91.4% vs 86.5%). No major complications were observed. CONCLUSIONS: Transjugular endovascular treatment of AVFs was successful and effective. Longer patency periods were observed when treated via transjugular access. ADVANCES IN KNOWLEDGE: This article compared the outcomes of transjugular approaches with those of conventional approaches in the endovascular treatment of native AVFs and showed higher patency periods/rates in the transjugular group than in the conventional group.

Development and validation of CT­based radiomics model of PET-negative residual CT masses: a potential biomarker for predicting relapse­free survival in non-Hodgkin lymphoma patients showing complete metabolic response.

PURPOSE: PET-negative residual CT masses (PnRCMs) are usually dismissed as nonviable post-treatment lesions in non-Hodgkin lymphoma (NHL) patients showing complete metabolic response (CMR). We aimed to develop and validate computed tomography (CT)-based radiomics model of PET-negative residual CT mass (PnRCM) for predicting relapse-free survival (RFS) in NHL patients showing CMR. METHODS: A total of 224 patients who showed CMR after completing first-line chemotherapy for PET-avid NHL were recruited for model development. Patients with PnRCM were selected in accordance with the Lugano classification. Three-dimensional segmentation was done by two readers. Radiomic scores (RS) were constructed using features extracted using the Least-absolute shrinkage and selection operator analysis among radiomics features of PnRCMs showing more than substantial interobserver agreement (> 0.6). Cox regression analysis was performed with clinical and radiologic features. The performance of the model was evaluated using area under the curve (AUC). For validation, 153 patients from an outside hospital were recruited and analyzed in the same way. RESULTS: In the model development cohort, 68 (30.4%) patients had PnRCM. Kaplan-Meier analysis showed that patients with PnRCM had significantly (p = 0.005) shorter RFS than those without PnRCM. In Kaplan-Meier analysis, the high RS group showed significantly (p = 0.038) shorter RFS than the low-scoring group. Multivariate Cox regression analysis showed that high IPI score [hazard ratio (HR) 2.46; p = 0.02], treatment without rituximab (HR 3.821; p = 0.019) were factors associated with shorter RFS. In estimating RFS, combined model in both development and validation cohort showed AUC values of 0.81. CONCLUSION: The combined model that incorporated both clinical parameters and CT-based RS showed good performance in predicting relapse in NHL patients with PnRCM.

Machine Learning-Based Magnetic Resonance Radiomics Analysis for Predicting Low- and High-Grade Clear Cell Renal Cell Carcinoma.

PURPOSE: To explore whether high- and low-grade clear cell renal cell carcinomas (ccRCC) can be distinguished using radiomics features extracted from magnetic resonance imaging. METHODS: In this retrospective study, 154 patients with pathologically proven clear ccRCC underwent contrast-enhanced 3 T magnetic resonance imaging and were assigned to the development (n = 122) and test (n = 32) cohorts in a temporal-split setup. A total of 834 radiomics features were extracted from whole-tumor volumes using 3 sequences: T2-weighted imaging (T2WI), diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging. A random forest regressor was used to extract important radiomics features that were subsequently used for model development using the random forest algorithm. Tumor size, apparent diffusion coefficient value, and percentage of tumor-to-renal parenchymal signal intensity drop in the tumors were recorded by 2 radiologists for quantitative analysis. The area under the receiver operating characteristic curve (AUC) was generated to predict ccRCC grade. RESULTS: In the development cohort, the T2WI-based radiomics model demonstrated the highest performance (AUC, 0.82). The T2WI-based radiomics and radiologic feature hybrid model showed AUCs of 0.79 and 0.83, respectively. In the test cohort, the T2WI-based radiomics model achieved an AUC of 0.82. The range of AUCs of the hybrid model of T2WI-based radiomics and radiologic features was 0.73 to 0.80. CONCLUSION: Magnetic resonance imaging-based classifier models using radiomics features and machine learning showed satisfactory diagnostic performance in distinguishing between high- and low-grade ccRCC, thereby serving as a helpful noninvasive tool for predicting ccRCC grade.

Hepatocellular carcinoma pathologic grade prediction using radiomics and machine learning models of gadoxetic acid-enhanced MRI: a two-center study.

PURPOSE: To develop a radiomics-based hepatocellular carcinoma (HCC) grade classifier model based on data from gadoxetic acid-enhanced MRI. METHODS: This retrospective study included 137 patients who underwent hepatectomy for a single HCC and gadoxetic acid-enhanced MRI within 60 days before surgery. HCC grade was categorized as low or high (modified Edmondson-Steiner grade I-II vs. III-IV). We used the hepatobiliary phase (HBP), portal venous phase, T2-weighted image(T2WI), and T1-weighted image(T1WI). From the volume of interest in HCC, 833 radiomic features were extracted. Radiomic and clinical features were selected using a random forest regressor, and the classification model was trained and validated using a random forest classifier and tenfold stratified cross-validation. Eight models were developed using the radiomic features alone or by combining the radiomic and clinical features. Models were validated with internal enrolled data (internal validation) and a dataset (28 patients) at a separate institution (external validation). The area under the curve (AUC) of the validation results was compared using the DeLong test. RESULTS: In internal and external validation, the HBP radiomics-only model showed the highest AUC (internal 0.80 ± 0.09, external 0.70 ± 0.09). In external validation, all models showed lower AUC than those for internal validation, while the T2WI and T1WI models failed to predict the HCC grade (AUC 0.30-0.58) in contrast to the internal validation results (AUC 0.67-0.78). CONCLUSION: The radiomics-based machine learning model from gadoxetic acid-enhanced liver MRI could distinguish between low- and high-grade HCCs. The radiomics-only HBP model showed the best AUC among the eight models, good performance in internal validation, and fair performance in external validation.

Radiomics Analysis of Magnetic Resonance Proton Density Fat Fraction for the Diagnosis of Hepatic Steatosis in Patients With Suspected Non-Alcoholic Fatty Liver Disease.

BACKGROUND: This study aimed to assess the diagnostic feasibility of radiomics analysis based on magnetic resonance (MR)-proton density fat fraction (PDFF) for grading hepatic steatosis in patients with suspected non-alcoholic fatty liver disease (NAFLD). METHODS: This retrospective study included 106 patients with suspected NAFLD who underwent a hepatic parenchymal biopsy. MR-PDFF and MR spectroscopy were performed on all patients using a 3.0-T scanner. Following whole-volume segmentation of the MR-PDFF images, 833 radiomic features were analyzed using a commercial program. Radiologic features were analyzed, including median and mean values of the multiple regions of interest and variable clinical features. A random forest regressor was used to extract the important radiomic, radiologic, and clinical features. The model was trained using 20 repeated 10-fold cross-validations to classify the NAFLD steatosis grade. The area under the receiver operating characteristic curve (AUROC) was evaluated using a classifier to diagnose steatosis grades. RESULTS: The levels of pathological hepatic steatosis were classified as low-grade steatosis (grade, 0-1; n = 82) and high-grade steatosis (grade, 2-3; n = 24). Fifteen important features were extracted from the radiomic analysis, with the three most important being wavelet-LLL neighboring gray tone difference matrix coarseness, original first-order mean, and 90th percentile. The MR spectroscopy mean value was extracted as a more important feature than the MR-PDFF mean or median in radiologic measures. Alanine aminotransferase has been identified as the most important clinical feature. The AUROC of the classifier using radiomics was comparable to that of radiologic measures (0.94 ± 0.09 and 0.96 ± 0.08, respectively). CONCLUSION: MR-PDFF-derived radiomics may provide a comparable alternative for grading hepatic steatosis in patients with suspected NAFLD.

Comparison between biparametric and multiparametric MRI in predicting muscle invasion by bladder cancer based on the VI-RADS.

This study aimed to compare the diagnostic validity of biparametric magnetic resonance imaging (bpMRI) with that of multiparametric MRI (mpMRI) based on the Vesicle Imaging-Reporting and Data System (VI-RADS) in predicting muscle invasion by bladder cancer (BCa). We retrospectively examined 357 patients with an initial diagnosis of BCa who underwent preoperative MRI; 257 and 100 patients underwent mpMRI and bpMRI, respectively. Two urogenital radiologists evaluated all bpMRI and mpMRI scans using VI-RADS, and the diagnostic validity of VI-RADS for predicting muscle invasion by BCa was analyzed based on histopathology of the first and/or second transurethral resection of bladder tumors and radical cystectomy. Receiver operating characteristic (ROC) curves were plotted with the calculation of area under the curves (AUCs), and the level of significance was P < 0.05. Both groups showed optimal performance with a VI-RADS score ≥ 3. BpMRI showed comparable diagnostic performance to mpMRI (reader 1: AUC, 0.903 [0.827-0.954] vs. 0.935 [0.884-0.968], p = 0.510; and reader 2: AUC, 0.901 [0.814-0.945] vs. 0.915 [0.874-0.946]; p = 0.655). The inter-reader agreement between both readers was excellent (Cohen's kappa value = 0.942 and 0.905 for bpMRI and mpMRI, respectively). This comparative study suggests that bpMRI has comparable diagnostic performance to mpMRI and may be an alternative option to predict muscle invasion by BCa.

Dual-Layer Spectral CT of Pancreas Ductal Adenocarcinoma: Can Virtual Monoenergetic Images of the Portal Venous Phase Be an Alternative to the Pancreatic-Phase Scan?

Objectives: To determine the performance of virtual monoenergetic images (VMIs) of the portal venous phase (PVP) compared with the pancreatic-phase image for pancreatic ductal adenocarcinoma (PDAC) evaluation. Materials and methods: This retrospective study enrolled 64 patients with PDAC who underwent pancreatic CT with dual-layer spectral CT between February 2018 and January 2020. A polychromatic pancreatic-phase image and VMIs at 40 (VMI40), 55 (VMI55), and 70 keV (VMI70) of the PVP were generated. The tumor-to-pancreas contrast-to-noise ratio (CNR), attenuation difference, peripancreatic vascular signal-to-noise ratio (SNR), and CNR were compared among the four images. Subjective image analysis was performed for tumor conspicuity, heterogeneity, size, and arterial invasion. Results: VMI40 and VMI55 demonstrated higher tumor-to-pancreas CNR, attenuation difference, and higher peripancreatic vascular CNR and SNR than the pancreatic-phase image and VMI70 (p < .001). On subjective analysis, VMI55 showed the best tumor conspicuity. Moreover, the inter-reader agreement for arterial invasion in VMIs from the PVP was not inferior to that in the pancreatic-phase image. Conclusion: For evaluating PDAC, the VMI55 of the PVP was superior to the pancreatic-phase image in terms of tumor conspicuity and peripancreatic vascular enhancement. Therefore, the VMI55 of the PVP could be an alternative to the pancreatic-phase scan in patients suspicious of PDAC.

Metastatic Renal Cell Carcinoma Manifesting as a Gastric Polyp on CT: A Case Report and Literature Review.

Gastric metastasis from renal cell carcinoma (RCC) is extremely rare, occurring in 0.2% of all RCC cases. Owing to its low prevalence, metachronous gastric metastasis from RCC may be underdiagnosed, and the imaging findings have not been well-established. Herein we present a case of metastatic RCC manifesting as a gastric polyp in a 70-year-old female along with a literature review on the imaging findings of gastric metastases from RCC. In patients presenting with gastric hyper-enhancing polypoid masses, metastasis from RCC should be considered as a differential diagnosis.

Computer-aided hepatocellular carcinoma detection on the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging using a convolutional neural network: Feasibility evaluation with multi-sequence data.

BACKGROUND AND OBJECTIVES: Diagnosis of hepatocellular carcinoma (HCC) on liver MRI needs analysis of multi-sequence images. However, developing computer-aided detection (CAD) for every single sequence requires considerable time and labor for image segmentation. Therefore, we developed CAD for HCC on the hepatobiliary phase (HBP) of gadoxetic acid-enhanced magnetic resonance imaging (MRI) using a convolutional neural network (CNN) and evaluated its feasibility on multi-sequence, multi-unit, and multi-center data. METHODS: Patients who underwent gadoxetic acid-enhanced MRI and surgery for HCC in Korea University Anam Hospital (KUAH) and Korea University Guro Hospital (KUGH) were reviewed. Finally, 170 nodules from 155 consecutive patients from KUAH and 28 nodules from 28 patients randomly selected from KUGH were included. Regions of interests were drawn on the whole HCC volume on HBP, T1-weighted (T1WI), T2-weighted (T2WI), and portal venous phase (PVP) images. The CAD was developed from the HBP images of KUAH using customized-nnUNet and post-processed for false-positive reduction. Internal and external validation of the CAD was performed with HBP, T1WI, T2WI, and PVP of KUAH and KUGH. RESULTS: The figure of merit and recall of the jackknife alternative free-response receiver operating characteristic of the CAD for HBP, T1WI, T2WI, and PVP at false-positive rate 0.5 were (0.87 and 87.0), (0.73 and 73.3), (0.13 and 13.3), and (0.67 and 66.7) in KUAH and (0.86 and 86.0), (0.61 and 53.6), (0.07 and 0.07), and (0.57 and 53.6) in KUGH, respectively. CONCLUSIONS: The CAD for HCC on gadoxetic acid-enhanced MRI developed by CNN from HBP detected HCCs feasibly on HBP, T1WI, and PVP of gadoxetic acid-enhanced MRI obtained from multiple units and centers. This result imply that the CAD developed using single MRI sequence may be applied to other similar sequences and this will reduce labor and time for CAD development in multi-sequence MRI.

Diagnostic Feasibility of Magnetic Resonance Elastography Radiomics Analysis for the Assessment of Hepatic Fibrosis in Patients With Nonalcoholic Fatty Liver Disease.

OBJECTIVE: The aim of the study was to investigate the diagnostic feasibility of radiomics analysis using magnetic resonance elastography (MRE) to assess hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD). METHODS: One hundred patients with suspected NAFLD were retrospectively enrolled. All patients underwent a liver parenchymal biopsy. Magnetic resonance elastography was performed using a 3.0-T scanner. After multislice segmentation of MRE images, 834 radiomic features were analyzed using a commercial program. Radiologic features, such as median and mean values of the regions of interest and variable clinical features, were analyzed. A random forest regressor was used to extract important radiomic, radiological, and clinical features. A random forest classifier model was trained to use these features to classify the fibrosis stage. The area under the receiver operating characteristic curve was evaluated using a classifier for fibrosis stage diagnosis. RESULTS: The pathological hepatic fibrosis stage was classified as low-grade fibrosis (stages F0-F1, n = 82) or clinically significant fibrosis (stages F2-F4, n = 18). Eight important features were extracted from radiomics analysis, with the 2 most important being wavelet-high high low gray level dependence matrix dependence nonuniformity-normalized and wavelet-high high low gray level dependence matrix dependence entropy. The median value of the multiple small regions of interest was identified as the most important radiologic feature. Platelet count has been identified as an important clinical feature. The area under the receiver operating characteristic curve of the classifier using radiomics was comparable with that of radiologic measures (0.97 ± 0.07 and 0.96 ± 0.06, respectively). CONCLUSIONS: Magnetic resonance elastography radiomics analysis provides diagnostic performance comparable with conventional MRE analysis for the assessment of clinically significant hepatic fibrosis in patients with NAFLD.

Accuracy of MRI for predicting anterior peritoneal reflection involvement in locally advanced rectal cancer: a comparison with operative findings.

PURPOSE: To assess the diagnostic accuracy of preoperative rectal MRI for anterior peritoneal reflection (APR) involvement in rectal cancer through comparison with the surgeon's operative findings. METHODS: This retrospective study was approved by the institutional review board; informed consent was waived. We enrolled 55 consecutive patients with suspected locally advanced mid-to-upper rectal cancer. All patients underwent rectal MRI using a 3T system. APR involvement in rectal cancer was assessed radiologically using a 5-point scale by two independent board-certified abdominal radiologists. The surgeon's evaluation during surgery was regarded as the gold standard for APR involvement. The accuracy of rectal MRI in predicting APR involvement was obtained. RESULTS: Rectal MRI showed good APR identification (rater 1, 92.7%; rater 2, 94.7%). On preoperative rectal MRI, rater 1 diagnosed 19 (34.5%) patients as having APR involvement and rater 2 diagnosed 28 (50.9%) as having APR involvement. There was moderate agreement (κ = 0.602, p < 0.001) between the two raters with regard to the evaluation of APR involvement. During surgery, the surgeon confirmed APR involvement in 13 (23.6%) patients. The sensitivity, specificity, PPV, and NPV of preoperative MRI for APR involvement were 69.2%, 76.2%, 47.4%, and 88.9%, respectively. The diagnostic accuracy of MRI for predicting APR involvement was 74.6%. CONCLUSION: Preoperative rectal MRI provides accurate anatomical information regarding APR involvement with high conspicuity. However, MRI has relatively low sensitivity (< 70%) and a low PPV (< 50%) with regard to the assessment of APR involvement in rectal tumors. Both rater 1 and rater 2 evaluated these images as positive involvement of APR. The patient underwent laparoscopic low anterior resection after preoperative evaluation. However, during surgery, the surgeon evaluated APR involvement as negative, and the final pathologic staging was confirmed as T3.

Active Learning for Efficient Segmentation of Liver with Convolutional Neural Network-Corrected Labeling in Magnetic Resonance Imaging-Derived Proton Density Fat Fraction.

This study aimed to propose an efficient method for self-automated segmentation of the liver using magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF) through deep active learning. We developed an active learning framework for liver segmentation using labeled and unlabeled data in MRI-PDFF. A total of 77 liver samples on MRI-PDFF were obtained from patients with nonalcoholic fatty liver disease. For the training, tuning, and testing of the liver segmentation, the ground truth of 71 (internal) and 6 (external) MRI-PDFF scans for training and testing were verified by an expert reviewer. For 100 randomly selected slices, manual and deep learning (DL) segmentations for visual assessments were classified, ranging from very accurate to mostly accurate. The dice similarity coefficients for each step were 0.69 ± 0.21, 0.85 ± 0.12, and 0.94 ± 0.01, respectively (p-value = 0.1389 between the first step and the second step or p-value = 0.0144 between the first step and the third step for paired t-test), indicating that active learning provides superior performance compared with non-active learning. The biases in the Bland-Altman plots for each step were - 24.22% (from - 82.76 to - 2.70), - 21.29% (from - 59.52 to 3.06), and - 0.67% (from - 10.43 to 4.06). Additionally, there was a fivefold reduction in the required annotation time after the application of active learning (2 min with, and 13 min without, active learning in the first step). The number of very accurate slices for DL (46 slices) was greater than that for manual segmentations (6 slices). Deep active learning enables efficient learning for liver segmentation on a limited MRI-PDFF.

A Predictive Model Based on Bi-parametric Magnetic Resonance Imaging and Clinical Parameters for Clinically Significant Prostate Cancer in the Korean Population.

PURPOSE: This study aimed to develop and validate a predictive model for the assessment of clinically significant prostate cancer (csPCa) in men, prior to prostate biopsies, based on bi-parametric magnetic resonance imaging (bpMRI) and clinical parameters. MATERIALS AND METHODS: We retrospectively analyzed 300 men with clinical suspicion of prostate cancer (prostate-specific antigen [PSA] ≥ 4.0 ng/mL and/or abnormal findings in a digital rectal examination), who underwent bpMRI-ultrasound fusion transperineal targeted and systematic biopsies in the same session, at a Korean university hospital. Predictive models, based on Prostate Imaging Reporting and Data Systems scores of bpMRI and clinical parameters, were developed to detect csPCa (intermediate/high grade [Gleason score ≥ 3+4]) and compared by analyzing the areas under the curves and decision curves. RESULTS: A predictive model defined by the combination of bpMRI and clinical parameters (age, PSA density) showed high discriminatory power (area under the curve, 0.861) and resulted in a significant net benefit on decision curve analysis. Applying a probability threshold of 7.5%, 21.6% of men could avoid unnecessary prostate biopsy, while only 1.0% of significant prostate cancers were missed. CONCLUSION: This predictive model provided a reliable and measurable means of risk stratification of csPCa, with high discriminatory power and great net benefit. It could be a useful tool for clinical decision-making prior to prostate biopsies.

How to accurately measure the distance from the anal verge to rectal cancer on MRI: a prospective study using anal verge markers.

PURPOSE: To determine an accurate method for localizing rectal cancer using the distance from the anal verge on preoperative MRI. METHODS: This prospective study included 50 patients scheduled for MRI evaluation of rectal cancer. After rectal filling with gel, MRI was performed with two markers attached at the anal verge. The distance between the tumor and the anal verge on a sagittal T2-weighted image (T2WI) was measured independently by two radiologists using six methods divided into three groups of similar measurement approaches, and compared to those obtained on rigid sigmoidoscopy. The anal verge location relative to the external anal sphincter was assessed on oblique coronal T2WI in reference to the markers. Correlation analysis was performed using the intraclass correlation coefficient (ICC) for verification, and a paired t test was used to evaluate the mean differences. RESULTS: The highest correlation (ICC 0.797-0.815) and the least mean difference (0.74-0.85 cm) with rigid sigmoidoscopy, and the least standard deviation (3.12-3.17 cm) were obtained in the direct methods group using a straight line from the anal verge to the tumor. The anal verge was localized within a range of - 1.4 to 1.5 cm (mean - 0.31 cm and - 0.22 cm) from the lower end of the external anal sphincter. CONCLUSION: The direct methods group provided the most accurate tumor distance among the groups. Among the direct methods, we recommend the direct mass method for its simplicity. Despite minor differences in location, the lower end of the external anal sphincter was a reliable anatomical landmark for the anal verge.

Efficacy of ZOOMit coronal diffusion-weighted imaging and MR texture analysis for differentiating between benign and malignant distal bile duct strictures.

PURPOSE: To investigate the diagnostic efficacy of ZOOMit coronal diffusion-weighted imaging (Z-DWI) and MR texture analysis (MRTA) for differentiating benign from malignant distal bile duct strictures. METHODS: We retrospectively enrolled a total of 71 patients with distal bile duct stricture who underwent magnetic resonance cholangiopancreatography (MRCP). For quantitative analysis, the average apparent diffusion coefficient (ADC) value at suspected stricture sites was assessed on both Z-DWI and conventional DWI (C-DWI). For qualitative analysis, two reviewers independently reviewed two image sets containing different diffusion-weighted images, and receiver operating characteristic (ROC) curve analysis was performed. Several MRTA parameters were extracted from the area of the stricture on the ADC map of the ZOOMit coronal diffusion-weighted images using commercially available software. RESULTS: Among 71 patients, 26 patients were diagnosed with malignant stricture. On quantitative analysis, the average ADC value of the malignant and benign strictures, using Z-DWI, was 1.124 × 10-3 mm2/s and 1.522 × 10-3 mm2/s, respectively (P < 0.001). The average ADC value of the malignant and benign strictures, using C-DWI, was 1.107 × 10-3 mm2/s and 1.519 × 10-3 mm2/s, respectively (P < 0.001). On qualitative analysis, for each reviewer, the area under the ROC curve (AUC) values for differentiating benign from malignant stricture was 0.928 and 0.939, respectively, for the ZOOMit diffusion set and 0.851 and 0.824, respectively, for the conventional diffusion set. Multiple MRTA parameters showed a significantly different distribution for the benign and malignant strictures, including mean, entropy, mean of positive pixels, and kurtosis at spatial filtration values of 0, 5, and 6 mm. CONCLUSION: The addition of Z-DWI to conventional MRCP is helpful in differentiating benign from malignant bile duct strictures, and some MRTA parameters also can be helpful in differentiating benign from malignant distal bile duct strictures.

Three-Dimensional Convolutional Neural Network for Prostate MRI Segmentation and Comparison of Prostate Volume Measurements by Use of Artificial Neural Network and Ellipsoid Formula.

OBJECTIVE. The purposes of this study were to assess the performance of a 3D convolutional neural network (CNN) for automatic segmentation of prostates on MR images and to compare the volume estimates from the 3D CNN with those of the ellipsoid formula. MATERIALS AND METHODS. The study included 330 MR image sets that were divided into 260 training sets and 70 test sets for automated segmentation of the entire prostate. Among these, 162 training sets and 50 test sets were used for transition zone segmentation. Assisted by manual segmentation by two radiologists, the following values were obtained: estimates of ground-truth volume (VGT), software-derived volume (VSW), mean of VGT and VSW (VAV), and automatically generated volume from the 3D CNN (VNET). These values were compared with the volume calculated with the ellipsoid formula (VEL). RESULTS. The Dice similarity coefficient for the entire prostate was 87.12% and for the transition zone was 76.48%. There was no significant difference between VNET and VAV (p = 0.689) in the test sets of the entire prostate, whereas a significant difference was found between VEL and VAV (p < 0.001). No significant difference was found among the volume estimates in the test sets of the transition zone. Overall intraclass correlation coefficients between the volume estimates were excellent (0.887-0.995). In the test sets of entire prostate, the mean error between VGT and VNET (2.5) was smaller than that between VGT and VEL (3.3). CONCLUSION. The fully automated network studied provides reliable volume estimates of the entire prostate compared with those obtained with the ellipsoid formula. Fast and accurate volume measurement by use of the 3D CNN may help clinicians evaluate prostate disease.

Percutaneous Closure of Gastrostomy Using a Suture-Mediated Vascular Closure Device in a Swine Model.

PURPOSE: After any procedure through the percutaneous gastrostomy (PG), a PG tube should be kept in place until a mature tract develops. For this period of maturation which takes about 2 to 4 weeks, tube dislodgement, leakage, or peritonitis can occur. Complications from PG tube maintenance can be prevented by closing the PG immediately after the procedure. The purpose of this study was to evaluate the feasibility and safety of immediate PG closure using Perclose ProGlide. MATERIALS AND METHODS: A 2-week survival study was performed in a swine model. We applied one Perclose ProGlide device for closing a 13-Fr PG (n = 3) and two devices for closing a 20-Fr PG (n = 3). Body weight, temperature and laboratory findings were observed. Autopsy and microscopic examination were performed after 2 weeks. RESULTS: All the swine subjects did not demonstrate any sign of systemic inflammatory responses in terms of fever and laboratory findings. From autopsy results, five pigs showed complete healing of the PG. One pig that underwent 20-Fr gastrostomy site closure with double Perclose ProGlide had scanty semitransparent fluid in the peritoneal cavity but that was not indicative of inflammation. En bloc tissue samples from all the pigs demonstrated complete wound healing of the PG sites. CONCLUSION: Percutaneous application of single or double Perclose ProGlide devices is feasible and safe for the PG closure in a swine model. LEVEL OF EVIDENCE: No level of evidence, Animal study.

Predicting cannulation difficulty in endoscopic retrograde cholangiopancreatography using CT image findings: a decision-tree analysis.

BACKGROUND: Difficult cannulation during endoscopic retrograde cholangiopancreatography (ERCP) is associated with increased complications; therefore, its prediction is important. PURPOSE: To identify radiologic risk factors of difficult cannulation during ERCP based on computed tomography (CT) findings and to develop a predictive model for a difficult cannulation. MATERIAL AND METHODS: A total of 171 patients with native papilla who underwent both enhanced CT and ERCP were recruited. Two radiologists independently measured the distal common bile duct (CBD) diameter and choledochoduodenal (CD) angle and analyzed CT images for presence of CBD stone and papilla bulging, size and type of periampullary diverticulum (PAD), and duodenal segment in which major papilla was located. Multivariate logistic regression analysis and decision-tree analysis were performed to identify risk factors for difficult cannulation. RESULTS: Thirty-nine patients underwent a difficult cannulation. The multivariate logistic regression analysis revealed that a smaller CBD diameter, presence of papilla bulging, location of the major papilla other than the descending duodenum, a smaller CD angle, and a higher worrisome PAD score were statistically relevant factors for difficult cannulation (P < 0.049). In the decision-tree analysis, a higher worrisome PAD score was the strongest predictor of difficult cannulation, followed by the presence of papilla bulging, smaller CD angle, and a smaller CBD diameter. The predictive model had an 82.5% overall predictive accuracy. CONCLUSION: The CT findings-based decision-tree analysis model showed a high accuracy in predicting cannulation difficulty and may be helpful for making pre-ERCP strategy.