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.

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.

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.

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.

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.

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.

Hepatic Parenchymal Heterogeneity as a Marker for Oxaliplatin-Induced Sinusoidal Obstruction Syndrome: Correlation With Treatment Response of Colorectal Cancer Liver Metastases.

OBJECTIVE: The objective of our study was to evaluate the influence of oxaliplatin-based chemotherapy (OBC)-induced hepatic parenchymal heterogeneity detected on contrast-enhanced CT scans on response of liver metastasis. We chose to study hepatic parenchymal heterogeneity on the basis of the assumption that hepatic parenchymal heterogeneity may indicate the presence of chemotherapy-induced sinusoidal obstruction syndrome (SOS). MATERIALS AND METHODS: For this retrospective study, 104 patients with hepatic metastases from colorectal cancer (male-female ratio, 66:38; age range, 20-80 years) who had undergone OBC and serial CT studies were consecutively registered. Two blinded imagers independently scored CT images using a 5-point scale to determine the severity of newly developed hepatic parenchymal heterogeneity after OBC. Subsequently, two radiologists evaluated tumor response to OBC using a 4-point ordinal scale. We performed generalized estimating equation (GEE) analysis using cumulative logits to account for the effect of hepatic parenchymal heterogeneity severity on the cumulative tumor response probability. RESULTS: The interobserver agreements for the severity of hepatic parenchymal heterogeneity were excellent (κ = 0.825). GEE analyses showed that the severity of post-OBC hepatic parenchymal heterogeneity, number of chemotherapy sessions, and presence of other organ metastases were significant predictors of tumor response; these three factors also showed significance in the final GEE model (p < 0.0001 for severity of hepatic parenchymal heterogeneity for both readers; p = 0.011 and 0.010 for the number of chemotherapy sessions for readers 1 and 2; p = 0.046 and 0.012 for the presence of other organ metastases for readers 1 and 2). CONCLUSION: Hepatic parenchymal heterogeneity detected on contrast-enhanced CT of patients with hepatic metastases from colorectal cancer who have undergone OBC may indicate the presence of SOS, and the more severe the SOS, the worse the tumor response of hepatic metastasis to oxaliplatin is expected to be.

Magnetic Resonance Imaging-Based Prostate-Specific Antigen Density for Prediction of Gleason Score Upgrade in Patients With Low-Risk Prostate Cancer on Initial Biopsy.

OBJECTIVE: The aim of this study was to assess the utility of prostate-specific antigen density (PSAD) calculated using magnetic resonance imaging for predicting Gleason score (GS) upgrade in patients with low-risk prostate cancer on biopsy. METHODS: Seventy-three patients were divided into 2 groups according to the concordance between biopsy and prostatectomy GS: group 1 (6/6) and group 2 (6/≥7). Magnetic resonance imaging-based PSAD, prostate volume, prostate-specific antigen (PSA), and age were compared between the 2 groups. Logistic regression and receiver operating characteristic curve analysis were performed. RESULTS: Gleason score was upgraded in 40 patients. Patients in group 2 had significantly higher PSAD and PSA values and smaller prostate volume than did those in group 1. Prostate-specific antigen density of 0.26 ng/mL per cm or higher, PSA of 7.63 ng/mL or higher, and prostate volume of 25.1 cm or less were related to GS upgrade, with area-under-the-curve values of 0.765, 0.721, and 0.639, respectively. CONCLUSIONS: Magnetic resonance imaging-based PSAD could help in predicting postoperative GS upgrade in patients with low-risk prostate cancer.

Correlation of CT imaging features and tumor size with Fuhrman grade of clear cell renal cell carcinoma.

Background Identification of clinical features to determine the aggressive potential of tumors is highly warranted to stratify patients for adequate treatment. Computed tomography (CT) imaging features of clear cell renal cell carcinoma (ccRCC) may contribute to personalized risk assessment. Purpose To assess the correlation between CT imaging features and Fuhrman grade of ccRCC, and to identify the predictors of high Fuhrman grade in conjunction with tumor size. Material and Methods CT scans of 169 patients with 173 pathologically proven ccRCCs were retrospectively reviewed in consensus by two radiologists for the presence of intratumoral necrosis and intratumoral cyst and tumor size. Histologic grade was classified as either low (Fuhrman grade I or II) or high (Fuhrman grade III or IV). Statistical significance was evaluated by using univariate, multivariate regression, receiver operating characteristic (ROC) curve, and Spearman correlation analyses. Results On CT, 20 of the 173 tumors had intratumoral cysts, 60 had intratumoral necrosis, and 93 showed entirely solid tumors. The odds of high grade were higher with intratumoral necrosis and entirely solid tumor than with intratumoral cyst ( P < 0.03). Intratumoral necrosis showed a significantly high odds ratio of 25.73 for high Fuhrman grade. The ROC curve showed a threshold tumor size of 36 mm to predict high Fuhrman grade for overall tumors (area under the ROC curve, 0.70). In ccRCCs with intratumoral necrosis or cyst, tumor size did not significantly correlate with Fuhrman grade. Conclusion Intratumoral necrosis on CT was a strong and independent predictor of biologically aggressive ccRCCs, irrespective of tumor size.

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.

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.

Magnetic resonance imaging improves stratification of fibrosis and steatosis in patients with chronic liver disease.

PURPOSE: We aimed to compare the diagnostic accuracy of magnetic resonance imaging (MRI) and transient elastography (TE) in assessing liver fibrosis and steatosis in patients with chronic liver disease (CLD). METHODS: Patients who underwent liver biopsy or liver surgery at two academic hospitals between 2017 and 2021 were retrospectively recruited. The stages of liver fibrosis and steatosis were evaluated using histologic examination. Liver stiffness (LS) was assessed using MR elastography (LSMRE) and TE (LSTE). Liver steatosis was assessed using proton density fat fraction (PDFF) and controlled attenuation parameter (CAP). RESULTS: The mean age of the study population (n = 280) was 53.6 years and male sex predominated (n = 199, 71.1%). Nonalcoholic fatty liver disease was the most prevalent (n = 127, 45.5%), followed by hepatitis B virus (n = 112, 40.0%). Hepatocellular carcinoma was identified in 130 patients (46.4%). The proportions of F0, F1, F2, F3, and F4 fibrosis were 13.2%, 31.1%, 9.6%, 16.4%, and 29.7%, respectively. LSMRE had a significantly greater AUROC value than LSTE for detecting F2-F4 (0.846 vs. 0.781, P = 0.046), whereas LSMRE and LSTE similarly predicted F1-4, F3-4, and F4 (all P > 0.05). The proportions of S0, S1, S2, and S3 steatosis were 34.7%, 49.6%, 12.5%, and 3.2%, respectively. PDFF had significantly greater AUROC values than CAP in predicting S1-3 (0.922 vs. 0.806, P < 0.001) and S2-3 (0.924 vs. 0.795, P = 0.005); however, PDFF and CAP similarly predicted S3 (P = 0.086). CONCLUSION: MRI exhibited significantly higher diagnostic accuracy than TE for detecting significant fibrosis and mild or moderate steatosis in patients with CLD.

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.

Effectiveness of Epidural Balloon Neuroplasty in Patients With Chronic Spinal Stenosis Accompanied by Redundant Nerve Roots: A Longitudinal Cohort Study.

BACKGROUND: Symptomatic patients with chronic lumbar spinal stenosis (LSS) accompanied by redundant nerve roots (RNR) have poor treatment outcomes. Recently, epidural balloon neuroplasty has been shown to be effective in patients with chronic LSS. OBJECTIVE: To evaluate the effectiveness of epidural balloon neuroplasty in patients with chronic LSS accompanied by RNR. STUDY DESIGN: Retrospective cohort study. SETTING: A single pain clinic of a tertiary medical center in Seoul, Republic of Korea. METHODS: Patients with chronic LSS were divided into groups with (RNR group) and without RNR (non-RNR group). The generalized estimating equations (GEE) model was used to evaluate the effectiveness of epidural balloon neuroplasty in both groups based on Numeric Rating Scale (NRS-11) score for pain intensity, Medication Quantification Scale III (MQS III), and proportion of functional improvement at one, 3, and 6 months postprocedure. RESULTS: GEE analyses showed a significant reduction of pain intensity in NRS-11 and functional improvement compared to baseline throughout the 6-month follow-up period in both groups (P < 0.001), without differences between groups. After adjusting for potential confounding variables, the NRS-11 of leg pain one month after the procedure in the RNR group was reduced less than that in the non-RNR group (P = 0.016), although we did not find a significant time and group interaction. After adjustment, less functional improvement was observed 3 months after the procedures in the RNR group than in the non-RNR group (P = 0.001), with a significant interaction between time and group (P = 0.003). The estimated mean MQS III values were unchanged at 6 months regardless of adjustment in both groups. LIMITATIONS: Retrospective design and a lack of information on adjuvant nonpharmacologic therapies. CONCLUSION: Epidural balloon neuroplasty may be an effective option for reducing pain in patients with chronic LSS accompanied by RNR.

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.

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.

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.

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.

Ectopic lesions in the abdomen and pelvis: a multimodality pictorial review.

Various ectopic lesions occur in the abdomen and pelvis and affect multiple organs including liver, gallbladder, pancreas, spleen, and organs of the genitourinary system. Ectopic organs may be present outside their normal positions, or ectopic tissues may develop while the original organ exists in its normal position. Both benign and malignant lesions can occur in ectopic organs and tissues. Owing to their unusual location, they can often be misdiagnosed as other lesions or even malignant lesions, such as metastasis or seeding. This multimodality pictorial review provides various cases of ectopic lesions in the abdomen and pelvis, which will help narrow the differential diagnosis and guide clinical decision-making.