Dynamic Computed Tomography Imaging Research Articles

A Novel Self-Supervised Learning-Based Method for Dynamic CT Brain Perfusion Imaging.

Dynamic computed tomography (CT)-based brain perfusion imaging is a non-invasive technique that can provide quantitative measurements of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). However, due to high radiation dose, dynamic CT scan with a low tube voltage and current protocol is commonly used. Because of this reason, the increased noise degrades the quality and reliability of perfusion maps. In this study, we aim to propose and investigate the feasibility of utilizing a convolutional neural network and a bi-directional long short-term memory model with an attention mechanism to self-supervisedly yield the impulse residue function (IRF) from dynamic CT images. Then, the predicted IRF can be used to compute the perfusion parameters. We evaluated the performance of the proposed method using both simulated and real brain perfusion data and compared the results with those obtained from two existing methods: singular value decomposition and tensor total-variation. The simulation results showed that the overall performance of parameter estimation obtained from the proposed method was superior to that obtained from the other two methods. The experimental results showed that the perfusion maps calculated from the three studied methods were visually similar, but small and significant differences in perfusion parameters between the proposed method and the other two methods were found. We also observed that there were several low-CBF and low-CBV lesions (i.e., suspected infarct core) found by all comparing methods, but only the proposed method revealed longer MTT. The proposed method has the potential to self-supervisedly yield reliable perfusion maps from dynamic CT images.

Exploring healthy knee kinematic phenotypes obtained through dynamic CT imaging: A cluster analysis study

Dynamic Computed Tomography (CT) emerges as a pivotal imaging modality for the assessment of knee joint kinematics. However, integrating dynamic CT into clinical practice necessitates a thorough understanding of healthy knee kinematics, as large variation in kinematics has been described within healthy populations. Therefore, this study aims to identify and describe healthy phenotypes with homogenous knee kinematics using a clustering approach. A total of 120 healthy knees from 64 participants underwent dynamic CT scanning during knee extension and flexion. Eight tibiofemoral (TF) and patellofemoral kinematic parameters were extracted, after which K-means clustering was applied to identify homogenous kinematic clusters. Kinematic phenotypes were obtained by calculating the median and interquartile range (IQR) for all kinematic parameters per cluster. Two distinct clusters were found, comprising 53 (Cluster 1) and 67 (Cluster 2) knees. Statistically significant differences between the clusters were found in six out of eight kinematic parameters. The most notable differences were observed in TF rotations, with cluster 1 exhibiting a greater amount of internal and adduction rotation of the tibia compared to cluster 2. The two kinematic phenotypes provide new insights into the nuanced variation within a healthy cohort and can serve as reference for future studies evaluating pathological kinematic phenotypes using dynamic CT.

A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images

BackgroundAutomatic segmentation of hepatocellular carcinoma (HCC) on computed tomography (CT) scans is in urgent need to assist diagnosis and radiomics analysis. The aim of this study is to develop a deep learning based network to detect HCC from dynamic CT images.MethodsDynamic CT images of 595 patients with HCC were used. Tumors in dynamic CT images were labeled by radiologists. Patients were randomly divided into training, validation and test sets in a ratio of 5:2:3, respectively. We developed a hierarchical fusion strategy of deep learning networks (HFS-Net). Global dice, sensitivity, precision and F1-score were used to measure performance of the HFS-Net model.ResultsThe 2D DenseU-Net using dynamic CT images was more effective for segmenting small tumors, whereas the 2D U-Net using portal venous phase images was more effective for segmenting large tumors. The HFS-Net model performed better, compared with the single-strategy deep learning models in segmenting small and large tumors. In the test set, the HFS-Net model achieved good performance in identifying HCC on dynamic CT images with global dice of 82.8%. The overall sensitivity, precision and F1-score were 84.3%, 75.5% and 79.6% per slice, respectively, and 92.2%, 93.2% and 92.7% per patient, respectively. The sensitivity in tumors < 2 cm, 2–3, 3–5 cm and > 5 cm were 72.7%, 92.9%, 94.2% and 100% per patient, respectively.ConclusionsThe HFS-Net model achieved good performance in the detection and segmentation of HCC from dynamic CT images, which may support radiologic diagnosis and facilitate automatic radiomics analysis.

Dynamic Computed Tomography of the Distal Radioulnar Joint Versus Magnetic Resonance Imaging in Detecting Foveal Tears of the Triangular Fibrocartilage Complex.

Diagnosis of foveal triangular fibrocartilage complex (TFCC) tears remains difficult. The purpose of this study was to evaluate whether dynamic computed tomography (CT) of the distal radioulnar joint (DRUJ) has a higher sensitivity in detecting foveal tears of the TFCC when compared with magnetic resonance imaging (MRI). A database query identified all patients above the age of 18 years with suspected foveal TFCC injuries who underwent bilateral dynamic CT imaging of the DRUJ, static 3T MRI, and subsequent wrist arthroscopy. All patients had evidence of foveal tenderness on clinical examination. The imaging results were validated with wrist arthroscopy. Twelve patients with a mean age of 31 years (range, 18-53 years) were identified. Eight patients were diagnosed with a complete foveal detachment of the TFCC on wrist arthroscopy. Of the 8 patients with a foveal tear on DRUJ arthroscopy, only 3 had a preoperative physical examination suggestive of DRUJ instability. Magnetic resonance imaging identified foveal injuries in 4 of these 8 patients (sensitivity, 50%), and 3 of these were identified as only partial tears. Dynamic CT identified instability of DRUJ in 6 of 8 patients (sensitivity, 75%). Seven of 8 patients had imaging findings suggestive of a foveal tear when including either MRI or CT imaging (sensitivity, 88%). Of the 4 patients with an intact foveal attachment on arthroscopy, 3 (specificity, 75%) had a stable DRUJ on CT and all 4 (specificity, 100%) had an intact foveal attachment on MRI. Sixty-two percent of patients with foveal tears on arthroscopy have a stable DRUJ on clinical examination. When combining MRI and dynamic CT imaging of the DRUJ, the sensitivity of detecting a foveal TFCC tear approaches 88% compared with a gold standard of wrist arthroscopy.

Segmentation and Rigid Registration of Liver Dynamic Computed Tomography Images for Diagnostic Assessment of Fatty Liver Disease

Segmentation and Rigid Registration of Liver Dynamic Computed Tomography Images for Diagnostic Assessment of Fatty Liver Disease

A Novel Observational Strategy for Nonfunctional Pancreatic Neuroendocrine Neoplasms With Texture Analysis: A Multicenter Retrospective Study.

Surgical resection is recommended for nonfunctional pancreatic neuroendocrine neoplasms (NF-pNENs). However, metastasis is rare in patients with small lesions with histological grade 1 (G1); thus, observation is an optional treatment approach for small NF-pNENs. Texture analysis (TA) is an imaging analysis mode for quantification of heterogeneity by extracting quantitative parameters from images. We retrospectively evaluated the utility of TA in predicting histological grade of resected NF-pNENs in a multicenter retrospective study. The utility of TA in preoperative prediction of grade were evaluated with 29 patients treated by pancreatectomy for NF-pNEN who underwent preoperative dynamic computed tomography scan between January 1, 2013 and December 31, 2020 at three hospitals affiliated with the Jikei University School of Medicine. TA was performed with dedicated software for medical imaging processing for determining histological tumor grade using dynamic computed tomography images. Histological tumor grades based on the 2017 World Health Organization Classification for Pancreatic Neuroendocrine Neoplasms were grade 1, 2 and 3 in 18, 10 and one patient, respectively. Preoperative grades by TA were 1 and 2/3 in 15 and 14 patients, respectively. The sensitivity, specificity and area under the curve for TA-oriented grade 1 lesions were 1.00, 0.889 and 0.965 (95% confidence interval=0.901-1.000), respectively. TA is useful for predicting grade 2/3 NF-pNEN and can provide a safe option for observation for patients with small grade 1 lesions.

In Vivo Length Changes Between the Attachments of the Medial Patellofemoral Complex Fibers in Knees With Anatomic Risk Factors for Patellar Instability

Background: Medial patellofemoral complex (MPFC) reconstruction plays an important role in the surgical treatment of patellar instability. Anatomic reconstruction is critical in re-creating the native function of the ligament, which includes minimizing length changes that occur in early flexion. Anatomic risk factors for patellar instability such as trochlear dysplasia, patella alta, and increased tibial tuberosity to trochlear groove (TT-TG) distance have been shown to influence the function of the MPFC graft in cadaveric studies, but the native length change patterns of the MPFC fibers in knees with anatomic risk factors have not been described. Purpose: To describe the in vivo length changes of the MPFC fibers in knees with anatomic risk factors for patellar instability and identify the optimal attachment sites for MPFC reconstruction. Study Design: Controlled laboratory study. Methods: Dynamic computed tomography imaging was performed on the asymptomatic knee in patients with contralateral patellar instability. Three-dimensional digital knee models were created to assess knees between 0° and 50° of flexion in 10° increments. MPFC fiber lengths were calculated at each flexion angle between known anatomic attachment points on the extensor mechanism (quadriceps tendon, MPFC midpoint [M], and patella) and femur (1, 2, and 3, representing the proximal to distal femoral footprint). Changes in MPFC fiber length were compared for each condition and assessed for their relationships to morphologic risk factors (trochlear depth, Caton Deschamps Index [CDI], and TT-TG distance). Results: In 22 knees, native MPFC fibers were found to be longer at 0° than at 20° to 50° of flexion. Length changes observed between 0° and 50° increased with the number of risk factors present. In the central fibers of the MPFC (M-2), 1.7% ± 3.1% length change was noted in knees with no anatomic risk factors, which increased to 5.6% ± 4.6%, 17.0% ± 6.4%, and 26.7% ± 6.8% in the setting of 1, 2, and 3 risk factors, respectively. Nonanatomic patella-based attachments were more likely to demonstrate unfavorable length change patterns, in which length was greater at 50° than 0°. In patellar attachments, an independent relationship was found between increasing length changes and TT-TG distance, while in quadriceps tendon attachments, a trend toward a negative relationship between length changes and CDI was noted. All configurations demonstrated a strong relationship between percentage change in length and number of morphologic risk factors present, with the greatest influence found in patella-based attachments. Conclusion: The MPFC fibers demonstrated increased length changes in knees when a greater number of morphological risk factors for patellar instability were present, which worsened in the setting of nonanatomic configurations. This suggests that the function of the intact MPFC in patients with anatomic risk factors may not reflect previously described findings in anatomically normal knees. Further studies are needed to understand the pathoanatomy related to these changes, as well as the implications for graft placement and assessment of length changes during MPFC reconstruction techniques. Clinical Relevance: MPFC length change patterns vary based on the number of morphologic risk factors for patellar instability present and should be considered during reconstructive procedures.

Design and CT imaging of casper, an anthropomorphic breathing thorax phantom

The goal of this work was to build an anthropomorphic thorax phantom capable of breathing motion with materials mimicking human tissues in x-ray imaging applications. The thorax phantom, named Casper, was composed of resin (body), foam (lungs), glow polyactic acid (bones) and natural polyactic acid (tumours placed in the lungs). X-ray attenuation properties of all materials prior to manufacturing were evaluated by means of photon-counting computed tomography (CT) imaging on a table-top system. Breathing motion was achieved by a scotch-yoke mechanism with diaphragm motion frequencies of 10−20 rpm and displacements of 1 to 2 cm. Casper was manufactured by means of 3D printing of moulds and ribs and assembled in a complex process. The final phantom was then scanned using a clinical CT scanner to evaluate material CT numbers and the extent of tumour motion. Casper CT numbers were close to human CT numbers for soft tissue (46 HU), ribs (125 HU), lungs (−840 HU) and tumours (−45 HU). For a 2 cm diaphragm displacement the largest tumour displacement was 0.7 cm. The five tumour volumes were accurately assessed in the static CT images with a mean absolute error of 4.3%. Tumour sizes were either underestimated for smaller tumours or overestimated for larger tumours in dynamic CT images due to motion blurring with a mean absolute difference from true volumes of 10.3%. More Casper information including a motion movie and manufacturing data can be downloaded from http://web.uvic.ca/~bazalova/Casper/.

Dual segmentation models for poorly and well-differentiated hepatocellular carcinoma using two-step transfer deep learning on dynamic contrast-enhanced CT images.

The aim of this study was to develop dual segmentation models for poorly and well-differentiated hepatocellular carcinoma (HCC), using two-step transfer learning (TSTL) based on dynamic contrast-enhanced (DCE) computed tomography (CT) images. From 2013 to 2019, DCE-CT images of 128 patients with 80 poorly differentiated and 48 well-differentiated HCCs were selected at our hospital. In the first transfer learning (TL) step, a pre-trained segmentation model with 192 CT images of lung cancer patients was retrained as a poorly differentiated HCC model. In the second TL step, a well-differentiated HCC model was built from a poorly differentiated HCC model. The average three-dimensional Dice's similarity coefficient (3D-DSC) and 95th-percentile of the Hausdorff distance (95% HD) were mainly employed to evaluate the segmentation accuracy, based on a nested fourfold cross-validation test. The DSC denotes the degree of regional similarity between the HCC reference regions and the regions estimated using the proposed models. The 95% HD is defined as the 95th-percentile of the maximum measures of how far two subsets of a metric space are from each other. The average 3D-DSC and 95% HD were 0.849 ± 0.078 and 1.98 ± 0.71mm, respectively, for poorly differentiated HCC regions, and 0.811 ± 0.089 and 2.01 ± 0.84mm, respectively, for well-differentiated HCC regions. The average 3D-DSC for both regions was 1.2 times superior to that calculated without the TSTL. The proposed model using TSTL from the lung cancer dataset showed the potential to segment poorly and well-differentiated HCC regions on DCE-CT images.

A radiomics nomogram analysis based on CT images and clinical features for preoperative Lauren classification in gastric cancer.

To develop a combined radiomics nomogram based on computed tomography (CT) images and clinical features to preoperatively distinguish Lauren's diffuse-type gastric cancer (GC) from intestinal-type GC. Ninety-five patients with Lauren's intestinal or diffuse-type GC confirmed by postoperative pathology had their preoperative clinical information and dynamic contrast CT images retrospectively analyzed and were subdivided into training and test groups in a 7:3 ratio. To select the optimal features and construct the radiomic signatures, we extracted, filtered, and minimized the radiomic features from arterial phase (AP) and venous phase (VP) CT images. We constructed four models (clinical model, AP radiomics model, VP radiomics model, and radiomics-clinical model) to assess and compare their predictive performance between the intestinal- and diffuse-type GC. Receiver-operating characteristic (ROC) curve, area under the ROC curve (AUC), and the DeLong test were used for assessment and comparison. In this study, radiomic nomograms integrating combined radiomic signatures and clinical characteristics were developed. Compared to the AP radiomics model, the VP radiomics model had better performance, with an AUC of 0.832 (95% confidence interval [CI], 0.735, 0.929) in the training cohort and 0.760 (95% CI 0.580, 0.940) in the test cohort. Among the combined models that assessed Lauren's type GC, the model including age and VP radiomics showed the best performance, with an AUC of 0.849 (95% CI 0.758, 0.940) in the training cohort and 0.793 (95% CI 0.629, 0.957) in the test cohort. Nomogram incorporating radiomic signatures and clinical features effectively differentiated Lauren's diffuse-type from intestinal-type GC.

Radiographic Evaluation of Carpal Mechanics and the Scapholunate Angle in a Clenched Fist with Dynamic Computed Tomography Imaging

The long-term consequences of injuries to the scapholunate joint can severely limit hand function, and the potential for posttraumatic deformity makes early recognition of these injuries important. The purpose of this study was to evaluate the motion of the scapholunate joint in normal wrists through the radial and ulnar deviation using novel dynamic computed tomography (CT) imaging. Fifteen participants consented to have their uninjured wrists scanned. A protocol was designed to ensure adequate time, yet limited exposure, for volunteers. Participants began with the hand in a relaxed fist position and then proceeded to clench the hand in a full fist and relax. Once relaxed again, the wrist was maximally ulnarly deviated and then maximally radially deviated in a fluid motion. Dynamic CT imaging was captured throughout the range of motion. The scapholunate angle was measured on dynamic wrist images. The mean range of the scapholunate angle that the wrists moved through was 37.2°-45.9°, and the mean midpoint angle was 41.2° ± 0.4°. All wrists had small, measurable differences in the scapholunate angle when moving from the maximum ulnar deviation to the maximal radial deviation. The average maximum angle change through the range is 11.7°, whereas the average minimum angle change was 0.9°. In this study, scapholunate angle calculations using dynamic wrist CT scans were within the range of accepted normal for the angle in uninjured wrists. With the increased focus on dynamic imaging for wrist motion, it may be possible to derive a standardized protocol for mapping the carpal motion that is clinically applicable and reproducible. Diagnostic IV.

A novel hierarchical fusion strategy of deep learning networks to detect hepatocellular carcinoma from dynamic computed tomography images

A novel hierarchical fusion strategy of deep learning networks to detect hepatocellular carcinoma from dynamic computed tomography images

Dynamic Changes and Nomogram Prediction for Sinistral Portal Hypertension in Moderate and Severe Acute Pancreatitis.

ObjectivesTo investigate the dynamic changes in gastric varices in patients with acute pancreatitis (AP) and to develop a novel nomogram for the early prediction of sinistral portal hypertension (SPH).MethodsThis was a retrospective, case-control study with an analysis of the quantitative, dynamic computed tomography imaging results of SPH in patients with moderate and severe AP with a long-term follow-up. Multivariate logistic regression analysis and nomogram were employed.ResultsThe SPH group (n = 94) and non-SPH group (n = 94) were matched. The dynamic changes showed an increasing trend in the diameter of gastric fundus, short gastric, gastric coronary, and gastroepiploic veins, which did not recover during the one-year follow-up. Multivariate analysis showed that male (adjusted odds ratio (adjOR), 8.71; 95% confidence interval (CI), 2.86–26.53; P < 0.001), body mass index ≥27.5 kg/m2 (adjOR, 5.49; 95% CI, 1.85–16.29; P = 0.002), prothrombin time ≥12.6 s (adjOR, 2.82; 95% CI, 1.11–7.17; P = 0.03), and the patency of splenic vein [stenosis (adjOR, 8.48; 95% CI, 2.13–33.71; P = 0.002), and occlusion (adjOR, 34.57; 95% CI, 10.87–110.00; P < 0.001)] were independently associated with the development of SPH. The nomogram incorporating these factors demonstrated good discrimination, calibration and clinical utility. The area under the curve was as high as 0.92 (95% CI, 0.87–0.95).ConclusionThe dynamic changes in varices in SPH are long-term and slowly progressing. Males and obese patients with abnormal splenic veins and coagulopathies are at high risk for developing SPH. A simple nomogram tool helps in the early, accurate prediction of SPH.

Dynamic three-dimensional computed tomographic imaging facilitates evaluation of the equine cervical articular process joint in motion.

Dynamic computed tomography (CT) imaging has been introduced in human orthopaedics and is continuing to gain popularity. With dynamic CT, video sequences of anatomical structures can be evaluated in motion. To investigate the feasibility of dynamic CT for diagnostic imaging of the equine cervical articular process joints (APJs) and to give a detailed description of the APJ movement pattern. Descriptive cadaver imaging. Cervical specimens of twelve Warmblood horses were included. A custom-made motorised testing device was used to position and manipulate the neck specimens and perform dynamic 2D and 3D CT imaging. Images were obtained with a 320-detector-row CT scanner with a 160mm wide-area (2D) solid-state detector design that allows image acquisition of a volumetric axial length of 160mm without moving the CT couch. Dynamic videos were acquired and divided into four phases of movement. Three blinded observers used a subjective scale of 1 (excellent) to 4 (poor) to grade the overall image quality in each phases of motion cycle. With an overall median score of 1 the image quality, a significantly lower score was observed in the dynamic 3D videos over the four phases by the three observers compared with the 2D videos for both flexion (3D 95% CI: 1-2 and 2D 95% CI: 1-3; P=.007) and extension movement (3D 95% CI: 1-2 and 2D 95% CI: 1-3; P=.008). Median Translational displacement of the APJ surface was significantly greater in flexion than in extension movement (P=.002). The small number of specimens included. Excision of spines and removal of musculature. The study is a first step in the investigation of the potential of dynamic 3D CT in veterinary medicine, a technique that has only begun to be explored and leaves much room for refinement prior to its introduction in routine practice. CT with a detector coverage of 16cm and a rotation speed of 0.32seconds provides high-quality images of moving objects and gives new insight into the movement pattern of equine cervical APJs.

Are the washout values currently accepted for lesion characterization in dedicated adrenal CT adequate for diagnosis?

We aimed to investigate the accuracy of density characteristics and washout values of lesions detected on computed tomography (CT) at the cutoff values obtained from the literature by taking the pathological results of adrenalectomy specimens as reference and to determine the cutoff values of parameters evaluated on CT for the differentiation of adenoma and nonadenoma lesions in the study group. Hospital records and standard CT imaging data (noncontrast early phase [65 s] and late phase [15 min] ) of 84 patients with 87 lesions who underwent adrenalectomy between January 2012 and December 2018 were retrospectively reevaluated by two radiologists in consensus. The patients were categorized as having adenoma and nonadenoma lesions according to the pathology results. The sensitivity, specificity and diagnostic accuracy of CT parameters (density values and washout percentages) were evaluated. Differences in the CT parameters (size, noncontrast and early-late enhancement density and absolute and relative washout values) were investigated. The optimal cutoff values of CT parameters were determined by ROC analysis. Noncontrast CT had a specificity of 87.75% and 95.9%, sensitivity of 60% and 48.6%, diagnostic accuracy of 77.7% and 89.47% for adenomas, at the cutoff values of ≤10 HU and ≤0 HU, respectively. For absolute washout value ≥ 60%, the sensitivity, specificity and accuracy were 64.7%, 52.38% and 56.75%, respectively; while these rates were 76.47%, 56.52% and 62.16%, respectively, for relative washout value ≥40%. Adenomas and nonadenomas showed significant difference in terms of size (p < 0.0001), unenhanced attenuation (p < 0.0001), relative washout (p = 0.020) and delay enhancement (p < 0.001). But there were no differences in terms of absolute washout (p = 0.230) and early enhancement (p = 0.264). The cutoff values for the differentiation of adenomas and nonadenomas were as follows: size ≤44 mm, noncontrast density <20 HU, early-phase density ≥45 HU, delayed-phase density ≤44 HU, absolute washout 74.83% and relative washout 57.76%. The current washout criteria used in the differentiation of adenoma and nonadenoma lesions in dynamic CT imaging can give false negative and positive results. According to the existing criteria, the most reliable parameter in adenoma-nonadenoma differentiation is ≤ 0 HU noncontrast CT density value.

Enhancement rate of venous phase to portal venous phase computed tomography and its correlation with ultrasound elastography determination of liver fibrosis

IntroductionThis study aimed to compare the correlation between the computed tomography (CT) enhancement rate of the venous to portal venous phase (VP-ER) and the extracellular volume (ECV) fraction with shear-wave ultrasound elastography (USE) findings in patients with liver fibrosis. MethodsWe included 450 patients with clinically suspected liver cirrhosis who underwent triphasic dynamic CT studies and USE. We compared the USE results with the unenhanced CT phase, with enhancement in the hepatic artery phase (HAP), portal venous phase (PVP), and venous phase (VP), and with the ECV fraction and the VP-ER. We also compared the area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the ECV fraction and VP-ER with that of the values obtained with USE. ResultsThe VP-ER was the most highly correlated with the liver stiffness value determined with USE (Pearson's correlation coefficient: r = 0.37), followed by enhancement in the PVP (r = −0.25), CT number on unenhanced CT scans (r = −0.22), the ECV fraction (r = 0.19), enhancement in the VP (r = 0.059), and enhancement in the HAP (r = −0.023) (all p < 0.01). The VP-ER showed a significantly higher AUC than the ECV fraction (0.75 vs 0.62) when the liver stiffness was >15 kPa in USE studies (p = 0.04). ConclusionCompared to the ECV fraction, the VP-ER is more useful for predicting all degrees of liver fibrosis on routine triphasic dynamic CT images. Implications for practiceAlthough improvement is needed, the VP-ER has a higher diagnostic ability for liver fibrosis than the ECV fraction in clinical practice.

Infiltrative tumor interface with normal renal parenchyma in locally advanced renal cell carcinoma: Clinical relevance and pathological implications.

Locally advanced renal cell carcinoma is considered clinically aggressive, despite heterogeneity in survival outcomes. We investigated the clinical relevance and pathological implications of infiltrative tumor interface with normal renal parenchyma on preoperative imaging in locally advanced renal cell carcinoma. A total of 77 patients with locally advanced renal cell carcinoma (≥pT3a Nany M0) who underwent radical or partial nephrectomy (2008-2018) were analyzed. Preoperative dynamic computed tomography images were reviewed to assess radiological infiltrative features. A radiological infiltrative feature was defined as an ill-defined tumor interface with normal renal parenchyma. The tumor interfaces were analyzed histologically and compared with radiological findings. The median tumor size was 6.4cm. Lymphadenopathy was observed in four patients (5.2%). Clear cell renal cell carcinoma was diagnosed in 66 patients (86%) and Fuhrman grade was 3-4 in 38 patients (49%). A total of 30 patients (39%) showed radiological infiltrative features, which were significantly associated with larger tumor size and higher clinical Tstage. The specificity and sensitivity of radiological infiltrative features in predicting pathological renal parenchymal infiltration were 90 and 64%, respectively. During a median follow-up period of 3.8years, 27 patients (35%) developed cancer recurrences, and six patients (7.8%) died of renal cell carcinoma. Multivariable analysis showed that the presence of radiological infiltrative features was an independent risk factor for cancer recurrence. Cancer recurrence and cancer-specific mortality were significantly stratified by the presence or absence of radiological infiltrative features (P<0.001 and P=0.02, respectively). Locally advanced renal cell carcinoma can show radiological infiltrative features preoperatively, which are significantly associated with unfavorable prognosis. Radiological infiltrative features on preoperative imaging correspond with a high specificity to pathological renal parenchymal infiltration.

Dynamic Enhancement Pattern on CT for Predicting Pancreatic Neuroendocrine Neoplasms with Low PAX6 Expression: A Retrospective Observational Study.

Paired box 6 (PAX6) is a transcription factor that plays a critical role in tumor suppression, implying that the downregulation of PAX6 promotes tumor growth and invasiveness. This study aimed to examine dynamic computed tomography (CT) features for predicting pancreatic neuroendocrine neoplasms (Pan-NENs) with low PAX6 expression. We retrospectively evaluated 51 patients with Pan-NENs without synchronous liver metastasis to assess the pathological expression of PAX6. Two radiologists analyzed preoperative dynamic CT images to determine morphological features and enhancement patterns. We compared the CT findings between low and high PAX6 expression groups. Pathological analysis identified 11 and 40 patients with low and high PAX6 expression, respectively. Iso- or hypoenhancement types in the arterial and portal phases were significantly associated with low PAX6 expression (p = 0.009; p = 0.001, respectively). Low PAX6 Pan-NENs showed a lower portal enhancement ratio than high PAX6 Pan-NENs (p = 0.044). The combination based on enhancement types (iso- or hypoenhancement during arterial and portal phases) and portal enhancement ratio (≤1.22) had 54.5% sensitivity, 92.5% specificity, and 84.3% accuracy in identifying low PAX6 Pan-NENs. Dynamic CT features, including iso- or hypoenhancement types in the arterial and portal phases and lower portal enhancement ratio may help predict Pan-NENs with low PAX6 expression.

Features of Dynamic Computed Tomography Imaging of Glycogenic Hepatopathy.

Features of Dynamic Computed Tomography Imaging of Glycogenic Hepatopathy.

Long-term outcomes of living donor liver transplantation after locoregional treatment for hepatocellular carcinoma: an experience from a single institute.

The precise role of downstaging or bridge therapy for cirrhotic patients with hepatocellular carcinoma (HCC) beyond or within the Milan criteria (MC) before living donor liver transplantation (LDLT) remains undefined. We conducted a single-center, retrospective cohort study of 40 cirrhotic patients with HCC who underwent LDLT from 2000 to 2018. Dynamic computed tomography images at the initial presentation and immediately before LDLT as well as the final histopathological findings were reviewed to determine whether they met or exceeded MC. Overall, 29 patients underwent various pre-transplant HCC treatments, including ablation and embolization (bridge therapy, n = 20; downstaging, n = 9). Of the 9 patients who were initially beyond the MC, 4 (44.4%) were successfully downstaged to within the MC. Five patients beyond the MC immediately before LDLT demonstrated a significantly worse 5-year overall survival rate than patients within the MC (16.7% vs. 82.2%, P = 0.004), regardless of the radiological HCC stage at presentation or the final pathological tumor status. All 3 recurrent patients had HCC beyond the MC immediately before transplant and died of their disease at 13, 24, and 50months after transplantation. Successful downstaging for HCC cases beyond the MC provides similar outcomes to those within the MC at presentation, regardless of the histopathological findings.