The purpose of this study was to evaluate the treatment efficacy of transcatheter arterial chemoembolization (TACE) for treatment-naive hepatocellular carcinoma (HCC) according to tumor location and burden.Between 2010 and 2019, consecutive patients who underwent TACE as the first treatment were enrolled. Tumors were classified into two categories based on their location, as central or peripheral tumors. Tumors in the central zone, which is within 1 cm of the main trunk or the first branch of the portal vein, were classified as central tumors, while those located in the peripheral zone were classified as peripheral tumors. Patients were grouped according to the HCC location and up-to-7 criteria. Patients with central tumors were classified into the central arm and those with only peripheral tumors were classified into the peripheral arm. Patients within and beyond the up-to-7 criteria were classified into the up-to-7 in and up-to-7 out-groups, respectively. Local recurrence-free survival (LRFS) and progression-free survival (PFS) were compared per nodule (central tumor vs. peripheral tumor) and per patient (central arm vs. peripheral arm), respectively. The prognostic factors of LRFS and PFS were analyzed by univariate and multivariate analyses.A total of 174 treatment-naive patients with 352 HCCs were retrospectively enrolled. Ninety-six patients and 130 lesions were selected by propensity score matching. Median LRFS was longer for peripheral tumors than central tumors (not reached vs. 3.3 months, p < 0.001). Median PFS was 17.1 months (8.3-24.9) in the peripheral arm and up-to-7 in, 7.0 months (3.3-12.7) in the peripheral arm and up-to-7 out, 8.4 months (4.0-12.6) in the central arm and up-to-7 in, and 3.0 months (1.2-4.9) in the central arm and up-to-7 out-groups. The peripheral arm and up-to-7 in-groups had significantly longer PFS than the other three groups (p = 0.013, p = 0.015, p < 0.001, respectively). Multivariate analysis confirmed that the central zone and central arm were associated with high adjusted hazard ratios for tumor recurrence or death (2.87, p < 0.001; 2.89, p < 0.001, respectively).Treatment-naive HCCs in the peripheral zone had a longer LRFS and PFS following TACE compared to those in the central zone.
Objective Therapeutic predictors derived from the venous pressure before therapy have not been identified for Budd-Chiari syndrome (BCS). The aim of this study was to determine whether or not measuring the distal pressure or pressure gradient was useful for predicting treatment efficacy in BCS.
Introduction: The purpose of this study was to evaluate the treatment efficacy of transcatheter arterial chemo-embolization (TACE) for treatment-naive hepatocellular carcinoma (HCC) according to tumor location and burden. Methods: Between 2010 and 2019, consecutive patients who underwent TACE as the first treatment were enrolled. Tumors were classified into two categories based on their location, as central or peripheral tumors. Tumors in the central zone, which is within 1 cm of the main trunk or the first branch of the portal vein, were classified as central tumors, while those located in the peripheral zone were classified as peripheral tumors. Patients were grouped according to the HCC location and up-to-7 criteria. Patients with central tumors were classified into the central arm and those with only peripheral tumors were classified into the peripheral arm. Patients within and beyond the up-to-7 criteria were classified into the up-to-7 in and up-to-7 out groups, respectively. Local recurrence-free survival (LRFS) and progression-free survival (PFS) were compared per-nodule (central tumor vs. peripheral tumor) and per-patient (central arm vs. peripheral arm), respectively. The prognostic factors of LRFS and PFS were analyzed by univariate and multivariate analyses. Results: A total of 174 treatment-naive patients with 352 HCCs were retrospectively enrolled. Ninety-six patients and 130 lesions were selected by propensity score matching. Median LRFS was longer for peripheral tumors than central tumors (not reached vs. 3.3 months, p<0.001). Median PFS was: 17.1 months (8.3-24.9) in the peripheral arm & up-to-7 in, 7.0 months (3.3-12.7) in the peripheral arm & up-to-7 out, 8.4 months (4.0-12.6) in the central arm & up-to-7 in, and 3.0 months (1.2-4.9) in the central arm & up-to-7 out groups. The peripheral arm & up-to-7 in group had significantly longer PFS than the other three groups (p=0.013, p=0.015, p<0.001, respectively). Multivariate analysis confirmed that the central zone and central arm were associated with high adjusted hazard ratios for tumor recurrence or death (2.87, p<0.001; 2.89, p<0.001, respectively). Conclusion: Treatment-naive HCCs in the peripheral zone had a longer LRFS and PFS following TACE compared to those in the central zone.
An automatic extraction of pulmonary emphysema area on 3-D chest CT images was performed using an adaptive thresholding technique. We proposed a method to estimate the ratio of the emphysema area to the whole lung volume. We employed 32 cases (15 normal and 17 abnormal) which had been already diagnosed by radiologists prior to the study. The ratio in all the normal cases was less than 0.02, and in abnormal cases, it ranged from 0.01 to 0.26. The effectiveness of our approach was confirmed through the results of the present study.
A 55-year-old patient was admitted for variceal treatment, a complication of chronic portal hypertension and liver cirrhosis. Imaging studies revealed prominent duodenal varices, the pancreaticoduodenal vein as its afferent pathway, a drainer vessel into the inferior vena cava, and a paraumbilical vein. We successfully performed complete obliteration of the varix, including its afferent and efferent vessels, via the paraumbilical vein approach.
Abstract Aims We aimed to develop models to detect aortic stenosis (AS) from chest radiographs—one of the most basic imaging tests—with artificial intelligence. Methods and results We used 10 433 retrospectively collected digital chest radiographs from 5638 patients to train, validate, and test three deep learning models. Chest radiographs were collected from patients who had also undergone echocardiography at a single institution between July 2016 and May 2019. These were labelled from the corresponding echocardiography assessments as AS-positive or AS-negative. The radiographs were separated on a patient basis into training [8327 images from 4512 patients, mean age 65 ± (standard deviation) 15 years], validation (1041 images from 563 patients, mean age 65 ± 14 years), and test (1065 images from 563 patients, mean age 65 ± 14 years) datasets. The soft voting-based ensemble of the three developed models had the best overall performance for predicting AS with an area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 0.83 (95% confidence interval 0.77–0.88), 0.78 (0.67–0.86), 0.71 (0.68–0.73), 0.71 (0.68–0.74), 0.18 (0.14–0.23), and 0.97 (0.96–0.98), respectively, in the validation dataset and 0.83 (0.78–0.88), 0.83 (0.74–0.90), 0.69 (0.66–0.72), 0.71 (0.68–0.73), 0.23 (0.19–0.28), and 0.97 (0.96–0.98), respectively, in the test dataset. Conclusion Deep learning models using chest radiographs have the potential to differentiate between radiographs of patients with and without AS. Lay Summary We created artificial intelligence (AI) models using deep learning to identify aortic stenosis (AS) from chest radiographs. Three AI models were developed and evaluated with 10 433 retrospectively collected radiographs and labelled from echocardiography reports. The ensemble AI model could detect AS in a test dataset with an area under the receiver operating characteristic curve of 0.83 (95% confidence interval 0.78–0.88). Since chest radiography is a cost-effective and widely available imaging test, our model can provide an additive resource for the detection of AS.
