Automated hepatic steatosis assessment on dual-energy CT-derived virtual non-contrast images through fully-automated 3D organ segmentation.

PURPOSE: To evaluate the efficacy of volumetric CT attenuation-based parameters obtained through automated 3D organ segmentation on virtual non-contrast (VNC) images from dual-energy CT (DECT) for assessing hepatic steatosis. MATERIALS AND METHODS: This retrospective study included living liver donor candidates having liver DECT and MRI-determined proton density fat fraction (PDFF) assessments. Employing a 3D deep learning algorithm, the liver and spleen were automatically segmented from VNC images (derived from contrast-enhanced DECT scans) and true non-contrast (TNC) images, respectively. Mean volumetric CT attenuation values of each segmented liver (L) and spleen (S) were measured, allowing for liver attenuation index (LAI) calculation, defined as L minus S. Agreements of VNC and TNC parameters for hepatic steatosis, i.e., L and LAI, were assessed using intraclass correlation coefficients (ICC). Correlations between VNC parameters and MRI-PDFF values were assessed using the Pearson's correlation coefficient. Their performance to identify MRI-PDFF ≥ 5% and ≥ 10% was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Of 252 participants, 56 (22.2%) and 16 (6.3%) had hepatic steatosis with MRI-PDFF ≥ 5% and ≥ 10%, respectively. LVNC and LAIVNC showed excellent agreement with LTNC and LAITNC (ICC = 0.957 and 0.968) and significant correlations with MRI-PDFF values (r = - 0.585 and - 0.588, Ps < 0.001). LVNC and LAIVNC exhibited areas under the ROC curve of 0.795 and 0.806 for MRI-PDFF ≥ 5%; and 0.916 and 0.932, for MRI-PDFF ≥ 10%, respectively. CONCLUSION: Volumetric CT attenuation-based parameters from VNC images generated by DECT, via automated 3D segmentation of the liver and spleen, have potential for opportunistic hepatic steatosis screening, as an alternative to TNC images.

Important Radiologic and Clinical Factors for Predicting Overall Survival in Pancreatic Adenocarcinoma Patients Who Underwent FOLFIRINOX.

BACKGROUND: To predict poor overall survival (OS) in pancreatic adenocarcinoma (PAC) who underwent FOLFIRINOX (5-fluorouracil/leucovorin/irinotecan/oxaliplatin) using clinical and computed tomography (CT) findings. METHODS: A total of 189 patients with PAC who received FOLFIRINOX were retrospectively included. Two reviewers assessed CT findings and resectability based on National Comprehensive Cancer Network guidelines. They determined tumor size changes according to Response Evaluation Criteria in Solid Tumors (RECIST 1.1). Delta measurements were performed. Clinical results, such as whether to perform surgery, were also investigated. A Cox proportional hazard model was used to identify significant predictors for OS. A CT-based nomogram was constructed to predict OS. RESULTS: Seventy-four patients (39.2%) underwent surgery. For OS, rim enhancement of PAC on baseline CT (hazard ratio [HR], 1.75; 95% confidence interval [CI], 1.10-2.77; P = 0.018), high delta tumor on baseline CT (HR, 2.46; 95% CI, 1.55-3.91; P < 0.001), progressive disease at follow-up CT (HR, 8.89; 95% CI, 2.94-26.87; P < 0.001), and without surgery (HR, 2.81; 95% CI, 1.49-5.30; P = 0.001) were important features related to poor prognosis. The nomogram showed good predictive ability for the survival. CONCLUSION: Both clinical and CT findings were useful for predicting OS after FOLFIRINOX in PAC.

Utilizing fully-automated 3D organ segmentation for hepatic steatosis assessment with CT attenuation-based parameters.

OBJECTIVES: To investigate the clinical utility of fully-automated 3D organ segmentation in assessing hepatic steatosis on pre-contrast and post-contrast CT images using magnetic resonance spectroscopy (MRS)-proton density fat fraction (PDFF) as reference standard. MATERIALS AND METHODS: This retrospective study analyzed 362 adult potential living liver donors with abdominal CT scans and MRS-PDFF. Using a deep learning-based tool, mean volumetric CT attenuation of the liver and spleen were measured on pre-contrast (liver(L)_pre and spleen(S)_pre) and post-contrast (L_post and S_post) images. Agreements between volumetric and manual region-of-interest (ROI)-based measurements were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. Diagnostic performances of volumetric parameters (L_pre, liver-minus-spleen (L-S)_pre, L_post, and L-S_post) were evaluated for detecting MRS-PDFF ≥ 5% and ≥ 10% using receiver operating characteristic (ROC) curve analysis and compared with those of ROI-based parameters. RESULTS: Among the 362 subjects, 105 and 35 had hepatic steatosis with MRS-PDFF ≥ 5% and ≥ 10%, respectively. Volumetric and ROI-based measurements revealed ICCs of 0.974, 0.825, 0.992, and 0.962, with mean differences of -4.2 HU, -3.4 HU, -1.2 HU, and -7.7 HU for L_pre, S_pre, L_post, and S_post, respectively. Volumetric L_pre, L-S_pre, L_post, and L-S_post yielded areas under the ROC curve of 0.813, 0.813, 0.734, and 0.817 for MRS-PDFF ≥ 5%; and 0.901, 0.915, 0.818, and 0.868 for MRS-PDFF ≥ 10%, comparable with those of ROI-based parameters (0.735-0.818; and 0.816-0.895, Ps = 0.228-0.911). CONCLUSION: Automated 3D segmentation of the liver and spleen in CT scans can provide volumetric CT attenuation-based parameters to detect and grade hepatic steatosis, applicable to pre-contrast and post-contrast images. CLINICAL RELEVANCE STATEMENT: Volumetric CT attenuation-based parameters of the liver and spleen, obtained through automated segmentation tools from pre-contrast or post-contrast CT scans, can efficiently detect and grade hepatic steatosis, making them applicable for large population data collection. KEY POINTS: • Automated organ segmentation enables the extraction of CT attenuation-based parameters for the target organ. • Volumetric liver and spleen CT attenuation-based parameters are highly accurate in hepatic steatosis assessment. • Automated CT measurements from pre- or post-contrast imaging show promise for hepatic steatosis screening in large cohorts.

Fully-automated multi-organ segmentation tool applicable to both non-contrast and post-contrast abdominal CT: deep learning algorithm developed using dual-energy CT images.

A novel 3D nnU-Net-based of algorithm was developed for fully-automated multi-organ segmentation in abdominal CT, applicable to both non-contrast and post-contrast images. The algorithm was trained using dual-energy CT (DECT)-obtained portal venous phase (PVP) and spatiotemporally-matched virtual non-contrast images, and tested using a single-energy (SE) CT dataset comprising PVP and true non-contrast (TNC) images. The algorithm showed robust accuracy in segmenting the liver, spleen, right kidney (RK), and left kidney (LK), with mean dice similarity coefficients (DSCs) exceeding 0.94 for each organ, regardless of contrast enhancement. However, pancreas segmentation demonstrated slightly lower performance with mean DSCs of around 0.8. In organ volume estimation, the algorithm demonstrated excellent agreement with ground-truth measurements for the liver, spleen, RK, and LK (intraclass correlation coefficients [ICCs] > 0.95); while the pancreas showed good agreements (ICC = 0.792 in SE-PVP, 0.840 in TNC). Accurate volume estimation within a 10% deviation from ground-truth was achieved in over 90% of cases involving the liver, spleen, RK, and LK. These findings indicate the efficacy of our 3D nnU-Net-based algorithm, developed using DECT images, which provides precise segmentation of the liver, spleen, and RK and LK in both non-contrast and post-contrast CT images, enabling reliable organ volumetry, albeit with relatively reduced performance for the pancreas.

Prognostic value of initial and longitudinal changes in body composition in metastatic pancreatic cancer.

BACKGROUND: Sarcopenia or visceral adipose tissue has been reported to be related to pancreatic cancer prognosis. However, clinical relevance of the comprehensive analysis of body compositions and their longitudinal changes is lacking. This study analysed the association between body composition changes after chemotherapy and survival in patients with metastatic pancreatic cancer. METHODS: We retrospectively included 456 patients (mean age ± standard deviation, 61.2 ± 10.0 years; 272 males and 184 females) with metastatic pancreatic cancer who received palliative chemotherapy from May 2011 to December 2019. Using deep learning-based, fully automated segmentation of contrast-enhanced computed tomography (CT) at the time of diagnosis, cross-sectional areas of muscle, subcutaneous adipose tissue and visceral adipose tissue were extracted from a single axial image of the portal venous phase at L3 level. Skeletal muscle index (SMI), visceral adipose tissue index (VATI), subcutaneous adipose tissue index (SATI) and mean skeletal muscle attenuation (MA) were calculated, and their effect on overall survival (OS) was analysed. Longitudinal changes in body composition and prognostic values were also analysed in a subgroup of patients with 2- and 6-month follow-up CT (n = 349). RESULTS: A total of 452 deaths occurred during follow-up in the entire cohort. The survival rate was 49.3% (95% confidence interval [CI], 44.9-54.2) at 1 year and 3.7% (95% CI, 2.0-6.8) at 5 years. In multivariable analysis, higher MA (≥44.4 HU in males and ≥34.8 HU in females) at initial CT was significantly associated with better OS in both males and females (adjusted hazard ratio [HR], 0.706; 95% CI, 0.538-0.925; P = 0.012 for males, and HR, 0.656; 95% CI, 0.475-0.906; P = 0.010 for females), whereas higher SATI (≥42.8 cm2/m2 in males and ≥65.8 cm2/m2 in females) was significantly associated with better OS in female patients only (adjusted HR, 0.568; 95% CI, 0.388-0.830; P = 0.003). In longitudinal analysis, SMI, VATI and SATI significantly decreased between initial and 2-month follow-up CT, whereas mean MA significantly decreased between 2- and 6-month follow-up CT. In multivariable Cox regression analysis of longitudinal changes, which was stratified by disease control state, SATI change was significantly associated with OS in male patients (adjusted HR, 0.513; 95% CI, 0.354-0.745; P < 0.001), while other body composition parameters were not. CONCLUSIONS: In patients with metastatic pancreatic cancer, body composition mostly changed during the first 2 months after starting chemotherapy, and the prognostic factors associated with OS differed between males and females. Initial and longitudinal changes of body composition are associated with OS of metastatic pancreatic cancer.

LI-RADS CT and MRI Ancillary Feature Association with Hepatocellular Carcinoma and Malignancy: An Individual Participant Data Meta-Analysis.

Background The independent contribution of each Liver Imaging Reporting and Data System (LI-RADS) CT or MRI ancillary feature (AF) has not been established. Purpose To evaluate the association of LI-RADS AFs with hepatocellular carcinoma (HCC) and malignancy while adjusting for LI-RADS major features through an individual participant data (IPD) meta-analysis. Materials and Methods Medline, Embase, Cochrane Central Register of Controlled Trials, and Scopus were searched from January 2014 to January 2022 for studies evaluating the diagnostic accuracy of CT and MRI for HCC using LI-RADS version 2014, 2017, or 2018. Using a one-step approach, IPD across studies were pooled. Adjusted odds ratios (ORs) and 95% CIs were derived from multivariable logistic regression models of each AF combined with major features except threshold growth (excluded because of infrequent reporting). Liver observation clustering was addressed at the study and participant levels through random intercepts. Risk of bias was assessed using a composite reference standard and Quality Assessment of Diagnostic Accuracy Studies 2. Results Twenty studies comprising 3091 observations (2456 adult participants; mean age, 59 years ± 11 [SD]; 1849 [75.3%] men) were included. In total, 89% (eight of nine) of AFs favoring malignancy were associated with malignancy and/or HCC, 80% (four of five) of AFs favoring HCC were associated with HCC, and 57% (four of seven) of AFs favoring benignity were negatively associated with HCC and/or malignancy. Nonenhancing capsule (OR = 3.50 [95% CI: 1.53, 8.01]) had the strongest association with HCC. Diffusion restriction (OR = 14.45 [95% CI: 9.82, 21.27]) and mild-moderate T2 hyperintensity (OR = 10.18 [95% CI: 7.17, 14.44]) had the strongest association with malignancy. The strongest negative associations with HCC were parallels blood pool enhancement (OR = 0.07 [95% CI: 0.01, 0.49]) and marked T2 hyperintensity (OR = 0.18 [95% CI: 0.07, 0.45]). Seventeen studies (85%) had a high risk of bias. Conclusion Most LI-RADS AFs were independently associated with HCC, malignancy, or benignity as intended when adjusting for major features. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Crivellaro in this issue.

Contrast-enhanced ultrasonography-CT/MRI fusion guidance for percutaneous ablation of inconspicuous, small liver tumors: improving feasibility and therapeutic outcome.

BACKGROUND: Percutaneous radiofrequency ablation (RFA) is pivotal for treating small malignant liver tumors, but tumors often remain inconspicuous on B-mode ultrasound (US). This study evaluates the potential of CEUS-CT/MRI fusion imaging (FI) to improve tumor visibility and the associated RFA outcomes for small (≤ 3 cm) malignant liver tumors that were inconspicuous on US. METHODS: Between January 2019 and April 2021, a prospective study enrolled 248 patients with liver malignancies (≤ 3 cm) that were poorly visible on B-mode US. Tumor visibility and ablation feasibility were assessed using B-mode US, US-CT/MRI FI, and CEUS-CT/MRI FI, and graded on a 4-point scale. CEUS was employed post-registration of US and CT/MRI images, utilizing either SonoVue or Sonazoid. Comparisons between US-based and CEUS-based fusion visibility and feasibility scores were undertaken using the Friedman test. Moreover, rates of technical success, technique efficacy, local tumor progression (LTP), and major complications were assessed. RESULTS: The cohort included 223 hepatocellular carcinomas (HCCs) (89.9%) and 23 metastases (9.3%), with an average tumor size of 1.6 cm. CEUS-CT/MRI FI demonstrated a significant advantage in tumor visibility (3.4 ± 0.7 vs. 1.9 ± 0.6, P < 0.001) and technical feasibility (3.6 ± 0.6 vs. 2.9 ± 0.8, P < 0.001) compared to US-FI. In 85.5% of patients, CEUS addition to US-FI ameliorated tumor visibility. Technical success was achieved in 99.6% of cases. No severe complications were reported. One and two-year post CEUS-CT/MRI FI-guided RFA estimates for LTP were 9.3% and 10.9%, respectively. CONCLUSIONS: CEUS-CT/MRI FI significantly improves the visualization of tumors not discernible on B-mode US, thus augmenting percutaneous RFA success and delivering improved therapeutic outcomes. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05445973. Registered 17 June 2022 - Retrospectively registered, http://clinicaltrials.gov/study/NCT05445973?id=NCT05445973&rank=1 .

Image Quality Improvement of Low-dose Abdominal CT using Deep Learning Image Reconstruction Compared with the Second Generation Iterative Reconstruction

BACKGROUND: Whether deep learning-based CT reconstruction could improve lesion conspicuity on abdominal CT when the radiation dose is reduced is controversial. OBJECTIVES: To determine whether DLIR can provide better image quality and reduce radiation dose in contrast-enhanced abdominal CT compared with the second generation of adaptive statistical iterative reconstruction (ASiR-V). AIMS: This study aims to determine whether deep-learning image reconstruction (DLIR) can improve image quality. METHOD: In this retrospective study, a total of 102 patients were included, who underwent abdominal CT using a DLIR-equipped 256-row scanner and routine CT of the same protocol on the same vendor's 64-row scanner within four months. The CT data from the 256-row scanner were reconstructed into ASiR-V with three blending levels (AV30, AV60, and AV100), and DLIR images with three strength levels (DLIR-L, DLIR-M, and DLIR-H). The routine CT data were reconstructed into AV30, AV60, and AV100. The contrast-to-noise ratio (CNR) of the liver, overall image quality, subjective noise, lesion conspicuity, and plasticity in the portal venous phase (PVP) of ASiR-V from both scanners and DLIR were compared. RESULTS: The mean effective radiation dose of PVP of the 256-row scanner was significantly lower than that of the routine CT (6.3±2.0 mSv vs. 2.4±0.6 mSv; p< 0.001). The mean CNR, image quality, subjective noise, and lesion conspicuity of ASiR-V images of the 256-row scanner were significantly lower than those of ASiR-V images at the same blending factor of routine CT, but significantly improved with DLIR algorithms. DLIR-H showed higher CNR, better image quality, and subjective noise than AV30 from routine CT, whereas plasticity was significantly better for AV30. CONCLUSION: DLIR can be used for improving image quality and reducing radiation dose in abdominal CT, compared with ASIR-V.

Intraductal papillary neoplasm of the bile duct: diagnostic value of MRI features in differentiating pathologic subclassifications-type 1 versus type 2.

OBJECTIVES: To identify MRI features for differentiating type 2 from type 1 intraductal papillary neoplasms of bile duct (IPNB) and assessing malignant potential of IPNB. METHODS: This retrospective study included 60 patients with surgically proven IPNB who had undergone preoperative MRI between January 2007 and December 2020. All surgical specimens were reviewed retrospectively to classify types 1 and 2 IPNBs and assess tumor grade. Significant MRI features for differentiating type 2 (n = 40) from type 1 IPNB (n = 20); and for IPNB with an associated invasive carcinoma (n = 43) from intraepithelial neoplasia (n = 17) were determined using logistic regression analysis. RESULTS: An associated invasive carcinoma was more frequently found in type 2 than in type 1 IPNB (85.0% [34/40] vs. 45.0% [9/20], p = 0.003). At univariable analysis, MRI features including extrahepatic location, no dilatation of tumor-bearing segment of bile duct, isolated upstream bile duct dilatation, and single lesion were associated with type 2 IPNB (all p ≤ 0.012). At multivariable analysis, significant MRI findings for differentiating type 2 from type 1 IPNB were extrahepatic location and no dilatation of tumor-bearing segment of bile duct (odds ratio [OR], 7.24 and 46.40, respectively). At univariable and multivariable analysis, tumor size ≥ 2.5 cm (OR, 8.45), bile duct wall thickening (OR, 4.82), and irregular polypoid or nodular tumor shape (OR, 6.44) were significant MRI features for differentiating IPNB with an associated invasive carcinoma from IPNB with intraepithelial neoplasia. CONCLUSION: MRI with MR cholangiopancreatography may be helpful in differentiating type 2 IPNB from type 1 IPNB and assessing malignant potential of IPNB. CLINICAL RELEVANCE STATEMENT: Preoperative MRI with MR cholangiopancreatography may be helpful in differentiating type 2 intraductal papillary neoplasms of bile duct (IPNB) from type 1 IPNB and assessing malignant potential of IPNB. KEY POINTS: • In terms of tumor grade, the incidence of invasive carcinoma was significantly higher in type 2 intraductal papillary neoplasm of the bile duct (IPNB) than in type 1 IPNB. • At MRI, extrahepatic location and no dilatation of tumor-bearing segment are significant features for differentiating type 2 IPNBs from type 1 IPNBs. • At MRI, large tumor size, bile duct wall thickening, and irregular polypoid or nodular tumor shape are significant features for differentiating IPNB with an associated invasive carcinoma from IPNB with intraepithelial neoplasia.

Comparative Performance of 2018 LI-RADS versus Modified LIRADS (mLI-RADS): An Individual Participant Data Meta-Analysis.

BACKGROUND: LI-RADS version 2018 (v2018) is used for non-invasive diagnosis of hepatocellular carcinoma (HCC). A recently proposed modification (known as mLI-RADS) demonstrated improved sensitivity while maintaining specificity and positive predictive value (PPV) of LI-RADS category 5 (definite HCC) for HCC. However, mLI-RADS requires multicenter validation. PURPOSE: To evaluate the performance of v2018 and mLI-RADS for liver lesions in a large, heterogeneous, multi-national cohort of patients at risk for HCC. STUDY TYPE: Systematic review and meta-analysis using individual participant data (IPD) [Study Protocol: https://osf.io/duys4]. POPULATION: 2223 observations from 1817 patients (includes all LI-RADS categories; females = 448, males = 1361, not reported = 8) at elevated risk for developing HCC (based on LI-RADS population criteria) from 12 retrospective studies. FIELD STRENGTH/SEQUENCE: 1.5T and 3T; complete liver MRI with gadoxetate disodium, including axial T2w images and dynamic axial fat-suppressed T1w images precontrast and in the arterial, portal venous, transitional, and hepatobiliary phases. Diffusion-weighted imaging was used when available. ASSESSMENT: Liver observations were categorized using v2018 and mLI-RADS. The diagnostic performance of each system's category 5 (LR-5 and mLR-5) for HCC were compared. STATISTICAL TESTS: The Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2 was applied to determine risk of bias and applicability. Diagnostic performances were assessed using the likelihood ratio test for sensitivity and specificity and the Wald test for PPV. The significance level was P < 0.05. RESULTS: 17% (2/12) of the studies were considered low risk of bias (244 liver observations; 164 patients). When compared to v2018, mLR-5 demonstrated higher sensitivity (61.3% vs. 46.5%, P < 0.001), similar PPV (85.3% vs. 86.3%, P = 0.89), and similar specificity (85.8% vs. 90.8%, P = 0.16) for HCC. DATA CONCLUSION: This study confirms mLR-5 has higher sensitivity than LR-5 for HCC identification, while maintaining similar PPV and specificity, validating the mLI-RADS proposal in a heterogeneous, international cohort. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Individual Participant Data Meta-Analysis of LR-5 in LI-RADS Version 2018 versus Revised LI-RADS for Hepatocellular Carcinoma Diagnosis.

Background A simplification of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 (v2018), revised LI-RADS (rLI-RADS), has been proposed for imaging-based diagnosis of hepatocellular carcinoma (HCC). Single-site data suggest that rLI-RADS category 5 (rLR-5) improves sensitivity while maintaining positive predictive value (PPV) of the LI-RADS v2018 category 5 (LR-5), which indicates definite HCC. Purpose To compare the diagnostic performance of LI-RADS v2018 and rLI-RADS in a multicenter data set of patients at risk for HCC by performing an individual patient data meta-analysis. Materials and Methods Multiple databases were searched for studies published from January 2014 to January 2022 that evaluated the diagnostic performance of any version of LI-RADS at CT or MRI for diagnosing HCC. An individual patient data meta-analysis method was applied to observations from the identified studies. Quality Assessment of Diagnostic Accuracy Studies version 2 was applied to determine study risk of bias. Observations were categorized according to major features and either LI-RADS v2018 or rLI-RADS assignments. Diagnostic accuracies of category 5 for each system were calculated using generalized linear mixed models and compared using the likelihood ratio test for sensitivity and the Wald test for PPV. Results Twenty-four studies, including 3840 patients and 4727 observations, were analyzed. The median observation size was 19 mm (IQR, 11-30 mm). rLR-5 showed higher sensitivity compared with LR-5 (70.6% [95% CI: 60.7, 78.9] vs 61.3% [95% CI: 45.9, 74.7]; P < .001), with similar PPV (90.7% vs 92.3%; P = .55). In studies with low risk of bias (n = 4; 1031 observations), rLR-5 also achieved a higher sensitivity than LR-5 (72.3% [95% CI: 63.9, 80.1] vs 66.9% [95% CI: 58.2, 74.5]; P = .02), with similar PPV (83.1% vs 88.7%; P = .47). Conclusion rLR-5 achieved a higher sensitivity for identifying HCC than LR-5 while maintaining a comparable PPV at 90% or more, matching the results presented in the original rLI-RADS study. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sirlin and Chernyak in this issue.

Prognostic role of computed tomography analysis using deep learning algorithm in patients with chronic hepatitis B viral infection.

BACKGROUND/AIMS: The prediction of clinical outcomes in patients with chronic hepatitis B (CHB) is paramount for effective management. This study aimed to evaluate the prognostic value of computed tomography (CT) analysis using deep learning algorithms in patients with CHB. METHODS: This retrospective study included 2,169 patients with CHB without hepatic decompensation who underwent contrast-enhanced abdominal CT for hepatocellular carcinoma (HCC) surveillance between January 2005 and June 2016. Liver and spleen volumes and body composition measurements including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle indices were acquired from CT images using deep learning-based fully automated organ segmentation algorithms. We assessed the significant predictors of HCC, hepatic decompensation, diabetes mellitus (DM), and overall survival (OS) using Cox proportional hazard analyses. RESULTS: During a median follow-up period of 103.0 months, HCC (n=134, 6.2%), hepatic decompensation (n=103, 4.7%), DM (n=432, 19.9%), and death (n=120, 5.5%) occurred. According to the multivariate analysis, standardized spleen volume significantly predicted HCC development (hazard ratio [HR]=1.01, P=0.025), along with age, sex, albumin and platelet count. Standardized spleen volume (HR=1.01, P<0.001) and VAT index (HR=0.98, P=0.004) were significantly associated with hepatic decompensation along with age and albumin. Furthermore, VAT index (HR=1.01, P=0.001) and standardized spleen volume (HR=1.01, P=0.001) were significant predictors for DM, along with sex, age, and albumin. SAT index (HR=0.99, P=0.004) was significantly associated with OS, along with age, albumin, and MELD. CONCLUSION: Deep learning-based automatically measured spleen volume, VAT, and SAT indices may provide various prognostic information in patients with CHB.

Newly developed sarcopenia after liver transplantation, determined by a fully automated 3D muscle volume estimation on abdominal CT, can predict post-transplant diabetes mellitus and poor survival outcomes.

BACKGROUND: Loss of muscle mass is the most common complication of end-stage liver disease and negatively affects outcomes for liver transplantation (LT) recipients. We aimed to determine the prognostic value of a fully automated three-dimensional (3D) muscle volume estimation using deep learning algorithms on abdominal CT in patients who underwent liver transplantation (LT). METHODS: This retrospective study included 107 patients who underwent LT from 2014 to 2015. Serial CT scans, including pre-LT and 1- and 2-year follow-ups were performed. From the CT scans, deep learning-based automated body composition segmentation software was used to calculate muscle volumes in 3D. Sarcopenia was calculated by dividing average skeletal muscle area by height squared. Newly developed-(ND) sarcopenia was defined as the onset of sarcopenia 1 or 2 years after LT in patients without a history of sarcopenia before LT. Patients' clinical characteristics, including post-transplant diabetes mellitus (PTDM) and Model for end-stage liver disease score, were compared according to the presence or absence of sarcopenia after LT. A subgroup analysis was performed in the post-LT sarcopenic group. The Kaplan-Meier method was used for overall survival (OS). RESULTS: Patients with ND-sarcopenia had poorer OS than those who did not (P = 0.04, hazard ratio [HR], 3.34; 95% confidence interval [CI] 1.05 - 10.7). In the subgroup analysis for post-LT sarcopenia (n = 94), 34 patients (36.2%) had ND-sarcopenia. Patients with ND-sarcopenia had significantly worse OS (P = 0.002, HR 7.12; 95% CI 2.00 - 25.32) and higher PTDM occurrence rates (P = 0.02, HR 4.93; 95% CI 1.18 - 20.54) than those with sarcopenia prior to LT. CONCLUSION: ND-sarcopenia determined by muscle volume on abdominal CT can predict poor survival outcomes and the occurrence of PTDM for LT recipients.

Anatomical ablation for small hepatocellular carcinomas using multiple applicators: a preliminary study.

BACKGROUND: Anatomical ablation, defined as thermal ablation of tumor-bearing small portal territories, may provide excellent local tumor control in peripherally-located small hepatocellular carcinomas (HCC), which has been a major concern with percutaneous ablation alone. PURPOSE: To evaluate the technical feasibility and therapeutic outcomes of anatomical ablation using multiple radiofrequency (RF) applicators for the ablation of tumor-bearing small portal territories of peripherally-located small (≤ 4 cm) HCCs. MATERIALS AND METHODS: Patients with peripherally-located single HCCs (≤ 4 cm) to be treated with anatomical ablation using multiple RF applicators between January 2020 and March 2022 were enrolled in this prospective study. Anatomical ablation was performed for the index tumor under real-time US-CT/MR fusion imaging guidance, with one or two clustered electrode needles placed across the tumor-bearing portal vein branches. Technical success and complications of anatomical ablations were assessed. Cumulative incidence of local tumor progression (LTP) and recurrence-free survival were estimated using the Kaplan-Meier method. RESULTS: Fifty-five HCCs (mean size, 1.77 ± 0.59 cm) in 55 participants (mean age, 66.4 ± 7.7 years; 39 men, 16 women) were treated with anatomical ablation; 98.2% (54/55) technical success was achieved. No major complications were noted. Among the 55 participants, LTP occurred in only one patient who had experienced technical failure of anatomical ablation. Estimated 1- and 2-year cumulative incidences of LTP were 0% and 3.7%, respectively. Five patients developed intrahepatic remote recurrence during the median follow-up period of 19.2 months (range, 3.7-28.8 months); therefore, estimated 1- and 2-year recurrence-free survival was 91.7% and 85.0%, respectively. CONCLUSION: Anatomical ablation using multiple RF applicators provided the excellent results of local tumor control in patients with peripherally-located small (≤ 4 cm) HCCs. TRIAL REGISTRATION: clinicaltrial.gov identifier: NCT05397860.

Clinical feasibility of radiofrequency ablation using novel adjustable separable electrodes with a multipurpose needle for treating small hepatocellular carcinomas: a prospective single center study.

BACKGROUND: The novel separable clustered electrode system with two adjustable active tips (ICAEs) and a fine multipurpose needle (MPN) for in situ temperature monitoring and adjuvant agent injection was developed and validated in an animal study. The purpose of this study was to evaluate the technical efficacy and complication of the novel electrode system for patients who have small HCC. METHODS: In this prospective, single-center clinical trial, ten participants with 14 small (≤ 2 cm, BCLC 0-A) HCCs referred for RFA were enrolled. A novel electrode system consisting of two ICAEs and one MPN with a thermometer and side holes was used for RFA. The RF energy was delivered using a multichannel RF system combining bipolar and switching monopolar modes. Technical success, efficacy, and complications were evaluated on immediate and one-month follow-up CT. RESULTS: Technical success was achieved in 92.9% (13/14) of tumors. One participant withdrew consent after RFA, and technical efficacy was achieved in 91.7% (11/12) of tumors. None showed thermal injury to nontarget organs. All patients were discharged the day after RFA without major complications. The active electrode lengths were adjusted in 60% (6/10) of patients during the procedure to tailor the ablation zone (83.3%, n = 5) or treat two tumors with different sizes (16.7%, n = 1). MPN was capable of continuous temperature monitoring during all ablations (100%, 14/14). CONCLUSIONS: RFA using a novel electrode system showed acceptable technical efficacy and safety in patients with small HCCs. Further comparative studies are needed for the investigation of the system's potential benefits compared to conventional electrodes.

Perfluorobutane-Enhanced Ultrasound for Characterization of Hepatocellular Carcinoma From Non-hepatocellular Malignancies or Benignancy: Comparison of Imaging Acquisition Methods.

OBJECTIVE: The aim of the work described here was to evaluate the diagnostic performance of perfluorobutane (PFB)-enhanced ultrasound in differentiating hepatocellular carcinoma (HCC) from non-HCC malignancies and other benign lesions using different acquisition methods. METHODS: This prospective study included 69 patients with solid liver lesions larger than 1 cm who were scheduled for biopsy or radiofrequency ablation between September 2020 and March 2021. Lesion diagnosis was designated by three blinded radiologists after reviewing three different sets of acquired images selected according to the following presumed acquisition methods: (i) method A, acquisition up to 5 min after contrast injection; (ii) method B, acquisition up to 1 min after contrast injection with additional Kupffer phase; and (iii) method C, acquisition up to 5 min after contrast injection with additional Kupffer phase. RESULTS: After excluding 7 technical failures, 62 patients with liver lesions (mean size: 24.2 ± 14.8 mm), which consisted of 7 benign lesions, 37 non-HCC malignancies and 18 HCCs. For the HCC diagnosis, method C had the highest sensitivity (75.9%), followed by method B (72.2%) and method A (68.5%), but failed to exhibit statistical significance (p = 0.12). There was no significant difference with respect to the pooled specificity between the three methods (p = 0.28). Diagnostic accuracy was the highest with method C (87.1%) but failed to exhibit statistical significance (p = 0.24). CONCLUSION: Image acquisition up to 5 min after contrast injection with additional Kupffer phase could potentially result in high accuracy and sensitivity without loss of specificity in diagnosing HCC with PFB-enhanced ultrasound.

Diagnosing Hepatocellular Carcinoma Using Sonazoid Contrast-Enhanced Ultrasonography: 2023 Guidelines From the Korean Society of Radiology and the Korean Society of Abdominal Radiology.

Sonazoid, a second-generation ultrasound contrast agent, was introduced for the diagnosis of hepatic nodules. To clarify the issues with Sonazoid contrast-enhanced ultrasonography for the diagnosis of hepatocellular carcinoma (HCC), the Korean Society of Radiology and Korean Society of Abdominal Radiology collaborated on the guidelines. The guidelines are de novo, evidence-based, and selected using an electronic voting system for consensus. These include imaging protocols, diagnostic criteria for HCC, diagnostic value for lesions that are inconclusive on other imaging results, differentiation from non-HCC malignancies, surveillance of HCC, and treatment response after locoregional and systemic treatment for HCC.

Deep Learning-Enabled Detection of Pneumoperitoneum in Supine and Erect Abdominal Radiography: Modeling Using Transfer Learning and Semi-Supervised Learning.

OBJECTIVE: Detection of pneumoperitoneum using abdominal radiography, particularly in the supine position, is often challenging. This study aimed to develop and externally validate a deep learning model for the detection of pneumoperitoneum using supine and erect abdominal radiography. MATERIALS AND METHODS: A model that can utilize "pneumoperitoneum" and "non-pneumoperitoneum" classes was developed through knowledge distillation. To train the proposed model with limited training data and weak labels, it was trained using a recently proposed semi-supervised learning method called distillation for self-supervised and self-train learning (DISTL), which leverages the Vision Transformer. The proposed model was first pre-trained with chest radiographs to utilize common knowledge between modalities, fine-tuned, and self-trained on labeled and unlabeled abdominal radiographs. The proposed model was trained using data from supine and erect abdominal radiographs. In total, 191212 chest radiographs (CheXpert data) were used for pre-training, and 5518 labeled and 16671 unlabeled abdominal radiographs were used for fine-tuning and self-supervised learning, respectively. The proposed model was internally validated on 389 abdominal radiographs and externally validated on 475 and 798 abdominal radiographs from the two institutions. We evaluated the performance in diagnosing pneumoperitoneum using the area under the receiver operating characteristic curve (AUC) and compared it with that of radiologists. RESULTS: In the internal validation, the proposed model had an AUC, sensitivity, and specificity of 0.881, 85.4%, and 73.3% and 0.968, 91.1, and 95.0 for supine and erect positions, respectively. In the external validation at the two institutions, the AUCs were 0.835 and 0.852 for the supine position and 0.909 and 0.944 for the erect position. In the reader study, the readers' performances improved with the assistance of the proposed model. CONCLUSION: The proposed model trained with the DISTL method can accurately detect pneumoperitoneum on abdominal radiography in both the supine and erect positions.

Post-neoadjuvant treatment pancreatic cancer resectability and outcome prediction using CT, 18F-FDG PET/MRI and CA 19-9.

BACKGROUND: CT prediction of resectability and prognosis following neoadjuvant treatment (NAT) in patients with pancreatic ductal adenocarcinoma (PDAC) remains challenging. This study aims to determine whether addition of 18F-fluorodeoxyglucose (FDG) postiron emission tomography (PET)/MRI and carbohydrate antigen (CA) 19-9 to contrast-enhanced CT (CECT) can improve accuracy of predicting resectability compared to CECT alone and predict prognosis in PDAC patients after NAT. METHODS: In this retrospective study, 120 PDAC patients (65 women; mean age, 66.7 years [standard deviation, 8.4]) underwent CECT, PET/MRI, and CA 19-9 examinations after NAT between January 2013 and June 2021. Three board-certified radiologists independently rated the overall resectability on a 5-point scale (score 5, definitely resectable) in three sessions (session 1, CECT; 2, CECT plus PET/MRI─no FDG avidity and no diffusion restriction at tumor-vessel contact indicated modification of CECT scores to ≥ 3; 3, CECT plus PET plus CA 19-9─no FDG avidity at tumor-vessel contact and normalized CA 19-9 indicated modification of CECT scores to ≥ 3). Jackknife free-response receiver operating characteristic method and generalized estimating equations were used to compare pooled area under the curve (AUC), sensitivity, and specificity of three sessions. Predictors for recurrence-free survival (RFS) were assessed using Cox regression analyses. RESULTS: Each session showed different pooled AUC (session 1 vs. 2 vs. 3, 0.853 vs. 0.873 vs. 0.874, p = 0.026), sensitivity (66.2% [137/207] vs. 86.0% [178/207] vs. 84.5% [175/207], p < 0.001) and specificity (67.3% [103/153] vs. 58.8% [90/153] vs. 60.1% [92/153], p = 0.048). According to pairwise comparison, specificity of CECT plus PET/MRI was lower than that of CECT alone (adjusted p = 0.042), while there was no significant difference in specificity between CECT alone and CECT plus PET plus CA 19-9 (adjusted p = 0.081). Twenty-eight of 69 patients (40.6%) with R0 resection experienced tumor recurrence (mean follow-up, 18.0 months). FDG avidity at tumor-vessel contact on post-NAT PET (HR = 4.37, p = 0.033) and pathologically confirmed vascular invasion (HR = 5.36, p = 0.004) predicted RFS. CONCLUSION: Combination of CECT, PET and CA 19-9 increased area under the curve and sensitivity for determining resectability, compared to CECT alone, without compromising the specificity. Furthermore, 18F-FDG avidity at tumor-vessel contact on post-NAT PET predicted RFS.