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.

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.

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.

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.

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.

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 .

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.

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.

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.