Exploring the impact of excluding intrahepatic segmental vessels on liver stiffness measurement and advanced fibrosis diagnosis using magnetic resonance elastography.

BACKGROUND: The impact of excluding intrahepatic segmental vessels from regions of interest (ROIs) on liver stiffness measurement (LSM) via magnetic resonance elastography (MRE) remains uncertain. PURPOSE: To determine the effect of excluding intrahepatic segmental vessels from ROIs on LSM obtained from MRE. MATERIAL AND METHODS: This retrospective analysis included 95 participants who underwent successful two-dimensional gradient recalled-echo MRE before hepatic tumor resection (n = 49) or living liver donation (n = 46). The conventional LSM was determined by manually drawing ROIs on the elastogram within the 95% confidence region, staying 1 cm within the liver capsule and excluding large hilar vessels, the gallbladder, hepatic lesions, and artifacts. In addition, the modified LSM was determined by excluding intrahepatic segmental vessels. LSMs obtained by the two methods were compared with paired sample signed-rank test. Diagnostic performance for advanced fibrosis was calculated and compared using McNemar's test and Delong's test. The stage of hepatic fibrosis was assessed using surgical specimens by the METAVIR system. RESULTS: The modified LSM was larger than the conventional LSM (2.4 kPa vs. 2.2 kPa in reader 1; 2.7 kPa vs. 2.4 kPa in reader 2; P < 0.001). The modified LSM showed superior sensitivity (0.841 vs. 0.659 in reader 1; 0.864 vs. 0.705 in reader 2; P < 0.05) and area under the curve (0.901 vs. 0.820 in reader 1; 0.912 vs. 0.843 in reader 2; P < 0.05) for detecting advanced fibrosis (≥F3) than conventional LSM. CONCLUSION: The exclusion of intrahepatic segmental vessels from ROIs in MRE affected the LSM and enhanced diagnostic performance for advanced fibrosis.

Prognostic significance of MRI features in patients with solitary large hepatocellular carcinoma following surgical resection.

OBJECTIVE: To assess the prognostic impact of preoperative MRI features on outcomes for single large hepatocellular carcinoma (HCC) (≥ 8 cm) after surgical resection. MATERIAL AND METHODS: This retrospective study included 151 patients (mean age: 59.2 years; 126 men) with a single large HCC who underwent gadoxetic acid-enhanced MRI and surgical resection between 2008 and 2020. Clinical variables, including tumor markers and MRI features (tumor size, tumor margin, and the proportion of hypovascular component on hepatic arterial phase (AP) (≥ 50% vs. < 50% tumor volume) were evaluated. Cox proportional hazards model analyzed overall survival (OS), recurrence-free survival (RFS), and associated factors. RESULTS: Among 151 HCCs, 37.8% and 62.2% HCCs were classified as ≥ 50% and < 50% AP hypovascular groups, respectively. The 5- and 10-year OS and RFS rates in all patients were 62.0%, 52.6% and 41.4%, 38.5%, respectively. Multivariable analysis revealed that ≥ 50% AP hypovascular group (hazard ratio [HR] 1.7, p = 0.048), tumor size (HR 1.1, p = 0.006), and alpha-fetoprotein ≥ 400 ng/mL (HR 2.6, p = 0.001) correlated with poorer OS. ≥ 50% AP hypovascular group (HR 1.9, p = 0.003), tumor size (HR 1.1, p = 0.023), and non-smooth tumor margin (HR 2.1, p = 0.009) were linked to poorer RFS. One-year RFS rates were lower in the ≥ 50% AP hypovascular group than in the < 50% AP hypovascular group (47.4% vs 66.9%, p = 0.019). CONCLUSION: MRI with ≥ 50% AP hypovascular component and larger tumor size were significant factors associated with poorer OS and RFS after resection of single large HCC (≥ 8 cm). These patients require careful multidisciplinary management to determine optimal treatment strategies. CLINICAL RELEVANCE STATEMENT: Preoperative MRI showing a ≥ 50% arterial phase hypovascular component and larger tumor size can predict worse outcomes after resection of single large hepatocellular carcinomas (≥ 8 cm), underscoring the need for tailored, multidisciplinary treatment strategies. KEY POINTS: MRI features offer insights into the postoperative prognosis for large hepatocellular carcinoma. Hypovascular component on arterial phase ≥ 50% and tumor size predicted poorer overall survival and recurrence-free survival. These findings can assist in prioritizing aggressive and multidisciplinary approaches for patients at risk for poor outcomes.

Should we suspect gallbladder cancer if which CT finding is observed in patients with localized gallbladder wall thickening?

PURPOSE: To identify high-risk computed tomography (CT) features for predicting gallbladder (GB) cancer in patients presenting with localized GB wall thickening. METHODS: This retrospective analysis included 120 patients (mean age: 63.9 ± 10.0 years; 51 men) exhibiting localized GB wall thickening on CT scans obtained between January 2008 and May 2017. Two radiologists independently evaluated CT imaging features for predicting GB cancer. The diagnostic performance of significant imaging features and their combinations was evaluated. High-risk CT features ranked by accuracy were delineated for predicting GB cancer. RESULTS: This study included 55 patients with GB cancer and 65 with benign GB conditions. The top-four most accurate CT imaging features for predicting GB cancer were identified: heterogeneously enhancing single layer or strongly enhancing thick inner layer; GB wall thickness > 6.5 mm; hyperenhancement on arterial phase; and absence of intramural small cystic lesions (accuracies of 90.0 %, 88.3 %, 85.0 %, and 85.0 %, respectively). The combination of any three high-risk features exhibited the highest accuracy (94.2 %). The presence of any high-risk feature yielded a sensitivity of 100 %, whereas that of all high-risk features indicated a specificity of 100 %. CONCLUSION: CT imaging features, whether alone or in combination, could effectively and accurately predict GB cancer among patients with localized GB wall thickening. This finding holds significance in guiding decisions regarding further diagnostic tests and treatment planning.

Using GPT-4 for LI-RADS feature extraction and categorization with multilingual free-text reports.

BACKGROUND AND AIMS: The Liver Imaging Reporting and Data System (LI-RADS) offers a standardized approach for imaging hepatocellular carcinoma. However, the diverse styles and structures of radiology reports complicate automatic data extraction. Large language models hold the potential for structured data extraction from free-text reports. Our objective was to evaluate the performance of Generative Pre-trained Transformer (GPT)-4 in extracting LI-RADS features and categories from free-text liver magnetic resonance imaging (MRI) reports. METHODS: Three radiologists generated 160 fictitious free-text liver MRI reports written in Korean and English, simulating real-world practice. Of these, 20 were used for prompt engineering, and 140 formed the internal test cohort. Seventy-two genuine reports, authored by 17 radiologists were collected and de-identified for the external test cohort. LI-RADS features were extracted using GPT-4, with a Python script calculating categories. Accuracies in each test cohort were compared. RESULTS: On the external test, the accuracy for the extraction of major LI-RADS features, which encompass size, nonrim arterial phase hyperenhancement, nonperipheral 'washout', enhancing 'capsule' and threshold growth, ranged from .92 to .99. For the rest of the LI-RADS features, the accuracy ranged from .86 to .97. For the LI-RADS category, the model showed an accuracy of .85 (95% CI: .76, .93). CONCLUSIONS: GPT-4 shows promise in extracting LI-RADS features, yet further refinement of its prompting strategy and advancements in its neural network architecture are crucial for reliable use in processing complex real-world MRI reports.

Ultrasonographic findings of metabolic dysfunction-associated fatty liver disease: A comparative study with non-alcoholic fatty liver disease and clinical characteristics.

PURPOSE: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a new term proposed to replace non-alcoholic fatty liver disease (NAFLD). We analyzed the ultrasonographic findings of MAFLD and NAFLD. METHODS: We conducted a retrospective cross-sectional study of subjects aged ≥19 years who underwent a health screening examination, including ultrasonography, (n = 17,066). Patients were separated into one of three groups; pure MAFLD (n = 5304), pure NAFLD (n = 579), and both NAFLD & MAFLD (n = 11,183). The outcomes were the degree of fatty liver disease and liver cirrhosis, defined by ultrasonography. In addition, the risk of ultrasonographic cirrhosis was assessed in the MAFLD group based on clinical characteristics. RESULTS: The pure NAFLD group had a lower risk of severe fatty liver disease than the both NAFLD & MAFLD groups (0.9 % vs. 4.4 %, p < 0.001). Cirrhosis was not diagnosed in the NAFLD group. Cirrhosis was more common in the pure MAFLD group than in the both NAFLD & MAFLD group (0.3 % vs. 0.0 %, p < 0.001). In the MAFLD group, multivariable analysis showed that diagnosis by hepatic steatosis index (Odds ratio [OR], 12.39; 95 % confidence interval [CI], 3.40-45.19; p < 0.001) or significant alcohol intake (OR, 9.58, 95 % CI, 1.93-47.61; p = 0.006) was independently associated with risk of liver cirrhosis on ultrasonography. CONCLUSION: Liver cirrhosis was more frequently identified on ultrasonography in patients with MAFLD than in NAFLD. MAFLD diagnosed using the hepatic steatosis index or significant alcohol intake is a risk factor for liver cirrhosis.

Impact and optimal timing of local therapy addition in borderline resectable or locally advanced pancreatic cancer after FOLFIRINOX chemotherapy.

Background: To evaluate the efficacy and optimal timing of local treatment in patients with borderline resectable (BR) or locally advanced pancreatic cancer (LAPC) treated with upfront FOLFIRINOX. Method: Between 2015 and 2020, 258 patients with pancreatic ductal adenocarcinoma (PDAC) were analysed. Treatment outcomes were compared between systemic treatment group (ST) and multimodality treatment groups (MT) using Kaplan-Meier curves and log-rank test. The MT were stratified as follows: FOLFIRINOX + radiation therapy (RT) (MT1), FOLFIRINOX + surgical resection (MT2), and FOLFIRINOX + RT + surgical resection (MT3). Results: With median follow-up period of 18 months, the 2-year overall survival (OS) for the ST was 22.0%, and it was significantly worse than MT (MT1, 46.3%; MT2, 65.7% and MT3; 90.2%; P < .001). The 2-year locoregional progression free survival (LRPFS) and overall PFS in ST were 10.7% and 7.0%, which were also significantly lower than those of MT (2-year LRPFS: MT1, 31.8%; MT2, 45.3%; MT3, 81.0%; 2-year overall PFS: MT1, 23.3%; MT2, 35.0%; MT3, 66.3%; P < .001). In time-varying multivariate Cox proportional hazard model, local treatment contributed to better treatment outcomes, with adjusted hazard ratios of 0.568 (95% confidence interval [CI], 0.398-0.811), 0.490 (95% CI, 0.331-0.726), and 0.656 (95% CI, 0.458-0.940) for OS, LRPFS, and overall PFS, respectively. The time window of 11-17 months after FOLFIRINOX appeared to demonstrate the maximal efficacy of local treatments in OS. Conclusions: Adding local treatment in BR/LAPC patients treated with upfront FOLFIRINOX seemed to contribute in improved treatment outcomes, and it showed maximal efficacy in OS when applied 11-17 months after the initiation of FOLFIRINOX. We suggest that administration of sufficient period of upfront FOLFIRINOX may intensify the efficacy of local treatments, and well controlled prospective trials are expected.

nnU-Net-Based Pancreas Segmentation and Volume Measurement on CT Imaging in Patients with Pancreatic Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a deep learning (DL)-based method for pancreas segmentation on CT and automatic measurement of pancreatic volume in pancreatic cancer. MATERIALS AND METHODS: This retrospective study used 3D nnU-net architecture for fully automated pancreatic segmentation in patients with pancreatic cancer. The study used 851 portal venous phase CT images (499 pancreatic cancer and 352 normal pancreas). This dataset was divided into training (n = 506), internal validation (n = 126), and external test set (n = 219). For the external test set, the pancreas was manually segmented by two abdominal radiologists (R1 and R2) to obtain the ground truth. In addition, the consensus segmentation was obtained using Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. Segmentation performance was assessed using the Dice similarity coefficient (DSC). Next, the pancreatic volumes determined by automatic segmentation were compared to those determined by manual segmentation by two radiologists. RESULTS: The DL-based model for pancreatic segmentation showed a mean DSC of 0.764 in the internal validation dataset and DSC of 0.807, 0.805, and 0.803 using R1, R2, and STAPLE as references in the external test dataset. The pancreas parenchymal volume measured by automatic and manual segmentations were similar (DL-based model: 65.5 ± 19.3 cm3 and STAPLE: 65.1 ± 21.4 cm3; p = 0.486). The pancreatic parenchymal volume difference between the DL-based model predictions and the manual segmentation by STAPLE was 0.5 cm3, with correlation coefficients of 0.88. CONCLUSION: The DL-based model efficiently generates automatic segmentation of the pancreas and measures the pancreatic volume in patients with pancreatic cancer.

Preoperative prediction of early recurrence in resectable pancreatic cancer integrating clinical, radiologic, and CT radiomics features.

OBJECTIVES: To use clinical, radiographic, and CT radiomics features to develop and validate a preoperative prediction model for the early recurrence of pancreatic cancer. METHODS: We retrospectively analyzed 190 patients (150 and 40 in the development and test cohort from different centers) with pancreatic cancer who underwent pancreatectomy between January 2018 and June 2021. Radiomics, clinical-radiologic (CR), and clinical-radiologic-radiomics (CRR) models were developed for the prediction of recurrence within 12 months after surgery. Performance was evaluated using the area under the curve (AUC), Brier score, sensitivity, and specificity. RESULTS: Early recurrence occurred in 36.7% and 42.5% of the development and test cohorts, respectively (P = 0.62). The features for the CR model included carbohydrate antigen 19-9 > 500 U/mL (odds ratio [OR], 3.60; P = 0.01), abutment to the portal and/or superior mesenteric vein (OR, 2.54; P = 0.054), and adjacent organ invasion (OR, 2.91; P = 0.03). The CRR model demonstrated significantly higher AUCs than the radiomics model in the internal (0.77 vs. 0.73; P = 0.048) and external (0.83 vs. 0.69; P = 0.038) validations. Although we found no significant difference between AUCs of the CR and CRR models (0.83 vs. 0.76; P = 0.17), CRR models showed more balanced sensitivity and specificity (0.65 and 0.87) than CR model (0.41 and 0.91) in the test cohort. CONCLUSIONS: The CRR model outperformed the radiomics and CR models in predicting the early recurrence of pancreatic cancer, providing valuable information for risk stratification and treatment guidance.