CT findings of primary clear cell carcinoma of liver: with analysis of 19 cases and review of the literature

Multimodal imaging findings of primary liver clear cell carcinoma: a case presentation

Primary clear cell carcinoma of liver (PCCCL) is a special and relatively rare subtype of hepatocellular carcinoma (HCC), which is more common in people over 50 years of age, with a preference for men and a history of hepatitis B or C and/or cirrhosis. Herein, we present a case of a 60-year-old woman who came to our hospital for medical help with right upper abdominal pain. The imaging examination showed a low-density mass in the right lobe of his liver. In contrast enhanced computed tomography (CT) or T1-weighted imaging, significant enhancement can appear around the tumor during the arterial phase, and over time, the degree of enhancement of the tumor gradually decreases. The lession showed obviously increased fluorine-18 fluorodeoxyglucose (18F-FDG) uptake on positron emission tomography/CT. These imaging findings contribute to the diagnosis of PCCCL and differentiate it from other types of liver tumors.

Fat fraction and R2 * values of various liver masses: Initial experience with 6-point Dixon method on a 3T MRI system

Purpose

To assess the feasibility of the 6-point Dixon method for evaluating liver masses. We also report our initial experience with the quantitative values in various liver masses on a 3T system.

Materials and methods

Of 251 consecutive patients for whom 6-point Dixon was employed in abdominal magnetic resonance imaging scans between October 2020 and October 2021, 117 nodules in 117 patients with a mass diameter of more than 1 cm were included in the study. Images for measuring the proton density fat fraction (PDFF) and R2 * values were obtained using the iterative decomposition of water and fat with echo asymmetry and least-squares estimation-quantitative technique for liver imaging. Two radiologists independently measured PDFF (%) and R2 * (Hz). Inter-reader agreement and the differences between readers were examined using intra-class correlation coefficient (ICC) and the Bland-Altman method, respectively. PDFF and R2 * values in differentiating liver masses were examined.

Results

The masses included hepatocellular carcinoma (n = 59), cyst (n = 20), metastasis (n = 14), hemangioma (n = 8), and others (n = 16). The ICCs for the region of interest (mm2), PDFF, and R2 * were 0.988 (95 % confidence interval (CI): 0.983, 0.992), 0.964 (95 % CI: 0.949, 0.975), and 0.962 (95 % CI: 0.941, 0.975), respectively. The differences of measurements between the readers showed that 5.1 % (6/117) and 6.0% (7/117) for PDFF and R2 * , respectively, were outside the 95 % CI.

Conclusion

Our observation indicates that the 6-point Dixon method is applicable to liver masses.

Diagnosis of primary clear cell carcinoma of the liver based on Faster region-based convolutional neural network

BACKGROUND

Distinguishing between primary clear cell carcinoma of the liver (PCCCL) and common hepatocellular carcinoma (CHCC) through traditional inspection methods before the operation is difficult. This study aimed to establish a Faster region-based convolutional neural network (RCNN) model for the accurate differential diagnosis of PCCCL and CHCC.

METHODS

In this study, we collected the data of 62 patients with PCCCL and 1079 patients with CHCC in Beijing YouAn Hospital from June 2012 to May 2020. A total of 109 patients with CHCC and 42 patients with PCCCL were randomly divided into the training validation set and the test set in a ratio of 4:1.The Faster RCNN was used for deep learning of patients' data in the training validation set, and established a convolutional neural network model to distinguish PCCCL and CHCC. The accuracy, average precision, and the recall of the model for diagnosing PCCCL and CHCC were used to evaluate the detection performance of the Faster RCNN algorithm.

RESULTS

A total of 4392 images of 121 patients (1032 images of 33 patients with PCCCL and 3360 images of 88 patients with CHCC) were uesd in test set for deep learning and establishing the model, and 1072 images of 30 patients (320 images of nine patients with PCCCL and 752 images of 21 patients with CHCC) were used to test the model. The accuracy of the model for accurately diagnosing PCCCL and CHCC was 0.962 (95% confidence interval [CI]: 0.931-0.992). The average precision of the model for diagnosing PCCCL was 0.908 (95% CI: 0.823-0.993) and that for diagnosing CHCC was 0.907 (95% CI: 0.823-0.993). The recall of the model for diagnosing PCCCL was 0.951 (95% CI: 0.916-0.985) and that for diagnosing CHCC was 0.960 (95% CI: 0.854-0.962). The time to make a diagnosis using the model took an average of 4 s for each patient.

CONCLUSION

The Faster RCNN model can accurately distinguish PCCCL and CHCC. This model could be important for clinicians to make appropriate treatment plans for patients with PCCCL or CHCC.

A case of primary clear cell hepatocellular carcinoma comprised mostly of clear cells

Clear cell hepatocellular carcinoma (CHCC) is defined as a tumor which contains more than 50% of clear cells. However, CHCC with more than 90% of clear cells are extremely rare. We report a case of a 65-year-old woman who was found to have a solitary mass, which was histologically diagnosed as clear cell hepatocellular carcinoma composed of 90% or more clear cells. The tumor presented rim arterial phase hyperenhancement in computed tomography, magnetic resonance imaging, and computed tomography during hepatic arteriography, and was classified as LR-M category according to The Liver Imaging Reporting and Data System version 2018(LI-RADS v2018). This tumor may mimic other tumors with similar radiographic features, such as intrahepatic cholangiocellular carcinoma and metastatic tumor.