scholarly journals Differential Diagnosis of Hepatic Mass with Central Scar: Focal Nodular Hyperplasia Mimicking Fibrolamellar Hepatocellular Carcinoma

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 44
Author(s):  
Teodoro Rudolphi-Solero ◽  
Eva María Triviño-Ibáñez ◽  
Antonio Medina-Benítez ◽  
Javier Fernández-Fernández ◽  
Daniel José Rivas-Navas ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Differential Diagnosis ◽  
Focal Nodular Hyperplasia ◽  
Radical Surgery ◽  
Fibrolamellar Hepatocellular Carcinoma ◽  
Central Scar ◽  
Hepatic Mass ◽  
Contrast Enhanced Ct ◽  
Pet Ct ◽  
Nodular Hyperplasia

Fibrolamellar hepatocellular carcinoma is a primary hepatic tumor that usually appears in young adults. Radical surgery is considered curative for this kind of tumor, so early diagnosis becomes essential for the prognosis of the patients. The main characteristic of this entity is the central scar, which is the center of differential diagnosis. We report the case of a 30-year-old man who was diagnosed with fibrolamellar hepatocellular carcinoma by ultrasonography. Contrast-enhanced CT confirmed this diagnosis, and the patient underwent a [18F] fluorocholine PET/CT. Hypermetabolism and the morphology in the nuclear medicine exploration suggest neoplastic nature of the lesion. Radical surgery was performed, and histopathologic analysis was performed, which resulted in focal nodular hyperplasia. Hepatic masses with central scar could have a difficult differential diagnosis, and focal nodular hyperplasia could mimic fibrolamellar hepatocellular carcinoma imaging patterns. These morphofunctional characteristics have not been described in [18F] Fluorocholine PET/CT, so there is a need to find out the potential role PET/CT in the differential diagnosis of hepatic mass with central scar.

Hepatocellular carcinoma with a central scar and a scalloped tumor margin resembling focal nodular hyperplasia in macroscopic appearance

2006 ◽  
Vol 94 (7) ◽  
pp. 587-591 ◽  
Author(s):  
Masakazu Yamamoto ◽  
Shyunichi Ariizumi ◽  
Kenji Yoshitoshi ◽  
Akiko Saito ◽  
Masayuki Nakano ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Focal Nodular Hyperplasia ◽  
Macroscopic Appearance ◽  
Central Scar ◽  
Tumor Margin ◽  
Nodular Hyperplasia

Calcification in focal nodular hyperplasia: a new problem for differentiation from fibrolamellar hepatocellular carcinoma.

Radiology ◽  
1996 ◽  
Vol 198 (3) ◽  
pp. 889-892 ◽  
Author(s):  
F Caseiro-Alves ◽  
M Zins ◽  
Mahfouz A-E ◽  
A Rahmouni ◽  
V Vilgrain ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Focal Nodular Hyperplasia ◽  
Fibrolamellar Hepatocellular Carcinoma ◽  
Nodular Hyperplasia

scholarly journals Machine Learning-Based Ultrasomics Improves the Diagnostic Performance in Differentiating Focal Nodular Hyperplasia and Atypical Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Wei Li ◽  
Xiao-Zhou Lv ◽  
Xin Zheng ◽  
Si-Min Ruan ◽  
Hang-Tong Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Differential Diagnosis ◽  
Focal Nodular Hyperplasia ◽  
Diagnostic Performance ◽  
Arterial Phase ◽  
Support Vector ◽  
Focal Liver Lesions ◽  
Machine Method ◽  
Nodular Hyperplasia

BackgroundThe typical enhancement patterns of hepatocellular carcinoma (HCC) on contrast-enhanced ultrasound (CEUS) are hyper-enhanced in the arterial phase and washed out during the portal venous and late phases. However, atypical variations make a differential diagnosis both challenging and crucial. We aimed to investigate whether machine learning-based ultrasonic signatures derived from CEUS images could improve the diagnostic performance in differentiating focal nodular hyperplasia (FNH) and atypical hepatocellular carcinoma (aHCC).Patients and MethodsA total of 226 focal liver lesions, including 107 aHCC and 119 FNH lesions, examined by CEUS were reviewed retrospectively. For machine learning-based ultrasomics, 3,132 features were extracted from the images of the baseline, arterial, and portal phases. An ultrasomics signature was generated by a machine learning model. The predictive model was constructed using the support vector machine method trained with the following groups: ultrasomics features, radiologist’s score, and combination of ultrasomics features and radiologist’s score. The diagnostic performance was explored using the area under the receiver operating characteristic curve (AUC).ResultsA total of 14 ultrasomics features were chosen to build an ultrasomics model, and they presented good performance in differentiating FNH and aHCC with an AUC of 0.86 (95% confidence interval [CI]: 0.80, 0.89), a sensitivity of 76.6% (95% CI: 67.5%, 84.3%), and a specificity of 80.5% (95% CI: 70.6%, 85.9%). The model trained with a combination of ultrasomics features and the radiologist’s score achieved a significantly higher AUC (0.93, 95% CI: 0.89, 0.96) than that trained with the radiologist’s score (AUC: 0.84, 95% CI: 0.79, 0.89, P < 0.001). For the sub-group of HCC with normal AFP value, the model trained with a combination of ultrasomics features, and the radiologist’s score remain achieved the highest AUC of 0.92 (95% CI: 0.87, 0.96) compared to that with the ultrasomics features (AUC: 0.86, 95% CI: 0.74, 0.89, P < 0.001) and radiologist’s score (AUC: 0.86, 95% CI: 0.79, 0.91, P < 0.001).ConclusionsMachine learning-based ultrasomics performs as well as the staff radiologist in predicting the differential diagnosis of FNH and aHCC. Incorporating an ultrasomics signature into the radiologist’s score improves the diagnostic performance in differentiating FNH and aHCC.

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