MRI-based deep learning model for differentiation of hepatic hemangioma and hepatoblastoma in early infancy

Conclusion:  The MRI-based integrated model, a noninvasive preoperative diagnostic tool, yielded favorable efficacy for differentiating HH and HBL in early infancy, which might reduce the patients ’ costs of repetitive and unnecessary examinations or over-treatment.Trial registration: ClinicalTrials.gov Identifier: NCT05170282.What is Known:• Hepatic hemangioma (HH) and hepatoblastoma (HBL) are common pediatric liver tumors and present with similar clinical manifestations with limited distinguishing value of serum AFP in early infancy.• Considering the rare incidence of infantile hepatic tumors, the distinguishing accuracy between HBL and HH for cases in early infancy is unsatisfactory for radiologists’ recognition solely.What is New:• The MRI-based integrated model, a noninvasive preoperative diagnostic tool yielded favorable efficacy for differentiating HH and HBL in early infancy, which might reduce the patients’ costs of repetitive and unnecessary examinations or over-treatment.
Source: European Journal of Pediatrics - Category: Pediatrics Source Type: research