Hepatocellular adenoma subtyping by qualitative MRI features and machine learning algorithm of integrated qualitative and quantitative features: a proof-of-concept study.

AIM: To evaluate hepatocellular adenoma (HCA) subtyping using qualitative magnetic resonance imaging (MRI) features and feasibility of differentiating HCA subtypes using machine learning (ML) of qualitative and quantitative MRI features with histopathology as the reference standard. MATERIALS AND METHODS: This retrospective study included 39 histopathologically subtyped HCAs (13 hepatocyte nuclear factor (HNF)-1-alpha mutated [HHCA], 11 inflammatory [IHCA], one beta-catenin-mutated [BHCA], and 14 unclassified [UHCA]) in 36 patients. HCA subtyping by two blinded radiologists using the proposed schema of qualitative MRI features and using the random forest algorithm was compared against histopathology. For quantitative features, 1,409 radiomic features were extracted after segmentation and reduced to 10 principle components. Support vector machine and logistic regression was applied to assess HCA subtyping. RESULTS: Qualitative MRI features with proposed flow chart yielded diagnostic accuracies of 87%, 82%, and 74% for HHCA, IHCA, and UHCA respectively. The ML algorithm based on qualitative MRI features showed AUCs (area under the receiver operating characteristic curve [ROC] curve) of 0.846, 0.642, and 0.766 for diagnosing HHCA, IHCA, and UHCA, respectively. Quantitative radiomic features from portal venous and hepatic venous phase MRI demonstrated AUCs of 0.83 and 0.82, with a sensitivity of 72% and a specificity of 85% in predicting HHCA subtype. CONCLUSIONS: The proposed schema of integrated qualitative MRI features with ML algorithm provided high accuracy for HCA subtyping while quantitative radiomic features provide value for diagnosis of HHCA. The key qualitative MRI features for differentiating HCA subtypes were concordant between the radiologists and the ML algorithm. These approaches appear promising to better inform clinical management for patients with HCA.

T2-FLAIR Mismatch Sign in Pediatric Low-Grade Glioma.

BACKGROUND AND PURPOSE: No qualitative imaging feature currently predicts molecular alterations of pediatric low-grade gliomas with high sensitivity or specificity. The T2-FLAIR mismatch sign predicts IDH-mutated 1p19q noncodeleted adult gliomas with high specificity. We aimed to assess the significance of the T2-FLAIR mismatch sign in pediatric low-grade gliomas. MATERIALS AND METHODS: Pretreatment MR images acquired between January 2001 and August 2018 in pediatric patients with pediatric low-grade gliomas were retrospectively identified. Inclusion criteria were the following: 1) 0-18 years of age, 2) availability of molecular information in histopathologically confirmed cases, and 3) availability of preoperative brain MR imaging with non-motion-degraded T2-weighted and FLAIR sequences. Spinal cord tumors were excluded. RESULTS: Three hundred forty-nine patients were included (187 boys; mean age, 8.7 [SD, 4.8] years; range, 0.5-17.7 years). KIAA1549-B-Raf proto-oncogene (BRAF) fusion and BRAF p.V600E mutation were the most common molecular markers (n = 148, 42%, and n = 73, 20.7%, respectively). The T2-FLAIR mismatch sign was present in 25 patients (7.2%). Of these, 9 were dysembryoplastic neuroepithelial tumors; 8, low-grade astrocytomas; 5, diffuse astrocytomas; 1, a pilocytic astrocytoma; 1, a glioneuronal tumor; and 1, an angiocentric glioma. None of the 25 T2-FLAIR mismatch pediatric low-grade gliomas were BRAF p.V600E-mutated. Fourteen of 25 pediatric low-grade gliomas with the T2-FLAIR mismatch sign had rare molecular alterations, while the molecular subtype was unknown for 11 tumors. CONCLUSIONS: The T2-FLAIR mismatch sign was not observed in the common molecular alterations, BRAF p.V600E-mutated and KIAA1549-BRAF fused pediatric low-grade gliomas, while it was encountered in pediatric low-grade gliomas with rare pediatric molecular alterations.

Radiomics of Pediatric Low-Grade Gliomas: Toward a Pretherapeutic Differentiation of BRAF-Mutated and BRAF-Fused Tumors.

BACKGROUND AND PURPOSE: B-Raf proto-oncogene, serine/threonine kinase (BRAF) status has important implications for prognosis and therapy of pediatric low-grade gliomas. Currently, BRAF status classification relies on biopsy. Our aim was to train and validate a radiomics approach to predict BRAF fusion and BRAF V600E mutation. MATERIALS AND METHODS: In this bi-institutional retrospective study, FLAIR MR imaging datasets of 115 pediatric patients with low-grade gliomas from 2 children's hospitals acquired between January 2009 and January 2016 were included and analyzed. Radiomics features were extracted from tumor segmentations, and the predictive model was tested using independent training and testing datasets, with all available tumor types. The model was selected on the basis of a grid search on the number of trees, opting for the best split for a random forest. We used the area under the receiver operating characteristic curve to evaluate model performance. RESULTS: The training cohort consisted of 94 pediatric patients with low-grade gliomas (mean age, 9.4 years; 45 boys), and the external validation cohort comprised 21 pediatric patients with low-grade gliomas (mean age, 8.37 years; 12 boys). A 4-fold cross-validation scheme predicted BRAF status with an area under the curve of 0.75 (SD, 0.12) (95% confidence interval, 0.62-0.89) on the internal validation cohort. By means of the optimal hyperparameters determined by 4-fold cross-validation, the area under the curve for the external validation was 0.85. Age and tumor location were significant predictors of BRAF status (P values = .04 and <.001, respectively). Sex was not a significant predictor (P value = .96). CONCLUSIONS: Radiomics-based prediction of BRAF status in pediatric low-grade gliomas appears feasible in this bi-institutional exploratory study.