Prediction of MYCN Gene Amplification in Pediatric Neuroblastomas: Development of a Deep Learning-Based Tool for Automatic Tumor Segmentation and Comparative Analysis of Computed Tomography-Based Radiomics Features Harmonization.

OBJECTIVE: MYCN oncogene amplification is closely linked to high-grade neuroblastoma with poor prognosis. Accurate quantification is essential for risk assessment, which guides clinical decision making and disease management. This study proposes an end-to-end deep-learning framework for automatic tumor segmentation of pediatric neuroblastomas and radiomics features-based classification of MYCN gene amplification. METHODS: Data from pretreatment contrast-enhanced computed tomography scans and MYCN status from 47 cases of pediatric neuroblastomas treated at a tertiary children's hospital from 2009 to 2020 were reviewed. Automated tumor segmentation and grading pipeline includes (1) a modified U-Net for tumor segmentation; (2) extraction of radiomic textural features; (3) feature-based ComBat harmonization for removal of variabilities across scanners; (4) feature selection using 2 approaches, namely, ( a ) an ensemble approach and ( b ) stepwise forward-and-backward selection method using logistic regression classifier; and (5) radiomics features-based classification of MYCN gene amplification using machine learning classifiers. RESULTS: Median train/test Dice score for modified U-Net was 0.728/0.680. The top 3 features from the ensemble approach were neighborhood gray-tone difference matrix (NGTDM) busyness, NGTDM strength, and gray-level run-length matrix (GLRLM) low gray-level run emphasis, whereas those from the stepwise approach were GLRLM low gray-level run emphasis, GLRLM high gray-level run emphasis, and NGTDM coarseness. The top-performing tumor classification algorithm achieved a weighted F1 score of 97%, an area under the receiver operating characteristic curve of 96.9%, an accuracy of 96.97%, and a negative predictive value of 100%. Harmonization-based tumor classification improved the accuracy by 2% to 3% for all classifiers. CONCLUSION: The proposed end-to-end framework achieved high accuracy for MYCN gene amplification status classification.

Oncofetal protein IGF2BP1 regulates IQGAP3 expression to maintain stem cell potential in cancer.

We reported earlier that IQGAP3 is an important stem cell factor in rapidly proliferating isthmus stem cells in the stomach and that IQGAP3 expression is robustly induced in terminally differentiated chief cells and de-differentiated cells following tissue damage. The elevated IQGAP3 expression in cancer and its association with metastasis suggest a fundamental role for IQGAP3 in proliferating cancer stem cells. What causes IQGAP3 upregulation in cancer is unclear. Here, we show that IGF2BP1 and IQGAP3 expression levels are highest in the blastocyst, with both decreasing during adulthood. This suggests that IQGAP3, like IGF2BP1, is an early developmental gene that is aberrantly upregulated upon re-expression of IGF2BP1 during carcinogenesis. IGF2BP1 binds and stabilizes m6A-modified IQGAP3 transcripts. Downstream targets of IGF2BP1, namely SRF and FOXM1, also upregulate IQGAP3 expression. These multiple layers of IQGAP3 regulation, which may safeguard against inappropriate stem cell proliferation, present additional drug targets to inhibit IQGAP3-driven malignant growth.