Current AI applications in the diagnosis of childhood intracranial tumor
| AI method | Medical field | Task | Tumor | Result | References |
|---|---|---|---|---|---|
| SVM | Radiology | Classifying pediatric posterior fossa tumors | Pediatric posterior fossa tumors | ACC: 75–85% | [32] |
| LR/XGB/LASSO | Radiology | Distinguishing pediatric supratentorial tumors, high-grade gliomas, and ependymomas | Supratentorial embryonal tumors, high-grade gliomas, and ependymomas | ACC: 81–91%; AUC: 0.82–0.98 | [33] |
| CNN | Radiology | Establishing a pre-trained ResNet18 with transfer learning to identify germinomas of the basal ganglia | Gliomas and germinomas | AUC: 0.88 | [34] |
| CNN | Radiology | Training on T1-weighted gadolinium-enhanced MRI scans of glioblastomas, atypical primary central nervous system lymphomas, and solitary brain metastasis | Glioblastomas, atypical primary central nervous system lymphomas, and solitary brain metastasis | AUC: 0.81–0.98 | [31] |
| CNN/RF/DT/KNN/SVM | Radiology | Classifying primary central nervous system lymphoma and glioma types | Primary central nervous system lymphoma and glioma | ACC: 84%; AUC: 0.839 | [35] |
| LR/ANN | Radiology | Developing a sequential ML classifier to distinguish medulloblastoma from ependymoma | Medulloblastoma and ependymoma | ACC: 94–95.5% | [36] |
| SVM/LR/KNN/RF/XGB/ANN | Radiology | Distinguishing atypical teratoid/rhabdoid tumors and medulloblastomas by MR imaging-based radiomic phenotypes | Atypical teratoid/rhabdoid tumors and medulloblastomas | ACC: 81%; AUC: 0.86 | [37] |
| CNN | Radiology | Characterizing and classifying multiple tumor histologic features in pediatric high-grade brain tumors employing diffusion basis spectrum imaging | Pediatric high-grade brain tumors | AUC: 0.950–0.991 | [38] |
| GBDT | Radiology | Applying multiparametric MRI to differentiate pilocytic astrocytoma from cystic oligodendrogliomas | Pilocytic astrocytoma and cystic oligodendrogliomas | AUC: 0.99 | [39] |
| CNN | Radiology | Developing an MR imaging-based DL model for posterior fossa tumor detection and tumor pathology classification | Diffuse midline glioma of the pons, medulloblastoma, pilocytic astrocytoma, and ependymoma | ACC: 92%; AUC: 0.99 | [40] |
| CNN | Radiology | Identifying the pediatric brain tumor, adamantinomatous craniopharyngioma | Adamantinomatous craniopharyngioma | ACC: 83.3–87.8% | [41] |
| CNN | Pathology | Proposing a time-efficient and reliable CAD for the automatic diagnosis of pediatric medulloblastoma and its subtypes from histopathological images | Medulloblastoma | ACC: 90–100% | [42, 43] |
| SVM | Others-blood markers | Differentiating malignant and benign pediatric brain tumors using blood markers | Pediatric brain tumors | ACC: 71.6% | [45] |
| LR | Others-Raman spectroscopy | Investigating the potential for Raman spectroscopy to accurately diagnose pediatric brain tumors intraoperatively | Pediatric brain tumors | AUC: 0.91–0.94 | [46] |
| RF | Others-DNA methylation profiles | Describing a fast and cost-efficient workflow for intraoperative classification of brain tumors based on DNA methylation profiles generated by low coverage nanopore sequencing and ML algorithms | Pediatric brain tumors | ACC: 89% | [47] |
| SVM | Others-proteomics of cerebrospinal fluid | Distinguishing among brain tumor versus non-tumor/hemorrhagic conditions and differentiating two large classes of brain tumors | Pediatric brain tumors | AUC: 0.97–1 | [48] |
| LASSO | Others-lncRNAs | Developing an RF-based ML algorithm identifying a lncRNA-based diagnostic signature | Medulloblastoma | AUC: 0.974–1 | [44] |
LR: logistic regression; XGB: extreme gradient boosting; LASSO: least absolute shrinkage and selection operator; ResNet18: residual neural network with 18-layer by using more 5-layer blocks; DT: decision tree; KNN: k-nearest neighbour; GBDT: gradient boosting decision tree; lncRNAs: long non-coding RNAs
