Table Info

Table 3.

Current AI applications in the diagnosis of childhood extracranial tumor

AI method	Medical field	Task	Tumor	Result	References
CNN	Radiology	Developing an AI algorithm to distinguish osteomyelitis from Ewing sarcoma	Ewing sarcoma and osteomyelitis	ACC: 86.7–94.4%	[53]
CNN/SVM	Radiology	Constructing image-based models to identify well-differentiated liposarcoma and lipoma	Well-differentiated liposarcomas and lipomas	ACC: 86.84%; AUC: 0.942	[54]
CNN/RF	Radiology	Developing a DL/ML model to classify primary bone tumors from preoperative radiographs and compare performance with radiologists	Malignant and benign bone tumors	AUC: 0.79–0.97	[49–51]
SVM/GLM/RF	Radiology	Constructing a radiomics-based machine method for differentiation between malignant and benign soft-tissue masses	Malignant and benign soft-tissue masses	AUC: 0.88–0.96; ACC: 80.8–90.5%	[52]
CNN	Pathology	Building CNNs for rhabdomyosarcoma histology subtype classification	Rhabdomyosarcoma	AUC: 0.92–0.94	[55]
CNN	Pathology	Developing a DL CNN-based differential diagnosis system on soft-tissue sarcoma subtypes based on whole histopathology tissue slides	Soft-tissue sarcoma	AUC: 0.889	[56]
LDA	Pathology	Identifying proteomic differences, which would more reliably differentiate between benign and malignant melanocytic lesions	Benign nevi and melanomas	SEN: 98.76%; SPE: 99.65%	[59]
CNN	Others-dermatological photos	Establishing an AI algorithm to diagnose infantile hemangiomas based on clinical images	Infantile hemangiomas	ACC: 91.7%	[61]
LR	Others-umbilical cord blood sera	Exploring prediction biomarkers for infantile hemangiomas using noninvasive umbilical cord blood	Infantile hemangiomas	AUC: 0.756–0.943	[62]
CNN	Others-dermoscopic examination	Developing a DCNN model to support dermatologists in the classification and management of atypical melanocytic skin lesions	Early melanomas and atypical nevi	AUC: 0.903	[60]
SVM	Others-cell-free DNA	Providing a comprehensive analysis of circulating tumor DNA beyond recurrent genetic aberrations for early diagnosis	Ewing sarcoma and other pediatric sarcomas	SEN: 73%; SPE: 100%	[57]
SVM	Others-electronic colorimeters	Determining the diagnostic utility of widely available colorimetric technology when differentiating port-wine birthmarks from infantile hemangiomas in photographs of infants less than 3 months old	Port-wine birthmarks and infantile hemangiomas	ACC: 90%	[63]
DT & RF	Others-array-generated DNA methylation data	Classifying soft tissue and bone tumors using an ML classifier algorithm based on array-generated DNA methylation data	Soft tissue and bone tumors	AUC: 0.999	[58]

GLM: general linear model; LDA: linear discriminant analysis; DCNN: deep CNN

Declarations

Author contributions

YY, YZ, and YL: Conceptualization, Writing—original draft, Writing—review & editing. YL: Validation, Writing—review & editing, Supervision.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Requests for access to these datasets should be directed to [Yuan Li, l13258389785@126.com].

Funding

Not applicable.

Copyright

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