Lymph node disease in 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography imaging: Advances in artificial intelligence-driven automatic segmentation and precise diagnosis

Table 1 Summary of deep learning-based studies for lymph node disease diagnosis using 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography imaging

Ref.	No. of patients	Disease	Technical methods	Performance metrics
Chen et al[45] 2019	59	Lymph node metastasis of head and neck cancer	Combining a hybrid model of MaO-radiomics and 3D-CNN	Accuracy = 0.88; Macro-average = 0.89; mean-OVA-AUC1 = 0.95; multi-class AUC2 = 0.95
Yang et al[6] 2023	165	Distinguish between lymph node metastases in swollen lymph nodes in the neck and lymphoma involvement	DL-CNN, DL-SVM and a combined model	DL-CNN: Best model: ResNet50; AUC=0.845; Accuracy = 78.13%, DL-SVM: Best model: ResNet50; AUC = 0.901; accuracy = 86.96%, Combination model: AUC = 0.948; accuracy = 84.00%; sensitivity = 100.00%; specificity = 75.00%
Zhang et al[46] 2023	689	Lymph node metastasis of ESCC	AI-CAD	Doctors vs AI-CAD: (1) Accuracy: 0.712→0.833; and (2) Specificity: 0.697→0.891. Diagnostic results in 12.1% of patients were corrected with AI-CAD assistance
Qiao et al[47] 2022	228	Lymph node metastases of NSCLC	Radiomics nomogram based on 18F-FDG PET/CT	Training set: AUC = 0.884; test set: AUC = 0.881
Trägårdh et al[48] 2022	660	Local recurrence of prostate cancer, lymph node metastasis and bone metastasis	UNet3D CNN model	Doctor sensitivity vs UNet3D CNN model sensitivity: (1) Local recurrence: 78% vs 79%; (2) Lymph node metastasis: 78% vs 79%; and (3) Bone metastasis: 59% vs 62%
Trägårdh et al[49] 2022	221	Pelvic lymph node metastasis of prostate cancer	CNN model	Doctor average sensitivity vs CNN model sensitivity = 77% vs 82%

¹Mean- one-vs-all- area under the curve is defined as the average of the one-versus-all area under the curve, which can be used as a measure of how well the classifier separates each class from all the other classes.

²Multi-class area under the curve is an extended definition of two-class area under the curve that averages pairwise comparisons to evaluate multi-class classification problems.

Mean-OVA-AUC: Mean one-vs-all area under the curve; ESCC: Esophageal squamous cell carcinoma; NSCLC: Non-small cell lung cancer; CNN: Convolutional neural network; AI-CAD: Artificial intelligence-based computer-aided diagnosis system; ¹⁸F-FDG PET/CT: 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography.