Table 1. Summary of the included studies (sorted based on cancer type).
| Author, year | Cancer | Study population | Purpose of the study |
|---|---|---|---|
| Lung cancer | |||
| Alilou, 2018 (9) | Lung | 290 patients (145 in training and 145 in validation) | Differentiating granulomas from adenocarcinoma |
| Buizza, 2018 (10) | Lung | 30 patients with 31 NSCLC tumors | Response assessment after chemoradiation |
| Chen, 2017 (11) | Lung | 85 patients | Differentiating benign and malignant solitary pulmonary nodules |
| Hyun, 2019 (12) | Lung | 396 NSCLC patients (210 adenocarcinoma, and 186 squamous cell carcinoma) | Predicting pathological subtype of NSCLC |
| Ikushima, 2017 (13) | Lung | 14 patients | Gross tumor volume segmentation |
| Kawata, 2017 (14) | Lung | 16 patients | Gross tumor volume segmentation |
| Kirienko, 2018 (15) | Lung | 472 patients; training (303 patients), validation (75 patients), and testing (94 patients) | Staging lung cancer |
| Ma, 2018 (16) | Lung | 341 NSCLC patients (125 adenocarcinoma, 174 squamous cell cancer, and 42 unknown subtype) | Differentiating different NSCLC subtypes |
| Schwyzer, 2018 (17) | Lung | 50 Lung cancer patients and 50 non-malignant patients | Tumor detection |
| Scott, 2019 (18) | Lung | 125 patients (85 training cases and 40 test cases) | Prediction of malignancy in ground glass opacities |
| Teramoto, 2016 (19) | Lung | 84 patients | Pulmonary nodule detection |
| Zhang, 2019 (20) | Lung | 135 patents (40% benign and 60% malignant) | Differentiating benign and malignant Lung lesions |
| Zhao, 2018 (21) | Lung | 84 lung cancer patients (48 randomly selected PET/CT images for training and the remaining 36 images for testing) | Tumor segmentation |
| Astaraki, 2019 (22) | Lung | 30 patients with 31 NSCLC tumors | Prediction of survival |
| He, 2020 (23) | Lung | 935 NSCLC patients with baseline 18F-FDG PET/CT were randomly and equally divided to training and testing groups | Prediction of overall survival |
| Wu, 2018 (24) | Lung | 12,186 patients | Cancer detection |
| Zhong, 2019 (25) | Lung | 60 NSCLC patients (38 pairs for training and the remaining 22 pairs for testing) | Gross tumor volume segmentation |
| Head and neck cancer | |||
| Guo, 2019 (26) | Head and neck | 250 patients (140 patients for training, 35 for validation and 75 for testing) | Gross tumor volume segmentation |
| Huang, 2018 (27) | Head and neck | 22 patients | Gross tumor volume segmentation |
| Chen, 2019 (28) | LAP head and neck | 59 patients (41 patients in training group and 18 patients in validation group) | Differentiate malignant from non-malignant lymph nodes |
| Zhou, 2018 (29) | LAP head and neck cancer | 59 patients (41 patients for training including 85 involved nodes, 55 suspicious nodes, and 30 normal nodes and the remaining 18 patients for validation including 22 involved nodes, 27 suspicious nodes, and 17 normal nodes) | Predicting lymph node metastasis |
| Parkinson, 2019 (30) | Oropharyngeal squamous cell carcinomas | 20 patients | Response assessment |
| Lymphoma | |||
| Sadik, 2019 (31) | lymphoma | 80 lymphoma patients for training and 6 lymphoma patients for validation | Response assessment |
| Bi, 2017 (32) | lymphoma | 11 patients | Classifying sites of normal physiologic 18F-FDG uptake and excretion |
| Ellmann, 2019 (33) | Breast cancer cell; detecting osseous metastasis | 28 rats | Prediction of early metastatic disease in bones |
| Pancreas | |||
| Zhang, 2019 (34) | Pancreas | Article in Chinese language | Differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma |
| Li, 2018 (35) | Pancreas | 80 patients (40 patients with pancreatic cancer and 40 normal cases). Tumor identification was tested on the 80 patients. Tumor segmentation was tested on another dataset with 82 patients | Pancreas cancer identification and segmentation |
| Other cancers | |||
| Dong, 2020 (36) | Lung cancer (8), lymphoma (4), head and neck cancer (4), skin cancer (3), breast cancer (2) and abdominal cancer (4); | 25 patients to train, 55 to evaluate the model | Attenuation correction in whole-body PET images without structural imaging |
| Shaish, 2019 (37) | Lymph node metastasis in malignancy | 136 patients (total of 400 lymph nodes) for training and 49 patients (total of 164 lymph nodes) for testing | Prediction of the SUVmax of lymph nodes determined based on unenhanced CT and pathology subtype |
| Shen, 2019 (38) | Cervical cancer | 142 patients (101 patients with no evidence of disease progression, whereas 41 patients did have disease progression) | Prediction of local relapse and distant metastasis |
| Peng, 2019 (39) | Soft tissue sarcoma | 48 patients with pathology proven soft tissue sarcoma (24 with and 24 without metastases) | Prediction of distant metastasis |
| Nakagawa, 2019 (40) | Uterine sarcoma | 67 patients (11 with uterine sarcoma, 56 with leiomyomas) | Distinguishing uterine sarcoma and benign leiomyoma |
NSCLC, non-small cell lung cancer.