TABLE 2.
Summary of key papers applying deep learning to cancer diagnosis and treatment
| Application | Reference | Task | Performance |
|---|---|---|---|
| Screening | |||
| Pathology | [14] | Automation of dual stain cytology in cervical cancer screening | Sensitivity, 87% |
| Endoscopy | [15] | Automation of polyp detection | False positive rate, 7.5% |
| Radiology | [16] | Predicting invasiveness of pulmonary adenocarcinomas | AUC, 0.788 |
| Radiology | [17] | Lung nodule classification: benign/malignant | Sensitivity, 98.45% |
| Radiology | [18] | Lung nodule classification: benign/malignant | Accuracy, 79.5% |
| Radiology | [19] | Lung nodule classification: benign/malignant | AUC, 0.944 |
| Radiology | [20] | Breast lesion classification: benign/malignant | AUC, 0.909 |
| Radiology | [21] | Breast lesion classification: benign/malignant | AUC, 0.860 |
| Radiology | [22] | Breast lesion classification: benign/malignant | AUC, 0.870 |
| Radiology | [23] | Breast lesion classification: benign/malignant | AUC, 0.860 |
| Radiology | [24] | Breast lesion classification: benign/malignant | AUC, 0.890 |
| Radiology | [25] | Breast cancer prediction | AUC, 0.8107 |
| Diagnosis | |||
| Pathology | [30] | Invasive breast cancer detection | DSC, 75.86% |
| Pathology | [31] | Breast cancer nodal metastasis detection | AUC, 0.994 |
| Pathology | [32] | Breast lesion classification: benign/malignant | Accuracy, 98.7% |
| Pathology | [33] | Detection of lymph node metastases in breast cancer | AUC, 0.994 |
| Pathology | [35] | Diagnosis of gastric cancer | AUC, 0.990‐0.996 |
| Pathology | [36] | Predicting origins for cancers of unknown primary | Accuracy, 80% |
| Pathology | [51] | Lung tumor classification: normal/ adenocarcinoma/squamous cell carcinoma | AUC, 0.97 |
| Pathology | [52] | Automated Gleason grading of prostate adenocarcinoma | Cohen's quadratic kappa statistic, 0.75 |
| Radiology | [37] | Brain tumor classification: normal/glioblastoma/sarcoma/metastatic bronchogenic carcinoma | AUC, 0.984 |
| Radiology | [38] | Liver cancer detection | Accuracy, 99.38% |
| Radiology | [39] | Prostate lesion classification: benign/malignant | AUC, 0.84 |
| Radiology | [40] | Detection of synchronous peritoneal carcinomatosis in colorectal cancer | Accuracy, 94.11% |
| Radiology | [41] | Detection of NPC using MRI | Accuracy, 97.77% |
| Radiology | [53] | Predicting grade of liver cancer | AUC, 0.83 |
| Endoscopy | [42] | Gastric lesion classification: normal/malignant | Accuracy, 96.49% |
| Endoscopy | [43] | Upper gastrointestinal cancer detection | Accuracy, 99.7% |
| Endoscopy | [44] | Polyps identification | Accuracy, 96% |
| Endoscopy | [50] | Polyps identification | AUC, 0.984 |
| Endoscopy | [45] | Invasive colorectal cancer diagnosis | Accuracy, 94.1% |
| Endoscopy | [46] | Diminutive colorectal polyps classification: hyperplastic/neoplastic | Accuracy, 90.1% |
| Endoscopy | [47] | cT1b colorectal cancer diagnosis | AUC, 0.871 |
| Endoscopy | [49] | Nasopharyngeal lesion classification: benign/malignant | Accuracy, 88% |
| Prediction of mutation | |||
| Pathology | [51] | Predicting genetic mutations of lung cancer: STK11, EGFR, FAT1, SETBP1, KRAS, and TP53 | AUC, 0.733‐0.856 |
| Pathology | [56] | Predicting genetic mutations of lung cancer: CTNNB1, FMN2, TP53, and ZFX4 | AUC>0.71 |
| Pathology | [59] | Predicting MSI status in colorectal cancer | AUC, 0.93 |
| Pathology | [60] | Predicting MSI status in colorectal cancer | AUC, 0.85 |
| Pathology | [61] | Predicting TMB status in gastric cancer | AUC, 0.75 |
| Pathology | [61] | Predicting TMB status in colon cancer | AUC, 0.82 |
| Radiology | [62] | Predicting EGFR status in NSCLC | AUC, 0.81 |
| Radiology | [63] | Predicting EGFR status in NSCLC | AUC, 0.81 |
| Radiology | [70] | Predicting TMB status in NSCLC | AUC, 0.81 |
| Predicting of prognosis | |||
| Pathology | [66] | Predicting outcome of colorectal cancer | AUC, 0.69 |
| Pathology | [67] | Predicting outcome of mesothelioma | Concordance index, 0.643 |
| Pathology | [68] | Predicting outcome of NSCLC | AUC, 0.85 |
| Immunotherapy | |||
| Radiology | [70] | Predicting response to immunotherapy in advanced NSCLC using TMB | AUC, 0.81 |
| Radiology | [74] | Predicting response to immunotherapy in NSCLC using MSI | AUC, 0.79 |
| Pathology | [72] | Predicting response to immunotherapy in advanced melanoma | AUC, 0.80 |
| Pathology | [73] | Predicting response to immunotherapy in gastrointestinal cancer using MSI | AUC > 0.99 |
| Chemotherapy | |||
| Radiology | [75] | Predicting response to NAC in breast cancer | AUC, 0.851 |
| Radiology | [76] | Predicting response to NAC in breast cancer | Accuracy, 88% |
| Radiology | [77] | Prediction response to NAC in rectal cancer | AUC, 0.83 |
| Radiology | [78] | Prediction response to NAC in NPC | Concordance index, 0.719‐0.757 |
| Radiology | [79] | Prediction response to NAC in NPC | Concordance index, 0.722 |
| Radiotherapy | |||
| Radiotherapy | [84] | Segmentation of OAR in head and neck | DSC, 37.4%‐89.5% |
| Radiotherapy | [85] | Segmentation of OAR in NPC | DSC, 86.1% |
| Radiotherapy | [86] | Segmentation of OAR in head and neck | DSC, 74% |
| Radiotherapy | [87] | Segmentation of OAR in head and neck | DSC, 60‐83% |
| Radiotherapy | [88] | Segmentation of OAR in head and neck | DSC, 53‐90% |
| Radiotherapy | [91] | 3D liver segmentation | DSC, 97.25% |
| Radiotherapy | [92] | Segmentation of CTV and OAR in rectal cancer | CTV: DSC, 87.7% |
| OAR: DSC, 61.8‐93.4% | |||
| Radiotherapy | [93] | Segmentation of OAR in esophageal cancer | DSC, 84‐97% |
| Radiotherapy | [94] | Contouring of GTV in NPC | DSC, 79% |
| Radiotherapy | [95] | Segmentation of CTV and OAR in cervical cancer | CTV: DSC, 86% |
| OAR: DSC, 82‐91% | |||
| Radiotherapy | [96] | Contouring of GTV in colorectal carcinoma | DSC, 75.5% |
| Radiotherapy | [97] | Contouring of CTV in NSCLC | DSC, 75% |
| Radiotherapy | [98] | Contouring of CTV in breast cancer | DSC, 91% |
| Radiotherapy | [99] | IMRT planning in NPC | Conformity index, 1.18‐1.42 |
| Radiotherapy | [102] | Prediction of dose distribution of IMRT in NPC | Dose difference, 4.7% |
| Radiotherapy | [103] | Prediction of three‐dimensional dose distribution of helical tomotherapy | Dose difference, 2‐4.2% |
| Radiotherapy | [104] | Prediction of dose distribution of IMRT in prostate cancer | Dose difference, 1.26‐5.07% |
| Radiotherapy | [105] | Prediction of three‐dimensional dose distribution | Dose difference < 0.5% |
Abbreviations: AUC, area under curve; NPC, nasopharyngeal carcinoma; MRI, magnetic resonance images; MSI, microsatellite instability; TMB, tumor mutation burden; NSCLC, non‐small cell lung cancer; NAC, neoadjuvant chemotherapy; DSC, Dice similarity coefficient; OAR, organs at risk; GTV, gross tumor volume; CTV, clinical target volume; IMRT, intensity‐modulated radiation therapy.