Table 2.
Representative research of AI for lung cancer treatment based on CT images.
| Application | Author | Year | Dataset/sample size | Imaging modality | Algorithm | Task | Performance |
|---|---|---|---|---|---|---|---|
| Gene mutation and molecular expression prediction | Wang et al.60 | 2019 | 844 LUADs | CT | Deep learning | EGFR mutation status prediction | AUC: 0.81 on independent validation dataset |
| Mu et al.61 | 2020 | 681 NSCLC patients | PET/CT | Deep learning | Quantification of EGFR mutation status | AUC: 0.81 on external testing dataset | |
| Tian et al.62 | 2020 | 939 consecutive stage IIIB–IV NSCLC patients | CT | Deep CNN | Assessment of PD-L1 expression and ICI responses | AUC: 0.76 on testing dataset, C-index: 0.66 | |
| Song et al.63 | 2021 | 937 patients | CT | Deep learning model and clinicopathological information | ALK fusion status prediction | AUC: 0.8481 on validation dataset | |
| Rossi et al.64 | 2021 | 109 patients | CT | Radiomics and SVM model | Detection of EGFR mutations | AUC: 0.85 on five-fold cross validation | |
| Zhang et al.65 | 2021 | 134 patients | CT | 1672 radiomic features | Simultaneous identification of EGFR, KRAS, ERBB2, and TP53 mutations | AUCs: 0.78–0.87 on five-fold cross validation | |
| Wang et al.17 | 2022 | 18,232 patients | CT | FAIS | Prediction of EGFR genotype and targeted therapy response | AUCs: 0.748–0.813 on six testing datasets | |
| Wang et al.66 | 2022 | 3816 patients | CT | Multitask AI system | EGFR and PD-L1 status prediction | AUCs: 0.928 for EGFR mutated status, 0.905 for PD-L1 expression on testing dataset | |
| Shao et al.67 | 2022 | 1096 Patients | CT | MMDL system | Identification of multiple actionable mutations and PD-L1 expression | AUCs: 0.862 for 8 mutated genes, 0.856 for 10 molecular statuses on testing dataset | |
| Treatment efficacy assessment | Song et al.68 | 2018 | 117 stage IV EGFR-mutant NSCLC patients | CT | CT-based phenotypic characteristics | Prediction of PFS with EGFR-TKI therapy | C-index: 0.718, 0.720 on two validation datasets |
| Xu et al.69 | 2019 | 179 patients with stage III NSCLC treated with definitive chemoradiation | CT | Transfer learning of CNN with RNN using single seed-point tumor localization | Prediction of OS | AUC: 0.74 for 2-year OS on validation dataset | |
| Lou et al.70 | 2019 | 944 patients | CT | Deep learning | Predict treatment failure and hence guide the individualization of radiotherapy dose | C-index: 0.77 on independent validation dataset | |
| Khorrami et al.71 | 2020 | 139 patients | CT | Compared changes in the radiomic texture (DelRADx) of CT patterns both within and outside tumor | Prediction of OS and response to ICIs | AUCs: 0.81, 0.85 on two independent validation datasets | |
| Dercle et al.72 | 2020 | Nivolumab, 92; docetaxel, 50; and gefitinib, 46 | CT | Radiomics | Prediction of systemic cancer therapies response | AUC: 0.77 for nivolumab, 0.67 for docetaxel, and 0.82 for gefitinib on validation dataset | |
| Mu et al.73 | 2020 | 194 patients with stage IIIB–IV NSCLC | PET/CT | Radiomics | Prediction of ICIs benefit | AUC: 0.81 on prospective testing dataset | |
| He et al.74 | 2020 | 327 patients | CT images | Deep learning radiomics | Prediction of ICIs response | AUC: 0.81 on testing dataset | |
| Dissaux et al.75 | 2020 | 27, 29, and 8 patients treated with SBRT from three different centers | 18F-FDG PET/CT | Radiomics | Prediction of local recurrence | AUC: 0.905 on testing dataset | |
| Deng et al.76 | 2022 | 570 patients with stage IV EGFR-mutant NSCLC treated with EGFR-TKIs and 129 patients with stage IV NSCLC treated with ICIs | CT | EfficientNetV2-based survival benefit prognosis (ESBP) | Survival benefit prediction of TKIs and ICIs | C-index: 0.690 on the EGFR-TKI external testing dataset | |
| Survival prognosis prediction | Aerts et al.77 | 2014 | 1019 patients with lung or head-and-neck cancer | CT | 440 features quantifying tumor image intensity, shape, and texture | Prediction of OS | C-index: 0.65 on validation dataset |
| Hosny et al.78 | 2018 | 1194 NSCLC patients | CT | 3D CNN | Mortality risk stratification | AUCs: 0.72 for 2-year OS from the start of respective treatment for radiotherapy and 0.71 for surgery on external validation dataset | |
| Arshad et al.79 | 2019 | 358 stage I–III NSCLC patients | FDG-PET/CT | Radiomics | Prediction of OS | C-index: 0.541–0.558 on testing dataset | |
| Jazieh et al.80 | 2022 | 133 patients with unresectable stage III NSCLC | CT | Radiomics risk score | Prediction of clinical outcomes | C-index: 0.77 for PFS, 0.77 for OS on testing dataset | |
| Huang et al.81 | 2022 | 1168 nodules of 965 patients | FDG-PET/CT scans | CNN | Prediction of OS | C-index: 0.737 for PET + CT + clinical ensemble model on testing dataset | |
AI: Artificial intelligence; ALK: Anaplastic lymphoma kinase; AUC: Area under the curve; CNN: Convolutional neural network; CT: Computed tomography; DFS: Disease-free survival; EGFR: Epidermal growth factor receptor; FAIS: Fully automated artificial intelligence system;18F-FDG PET/CT: Fluorine-18-fluorodeoxyglucose (FDG) positron emission tomography (PET) computed tomography (CT); ICI: Immune checkpoint inhibitors; LUAD: Lung adenocarcinoma; MMDL: Multi-label multi-task deep learning; NSCLC: Non-small cell lung cancer; OS: Overall survival; PD-L1: Programed death ligand-1; PFS: Progression-free survival; RNN: Recurrent neural networks; SBRT: Stereotactic body radiotherapy; SVM: Support vector machine; TKIs: Tyrosine kinase inhibitors; TMB: Tumor mutational burden.