| AI, artificial intelligence; SVM, support vector machine; LASSO, least absolute shrinkage and selection operator; CT, computed tomography; ML, machine learning; LDA, linear discriminant analysis; ANN, artificial neural network; PET, positron emission tomography; CADx, computer-aided diagnosis; CNN, convolutional neural network; LUNA16, Lung Nodule Analysis 2016; DL, deep learning; Res-Net, residual neural network; CMixNet, comprehensive mixed network; DI2IM, deep image-to-image network; RCNN, region-based convolutional neural network; GLCM, Gray Level Co-occurrence Matrices; LCP, lung cancer prediction; NLST, National Lung Screening Test; 18F-FDG, 18F-2’-deoxy-2-fluoro-D-glucose; CXR-LC, lung cancer incidence risk model; EHR, electronic health record; RADS, reporting and data system; LDCT, low-dose CT; AdaBoost, Adaptive Boosting; SNMV, Self-Normalized Multi-View; CAD, computer-aided diagnosis/detection; B-RGS, bates distribution coati optimization; MET, mesenchymal epithelial transformation; 3D-UTE, three-dimensional ultrashort echo time; MRI, magnetic resonance imaging; NSCLC, non-small cell lung cancer; LGBM, light gradient boosting machine; XGBoost, extreme gradient boosting; LR, logistic regression; AUC, area under the receiver operating characteristic curve; 95% CI, 95% confidence interval; CMS, Centers for Medicare & Medicalaid Services; LIDC-IDRI, Lung Image Database Consortium and Image Database Resource Initiative; FP, false positives; MGH, Massachusetts General Hospital; CGMH, Chang Gung Memorial Hospital; LCRAT, Lung Cancer Risk Assessment Tool. |
| SVM-LASSO model utilizes two CT radiomic features. |
Predicted malignancy of pulmonary nodules with 84.6% accuracy, outperforming that of Lung-RADS. |
(39) |
2018 |
| Combination of ML techniques SVM and sensor array technique LDA |
Produced AUCs of 0.91 (95% CI: 0.79−1.00) with LDA and 0.90 (95% CI: 0.80−0.99) with SVM, reflecting strong diagnostic accuracy. |
(40) |
2018 |
| Feed-forward autoencoder-based deep neural network classifier |
Classified pulmonary nodules with 91.2% accuracy, demonstrating it is a prospective tool for lung cancer detection with prospective validation. |
(41) |
2018 |
| Multi-parameterized ANN based on personal health information |
In validation set for predicting lung cancer risk, sensitivity was 75.3% (68.9%−81.6%), specificity was 80.6% (80.3%−80.8%), and AUC was 0.86 (0.84−0.89). |
(42) |
2018 |
| Deep neural networks applied to ultralow dose PET scans |
In automated detection of lung cancer, sensitivity was 91.5% and specificity is 94.2%. |
(43) |
2018 |
| Collaborative CADx system unifying eye-tracking systems and CADx |
Achieved a classification accuracy of 97% and a 91% average Dice Similarity Coefficient for distinguishing between nodules and non-nodules. |
(44) |
2019 |
| Amalgamated-CNN analyzed with LUNA16 and Ali Tianchi data |
Sensitivity was 81.7% and 85.1%, with average false positives per scan of 0.125 and 0.25, respectively. |
(45) |
2019 |
| Fusion algorithm combining features learned by 3D deep CNN and handcrafted features |
Achieved the highest accuracy, AUC, specificity, and sensitivity within all competitive classification models. |
(46) |
2019 |
| DL model utilizing CMixNet architectures in conjunction with clinical factors |
Achieved 94% sensitivity and 91% specificity, which is better than existing methods. |
(47) |
2019 |
| In-house-deployed AI algorithms complexes consist of DI2IN, region growing, and faster RCNN. |
Facilitated reliable 3D segmentation and detection of T1/T2 lung tumors on FDG-PET/CTs |
(48) |
2019 |
| GLCM technique |
GLCM features are accurate in predicting lung tumors, albeit at a slower rate than histogram features. |
(49) |
2019 |
| LCP-CNN |
Achieved an AUC of 89.6% and yielded only one false negative with 234 nodules in 229 patients (19.3%) when using the predefined thresholds. |
(50) |
2019 |
| DL-based AI algorithm for detecting lung cancer and pulmonary nodules on chest radiographs using data from NLST |
Outperformed NLST radiologists in detecting pulmonary nodules on digital radiographs. |
(51) |
2020 |
| A DL algorithm using pre-trained Res-Net to automatically identify small 18F-FDG-avid pulmonary nodules in PET scans |
Accurately identified small 18F-FDG-avid pulmonary nodules, supporting automated clinical PET/CT interpretation. |
(52) |
2020 |
| A fusion CNN (CXR-LC) using EHRs |
Predicted incident lung cancer with high discrimination than CMS eligibility (AUC: 0.755 vs. 0.634). |
(53) |
2020 |
| A radiomics model applied LASSO feature selection and score calculation, followed by multivariate logistic regression to construct a classification model and nomogram. |
Achieved AUCs of 0.836 and 0.809 in training cohort and validation cohorts, respectively to classify benign and malignant pulmonary nodules. |
(54) |
2020 |
| An AI-CNN prototype (Siemens Healthiness, AI-RAD Companion) to detect pulmonary nodules on LDCT |
The agreement between the AI findings and experts was excellent (lung nodules Cohen’s kappa=0.846), with 0.99 sensitivity and 0.708 specificity. |
(55) |
2021 |
| Fusion CNN integrating four advanced object detectors |
Malignancy risk prediction accuracy was slightly lower than the performance of the observers, but the model is useful for assisting less experienced radiologists. |
(56) |
2021 |
| Innovative model called AdaBoost-SNMV-CNN |
Achieved 0.93 sensitivity, 0.92 accuracy, and 0.92 specificity for lung nodules detection and outperformed the majority of the models based on LIDC-IDRI dataset. |
(57) |
2022 |
| Open-source DL tool (CXR-LC) based on X-ray images and common EHRs |
Identified individuals at elevated risk who are potential candidates for lung cancer screening CT. |
(58) |
2022 |
| A DL-based CAD system developed for a Chinese low-dose CT lung cancer screening program |
For lung nodule detection independent of size or type, sensitivity was 90.1% with 1.0 FP/scan, compared with 76.0% and 0.04 FP/scan for double reading (P=0.001). |
(59) |
2022 |
| An optimized early warning model for lung cancer risk (OWL) based on XGBoost algorithm |
Demonstrated robust accuracy and clinical utility for identifying individuals at high risk of lung cancer. |
(60) |
2023 |
| The Sybil model, trained on NLST LDCTs, requires only one scan and no annotations, enabling real-time use on radiology stations. |
AUCs were 0.92 (NLST), 0.86 (MGH), and 0.94 (CGMH) for 1-year lung cancer prediction. |
(61) |
2023 |
| Recalibrated LCP-CNN |
Predicted 1-year lung cancer risk with an AUC of 0.87, exceeding that of LCRAT+CT (0.79) or Lung-RADS (0.69). It showed the highest accuracy for 1-year risk prediction and the lowest likelihood of delayed diagnosis under biennial screening. |
(62) |
2023 |
| An advanced lung cancer risk prediction model using transfer learning (PResNet classifier) and a modified B-RGS algorithm |
Achieved 98.2% accuracy, 98.7% precision, and 97.5% recall rates, which is comparable to other state-of-the-art methods. |
(63) |
2024 |
| A CT-based deep learning model, METnet, employs grouped convolution blocks to predict MET dysregulation. |
Predicted MET dysregulation with 0.746 accuracy and an AUC of 0.793. The method could potentially aid precise diagnosis and therapy at the molecular scale with prospective validation. |
(64) |
2024 |
| A random forest model combing 18F FDG-PET and 3D-UTE MRI to assess lymph node status in NSCLC |
Achieved AUCs of 0.912 and 0.791 in the training and test sets, respectively, demonstrating different degrees of improvement over individual models. |
(65) |
2024 |
| An ML model based on a dynamic ensemble model for lung cancer detection, which combines four classification models: LGBM, XGBoost, LR, and SVM. |
Achieved 76.2% sensitivity, an AUC of 0.77, and 63.8% specificity in 9,940 samples, outperforming five pulmonologists with a sensitivity of 67.4% and a specificity of 70.3%. |
(66) |
2024 |
| A model for early detection of lung cancer in primary care using ML with deep ‘transformer’ models on EHR data |
Achieved an AUC of 0.924 with a positive predictive value of 3.6% and 96.6% sensitivity based on three years of data. |
(35) |
2024 |
