Table 2. Summary of recent studies with radiomics model that can be used as ‘virtual biopsy’ tools. MIA: minimally invasive adenocarcinoma; IA: invasive adenocarcinoma, AAH: atypical adenomatous hyperplasia, AIS: adenocarcinoma insitu.
| Study | Dataset | Model description | Model performance |
|---|---|---|---|
| Wu et al. (29) | Training set: 152 adenocarcinoma 51 squamous cell carcinoma Validation set: 62 adenocarcinoma 90 squamous cell carcinoma |
- Three classifiers: random Forests, Naive Baye’s, and K-nearest neighbors were evaluated. - 67 out of 440 features selected in multivariate analysis |
Naive Baye’s classifier performed the best with AUC 0.72 in identifying adenocarcinoma and squamous cell carcinoma |
| Digumarthy et al. (30) | 69 adenocarcinoma 25 squamous cell carcinoma |
- 11 radiomic features - Radiomic analysis comprised an initial image filtration step followed by quantification of texture within the lesion |
- 3/11 radiomic features were significantly different between adenocarcinoma and squamous cell carcinoma (AUC 0.686–0.744) - For probability variables, ROC analysis showed higher AUC value for radiomics (AUC 0.800) than clinical (AUC 0.780) and imaging (AUC 0.694) for differentiating adenocarcinomas and squamous cell carcinomas |
| Chae et al. (31) | 58 invasive pulmonary adenocarcinoma (7 MIA and 51 IA) 28 pre-invasive pulmonary adenocarcinoma (4 AAH and 24 AIS) |
- Investigate the value of computerized three-dimensional texture analysis for differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas - Three-layered artificial neural networks (ANNs) with a back-propagation algorithm used |
- Smaller mass (adjusted OR: 0.092) and higher kurtosis (adjusted OR: 3.319) were significant differentiators of preinvasive lesions from invasive lesions (P<0.05). - ANNs model showed excellent accuracy in differentiation of preinvasive lesions from invasive lesions (AUC 0.981). |
| Li et al. (32) | 77 invasive pulmonary adenocarcinoma (37 MIA and 40 IA) 32 pre-invasive pulmonary adenocarcinoma (22 AAH and 10 AIS) |
- Stepwise model selection that mixed both forward and backward methods of variable selection using Akaike’s information criterion (AIC) was used to select the final predictive model | - Voxel count feature was significantly different between the invasive and preinvasive Lesions (82.5% sensitivity and 62.5% specificity) - Correlation feature predicted preinvasive lesions and MIAs better (sensitivity 81.1% and specificity 53.1%) |
| Son et al. (33) | 26 IA 9 MIA 4 AIS |
- Looked into utility of iodine enhanced imaging and virtual non-contrast (VNC) imaging in differentiating histologic subtypes of adenocarcinoma | The power of diagnosing IA improved after adding the iodine-enhanced imaging parameters compared to VNC imaging alone (AUC 0.959 vs. 0.888) |
| Maldonado et al. (34) | Training set: 2 AIS 20 MIA 32 IA Validation set: 1 AIS 10 MIA 75 IA |
- Development of computer-aided nodule assessment and risk yield (CANARY) software - Nonparametric Spearman correlation was used to analyze the relationship between histopathologic and radiologic invasion as determined by CANARY |
- Identified nine unique exemplars - Spearman R =0.87, P<0.0001 and 0.89 and P<0.0001 for the training and the validation set, respectively |