Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer Journal Article


Authors: Choi, W.; Oh, J. H.; Riyahi, S.; Liu, C. J.; Jiang, F.; Chen, W.; White, C.; Rimner, A.; Mechalakos, J. G.; Deasy, J. O.; Lu, W.
Article Title: Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer
Abstract: Purpose: To develop a radiomics prediction model to improve pulmonary nodule (PN) classification in low-dose CT. To compare the model with the American College of Radiology (ACR) Lung CT Screening Reporting and Data System (Lung-RADS) for early detection of lung cancer. Methods: We examined a set of 72 PNs (31 benign and 41 malignant) from the Lung Image Database Consortium image collection (LIDC-IDRI). One hundred three CT radiomic features were extracted from each PN. Before the model building process, distinctive features were identified using a hierarchical clustering method. We then constructed a prediction model by using a support vector machine (SVM) classifier coupled with a least absolute shrinkage and selection operator (LASSO). A tenfold cross-validation (CV) was repeated ten times (10 × 10-fold CV) to evaluate the accuracy of the SVM-LASSO model. Finally, the best model from the 10 × 10-fold CV was further evaluated using 20 × 5- and 50 × 2-fold CVs. Results: The best SVM-LASSO model consisted of only two features: the bounding box anterior–posterior dimension (BB_AP) and the standard deviation of inverse difference moment (SD_IDM). The BB_AP measured the extension of a PN in the anterior–posterior direction and was highly correlated (r = 0.94) with the PN size. The SD_IDM was a texture feature that measured the directional variation of the local homogeneity feature IDM. Univariate analysis showed that both features were statistically significant and discriminative (P = 0.00013 and 0.000038, respectively). PNs with larger BB_AP or smaller SD_IDM were more likely malignant. The 10 × 10-fold CV of the best SVM model using the two features achieved an accuracy of 84.6% and 0.89 AUC. By comparison, Lung-RADS achieved an accuracy of 72.2% and 0.77 AUC using four features (size, type, calcification, and spiculation). The prediction improvement of SVM-LASSO comparing to Lung-RADS was statistically significant (McNemar's test P = 0.026). Lung-RADS misclassified 19 cases because it was mainly based on PN size, whereas the SVM-LASSO model correctly classified 10 of these cases by combining a size (BB_AP) feature and a texture (SD_IDM) feature. The performance of the SVM-LASSO model was stable when leaving more patients out with five- and twofold CVs (accuracy 84.1% and 81.6%, respectively). Conclusion: We developed an SVM-LASSO model to predict malignancy of PNs with two CT radiomic features. We demonstrated that the model achieved an accuracy of 84.6%, which was 12.4% higher than Lung-RADS. © 2018 American Association of Physicists in Medicine
Keywords: lung cancer; ct; pulmonary nodule; radiomics; svm
Journal Title: Medical Physics
Volume: 45
Issue: 4
ISSN: 0094-2405
Publisher: American Association of Physicists in Medicine  
Date Published: 2018-04-01
Start Page: 1537
End Page: 1549
Language: English
DOI: 10.1002/mp.12820
PROVIDER: scopus
PMCID: PMC5903960
PUBMED: 29457229
DOI/URL:
Notes: Article -- Export Date: 1 May 2018 -- Source: Scopus
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MSK Authors
  1. Andreas Rimner
    392 Rimner
  2. Jung Hun Oh
    152 Oh
  3. Joseph Owen Deasy
    410 Deasy
  4. Wei   Lu
    48 Lu
  5. Wookjin   Choi
    18 Choi
  6. Chia-Ju Liu
    8 Liu