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21.05.2019 | Chest

Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study

verfasst von: Wei Wu, Larry A. Pierce, Yuzheng Zhang, Sudhakar N. J. Pipavath, Timothy W. Randolph, Kristin J. Lastwika, Paul D. Lampe, A. McGarry Houghton, Haining Liu, Liming Xia, Paul E. Kinahan

Erschienen in: European Radiology | Ausgabe 11/2019

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Abstract

Purpose

To compare the ability of radiological semantic and quantitative texture features in lung cancer diagnosis of pulmonary nodules.

Materials and methods

A total of N = 121 subjects with confirmed non-small-cell lung cancer were matched with 117 controls based on age and gender. Radiological semantic and quantitative texture features were extracted from CT images with or without contrast enhancement. Three different models were compared using LASSO logistic regression: “CS” using clinical and semantic variables, “T” using texture features, and “CST” using clinical, semantic, and texture variables. For each model, we performed 100 trials of fivefold cross-validation and the average receiver operating curve was accessed. The AUC of the cross-validation study (AUC_CV) was calculated together with its 95% confidence interval.

Results

The AUC_CV (and 95% confidence interval) for models T, CS, and CST was 0.85 (0.71–0.96), 0.88 (0.77–0.96), and 0.88 (0.77–0.97), respectively. After separating the data into two groups with or without contrast enhancement, the AUC (without cross-validation) of the model T was 0.86 both for images with and without contrast enhancement, suggesting that contrast enhancement did not impact the utility of texture analysis.

Conclusions

The models with semantic and texture features provided cross-validated AUCs of 0.85–0.88 for classification of benign versus cancerous nodules, showing potential in aiding the management of patients.

Key Points

• Pretest probability of cancer can aid and direct the physician in the diagnosis and management of pulmonary nodules in a cost-effective way.

• Semantic features (qualitative features reported by radiologists to characterize lung lesions) and radiomic (e.g., texture) features can be extracted from CT images.

• Input of these variables into a model can generate a pretest likelihood of cancer to aid clinical decision and management of pulmonary nodules.

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Titel: Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study
verfasst von: Wei Wu
Larry A. Pierce
Yuzheng Zhang
Sudhakar N. J. Pipavath
Timothy W. Randolph
Kristin J. Lastwika
Paul D. Lampe
A. McGarry Houghton
Haining Liu
Liming Xia
Paul E. Kinahan
Publikationsdatum: 21.05.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 11/2019
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-019-06213-9

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Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study