Change descriptors for determining nodule malignancy in national lung screening trial CT screening images
| Autor: | Robert J. Gillies, Lawrence O. Hall, Dmitry B. Goldgof, Yoganand Balagurunathan, Samuel H. Hawkins, Benjamin Geiger, Robert A. Gatenby |
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| Rok vydání: | 2016 |
| Předmět: |
medicine.medical_specialty
Lung medicine.diagnostic_test business.industry Feature selection 02 engineering and technology Malignancy medicine.disease 030218 nuclear medicine & medical imaging Random forest 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure Computer-aided diagnosis Biopsy 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing National Lung Screening Trial Radiology Lung cancer business |
| Zdroj: | Medical Imaging: Computer-Aided Diagnosis |
| ISSN: | 0277-786X |
| Popis: | Pulmonary nodules are effectively diagnosed in CT scans, but determining their malignancy has been a challenge. The rate of change of the volume of a pulmonary nodule is known to be a prognostic factor for cancer development. In this study, we propose that other changes in imaging characteristics are similarly informative. We examined the combination of image features across multiple CT scans, taken from the National Lung Screening Trial, with individual scans of the same patient separated by approximately one year. By subtracting the values of existing features in multiple scans for the same patient, we were able to improve the ability of existing classification algorithms to determine whether a nodule will become malignant. We trained each classifier on 83 nodules determined to be malignant by biopsy and 172 nodules determined to be benign by their clinical stability through two years of no change; classifiers were tested on 77 malignant and 144 benign nodules, using a set of features that in a test-retest experiment were shown to be stable. An accuracy of 83.71% and AUC of 0.814 were achieved with the Random Forests classifier on a subset of features determined to be stable via test-retest reproducibility analysis, further reduced with the Correlation-based Feature Selection algorithm. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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