An Intelligent Radiomic Approach for Lung Cancer Screening

The efficiency of lung cancer screening for reducing mortality is hindered by the high rate of false positives. Artificial intelligence applied to radiomics could help to early discard benign cases from the analysis of CT scans. The available amount of data and the fact that benign cases are a minor...

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Bibliographic Details
Published in:Applied sciences 2022-02, Vol.12 (3), p.1568
Main Authors: Torres, Guillermo, Baeza, Sonia, Sanchez, Carles, Guasch, Ignasi, Rosell, Antoni, Gil, Debora
Format: Article
Language:English
Subjects:
architecture optimization
Artificial intelligence
Biopsy
Cancer screening
Classification
Computed tomography
Deep learning
early diagnosis
image embedding
Lung cancer
Machine learning
Malignancy
Medical screening
Neural networks
Nodules
Patients
Radiomics
screening
Skin cancer
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Summary:The efficiency of lung cancer screening for reducing mortality is hindered by the high rate of false positives. Artificial intelligence applied to radiomics could help to early discard benign cases from the analysis of CT scans. The available amount of data and the fact that benign cases are a minority, constitutes a main challenge for the successful use of state of the art methods (like deep learning), which can be biased, over-fitted and lack of clinical reproducibility. We present an hybrid approach combining the potential of radiomic features to characterize nodules in CT scans and the generalization of the feed forward networks. In order to obtain maximal reproducibility with minimal training data, we propose an embedding of nodules based on the statistical significance of radiomic features for malignancy detection. This representation space of lesions is the input to a feed forward network, which architecture and hyperparameters are optimized using own-defined metrics of the diagnostic power of the whole system. Results of the best model on an independent set of patients achieve 100% of sensitivity and 83% of specificity (AUC = 0.94) for malignancy detection.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12031568