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Author (up) Guillermo Torres; Sonia Baeza; Carles Sanchez; Ignasi Guasch; Antoni Rosell; Debora Gil edit  doi
openurl 
  Title An Intelligent Radiomic Approach for Lung Cancer Screening Type Journal Article
  Year 2022 Publication Applied Sciences Abbreviated Journal APPLSCI  
  Volume 12 Issue 3 Pages 1568  
  Keywords Lung cancer; Early diagnosis; Screening; Neural networks; Image embedding; Architecture optimization  
  Abstract 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.
 
  Address Jan 2022  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ Serial 3699  
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