# An Intelligent Radomic Approach for Lung Cancer Screening
###### 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. lung cancer; early diagnosis; screening; neural networks; image embedding; architecture optimization +
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significantly better predictive value than volumetry alone (AUC = 0.87 vs. 0.74). More recently, [PERSON] et al. [5] built a radiomic classifier based upon eight quantitative radiologic features selected by the least absolute shrinkage and selection operator (LASSO) method from 726 indeterminate nodules of the LCST. These eight features include variables capturing location, size, shape descriptors and texture analysis. In this retrospective study, the optimism-corrected AUC for these eight features was 0.939 with a sensitivity and specificity of, respectively, 90% and 85%. An alternative to classic radiomics is the use of machine learning methods that extract image features using well known methods such as, Gabor, Local Binary Patterns (LBP), or SIFT descriptor to represent a nodule. Then machine learning technics (e.g., Support Vector Machine (SVM) and Random Forest) are used to define a classification of nodules in this representation space according to their diagnosis [6; 7]. This methods achieve better diagnostic power than radiomic methods with AUC equal to 0.97, sensitivity equal to 96% with 95% of specificity for [6]. Recently, deep CNN (CNN stands for Convolutional Neural Network) have achieved great success in various computer vision tasks, such as image classification, segmentation, and enhancement. Researchers are therefore inspired to classify nodules by using CNNs. Existing works based on CNNs can be classified according to some key points: input data (2D or 3D), level of the input image (volume, slice, nodule), CNN architecture, resources needed and obtained performance. The early work of [PERSON] et al. proposed to use a multi-crop CNN [8] to make the model robust to scales of nodules while keeping 2D input images. Results showed an overall accuracy (including malign and benign cases) of 87%. However, the authors did not report sensitivity for malignancy detection and specificity for discarding benign nodules and, thus, its true clinical value is uncertain. Since nodules are 3D structures, recent works have addressed the problem using 3D CNNs. [PERSON] et al. [9] explored 3D CNNs for pulmonary nodule classification in comparison to a slice-level 2D CNN and a nodule-level 2D CNN analysis. The 3D approach was the best performer with a 87% of overall accuracy and similar specificity and sensitivity at the cost of a significantly higher demand of computational resources and annotated data. [PERSON] et al. [10] used 3D deep dual path networks (DPNs) a 3D Faster Regions with Convolutional Neural Net (R-CNN) designed for nodule detection with 3D dual path blocks and a U-net-like encoder-decoder structure to effectively learn nodule features.