Prediction of Non-small Cell Lung Cancer Histology by a Deep Ensemble of Convolutional and Bidirectional Recurrent Neura
- PDF / 622,640 Bytes
- 8 Pages / 595.276 x 790.866 pts Page_size
- 82 Downloads / 166 Views
ORIGINAL PAPER
Prediction of Non-small Cell Lung Cancer Histology by a Deep Ensemble of Convolutional and Bidirectional Recurrent Neural Network Dipanjan Moitra 1 & Rakesh Kumar Mandal 1
# Society for Imaging Informatics in Medicine 2020
Abstract Histology subtype prediction is a major task for grading non-small cell lung cancer (NSCLC) tumors. Invasive methods such as biopsy often lack in tumor sample, and as a result radiologists or oncologists find it difficult to detect proper histology of NSCLC tumors. The non-invasive methods such as machine learning may play a useful role to predict NSCLC histology by using medical image biomarkers. Few attempts have so far been made to predict NSCLC histology by considering all the major subtypes. The present study aimed to develop a more accurate deep learning model by clubbing convolutional and bidirectional recurrent neural networks. The NSCLC Radiogenomics dataset having 211 subjects was used in the study. Ten best models found during experimentation were averaged to form an ensemble. The model ensemble was executed with 10-fold repeated stratified cross-validation, and the results got were tested with metrics like accuracy, recall, precision, F1-score, Cohen’s kappa, and ROC-AUC score. The accuracy of the ensemble model showed considerable improvement over the best model found with the single model. The proposed model may help significantly in the automated prognosis of NSCLC and other types of cancers. Keywords Lung cancer . Histology . Bidirectional . Recurrent . Neural network
Introduction Non-small cell lung cancer (NSCLC) accounts for nearly 85% of all lung cancers and a leading cause of cancer-related death worldwide [19]. Prediction of histological subtypes is an important determinant of therapy in NSCLC as it may boost the histopathological grading workup to a significant extent [2]. Major NSCLC subtypes are lung adenocarcinoma and squamous cell carcinoma. Pathological diagnosis of NSCLC often experiences difficulties, as most NSCLC is detected at an advanced stage and samples got from surgical resection are tiny with limited tumor content [1]. This affects the biopsy results, and a proper histology subtype prediction may not be very easy for the radiologists or oncologists. With the advances in precision medicine, medical image biomarkers provide an * Dipanjan Moitra [email protected] Rakesh Kumar Mandal [email protected] 1
University of North Bengal, Siliguri, India
immense improvement in characterizing a heterogeneous tumor, compared with genomic biomarkers [18]. Thus, there is a need for further study of histological prediction in NSCLC [5] by using non-invasive procedures.
Related Work Many studies have so far been conducted to classify NSCLC tumors either as benign or malignant [16], but only a few attempts were aimed to predict NSCLC histology subtypes by using non-invasive methods [10]. Most of these studies were gene expression based [3, 7, 11], and others were traditional and advanced machine learning based [8, 9] powered by extracted
Data Loading...
