Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey

• Published in: Computers in biology and medicine 2022-07, Vol.146, p.105691-105691, Article 105691
• Main Authors: Tomassini, Selene, Falcionelli, Nicola, Sernani, Paolo, Burattini, Laura, Dragoni, Aldo Franco
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Biopsy; Classification; Computed tomography; Computer networks; Convolutional neural network; Deep learning; Diagnosis; Histology; Lung cancer; Lung cancer histology classification; Lung nodule diagnosis; Lung nodules; Medical diagnosis; Neural networks; Nodules; Supervised learning; Surveys; Survival; Tomography
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.105691

Lung cancer is among the deadliest cancers. Besides lung nodule classification and diagnosis, developing non-invasive systems to classify lung cancer histological types/subtypes may help clinicians to make targeted treatment decisions timely, having a positive impact on patients’ comfort and survival rate. As convolutional neural networks have proven to be responsible for the significant improvement of the accuracy in lung cancer diagnosis, with this survey we intend to: show the contribution of convolutional neural networks not only in identifying malignant lung nodules but also in classifying lung cancer histological types/subtypes directly from computed tomography data; point out the strengths and weaknesses of slice-based and scan-based approaches employing convolutional neural networks; and highlight the challenges and prospective solutions to successfully apply convolutional neural networks for such classification tasks. To this aim, we conducted a comprehensive analysis of relevant Scopus-indexed studies involved in lung nodule diagnosis and cancer histology classification up to January 2022, dividing the investigation in convolutional neural network-based approaches fed with planar or volumetric computed tomography data. Despite the application of convolutional neural networks in lung nodule diagnosis and cancer histology classification is a valid strategy, some challenges raised, mainly including the lack of publicly-accessible annotated data, together with the lack of reproducibility and clinical interpretability. We believe that this survey will be helpful for future studies involved in lung nodule diagnosis and cancer histology classification prior to lung biopsy by means of convolutional neural networks. [Display omitted] •This survey focuses on lung cancer diagnosis from computed tomography data.•This survey analyzes relevant Scopus-indexed studies in this domain.•The investigation is divided in slice-based and scan-based approaches.•The application of convolutional neural networks in this domain is a valid strategy.•This survey will be helpful for future on-topic studies.