Non-small-cell lung cancer classification via RNA-Seq and histology imaging probability fusion

Abstract

Background Adenocarcinoma and squamous cell carcinoma are the two most prevalent lung cancer types, and their distinction requires different screenings, such as the visual inspection of histology slides by an expert pathologist, the analysis of gene expression or computer tomography scans, among others. In recent years, there has been an increasing gathering of biological data for decision support systems in the diagnosis (e.g. histology imaging, next-generation sequencing technologies data, clinical information, etc.). Using all these sources to design integrative classification approaches may improve the final diagnosis of a patient, in the same way that doctors can use multiple types of screenings to reach a final decision on the diagnosis. In this work, we present a late fusion classification model using histology and RNA-Seq data for adenocarcinoma, squamous-cell carcinoma and healthy lung tissue. Results The classification model improves results over using each source of information separately, being able to reduce the diagnosis error rate up to a 64% over the isolate histology classifier and a 24% over the isolate gene expression classifier, reaching a mean F1-Score of 95.19% and a mean AUC of 0.991. Conclusions These findings suggest that a classification model using a late fusion methodology can considerably help clinicians in the diagnosis between the aforementioned lung cancer cancer subtypes over using each source of information separately. This approach can also be applied to any cancer type or disease with heterogeneous sources of information.

Identification of a apoptosis-related LncRNA signature to improve prognosis prediction and immunotherapy response in lung adenocarcinoma patients

“…Lung cancer is the leading cause of cancer deaths worldwide. LUAD is one of the most common histological types of lung cancer (Carrillo-Perez et al, 2021). In recent years, Frontiers in Genetics frontiersin.org Frontiers in Genetics frontiersin.org 13 chemotherapy and molecular targeted therapy can prolong the overall survival of patients with LUAD, and the emergence of immunotherapy also brings a promising future to LUAD treatment (Lou et al, 2020;Deshpand et al, 2022).…”

An emerging era of computational cytology

“…This is an open sourced web-based tool that may be helpful in computational cytology in future. Carrillo-Perez et al 19 designed a late fusion technique to amalgamate the WSI data and RNA-Seq data for the classification of the various lung carcinomas.…”

Deep Learning Facilitates Distinguishing Histologic Subtypes of Pulmonary Neuroendocrine Tumors on Digital Whole-Slide Images

“…The approach used in our case study showed state-of-the-art results on publicly available histopathology image analysis challenges, namely CAMELYON16 and CAMELYON17, to automate identification and staging of lymph node metastases in breast cancer [ 18 , 19 ]. In thoracic pathology, several prior studies have demonstrated that CNNs can correctly identify morphological patterns and predict survival outcomes or mutation profile using whole-slide images [ 20 , 21 , 22 , 23 ]. The CAD was assessed only in distinguishing lung adenocarcinoma from squamous cell carcinoma [ 20 , 21 , 22 , 23 ].…”

“…In thoracic pathology, several prior studies have demonstrated that CNNs can correctly identify morphological patterns and predict survival outcomes or mutation profile using whole-slide images [ 20 , 21 , 22 , 23 ]. The CAD was assessed only in distinguishing lung adenocarcinoma from squamous cell carcinoma [ 20 , 21 , 22 , 23 ].…”