Non‑small‑cell lung cancer classification via RNA‑Seq and histology imaging probability fusion
MetadataShow full item record
AuthorCarrillo Pérez, Francisco; Morales Vega, Juan Carlos; Castillo Secilla, Daniel; Molina Castro, Yésica; Guillén Perales, Alberto; Rojas Ruiz, Ignacio; Herrera Maldonado, Luis Javier
Deep learningGene expressionLate fusionWhole slide imagingNSCLC
Carrillo-Perez, F... [et al.]. Non-small-cell lung cancer classification via RNA-Seq and histology imaging probability fusion. BMC Bioinformatics 22, 454 (2021). [https://doi.org/10.1186/s12859-021-04376-1]
SponsorshipSpanish Ministry of Sciences, Innovation and Universities RTI2018-101674B-I00; Government of Andalusia CV20-64934
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.