@misc{10481/71005,
year = {2021},
month = {9},
url = {http://hdl.handle.net/10481/71005},
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.},
organization = {Spanish Ministry of Sciences, Innovation and Universities	RTI2018-101674B-I00},
organization = {Government of Andalusia	CV20-64934},
publisher = {BMC},
keywords = {Deep learning},
keywords = {Gene expression},
keywords = {Late fusion},
keywords = {Whole slide imaging},
keywords = {NSCLC},
title = {Non‑small‑cell lung cancer classification via RNA‑Seq and histology imaging probability fusion},
doi = {10.1186/s12859-021-04376-1},
author = {Carrillo Pérez, Francisco and Morales Vega, Juan Carlos and Castillo Secilla, Daniel and Molina Castro, Yésica and Guillén Perales, Alberto and Rojas Ruiz, Ignacio and Herrera Maldonado, Luis Javier},
}