Non‑small‑cell lung cancer classification via RNA‑Seq and histology imaging probability fusion
Metadatos
Mostrar el registro completo del ítemAutor
Carrillo Pérez, Francisco; Morales Vega, Juan Carlos; Castillo Secilla, Daniel; Molina Castro, Yésica; Guillén Perales, Alberto; Rojas Ruiz, Ignacio; Herrera Maldonado, Luis JavierEditorial
BMC
Materia
Deep learning Gene expression Late fusion Whole slide imaging NSCLC
Fecha
2021-09-22Referencia bibliográfica
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]
Patrocinador
Spanish Ministry of Sciences, Innovation and Universities RTI2018-101674B-I00; Government of Andalusia CV20-64934Resumen
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.