Identification of Tissue Types and Gene Mutations From Histopathology Images for Advancing Colorectal Cancer Biology.
IEEE Open J Eng Med Biol
; 3: 115-123, 2022.
Article
in En
| MEDLINE
| ID: mdl-35937101
ABSTRACT
Objective:
Colorectal cancer (CRC) patients respond differently to treatments and are sub-classified by different approaches. We evaluated a deep learning model, which adopted endoscopic knowledge learnt from AI-doscopist, to characterise CRC patients by histopathological features.Results:
Data of 461 patients were collected from TCGA-COAD database. The proposed framework was able to 1) differentiate tumour from normal tissues with an Area Under Receiver Operating Characteristic curve (AUROC) of 0.97; 2) identify certain gene mutations (MYH9, TP53) with an AUROC > 0.75; 3) classify CMS2 and CMS4 better than the other subtypes; and 4) demonstrate the generalizability of predicting KRAS mutants in an external cohort.Conclusions:
Artificial intelligent can be used for on-site patient classification. Although KRAS mutants were commonly associated with therapeutic resistance and poor prognosis, subjects with predicted KRAS mutants in this study have a higher survival rate in 30 months after diagnoses.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Diagnostic_studies
/
Prognostic_studies
Language:
En
Journal:
IEEE Open J Eng Med Biol
Year:
2022
Document type:
Article