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Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging
- Source :
- BMC Medical Imaging, Vol 20, Iss 1, Pp 1-9 (2020), BMC Medical Imaging
- Publication Year :
- 2020
- Publisher :
- BMC, 2020.
-
Abstract
- Background The detection of Kirsten rat sarcoma viral oncogene homolog (KRAS) gene mutations in colorectal cancer (CRC) is key to the optimal design of individualized therapeutic strategies. The noninvasive prediction of the KRAS status in CRC is challenging. Deep learning (DL) in medical imaging has shown its high performance in diagnosis, classification, and prediction in recent years. In this paper, we investigated predictive performance by using a DL method with a residual neural network (ResNet) to estimate the KRAS mutation status in CRC patients based on pre-treatment contrast-enhanced CT imaging. Methods We have collected a dataset consisting of 157 patients with pathology-confirmed CRC who were divided into a training cohort (n = 117) and a testing cohort (n = 40). We developed an ResNet model that used portal venous phase CT images to estimate KRAS mutations in the axial, coronal, and sagittal directions of the training cohort and evaluated the model in the testing cohort. Several groups of expended region of interest (ROI) patches were generated for the ResNet model, to explore whether tissues around the tumor can contribute to cancer assessment. We also explored a radiomics model with the random forest classifier (RFC) to predict KRAS mutations and compared it with the DL model. Results The ResNet model in the axial direction achieved the higher area under the curve (AUC) value (0.90) in the testing cohort and peaked at 0.93 with an input of ’ROI and 20-pixel’ surrounding area. AUC of radiomics model in testing cohorts were 0.818. In comparison, the ResNet model showed better predictive ability. Conclusions Our experiments reveal that the computerized assessment of the pre-treatment CT images of CRC patients using a DL model has the potential to precisely predict KRAS mutations. This new model has the potential to assist in noninvasive KRAS mutation estimation.
- Subjects :
- Oncology
Adult
Male
medicine.medical_specialty
lcsh:Medical technology
Colorectal cancer
Colorectal Neoplasm
Gene mutation
medicine.disease_cause
030218 nuclear medicine & medical imaging
Proto-Oncogene Proteins p21(ras)
03 medical and health sciences
0302 clinical medicine
Region of interest
Internal medicine
medicine
Medical imaging
Humans
Radiology, Nuclear Medicine and imaging
neoplasms
Aged
Aged, 80 and over
business.industry
Area under the curve
Cancer
High-Throughput Nucleotide Sequencing
Deep learning
Sequence Analysis, DNA
Middle Aged
medicine.disease
digestive system diseases
lcsh:R855-855.5
030220 oncology & carcinogenesis
Cohort
Mutation
Radiographic Image Interpretation, Computer-Assisted
Female
KRAS
business
Colorectal Neoplasms
Tomography, X-Ray Computed
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 14712342
- Volume :
- 20
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- BMC Medical Imaging
- Accession number :
- edsair.doi.dedup.....d68742a052cf5171baaac9b11b2225dd
- Full Text :
- https://doi.org/10.1186/s12880-020-00457-4