Evidence map›Paper›PMID 32487083›Full record

ArticleBMC medical imaging2020

Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging.

Kan He, Xiaoming Liu, Mingyang Li, Xueyan Li, Hualin Yang, Huimao Zhang

Open access · goldAbstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
3.7field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 56 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Kan HeThe First Hospital of Jilin University, Department of Radiology, Changchun, China.
Xiaoming LiuJilin University, College of Electronic Science and Engineering, State Key Laboratory on Integrated Optoelectronics, No.2699 Qianjin Street, Changchun, China.
Mingyang LiJilin University, College of Electronic Science and Engineering, State Key Laboratory on Integrated Optoelectronics, No.2699 Qianjin Street, Changchun, China.
Xueyan LiJilin University, College of Electronic Science and Engineering, State Key Laboratory on Integrated Optoelectronics, No.2699 Qianjin Street, Changchun, China.
Hualin YangJilin University, College of Electronic Science and Engineering, State Key Laboratory on Integrated Optoelectronics, No.2699 Qianjin Street, Changchun, China.
Huimao ZhangThe First Hospital of Jilin University, Department of Radiology, Changchun, China. huimaozhanglinda@163.com.
Jilin University · CNState Key Laboratory on Integrated Optoelectronics · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodsWe 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.

resultsThe 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.

conclusionsOur 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.

Indexed as

MutationAdultAgedAged, 80 and overColorectal NeoplasmsDeep LearningFemaleHigh-Throughput Nucleotide SequencingHumansMaleMiddle AgedProto-Oncogene Proteins p21(ras)Radiographic Image Interpretation, Computer-AssistedSequence Analysis, DNATomography, X-Ray ComputedKRAS protein, humanProto-Oncogene Proteins p21(ras)Colorectal NeoplasmDeep learningMutation

Identifiers

PMID32487083
PMCPMC7268438
OpenAlexW3030421963

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.