Multiparametric MRI-based machine learning models for preoperatively predicting rectal adenoma with canceration

Abstract

Objectives

To propose multiparametric MRI-based machine learning models and assess their ability to preoperatively predict rectal adenoma with canceration.

Materials and methods

A total of 53 patients with postoperative pathology confirming rectal adenoma (n = 29) and adenoma with canceration (n = 24) were enrolled in this retrospective study. All patients were divided into a training cohort (n = 42) and a test cohort (n = 11). All patients underwent preoperative pelvic MR examination, including high-resolution T2-weighted imaging (HR-T2WI) and diffusion-weighted imaging (DWI). A total of 1396 radiomics features were extracted from the HR-T2WI and DWI sequences, respectively. The least absolute shrinkage and selection operator (LASSO) was utilized for feature selection from the radiomics feature sets from the HR-T2WI and DWI sequences and from the combined feature set with 2792 radiomics features incorporating two sequences. Five-fold cross-validation and two machine learning algorithms (logistic regression, LR; support vector machine, SVM) were utilized for model construction in the training cohort. The diagnostic performance of the models was evaluated by sensitivity, specificity and area under the curve (AUC) and compared with the Delong’s test.

Results

Ten, 8, and 25 optimal features were selected from 1396 HR-T2WI, 1396 DWI and 2792 combined features, respectively. Three group models were constructed using the selected features from HR-T2WI (ModelT2), DWI (ModelDWI) and the two sequences combined (Modelcombined). Modelcombined showed better prediction performance than ModelT2 and ModelDWI. In Modelcombined, there was no significant difference between the LR and SVM algorithms (p = 0.4795), with AUCs in the test cohort of 0.867 and 0.900, respectively.

Conclusions

Multiparametric MRI-based machine learning models have the potential to predict rectal adenoma with canceration. Compared with ModelT2 and ModelDWI, Modelcombined showed the best performance. Moreover, both LR and SVM have equal excellent performance for model construction.

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Abbreviations

MRI :

Magnetic resonance imaging

HR-T2WI:

High-resolution T2-weighted imaging

DWI:

Diffusion-weighted imaging

LASSO:

Least absolute shrinkage and selection operator

LR:

Logistic regression

SVM:

Support vector machine

AUC:

Area under the curve

CRC:

Colorectal cancer

EMR:

Endoscopic mucosal resection

ESD:

Endoscopic submucosal dissection

ERC:

Early rectal cancer

MP:

Muscularis propria

TE:

Echo time

TR:

Repetition time

FOV:

Field of view

ROI:

Region of interest

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

GLSZM:

Gray-level size zone matrix

GLDM:

Gray-level dependence matrix

NGTDM:

Neighbouring gray-tone difference matrix

ICC:

Interclass correlation coefficient

SMOTE:

Synthetic minority over-sampling Technique

ROC:

Receiver operating characteristic

CEA:

Carcinoembryonic antigen

CA199:

Carbohydrate antigen 19–9

CA724:

Carbohydrate antigen 724

FPR :

False-positive rate

FNR :

False-negative rate

SD:

Standard deviation

Sen :

Sensitivity

Spe :

Specificity

MSE :

Mean-square error

NM:

Normal mucosa

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Author information

Affiliations

Authors

Contributions

PL: analysis and interpretation of the data, drafting of the manuscript; AL: study conception and design; HL: acquisition of the data; RW: acquisition of the data; GS: critical revision; RZ: analysis and interpretation of the data; and PZ: analysis and interpretation of the data.

Corresponding author

Correspondence to Aiyin Li.

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The authors declare that they have no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were approved by the Ethics Commission of the Shandong Provincial Qianfoshan Hospital of Shandong University.

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Informed consent was obtained from all individual participants included in the study.

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Cite this article

Li, P., Song, G., Wu, R. et al. Multiparametric MRI-based machine learning models for preoperatively predicting rectal adenoma with canceration. Magn Reson Mater Phy (2021). https://doi.org/10.1007/s10334-021-00915-2

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Keywords

  • Machine learning model
  • Rectal adenoma with canceration
  • MRI