Objective:

To explore the application value of radiomics model based on multi-parameter magnetic resonance imaging (mpMRI) in Gleason grading of prostate cancer.

Methods:

The data of patients who underwent prostate mpMRI examination with surgical or pathological puncture results confirming prostate cancer at Ma’anshan People’s Hospital from November 2020 to August 2023 were retrospectively analyzed. MpMRI data were extracted

including T2- weighted imaging (T2WI)

zoomed imaging technique with parallel transmission diffusion-weighted imaging (ZOOMit DWI) and apparent diffusion coefficients (ADC). Spearman’s correlation coefficient was used to preliminarily screen the histological features

the least absolute shrinkage and selection operator (LASSO) algorithm and ten-fold cross-validation were used to further screen

logistic regression was usedto construct the model

and the receiver operating characteristic (ROC) curve was used to judge the results. And the area under the ROC curve (AUC) was compared between models using the DeLong test.

Results:

A total of 176 patients were included

including 72 patients in the low-grade group (Gleason score≤3+4) and 104 patients in the high-grade group (Gleason score≥4+3)

who werer andomly divided into training group (

n

=141) and test group (

n

=35) according to 7∶3. A variety of classifiers were used to construct the multi-parameter model

and the results showed that the AUC of support vector machine (SVM) in the test set was 0.891

and the AUC in the training set was 0.905. Light gradient boosting machine (LightGBM) had the highest AUC of 0.931 in the training set

but it performed poorly in the test set with an AUC of 0.808. The AUCs of multilayer perceptron (MLP) in the test set and the training set were 0.883

and 0.855

respectively

which were weaker than that of SVM

which showed that the stability of LightGBM and MLP models were slightly worse than that of SVM. In addition

the overall performance of the four methods ［k-nearest neighbor (KNN)

extra trees (ET)

random forest (RF)

extreme gradient boosting (XGBoost)］were not as good as SVM

and some of them are overfitted. In general

in terms of Gleason grading of prostate cancer

the SVM model had a higher AUC in both the test set and the training set

and its stability and model classification ability were better.

Conclusion:

Constructing a multimodal imaging histology model based on mpMRI has significant clinical application value in Gleason grading of prostate cancer

of which the SVM model is the best.