Radiomic Analysis Based on Gd-EOB-DTPA Enhanced MRI for the Preoperative Prediction of Ki-67 Expression in Hepatocellular Carcinoma

Yan, Yang; Lin, Xiao Shi; Ming, Wang Zheng; Chuan, Zhang Qi; Hui, Gan; Juan, Sun Ya; Shuang, Wang; Yang Fan, LV; Dong, Zhang

doi:10.1016/j.acra.2023.07.019

Cited by 2 publications

(1 citation statement)

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“…In terms of automatic brain tumor segmentation, nnU-NetV2 achieves good segmentation performance by implementing automatic network architecture and training hyper-parameter configuration based on the training experience of several public medical databases. Compared to previous studies ( 10 , 11 , 13 , 48 ), this study provides a novel interpretable machine learning radiomics framework that offers an efficient solution for the study of other medical tasks.…”

Section: Discussionmentioning

confidence: 99%

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma

Zhao,

Yang,

Bai

et al. 2024

Front. Med.

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ObjectivesTo investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL).Materials and methodsMRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction.ResultsThe mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p < 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision.ConclusionIn this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task.Clinical relevance statementKi-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis.

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