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作者

Gu, Mengyao Chen, Qi Liu, Li Qi, Jiajia Li, Wanhu

作者单位

^aDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, 250117, China

刊名

Chinese Journal of Medical Imaging Technology

年份

2025

卷号

Vol.41 No.3

页码

429-433

ISSN

1003-3289

关键词

breast neoplasms Ki-67 mammography radiomics

摘要

Objective To observe the value of digital breast tomosynthesis -based radiomics nomogram for predicting Ki-67 expression levels in breast invasive ductal carcinoma . Methods Data of 374 eases of BIDC were retrospectively analyzed and divided into high-expression group and low-expression group according to expression of Ki-67 as well as training set and test set at the ratio of 7 : 3. Clinical characteristics and lesion's image manifestations were compared between groups, and radiomic feature...更多

Objective To observe the value of digital breast tomosynthesis -based radiomics nomogram for predicting Ki-67 expression levels in breast invasive ductal carcinoma . Methods Data of 374 eases of BIDC were retrospectively analyzed and divided into high-expression group and low-expression group according to expression of Ki-67 as well as training set and test set at the ratio of 7 : 3. Clinical characteristics and lesion's image manifestations were compared between groups, and radiomic features were extracted and filtered. Then imaging radiomics models were constructed with 8 classifiers, respectively, and the optimal classifier was selected. A nomogram model was subsequently developed through integrating image features and radiomics scores. Receiver operating characteristic curvcs were drawn, and the area under the curvcs were calculated to evaluate the performance of the above models. Results Significant differences of the maximum diameter and spiculatcd margin of the lesions were found between groups . and AUC of multi-layer perceptron image model constructed based on these indexes for predicting BIDC expression level of Ki-67 was 0. 654 in training set and 0. 715 in test set, of MLP radiomics model constructed based on 8 radiomics features was 0. 802 in training set and 0. 806 in test set, while of the nomogram model constructed based on image features and radiomics scores was 0. 802 in training set and 0. 806 in test set, respectively. Conclusion DBT-based radiomics nomogram could be used to effectively predict Ki-67 expression levels in BIDC.收起

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