The value of multiparametric MRI radiomics and machine learning in predicting preoperative Ki-67 expression level in breast cancer
Abstract Objective This study was to develop a multi-parametric MRI radiomics model to predict preoperative Ki-67 status. Materials and methods A total of 120 patients with pathologically confirmed breast cancer were retrospectively enrolled and randomly divided into a training set (n = 84) and a va...
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BMC
2025-01-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01553-z |
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| author | Yan Lu Long Jin Ning Ding Mengjuan Li Shengnan Yin Yiding Ji |
| author_facet | Yan Lu Long Jin Ning Ding Mengjuan Li Shengnan Yin Yiding Ji |
| author_sort | Yan Lu |
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| description | Abstract Objective This study was to develop a multi-parametric MRI radiomics model to predict preoperative Ki-67 status. Materials and methods A total of 120 patients with pathologically confirmed breast cancer were retrospectively enrolled and randomly divided into a training set (n = 84) and a validation set (n = 36). Radiomic features were derived from both the intratumoral and peritumoral regions, extending 5 mm from the tumor boundary, using magnetic resonance imaging (MRI). The MRI sequences employed included T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE) imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. The T-test and the Least Absolute Shrinkage and Selection Operator Cross-Validation (LASSO CV) were conducted for feature selection. Modelintra, modelperi, modelintra+peri were established by eleven supervised machine learning (ML) algorithms to predict the expression status of Ki-67 in breast cancer and were verified by the validation groups. The model’s performance was evaluated by employing metrics such as the area under the curve (AUC), accuracy, sensitivity, and specificity. Results The features of intratumor, peritumor, intratumor + peritumor were extracted 851, 851 and 1702 samples respectively, 14, 23 and 35 features were selected by LASSO. ML algorithms based on modelintra and modelperi consistently yield AUCs that are below 80% in the validation set. Hower, Logistic regression (LR) and linear discriminant analysis (LDA) based on modelintra+peri demonstrated significant advantages over other algorithms, achieving AUCs of 0.92 and 0.98, accuracies of 0.94 and 0.97, sensitivities of 1 and 0.96, and specificities of 0.85 and 1 respectively in the validation set. Conclusion The integrated intra- and peritumoral radiomics model, developed using multiparametric MRI data and machine learning classifiers, exhibits significant predictive power for Ki-67 expression levels. This model could facilitate personalized clinical treatment strategies for individuals diagnosed with breast cancer (BC). Clinical trial number Not applicable. |
| format | Article |
| id | doaj-art-c83df7938e6247129e2d3c1f492421f3 |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
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| series | BMC Medical Imaging |
| spelling | doaj-art-c83df7938e6247129e2d3c1f492421f32025-01-12T12:44:44ZengBMCBMC Medical Imaging1471-23422025-01-012511910.1186/s12880-025-01553-zThe value of multiparametric MRI radiomics and machine learning in predicting preoperative Ki-67 expression level in breast cancerYan Lu0Long Jin1Ning Ding2Mengjuan Li3Shengnan Yin4Yiding Ji5Department of Radiology, Suzhou Ninth People’s HospitalDepartment of Radiology, Suzhou Ninth People’s HospitalDepartment of Radiology, Suzhou Ninth People’s HospitalDepartment of Radiology, Suzhou Ninth People’s HospitalDepartment of Radiology, Suzhou Ninth People’s HospitalDepartment of Radiology, Suzhou Ninth People’s HospitalAbstract Objective This study was to develop a multi-parametric MRI radiomics model to predict preoperative Ki-67 status. Materials and methods A total of 120 patients with pathologically confirmed breast cancer were retrospectively enrolled and randomly divided into a training set (n = 84) and a validation set (n = 36). Radiomic features were derived from both the intratumoral and peritumoral regions, extending 5 mm from the tumor boundary, using magnetic resonance imaging (MRI). The MRI sequences employed included T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE) imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. The T-test and the Least Absolute Shrinkage and Selection Operator Cross-Validation (LASSO CV) were conducted for feature selection. Modelintra, modelperi, modelintra+peri were established by eleven supervised machine learning (ML) algorithms to predict the expression status of Ki-67 in breast cancer and were verified by the validation groups. The model’s performance was evaluated by employing metrics such as the area under the curve (AUC), accuracy, sensitivity, and specificity. Results The features of intratumor, peritumor, intratumor + peritumor were extracted 851, 851 and 1702 samples respectively, 14, 23 and 35 features were selected by LASSO. ML algorithms based on modelintra and modelperi consistently yield AUCs that are below 80% in the validation set. Hower, Logistic regression (LR) and linear discriminant analysis (LDA) based on modelintra+peri demonstrated significant advantages over other algorithms, achieving AUCs of 0.92 and 0.98, accuracies of 0.94 and 0.97, sensitivities of 1 and 0.96, and specificities of 0.85 and 1 respectively in the validation set. Conclusion The integrated intra- and peritumoral radiomics model, developed using multiparametric MRI data and machine learning classifiers, exhibits significant predictive power for Ki-67 expression levels. This model could facilitate personalized clinical treatment strategies for individuals diagnosed with breast cancer (BC). Clinical trial number Not applicable.https://doi.org/10.1186/s12880-025-01553-zRadiomicsMachine learningBreast cancerKi-67 expression level |
| spellingShingle | Yan Lu Long Jin Ning Ding Mengjuan Li Shengnan Yin Yiding Ji The value of multiparametric MRI radiomics and machine learning in predicting preoperative Ki-67 expression level in breast cancer BMC Medical Imaging Radiomics Machine learning Breast cancer Ki-67 expression level |
| title | The value of multiparametric MRI radiomics and machine learning in predicting preoperative Ki-67 expression level in breast cancer |
| title_full | The value of multiparametric MRI radiomics and machine learning in predicting preoperative Ki-67 expression level in breast cancer |
| title_fullStr | The value of multiparametric MRI radiomics and machine learning in predicting preoperative Ki-67 expression level in breast cancer |
| title_full_unstemmed | The value of multiparametric MRI radiomics and machine learning in predicting preoperative Ki-67 expression level in breast cancer |
| title_short | The value of multiparametric MRI radiomics and machine learning in predicting preoperative Ki-67 expression level in breast cancer |
| title_sort | value of multiparametric mri radiomics and machine learning in predicting preoperative ki 67 expression level in breast cancer |
| topic | Radiomics Machine learning Breast cancer Ki-67 expression level |
| url | https://doi.org/10.1186/s12880-025-01553-z |
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