Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study
Abstract Objectives To examine the correlation of apparent diffusion coefficient (ADC), diffusion weighted imaging (DWI), and T1 contrast enhanced (T1-CE) with Ki-67 in primary central nervous system lymphomas (PCNSL). And to assess the diagnostic performance of MRI radiomics-based machine-learning...
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BMC
2025-02-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01585-5 |
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| author | Yelong Shen Siyu Wu Yanan Wu Chao Cui Haiou Li Shuang Yang Xuejun Liu Xingzhi Chen Chencui Huang Ximing Wang |
| author_facet | Yelong Shen Siyu Wu Yanan Wu Chao Cui Haiou Li Shuang Yang Xuejun Liu Xingzhi Chen Chencui Huang Ximing Wang |
| author_sort | Yelong Shen |
| collection | DOAJ |
| description | Abstract Objectives To examine the correlation of apparent diffusion coefficient (ADC), diffusion weighted imaging (DWI), and T1 contrast enhanced (T1-CE) with Ki-67 in primary central nervous system lymphomas (PCNSL). And to assess the diagnostic performance of MRI radiomics-based machine-learning algorithms in differentiating the high proliferation and low proliferation groups of PCNSL. Methods 83 patients with PCNSL were included in this retrospective study. ADC, DWI and T1-CE sequences were collected and their correlation with Ki-67 was examined using Spearman’s correlation analysis. The Kaplan-Meier method and log-rank test were used to compare the survival rates of the high proliferation and low proliferation groups. The radiomics features were extracted respectively, and the features were screened by machine learning algorithm and statistical method. Radiomics models of seven different sequence permutations were constructed. The area under the receiver operating characteristic curve (ROC AUC) was used to evaluate the predictive performance of all models. DeLong test was utilized to compare the differences of models. Results Relative mean apparent diffusion coefficient (rADCmean) (ρ=-0.354, p = 0.019), relative mean diffusion weighted imaging (rDWImean) (b = 1000) (ρ = 0.273, p = 0.013) and relative mean T1 contrast enhancement (rT1-CEmean) (ρ = 0.385, p = 0.001) was significantly correlated with Ki-67. Interobserver agreements between the two radiologists were almost perfect for all parameters (rADCmean ICC = 0.978, 95%CI 0.966–0.986; rDWImean (b = 1000) ICC = 0.931, 95% CI 0.895–0.955; rT1-CEmean ICC = 0.969, 95% CI 0.953–0.980). The differences in PFS (p = 0.016) and OS (p = 0.014) between the low and high proliferation groups were statistically significant. The best prediction model in our study used a combination of ADC, DWI, and T1-CE achieving the highest AUC of 0.869, while the second ranked model used ADC and DWI, achieving an AUC of 0.828. Conclusion rDWImean, rADCmean and rT1-CEmean were correlated with Ki-67. The radiomics model based on MRI sequences combined is promising to distinguish low proliferation PCNSL from high proliferation PCNSL. |
| format | Article |
| id | doaj-art-71d44939571743a68f2c1c85edd5da5c |
| institution | DOAJ |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
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| series | BMC Medical Imaging |
| spelling | doaj-art-71d44939571743a68f2c1c85edd5da5c2025-08-20T03:10:56ZengBMCBMC Medical Imaging1471-23422025-02-0125111210.1186/s12880-025-01585-5Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter studyYelong Shen0Siyu Wu1Yanan Wu2Chao Cui3Haiou Li4Shuang Yang5Xuejun Liu6Xingzhi Chen7Chencui Huang8Ximing Wang9Department of Radiology, Shandong Provincial HospitalDepartment of Radiology, Shandong Provincial HospitalDepartment of Radiology, Shandong Provincial HospitalQilu Hospital of Shandong University Dezhou HospitalCheeloo College of Medicine, Qilu Hospital, Shandong UniversityDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University& Shandong Provincial Qianfoshan HospitalDepartment of Radiology, the Affiliated Hospital of Qingdao UniversityDepartment of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., LtdDepartment of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., LtdDepartment of Radiology, Shandong Provincial HospitalAbstract Objectives To examine the correlation of apparent diffusion coefficient (ADC), diffusion weighted imaging (DWI), and T1 contrast enhanced (T1-CE) with Ki-67 in primary central nervous system lymphomas (PCNSL). And to assess the diagnostic performance of MRI radiomics-based machine-learning algorithms in differentiating the high proliferation and low proliferation groups of PCNSL. Methods 83 patients with PCNSL were included in this retrospective study. ADC, DWI and T1-CE sequences were collected and their correlation with Ki-67 was examined using Spearman’s correlation analysis. The Kaplan-Meier method and log-rank test were used to compare the survival rates of the high proliferation and low proliferation groups. The radiomics features were extracted respectively, and the features were screened by machine learning algorithm and statistical method. Radiomics models of seven different sequence permutations were constructed. The area under the receiver operating characteristic curve (ROC AUC) was used to evaluate the predictive performance of all models. DeLong test was utilized to compare the differences of models. Results Relative mean apparent diffusion coefficient (rADCmean) (ρ=-0.354, p = 0.019), relative mean diffusion weighted imaging (rDWImean) (b = 1000) (ρ = 0.273, p = 0.013) and relative mean T1 contrast enhancement (rT1-CEmean) (ρ = 0.385, p = 0.001) was significantly correlated with Ki-67. Interobserver agreements between the two radiologists were almost perfect for all parameters (rADCmean ICC = 0.978, 95%CI 0.966–0.986; rDWImean (b = 1000) ICC = 0.931, 95% CI 0.895–0.955; rT1-CEmean ICC = 0.969, 95% CI 0.953–0.980). The differences in PFS (p = 0.016) and OS (p = 0.014) between the low and high proliferation groups were statistically significant. The best prediction model in our study used a combination of ADC, DWI, and T1-CE achieving the highest AUC of 0.869, while the second ranked model used ADC and DWI, achieving an AUC of 0.828. Conclusion rDWImean, rADCmean and rT1-CEmean were correlated with Ki-67. The radiomics model based on MRI sequences combined is promising to distinguish low proliferation PCNSL from high proliferation PCNSL.https://doi.org/10.1186/s12880-025-01585-5Primary central nervous system lymphomaKi-67Multiparametric magnetic resonance imagingRadiomics |
| spellingShingle | Yelong Shen Siyu Wu Yanan Wu Chao Cui Haiou Li Shuang Yang Xuejun Liu Xingzhi Chen Chencui Huang Ximing Wang Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study BMC Medical Imaging Primary central nervous system lymphoma Ki-67 Multiparametric magnetic resonance imaging Radiomics |
| title | Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study |
| title_full | Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study |
| title_fullStr | Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study |
| title_full_unstemmed | Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study |
| title_short | Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study |
| title_sort | radiomics model building from multiparametric mri to predict ki 67 expression in patients with primary central nervous system lymphomas a multicenter study |
| topic | Primary central nervous system lymphoma Ki-67 Multiparametric magnetic resonance imaging Radiomics |
| url | https://doi.org/10.1186/s12880-025-01585-5 |
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