1. Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study.
- Author
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Shen Y, Wu S, Wu Y, Cui C, Li H, Yang S, Liu X, Chen X, Huang C, and Wang X
- Subjects
- Humans, Female, Male, Middle Aged, Retrospective Studies, Adult, Aged, Diffusion Magnetic Resonance Imaging methods, ROC Curve, Algorithms, Contrast Media, Radiomics, Ki-67 Antigen metabolism, Ki-67 Antigen analysis, Multiparametric Magnetic Resonance Imaging methods, Lymphoma diagnostic imaging, Lymphoma metabolism, Machine Learning, Central Nervous System Neoplasms diagnostic imaging, Central Nervous System Neoplasms metabolism
- 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., Competing Interests: Declarations. Ethics approval and consent to participate: This retrospective study was approved by the Ethics Committee of the Shandong Provincial Hospital and written informed consent for the participants over 16 years of age was discarded because of the retrospective nature of this study. For participants less than 16 years of age, written informed consent has been obtained from their parents/guardians after a brief description of the purpose and objectives of the study has been given to them. We confirmed that the study was carried out in accordance with relevant guidelines and regulations of Helsinki Declaration. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Clinical trial number: Not applicable., (© 2025. The Author(s).)
- Published
- 2025
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