BMC medical imaging · 2026
Non-invasive prediction of B-cell lymphoma-2 and Ki-67 expression in primary central nervous system lymphoma using whole-tumor histogram analysis of diffusion weighted imaging, diffusional kurtosis imaging and intravoxel incoherent motion.
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Zhou Xiaofang, Wang Feng, Yu Shujie, Yu Feifei, Lin Xiaoye, Cao Dairong, Xing Zhen
Аннотация
OBJECTIVE: To evaluate and compare the diagnostic performance of whole-tumor histogram analysis using multiple diffusion MRI models for prediction of B-cell lymphoma-2 (BCL-2) and Ki-67 expression in PCNSL.
METHODS: Eighty-one participants who underwent conventional diffusion weighted imaging (DWI), diffusional kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) examinations between January 2020 and January 2025 were enrolled in this study. Whole-tumor histogram features extracted from apparent diffusion coefficient (ADC), diffusion coefficient (Dk), diffusional kurtosis (K), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) maps were compared between different BCL-2 and Ki-67 expression groups using Mann-Whitney U test. Receiver operating characteristic (ROC) analysis and logistic regression analysis were used to evaluate the diagnostic performance of different diffusion models. Internal validation was performed using bootstrap resampling with 1000 repetitions.
RESULTS: A total of 21 and 26 histogram features derived from diffusion maps were identified as effective for distinguishing different BCL-2 and Ki-67 expression statuses in PCNSL (all P < 0.05), respectively. Among the DWI, DKI, IVIM, and combined models, no significant differences were observed in the areas under the receiver operating characteristic curves (AUCs) for predicting BCL-2 expression (AUCs (95%CI): 0.810 (0.683-0.903), 0.830 (0.706-0.917), 0.901 (0.798-1.000), and 0.877 (0.769-0.984), respectively; all corrected P > 0.05, Bonferroni correction) or Ki-67 index (AUCs (95%CI): 0.832 (0.732-0.906), 0.769 (0.662-0.856), 0.792 (0.688-0.874), and 0.824 (0.724-0.900), respectively; all corrected P > 0.05, Bonferroni correction) in PCNSL. Bootstrap internal validation suggested limited model optimism and acceptable calibration.
CONCLUSIONS: Whole-tumor histogram analysis based on diffusion MRI is an effective noninvasive method for predicting BCL-2 expression and Ki-67 index in PCNSL. No statistically significant difference was detected among the three diffusion models in this preliminary study.
METHODS: Eighty-one participants who underwent conventional diffusion weighted imaging (DWI), diffusional kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) examinations between January 2020 and January 2025 were enrolled in this study. Whole-tumor histogram features extracted from apparent diffusion coefficient (ADC), diffusion coefficient (Dk), diffusional kurtosis (K), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) maps were compared between different BCL-2 and Ki-67 expression groups using Mann-Whitney U test. Receiver operating characteristic (ROC) analysis and logistic regression analysis were used to evaluate the diagnostic performance of different diffusion models. Internal validation was performed using bootstrap resampling with 1000 repetitions.
RESULTS: A total of 21 and 26 histogram features derived from diffusion maps were identified as effective for distinguishing different BCL-2 and Ki-67 expression statuses in PCNSL (all P < 0.05), respectively. Among the DWI, DKI, IVIM, and combined models, no significant differences were observed in the areas under the receiver operating characteristic curves (AUCs) for predicting BCL-2 expression (AUCs (95%CI): 0.810 (0.683-0.903), 0.830 (0.706-0.917), 0.901 (0.798-1.000), and 0.877 (0.769-0.984), respectively; all corrected P > 0.05, Bonferroni correction) or Ki-67 index (AUCs (95%CI): 0.832 (0.732-0.906), 0.769 (0.662-0.856), 0.792 (0.688-0.874), and 0.824 (0.724-0.900), respectively; all corrected P > 0.05, Bonferroni correction) in PCNSL. Bootstrap internal validation suggested limited model optimism and acceptable calibration.
CONCLUSIONS: Whole-tumor histogram analysis based on diffusion MRI is an effective noninvasive method for predicting BCL-2 expression and Ki-67 index in PCNSL. No statistically significant difference was detected among the three diffusion models in this preliminary study.