Non-invasive MRI-based assessment of reactive stromal grade in prostate cancer using diffusion kurtosis imaging and stretched-exponential model
Abstract Objectives Reactive stroma plays a pivotal role in the genesis, progression, and metastasis of prostate cancer (PCa). Higher reactive stromal grade (RSG) generally portends a poorer prognosis. The aim of the study is non-invasively evaluate RSG by preoperative mono-exponential model, stretc...
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BMC
2025-08-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01881-0 |
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| author | Kun-Peng Zhou Hua-Bin Huang Shu-Yi Li Zhong-Xing Luo Xian-Wen Cheng Di-Min Liu Jie Bian Qing-Yu Liu |
| author_facet | Kun-Peng Zhou Hua-Bin Huang Shu-Yi Li Zhong-Xing Luo Xian-Wen Cheng Di-Min Liu Jie Bian Qing-Yu Liu |
| author_sort | Kun-Peng Zhou |
| collection | DOAJ |
| description | Abstract Objectives Reactive stroma plays a pivotal role in the genesis, progression, and metastasis of prostate cancer (PCa). Higher reactive stromal grade (RSG) generally portends a poorer prognosis. The aim of the study is non-invasively evaluate RSG by preoperative mono-exponential model, stretch-exponent model (SEM) and diffusion kurtosis imaging (DKI), and isolate the independent predictor of high RSG (> 50% reactive stroma) in parameters of mono-exponential model, SEM and DKI. Methods Totally, 54 low RSG (≤ 50% reactive stroma) patients and 26 high RSG patients were prospectively enrolled in the study. Apparent diffusion coefficient (ADC), mean kurtosis (MK), mean diffusivity (MD), distributed diffusion coefficient (DDC), and heterogeneity index (α) values of all lesions were measured on GE Workstation 4.6. Spearman’s rank correlation analysis was used to analysis the correlation between RSG and parameters of SEM and DKI. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of those parameters in differentiating low RSG and high RSG. DeLong’s test was used to assess whether the differences of AUC for each parameter were statistically significant. Binary logistic regression analysis was performed to identify independent predictors of high RSG. Results ADC (r = − 0.352, p = 0.001), DDC (r = − 0.579, p < 0.001) and MD (r = − 0.597, p < 0.001) values showed significant negative correlations with RSG, while MK value (r = 0.658, p < 0.001) demonstrated a significant positive correlation. MK (AUC = 0.816, p < 0.001) was superior to ADC (AUC = 0.717, p < 0.001), DDC (AUC = 0.781, p < 0.001) and MD (AUC = 0.774, p < 0.001) in differentiating low and high RSG, but the differences between these AUCs were not statistically significant (all p > 0.05). Binary logistic regression analysis demonstrated a statistically significant model (χ² =43.222, p < 0.001), and showed that MK (odds ratio = 10.185; 95% CI: 2.467 ~ 21.694; p < 0.001) and MD (odds ratio = 0.014; 95% CI: 0.003 ~ 0.367; p < 0.001) were the independent predictors of high RSG. Conclusion Although ADC, DDC, and MD values were significantly negatively correlated with RSG, and MK was significantly positively correlated, and all three models—mono-exponential model, SEM, and DKI—demonstrated good performance in differentiating between low and high RSG, only parameters MD and MK values of DKI were identified as independent predictors of high RSG. |
| format | Article |
| id | doaj-art-263237fc67ce40d2ba3ef35ce22a7619 |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-08-01 |
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| spelling | doaj-art-263237fc67ce40d2ba3ef35ce22a76192025-08-24T11:57:41ZengBMCBMC Medical Imaging1471-23422025-08-0125111010.1186/s12880-025-01881-0Non-invasive MRI-based assessment of reactive stromal grade in prostate cancer using diffusion kurtosis imaging and stretched-exponential modelKun-Peng Zhou0Hua-Bin Huang1Shu-Yi Li2Zhong-Xing Luo3Xian-Wen Cheng4Di-Min Liu5Jie Bian6Qing-Yu Liu7Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen UniversityDepartment of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen UniversityDepartment of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen UniversityDepartment of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen UniversityDepartment of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen UniversityDepartment of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen UniversityDepartment of Radiology, Second Affiliated Hospital of Dalian Medical UniversityDepartment of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen UniversityAbstract Objectives Reactive stroma plays a pivotal role in the genesis, progression, and metastasis of prostate cancer (PCa). Higher reactive stromal grade (RSG) generally portends a poorer prognosis. The aim of the study is non-invasively evaluate RSG by preoperative mono-exponential model, stretch-exponent model (SEM) and diffusion kurtosis imaging (DKI), and isolate the independent predictor of high RSG (> 50% reactive stroma) in parameters of mono-exponential model, SEM and DKI. Methods Totally, 54 low RSG (≤ 50% reactive stroma) patients and 26 high RSG patients were prospectively enrolled in the study. Apparent diffusion coefficient (ADC), mean kurtosis (MK), mean diffusivity (MD), distributed diffusion coefficient (DDC), and heterogeneity index (α) values of all lesions were measured on GE Workstation 4.6. Spearman’s rank correlation analysis was used to analysis the correlation between RSG and parameters of SEM and DKI. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of those parameters in differentiating low RSG and high RSG. DeLong’s test was used to assess whether the differences of AUC for each parameter were statistically significant. Binary logistic regression analysis was performed to identify independent predictors of high RSG. Results ADC (r = − 0.352, p = 0.001), DDC (r = − 0.579, p < 0.001) and MD (r = − 0.597, p < 0.001) values showed significant negative correlations with RSG, while MK value (r = 0.658, p < 0.001) demonstrated a significant positive correlation. MK (AUC = 0.816, p < 0.001) was superior to ADC (AUC = 0.717, p < 0.001), DDC (AUC = 0.781, p < 0.001) and MD (AUC = 0.774, p < 0.001) in differentiating low and high RSG, but the differences between these AUCs were not statistically significant (all p > 0.05). Binary logistic regression analysis demonstrated a statistically significant model (χ² =43.222, p < 0.001), and showed that MK (odds ratio = 10.185; 95% CI: 2.467 ~ 21.694; p < 0.001) and MD (odds ratio = 0.014; 95% CI: 0.003 ~ 0.367; p < 0.001) were the independent predictors of high RSG. Conclusion Although ADC, DDC, and MD values were significantly negatively correlated with RSG, and MK was significantly positively correlated, and all three models—mono-exponential model, SEM, and DKI—demonstrated good performance in differentiating between low and high RSG, only parameters MD and MK values of DKI were identified as independent predictors of high RSG.https://doi.org/10.1186/s12880-025-01881-0Prostate cancerReactive stromal gradeDiffusion kurtosis imagingStretch-exponent model |
| spellingShingle | Kun-Peng Zhou Hua-Bin Huang Shu-Yi Li Zhong-Xing Luo Xian-Wen Cheng Di-Min Liu Jie Bian Qing-Yu Liu Non-invasive MRI-based assessment of reactive stromal grade in prostate cancer using diffusion kurtosis imaging and stretched-exponential model BMC Medical Imaging Prostate cancer Reactive stromal grade Diffusion kurtosis imaging Stretch-exponent model |
| title | Non-invasive MRI-based assessment of reactive stromal grade in prostate cancer using diffusion kurtosis imaging and stretched-exponential model |
| title_full | Non-invasive MRI-based assessment of reactive stromal grade in prostate cancer using diffusion kurtosis imaging and stretched-exponential model |
| title_fullStr | Non-invasive MRI-based assessment of reactive stromal grade in prostate cancer using diffusion kurtosis imaging and stretched-exponential model |
| title_full_unstemmed | Non-invasive MRI-based assessment of reactive stromal grade in prostate cancer using diffusion kurtosis imaging and stretched-exponential model |
| title_short | Non-invasive MRI-based assessment of reactive stromal grade in prostate cancer using diffusion kurtosis imaging and stretched-exponential model |
| title_sort | non invasive mri based assessment of reactive stromal grade in prostate cancer using diffusion kurtosis imaging and stretched exponential model |
| topic | Prostate cancer Reactive stromal grade Diffusion kurtosis imaging Stretch-exponent model |
| url | https://doi.org/10.1186/s12880-025-01881-0 |
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