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Sub-differentiation of PI-RADS 3 lesions in TZ by advanced diffusion-weighted imaging to aid the biopsy decision process

BACKGROUND: Performing biopsy for intermediate lesions with PI-RADS 3 has always been controversial. Moreover, it is difficult to differentiate prostate cancer (PCa) and benign prostatic hyperplasia (BPH) nodules in PI-RADS 3 lesions by conventional scans, especially for transition zone (TZ) lesions...

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Autores principales: Zhou, Kun-Peng, Huang, Hua-Bin, Bu, Chao, Luo, Zhong-Xing, Huang, Wen-Sheng, Xie, Li-Zhi, Liu, Qing-Yu, Bian, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9950630/
https://www.ncbi.nlm.nih.gov/pubmed/36845749
http://dx.doi.org/10.3389/fonc.2023.1092073
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author Zhou, Kun-Peng
Huang, Hua-Bin
Bu, Chao
Luo, Zhong-Xing
Huang, Wen-Sheng
Xie, Li-Zhi
Liu, Qing-Yu
Bian, Jie
author_facet Zhou, Kun-Peng
Huang, Hua-Bin
Bu, Chao
Luo, Zhong-Xing
Huang, Wen-Sheng
Xie, Li-Zhi
Liu, Qing-Yu
Bian, Jie
author_sort Zhou, Kun-Peng
collection PubMed
description BACKGROUND: Performing biopsy for intermediate lesions with PI-RADS 3 has always been controversial. Moreover, it is difficult to differentiate prostate cancer (PCa) and benign prostatic hyperplasia (BPH) nodules in PI-RADS 3 lesions by conventional scans, especially for transition zone (TZ) lesions. The purpose of this study is sub-differentiation of transition zone (TZ) PI-RADS 3 lesions using intravoxel incoherent motion (IVIM), stretched exponential model, and diffusion kurtosis imaging (DKI) to aid the biopsy decision process. METHODS: A total of 198 TZ PI-RADS 3 lesions were included. 149 lesions were BPH, while 49 lesions were PCa, including 37 non-clinical significant PCa (non-csPCa) lesions and 12 clinical significant PCa (csPCa) lesions. Binary logistic regression analysis was used to examine which parameters could predict PCa in TZ PI-RADS 3 lesions. The ROC curve was used to test diagnostic efficiency in distinguishing PCa from TZ PI-RADS 3 lesions, while one-way ANOVA analysis was used to examine which parameters were statistically significant among BPH, non-csPCa and csPCa. RESULTS: The logistic model was statistically significant (χ2 = 181.410, p<0.001) and could correctly classify 89.39% of the subjects. Parameters of fractional anisotropy (FA) (p=0.004), mean diffusion (MD) (p=0.005), mean kurtosis (MK) (p=0.015), diffusion coefficient (D) (p=0.001), and distribute diffusion coefficient (DDC) (p=0.038) were statistically significant in the model. ROC analysis showed that AUC was 0.9197 (CI 95%: 0.8736-0.9659). Sensitivity, specificity, positive predictive value and negative predictive value were 92.1%, 80.4%, 93.9% and 75.5%, respectively. FA and MK of csPCa were higher than those of non-csPCa (all p<0.05), while MD, ADC, D, and DDC of csPCa were lower than those of non-csPCa (all p<0.05). CONCLUSION: FA, MD, MK, D, and DDC can predict PCa in TZ PI-RADS 3 lesions and inform the decision-making process of whether or not to perform a biopsy. Moreover, FA, MD, MK, D, DDC, and ADC may have ability to identify csPCa and non-csPCa in TZ PI-RADS 3 lesions.
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spelling pubmed-99506302023-02-25 Sub-differentiation of PI-RADS 3 lesions in TZ by advanced diffusion-weighted imaging to aid the biopsy decision process Zhou, Kun-Peng Huang, Hua-Bin Bu, Chao Luo, Zhong-Xing Huang, Wen-Sheng Xie, Li-Zhi Liu, Qing-Yu Bian, Jie Front Oncol Oncology BACKGROUND: Performing biopsy for intermediate lesions with PI-RADS 3 has always been controversial. Moreover, it is difficult to differentiate prostate cancer (PCa) and benign prostatic hyperplasia (BPH) nodules in PI-RADS 3 lesions by conventional scans, especially for transition zone (TZ) lesions. The purpose of this study is sub-differentiation of transition zone (TZ) PI-RADS 3 lesions using intravoxel incoherent motion (IVIM), stretched exponential model, and diffusion kurtosis imaging (DKI) to aid the biopsy decision process. METHODS: A total of 198 TZ PI-RADS 3 lesions were included. 149 lesions were BPH, while 49 lesions were PCa, including 37 non-clinical significant PCa (non-csPCa) lesions and 12 clinical significant PCa (csPCa) lesions. Binary logistic regression analysis was used to examine which parameters could predict PCa in TZ PI-RADS 3 lesions. The ROC curve was used to test diagnostic efficiency in distinguishing PCa from TZ PI-RADS 3 lesions, while one-way ANOVA analysis was used to examine which parameters were statistically significant among BPH, non-csPCa and csPCa. RESULTS: The logistic model was statistically significant (χ2 = 181.410, p<0.001) and could correctly classify 89.39% of the subjects. Parameters of fractional anisotropy (FA) (p=0.004), mean diffusion (MD) (p=0.005), mean kurtosis (MK) (p=0.015), diffusion coefficient (D) (p=0.001), and distribute diffusion coefficient (DDC) (p=0.038) were statistically significant in the model. ROC analysis showed that AUC was 0.9197 (CI 95%: 0.8736-0.9659). Sensitivity, specificity, positive predictive value and negative predictive value were 92.1%, 80.4%, 93.9% and 75.5%, respectively. FA and MK of csPCa were higher than those of non-csPCa (all p<0.05), while MD, ADC, D, and DDC of csPCa were lower than those of non-csPCa (all p<0.05). CONCLUSION: FA, MD, MK, D, and DDC can predict PCa in TZ PI-RADS 3 lesions and inform the decision-making process of whether or not to perform a biopsy. Moreover, FA, MD, MK, D, DDC, and ADC may have ability to identify csPCa and non-csPCa in TZ PI-RADS 3 lesions. Frontiers Media S.A. 2023-02-10 /pmc/articles/PMC9950630/ /pubmed/36845749 http://dx.doi.org/10.3389/fonc.2023.1092073 Text en Copyright © 2023 Zhou, Huang, Bu, Luo, Huang, Xie, Liu and Bian https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Zhou, Kun-Peng
Huang, Hua-Bin
Bu, Chao
Luo, Zhong-Xing
Huang, Wen-Sheng
Xie, Li-Zhi
Liu, Qing-Yu
Bian, Jie
Sub-differentiation of PI-RADS 3 lesions in TZ by advanced diffusion-weighted imaging to aid the biopsy decision process
title Sub-differentiation of PI-RADS 3 lesions in TZ by advanced diffusion-weighted imaging to aid the biopsy decision process
title_full Sub-differentiation of PI-RADS 3 lesions in TZ by advanced diffusion-weighted imaging to aid the biopsy decision process
title_fullStr Sub-differentiation of PI-RADS 3 lesions in TZ by advanced diffusion-weighted imaging to aid the biopsy decision process
title_full_unstemmed Sub-differentiation of PI-RADS 3 lesions in TZ by advanced diffusion-weighted imaging to aid the biopsy decision process
title_short Sub-differentiation of PI-RADS 3 lesions in TZ by advanced diffusion-weighted imaging to aid the biopsy decision process
title_sort sub-differentiation of pi-rads 3 lesions in tz by advanced diffusion-weighted imaging to aid the biopsy decision process
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9950630/
https://www.ncbi.nlm.nih.gov/pubmed/36845749
http://dx.doi.org/10.3389/fonc.2023.1092073
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