Combination of Prostate Cancer Antigen 3 (PCA3), Sarcosine, Glypican-1 (GPC1), Urokinase Plasminogen Activator Receptor (uPAR), and Thymidine Kinase 1 (TK1), and T2WI and DWI Radiomics Model for Distinguishing Benign Prostatic Hyperplasia, Prostate Cancer
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Reference
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2. Risbridger, G.P., G.A. Almahbobi, and R.A. Taylor, Early prostate development and its association with late-life prostate disease. Cell Tissue Res, 2005. 322(1): p. 173-81.
3. Ratajczak, W., M. Lubkowski, and A. Lubkowska, Heat Shock Proteins in Benign Prostatic Hyperplasia and Prostate Cancer. Int J Mol Sci, 2022. 23(2).
4. Al-Khalil, S., et al., Interactions between benign prostatic hyperplasia (BPH) and prostate cancer in large prostates: a retrospective data review. Int Urol Nephrol, 2016. 48(1): p. 91-7.
5. Cavada, B.S., et al., Lectins applied to diagnosis and treatment of prostate cancer and benign hyperplasia: A review. Int J Biol Macromol, 2021. 190: p. 543-553.
6. Williams, I.S., et al., Modern paradigms for prostate cancer detection and management. Med J Aust, 2022. 217(8): p. 424-433.
7. Jiao, J., et al., Establishment and prospective validation of an SUV(max) cutoff value to discriminate clinically significant prostate cancer from benign prostate diseases in patients with suspected prostate cancer by (68)Ga-PSMA PET/CT: a real-world study. Theranostics, 2021. 11(17): p. 8396-8411.
8. Kumar, V., et al., Prebiopsy magnetic resonance spectroscopy and imaging in the diagnosis of prostate cancer. Int J Urol, 2012. 19(7): p. 602-13.
9. Giganti, F., et al., Prostate Imaging Quality (PI-QUAL): A New Quality Control Scoring System for Multiparametric Magnetic Resonance Imaging of the Prostate from the PRECISION trial. Eur Urol Oncol, 2020. 3(5): p. 615-619.
10. Hu, L., et al., Better lesion conspicuity translates into improved prostate cancer detection: comparison of non-parallel-transmission-zoomed-DWI with conventional-DWI. Abdom Radiol (NY), 2021. 46(12): p. 5659-5668.
11. Liu, Y., et al., The applied research of simultaneous image acquisition of T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) in the assessment of patients with prostate cancer. Asian J Androl, 2019. 21(2): p. 177-182.
12. Mayerhoefer, M.E., et al., Introduction to Radiomics. J Nucl Med, 2020. 61(4): p. 488-495.
13. Chen, Q., et al., Radiomics in precision medicine for gastric cancer: opportunities and challenges. Eur Radiol, 2022. 32(9): p. 5852-5868.
14. Li, Y., et al., Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis. Eur Radiol, 2022. 32(11): p. 8039-8051.
15. Conti, A., et al., Radiomics in breast cancer classification and prediction. Semin Cancer Biol, 2021. 72: p. 238-250.
16. Shin, J., et al., MRI Radiomics Model Predicts Pathologic Complete Response of Rectal Cancer Following Chemoradiotherapy. Radiology, 2022. 303(2): p. 351-358.
17. Scott, R., et al., PI-RADS v2.1: What has changed and how to report. SA J Radiol, 2021. 25(1): p. 2062.
18. Prostate cancer. Nat Rev Dis Primers, 2021. 7(1): p. 8.
19. Nordström, T., et al., Prostate-specific antigen (PSA) density in the diagnostic algorithm of prostate cancer. Prostate Cancer Prostatic Dis, 2018. 21(1): p. 57-63.
20. Yacoub, J.H. and A. Oto, MR Imaging of Prostate Zonal Anatomy. Radiol Clin North Am, 2018. 56(2): p. 197-209.
21. Zhang, K.S., et al., Are T2WI PI-RADS sub-scores of transition zone prostate lesions biased by DWI information? A multi-reader, single-center study. Eur J Radiol, 2023. 167: p. 111026.
22. Belue, M.J., et al., Current Status of Biparametric MRI in Prostate Cancer Diagnosis: Literature Analysis. Life (Basel), 2022. 12(6).
23. Iyama, Y., et al., Development and validation of a logistic regression model to distinguish transition zone cancers from benign prostatic hyperplasia on multi-parametric prostate MRI. Eur Radiol, 2017. 27(9): p. 3600-3608.
24. Woo, S., et al., Head-To-Head Comparison Between High- and Standard-b-Value DWI for Detecting Prostate Cancer: A Systematic Review and Meta-Analysis. AJR Am J Roentgenol, 2018. 210(1): p. 91-100.
25. Jendoubi, S., et al., MRI for prostate cancer: can computed high b-value DWI replace native acquisitions? Eur Radiol, 2019. 29(10): p. 5197-5204.
26. Sun, H., et al., DCE-MRI and DWI can differentiate benign from malignant prostate tumors when serum PSA is ≥10 ng/ml. Front Oncol, 2022. 12: p. 925186.
27. Xiaohang, L., et al., Differentiation of prostate cancer and stromal hyperplasia in the transition zone with histogram analysis of the apparent diffusion coefficient. Acta Radiol, 2017. 58(12): p. 1528-1534.
28. O'Shea, A. and M. Harisinghani, PI-RADS: multiparametric MRI in prostate cancer. Magma, 2022. 35(4): p. 523-532.
29. Turkbey, B. and A.S. Purysko, PI-RADS: Where Next? Radiology, 2023. 307(5): p. e223128.
30. Gupta, R.T., et al., PI-RADS: Past, present, and future. J Magn Reson Imaging, 2020. 52(1): p. 33-53.
31. Smith, C.P. and B. Türkbey, PI-RADS v2: Current standing and future outlook. Turk J Urol, 2018. 44(3): p. 189-194.
32. Rizzo, S., et al., Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp, 2018. 2(1): p. 36.
33. Ferro, M., et al., Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol, 2022. 14: p. 17562872221109020.
34. Jing, G., et al., Prediction of clinically significant prostate cancer with a multimodal MRI-based radiomics nomogram. Front Oncol, 2022. 12: p. 918830.
35. Zhang, Y., et al., Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics. Med Biol Eng Comput, 2023. 61(3): p. 757-771.
36. Lo Gullo, R., et al., Combining molecular and imaging metrics in cancer: radiogenomics. Insights Imaging, 2020. 11(1): p. 1.
37. Shaikh, F., et al., The Role of Imaging Biomarkers Derived From Advanced Imaging and Radiomics in the Management of Brain Tumors. Frontiers in Oncology, 2020. 10.
38. Lee, G., et al., Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art. European journal of radiology, 2017. 86: p. 297-307.
39. Xu, M., et al., Using biparametric MRI radiomics signature to differentiate between benign and malignant prostate lesions. Eur J Radiol, 2019. 114: p. 38-44.
40. Li, M., et al., Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer. Front Oncol, 2020. 10: p. 631831.
2. Risbridger, G.P., G.A. Almahbobi, and R.A. Taylor, Early prostate development and its association with late-life prostate disease. Cell Tissue Res, 2005. 322(1): p. 173-81.
3. Ratajczak, W., M. Lubkowski, and A. Lubkowska, Heat Shock Proteins in Benign Prostatic Hyperplasia and Prostate Cancer. Int J Mol Sci, 2022. 23(2).
4. Al-Khalil, S., et al., Interactions between benign prostatic hyperplasia (BPH) and prostate cancer in large prostates: a retrospective data review. Int Urol Nephrol, 2016. 48(1): p. 91-7.
5. Cavada, B.S., et al., Lectins applied to diagnosis and treatment of prostate cancer and benign hyperplasia: A review. Int J Biol Macromol, 2021. 190: p. 543-553.
6. Williams, I.S., et al., Modern paradigms for prostate cancer detection and management. Med J Aust, 2022. 217(8): p. 424-433.
7. Jiao, J., et al., Establishment and prospective validation of an SUV(max) cutoff value to discriminate clinically significant prostate cancer from benign prostate diseases in patients with suspected prostate cancer by (68)Ga-PSMA PET/CT: a real-world study. Theranostics, 2021. 11(17): p. 8396-8411.
8. Kumar, V., et al., Prebiopsy magnetic resonance spectroscopy and imaging in the diagnosis of prostate cancer. Int J Urol, 2012. 19(7): p. 602-13.
9. Giganti, F., et al., Prostate Imaging Quality (PI-QUAL): A New Quality Control Scoring System for Multiparametric Magnetic Resonance Imaging of the Prostate from the PRECISION trial. Eur Urol Oncol, 2020. 3(5): p. 615-619.
10. Hu, L., et al., Better lesion conspicuity translates into improved prostate cancer detection: comparison of non-parallel-transmission-zoomed-DWI with conventional-DWI. Abdom Radiol (NY), 2021. 46(12): p. 5659-5668.
11. Liu, Y., et al., The applied research of simultaneous image acquisition of T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) in the assessment of patients with prostate cancer. Asian J Androl, 2019. 21(2): p. 177-182.
12. Mayerhoefer, M.E., et al., Introduction to Radiomics. J Nucl Med, 2020. 61(4): p. 488-495.
13. Chen, Q., et al., Radiomics in precision medicine for gastric cancer: opportunities and challenges. Eur Radiol, 2022. 32(9): p. 5852-5868.
14. Li, Y., et al., Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis. Eur Radiol, 2022. 32(11): p. 8039-8051.
15. Conti, A., et al., Radiomics in breast cancer classification and prediction. Semin Cancer Biol, 2021. 72: p. 238-250.
16. Shin, J., et al., MRI Radiomics Model Predicts Pathologic Complete Response of Rectal Cancer Following Chemoradiotherapy. Radiology, 2022. 303(2): p. 351-358.
17. Scott, R., et al., PI-RADS v2.1: What has changed and how to report. SA J Radiol, 2021. 25(1): p. 2062.
18. Prostate cancer. Nat Rev Dis Primers, 2021. 7(1): p. 8.
19. Nordström, T., et al., Prostate-specific antigen (PSA) density in the diagnostic algorithm of prostate cancer. Prostate Cancer Prostatic Dis, 2018. 21(1): p. 57-63.
20. Yacoub, J.H. and A. Oto, MR Imaging of Prostate Zonal Anatomy. Radiol Clin North Am, 2018. 56(2): p. 197-209.
21. Zhang, K.S., et al., Are T2WI PI-RADS sub-scores of transition zone prostate lesions biased by DWI information? A multi-reader, single-center study. Eur J Radiol, 2023. 167: p. 111026.
22. Belue, M.J., et al., Current Status of Biparametric MRI in Prostate Cancer Diagnosis: Literature Analysis. Life (Basel), 2022. 12(6).
23. Iyama, Y., et al., Development and validation of a logistic regression model to distinguish transition zone cancers from benign prostatic hyperplasia on multi-parametric prostate MRI. Eur Radiol, 2017. 27(9): p. 3600-3608.
24. Woo, S., et al., Head-To-Head Comparison Between High- and Standard-b-Value DWI for Detecting Prostate Cancer: A Systematic Review and Meta-Analysis. AJR Am J Roentgenol, 2018. 210(1): p. 91-100.
25. Jendoubi, S., et al., MRI for prostate cancer: can computed high b-value DWI replace native acquisitions? Eur Radiol, 2019. 29(10): p. 5197-5204.
26. Sun, H., et al., DCE-MRI and DWI can differentiate benign from malignant prostate tumors when serum PSA is ≥10 ng/ml. Front Oncol, 2022. 12: p. 925186.
27. Xiaohang, L., et al., Differentiation of prostate cancer and stromal hyperplasia in the transition zone with histogram analysis of the apparent diffusion coefficient. Acta Radiol, 2017. 58(12): p. 1528-1534.
28. O'Shea, A. and M. Harisinghani, PI-RADS: multiparametric MRI in prostate cancer. Magma, 2022. 35(4): p. 523-532.
29. Turkbey, B. and A.S. Purysko, PI-RADS: Where Next? Radiology, 2023. 307(5): p. e223128.
30. Gupta, R.T., et al., PI-RADS: Past, present, and future. J Magn Reson Imaging, 2020. 52(1): p. 33-53.
31. Smith, C.P. and B. Türkbey, PI-RADS v2: Current standing and future outlook. Turk J Urol, 2018. 44(3): p. 189-194.
32. Rizzo, S., et al., Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp, 2018. 2(1): p. 36.
33. Ferro, M., et al., Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol, 2022. 14: p. 17562872221109020.
34. Jing, G., et al., Prediction of clinically significant prostate cancer with a multimodal MRI-based radiomics nomogram. Front Oncol, 2022. 12: p. 918830.
35. Zhang, Y., et al., Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics. Med Biol Eng Comput, 2023. 61(3): p. 757-771.
36. Lo Gullo, R., et al., Combining molecular and imaging metrics in cancer: radiogenomics. Insights Imaging, 2020. 11(1): p. 1.
37. Shaikh, F., et al., The Role of Imaging Biomarkers Derived From Advanced Imaging and Radiomics in the Management of Brain Tumors. Frontiers in Oncology, 2020. 10.
38. Lee, G., et al., Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art. European journal of radiology, 2017. 86: p. 297-307.
39. Xu, M., et al., Using biparametric MRI radiomics signature to differentiate between benign and malignant prostate lesions. Eur J Radiol, 2019. 114: p. 38-44.
40. Li, M., et al., Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer. Front Oncol, 2020. 10: p. 631831.
Objavljeno
2025/07/07
Broj časopisa
Rubrika
Original paper
Sva prava zadržana (c) 2025 Fan Yang, Wei Guo, Siqin Sun, Yanan Huang
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