|
传统影像与纹理分析对术前肾癌Fuhrman分级评估的应用价值
|
Abstract:
肾癌是常见的泌尿系恶性肿瘤,肾癌的Fuhrman分级在诊疗过程中至关重要,因此能否在对肿瘤进行非侵袭性方法中准确评估其分级,对患者最优治疗方案的选择及预后都有着很大的影响。传统影像学检查包括计算机断层扫描(CT)、磁共振成像(MRI)及超声等技术,但这些技术在对肿瘤的检出及判断等方面存在一定的局限性,近年来影像组学技术中的纹理分析广泛应用于多个系统疾病的诊疗过程,为提高认识,本文以传统影像技术与纹理分析的方法在评估肾癌Fuhrman分级中应用的价值展开综述。
Renal carcinoma is a common malignant tumor of urinary system. The Fuhrman grade of renal car-cinoma is very important in the process of diagnosis and treatment. Therefore, whether the grade can be accurately evaluated in the non-invasive method of tumor has a great impact on the selection of the optimal treatment plan and prognosis of patients. Traditional imaging examinations include computed tomography (CT), magnetic resonance imaging (MRI), ultrasound and other technologies, but these technologies have certain limitations in the detection and judgment of tumors. In recent years, texture analysis in imaging omics technology has been widely used in the diagnosis and treatment of multiple systems of diseases. This article reviews the value of traditional imaging techniques and texture analysis in evaluating Fuhrman grading of renal carcinoma.
[1] | Nazari, M., Shiri, I., Hajianfar, G., et al. (2020) Noninvasive Fuhrman Grading of Clear Cell Renal Cell Carcinoma Us-ing Computed Tomography Radiomic Features and Machine Learning. La radiologia medica, 125, 754-762.
https://doi.org/10.1007/s11547-020-01169-z |
[2] | Minardi, D., Lucarini, G., Mazzucchelli, R., Milanese, G., Natali, D., Galosi, A.B., et al. (2005) Prognostic Role of Fuhrman Grade and Vascular Endothelial Growth Factor in pT1a Clear Cell Carcinoma in Partial Nephrectomy Specimens. Journal of Urology, 174, 1208-1212. https://doi.org/10.1097/01.ju.0000173078.57871.2d |
[3] | Li, X.S., Yao, L., Gong, K., Yu, W., He, Q., Zhou, L.Q., et al. (2012) Growth Pattern of Renal Cell Carcinoma (RCC) in Patients with Delayed Surgical Intervention. Journal of Cancer Research and Clinical Oncology, 138, 269-274.
https://doi.org/10.1007/s00432-011-1083-0 |
[4] | Sahni, V.A. and Silverman, S.G. (2014) Imaging Management of Incidentally Detected Small Renal Masses. Seminars in Interventional Radiology, 31, 9-19. https://doi.org/10.1055/s-0033-1363838 |
[5] | Sun, J., Pan, L., Zha, T., Xing, W., Chen, J. and Duan, S. (2021) The Role of MRI Texture Analysis Based on Susceptibility-Weighted Imaging in Predicting Fuhrman Grade of Clear Cell Renal Cell Carcinoma. Acta Radiologica, 62, 1104-1111. https://doi.org/10.1177/0284185120951964 |
[6] | Fuhrman, S.A., Lasky, L.C. and Limas, C. (2004) Prognostic Significance of Morphologic Parameters in Renal Cell Carcinoma. International Journal of Clinical Practice, 58, 333-336. https://doi.org/10.1111/j.1368-5031.2004.00008.x |
[7] | Donat, S.M., Diaz, M., Bishoff, J.T., Coleman, J.A., Dahm, P., Derweesh, I.H., et al. (2013) Follow-Up for Clinically Localized Renal Neoplasms: AUA Guideline. Journal of Urology, 190, 407-416.
https://doi.org/10.1016/j.juro.2013.04.121 |
[8] | Suzuki, K., Mizuno, R., Mikami, S., Tanaka, N., Kanao, K., Kiku-chi, E., et al. (2012) Prognostic Significance of High Nuclear Grade in Patients with Pathologic T1a Renal Cell Carcino-ma. Japanese Journal of Clinical Oncology, 42, 831-835. https://doi.org/10.1093/jjco/hys109 |
[9] | Hussain, M.A., Hamarneh, G. and Garbi, R. (2021) Learnable Image Histograms-Based Deep Radiomics for Renal Cell Carcinoma Grading and staging. Computerized Medical Imaging and Graphics, 90, Article ID: 101924.
https://doi.org/10.1016/j.compmedimag.2021.101924 |
[10] | Choi, S.Y., Sung, D.J., Yang, K.S., et al. (2016) Small (<4 cm) Clear Cell Renal Cell Carcinoma: Correlation between CT Findings and Histologic Grade. Abdominal Radiology, 41, 1160-1169. https://doi.org/10.1007/s00261-016-0732-9 |
[11] | Yi, X., Xiao, Q., Zeng, F., Yin, H., Li, Z., Qian, C., Wang, C., Lei, G., Xu, Q., Li, C., Li, M., Gong, G., Zee, C., Guan, X., Liu, L. and Chen, B.T. (2021) Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma. Frontiers in Oncology, 10, Article 570396. https://doi.org/10.3389/fonc.2020.570396 |
[12] | Chiarello, M.A., Mali, R.D. and Kang, S.K. (2018) Diag-nostic Accuracy of MRI for Detection of Papillary Renal Cell Carcinoma: A Systematic Review and Meta-Analysis. American Journal of Roentgenology, 211, 812-821.
https://doi.org/10.2214/AJR.17.19462 |
[13] | Woo, S., Suh, C.H., Kim, S.Y., Cho, J.Y. and Kim, S.H. (2017) Diag-nostic Performance of DWI for Differentiating High- From Low-Grade Clear Cell Renal Cell Carcinoma: A Systematic Review and Meta-Analysis. American Journal of Roentgenology, 209, W374-W381. https://doi.org/10.2214/AJR.17.18283 |
[14] | Singh, H., Arora, G., Nayak, B., Sharma, A., Singh, G., Kumari, K., Jana, S., Patel, C., Pandey, A.K., Seth, A. and Kumar, R. (2020) Semi-Quantitative F-18-FDG PET/Computed Tomog-raphy Parameters for Prediction of Grade in Patients with Renal Cell Carcinoma and the Incremental Value of Diuretics. Nuclear Medicine Communications, 41, 485-493. https://doi.org/10.1097/MNM.0000000000001169 |
[15] | Yu, W., Liang, G., Zeng, L., Yang, Y. and Wu, Y. (2021) Accuracy of CT Texture Analysis for Differentiating Low-Grade and High-Grade Renal Cell Carcinoma: Systematic Review and Meta-Analysis. BMJ Open, 11, e051470.
https://doi.org/10.1136/bmjopen-2021-051470 |
[16] | Goyal, A., Razik, A., Kandasamy, D., et al. (2019) Role of MR Texture Analysis in Histological Subtyping and Grading of Renal Cell Carcinoma: A Preliminary Study. Abdominal Ra-diology, 44, 3336-3349.
https://doi.org/10.1007/s00261-019-02122-z |
[17] | Xing, J., Liu, Y., Wang, Z., Xu, A., Su, S., Shen, S. and Wang, Z. (2023) Incremental Value of Radiomics with Machine Learning to the Existing Prognostic Models for Predicting Out-come in Renal Cell Carcinoma. Frontiers in Oncology, 13, Article 1036734. https://doi.org/10.3389/fonc.2023.1036734 |
[18] | Greco, F., Beomonte Zobel, B., Di Gennaro, G. and Mallio, C.A. (2023) Advanced CT Imaging, Radiomics, and Artificial Intelligence to Evaluate Immune Checkpoint Inhibitors’ Effects on Metastatic Renal Cell Carcinoma. Applied Sciences, 13, Article 3779. https://doi.org/10.3390/app13063779 |
[19] | Yu, G., Mao, N., Song, X., et al. (2022) Radiomics Model for Predict-ing TP53 Status Using CT and Machine Learning Approach in Laryngeal Squamous Cell Carcinoma. Frontiers in On-cology, 12, Article 823428.
https://doi.org/10.3389/fonc.2022.823428 |
[20] | Litvin, A.A., Burkin, D.A., Kropinov, A.A., et al. (2021) Radi-omics and Digital Image Texture Analysis in Oncology (Review). Sovremennye Tehnologii v Medicine, 13, 97-104. https://doi.org/10.17691/stm2021.13.2.11 |
[21] | Gillies, R.J., Kinahan, P.E. and Hricak, H. (2016) Radiomics: Im-ages Are More than Pictures, They Are Data. Radiology, 278, 563-577. https://doi.org/10.1148/radiol.2015151169 |
[22] | Wang, Y., Zhang, X., Zhang, J., et al. (2023) MR Texture Analysis in Differentiation of Small and Very Small Renal Cell Carcinoma Subtypes. Abdominal Radiology, 48, 1044-1050. https://doi.org/10.1007/s00261-022-03794-w |
[23] | Ankur, G., Abdul, R., Devasenathipathy, K., et al. (2019) Role of MR Texture Analysis in Histological Subtyping and Grading of Renal Cell Carcinoma: A Preliminary Study. Ab-dominal Radiology, 44, 3336-3349.
https://doi.org/10.1007/s00261-019-02122-z |
[24] | Wei, W., Cao, K.M., Jin, S.M., et al. (2020) Differentiation of Renal Cell Carcinoma Subtypes through MRI-Based Radiomics Analysis. European Radiology, 30, 5738-5747. https://doi.org/10.1007/s00330-020-06896-5 |
[25] | Uyen, N.H., Mirmomen, S.M., Osorio, M., et al. (2018) As-sessment of Multiphasic Contrast-Enhanced MR Textures in Differentiating Small Renal Mass Subtypes. Abdominal Ra-diology, 43, 3400-3409.
https://doi.org/10.1007/s00261-018-1625-x |
[26] | Lim, C.S., Tirumani, S., van der Pol, C.B., Alessandrino, F., Sonpavde, G.P., Silverman, S.G. and Shinagare, A.B. (2019) Use of Quantitative T2-Weighted and Apparent Diffusion Coefficient Texture Features of Bladder Cancer and Extravesical Fat for Local Tumor Staging after Transurethral Resec-tion. American Journal of Roentgenology, 212, 1060-1069. https://doi.org/10.2214/AJR.18.20718 |
[27] | van der Pol, C.B., Shinagare, A.B., Tirumani, S.H., Preston, M.A., Vangel, M.G. and Silverman, S.G. (2018) Bladder Cancer Local Staging: Multiparametric MRI Performance following Transurethral Resection. Abdominal Radiology, 43, 2412-2423. https://doi.org/10.1007/s00261-017-1449-0 |
[28] | Xu, X., Zhang, X., Tian, Q., et al. (2017) Three-Dimensional Texture Features from Intensity and High-Order Derivative Maps for the Discrimination between Bladder Tumors and Wall Tissues via MRI. International Journal of Computer Assisted Radiology and Surgery, 12, 645-656. https://doi.org/10.1007/s11548-017-1522-8 |
[29] | Yang, X., Yuan, B.R., Zhang, Y.D., Zhuang, J.T., Cai, L.K., Wu, Q.K., Cao, Q., Li, P.C., Lu, Q. and Sun, X.Y. (2022) Quantitative Multiparametric MRI as a Promising Tool for the As-sessment of Early Response to Neoadjuvant Chemotherapy in Bladder Cancer. European Journal of Radiology, 157, Ar-ticle ID: 110587.
https://doi.org/10.1016/j.ejrad.2022.110587 |
[30] | Meng, X., Li, S., He, K., Hu, H., Feng, C., Li, Z. and Wang, Y. (2023) Evaluation of Whole-Tumor Texture Analysis Based on MRI Diffusion Kurtosis and Biparametric VI-RADS Model for Staging and Grading Bladder Cancer. Bioengineering, 10, Article 745. https://doi.org/10.3390/bioengineering10070745 |
[31] | Song, J., Yin, Y., Wang, H., Chang, Z., Liu, Z. and Cui, L. (2020) A Review of Original Articles Published in the Emerging Field of Radiomics. European Journal of Radiology, 127, Article ID: 108991.
https://doi.org/10.1016/j.ejrad.2020.108991 |
[32] | Scalco, E. and Rizzo, G. (2017) Texture Analysis of Medical Im-ages for Radiotherapy Applications. The British Journal of Radiology, 90, Article ID: 20160642. https://doi.org/10.1259/bjr.20160642 |
[33] | Stephan, U., Lucian, B., Annemarie, B., et al. (2020) Radiomics of Computed Tomography and Magnetic Resonance Imaging in Renal Cell Carcinoma—A Systematic Review and Me-ta-Analysis. European Radiology, 30, 3558-3566.
https://doi.org/10.1007/s00330-020-06666-3 |
[34] | Cui, E., Li, Z., Ma, C., et al. (2020) Predicting the ISUP Grade of Clear Cell Renal Cell Carcinoma with Multiparametric MR and Multiphase CT Radiomics. European Radiology, 30, 2912-2921.
https://doi.org/10.1007/s00330-019-06601-1 |
[35] | Yao, Z., Shuai, W., Yan, C. and Du, H.Q. (2021) Deep Learning with a Convolutional Neural Network Model to diFferentiate Renal Parenchymal Tumors: A Preliminary Study. Ab-dominal Radiology, 46, 3260-3268.
https://doi.org/10.1007/s00261-021-02981-5 |
[36] | Xi, I.L., Zhao, Y., Wang, R., et al. (2020) Deep Learning to Dis-tinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging. Clinical Cancer Research, 26, 1944-1952. https://doi.org/10.1158/1078-0432.CCR-19-0374 |
[37] | Meghan, G.L. (2020) Radiomics and Artificial Intelligence for Renal Mass Characterization. Radiologic Clinics, 58, 995-1008. https://doi.org/10.1016/j.rcl.2020.06.001 |
[38] | Buch, K., Kuno, H., Qureshi, M.M., Li, B. and Sakai, O. (2018) Quantitative Variations in Texture Analysis Features Dependent on MRI Scanning Parameters: A Phantom Model. Journal of Applied Clinical Medical Physics, 19, 253-264.
https://doi.org/10.1002/acm2.12482 |