1 Quantitative Assessment of Bone Lesions in Prostate Cancer: Diagnostic Performance of CT Hounsfield Units in Differentiating Metastases From Benign Enostoses https://brieflands.com/journals/ijradiology/articles/170671 10.5812/iranjradiol-170671 1 Background :Prostate carcinoma is the third most common malignancy among Malaysian men, and the skeleton is the most frequent metastatic site. Differentiating benign enostoses from osteoblastic metastases on computed tomography (CT) remains challenging. Although 99mTc-MDP bone scintigraphy is the gold standard, capacity constraints often delay treatment. Objectives :To evaluate the diagnostic performance of CT Hounsfield Unit (HU) measurements in differentiating these lesions and their correlation with systemic biological markers. Patients and Methods :We retrospectively evaluated 1041 sclerotic lesions (860 metastases and 181 benign enostoses) from 105 patients with prostate carcinoma. Mean HU values were recorded. Diagnostic metrics were computed at the lesion level, with adjustment for within-patient clustering using Generalized Estimating Equations (GEE). Optimal thresholds were determined using Receiver Operating Characteristic (ROC) analysis. Biological correlations with Gleason scores and Prostate-Specific Antigen (PSA) levels were analyzed using Spearman correlation and validated with GEE. Results :Mean HU demonstrated exceptional discriminatory power (area under the curve [AUC] = 0.984; 95% confidence interval [CI]: 0.975 - 0.993; P < 0.001). The optimal 944.99 HU threshold yielded 90.6% sensitivity, 97.8% specificity, and 99.5% positive predictive value (PPV). After GEE adjustment, histological Gleason grade (P = 0.098), systemic PSA (P = 0.301), and regional density differences (P > 0.05) showed no significant association with lesion density. Conclusion :Quantitative CT attenuation is a highly accurate triage adjunct. In a high-pretest-probability oncological setting, the 944.99 HU threshold confidently rules in benign enostoses, allowing clinicians to safely avoid unnecessary biopsies and optimize nuclear imaging resources. 1 12 Tze Hui Soo Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Malaysia Malaysia suzyhui88@upm.edu.my Siti Nur Atiqah Mohd Jamil Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Malaysia Malaysia gs70338@student.upm.edu.my Subapriya Suppiah Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Malaysia Malaysia subapriya@upm.edu.my No Keyword 1.pdf [0]Malaysian National Cancer Registry.Malaysian National Cancer Registry Report 2017 - 2021. Malaysian National Cancer Registry Report. 2024.##[1]Lim J, Malek R, Jr S, Toh CC, Sundram M, Woo SYY, et al.Prostate cancer in multi-ethnic Asian men: Real-world experience in the Malaysia Prostate Cancer (M-CaP) Study. Cancer Med. 2021;10(22):8020-8028. [PubMed ID: 34626088]. [PubMed Central ID: PMC8607241]. doi: 10.1002/cam4.4319.##[2]Mottet Nicolas, van den Bergh RoderickCN, Briers Erik, Van den Broeck Thomas, Cumberbatch MarcusG, De Santis Maria, et al.EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer - 2020 Update. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent. Eur Urol. 2021;79(2):243-262. [PubMed ID: 33172724]. doi: 10.1016/j.eururo.2020.09.042.##[3]Ulano A, Bredella MA, Burke P, Chebib I, Simeone FJ, Huang AJ, et al.Distinguishing Untreated Osteoblastic Metastases From Enostoses Using CT Attenuation Measurements. AJR Am J Roentgenol. 2016;207(2):362-368. [PubMed ID: 27101076]. doi: 10.2214/AJR.15.15559.##[4]Sala F, Dapoto A, Morzenti C, Firetto MC, Valle C, Tomasoni A, et al.Bone Islands Incidentally Detected on Computed Tomography: Frequency of Enostosis and Differentiation From Untreated Osteoblastic Metastases Based on CT Attenuation Value. Br J Radiol. 2019;92(1103):20190249. [PubMed ID: 31469323]. [PubMed Central ID: PMC6849660]. doi: 10.1259/bjr.20190249.##[5]Elangovan SM, Sebro R.Accuracy of CT Attenuation Measurement for Differentiating Treated Osteoblastic Metastases From Enostoses. AJR Am J Roentgenol. 2018;210(3):615-620. [PubMed ID: 29323547]. doi: 10.2214/AJR.17.18638.##[6]Hao B, Ma J, Wan N, Xu S, Wang Z.Therapeutic Effects of CT-Guided Microwave Ablation Combined with Cementoplasty in the Treatment of Bone Metastasis. I J Radiol. 2023;20(3):e128065. doi: 10.5812/ijradiol-128065.##[7]Kosinipalli Naveen kumar, Patnaik Sujata, S Rammurti.Role of CT Attenuation Value in Differentiating Enostosis From Osteoblastic Metastases. Global Journal for Research Analysis. 2020;9(10).##[8]Priyanka K, Deep ChaitanyaDSJ, Babu OSridhar.Utility of CT Attenuation Value in Differentiating Enostosis From Untreated Osteoblastic Metastases. Int J Med Pub Health. 2025;15(4):170-174.##[9]Lee HarrisonT, Pryma DanielA, Sebro Ronnie.Optimized CT Attenuation and SUV Prediction Thresholds for Differentiating Enostoses From Untreated and Treated Metastases on Attenuation-Corrected 18F-FDG PET/CT. Clin Nucl Med. 2020;45(1):32-37. [PubMed ID: 31693615]. doi: 10.1097/RLU.0000000000002808.##[10]Kivell TL.A Review of Trabecular Bone Functional Adaptation: What Have We Learned From Trabecular Analyses in Extant Hominoids and What Can We Apply to Fossils? J Anat. 2016;228(4):569-594. [PubMed ID: 26879841]. [PubMed Central ID: PMC4804137]. doi: 10.1111/joa.12446.##[11]Wang H, Zhang W, Bado I, Zhang XH.Bone Tropism in Cancer Metastases. Cold Spring Harb Perspect Med. 2019;10(10):a036848. [PubMed ID: 31615871]. [PubMed Central ID: PMC7528862]. doi: 10.1101/cshperspect.a036848.##[12]Huang G, Hou T, Song D, Meng T.The Regulatory Networks and Mechanisms of Bone Microenvironment in Tumorigenesis and Metastasis. J Bone Oncol. 2025;55:100729. [PubMed ID: 41399767]. [PubMed Central ID: PMC12702220]. doi: 10.1016/j.jbo.2025.100729.##[13]Zhang X, Jiang P, Wang C.The Role of Prostate-Specific Antigen in the Osteoblastic Bone Metastasis of Prostate Cancer: A Literature Review. Front Oncol. 2023;13:1127637. [PubMed ID: 37746292]. [PubMed Central ID: PMC10513387]. doi: 10.3389/fonc.2023.1127637.##[14]Wong SK, Mohamad NV, Giaze TR, et al.Prostate Cancer and Bone Metastases: The Underlying Mechanisms. Int J Mol Sci. 2019;20(10):2587. [PubMed ID: 31137764]. [PubMed Central ID: PMC6567184]. doi: 10.3390/ijms20102587.##

1 Comparative Image Quality of Deep Learning Reconstruction Against Conventional Reconstruction for T2-weighted Prostate Imaging: A Systematic Review and Meta-Analysis https://brieflands.com/journals/ijradiology/articles/168277 10.5812/iranjradiol-168277 1 Context :Deep learning reconstruction is increasingly proposed to accelerate prostate T2-weighted magnetic resonance imaging (MRI) acquisition without loss of diagnostic quality. However, the magnitude of image quality benefits and the influence of deep learning network design remain unclear. Objectives :We aimed to compare the image quality of deep learning versus conventional prostate T2 MRI reconstruction and assess how acceleration factors and network architecture influence performance. Methods :A PRISMA search of PubMed/Medline, Embase, Web of Science, and Scopus (22 February 2026) identified studies comparing deep learning with conventional T2 MRI reconstruction. Paired standardized mean change (SMCC) values were pooled with a three-level random-effects model from extracted paired image-quality scores; meta-regressions examined scan-time acceleration. Small-study bias was tested with a precision-effect method. Results :Five studies (602 participants; 20 comparisons) included in the study. Overall deep learning reconstruction benefit was +0.14 standard deviation (SD) [95% confidence interval (CI) -2.00 to 2.28; I2 = 99 %]. At equal scan times deep learning reconstruction improved quality by +2.09 SD; for each fold increase in acceleration, image quality declined by 0.48 SD (95% CI -0.91 to -0.05; P = 0.030). C-SENSE AI at acceleration factors of 1.7, 3.4, and 4.8 demonstrated superior performance, whereas CycleGAN showed inferior results. Small-study effects were evident. Conclusion :Deep learning reconstruction can increase prostate T2-weighted MRI speed without significant changes in overall image quality; however, overly aggressive implementations may lead to image degradation. Careful validation and protocol-specific tuning are therefore essential prior to clinical adoption. 1 10 Narges Azizi Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran Iran nargesazizi05@gmail.com Hoda Borooghani Iran University of Medical Science, Tehran, Iran Iran hoda.brn0878@gmail.com Manouchehr Nasrollahzadeh Saravi Shahid Beheshti University of Medical Science, Tehran, Iran Iran manouchehrnasr@gmail.com Sina Delazar Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran Iran sina.delazar.md@gmail.com No Keyword 2.pdf [0]Rawla P, et al.Epidemiology of prostate cancer. World J Oncol. 2019;10(2):63. [PubMed ID: 31068988]. [PubMed Central ID: PMC6497009]. doi: 10.14740/wjon1191.##[1]Wu L-M, et al.The clinical value of diffusion-weighted imaging in combination with T2-weighted imaging in diagnosing prostate carcinoma: a systematic review and meta-analysis. Am J Roentgenol. 2012;199(1):103-110. [PubMed ID: 22733900]. doi: 10.2214/AJR.11.7634.##[2]Gassenmaier S, et al.Thin-slice prostate MRI enabled by deep learning image reconstruction. Cancers. 2023;15(3):578. [PubMed ID: 36765539]. [PubMed Central ID: PMC9913660]. doi: 10.3390/cancers15030578.##[3]Hoffman RM, et al.Screening for prostate cancer. N Engl J Med. 2011;365(21):2013-2019. [PubMed ID: 22029754]. doi: 10.1056/NEJMcp1103642.##[4]Litwin MS, et al.The diagnosis and treatment of prostate cancer: a review. JAMA. 2017;317(24):2532-2542. [PubMed ID: 28655021]. doi: 10.1001/jama.2017.7248.##[5]Li H, et al.Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities. Diagnostics. 2022;12(2):289. [PubMed ID: 35204380]. [PubMed Central ID: PMC8870978]. doi: 10.3390/diagnostics12020289.##[6]Ueda T, et al.Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging. Radiology. 2022;303(2):373-381. [PubMed ID: 35103536]. doi: 10.1148/radiol.204097.##[7]Cho J, et al.Biparametric versus multiparametric magnetic resonance imaging of the prostate: detection of clinically significant cancer in a perfect match group. Prostate Int. 2020;8(4):146-151. [PubMed ID: 33425791]. [PubMed Central ID: PMC7767942]. doi: 10.1016/j.prnil.2019.12.004.##[8]Bischoff LM, et al.Deep Learning Super-Resolution Reconstruction for Fast and Motion-Robust T2-weighted Prostate MRI. Radiology. 2023;308(3):e230427. [PubMed ID: 37750774]. doi: 10.1148/radiol.230427.##[9]Cuocolo R, et al.Machine learning applications in prostate cancer magnetic resonance imaging. Eur Radiol Exp. 2019;3(1):35. [PubMed ID: 31392526]. [PubMed Central ID: PMC6686027]. doi: 10.1186/s41747-019-0109-2.##[10]Gloe JN, et al.Deep learning for quality assessment of axial T2-weighted prostate MRI: a tool to reduce unnecessary rescanning. Eur Radiol Exp. 2025;9(1):44. [PubMed ID: 40299162]. [PubMed Central ID: PMC12040773]. doi: 10.1186/s41747-025-00584-z.##[11]Wang G, et al.Deep learning for tomographic image reconstruction. Nat Mach Intell. 2020;2(12):737-748. doi: 10.1038/s42256-020-00273-z.##[12]Antun V, et al.On instabilities of deep learning in image reconstruction and the potential costs of AI. Proc Natl Acad Sci U S A. 2020;117(48):30088-30095. [PubMed ID: 32393633]. [PubMed Central ID: PMC7720232]. doi: 10.1073/pnas.1907377117.##[13]van Lohuizen Q, et al.Assessing deep learning reconstruction for faster prostate MRI: visual vs. diagnostic performance metrics. Eur Radiol. 2024;34(11):7364-7372. [PubMed ID: 38724765]. 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1 Differences in Ultrasound Characteristics Among Molecular Subtypes of Breast Cancer: A Retrospective Cross-Sectional Analysis https://brieflands.com/journals/ijradiology/articles/167871 10.5812/iranjradiol-167871 1 Background :Breast cancer molecular subtyping is crucial for prognosis and treatment planning, prompting interest in noninvasive ultrasound-based approaches for identifying relevant correlations. Objectives :Breast cancer, which is highly prevalent worldwide, comprises several molecular subtypes, including luminal A (LA), luminal B (LB), triple-negative (TN), and human epidermal growth factor receptor 2-positive (HER2+). These subtypes have distinct prognostic and therapeutic implications. However, differences in ultrasound features among these subtypes and their potential associations with molecular classification remain underexplored. Patients and Methods :This retrospective cross-sectional study analyzed data from 140 women with primary invasive breast cancer treated at a referral hospital between April 2022 and March 2023. Standard ultrasound imaging and comprehensive clinicopathological data, including molecular subtypes identified by immunohistochemistry, were evaluated. Statistical analyses were performed using SPSS version 22 to assess correlations between ultrasound features and molecular subtypes. Results :The mean age of the participants was 49.75 years, and the mean tumor size was 27.1 mm. LA was the most common subtype (48%), followed by LB (25%), TN (18%), and HER2+ (7%). Adjusted associations between ultrasound features and molecular subtypes showed a nonsignificant trend, particularly for calcification, tumor shape, and tumor location. The TN subtype had the highest calcification rate, followed by the LA subtype (adjusted P = 0.107). Irregular shapes were common across all subtypes, whereas variation in the frequencies of round and oval shapes suggested potential subtype-specific differences. Conclusion :Certain ultrasound features showed nonsignificant trends across molecular subtypes; however, these findings do not currently support reliable differentiation among subtypes. Further research is needed to validate these preliminary results and potentially expand the diagnostic role of ultrasound in breast cancer subtyping. 1 8 Mobina Abbasi Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran Department of Radiology, Tehran University of Medical Sciences, Imam Khomeini Hospital, Tehran, Iran Iran Iran mobinaabbasi161044@gmail.com Mohammadreza Tahamtan Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran Department of Radiology, Tehran University of Medical Sciences, Imam Khomeini Hospital, Tehran, Iran Iran Iran mr.tahamtan@gmail.com Nahid Sadighi Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran Department of Radiology, Tehran University of Medical Sciences, Imam Khomeini Hospital, Tehran, Iran Iran Iran sadighii@yahoo.com Saeed Mohammadzadeh Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran Department of Radiology, Tehran University of Medical Sciences, Imam Khomeini Hospital, Tehran, Iran Iran Iran saeed.mohamadzadeh2001@gmail.com Peyman Kamali Hakim Radiology Department, Iran University of Medical Sciences, Tehran, Iran Iran peyman_kamali_hakim@yahoo.com Fatemeh Shakki Katouli Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran Department of Radiology, Tehran University of Medical Sciences, Imam Khomeini Hospital, Tehran, Iran Iran Iran fatemeh.shakki@gmail.com Maryam Aghasi Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran Department of Radiology, Tehran University of Medical Sciences, Imam Khomeini Hospital, Tehran, Iran Iran Iran maryamaghasi396@gmail.com Fahimeh Zeinalkhani Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran Department of Radiology, Tehran University of Medical Sciences, Imam Khomeini Hospital, Tehran, Iran Iran Iran f_zeinalkhani@yahoo.com No Keyword 3.pdf [0]Mohammadzadeh S, Mohebbi A, Moradi Z, Abdi A, Mohammadi A, Hakim PK, et al.Diagnostic performance of Kaiser score in the evaluation of breast cancer using MRI: A systematic review and meta-analysis. European Journal of Radiology. 2025;186. 112055. [PubMed ID: 40121897]. doi: 10.1016/j.ejrad.2025.112055.##[1]Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al.Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249.##[2]Wang GS, Zhu H, Bi SJ.Pathological features and prognosis of different molecular subtypes of breast cancer. Mol Med Rep. 2012;6(4):779-782. [PubMed ID: 22797840]. doi: 10.3892/mmr.2012.981.##[3]Turkoz FP, Solak M, Petekkaya I, Keskin O, Kertmen N, Sarici F, et al.Association between common risk factors and molecular subtypes in breast cancer patients. Breast. 2013;22(3):344-350. [PubMed ID: 22981738]. doi: 10.1016/j.breast.2012.08.005.##[4]Orrantia-Borunda E, Orrantia-Borunda E, Anchondo-Nuñe P, Acuña-Aguilar LE, Gómez-Valles FO, Ramírez-Valdespino CA, et al.Subtypes of breast cancer. In: Mayrovitz HN, editor. Breast Cancer. Exon Publications; 2022.##[5]Wu T, Li J, Wang D, Leng X, Zhang L, Li Z, et al.Identification of a correlation between the sonographic appearance and molecular subtype of invasive breast cancer: A review of 311 cases. Clin Imaging. 2019;53:179-185. [PubMed ID: 30415183]. doi: 10.1016/j.clinimag.2018.10.020.##[6]Khalaf LMR, Herdan RA.Role of ultrasound in predicting the molecular subtypes of invasive breast ductal carcinoma. Egyptian Journal of Radiology and Nuclear Medicine. 2020;51(1). 138. doi: 10.1186/s43055-020-00240-z.##[7]Kittaneh M, Montero AJ, Glück S.Molecular profiling for breast cancer: A comprehensive review. Biomark Cancer. 2013;5:61-70. [PubMed ID: 24250234]. [PubMed Central ID: PMC3825646]. doi: 10.4137/bic.s9455.##[8]Wu M, Ma J.Association between imaging characteristics and different molecular subtypes of breast cancer. Academic Radiology. 2016;24.##[9]Rashmi S, Kamala S, Murthy SS, Kotha S, Rao YS, Chaudhary KV.Predicting the molecular subtype of breast cancer based on mammography and ultrasound findings. Indian J Radiol Imaging. 2018;28(3):354-361. [PubMed ID: 30319215]. [PubMed Central ID: PMC6176670]. doi: 10.4103/ijri.ijri_78_18.##[10]Zhang L, Li J, Xiao Y, Cui H, Du G, Wang Y, et al.Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision. Sci Rep. 2015;5(1). 11085. [PubMed ID: 26046791]. [PubMed Central ID: PMC4457139]. doi: 10.1038/srep11085.##[11]Yang Q, Liu HY, Liu D, Song YQ.Ultrasonographic features of triple-negative breast cancer: A comparison with other breast cancer subtypes. Asian Pac J Cancer Prev. 2015;16(8):3229-3232. [PubMed ID: 25921124]. doi: 10.7314/apjcp.2015.16.8.3229.##[12]Wojcinski S, Soliman AA, Schmidt J, Makowski L, Degenhardt F, Hillemanns P.Sonographic features of triple-negative and non-triple-negative breast cancer. J Ultrasound Med. 2012;31(10):1531-1541. [PubMed ID: 23011616]. doi: 10.7863/jum.2012.31.10.1531.##[13]Eslami B, Omranipour R, Bayani L, Seifollahi A, Saberi A, Jahanbin B, et al.Comparison of Standard and Vacuum Specimen Mammography in the Detection of Margin Status in Conservative Surgery for Breast Cancer: a Cross-Sectional Diagnostic Study: Margin detection with specimen mammography. Arch Breast Cancer. 2024;11(1).##[14]Liang X, Li Z, Zhang L, Wang D, Tian J.Application of contrast-enhanced ultrasound in the differential diagnosis of different molecular subtypes of breast cancer. Ultrason Imaging. 2020;42(6):261-270. [PubMed ID: 33019918]. doi: 10.1177/0161734620959780.##[15]Shah A, Haider G, Abro N, Hashmat S, Chandio S, Shaikh A, et al.Correlation between site and stage of breast cancer in women. Cureus. 2022;14(2). e22672. [PubMed ID: 35386160]. [PubMed Central ID: PMC8967127]. doi: 10.7759/cureus.22672.##[16]Rummel S, Hueman MT, Costantino N, Shriver CD, Ellsworth RE.Tumour location within the breast: Does tumour site have prognostic ability? Ecancermedicalscience. 2015;9:552. [PubMed ID: 26284116]. [PubMed Central ID: PMC4531129]. doi: 10.3332/ecancer.2015.552.##[17]Ebrahimian S, Akbari A, Soleimani Varaki S, Amirhosseini S, Akbari ME, Ashoftebargi S.Accuracy of MRI versus ultrasound/mammography in detecting axillary lymph node involvement in patients with breast cancer. Int J Cancer Manag. 2025;18(1). e162212. doi: 10.5812/ijcm-162212.##[18]Heidarzadeh H, Nazeri N, Forghani MN, Bakhtiari E, Alamdaran SA.Can highly specific ultrasound criteria obviate the need for sentinel lymph node biopsy in breast cancer patients with axillary lymph node metastasis? I J Radiol. 2024;21(1). e139030. doi: 10.5812/ijradiol-139030.##

1 Ureteral Intraluminal Dissection Caused by Spontaneous Ureteropelvic Rupture: A Rare Case Report and Review of the Literature https://brieflands.com/journals/ijradiology/articles/167111 10.5812/iranjradiol-167111 1 Introduction :Spontaneous ureteropelvic rupture (SUPR) is rare, and its combination with ureteral intraluminal dissection (UID) is even rarer. Case Presentation :A 62-year-old Chinese man was admitted to the hospital with acute, severe right lumbar pain accompanied by nausea and vomiting. Contrast-enhanced computed tomography of the abdomen and pelvis showed a double-lumen appearance and septum formation in the middle and upper segments of the right ureter during the excretory phase. Based on these imaging findings, UID caused by SUPR was diagnosed. The patient was managed conservatively with antibiotics for three weeks without ureteral stenting. Conclusion :This case involved SUPR combined with UID; however, the source remained elusive. Limited clinical awareness and variable clinical manifestations often make the diagnosis challenging. Diagnosis relies on delayed contrast-enhanced computed tomography (CT) of the abdomen and pelvis. Currently, no standardized guidelines are available, and minimally invasive endourological approaches are generally accepted as the preferred first-line option. 1 6 Dawei Su Department of Urology, The First Ren Ming Hospital of Lanzhou, Lanzhou, China China 1763981719@qq.com Tingting Liu Department of Imageology, The First Ren Ming Hospital of Lanzhou, Lanzhou, China China 3687765171@qq.com Hongjie Chen Department of Urology, The First Ren Ming Hospital of Lanzhou, Lanzhou, China China cyr2000816@sina.com No Keyword 4.pdf [0]Jamil SB, Munir M, Patoli I, Rehmani S.An interesting case of critical spontaneous ureteral rupture. Cureus. 2021;13(8):e17497-503. [PubMed ID: 34595074]. 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