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Estrade V, Denis de Senneville B, Facq L, Daudon M. Endoscopic in-situ recognition of urinary stones during LASER-induced stone fragmentation: a modern, effective and essential approach in the diagnostic process in urolithiasis. CR CHIM 2022. [DOI: 10.5802/crchim.162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Chen HW, Chen YC, Lee JT, Yang FM, Kao CY, Chou YH, Chu TY, Juan YS, Wu WJ. Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model. Nutrients 2022; 14:nu14091829. [PMID: 35565794 PMCID: PMC9103478 DOI: 10.3390/nu14091829] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/25/2022] [Accepted: 04/26/2022] [Indexed: 02/04/2023] Open
Abstract
There is a great need for a diagnostic tool using simple clinical information collected from patients to diagnose uric acid (UA) stones in nephrolithiasis. We built a predictive model making use of machine learning (ML) methodologies entering simple parameters easily obtained at the initial clinical visit. Socio-demographic, health, and clinical data from two cohorts (A and B), both diagnosed with nephrolithiasis, one between 2012 and 2016 and the other between June and December 2020, were collected before nephrolithiasis treatment. A ML-based model for predicting UA stones in nephrolithiasis was developed using eight simple parameters-sex, age, gout, diabetes mellitus, body mass index, estimated glomerular filtration rate, bacteriuria, and urine pH. Data from Cohort A were used for model training and validation (ratio 3:2), while data from Cohort B were used only for validation. One hundred and forty-six (13.3%) out of 1098 patients in Cohort A and 3 (4.23%) out of 71 patients in Cohort B had pure UA stones. For Cohort A, our model achieved a validation AUC (area under ROC curve) of 0.842, with 0.8475 sensitivity and 0.748 specificity. For Cohort B, our model achieved 0.936 AUC, with 1.0 sensitivity, and 0.912 specificity. This ML-based model provides a convenient and reliable method for diagnosing urolithiasis. Using only eight readily available clinical parameters, including information about metabolic disorder and obesity, it distinguished pure uric acid stones from other stones before treatment.
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Affiliation(s)
- Hao-Wei Chen
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, 100, Shih-Chuan 1st Road, Kaohsiung 80708, Taiwan; (H.-W.C.); (Y.-C.C.); (Y.-H.C.); (Y.-S.J.)
- Department of Urology, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, 80145, Taiwan
- Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Yu-Chen Chen
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, 100, Shih-Chuan 1st Road, Kaohsiung 80708, Taiwan; (H.-W.C.); (Y.-C.C.); (Y.-H.C.); (Y.-S.J.)
- Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Jung-Ting Lee
- Si Wan College, National Sun-Yat Sen University, Kaohsiung 80424, Taiwan;
| | - Frances M. Yang
- School of Nursing, University of Kansas, Kansas City, KS 66160, USA;
| | - Chung-Yao Kao
- Department of Electrical Engineering, National Sun-Yat Sen University, Kaohsiung 80424, Taiwan;
| | - Yii-Her Chou
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, 100, Shih-Chuan 1st Road, Kaohsiung 80708, Taiwan; (H.-W.C.); (Y.-C.C.); (Y.-H.C.); (Y.-S.J.)
- Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Ting-Yin Chu
- Department of Business Management, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan;
| | - Yung-Shun Juan
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, 100, Shih-Chuan 1st Road, Kaohsiung 80708, Taiwan; (H.-W.C.); (Y.-C.C.); (Y.-H.C.); (Y.-S.J.)
- Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Wen-Jeng Wu
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, 100, Shih-Chuan 1st Road, Kaohsiung 80708, Taiwan; (H.-W.C.); (Y.-C.C.); (Y.-H.C.); (Y.-S.J.)
- Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Correspondence:
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Tang L, Li W, Zeng X, Wang R, Yang X, Luo G, Chen Q, Wang L, Song B. Value of artificial intelligence model based on unenhanced computed tomography of urinary tract for preoperative prediction of calcium oxalate monohydrate stones in vivo. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1129. [PMID: 34430570 PMCID: PMC8350703 DOI: 10.21037/atm-21-965] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 06/03/2021] [Indexed: 02/05/2023]
Abstract
Background Urolithiasis is a global disease with a high incidence and recurrence rate, and stone composition is closely related to the choice of treatment and preventive measures. Calcium oxalate monohydrate (COM) is the most common in clinical practice, which is hard and difficult to fragment. Preoperative identification of its components and selection of effective surgical methods can reduce the risk of patients having a second operation. Methods that can be used for stone composition analysis include infrared spectroscopy, X-ray diffraction, and polarized light microscopy, but they are all performed on stone specimens in vitro after surgery. This study aimed to design and develop an artificial intelligence (AI) model based on unenhanced computed tomography (CT) images of the urinary tract, and to investigate the predictive ability of the model for COM stones in vivo preoperatively, so as to provide surgeons with more accurate diagnostic information. Methods Preoperative unenhanced CT images of patients with urinary calculi whose components were determined by infrared spectroscopy in a single center were retrospectively analyzed, including 337 cases of COM stones and 170 of non-COM stones. All images were manually segmented and the image features were extracted, and randomly divided into the training and testing sets in a ratio of 7:3. The least absolute shrinkage and selection operation algorithm (LASSO) was used to construct the AI model, and classification of the training and testing sets was carried out. Results A total of 1,218 radiomics imaging features were extracted, and 8 features with non-zero coefficients were finally obtained. The sensitivity, specificity and accuracy of the AI model were 90.5%, 84.3% and 88.5% for the training set, and 90.1%, 84.3% and 88.3% for the testing set. The area under the curve was 0.935 for the training set and 0.933 for the testing set. Conclusions The AI model based on unenhanced CT images of the urinary tract can predict COM and non-COM stones in vivo preoperatively, and the model has high sensitivity, specificity and accuracy.
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Affiliation(s)
- Lei Tang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Wuchao Li
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Xianchun Zeng
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Rongpin Wang
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Xiushu Yang
- Department of Urological Surgery, Guizhou Provincial People's Hospital, Guiyang, China
| | - Guangheng Luo
- Department of Urological Surgery, Guizhou Provincial People's Hospital, Guiyang, China
| | - Qijian Chen
- College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Lihui Wang
- College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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Zheng J, Yu H, Batur J, Shi Z, Tuerxun A, Abulajiang A, Lu S, Kong J, Huang L, Wu S, Wu Z, Qiu Y, Lin T, Zou X. A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning. Kidney Int 2021; 100:870-880. [PMID: 34129883 DOI: 10.1016/j.kint.2021.05.031] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 04/15/2021] [Accepted: 05/14/2021] [Indexed: 02/06/2023]
Abstract
Urolithiasis is a common urological disease, and treatment strategy options vary between different stone types. However, accurate detection of stone composition can only be performed in vitro. The management of infection stones is particularly challenging with yet no effective approach to pre-operatively identify infection stones from non-infection stones. Therefore, we aimed to develop a radiomic model for preoperatively identifying infection stones with multicenter validation. In total, 1198 eligible patients with urolithiasis from three centers were divided into a training set, an internal validation set, and two external validation sets. Stone composition was determined by Fourier transform infrared spectroscopy. A total of 1316 radiomic features were extracted from the pre-treatment Computer Tomography images of each patient. Using the least absolute shrinkage and selection operator algorithm, we identified a radiomic signature that achieved favorable discrimination in the training set, which was confirmed in the validation sets. Moreover, we then developed a radiomic model incorporating the radiomic signature, urease-producing bacteria in urine, and urine pH based on multivariate logistic regression analysis. The nomogram showed favorable calibration and discrimination in the training and three validation sets (area under the curve [95% confidence interval], 0.898 [0.840-0.956], 0.832 [0.742-0.923], 0.825 [0.783-0.866], and 0.812 [0.710-0.914], respectively). Decision curve analysis demonstrated the clinical utility of the radiomic model. Thus, our proposed radiomic model can serve as a non-invasive tool to identify urinary infection stones in vivo, which may optimize disease management in urolithiasis and improve patient prognosis.
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Affiliation(s)
- Junjiong Zheng
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Hao Yu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Jesur Batur
- Department of Urology, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China
| | - Zhenfeng Shi
- Department of Urology, the People's Hospital of Xinjiang Uyghur Autonomous Region, Xinjiang, People's Republic of China
| | - Aierken Tuerxun
- Department of Urology, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China
| | - Abudukeyoumu Abulajiang
- Department of Information Technology, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China
| | - Sihong Lu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Jianqiu Kong
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Lifang Huang
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Shaoxu Wu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Zhuo Wu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Ya Qiu
- Department of Radiology, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China
| | - Tianxin Lin
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China; State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Urological Diseases, Guangdong, People's Republic of China.
| | - Xiaoguang Zou
- Department of Pharmacy, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China.
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Single-energy CT predicts uric acid stones with accuracy comparable to dual-energy CT-prospective validation of a quantitative method. Eur Radiol 2021; 31:5980-5989. [PMID: 33635394 PMCID: PMC8270827 DOI: 10.1007/s00330-021-07713-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 12/10/2020] [Accepted: 01/21/2021] [Indexed: 12/26/2022]
Abstract
OBJECTIVES To prospectively validate three quantitative single-energy CT (SE-CT) methods for classifying uric acid (UA) and non-uric acid (non-UA) stones. METHODS Between September 2018 and September 2019, 116 study participants were prospectively included in the study if they had at least one 3-20-mm urinary stone on an initial urinary tract SE-CT scan. An additional dual-energy CT (DE-CT) scan was performed, limited to the stone of interest. Additionally, to include a sufficient number of UA stones, eight participants with confirmed UA stone on DE-CT were retrospectively included. The SE-CT stone features used in the prediction models were (1) maximum attenuation (maxHU) and (2) the peak point Laplacian (ppLapl) calculated at the position in the stone with maxHU. Two prediction models were previously published methods (ppLapl-maxHU and maxHU) and the third was derived from the previous results based on the k-nearest neighbors (kNN) algorithm (kNN-ppLapl-maxHU). The three methods were evaluated on this new independent stone dataset. The reference standard was the CT vendor's DE-CT application for kidney stones. RESULTS Altogether 124 participants (59 ± 14 years, 91 men) with 106 non-UA and 37 UA stones were evaluated. For classification of UA and non-UA stones, the sensitivity, specificity, and accuracy were 100% (37/37), 97% (103/106), and 98% (140/143), respectively, for kNN-ppLapl-maxHU; 95% (35/37), 98% (104/106), and 97% (139/143) for ppLapl-maxHU; and 92% (34/37), 94% (100/106), and 94% (134/143) for maxHU. CONCLUSION A quantitative SE-CT method (kNN-ppLapl-maxHU) can classify UA stones with accuracy comparable to DE-CT. KEY POINTS • Single-energy CT is the first-line diagnostic tool for suspected renal colic. • A single-energy CT method based on the internal urinary stone attenuation distribution can classify urinary stones into uric acid and non-uric acid stones with high accuracy. • This immensely increases the availability of in vivo stone analysis.
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Abstract
PURPOSE OF REVIEW Conventional CT imaging is an excellent tool for the diagnosis of nephrolithiasis however is limited in its ability to detect stone composition. Dual-energy CT (DECT) scans have demonstrated promise in overcoming this limitation. We review the current utility of DECT in nephrolithiasis. RECENT FINDINGS DECT is superior to conventional CT in differentiating uric acid stones from non-uric acid stones, with numerous studies reporting sensitivities and specificities approaching > 95%. Dose reduction protocols incorporating low-dose CT scans are commonly used, providing significantly lower effective radiation doses compared to conventional CT. DECT remains an effective diagnostic tool in patients with large body habitus. DECT can accurately detect uric acid stones, which can help guide which stones may be suitable to medical dissolution. Further studies evaluating the effectiveness of DECT in guiding management of patients with nephrolithiasis can help to promote its widespread use.
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Cannella R, Shahait M, Furlan A, Zhang F, Bigley JD, Averch TD, Borhani AA. Efficacy of single-source rapid kV-switching dual-energy CT for characterization of non-uric acid renal stones: a prospective ex vivo study using anthropomorphic phantom. Abdom Radiol (NY) 2020; 45:1092-1099. [PMID: 31385007 DOI: 10.1007/s00261-019-02164-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
PURPOSE To investigate the accuracy of rapid kV-switching single-source dual-energy computed tomography (rsDECT) for prediction of classes of non-uric-acid stones. MATERIALS AND METHODS Non-uric-acid renal stones retrieved via percutaneous nephrolithotomy were prospectively collected between January 2017 and February 2018 in a single institution. Only stones ≥ 5 mm and with pure composition (i.e., ≥ 80% composed of one component) were included. Stone composition was determined using Fourier Transform Infrared Spectroscopy. The stones were scanned in 32-cm-wide anthropomorphic whole-body phantom using rsDECT. The effective atomic number (Zeff), the attenuation at 40 keV (HU40), 70 keV (HU70), and 140 keV (HU140) virtual monochromatic sets of images as well as the ratios between the attenuations were calculated. Values of stone classes were compared using ANOVA and Mann-Whitney U test. Receiver operating curves and area under curve (AUC) were calculated. A p value < 0.05 was considered statistically significant. RESULTS The final study sample included 31 stones from 31 patients consisting of 25 (81%) calcium-based, 4 (13%) cystine, and 2 (6%) struvite pure stones. The mean size of the stones was 9.9 ± 2.4 mm. The mean Zeff of the stones was 12.01 ± 0.54 for calcium-based, 11.10 ± 0.68 for struvite, and 10.23 ± 0.75 for cystine stones (p < 0.001). Zeff had the best efficacy to separate different classes of stones. The calculated AUC was 0.947 for Zeff; 0.833 for HU40; 0.880 for HU70; and 0.893 for HU140. CONCLUSION Zeff derived from rsDECT has superior performance to HU and attenuation ratios for separation of different classes of non-uric-acid stones.
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Affiliation(s)
- Roberto Cannella
- Division of Abdominal Imaging, Department of Radiology, University of Pittsburgh, UPMC Presbyterian, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
- Section of Radiology - BiND, University Hospital "Paolo Giaccone", Via del Vespro 129, 90127, Palermo, Italy
| | - Mohammed Shahait
- Department of Urology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Alessandro Furlan
- Division of Abdominal Imaging, Department of Radiology, University of Pittsburgh, UPMC Presbyterian, 200 Lothrop Street, Pittsburgh, PA, 15213, USA
| | - Feng Zhang
- Department of Radiology, St. Joseph's Medical Center, Stockton, CA, USA
| | - Joel D Bigley
- Department of Urology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Timothy D Averch
- Department of Radiology, Palmetto Health-Health-University of South Carolina Medical Group, Columbia, SC, USA
| | - Amir A Borhani
- Division of Abdominal Imaging, Department of Radiology, University of Pittsburgh, UPMC Presbyterian, 200 Lothrop Street, Pittsburgh, PA, 15213, USA.
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Comment on "Deep learning computer vision algorithm for detecting kidney stone composition". World J Urol 2020; 39:291. [PMID: 32239266 DOI: 10.1007/s00345-020-03181-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 03/23/2020] [Indexed: 10/24/2022] Open
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Development of Microwave-Assisted Hydrothermal Extraction Coupled to Ion Chromatography for Comprehensive Analysis of Chemical Composition in Intracorporeal Stone. Chromatographia 2020. [DOI: 10.1007/s10337-020-03883-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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Seitz C, Bach T, Bader M, Berg W, Knoll T, Neisius A, Netsch C, Nothacker M, Schmidt S, Schönthaler M, Siener R, Stein R, Straub M, Strohmaier W, Türk C, Volkmer B. Aktualisierung der S2k-Leitlinie zur Diagnostik, Therapie und Metaphylaxe der Urolithiasis (AWMF Registernummer 043-025). Urologe A 2019; 58:1304-1312. [DOI: 10.1007/s00120-019-01033-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Zusammenfassung
Die Zunahme des medizinischen Wissens, technische Neuerungen gemeinsam mit demographischem Wandel stellen eine Herausforderung an die Neukonzeption von Leitlinien und klinischen Studien dar. Die vorliegende S2k-Leitlinie, die sich ausschließlich mit Nieren- und Harnleitersteinen beschäftigt, soll die Behandlung von Harnsteinpatienten in Klinik und Praxis unterstützen, aber auch Patienteninformationen zur Urolithiasis geben. Die zunehmende interdisziplinäre Zusammenarbeit in der Steintherapie zeigt sich auch an der Anzahl beteiligter Fachgruppen und Arbeitsgemeinschaften in der Erstellung des neuen Leitlinienupdates. Die vorliegende, aus einem interdisziplinären Konsensusprozess hervorgegangene S2k-Leitlinie stellt die aktuellen Empfehlungen praxisnah dar und gibt Entscheidungshilfen für Diagnostik‑, Therapie- und Metaphylaxemaßnahmen auf Basis von Expertenmeinungen und verfügbaren Evidenzgrundlagen aus der Literatur.
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Magistro G, Bregenhorn P, Krauß B, Nörenberg D, D'Anastasi M, Graser A, Weinhold P, Strittmatter F, Stief CG, Staehler M. Optimized management of urolithiasis by coloured stent-stone contrast using dual-energy computed tomography (DECT). BMC Urol 2019; 19:29. [PMID: 31039768 PMCID: PMC6492318 DOI: 10.1186/s12894-019-0459-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 04/12/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND We analysed in vitro the appearance of commonly used ureteral stents with dual-energy computed tomography (DECT) and we used these characteristics to optimize the differentiation between stents and adjacent stone. METHODS We analysed in vitro a selection of 36 different stents from 7 manufacturers. They were placed in a self-build phantom model and measured using the SOMATOM® Force Dual Source CT-Scanner (Siemens, Forchheim, Germany). Each sample was scanned at various tube potentials of 80 and 150 peak kilovoltage (kVp), 90 and 150 kVp and 100 and 150 kVp. The syngo Post-Processing Suite software program (Siemens, Forchheim, Germany) was used for differentiation based on a 3-material decomposition algorithm (UA, calcium, urine) according to our standard stone protocol. RESULTS Stents composed of polyurethane appeared blue and silicon-based stents were red on the image. The determined appearances were constant for various peak kilovoltage (kVp) values. The coloured stent-stone-contrast displayed on DECT improves monitoring, especially of small calculi adjacent to indwelling ureteral stents. CONCLUSION Both urinary calculi and ureteral stents can be accurately differentiated by a distinct appearance on DECT. For the management of urolithiasis patients can be monitored more easily and accurately using DECT if the stent shows a different colour than the adjacent stone.
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Affiliation(s)
- Giuseppe Magistro
- Department of Urology, Ludwig-Maximilians-University of Munich, Marchioninistrasse 15, 81377, Munich, Germany.
| | - Patrick Bregenhorn
- Department of Radiology, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Bernhard Krauß
- Siemens Healthcare GmbH, Research and Development, Forchheim, Germany
| | - Dominik Nörenberg
- Siemens Healthcare GmbH, Research and Development, Forchheim, Germany
| | - Melvin D'Anastasi
- Siemens Healthcare GmbH, Research and Development, Forchheim, Germany
| | - Anno Graser
- Gemeinschaftspraxis Radiologie München, Munich, Germany
| | - Philipp Weinhold
- Department of Urology, Ludwig-Maximilians-University of Munich, Marchioninistrasse 15, 81377, Munich, Germany
| | - Frank Strittmatter
- Department of Urology, Ludwig-Maximilians-University of Munich, Marchioninistrasse 15, 81377, Munich, Germany
| | - Christian G Stief
- Department of Urology, Ludwig-Maximilians-University of Munich, Marchioninistrasse 15, 81377, Munich, Germany
| | - Michael Staehler
- Department of Urology, Ludwig-Maximilians-University of Munich, Marchioninistrasse 15, 81377, Munich, Germany
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Nestler T, Nestler K, Neisius A, Isbarn H, Netsch C, Waldeck S, Schmelz HU, Ruf C. Diagnostic accuracy of third-generation dual-source dual-energy CT: a prospective trial and protocol for clinical implementation. World J Urol 2018; 37:735-741. [DOI: 10.1007/s00345-018-2430-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 08/01/2018] [Indexed: 12/01/2022] Open
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13
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Zhang GMY, Sun H, Shi B, Xu M, Xue HD, Jin ZY. Uric acid versus non-uric acid urinary stones: differentiation with single energy CT texture analysis. Clin Radiol 2018; 73:792-799. [PMID: 29793721 DOI: 10.1016/j.crad.2018.04.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 04/17/2018] [Indexed: 02/03/2023]
Abstract
AIM To evaluate the accuracy of computed tomography (CT) texture analysis (TA) to differentiate uric acid (UA) stones from non-UA stones on unenhanced CT in patients with urinary calculi with ex vivo Fourier transform infrared spectroscopy (FTIR) as the reference standard. MATERIALS AND METHODS Fourteen patients with 18 UA stones and 31 patients with 32 non-UA stones were included. All the patients had preoperative CT evaluation and subsequent surgical removal of the stones. CTTA was performed on CT images using commercially available research software. Each texture feature was evaluated using the non-parametric Mann-Whitney test. Receiver operating characteristic (ROC) curves were created and the area under the ROC curve (AUC) was calculated for texture parameters that were significantly different. The features were used to train support vector machine (SVM) classifiers. Diagnostic accuracy was evaluated. RESULTS Compared to non-UA stones, UA stones had significantly lower mean, standard deviation and mean of positive pixels but higher kurtosis (p<0.001) on both unfiltered and filtered texture scales. There were no significant differences in entropy or skewness between UA and non-UA stones. The average SVM accuracy of texture features for differentiating UA from non-UA stones ranged from 88% to 92% (after 10-fold cross validation). A model incorporating standard deviation, skewness, and kurtosis from unfiltered texture scale images resulted in an AUC of 0.965±00.029 with a sensitivity of 94.4% and specificity of 93.7%. CONCLUSION CTTA can be used to accurately differentiate UA stones from non-UA stones in vivo using unenhanced CT images.
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Affiliation(s)
- G-M-Y Zhang
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences. Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing 100730, China
| | - H Sun
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences. Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing 100730, China.
| | - B Shi
- Department of Radiology, Shenzhen Sun Yat-Sen Cardiovascular Hospital, No. 1021 Dongmen Road North, Luohu District, Shenzhen 518001, China
| | - M Xu
- Siemens Healthcare Ltd, Beijing, China. No.7 Zhonghuan Nanlu, Chaoyang District, Beijing 100102, China
| | - H-D Xue
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences. Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing 100730, China.
| | - Z-Y Jin
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences. Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing 100730, China.
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Matsubara K, Nagata H, Okubo R, Takata T, Kobayashi M. Method for determining the half-value layer in computed tomography scans using a real-time dosimeter: Application to dual-source dual-energy acquisition. Phys Med 2017; 44:227-231. [DOI: 10.1016/j.ejmp.2017.10.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 08/09/2017] [Accepted: 10/21/2017] [Indexed: 10/18/2022] Open
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CT-calculometry (CT-CM): advanced NCCT post-processing to investigate urinary calculi. World J Urol 2017; 36:117-123. [DOI: 10.1007/s00345-017-2092-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 09/19/2017] [Indexed: 10/18/2022] Open
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17
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Tailly T, Denstedt J. Innovations in percutaneous nephrolithotomy. Int J Surg 2016; 36:665-672. [DOI: 10.1016/j.ijsu.2016.11.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Accepted: 11/02/2016] [Indexed: 12/26/2022]
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