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Xiong Z, Ge Y, Ma S, Wang Y, Li L, Chao Z, Wang X, Sulaiman M, Li C, Luan Y, Yang C, Zeng X, Yu G, Zou S, Zhu X, Wang S, Hu Z, Yang Q, Qin B, Wang Z. Comparing the diagnostic value of 68Ga-prostate-specific membrane antigen PET/CT and multiparametric MRI in pelvic lymph node metastasis of locally advanced prostate cancer. Transl Androl Urol 2024; 13:1219-1227. [PMID: 39100834 PMCID: PMC11291416 DOI: 10.21037/tau-24-15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/25/2024] [Indexed: 08/06/2024] Open
Abstract
Background Multiparametric magnetic resonance imaging (mpMRI) is a commonly used method to diagnose pelvic lymph node metastasis (PLNM) in prostate cancer (PCa) patients, but there are few comparative studies on mpMRI and 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) in locally advanced PCa (LAPC) patients. Therefore, we designed a retrospective study to compare the diagnostic value of 68Ga-PSMA PET/CT and mpMRI for PLNM of LAPC. Methods A retrospective study was performed on 50 patients with LAPC who underwent radical prostatectomy (RP) in Tongji Hospital from 2021 to 2023. All patients underwent PET/CT and mpMRI examination, and were diagnosed as LAPC before surgery, followed by robot-assisted laparoscopic prostatectomy or laparoscopic RP and extended pelvic lymph node dissection (ePLND). Routine postoperative pathological examination was performed. According to the results, the sensitivity, specificity, positive predictive value, and negative predictive value of 68Ga-PSMA PET/CT and mpMRI for the diagnosis of PLNM of LAPC were compared. Results Among the 50 patients, the mean age was 65.5±10.3 years, the preoperative total serum prostate-specific antigen (PSA) was 30.7±12.3 ng/mL, and the Gleason score was 7 [7, 8]. The difference in diagnostic efficacy between 68Ga-PSMA PET/CT and mpMRI in the preoperative diagnosis of PLNM of PCa was determined by postoperative pathological results. Based on the number of patients who developed PLNM, the sensitivity, specificity, positive predictive value, and negative predictive value of 68Ga-PSMA PET/CT were as follows: 93.75%, 100.00%, 100.00%, 97.14%, and 68.75%, 97.06%, 91.67%, 86.84% for mpMRI, respectively. Based on the number of pelvic metastatic lymph nodes, the sensitivity, specificity, positive predictive value, and negative predictive value of 68Ga-PSMA PET/CT were 95.24%, 100.00%, 100.00%, 99.48%, and 65.08%, 99.13%, 89.13%, 96.30% for mpMRI, respectively. It turned out that PET/CT was more sensitive than mpMRI in detecting PLNM of PCa, and the difference was statistically significant. Conclusions 68Ga-PSMA PET/CT is more sensitive than mpMRI in the detection of PLNM in patients with LAPC. It is a promising method in the diagnosis and preoperative assessment of PLNM in LAPC.
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Affiliation(s)
- Zezhong Xiong
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yue Ge
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng Ma
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yanan Wang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Le Li
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zheng Chao
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xia Wang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Manan Sulaiman
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Cong Li
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Luan
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chunguang Yang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xing Zeng
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Gan Yu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sijuan Zou
- Department of Nuclear Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaohua Zhu
- Department of Nuclear Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shaogang Wang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhiquan Hu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qin Yang
- Department of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Baolong Qin
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhihua Wang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Tarchi SM, Salvatore M, Lichtenstein P, Sekar T, Capaccione K, Luk L, Shaish H, Makkar J, Desperito E, Leb J, Navot B, Goldstein J, Laifer S, Beylergil V, Ma H, Jambawalikar S, Aberle D, D'Souza B, Bentley-Hibbert S, Marin MP. Radiology of fibrosis part III: genitourinary system. J Transl Med 2024; 22:616. [PMID: 38961396 PMCID: PMC11223291 DOI: 10.1186/s12967-024-05333-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 05/20/2024] [Indexed: 07/05/2024] Open
Abstract
Fibrosis is a pathological process involving the abnormal deposition of connective tissue, resulting from improper tissue repair in response to sustained injury caused by hypoxia, infection, or physical damage. It can impact any organ, leading to their dysfunction and eventual failure. Additionally, tissue fibrosis plays an important role in carcinogenesis and the progression of cancer.Early and accurate diagnosis of organ fibrosis, coupled with regular surveillance, is essential for timely disease-modifying interventions, ultimately reducing mortality and enhancing quality of life. While extensive research has already been carried out on the topics of aberrant wound healing and fibrogenesis, we lack a thorough understanding of how their relationship reveals itself through modern imaging techniques.This paper focuses on fibrosis of the genito-urinary system, detailing relevant imaging technologies used for its detection and exploring future directions.
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Affiliation(s)
- Sofia Maria Tarchi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy.
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA.
| | - Mary Salvatore
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Philip Lichtenstein
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Thillai Sekar
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Kathleen Capaccione
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Lyndon Luk
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Hiram Shaish
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Jasnit Makkar
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Elise Desperito
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Jay Leb
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Benjamin Navot
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Jonathan Goldstein
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Sherelle Laifer
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Volkan Beylergil
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Hong Ma
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Sachin Jambawalikar
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Dwight Aberle
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Belinda D'Souza
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Stuart Bentley-Hibbert
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
| | - Monica Pernia Marin
- Department of Radiology, Columbia University Irving Medical Center, 630 W 168th Street, New York, NY, 10032, USA
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Zhang M, Liu Y, Yao J, Wang K, Tu J, Hu Z, Jin Y, Du Y, Sun X, Chen L, Wang Z. Value of machine learning-based transrectal multimodal ultrasound combined with PSA-related indicators in the diagnosis of clinically significant prostate cancer. Front Endocrinol (Lausanne) 2023; 14:1137322. [PMID: 36967794 PMCID: PMC10031096 DOI: 10.3389/fendo.2023.1137322] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 02/20/2023] [Indexed: 03/11/2023] Open
Abstract
Objective To investigate the effect of transrectal multimodal ultrasound combined with serum prostate-specific antigen (PSA)-related indicators and machine learning for the diagnosis of clinically significant prostate cancer. Methods Based on Gleason score of postoperative pathological results, the subjects were divided into clinically significant prostate cancer groups(GS>6)and non-clinically significant prostate cancer groups(GS ≤ 6). The independent risk factors were obtained by univariate logistic analysis. Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) machine learning models were combined with clinically significant prostate cancer risk factors to establish the machine learning model, calculate the model evaluation indicators, construct the receiver operating characteristic curve (ROC), and calculate the area under the curve (AUC). Results Independent risk factor items (P< 0.05) were entered into the machine learning model. A comparison of the evaluation indicators of the model and the area under the ROC curve showed the ANN model to be best at predicting clinically significant prostate cancer, with a sensitivity of 80%, specificity of 88.6%, F1 score of 0.897, and the AUC was 0.855. Conclusion Establishing a machine learning model by rectal multimodal ultrasound and combining it with PSA-related indicators has definite application value in predicting clinically significant prostate cancer.
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Affiliation(s)
- Maoliang Zhang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Yuanzhen Liu
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jincao Yao
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Kai Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Jing Tu
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Zhengbiao Hu
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Yun Jin
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Yue Du
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Xingbo Sun
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Liyu Chen
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Zhengping Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
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S. Merseburger A, Krabbe LM, Joachim Krause B, Böhmer D, Perner S, von Amsberg G. The Treatment of Metastatic, Hormone-Sensitive Prostatic Carcinoma. DEUTSCHES ARZTEBLATT INTERNATIONAL 2022; 119:622-632. [PMID: 35912436 PMCID: PMC9756320 DOI: 10.3238/arztebl.m2022.0294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 04/08/2022] [Accepted: 06/26/2022] [Indexed: 01/05/2023]
Abstract
BACKGROUND For many years, the standard treatment of metastatic, hormone-sensitive prostatic carcinoma (mHSPC) was androgen deprivation therapy (ADT) alone. By lowering the testosterone level into the castration range, ADT deprives the tumor of a key growth factor. METHODS For this article, we evaluated the treatment recommendations contained in national and international guidelines (German S3 guidelines and those of the European Society for Medical Oncology [ESMO], European Association of Urology [EAU], and National Comprehensive Cancer Network [NCCN]), as well as pertinent publications revealed by a PubMed search and the congress abstracts of the ESMO and of the American Society of Clinical Oncology [ASCO]. RESULTS The past few years have witnessed fundamental changes in the treatment of mHSPC. Treatment intensification with docetaxel or with the new drugs directed against the androgen receptor signal pathway (abiraterone, apalutamide and enzalutamide) has been found to lower mortality by 19-40% and is now an integral component of first-line therapy. Relevant new findings have also been obtained with threefold combinations of ADT, docetaxel, and abiraterone or darolutamide. For patients with a light tumor burden, local radiotherapy of the primary tumor improves the probability of survival at 3 years by 8% (45.4 versus 49.1 months, difference 3.6 months; 95% confidence interval, 1.0 to 6.2 months). CONCLUSION The treatment of mHSPC is constantly changing. Phase III trials that are now in the recruitment stage, as well as our continually improving understanding of the underlying molecular-pathological mechanisms, will be altering the treatment landscape still further in the years to come.
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Affiliation(s)
- Axel S. Merseburger
- Department of Urology, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany,*Klinik für Urologie, Universitätsklinikum Schleswig-Holstein Campus Lübeck Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Laura-Maria Krabbe
- Department of Urology and Pediatric Urology, University Hospital Münster, Münster, Germany
| | - Bernd Joachim Krause
- Department of Nuclear Medicine, Rostock University Medical Center, Rostock, Germany
| | - Dirk Böhmer
- Department of Radiation Oncology and Radiation Therapy, Charité Universitätsmedizin – Campus Benjamin Franklin, Berlin, Germany
| | - Sven Perner
- University Hospital of Schleswig-Holstein, Campus Lübeck and Research Center Borstel, Leibniz Lung Center, Borstel, Germany,University Hospital Schleswig-Holstein, Campus Lübeck, Institute of Pathology, Lübeck, Germany
| | - Gunhild von Amsberg
- Department of Uro-Oncology of the Oncology Center and the Martini Clinic, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Contrast-Enhanced Ultrasound-Magnetic Resonance Imaging Radiomics Based Model for Predicting the Biochemical Recurrence of Prostate Cancer: A Feasibility Study. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8090529. [PMID: 35529273 PMCID: PMC9071874 DOI: 10.1155/2022/8090529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/08/2022] [Accepted: 04/15/2022] [Indexed: 11/17/2022]
Abstract
Objective This study was aimed at developing a model for predicting postoperative biochemical recurrence of prostate cancer (PCa) using clinical data-CEUS-MRI radiomics and at verifying its clinical effectiveness. Methods The clinical imaging data of 159 patients pathologically confirmed with PCa and who underwent radical prostatectomy in Xiangyang No. 1 People's Hospital and Jiangsu Hospital of Chinese Medicine from March 2016 to December 2020 were retrospectively analyzed. According to the 2-5-year follow-up results, the patients were divided into the biochemical recurrence (BCR) group (n = 59) and the control group (n = 100). The training set and test set were established in the proportion of 7/3; 4 prediction models were established based on the clinical imaging data. In training set, the area under the curve (AUC) and decision curve analysis (DCA) by R was conducted to compare the efficiency of 4 prediction models, and then, external validation was performed using the test set. Finally, a nomogram tool for predicting BCR was developed. Results Univariate regression analysis confirmed that the SmallAreaHighGrayLevelEmphasis, RunVariance, Contrast, tumor diameter, clinical T stage, lymph node metastasis, distant metastasis, Gleason score, preoperative PSA, treatment method, CEUS-peak intensity (PI), time to peak (TTP), arrival time (AT), and elastography grade were the influencing factors for predicting BCR. In the training set, the AUC of combinatorial model demonstrated the highest efficiency in predicting BCR [AUC: 0.914 (OR 0.0305, 95% CI: 0.854-0.974)] vs. the general clinical data model, the CEUS model, and the MRI radiomics model. The DCA confirmed the largest net benefits of the combinatorial model. The test set validation gave consistent results. The nomogram tool has been well applied clinically. Conclusion The previous clinical and imaging data alone did not perform well for predicting BCR. Our combinatorial model firstly using clinical data-CEUS-MRI radiomics provided an opportunity for clinical screening of BCR and help improve its prognosis.
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Joachim Krause B. In Reply. DEUTSCHES ARZTEBLATT INTERNATIONAL 2022; 119:277. [PMID: 35811352 PMCID: PMC9400197 DOI: 10.3238/arztebl.m2022.0115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
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Bruder W. Qualitative Data Are Lacking. DEUTSCHES ARZTEBLATT INTERNATIONAL 2022; 119:277. [PMID: 35811351 PMCID: PMC9400193 DOI: 10.3238/arztebl.m2022.0114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
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Bonekamp D, Schlemmer HP. [Artificial intelligence (AI) in radiology? : Do we need as many radiologists in the future?]. Urologe A 2022; 61:392-399. [PMID: 35277758 DOI: 10.1007/s00120-022-01768-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/14/2022] [Indexed: 11/27/2022]
Abstract
We are in the middle of a digital revolution in medicine. This raises the question of whether subjects such as radiology, which is superficially concerned with the interpretation of images, will be particularly changed by this revolution. In particular, it should be discussed whether in the future the completion of initially simpler, then more complex image analysis tasks by computer systems may lead to a reduced need for radiologists. What distinguishes radiology in particular is its key position between advanced technology and medical care. This article discusses that not only radiology but every medical discipline will be affected by innovations due to the digital revolution, and that a redefinition of medical specialties focusing on imaging and visual interpretation makes sense and that the arrival of artificial intelligence (AI) in radiology is to be welcomed in the context of ever larger amounts of image data-to at all be able to handle the increasing amount of image data in the future at the current number of radiologists. In this respect, the balance between research and teaching in comparison to patient care is more difficult to maintain in the academic environment. AI can help improve efficiency and balance in the areas mentioned. With regard to specialist training, information technology topics are expected to be integrated into the radiological curriculum. Radiology acts as a pioneer designing the entry of AI into medicine. It is to be expected that by the time radiologists can be substantially replaced by AI, the replacement of human contributions in other medical and non-medical fields will also be well advanced.
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Affiliation(s)
- David Bonekamp
- Abteilung für Radiologie (E010), Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland.
| | - H-P Schlemmer
- Abteilung für Radiologie (E010), Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland
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