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Abreu-Gomez J, Lim C, Haider MA. Contemporary Approach to Prostate Imaging and Data Reporting System Score 3 Lesions. Radiol Clin North Am 2024; 62:37-51. [PMID: 37973244 DOI: 10.1016/j.rcl.2023.06.008] [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] [Indexed: 11/19/2023]
Abstract
The aim of this article is to review the technical and clinical considerations encountered with PI-RADS 3 lesions, which are equivocal for clinically significant Prostate Cancer (csPCa) with detection rates ranging between 10% and 35%. The number of PI-RADS 3 lesions reported vary according to several factors including MRI quality and radiologist training/expertise among the most influential. PI-RADS v.2.1 updated definitions for scores 2 and 3 in the PZ and scores 1 and 2 in the TZ is reviewed. The role of DWI role is highlighted in the assessment of the TZ with the possibility of upgrading score 2 lesions to score 3 based on DWI score. Given the increased utilization for prostate MRI, biparametric MRI can be considered as an alternative for low-risk patients where there is a need to rule out csPCa acknowledging this technique may increase the number of indeterminate cases going for biopsies. Management of patients with equivocal lesions at mpMRI and factors influencing biopsy decision process remain as an unmet need and additional studies using molecular/imaging markers as well as artificial intelligence tools are needed to further address their role in proper patient selection for biopsy.
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Affiliation(s)
- Jorge Abreu-Gomez
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, 610 University Avenue, Suite 3-920, Toronto, ON M5G 2M9, Canada.
| | - Christopher Lim
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, Room AB 279, Toronto, ON M4N 3M5, Canada
| | - Masoom A Haider
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System and the Joint Department of Medical Imaging, Sinai Health System, Princess Margaret Hospital, University of Toronto, 600 University Avenue, Toronto, ON, Canada M5G 1X5
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Kang Z, Margolis DJ, Wang S, Li Q, Song J, Wang L. Management Strategy for Prostate Imaging Reporting and Data System Category 3 Lesions. Curr Urol Rep 2023; 24:561-570. [PMID: 37936016 DOI: 10.1007/s11934-023-01187-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2023] [Indexed: 11/09/2023]
Abstract
PURPOSE OF REVIEW Prostate Imaging Reporting and Data System (PI-RADS) category 3 lesions present a clinical dilemma due to their uncertain nature, which complicates the development of a definitive management strategy. These lesions have an incidence rate of approximately 22-32%, with clinically significant prostate cancer (csPCa) accounting for about 10-30%. Therefore, a thorough evaluation is warranted. RECENT FINDINGS This review highlights the need for radiology peer review, including the confirmation of dynamic contrast-enhanced (DCE) compliance, as the initial step. Additional MRI models such as VERDICT or Tofts need to be verified. Current evidence shows that imaging and clinical indicators can be used for risk stratification of PI-RADS 3 lesions. For low-risk lesions, a safety net monitoring approach involving annual repeat MRI can be employed. In contrast, lesions deemed potentially risky based on prostate-specific antigen density (PSAD), 68 Ga-PSMA PET/CT, MPS, Proclarix, or AI/machine learning models should undergo biopsy. It is recommended to establish a multidisciplinary team that takes into account factors such as age, PSAD, prostate, and lesion size, as well as previous biopsy pathological findings. Combining expert opinions, clinical-imaging indicators, and emerging methods will contribute to the development of management strategies for PI-RADS 3 lesions.
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Affiliation(s)
- Zhen Kang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 36 Yong'an Rd, Xicheng District, Beijing, 100016, China
| | - Daniel J Margolis
- Department of Radiology, Weill Cornell Medicine/New York Presbyterian, New York, NY, USA
| | - Shaogang Wang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiubai Li
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Jian Song
- Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Liang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 36 Yong'an Rd, Xicheng District, Beijing, 100016, China.
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Bonaffini PA, De Bernardi E, Corsi A, Franco PN, Nicoletta D, Muglia R, Perugini G, Roscigno M, Occhipinti M, Da Pozzo LF, Sironi S. Towards the Definition of Radiomic Features and Clinical Indices to Enhance the Diagnosis of Clinically Significant Cancers in PI-RADS 4 and 5 Lesions. Cancers (Basel) 2023; 15:4963. [PMID: 37894330 PMCID: PMC10605400 DOI: 10.3390/cancers15204963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/07/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
Prostate cancer (PC) is the most frequently diagnosed cancer among adult men, and its incidence is increasing worldwide [...].
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Affiliation(s)
- Pietro Andrea Bonaffini
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS, 24127 Bergamo, BG, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo, 1, 20126 Milan, MI, Italy
| | - Elisabetta De Bernardi
- Medicine and Surgery Department, Via Cadore, 48, 20900 Monza, MB, Italy
- Interdepartmental Research Centre Bicocca Bioinformatics Biostatistics and Bioimaging Centre-B4, University of Milano-Bicocca, Via Follereau 3, 20854 Vedano al Lambro, MB, Italy
| | - Andrea Corsi
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS, 24127 Bergamo, BG, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo, 1, 20126 Milan, MI, Italy
| | - Paolo Niccolò Franco
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS, 24127 Bergamo, BG, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo, 1, 20126 Milan, MI, Italy
| | - Dario Nicoletta
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS, 24127 Bergamo, BG, Italy
| | - Riccardo Muglia
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS, 24127 Bergamo, BG, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo, 1, 20126 Milan, MI, Italy
| | - Giovanna Perugini
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS, 24127 Bergamo, BG, Italy
| | - Marco Roscigno
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo, 1, 20126 Milan, MI, Italy
- Department of Urology, ASST Papa Giovanni XXIII, Piazza OMS, 1, 24127 Bergamo, BG, Italy
| | | | - Luigi Filippo Da Pozzo
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo, 1, 20126 Milan, MI, Italy
- Department of Urology, ASST Papa Giovanni XXIII, Piazza OMS, 1, 24127 Bergamo, BG, Italy
| | - Sandro Sironi
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS, 24127 Bergamo, BG, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo, 1, 20126 Milan, MI, Italy
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Nguyen TA, Fourcade A, Zambon A, Saout K, Deruelle C, Joulin V, Tissot V, Doucet L, Rozet F, Fournier G, Valeri A. Optimal PSA density threshold and predictive factors for the detection of clinically significant prostate cancer in patient with a PI-RADS 3 lesion on MRI. Urol Oncol 2023:S1078-1439(23)00165-5. [PMID: 37391283 DOI: 10.1016/j.urolonc.2023.05.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 04/06/2023] [Accepted: 05/04/2023] [Indexed: 07/02/2023]
Abstract
INTRODUCTION While Prostate Imaging Reporting and Data System (PI-RADS) 4 and 5 lesions usually justify prostate biopsy (PBx), the management of a PI-RADS 3 lesion can be discussed. The aim of our study was to determine the optimal prostate-specific antigen density (PSAD) threshold and predictive factors of clinically significant prostate cancer (csPCa) in patients with a PI-RADS 3 lesion on MRI. PATIENTS AND METHODS Using our prospectively maintained database, we conducted a monocentric retrospective study, including all patients with a clinical suspicious of prostate cancer (PCa), all of them had a PI-RADS 3 lesion on the mpMRI prior to PBx. Patients under active surveillance or displaying suspicious digital rectal examination were excluded. Clinically significant (csPCa) was defined as PCa with any ISUP grade group ≥ 2 (Gleason ≥ 3 + 4). RESULTS We included 158 patients. The detection rate of csPCa was 22.2%. In case of PSAD ≤ 0.15 ng/ml/cm3, PBx would be omitted in 71.5% (113/158) of men at the cost of missing 15.0% (17/113) of csPCa. With a threshold of 0.15 ng/ml/cm3, the sensitivity and the specificity were 0.51 and 0.78 respectively. The positive predictive value was 0.40 and the negative predictive value was 0.85. According to multivariate analysis, age (OR = 1.10, CI95% 1.03-1.19, P = 0.007), and PSAD ≥ 0.15 ng/ml/cm3 (OR = 3.59, CI95% 1.41-9.47, P = 0.008) were independent predictive factors of csPCa. Previous negative PBx was negatively associated with csPCa (OR = 0.24, CI 95% 0.07-0.66, P = 0.01). CONCLUSION Our result suggests that the optimal PSAD threshold was 0.15 ng/ml/cm3. However, in this case omitting PBx in 71.5% of cases would be at the cost of missing 15.0% of csPCa. PSAD should not be used alone, other predictive factors as age and PBx history should also be considered in the discussion with the patient, to avoid PBx while missing few csPCa.
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Affiliation(s)
- Truong-An Nguyen
- Urology Department, CHU Brest, Brest, France; Faculté de Médecine et des Sciences de la Santé, Université de Brest, Brest, France; LaTIM, INSERM, UMR 1101, CHU Brest, Brest, France.
| | | | - Audrey Zambon
- Urology Department, CHU Brest, Brest, France; Faculté de Médecine et des Sciences de la Santé, Université de Brest, Brest, France
| | - Kevin Saout
- Urology Department, CHU Brest, Brest, France
| | | | | | | | | | - François Rozet
- Urology Department, Institut Mutualiste Montsouris, Paris, France
| | - Georges Fournier
- Urology Department, CHU Brest, Brest, France; Faculté de Médecine et des Sciences de la Santé, Université de Brest, Brest, France; LaTIM, INSERM, UMR 1101, CHU Brest, Brest, France
| | - Antoine Valeri
- Urology Department, CHU Brest, Brest, France; Faculté de Médecine et des Sciences de la Santé, Université de Brest, Brest, France; LaTIM, INSERM, UMR 1101, CHU Brest, Brest, France
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Zhao LT, Liu ZY, Xie WF, Shao LZ, Lu J, Tian J, Liu JG. What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments? Mil Med Res 2023; 10:29. [PMID: 37357263 DOI: 10.1186/s40779-023-00464-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 06/07/2023] [Indexed: 06/27/2023] Open
Abstract
The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0-20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.
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Affiliation(s)
- Li-Tao Zhao
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Zhen-Yu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Wan-Fang Xie
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Li-Zhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
| | - Jian Lu
- Department of Urology, Peking University Third Hospital, Peking University, 100191, Beijing, China.
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, 100191, Beijing, China.
| | - Jian-Gang Liu
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, 100191, Beijing, China.
- Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment, Beijing, 100029, China.
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Jin P, Shen J, Yang L, Zhang J, Shen A, Bao J, Wang X. Machine learning-based radiomics model to predict benign and malignant PI-RADS v2.1 category 3 lesions: a retrospective multi-center study. BMC Med Imaging 2023; 23:47. [PMID: 36991347 PMCID: PMC10053087 DOI: 10.1186/s12880-023-01002-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
Purpose To develop machine learning-based radiomics models derive from different MRI sequences for distinction between benign and malignant PI-RADS 3 lesions before intervention, and to cross-institution validate the generalization ability of the models. Methods The pre-biopsy MRI datas of 463 patients classified as PI-RADS 3 lesions were collected from 4 medical institutions retrospectively. 2347 radiomics features were extracted from the VOI of T2WI, DWI and ADC images. The ANOVA feature ranking method and support vector machine classifier were used to construct 3 single-sequence models and 1 integrated model combined with the features of three sequences. All the models were established in the training set and independently verified in the internal test and external validation set. The AUC was used to compared the predictive performance of PSAD with each model. Hosmer–lemeshow test was used to evaluate the degree of fitting between prediction probability and pathological results. Non-inferiority test was used to check generalization performance of the integrated model. Results The difference of PSAD between PCa and benign lesions was statistically significant (P = 0.006), with the mean AUC of 0.701 for predicting clinically significant prostate cancer (internal test AUC = 0.709 vs. external validation AUC = 0.692, P = 0.013) and 0.630 for predicting all cancer (internal test AUC = 0.637 vs. external validation AUC = 0.623, P = 0.036). T2WI-model with the mean AUC of 0.717 for predicting csPCa (internal test AUC = 0.738 vs. external validation AUC = 0.695, P = 0.264) and 0.634 for predicting all cancer (internal test AUC = 0.678 vs. external validation AUC = 0.589, P = 0.547). DWI-model with the mean AUC of 0.658 for predicting csPCa (internal test AUC = 0.635 vs. external validation AUC = 0.681, P = 0.086) and 0.655 for predicting all cancer (internal test AUC = 0.712 vs. external validation AUC = 0.598, P = 0.437). ADC-model with the mean AUC of 0.746 for predicting csPCa (internal test AUC = 0.767 vs. external validation AUC = 0.724, P = 0.269) and 0.645 for predicting all cancer (internal test AUC = 0.650 vs. external validation AUC = 0.640, P = 0.848). Integrated model with the mean AUC of 0.803 for predicting csPCa (internal test AUC = 0.804 vs. external validation AUC = 0.801, P = 0.019) and 0.778 for predicting all cancer (internal test AUC = 0.801 vs. external validation AUC = 0.754, P = 0.047). Conclusions The radiomics model based on machine learning has the potential to be a non-invasive tool to distinguish cancerous, noncancerous and csPCa in PI-RADS 3 lesions, and has relatively high generalization ability between different date set.
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Affiliation(s)
- Pengfei Jin
- grid.509676.bDepartment of Radiology, The Cancer Hospital of the University of Chinese Academy of Science (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Science, 1# Banshan East Road, Hangzhou, 310022 Zhejiang China
| | - Junkang Shen
- grid.452666.50000 0004 1762 8363Department of Radiology, The Second Affiliated Hospital of Soochow University, 1055# Sanxiang Road, Suzhou, 215000 China
| | - Liqin Yang
- grid.429222.d0000 0004 1798 0228Department of Radiology, The First Affiliated Hospital of SooChow University, 188#, Shizi Road, Suzhou, 215006 Jiangsu China
| | - Ji Zhang
- grid.479690.50000 0004 1789 6747Department of Radiology, Taizhou People’s Hospital of Jiangsu Province, 10# Yigchun Road, Taizhou, 225300 Jiangsu China
| | - Ao Shen
- grid.9227.e0000000119573309Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88# Keling Road, Suzhou, 215163 Jiangsu China
| | - Jie Bao
- grid.429222.d0000 0004 1798 0228Department of Radiology, The First Affiliated Hospital of SooChow University, 188#, Shizi Road, Suzhou, 215006 Jiangsu China
| | - Ximing Wang
- grid.429222.d0000 0004 1798 0228Department of Radiology, The First Affiliated Hospital of SooChow University, 188#, Shizi Road, Suzhou, 215006 Jiangsu China
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Corsi A, De Bernardi E, Bonaffini PA, Franco PN, Nicoletta D, Simonini R, Ippolito D, Perugini G, Occhipinti M, Da Pozzo LF, Roscigno M, Sironi S. Radiomics in PI-RADS 3 Multiparametric MRI for Prostate Cancer Identification: Literature Models Re-Implementation and Proposal of a Clinical-Radiological Model. J Clin Med 2022; 11:6304. [PMID: 36362530 PMCID: PMC9656103 DOI: 10.3390/jcm11216304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/18/2022] [Accepted: 10/22/2022] [Indexed: 10/29/2023] Open
Abstract
PI-RADS 3 prostate lesions clinical management is still debated, with high variability among different centers. Identifying clinically significant tumors among PI-RADS 3 is crucial. Radiomics applied to multiparametric MR (mpMR) seems promising. Nevertheless, reproducibility assessment by external validation is required. We retrospectively included all patients with at least one PI-RADS 3 lesion (PI-RADS v2.1) detected on a 3T prostate MRI scan at our Institution (June 2016-March 2021). An MRI-targeted biopsy was used as ground truth. We assessed reproducible mpMRI radiomic features found in the literature. Then, we proposed a new model combining PSA density and two radiomic features (texture regularity (T2) and size zone heterogeneity (ADC)). All models were trained/assessed through 100-repetitions 5-fold cross-validation. Eighty patients were included (26 with GS ≥ 7). In total, 9/20 T2 features (Hector's model) and 1 T2 feature (Jin's model) significantly correlated to biopsy on our dataset. PSA density alone predicted clinically significant tumors (sensitivity: 66%; specificity: 71%). Our model obtained a sensitivity of 80% and a specificity of 76%. Standard-compliant works with detailed methodologies achieve comparable radiomic feature sets. Therefore, efforts to facilitate reproducibility are needed, while complex models and imaging protocols seem not, since our model combining PSA density and two radiomic features from routinely performed sequences appeared to differentiate clinically significant cancers.
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Affiliation(s)
- Andrea Corsi
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Elisabetta De Bernardi
- Medicine and Surgery Department, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy
- Interdepartmental Research Centre Bicocca Bioinformatics Biostatistics and Bioimaging Centre-B4, University of Milano-Bicocca, Via Follereau 3, 20854 Vedano al Lambro, Italy
| | - Pietro Andrea Bonaffini
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Paolo Niccolò Franco
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Dario Nicoletta
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Roberto Simonini
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
| | - Davide Ippolito
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
- Department of Radiology, San Gerardo Hospital, Via G. B. Pergolesi 33, 20900 Monza, Italy
| | - Giovanna Perugini
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
| | | | - Luigi Filippo Da Pozzo
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
- Department of Urology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
| | - Marco Roscigno
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
- Department of Urology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
| | - Sandro Sironi
- Department of Radiology, ASST Papa Giovanni XXIII, Piazza OMS 1, 24127 Bergamo, Italy
- School of Medicine, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy
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Magnetic Resonance Imaging-Based Predictive Models for Clinically Significant Prostate Cancer: A Systematic Review. Cancers (Basel) 2022; 14:cancers14194747. [PMID: 36230670 PMCID: PMC9562712 DOI: 10.3390/cancers14194747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 11/18/2022] Open
Abstract
Simple Summary Magnetic resonance imaging (MRI) has allowed the early detection of PCa to evolve towards clinically significant PCa (csPCa), decreasing unnecessary prostate biopsies and overdetection of insignificant tumours. MRI identifies suspicious lesions of csPCa, predicting the semi-quantitative risk through the prostate imaging report and data system (PI-RADS), and enables guided biopsies, increasing the sensitivity of csPCa. Predictive models that individualise the risk of csPCa have also evolved adding PI-RADS score (MRI-PMs), improving the selection of candidates for prostate biopsy beyond the PI-RADS category. During the last five years, many MRI-PMs have been developed. Our objective is to analyse the current developed MRI-PMs and define their clinical usefulness through a systematic review. We have found high heterogeneity between MRI technique, PI-RADS versions, biopsy schemes and approaches, and csPCa definitions. MRI-PMs outperform the selection of candidates for prostate biopsy beyond MRI alone and PMs based on clinical predictors. However, few developed MRI-PMs are externally validated or have available risk calculators (RCs), which constitute the appropriate requirements used in routine clinical practice. Abstract MRI can identify suspicious lesions, providing the semi-quantitative risk of csPCa through the Prostate Imaging-Report and Data System (PI-RADS). Predictive models of clinical variables that individualise the risk of csPCa have been developed by adding PI-RADS score (MRI-PMs). Our objective is to analyse the current developed MRI-PMs and define their clinical usefulness. A systematic review was performed after a literature search performed by two independent investigators in PubMed, Cochrane, and Web of Science databases, with the Medical Subjects Headings (MESH): predictive model, nomogram, risk model, magnetic resonance imaging, PI-RADS, prostate cancer, and prostate biopsy. This review was made following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) criteria and studied eligibility based on the Participants, Intervention, Comparator, and Outcomes (PICO) strategy. Among 723 initial identified registers, 18 studies were finally selected. Warp analysis of selected studies was performed with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Clinical predictors in addition to the PI-RADS score in developed MRI-PMs were age, PCa family history, digital rectal examination, biopsy status (initial vs. repeat), ethnicity, serum PSA, prostate volume measured by MRI, or calculated PSA density. All MRI-PMs improved the prediction of csPCa made by clinical predictors or imaging alone and achieved most areas under the curve between 0.78 and 0.92. Among 18 developed MRI-PMs, 7 had any external validation, and two RCs were available. The updated PI-RADS version 2 was exclusively used in 11 MRI-PMs. The performance of MRI-PMs according to PI-RADS was only analysed in a single study. We conclude that MRI-PMs improve the selection of candidates for prostate biopsy beyond the PI-RADS category. However, few developed MRI-PMs meet the appropriate requirements in routine clinical practice.
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Quantib Prostate Compared to an Expert Radiologist for the Diagnosis of Prostate Cancer on mpMRI: A Single-Center Preliminary Study. Tomography 2022; 8:2010-2019. [PMID: 36006066 PMCID: PMC9415513 DOI: 10.3390/tomography8040168] [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: 05/23/2022] [Revised: 08/07/2022] [Accepted: 08/08/2022] [Indexed: 11/30/2022] Open
Abstract
Background: To evaluate the clinical utility of an Artificial Intelligence (AI) radiology solution, Quantib Prostate, for prostate cancer (PCa) lesions detection on multiparametric Magnetic Resonance Images (mpMRI). Methods: Prostate mpMRI exams of 108 patients were retrospectively studied. The diagnostic performance of an expert radiologist (>8 years of experience) and of an inexperienced radiologist aided by Quantib software were compared. Three groups of patients were assessed: patients with positive mpMRI, positive target biopsy, and/or at least one positive random biopsy (group A, 73 patients); patients with positive mpMRI and a negative biopsy (group B, 14 patients), and patients with negative mpMRI who did not undergo biopsy (group-C, 21 patients). Results: In group A, the AI-assisted radiologist found new lesions with positive biopsy correlation, increasing the diagnostic PCa performance when compared with the expert radiologist, reaching an SE of 92.3% and a PPV of 90.1% (vs. 71.7% and 84.4%). In group A, the expert radiologist found 96 lesions on 73 mpMRI exams (17.7% PIRADS3, 56.3% PIRADS4, and 26% PIRADS5). The AI-assisted radiologist found 121 lesions (0.8% PIRADS3, 53.7% PIRADS4, and 45.5% PIRADS5). At biopsy, 33.9% of the lesions were ISUP1, 31.4% were ISUP2, 22% were ISUP3, 10.2% were ISUP4, and 2.5% were ISUP5. In group B, where biopsies were negative, the AI-assisted radiologist excluded three lesions but confirmed all the others. In group-C, the AI-assisted radiologist found 37 new lesions, most of them PIRADS 3, with 32.4% localized in the peripherical zone and 67.6% in the transition zone. Conclusions: Quantib software is a very sensitive tool to use specifically in high-risk patients (high PIRADS and high Gleason score).
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More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis—A Systematic Review. J Pers Med 2022; 12:jpm12060983. [PMID: 35743766 PMCID: PMC9225075 DOI: 10.3390/jpm12060983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/12/2022] [Accepted: 06/14/2022] [Indexed: 11/30/2022] Open
Abstract
(1) Introduction: Multiparametric magnetic resonance imaging (mpMRI) is the main imagistic tool employed to assess patients suspected of harboring prostate cancer (PCa), setting the indication for targeted prostate biopsy. However, both mpMRI and targeted prostate biopsy are operator dependent. The past decade has been marked by the emerging domain of radiomics and artificial intelligence (AI), with extended application in medical diagnosis and treatment processes. (2) Aim: To present the current state of the art regarding decision support tools based on texture analysis and AI for the prediction of aggressiveness and biopsy assistance. (3) Materials and Methods: We performed literature research using PubMed MeSH, Scopus and WoS (Web of Science) databases and screened the retrieved papers using PRISMA principles. Articles that addressed PCa diagnosis and staging assisted by texture analysis and AI algorithms were included. (4) Results: 359 papers were retrieved using the keywords “prostate cancer”, “MRI”, “radiomics”, “textural analysis”, “artificial intelligence”, “computer assisted diagnosis”, out of which 35 were included in the final review. In total, 24 articles were presenting PCa diagnosis and prediction of aggressiveness, 7 addressed extracapsular extension assessment and 4 tackled computer-assisted targeted prostate biopsies. (5) Conclusions: The fusion of radiomics and AI has the potential of becoming an everyday tool in the process of diagnosis and staging of the prostate malignancies.
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Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review. Diagnostics (Basel) 2022; 12:diagnostics12040799. [PMID: 35453847 PMCID: PMC9027206 DOI: 10.3390/diagnostics12040799] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/19/2022] [Accepted: 03/23/2022] [Indexed: 02/04/2023] Open
Abstract
Prostate cancer detection with magnetic resonance imaging is based on a standardized MRI-protocol according to the PI-RADS guidelines including morphologic imaging, diffusion weighted imaging, and perfusion. To facilitate data acquisition and analysis the contrast-enhanced perfusion is often omitted resulting in a biparametric prostate MRI protocol. The intention of this review is to analyze the current value of biparametric prostate MRI in combination with methods of machine-learning and deep learning in the detection, grading, and characterization of prostate cancer; if available a direct comparison with human radiologist performance was performed. PubMed was systematically queried and 29 appropriate studies were identified and retrieved. The data show that detection of clinically significant prostate cancer and differentiation of prostate cancer from non-cancerous tissue using machine-learning and deep learning is feasible with promising results. Some techniques of machine-learning and deep-learning currently seem to be equally good as human radiologists in terms of classification of single lesion according to the PIRADS score.
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12
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Jin P, Yang L, Qiao X, Hu C, Hu C, Wang X, Bao J. Utility of Clinical-Radiomic Model to Identify Clinically Significant Prostate Cancer in Biparametric MRI PI-RADS V2.1 Category 3 Lesions. Front Oncol 2022; 12:840786. [PMID: 35280813 PMCID: PMC8913337 DOI: 10.3389/fonc.2022.840786] [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: 12/21/2021] [Accepted: 02/01/2022] [Indexed: 11/21/2022] Open
Abstract
Purpose To determine the predictive performance of the integrated model based on clinical factors and radiomic features for the accurate identification of clinically significant prostate cancer (csPCa) among Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions. Materials and Methods A retrospective study of 103 patients with PI-RADS 3 lesions who underwent pre-operative 3.0-T MRI was performed. Patients were randomly divided into the training set and the testing set at a ratio of 7:3. Radiomic features were extracted from axial T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images of each patient. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) feature selection methods were used to identify the radiomic features and construct a radiomic model for csPCa identification. Moreover, multivariable logistic regression analysis was used to integrate the clinical factors with radiomic feature model to further improve the accuracy of csPCa identification, and the two are presented in the form of normogram. The performance of the integrated model was compared with radiomic model and clinical model on testing set. Results A total of four radiomic features were selected and used for radiomic model construction producing a radiomic score (Radscore). Radscore was significantly different between the csPCa and the non-csPCa patients (training set: p < 0.001; testing set: p = 0.035). Multivariable logistic regression analysis showed that age and PSA could be used as independent predictors for csPCa identification. The clinical–radiomic model produced the receiver operating characteristic (ROC) curve (AUC) in the testing set was 0.88 (95%CI, 0.75–1.00), which was similar to clinical model (AUC = 0.85; 95%CI, 0.52–0.90) (p = 0.048) and higher than the radiomic model (AUC = 0.71; 95%CI, 0.68–1.00) (p < 0.001). The decision curve analysis implies that the clinical–radiomic model could be beneficial in identifying csPCa among PI-RADS 3 lesions. Conclusion The clinical–radiomic model could effectively identify csPCa among biparametric PI-RADS 3 lesions and thus could help avoid unnecessary biopsy and improve the life quality of patients.
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Affiliation(s)
- Pengfei Jin
- Department of Radiology, The First Affifiliated Hospital of Soochow University, Suzhou, China.,Institute of Medical Imaging, Soochow University, Suzhou, China
| | - Liqin Yang
- Department of Radiology, The First Affifiliated Hospital of Soochow University, Suzhou, China.,Institute of Medical Imaging, Soochow University, Suzhou, China
| | - Xiaomeng Qiao
- Department of Radiology, The First Affifiliated Hospital of Soochow University, Suzhou, China.,Institute of Medical Imaging, Soochow University, Suzhou, China
| | - Chunhong Hu
- Department of Radiology, The First Affifiliated Hospital of Soochow University, Suzhou, China.,Institute of Medical Imaging, Soochow University, Suzhou, China
| | - Chenhan Hu
- Department of Radiology, The First Affifiliated Hospital of Soochow University, Suzhou, China.,Institute of Medical Imaging, Soochow University, Suzhou, China
| | - Ximing Wang
- Department of Radiology, The First Affifiliated Hospital of Soochow University, Suzhou, China.,Institute of Medical Imaging, Soochow University, Suzhou, China
| | - Jie Bao
- Department of Radiology, The First Affifiliated Hospital of Soochow University, Suzhou, China.,Institute of Medical Imaging, Soochow University, Suzhou, China
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Li T, Sun L, Li Q, Luo X, Luo M, Xie H, Wang P. Development and Validation of a Radiomics Nomogram for Predicting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions. Front Oncol 2022; 11:825429. [PMID: 35155214 PMCID: PMC8825569 DOI: 10.3389/fonc.2021.825429] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 12/30/2021] [Indexed: 12/22/2022] Open
Abstract
Purpose To develop and validate a radiomics nomogram for the prediction of clinically significant prostate cancer (CsPCa) in Prostate Imaging-Reporting and Data System (PI-RADS) category 3 lesions. Methods We retrospectively enrolled 306 patients within PI-RADS 3 lesion from January 2015 to July 2020 in institution 1; the enrolled patients were randomly divided into the training group (n = 199) and test group (n = 107). Radiomics features were extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast-enhanced (DCE) imaging. Synthetic minority oversampling technique (SMOTE) was used to address the class imbalance. The ANOVA and least absolute shrinkage and selection operator (LASSO) regression model were used for feature selection and radiomics signature building. Then, a radiomics score (Rad-score) was acquired. Combined with serum prostate-specific antigen density (PSAD) level, a multivariate logistic regression analysis was used to construct a radiomics nomogram. Receiver operating characteristic (ROC) curve analysis was used to evaluate radiomics signature and nomogram. The radiomics nomogram calibration and clinical usefulness were estimated through calibration curve and decision curve analysis (DCA). External validation was assessed, and the independent validation cohort contained 65 patients within PI-RADS 3 lesion from January 2020 to July 2021 in institution 2. Results A total of 75 (24.5%) and 16 (24.6%) patients had CsPCa in institution 1 and 2, respectively. The radiomics signature with SMOTE augmentation method had a higher area under the ROC curve (AUC) [0.840 (95% CI, 0.776–0.904)] than that without SMOTE method [0.730 (95% CI, 0.624–0.836), p = 0.08] in the test group and significantly increased in the external validation group [0.834 (95% CI, 0.709–0.959) vs. 0.718 (95% CI, 0.562–0.874), p = 0.017]. The radiomics nomogram showed good discrimination and calibration, with an AUC of 0.939 (95% CI, 0.913–0.965), 0.884 (95% CI, 0.831–0.937), and 0.907 (95% CI, 0.814–1) in the training, test, and external validation groups, respectively. The DCA demonstrated the clinical usefulness of radiomics nomogram. Conclusion The radiomics nomogram that incorporates the MRI-based radiomics signature and PSAD can be conveniently used to individually predict CsPCa in patients within PI-RADS 3 lesion.
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Affiliation(s)
- Tianping Li
- Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China.,School of Medical Imaging, Binzhou Medical University, Yantai, China
| | - Linna Sun
- School of Medical Imaging, Binzhou Medical University, Yantai, China
| | - Qinghe Li
- School of Medical Imaging, Binzhou Medical University, Yantai, China
| | - Xunrong Luo
- School of Medical Imaging, Binzhou Medical University, Yantai, China
| | - Mingfang Luo
- School of Medical Imaging, Binzhou Medical University, Yantai, China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Peiyuan Wang
- Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
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Ferro M, de Cobelli O, Musi G, del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022; 14:17562872221109020. [PMID: 35814914 PMCID: PMC9260602 DOI: 10.1177/17562872221109020] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy, via Ripamonti 435 Milano, Italy
| | - Ottavio de Cobelli
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Gennaro Musi
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco del Giudice
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Carrieri
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | | | - Alessandro Sciarra
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Martina Maggi
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Biagio Barone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Vincenzo Francesco Caputo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, Chieti, Italy; Urology Unit, ‘SS. Annunziata’ Hospital, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti, Italy
| | - Giuseppe Lucarelli
- Department of Emergency and Organ Transplantation, Urology, Andrology and Kidney Transplantation Unit, University of Bari, Bari, Italy
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Stefano Luzzago
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Luigi Cormio
- Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
- Urology Unit, Bonomo Teaching Hospital, Foggia, Italy
| | | | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral Studies, I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
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Lim CS, Abreu-Gomez J, Thornhill R, James N, Al Kindi A, Lim AS, Schieda N. Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis. Abdom Radiol (NY) 2021; 46:5647-5658. [PMID: 34467426 DOI: 10.1007/s00261-021-03235-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 07/31/2021] [Accepted: 07/31/2021] [Indexed: 12/22/2022]
Abstract
PURPOSE To evaluate if machine learning (ML) of radiomic features extracted from apparent diffusion coefficient (ADC) and T2-weighted (T2W) MRI can predict prostate cancer (PCa) diagnosis in Prostate Imaging-Reporting and Data System (PI-RADS) version 2.1 category 3 lesions. METHODS This multi-institutional review board-approved retrospective case-control study evaluated 158 men with 160 PI-RADS category 3 lesions (79 peripheral zone, 81 transition zone) diagnosed at 3-Tesla MRI with histopathology diagnosis by MRI-TRUS-guided targeted biopsy. A blinded radiologist confirmed PI-RADS v2.1 score and segmented lesions on axial T2W and ADC images using 3D Slicer, extracting radiomic features with an open-source software (Pyradiomics). Diagnostic accuracy for (1) any PCa and (2) clinically significant (CS; International Society of Urogenital Pathology Grade Group ≥ 2) PCa was assessed using XGBoost with tenfold cross -validation. RESULTS From 160 PI-RADS 3 lesions, there were 50.0% (80/160) PCa, including 36.3% (29/80) CS-PCa (63.8% [51/80] ISUP 1, 23.8% [19/80] ISUP 2, 8.8% [7/80] ISUP 3, 3.8% [3/80] ISUP 4). The remaining 50.0% (80/160) lesions were benign. ML of all radiomic features from T2W and ADC achieved area under receiver operating characteristic curve (AUC) for diagnosis of (1) CS-PCa 0.547 (95% Confidence Intervals 0.510-0.584) for T2W and 0.684 (CI 0.652-0.715) for ADC and (2) any PCa 0.608 (CI 0.579-0.636) for T2W and 0.642 (CI 0.614-0.0.670) for ADC. CONCLUSION Our results indicate ML of radiomic features extracted from T2W and ADC achieved at best moderate accuracy for determining which PI-RADS category 3 lesions represent PCa.
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Affiliation(s)
- Christopher S Lim
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, Rm AB 279, Toronto, ON, M4N 3M5, Canada.
| | - Jorge Abreu-Gomez
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
- Department of Medical Imaging, Joint Department of Medical Imaging, University of Toronto, 585 University Avenue PMB-298, Toronto, ON, M5G2N2, Canada
| | - Rebecca Thornhill
- Department of Medical Imaging, The Ottawa Hospital, The University of Ottawa, 1053 Carling Ave, Civic Campus C1, Ottawa, ON, K1Y 4E9, Canada
| | - Nick James
- Software Solutions, The Ottawa Hospital, Ottawa, Canada
| | - Ahmed Al Kindi
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, Rm AB 279, Toronto, ON, M4N 3M5, Canada
| | - Andrew S Lim
- Department of Radiation Oncology, Seattle Cancer Care Alliance, University of Washington, 825 Eastlake Ave. E, Seattle Washington, 98109-1023, USA
| | - Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, The University of Ottawa, 1053 Carling Ave, Civic Campus C1, Ottawa, ON, K1Y 4E9, Canada
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Ghezzo S, Bezzi C, Presotto L, Mapelli P, Bettinardi V, Savi A, Neri I, Preza E, Samanes Gajate AM, De Cobelli F, Scifo P, Picchio M. State of the art of radiomic analysis in the clinical management of prostate cancer: A systematic review. Crit Rev Oncol Hematol 2021; 169:103544. [PMID: 34801699 DOI: 10.1016/j.critrevonc.2021.103544] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/18/2021] [Accepted: 10/18/2021] [Indexed: 02/04/2023] Open
Abstract
We present the current clinical applications of radiomics in the context of prostate cancer (PCa) management. Several online databases for original articles using a combination of the following keywords: "(radiomic or radiomics) AND (prostate cancer or prostate tumour or prostate tumor or prostate neoplasia)" have been searched. The selected papers have been pooled as focus on (i) PCa detection, (ii) assessing the clinical significance of PCa, (iii) biochemical recurrence prediction, (iv) radiation-therapy outcome prediction and treatment efficacy monitoring, (v) metastases detection, (vi) metastases prediction, (vii) prediction of extra-prostatic extension. Seventy-six studies were included for qualitative analyses. Classifiers powered with radiomic features were able to discriminate between healthy tissue and PCa and between low- and high-risk PCa. However, before radiomics can be proposed for clinical use its methods have to be standardized, and these first encouraging results need to be robustly replicated in large and independent cohorts.
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Affiliation(s)
| | | | - Luca Presotto
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Mapelli
- Vita-Salute San Raffaele University, Milan, Italy; Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Valentino Bettinardi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Annarita Savi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Ilaria Neri
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Erik Preza
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Francesco De Cobelli
- Vita-Salute San Raffaele University, Milan, Italy; Radiology Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Scifo
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria Picchio
- Vita-Salute San Raffaele University, Milan, Italy; Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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Mendes B, Domingues I, Silva A, Santos J. Prostate Cancer Aggressiveness Prediction Using CT Images. Life (Basel) 2021; 11:life11111164. [PMID: 34833040 PMCID: PMC8618689 DOI: 10.3390/life11111164] [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: 09/30/2021] [Revised: 10/25/2021] [Accepted: 10/26/2021] [Indexed: 11/16/2022] Open
Abstract
Prostate Cancer (PCa) is mostly asymptomatic at an early stage and often painless requiring active surveillance screening. Transrectal Ultrasound Guided Biopsy (TRUS) is the principal method to diagnose PCa following a histological examination by observing cell pattern irregularities and assigning the Gleason Score (GS) according to the recommended guidelines. This procedure presents sampling errors and, being invasive may cause complications to the patients. External Beam Radiotherapy Treatment (EBRT) is presented as curative option for localised and locally advanced disease, as a palliative option for metastatic low-volume disease or after prostatectomy for prostate bed and pelvic nodes salvage. In the EBRT worflow a Computed Tomography (CT) scan is performed as the basis for dose calculations and volume delineations. In this work, we evaluated the use of data-characterization algorithms (radiomics) from CT images for PCa aggressiveness assessment. The fundamental motivation relies on the wide availability of CT images and the need to provide tools to assess EBRT effectiveness. We used Pyradiomics and Local Image Features Extraction (LIFEx) to extract features and search for a radiomic signature within CT images. Finnaly, when applying Principal Component Analysis (PCA) to the features, we were able to show promising results.
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Affiliation(s)
- Bruno Mendes
- Centro de Investigação do Instituto Português de Oncologia do Porto (CI-IPOP), Grupo de Física Médica, Radiobiologia e Protecção Radiológica, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Faculdade de Engenharia da Universidade do Porto (FEUP), 4200-465 Porto, Portugal
- Correspondence:
| | - Inês Domingues
- Centro de Investigação do Instituto Português de Oncologia do Porto (CI-IPOP), Grupo de Física Médica, Radiobiologia e Protecção Radiológica, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Instituto Superior de Engenharia de Coimbra (ISEC), 3030-199 Coimbra, Portugal
| | - Augusto Silva
- IEETA, Universidade de Aveiro (UA), 3810-193 Aveiro, Portugal;
| | - João Santos
- Centro de Investigação do Instituto Português de Oncologia do Porto (CI-IPOP), Grupo de Física Médica, Radiobiologia e Protecção Radiológica, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Instituto de Ciências Biomédicas Abel Salazar (ICBAS), 4050-313 Porto, Portugal
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18
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Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature. Diagnostics (Basel) 2021; 11:diagnostics11101829. [PMID: 34679527 PMCID: PMC8534893 DOI: 10.3390/diagnostics11101829] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 09/26/2021] [Accepted: 09/27/2021] [Indexed: 12/22/2022] Open
Abstract
Prostate cancer (PCa) represents the fourth most common cancer and the fifth leading cause of cancer death of men worldwide. Multiparametric MRI (mp-MRI) has high sensitivity and specificity in the detection of PCa, and it is currently the most widely used imaging technique for tumor localization and cancer staging. mp-MRI plays a key role in risk stratification of naïve patients, in active surveillance for low-risk patients, and in monitoring recurrence after definitive therapy. Radiomics is an emerging and promising tool which allows a quantitative tumor evaluation from radiological images via conversion of digital images into mineable high-dimensional data. The purpose of radiomics is to increase the features available to detect PCa, to avoid unnecessary biopsies, to define tumor aggressiveness, and to monitor post-treatment recurrence of PCa. The integration of radiomics data, including different imaging modalities (such as PET-CT) and other clinical and histopathological data, could improve the prediction of tumor aggressiveness as well as guide clinical decisions and patient management. The purpose of this review is to describe the current research applications of radiomics in PCa on MR images.
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Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization. Int J Mol Sci 2021; 22:ijms22189971. [PMID: 34576134 PMCID: PMC8465891 DOI: 10.3390/ijms22189971] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/06/2021] [Accepted: 09/10/2021] [Indexed: 12/24/2022] Open
Abstract
Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.
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Spohn SK, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH, Fechter T, Hölscher T, Grosu R, Chiti A, Grosu AL, Zamboglou C. Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics 2021; 11:8027-8042. [PMID: 34335978 PMCID: PMC8315055 DOI: 10.7150/thno.61207] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/17/2021] [Indexed: 12/14/2022] Open
Abstract
Prostate cancer (PCa) is one of the most frequently diagnosed malignancies of men in the world. Due to a variety of treatment options in different risk groups, proper diagnostic and risk stratification is pivotal in treatment of PCa. The development of precise medical imaging procedures simultaneously to improvements in big data analysis has led to the establishment of radiomics - a computer-based method of extracting and analyzing image features quantitatively. This approach bears the potential to assess and improve PCa detection, tissue characterization and clinical outcome prediction. This article gives an overview on the current aspects of methodology and systematically reviews available literature on radiomics in PCa patients, showing its potential for personalized therapy approaches. The qualitative synthesis includes all imaging modalities and focuses on validated studies, putting forward future directions.
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Affiliation(s)
- Simon K.B. Spohn
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
| | - Alisa S. Bettermann
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Fabian Bamberg
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Matthias Benndorf
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Michael Mix
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Nils H. Nicolay
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Tobias Fechter
- Department of Radiation Oncology - Division of Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Tobias Hölscher
- Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Radu Grosu
- Institute of Computer Engineering, Vienne University of Technology, Vienna, Austria
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele - Milan, Italy
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano - Milan, Italy
| | - Anca L. Grosu
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
- German Oncology Center, European University of Cyprus, Limassol, Cyprus
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MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance. Sci Rep 2021; 11:12917. [PMID: 34155265 PMCID: PMC8217549 DOI: 10.1038/s41598-021-92341-6] [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: 11/25/2020] [Accepted: 06/03/2021] [Indexed: 02/05/2023] Open
Abstract
Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the first five years, highlighting the unmet clinical need for robust baseline risk-stratification tools that enable timely and accurate prediction of tumour progression. In this proof-of-concept study, we sought to investigate the added value of MRI-derived radiomic features to standard-of-care clinical parameters for improving baseline prediction of PCa progression in AS patients. Tumour T2-weighted imaging (T2WI) and apparent diffusion coefficient radiomic features were extracted, with rigorous calibration and pre-processing methods applied to select the most robust features for predictive modelling. Following leave-one-out cross-validation, the addition of T2WI-derived radiomic features to clinical variables alone improved the area under the ROC curve for predicting progression from 0.61 (95% confidence interval [CI] 0.481-0.743) to 0.75 (95% CI 0.64-0.86). These exploratory findings demonstrate the potential benefit of MRI-derived radiomics to add incremental benefit to clinical data only models in the baseline prediction of PCa progression on AS, paving the way for future multicentre studies validating the proposed model and evaluating its impact on clinical outcomes.
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Cutaia G, La Tona G, Comelli A, Vernuccio F, Agnello F, Gagliardo C, Salvaggio L, Quartuccio N, Sturiale L, Stefano A, Calamia M, Arnone G, Midiri M, Salvaggio G. Radiomics and Prostate MRI: Current Role and Future Applications. J Imaging 2021; 7:jimaging7020034. [PMID: 34460633 PMCID: PMC8321264 DOI: 10.3390/jimaging7020034] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 02/07/2021] [Accepted: 02/08/2021] [Indexed: 02/07/2023] Open
Abstract
Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.
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Affiliation(s)
- Giuseppe Cutaia
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Giuseppe La Tona
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Albert Comelli
- Ri.Med Foundation, Via Bandiera 11, 90133 Palermo, Italy;
| | - Federica Vernuccio
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Francesco Agnello
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Cesare Gagliardo
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Leonardo Salvaggio
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
- Correspondence:
| | - Natale Quartuccio
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90133 Palermo, Italy; (N.Q.); (L.S.); (G.A.)
| | - Letterio Sturiale
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90133 Palermo, Italy; (N.Q.); (L.S.); (G.A.)
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy;
| | - Mauro Calamia
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Gaspare Arnone
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90133 Palermo, Italy; (N.Q.); (L.S.); (G.A.)
| | - Massimo Midiri
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Giuseppe Salvaggio
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
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