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Fennessy FM, Maier SE. Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment. Eur J Radiol 2023; 167:111066. [PMID: 37651828 PMCID: PMC10623580 DOI: 10.1016/j.ejrad.2023.111066] [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] [Received: 07/05/2023] [Revised: 08/19/2023] [Accepted: 08/24/2023] [Indexed: 09/02/2023]
Abstract
Diffusion-weighted imaging is a dependable method for detection of clinically significant prostate cancer. In prostate tissue, there are several compartments that can be distinguished from each other, based on different water diffusion decay signals observed. Alterations in cell architecture, such as a relative increase in tumor infiltration and decrease in stroma, will influence the observed diffusion signal in a voxel due to impeded random motion of water molecules. The amount of restricted diffusion can be assessed quantitatively by measuring the apparent diffusion coefficient (ADC) value. This is traditionally calculated using a monoexponential decay formula represented by the slope of a line produced between the logarithm of signal intensity decay plotted against selected b-values. However, the choice and number of b-values and their distribution, has a significant effect on the measured ADC values. There have been many models that attempt to use higher-order functions to better describe the observed diffusion signal decay, requiring an increased number and range of b-values. While ADC can probe heterogeneity on a macroscopic level, there is a need to optimize advanced diffusion techniques to better interrogate prostate tissue microstructure. This could be of benefit in clinical challenges such as identifying sparse tumors in normal prostate tissue or better defining tumor margins. This paper reviews the principles of diffusion MRI and novel higher order diffusion signal analysis techniques to improve the detection of prostate cancer.
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Affiliation(s)
- Fiona M Fennessy
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
| | - Stephan E Maier
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Chiacchio G, Castellani D, Nedbal C, De Stefano V, Brocca C, Tramanzoli P, Galosi AB, Donalisio da Silva R, Teoh JYC, Tiong HY, Naik N, Somani BK, Merseburger AS, Gauhar V. Radiomics vs radiologist in prostate cancer. Results from a systematic review. World J Urol 2023; 41:709-724. [PMID: 36867239 DOI: 10.1007/s00345-023-04305-2] [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: 08/26/2022] [Accepted: 01/20/2023] [Indexed: 03/04/2023] Open
Abstract
PURPOSE Radiomics in uro-oncology is a rapidly evolving science proving to be a novel approach for optimizing the analysis of massive data from medical images to provide auxiliary guidance in clinical issues. This scoping review aimed to identify key aspects wherein radiomics can potentially improve the accuracy of diagnosis, staging, and extraprostatic extension in prostate cancer (PCa). METHODS The literature search was performed on June 2022 using PubMed, Embase, and Cochrane Central Controlled Register of Trials. Studies were included if radiomics were compared with radiological reports only. RESULTS Seventeen papers were included. The combination of PIRADS and radiomics score models improves the PIRADS score reporting of 2 and 3 lesions even in the peripheral zone. Multiparametric MRI-based radiomics models suggest that by simply omitting diffusion contrast enhancement imaging in radiomics models can simplify the process of analysis of clinically significant PCa by PIRADS. Radiomics features correlated with the Gleason grade with excellent discriminative ability. Radiomics has higher accuracy in predicting not only the presence but also the side of extraprostatic extension. CONCLUSIONS Radiomics research on PCa mainly uses MRI as an imaging modality and is focused on diagnosis and risk stratification and has the best future possibility of improving PIRADS reporting. Radiomics has established its superiority over radiologist-reported outcomes but the variability has to be taken into consideration before translating it to clinical practice.
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Affiliation(s)
- Giuseppe Chiacchio
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Daniele Castellani
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy.
| | - Carlotta Nedbal
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Virgilio De Stefano
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Carlo Brocca
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Pietro Tramanzoli
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Andrea Benedetto Galosi
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | | | - Jeremy Yuen-Chun Teoh
- Department of Surgery, S.H.Ho Urology Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Ho Yee Tiong
- Department of Urology, National University Hospital, Singapore, Singapore
| | - Nithesh Naik
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Bhaskar K Somani
- Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, UK
| | - Axel S Merseburger
- Clinic of Urology, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Vineet Gauhar
- Department of Urology, Ng Teng Fong General Hospital, Singapore, Singapore
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Banerjee V, Wang S, Drescher M, Russell R, Siddiqui MM. Radiogenomics influence on the future of prostate cancer risk stratification. Ther Adv Urol 2022; 14:17562872221125317. [PMID: 36160762 PMCID: PMC9490455 DOI: 10.1177/17562872221125317] [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: 03/28/2022] [Accepted: 08/15/2022] [Indexed: 11/25/2022] Open
Abstract
In an era of powerful computing tools, radiogenomics provides a personalized, precise approach to the detection and diagnosis in patients with prostate cancer (PCa). Radiomics data are obtained through artificial intelligence (AI) and neural networks that analyze imaging, usually MRI, to assess statistical, geometrical, and textural features of images to provide quantitative data of shape, heterogeneity, and intensity of tumors. Genomics involves assessing the genomic markers that are present from tumor biopsies. In this article, we separately investigate the current landscape of radiomics and genomics within the realm of PCa and discuss the integration and validity of both into radiogenomics using the data from three papers on the topic. We also conducted a clinical trials search using the NIH’s database, where we found two relevant actively recruiting studies. Although there is more research needed to be done on radiogenomics to fully adopt it as a viable diagnosis tool, its potential by providing personalized data regarding each tumor cannot be overlooked as it may be the future of PCa risk-stratification techniques.
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Affiliation(s)
- Vinayak Banerjee
- Division of Urology, Department of Surgery, University of Maryland Medical Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Shu Wang
- Division of Urology, Department of Surgery, University of Maryland Medical Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Max Drescher
- Division of Urology, Department of Surgery, University of Maryland Medical Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ryan Russell
- Division of Urology, Department of Surgery, University of Maryland Medical Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - M Minhaj Siddiqui
- Division of Urology, Department of Surgery, University of Maryland Medical Center, University of Maryland School of Medicine, 29 S. Greene Street, Suite 500, Baltimore, MD 21201, USA
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Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms. JOURNAL OF ONCOLOGY 2021; 2021:8615450. [PMID: 34671399 PMCID: PMC8523238 DOI: 10.1155/2021/8615450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/13/2021] [Accepted: 09/20/2021] [Indexed: 12/24/2022]
Abstract
Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.
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A review on the role of tissue-based molecular biomarkers for active surveillance. World J Urol 2021; 40:27-34. [PMID: 33590277 DOI: 10.1007/s00345-021-03610-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 01/25/2021] [Indexed: 12/26/2022] Open
Abstract
PURPOSE Over the last decade, we have seen the emergence of tissue-based genomic prognostic markers that can be used for decision-making regarding the need for treatment. This review provides an up-to-date summary of the relevant literature surrounding these markers with a discussion of the relevant strength and limitations. METHODS We performed a literature search of tissue-based genomic prognostic markers and selected those that were currently available for clinical use. We selected the following markers for further review: Decipher (Decipher Bioscience), Polaris (Myriad), Genome Prostate Score (Oncotype Dx), and Promark. We selected the initial validation study for each marker along with other validation studies in independent cohorts. Furthermore, we selected available clinical utility studies or studies combining multi parametric MRI. RESULTS In this article, we provide an in-depth review of four commercially available biomarkers and discuss the current literature surrounding these markers, including the benefits and limitations of their use. We found that each of these markers has evidence supporting their role as an independent predictor of relevant prostate cancer endpoints, which can be helpful for clinical decision-making. However, issues related to heterogeneity and a lack of prospective randomized studies supporting their utility are limitations. Evidence appears to suggest that MRI and genomic risk assessment maybe complementary. CONCLUSION Although these markers can help in improved risk stratification of patients eligible for AS, more prospective studies with head to head comparison between markers are needed to elucidate the true potential of these markers in AS.
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