Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy Article Swipe
Rajiv Jayadevan
,
Steve Zhou
,
Alan Priester
,
Merdie Delfin
,
Leonard S. Marks
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3791/59231-v
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3791/59231-v
Here, we present a protocol to perform targeted prostate biopsy using a magnetic resonance imaging-ultrasound (MRI/US) fusion system. Prostate cancer has traditionally been diagnosed via transrectal ultrasound (TRUS) biopsy. Though considered the gold standard, TRUS is unable to visualize most prostate cancer lesions and therefore requires sampling of the entire prostate. This biopsy method often undergrades prostate cancer and fails to detect up to 35% of cancers on initial biopsy. Prostate MRI has been shown to have excellent sensitivity in the detection of cancerous lesions, and advancements in MRI technology during the last decade have led to the development of targeted biopsy. In targeted biopsy, a software platform overlays MRI data onto live TRUS images to create a fused MRI/US three-dimensional model of the prostate. Regions suspicious for malignancy on MRI are contoured by a radiologist, uploaded into the fusion system, and then displayed within the live MRI/US fused model. The urologist is then able to directly biopsy these targets. When compared to conventional TRUS biopsy, MRI/US fusion technology has been demonstrated to improve the detection of clinically significant cancer while reducing insignificant cancer detection. This technology, therefore, has the potential to diagnose prostate cancer primarily in men who would benefit from treatment.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3791/59231-v
- https://www.jove.com/pdf/59231/use-of-mri-ultrasound-fusion-to-achieve-targeted-prostate-biopsy
- OA Status
- bronze
- Cited By
- 2
- References
- 20
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4300191650Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3791/59231-vDigital Object Identifier
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Use of MRI-ultrasound Fusion to Achieve Targeted Prostate BiopsyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-04-09Full publication date if available
- Authors
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Rajiv Jayadevan, Steve Zhou, Alan Priester, Merdie Delfin, Leonard S. MarksList of authors in order
- Landing page
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https://doi.org/10.3791/59231-vPublisher landing page
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https://www.jove.com/pdf/59231/use-of-mri-ultrasound-fusion-to-achieve-targeted-prostate-biopsyDirect link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
- OA URL
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https://www.jove.com/pdf/59231/use-of-mri-ultrasound-fusion-to-achieve-targeted-prostate-biopsyDirect OA link when available
- Concepts
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Prostate cancer, Medicine, Biopsy, Prostate biopsy, Prostate, Magnetic resonance imaging, Malignancy, Radiology, Ultrasound, Cancer, Pathology, Internal medicineTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
- Citations by year (recent)
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2021: 2Per-year citation counts (last 5 years)
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20Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.often | 54 |
| abstract_inverted_index.shown | 74 |
| abstract_inverted_index.these | 158 |
| abstract_inverted_index.using | 10 |
| abstract_inverted_index.while | 180 |
| abstract_inverted_index.would | 199 |
| abstract_inverted_index.(TRUS) | 27 |
| abstract_inverted_index.MRI/US | 119, 147, 166 |
| abstract_inverted_index.Though | 29 |
| abstract_inverted_index.biopsy | 9, 52, 157 |
| abstract_inverted_index.cancer | 19, 41, 57, 179, 183, 194 |
| abstract_inverted_index.create | 116 |
| abstract_inverted_index.decade | 93 |
| abstract_inverted_index.detect | 61 |
| abstract_inverted_index.during | 90 |
| abstract_inverted_index.entire | 49 |
| abstract_inverted_index.fusion | 16, 139, 167 |
| abstract_inverted_index.images | 114 |
| abstract_inverted_index.method | 53 |
| abstract_inverted_index.model. | 149 |
| abstract_inverted_index.unable | 36 |
| abstract_inverted_index.within | 144 |
| abstract_inverted_index.Regions | 125 |
| abstract_inverted_index.benefit | 200 |
| abstract_inverted_index.biopsy, | 104, 165 |
| abstract_inverted_index.biopsy. | 28, 69, 101 |
| abstract_inverted_index.cancers | 66 |
| abstract_inverted_index.improve | 173 |
| abstract_inverted_index.initial | 68 |
| abstract_inverted_index.lesions | 42 |
| abstract_inverted_index.perform | 6 |
| abstract_inverted_index.present | 2 |
| abstract_inverted_index.system, | 140 |
| abstract_inverted_index.system. | 17 |
| abstract_inverted_index.(MRI/US) | 15 |
| abstract_inverted_index.Prostate | 18, 70 |
| abstract_inverted_index.compared | 161 |
| abstract_inverted_index.diagnose | 192 |
| abstract_inverted_index.directly | 156 |
| abstract_inverted_index.lesions, | 84 |
| abstract_inverted_index.magnetic | 12 |
| abstract_inverted_index.overlays | 108 |
| abstract_inverted_index.platform | 107 |
| abstract_inverted_index.prostate | 8, 40, 56, 193 |
| abstract_inverted_index.protocol | 4 |
| abstract_inverted_index.reducing | 181 |
| abstract_inverted_index.requires | 45 |
| abstract_inverted_index.sampling | 46 |
| abstract_inverted_index.software | 106 |
| abstract_inverted_index.targeted | 7, 100, 103 |
| abstract_inverted_index.targets. | 159 |
| abstract_inverted_index.uploaded | 136 |
| abstract_inverted_index.cancerous | 83 |
| abstract_inverted_index.contoured | 132 |
| abstract_inverted_index.detection | 81, 175 |
| abstract_inverted_index.diagnosed | 23 |
| abstract_inverted_index.displayed | 143 |
| abstract_inverted_index.excellent | 77 |
| abstract_inverted_index.potential | 190 |
| abstract_inverted_index.primarily | 195 |
| abstract_inverted_index.prostate. | 50, 124 |
| abstract_inverted_index.resonance | 13 |
| abstract_inverted_index.standard, | 33 |
| abstract_inverted_index.therefore | 44 |
| abstract_inverted_index.urologist | 151 |
| abstract_inverted_index.visualize | 38 |
| abstract_inverted_index.clinically | 177 |
| abstract_inverted_index.considered | 30 |
| abstract_inverted_index.detection. | 184 |
| abstract_inverted_index.malignancy | 128 |
| abstract_inverted_index.suspicious | 126 |
| abstract_inverted_index.technology | 89, 168 |
| abstract_inverted_index.therefore, | 187 |
| abstract_inverted_index.treatment. | 202 |
| abstract_inverted_index.ultrasound | 26 |
| abstract_inverted_index.development | 98 |
| abstract_inverted_index.sensitivity | 78 |
| abstract_inverted_index.significant | 178 |
| abstract_inverted_index.technology, | 186 |
| abstract_inverted_index.transrectal | 25 |
| abstract_inverted_index.undergrades | 55 |
| abstract_inverted_index.advancements | 86 |
| abstract_inverted_index.conventional | 163 |
| abstract_inverted_index.demonstrated | 171 |
| abstract_inverted_index.radiologist, | 135 |
| abstract_inverted_index.insignificant | 182 |
| abstract_inverted_index.traditionally | 21 |
| abstract_inverted_index.three-dimensional | 120 |
| abstract_inverted_index.imaging-ultrasound | 14 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 93 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.68233454 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |