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MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach
SIMPLE SUMMARY: The incidence of solid renal masses has been steadily increasing over the past couple of decades. Most renal masses are now incidental findings on ultrasound or cross-sectional imaging studies performed for unrelated complaints. Our aim for this review is to provide a comprehensive d...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10216374/ https://www.ncbi.nlm.nih.gov/pubmed/37345136 http://dx.doi.org/10.3390/cancers15102799 |
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author | Chartier, Stephane Arif-Tiwari, Hina |
author_facet | Chartier, Stephane Arif-Tiwari, Hina |
author_sort | Chartier, Stephane |
collection | PubMed |
description | SIMPLE SUMMARY: The incidence of solid renal masses has been steadily increasing over the past couple of decades. Most renal masses are now incidental findings on ultrasound or cross-sectional imaging studies performed for unrelated complaints. Our aim for this review is to provide a comprehensive discussion of the typical magnetic resonance (MR) findings for solid renal masses and create an algorithmic approach that could guide future imagers. With its low risk of harm to patients and ability to characterize multiple tissue parameters, MR imaging has supplanted computed tomography (CT) imaging as the most accurate way to “virtually biopsy” a small renal tumor. Modern-day MR protocols can provide clinicians a robust toolset for differentiating tumor subtypes, prediction of tumor histology, and pre-operative planning. ABSTRACT: Between 1983 and 2002, the incidence of solid renal tumors increased from 7.1 to 10.8 cases per 100,000. This is in large part due to the increase in the volume of ultrasound and cross-sectional imaging, although a majority of solid renal tumors are still found incidentally. Ultrasound and computed tomography (CT) have been the mainstay of renal mass screening and diagnosis but recent advances in magnetic resonance (MR) technology have made this the optimal choice when diagnosing and staging renal tumors. Our purpose in writing this review is to survey the modern MR imaging approach to benign and malignant solid renal tumors, consolidate the various imaging findings into an easy-to-read reference, and provide an imaging-based, algorithmic approach to renal mass characterization for clinicians. MR is at the forefront of renal mass characterization, surpassing ultrasound and CT in its ability to describe multiple tissue parameters and predict tumor biology. Cutting-edge MR protocols and the integration of diagnostic algorithms can improve patient outcomes, allowing the imager to narrow the differential and better guide oncologic and surgical management. |
format | Online Article Text |
id | pubmed-10216374 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102163742023-05-27 MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach Chartier, Stephane Arif-Tiwari, Hina Cancers (Basel) Review SIMPLE SUMMARY: The incidence of solid renal masses has been steadily increasing over the past couple of decades. Most renal masses are now incidental findings on ultrasound or cross-sectional imaging studies performed for unrelated complaints. Our aim for this review is to provide a comprehensive discussion of the typical magnetic resonance (MR) findings for solid renal masses and create an algorithmic approach that could guide future imagers. With its low risk of harm to patients and ability to characterize multiple tissue parameters, MR imaging has supplanted computed tomography (CT) imaging as the most accurate way to “virtually biopsy” a small renal tumor. Modern-day MR protocols can provide clinicians a robust toolset for differentiating tumor subtypes, prediction of tumor histology, and pre-operative planning. ABSTRACT: Between 1983 and 2002, the incidence of solid renal tumors increased from 7.1 to 10.8 cases per 100,000. This is in large part due to the increase in the volume of ultrasound and cross-sectional imaging, although a majority of solid renal tumors are still found incidentally. Ultrasound and computed tomography (CT) have been the mainstay of renal mass screening and diagnosis but recent advances in magnetic resonance (MR) technology have made this the optimal choice when diagnosing and staging renal tumors. Our purpose in writing this review is to survey the modern MR imaging approach to benign and malignant solid renal tumors, consolidate the various imaging findings into an easy-to-read reference, and provide an imaging-based, algorithmic approach to renal mass characterization for clinicians. MR is at the forefront of renal mass characterization, surpassing ultrasound and CT in its ability to describe multiple tissue parameters and predict tumor biology. Cutting-edge MR protocols and the integration of diagnostic algorithms can improve patient outcomes, allowing the imager to narrow the differential and better guide oncologic and surgical management. MDPI 2023-05-17 /pmc/articles/PMC10216374/ /pubmed/37345136 http://dx.doi.org/10.3390/cancers15102799 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Chartier, Stephane Arif-Tiwari, Hina MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach |
title | MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach |
title_full | MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach |
title_fullStr | MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach |
title_full_unstemmed | MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach |
title_short | MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach |
title_sort | mr virtual biopsy of solid renal masses: an algorithmic approach |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10216374/ https://www.ncbi.nlm.nih.gov/pubmed/37345136 http://dx.doi.org/10.3390/cancers15102799 |
work_keys_str_mv | AT chartierstephane mrvirtualbiopsyofsolidrenalmassesanalgorithmicapproach AT ariftiwarihina mrvirtualbiopsyofsolidrenalmassesanalgorithmicapproach |