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CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis
PURPOSE: Differentiation of fat-poor angiomyolipoma (fp-AMLs) from renal cell carcinoma (RCC) is often not possible from just visual interpretation of conventional cross-sectional imaging, typically requiring biopsy or surgery for diagnostic confirmation. However, radiomics has the potential to char...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10374097/ https://www.ncbi.nlm.nih.gov/pubmed/37498830 http://dx.doi.org/10.1371/journal.pone.0287299 |
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author | Dehghani Firouzabadi, Fatemeh Gopal, Nikhil Hasani, Amir Homayounieh, Fatemeh Li, Xiaobai Jones, Elizabeth C. Yazdian Anari, Pouria Turkbey, Evrim Malayeri, Ashkan A. |
author_facet | Dehghani Firouzabadi, Fatemeh Gopal, Nikhil Hasani, Amir Homayounieh, Fatemeh Li, Xiaobai Jones, Elizabeth C. Yazdian Anari, Pouria Turkbey, Evrim Malayeri, Ashkan A. |
author_sort | Dehghani Firouzabadi, Fatemeh |
collection | PubMed |
description | PURPOSE: Differentiation of fat-poor angiomyolipoma (fp-AMLs) from renal cell carcinoma (RCC) is often not possible from just visual interpretation of conventional cross-sectional imaging, typically requiring biopsy or surgery for diagnostic confirmation. However, radiomics has the potential to characterize renal masses without the need for invasive procedures. Here, we conducted a systematic review on the accuracy of CT radiomics in distinguishing fp-AMLs from RCCs. METHODS: We conducted a search using PubMed/MEDLINE, Google Scholar, Cochrane Library, Embase, and Web of Science for studies published from January 2011–2022 that utilized CT radiomics to discriminate between fp-AMLs and RCCs. A random-effects model was applied for the meta-analysis according to the heterogeneity level. Furthermore, subgroup analyses (group 1: RCCs vs. fp-AML, and group 2: ccRCC vs. fp-AML), and quality assessment were also conducted to explore the possible effect of interstudy differences. To evaluate CT radiomics performance, the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were assessed. This study is registered with PROSPERO (CRD42022311034). RESULTS: Our literature search identified 10 studies with 1456 lesions in 1437 patients. Pooled sensitivity was 0.779 [95% CI: 0.562–0.907] and 0.817 [95% CI: 0.663–0.910] for groups 1 and 2, respectively. Pooled specificity was 0.933 [95% CI: 0.814–0.978]and 0.926 [95% CI: 0.854–0.964] for groups 1 and 2, respectively. Also, our findings showed higher sensitivity and specificity of 0.858 [95% CI: 0.742–0.927] and 0.886 [95% CI: 0.819–0.930] for detecting ccRCC from fp-AML in the unenhanced phase of CT scan as compared to the corticomedullary and nephrogenic phases of CT scan. CONCLUSION: This study suggested that radiomic features derived from CT has high sensitivity and specificity in differentiating RCCs vs. fp-AML, particularly in detecting ccRCCs vs. fp-AML. Also, an unenhanced CT scan showed the highest specificity and sensitivity as compared to contrast CT scan phases. Differentiating between fp-AML and RCC often is not possible without biopsy or surgery; radiomics has the potential to obviate these invasive procedures due to its high diagnostic accuracy. |
format | Online Article Text |
id | pubmed-10374097 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103740972023-07-28 CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis Dehghani Firouzabadi, Fatemeh Gopal, Nikhil Hasani, Amir Homayounieh, Fatemeh Li, Xiaobai Jones, Elizabeth C. Yazdian Anari, Pouria Turkbey, Evrim Malayeri, Ashkan A. PLoS One Research Article PURPOSE: Differentiation of fat-poor angiomyolipoma (fp-AMLs) from renal cell carcinoma (RCC) is often not possible from just visual interpretation of conventional cross-sectional imaging, typically requiring biopsy or surgery for diagnostic confirmation. However, radiomics has the potential to characterize renal masses without the need for invasive procedures. Here, we conducted a systematic review on the accuracy of CT radiomics in distinguishing fp-AMLs from RCCs. METHODS: We conducted a search using PubMed/MEDLINE, Google Scholar, Cochrane Library, Embase, and Web of Science for studies published from January 2011–2022 that utilized CT radiomics to discriminate between fp-AMLs and RCCs. A random-effects model was applied for the meta-analysis according to the heterogeneity level. Furthermore, subgroup analyses (group 1: RCCs vs. fp-AML, and group 2: ccRCC vs. fp-AML), and quality assessment were also conducted to explore the possible effect of interstudy differences. To evaluate CT radiomics performance, the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were assessed. This study is registered with PROSPERO (CRD42022311034). RESULTS: Our literature search identified 10 studies with 1456 lesions in 1437 patients. Pooled sensitivity was 0.779 [95% CI: 0.562–0.907] and 0.817 [95% CI: 0.663–0.910] for groups 1 and 2, respectively. Pooled specificity was 0.933 [95% CI: 0.814–0.978]and 0.926 [95% CI: 0.854–0.964] for groups 1 and 2, respectively. Also, our findings showed higher sensitivity and specificity of 0.858 [95% CI: 0.742–0.927] and 0.886 [95% CI: 0.819–0.930] for detecting ccRCC from fp-AML in the unenhanced phase of CT scan as compared to the corticomedullary and nephrogenic phases of CT scan. CONCLUSION: This study suggested that radiomic features derived from CT has high sensitivity and specificity in differentiating RCCs vs. fp-AML, particularly in detecting ccRCCs vs. fp-AML. Also, an unenhanced CT scan showed the highest specificity and sensitivity as compared to contrast CT scan phases. Differentiating between fp-AML and RCC often is not possible without biopsy or surgery; radiomics has the potential to obviate these invasive procedures due to its high diagnostic accuracy. Public Library of Science 2023-07-27 /pmc/articles/PMC10374097/ /pubmed/37498830 http://dx.doi.org/10.1371/journal.pone.0287299 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Dehghani Firouzabadi, Fatemeh Gopal, Nikhil Hasani, Amir Homayounieh, Fatemeh Li, Xiaobai Jones, Elizabeth C. Yazdian Anari, Pouria Turkbey, Evrim Malayeri, Ashkan A. CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis |
title | CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis |
title_full | CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis |
title_fullStr | CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis |
title_full_unstemmed | CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis |
title_short | CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis |
title_sort | ct radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: systematic review and meta-analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10374097/ https://www.ncbi.nlm.nih.gov/pubmed/37498830 http://dx.doi.org/10.1371/journal.pone.0287299 |
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