Meta-analysis of dynamic contrast enhancement and diffusion-weighted MRI for differentiation of benign from malignant non-mass enhancement breast lesions
PurposeThe objective of this study was to conduct a meta-analysis comparing the diagnostic efficacy of models based on diffusion-weighted imaging (DWI)-MRI, dynamic contrast enhancement (DCE)-MRI, and combination models (DCE and DWI) in distinguishing benign from malignant non-mass enhancement (NME)...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1332783/full |
| _version_ | 1797265490685460480 |
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| author | Jing Zhang Longchao Li Li Zhang Xia Zhe Min Tang Xiaoyan Lei Xiaoling Zhang |
| author_facet | Jing Zhang Longchao Li Li Zhang Xia Zhe Min Tang Xiaoyan Lei Xiaoling Zhang |
| author_sort | Jing Zhang |
| collection | DOAJ |
| description | PurposeThe objective of this study was to conduct a meta-analysis comparing the diagnostic efficacy of models based on diffusion-weighted imaging (DWI)-MRI, dynamic contrast enhancement (DCE)-MRI, and combination models (DCE and DWI) in distinguishing benign from malignant non-mass enhancement (NME) breast lesions.Materials and methodsPubMed, Embase, and Cochrane Library were searched, from inception to January 30, 2023, for studies that used DCE or DWI-MRI for the prediction of NME breast cancer patients. A bivariate random-effects model was used to calculate the meta-analytic sensitivity, specificity, and area under the curve (AUC) of the DCE, DWI, and combination models. Subgroup analysis and meta-regression analysis were performed to find the source of heterogeneity.ResultsOf the 838 articles screened, 18 were eligible for analysis (13 on DCE, five on DWI, and four studies reporting the diagnostic accuracy of both DCE and DWI). The funnel plot showed no publication bias (p > 0.5). The pooled sensitivity and specificity and the AUC of the DCE, DWI, and combination models were 0.58, 0.72, and 0.70, respectively; 0.84, 0.69, and 0.84, respectively; and 0.88, 0.79, 0.90, respectively. The meta-analysis found no evidence of a threshold effect and significant heterogeneity among trials in terms of DCE sensitivity and specificity, as well as DWI specificity alone (I2 > 75%). The meta-regression revealed that different diagnostic criteria contributed to the DCE study’s heterogeneity (p < 0.05). Different reference criteria significantly influenced the heterogeneity of the DWI model (p < 0.05). Subgroup analysis revealed that clustered ring enhancement (CRE) had the highest pooled specificity (0.92) among other DCE features. The apparent diffusion coefficient (ADC) with a mean threshold <1.3 × 10−3 mm2/s had a slightly higher sensitivity of 0.86 compared to 0.82 with an ADC of ≥1.3 × 10−3 mm2/s.ConclusionThe combination model (DCE and DWI) outperformed DCE or DWI alone in identifying benign and malignant NME lesions. The DCE-CRE feature was the most specific test for ruling in NME cancers. |
| first_indexed | 2024-04-25T00:45:38Z |
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| id | doaj.art-de96210ff6b54ad8a31b19384bfbfb57 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-25T00:45:38Z |
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| series | Frontiers in Oncology |
| spelling | doaj.art-de96210ff6b54ad8a31b19384bfbfb572024-03-12T04:58:22ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2024-03-011410.3389/fonc.2024.13327831332783Meta-analysis of dynamic contrast enhancement and diffusion-weighted MRI for differentiation of benign from malignant non-mass enhancement breast lesionsJing ZhangLongchao LiLi ZhangXia ZheMin TangXiaoyan LeiXiaoling ZhangPurposeThe objective of this study was to conduct a meta-analysis comparing the diagnostic efficacy of models based on diffusion-weighted imaging (DWI)-MRI, dynamic contrast enhancement (DCE)-MRI, and combination models (DCE and DWI) in distinguishing benign from malignant non-mass enhancement (NME) breast lesions.Materials and methodsPubMed, Embase, and Cochrane Library were searched, from inception to January 30, 2023, for studies that used DCE or DWI-MRI for the prediction of NME breast cancer patients. A bivariate random-effects model was used to calculate the meta-analytic sensitivity, specificity, and area under the curve (AUC) of the DCE, DWI, and combination models. Subgroup analysis and meta-regression analysis were performed to find the source of heterogeneity.ResultsOf the 838 articles screened, 18 were eligible for analysis (13 on DCE, five on DWI, and four studies reporting the diagnostic accuracy of both DCE and DWI). The funnel plot showed no publication bias (p > 0.5). The pooled sensitivity and specificity and the AUC of the DCE, DWI, and combination models were 0.58, 0.72, and 0.70, respectively; 0.84, 0.69, and 0.84, respectively; and 0.88, 0.79, 0.90, respectively. The meta-analysis found no evidence of a threshold effect and significant heterogeneity among trials in terms of DCE sensitivity and specificity, as well as DWI specificity alone (I2 > 75%). The meta-regression revealed that different diagnostic criteria contributed to the DCE study’s heterogeneity (p < 0.05). Different reference criteria significantly influenced the heterogeneity of the DWI model (p < 0.05). Subgroup analysis revealed that clustered ring enhancement (CRE) had the highest pooled specificity (0.92) among other DCE features. The apparent diffusion coefficient (ADC) with a mean threshold <1.3 × 10−3 mm2/s had a slightly higher sensitivity of 0.86 compared to 0.82 with an ADC of ≥1.3 × 10−3 mm2/s.ConclusionThe combination model (DCE and DWI) outperformed DCE or DWI alone in identifying benign and malignant NME lesions. The DCE-CRE feature was the most specific test for ruling in NME cancers.https://www.frontiersin.org/articles/10.3389/fonc.2024.1332783/fullnon-mass enhancement lesionsmeta-analysisbreast cancerdynamic contrast enhancementdiffusion-weighted imaging |
| spellingShingle | Jing Zhang Longchao Li Li Zhang Xia Zhe Min Tang Xiaoyan Lei Xiaoling Zhang Meta-analysis of dynamic contrast enhancement and diffusion-weighted MRI for differentiation of benign from malignant non-mass enhancement breast lesions Frontiers in Oncology non-mass enhancement lesions meta-analysis breast cancer dynamic contrast enhancement diffusion-weighted imaging |
| title | Meta-analysis of dynamic contrast enhancement and diffusion-weighted MRI for differentiation of benign from malignant non-mass enhancement breast lesions |
| title_full | Meta-analysis of dynamic contrast enhancement and diffusion-weighted MRI for differentiation of benign from malignant non-mass enhancement breast lesions |
| title_fullStr | Meta-analysis of dynamic contrast enhancement and diffusion-weighted MRI for differentiation of benign from malignant non-mass enhancement breast lesions |
| title_full_unstemmed | Meta-analysis of dynamic contrast enhancement and diffusion-weighted MRI for differentiation of benign from malignant non-mass enhancement breast lesions |
| title_short | Meta-analysis of dynamic contrast enhancement and diffusion-weighted MRI for differentiation of benign from malignant non-mass enhancement breast lesions |
| title_sort | meta analysis of dynamic contrast enhancement and diffusion weighted mri for differentiation of benign from malignant non mass enhancement breast lesions |
| topic | non-mass enhancement lesions meta-analysis breast cancer dynamic contrast enhancement diffusion-weighted imaging |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1332783/full |
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