Double reading of diffusion-weighted magnetic resonance imaging for breast cancer detection

Rotili, Anna; Trimboli, Rubina Manuela; Penco, Silvia; Pesapane, Filippo; Tantrige, Priyan; Cassano, Enrico; Sardanelli, Francesco

doi:10.1007/s10549-019-05519-y

Cited by 41 publications

(29 citation statements)

References 55 publications

(62 reference statements)

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“…The introduction of DWI and apparent diffusion coefficient (ADC) improved the accuracy of MRI for metastases detection [99][100][101]. Using whole-body DWI with STIR fat suppression in free breathing optimises the image signal-to-noise ratio and image quality [102,103].…”

Section: Whole-body Mrimentioning

confidence: 99%

Imaging diagnosis of metastatic breast cancer

et al. 2020

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Numerous imaging modalities may be used for the staging of women with advanced breast cancer. Although bone scintigraphy and multiplanar-CT are the most frequently used tests, others including PET, MRI and hybrid scans are also utilised, with no specific recommendations of which test should be preferentially used. We review the evidence behind the imaging modalities that characterise metastases in breast cancer and to update the evidence on comparative imaging accuracy.

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Section: Whole-body Mrimentioning

confidence: 99%

Imaging diagnosis of metastatic breast cancer

et al. 2020

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“…During the last two decades, new methods have been developed and tested to improve the diagnostic performance of breast imaging [ 7 , 8 ]. They are based on the biological principle of neo-angiogenesis that render malignancy associated vessels more permeable to contrast agent than healthy tissue, resulting in tumour enhancement [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnostic accuracy of contrast-enhanced spectral mammography for breast lesions: A systematic review and meta-analysis

Suter¹,

Pesapane

Agazzi

et al. 2020

The Breast

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Breast cancer diagnosis and staging is based on mammography, ultrasound, and magnetic resonance imaging (MRI). Contrast enhanced spectral mammography (CESM) has gained momentum as an innovative and clinically useful method for breast assessment. CESM is based on abnormal enhancement of neoplastic tissue compared to surrounding breast tissue. We performed a systematic review of prospective trial to evaluate its diagnostic performance, following standard PRISMA-DTA. We used a bivariate random-effects regression approach to obtain summary estimates of both sensitivity and specificity of CESM. 8 studies published between 2003 and 2019 were included in the meta-analysis for a total of 945 lesions. The summary area under the curve obtained from all the study was 89% [95% CI 86%–91%], with a sensitivity of 85% [95% CI 73%–93%], and a specificity of 77% [95% CI 60%–88%]. With a pre-test probability of malignancy of 57% a positive finding at CESM gives a post-test probability of 83% while a negative finding a post-test probability of 20%. CESM shows a suboptimal sensitivity and specificity in the diagnosis of breast cancer in a selected population, and at present time, it could be considered only as a possible alternative test for breast lesions assessment when mammography and ultrasound are not conclusive or MRI is contraindicated or not available.

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“…Finally, we did not compare or combine HTR DCE MR with other abbreviated protocols based on DW Imaging. This combination of HTR DCE and DW could be interesting and will certainly be explored in further studies (55).…”

Section: Discussionmentioning

confidence: 99%

Radiomic analysis of HTR-DCE MR sequences improves diagnostic performance compared to BI-RADS analysis of breast MR lesions

et al. 2021

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PURPOSE:To assess the diagnostic performance of radiomic analysis using High Temporal Resolution (HTR)-Dynamic Contrast Enhancement (DCE) MR sequences compared to BI-RADS analysis to distinguish benign from malignant breast lesions. MATERIALS AND METHODS:We retrospectively analyzed data from consecutive women who underwent breast MRI including HTR-DCE MR sequencing for abnormal enhancing lesions and who had subsequent pathological analysis at our tertiary center. Semiquantitative enhancement parameters and textural features were extracted. Temporal change across each phase of textural features in HTR-DCE MR sequences were calculated and called "kinetic textural parameters". Statistical analysis by LASSO-logistic regression and cross validation were performed to build a model. The diagnostic performance of the radiomic model was compared to the results of BI-RADS MR score analysis. RESULTS:We included 117 women with a mean age of 54 years (28-88). Of the 174 lesions analyzed 75 were benign and 99 malignant. Seven semi-quantitative enhancement parameters and 57 textural features were extracted. Regression analysis selected 15 significant variables in a radiomic model (called "malignant probability score") which displayed an AUC=0.876 (sensitivity=0.98, specificity=0.52, accuracy=0.78). The performance of the malignant probability score to distinguish benign from malignant breast lesions (AUC=0.876, 95%CI 0.825-0.925) was significantly better than that of BI-RADS analysis (AUC=0.831, 95%CI 0.769-0.892). The radiomic model significantly reduced false positives (42%) with the same number of missed cancers (n=2) 2 CONCLUSION: A radiomic model including kinetic textural features extracted from an HTR-DCE MR sequence improves diagnostic performance over BI-RADS analysis.

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Double reading of diffusion-weighted magnetic resonance imaging for breast cancer detection

Cited by 41 publications

References 55 publications

Imaging diagnosis of metastatic breast cancer

Imaging diagnosis of metastatic breast cancer

Diagnostic accuracy of contrast-enhanced spectral mammography for breast lesions: A systematic review and meta-analysis

Radiomic analysis of HTR-DCE MR sequences improves diagnostic performance compared to BI-RADS analysis of breast MR lesions

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