Can high school students help to improve breast radiologists in detecting missed breast cancer lesions on full-field digital mammography?

Nijnatten, Thiemo J A van; Smidt, Marjolein L.; Goorts, B.; Samiei, Sanaz; Houben, Ivo; Kok, Esther T.; Wildberger, Joachim E.; Robben, Simon G. F.; Lobbes, Marc

doi:10.7150/jca.30494

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…CEM has shown promising diagnostic value, with reported summary AUCs between 0.93-0.96 (8,34) in two meta-analyses. The sensitivity of CEM has been reported to be quite satisfactory even when interpreted by nonexperienced human readers (35). However, like breast MRI, the specificity of CEM is limited and can be affected by the experience of the readers.…”

Section: Discussionmentioning

confidence: 99%

Incorporating the clinical and radiomics features of contrast-enhanced mammography to classify breast lesions: a retrospective study

Wang¹,

Sun²,

Mao³

et al. 2021

Quant Imaging Med Surg

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Background: Contrast-enhanced mammography (CEM) is a promising breast imaging technique. A limited number of studies have focused on the radiomics analysis of CEM. We intended to explore whether a model constructed with both clinical and radiomics features of CEM can better classify benign and malignant breast lesions. Methods: This retrospective, double-center study included women who underwent CEM between August 2017 and February 2020. The data from Center 1 were used as training set and the data from Center 2 were used as external testing set (training: testing =2:1). Models were constructed with the clinical, radiomics, and clinical + radiomics features of CEM. The clinical features included patient age and clinical image features interpreted by the radiologists. The radiomics features were extracted from high-energy (HE), low-energy (LE), and dual-energy subtraction (DES) images of CEM. The Mann-Whitney U test, Pearson correlationand Boruta's approach were used to select the radiomics features. Random Forest (RF) and logistic regression were used to establish the models. For the testing set, the areas under the curve (AUCs) and 95% confidence intervals (CIs) were employed to evaluate the performance of the models. For the training set, the mean AUCs were obtained by performing internal validation for 100 iterations and then compared by the Kruskal-Wallis and Mann-Whitney U tests.Results: A total of 226 women (mean age: 47.4±10.1 years) with 226 pathologically proven breast lesions (101 benign; 125 malignant) were included. For the external testing set, the AUCs were 0.964 (95% CI: 0.918-1.000) for the combined model, 0.947 (95% CI: 0.891-0.997) for the radiomics model, and 0.882 (95% CI: 0.803-0.962) for the clinical model. In the internal validation process, the combined model achieved a mean AUC of 0.934±0.030, which was significantly higher than those of the radiomics (mean AUC =0.921±0.031, adjusted P<0.050) and clinical models (mean AUC =0.907±0.036; adjusted P<0.050). Conclusion:Incorporating both clinical and radiomics features of CEM may achieve better classification results for breast lesions.

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Section: Discussionmentioning

confidence: 99%

Incorporating the clinical and radiomics features of contrast-enhanced mammography to classify breast lesions: a retrospective study

Wang¹,

Sun²,

Mao³

et al. 2021

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

“…Reading CEM examinations utilizes the fifth edition of the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon for mammography, including the supplemental chapter on CEM, combined with that for MRI without the contrast kinetics evaluation and with some descriptors specific for CEM [11,12]. Also, CEM seems to be easy to learn [13][14][15][16]. The costs of a CEM exam are significantly lower than that of breast MRI and patients tend to prefer CEM over breast MRI [4,17,18].…”

Section: Introductionmentioning

confidence: 99%

Comparing the Diagnostic Performance of Contrast-Enhanced Mammography and Breast MRI: a Systematic Review and Meta-Analysis

Neeter¹,

Robbe²,

Nijnatten³

et al. 2023

J. Cancer

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Background: To provide a systematic review and meta-analysis that evaluates the diagnostic accuracy of contrast-enhanced mammography (CEM) compared to standard contrast-enhanced breast magnetic resonance imaging (breast MRI). Like breast MRI, CEM enables tumour visualization by contrast accumulation. CEM seems to be a viable substitute for breast MRI. Methods: This systematic search assessed the diagnostic accuracy of these techniques in women with suspicious breast lesions on prior imaging or physical examination, who have undergone both breast MRI and CEM. CEM had to be performed on a commercially available system. The MRI sequence parameters had to be described sufficiently to ensure that standard breast MRI sequence protocols were used. Pooled values of sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio (DOR), were estimated using bivariate mixed-effects logistic regression modeling. Hierarchical summary receiver operating characteristic curves for CEM and breast MRI were also constructed. Results: Six studies (607 patients with 775 lesions) met the predefined inclusion criteria. Pooled sensitivity was 96% for CEM and 97% for breast MRI. Pooled specificity was 77% for both modalities. DOR was 79.5 for CEM and 122.9 for breast MRI. Between-study heterogeneity expressed as the I 2 -index was substantial with values over 80%. Conclusion: Pooled sensitivity was high for both CEM and breast MRI, with moderate specificity. The pooled DOR estimates, however, indicate higher overall diagnostic performance of breast MRI compared to CEM. Nonetheless, current scientific evidence is too limited to prematurely discard CEM as an alternative for breast MRI.

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“… 24 This is supported by another study in which non-experienced high-school students, after a short introduction to breast cancer and CEM in general, evaluated the cases used in the study by Lalji et al These students immediately reached a sensitivity of more than 80% in detecting breast cancers on recombined images. 25 This also implies that semi-automatic software tools that are being developed might show steep learning curves (see ‘Future developments’).…”

Section: Introductionmentioning

confidence: 99%

Contrast-enhanced mammography: what the radiologist needs to know

et al. 2021

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Contrast-enhanced mammography (CEM) is a combination of standard mammography and iodinated contrast material administration. During the last decade, CEM has found its place in breast imaging protocols: after i.v. administration of iodinated contrast material, low-energy and high-energy images are retrieved in one acquisition using a dual-energy technique, and a recombined image is constructed enabling visualisation of areas of contrast uptake. The increased incorporation of CEM into everyday clinical practice is reflected in the installation of dedicated equipment worldwide, the (commercial) availability of systems from different vendors, the number of CEM examinations performed, and the number of scientific articles published on the subject. It follows that ever more radiologists will be confronted with this technique, and thus be required to keep up to date with the latest developments in the field. Most importantly, radiologists must have sufficient knowledge on how to interpret CEM images and be acquainted with common artefacts and pitfalls. This comprehensive review provides a practical overview of CEM technique, including CEM-guided biopsy; reading, interpretation and structured reporting of CEM images, including the accompanying learning curve, CEM artefacts and interpretation pitfalls; indications for CEM; disadvantages of CEM; and future developments.

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Can high school students help to improve breast radiologists in detecting missed breast cancer lesions on full-field digital mammography?

Cited by 6 publications

References 29 publications

Incorporating the clinical and radiomics features of contrast-enhanced mammography to classify breast lesions: a retrospective study

Incorporating the clinical and radiomics features of contrast-enhanced mammography to classify breast lesions: a retrospective study

Comparing the Diagnostic Performance of Contrast-Enhanced Mammography and Breast MRI: a Systematic Review and Meta-Analysis

Contrast-enhanced mammography: what the radiologist needs to know

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