Breast imaging: Beyond the detection

Galati, Francesca; Moffa, Giuliana; Pediconi, Federica

doi:10.1016/j.ejrad.2021.110051

Cited by 18 publications

(17 citation statements)

References 104 publications

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“… 10 Ordinal biomarkers are categories with intrinsic rankings that can be arranged in a meaningful order. 9 Breast MRI background parenchymal enhancement (BPE) with minimal, mild, moderate and marked categories is an example of ordinal biomarkers. Quantitative biomarkers are objective, measurable and reproducible parameters.…”

Section: Biomarkers Classificationmentioning

confidence: 99%

“…Quantitative biomarkers are objective, measurable and reproducible parameters. 9 Anatomic structures 2D and 3D measurements are examples of quantitative biomarkers essential in diagnosis, staging and monitoring of response to treatment, as it is when applying the Response Evaluation Criteria in Solid Tumors (RECIST 1.1) criteria. 11 Additional quantitative biomarkers derived from breast Mp-MRI, are apparent diffusion coefficient (ADC) maps values of DWI and the transfer constant (K trans ) that provides a measure of capillary permeability with CE-MRI perfusion.…”

Section: Biomarkers Classificationmentioning

confidence: 99%

“…Over the past two decades, CE-MRI has improved breast MRI diagnostic accuracy with sensitivity up to 99% and variable specificities ranging from 47 to 97% in the detection and characterization of breast lesions, 9,19 assessing breast tissue vascular microenvironment and tissue permeability. As angiogenesis plays an important role in tumor biology, CE-MRI biomarkers and pharmacokinetic parameters were widely investigated.…”

Section: Techniquesmentioning

confidence: 99%

“…Furthermore, in the era of new cellular signaling pathways and molecular therapies, CE-MRI can be used for quantitative assessment of the vascular microenvironment and the tissue permeability. 9 Breast lesions kinetic patterns differ between malignant and benign lesions; thus, enhancement time/intensity curve characteristics can be used in combination with morphologic features to improve differential diagnosis. Semi-quantitative parameters can be extracted from the enhancement curves, including the onset time, maximum signal intensity, gradient or rate of contrast uptake and washout, and initial area under the time signal curve.…”

Section: Techniquesmentioning

confidence: 99%

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MRI as a biomarker for breast cancer diagnosis and prognosis

et al. 2022

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Breast cancer (BC) is the most frequently diagnosed female invasive cancer in Western countries and the leading cause of cancer-related death worldwide. Nowadays tumor heterogeneity is a well-known characteristic of BC, since it includes several nosological entities characterized by morphologic features, clinical course and response to treatment. Thus, with the spread of molecular biology technologies and the growing knowledge of the biological processes underlying the development of BC, the importance of imaging biomarkers as noninvasive information about tissue characteristics has progressively grown. To date, breast magnetic resonance imaging (MRI) is considered indispensable in breast imaging practice, with widely recognized indications such as BC screening in females at increased risk, locoregional staging and neo-adjuvant (NAT) therapy monitoring. Moreover, breast MRI is increasingly used to assess not only the morphologic features of the pathological process but also to characterize individual phenotypes for targeted therapies, building on developments in genomics and molecular biology features. The aim of this review is to explore the role of breast multi parametric MRI (Mp-MRI) in providing imaging biomarkers, leading to an improved differentiation of benign and malignant breast lesions and to a costumized management of BC patients in monitoring and predicting response to treatment. Finally, we discuss how breast MRI biomarkers offer one of the most fertile ground for artificial intelligence (AI) applications. In the era of personalized medicine, with the development of omics-technologies, machine learning and big data, the role of imaging biomarkers is embracing new opportunities for BC diagnosis and treatment.

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Section: Biomarkers Classificationmentioning

confidence: 99%

Section: Biomarkers Classificationmentioning

confidence: 99%

Section: Techniquesmentioning

confidence: 99%

Section: Techniquesmentioning

confidence: 99%

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MRI as a biomarker for breast cancer diagnosis and prognosis

et al. 2022

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“…Breast ultrasound (US) is one of the most important imaging technology and is used in clinical practice, which aims to monitor neoadjuvant chemotherapy (NAC) treatment and characterize breast lesions and axillary lymph nodes ( 2 , 3 ). Various new US imaging techniques and quantitative analysis methods have been proposed, including US elastography and contrast-enhanced ultrasound (CEUS), to improve the sensitivity of conventional US and increase the accuracy of monitoring and prognostic prediction ( 4 ). However, it is difficult for radiologists to perform a comprehensive analysis of tumors with the information obtained by looking at various ultrasound images ( 5 , 6 ).…”

Section: Introductionmentioning

confidence: 99%

Ultrasound radiomics in personalized breast management: Current status and future prospects

2022

Front. Oncol.

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Breast cancer is the most common cancer in women worldwide. Providing accurate and efficient diagnosis, risk stratification and timely adjustment of treatment strategies are essential steps in achieving precision medicine before, during and after treatment. Radiomics provides image information that cannot be recognized by the naked eye through deep mining of medical images. Several studies have shown that radiomics, as a second reader of medical images, can assist physicians not only in the detection and diagnosis of breast lesions but also in the assessment of risk stratification and prediction of treatment response. Recently, more and more studies have focused on the application of ultrasound radiomics in breast management. We summarized recent research advances in ultrasound radiomics for the diagnosis of benign and malignant breast lesions, prediction of molecular subtype, assessment of lymph node status, prediction of neoadjuvant chemotherapy response, and prediction of survival. In addition, we discuss the current challenges and future prospects of ultrasound radiomics.

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Reader bias in breast cancer screening related to cancer prevalence and artificial intelligence decision support—a reader study

Al-Bazzaz,

Janicijevic,

Strand

2024

Eur Radiol

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Objectives The aim of our study was to examine how breast radiologists would be affected by high cancer prevalence and the use of artificial intelligence (AI) for decision support. Materials and method This reader study was based on selection of screening mammograms, including the original radiologist assessment, acquired in 2010 to 2013 at the Karolinska University Hospital, with a ratio of 1:1 cancer versus healthy based on a 2-year follow-up. A commercial AI system generated an exam-level positive or negative read, and image markers. Double-reading and consensus discussions were first performed without AI and later with AI, with a 6-week wash-out period in between. The chi-squared test was used to test for differences in contingency tables. Results Mammograms of 758 women were included, half with cancer and half healthy. 52% were 40–55 years; 48% were 56–75 years. In the original non-enriched screening setting, the sensitivity was 61% (232/379) at specificity 98% (323/379). In the reader study, the sensitivity without and with AI was 81% (307/379) and 75% (284/379) respectively (p < 0.001). The specificity without and with AI was 67% (255/379) and 86% (326/379) respectively (p < 0.001). The tendency to change assessment from positive to negative based on erroneous AI information differed between readers and was affected by type and number of image signs of malignancy. Conclusion Breast radiologists reading a list with high cancer prevalence performed at considerably higher sensitivity and lower specificity than the original screen-readers. Adding AI information, calibrated to a screening setting, decreased sensitivity and increased specificity. Clinical relevance statement Radiologist screening mammography assessments will be biased towards higher sensitivity and lower specificity by high-risk triaging and nudged towards the sensitivity and specificity setting of AI reads. After AI implementation in clinical practice, there is reason to carefully follow screening metrics to ensure the impact is desired. Key Points • Breast radiologists’ sensitivity and specificity will be affected by changes brought by artificial intelligence. • Reading in a high cancer prevalence setting markedly increased sensitivity and decreased specificity. • Reviewing the binary reads by AI, negative or positive, biased screening radiologists towards the sensitivity and specificity of the AI system. Graphical abstract

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Breast imaging: Beyond the detection

Cited by 18 publications

References 104 publications

MRI as a biomarker for breast cancer diagnosis and prognosis

MRI as a biomarker for breast cancer diagnosis and prognosis

Ultrasound radiomics in personalized breast management: Current status and future prospects

Reader bias in breast cancer screening related to cancer prevalence and artificial intelligence decision support—a reader study

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