Nonpalpable breast lesions: impact of a second-opinion review at a breast unit on BI-RADS classification

Margerie-Mellon, Constance de; Debry, Jean-Baptiste; Dupont, Axelle; Cuvier, C.; Giacchetti, Sylvie; Teixeira, Luis; Espié, Marc; Bazelaire, Cédric de

doi:10.1007/s00330-020-07664-1

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…BI-RADS 0 refers to an incomplete diagnosis that needs an additional image for reclassification. Table 1 shows the categories for BI-RADS in detail [13,14]. The authors follow the Al-Amoudi Center of Excellence in Breast Cancer to scale cases from 0 to 6.…”

Section: Discussionmentioning

confidence: 99%

King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD)

Alsolami

Shalash

Alsaggaf

et al. 2021

Data

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The current era is characterized by the rapidly increasing use of computer-aided diagnosis (CAD) systems in the medical field. These systems need a variety of datasets to help develop, evaluate, and compare their performances fairly. Physicians indicated that breast anatomy, especially dense ones, and the probability of breast cancer and tumor development, vary highly depending on race. Researchers reported that breast cancer risk factors are related to culture and society. Thus, there is a massive need for a local dataset representing breast cancer in our region to help develop and evaluate automatic breast cancer CAD systems. This paper presents a public mammogram dataset called King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD) version 1. To our knowledge, KAU-BCMD is the first dataset in Saudi Arabia that deals with a large number of mammogram scans. The dataset was collected from the Sheikh Mohammed Hussein Al-Amoudi Center of Excellence in Breast Cancer at King Abdulaziz University. It contains 1416 cases. Each case has two views for both the right and left breasts, resulting in 5662 images based on the breast imaging reporting and data system. It also contains 205 ultrasound cases corresponding to a part of the mammogram cases, with 405 images as a total. The dataset was annotated and reviewed by three different radiologists. Our dataset is a promising dataset that contains different imaging modalities for breast cancer with different cancer grades for Saudi women.

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Section: Discussionmentioning

confidence: 99%

King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD)

Alsolami

Shalash

Alsaggaf

et al. 2021

Data

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“…Class 3: High probability of benign or low grade malignancy, the probability of malignancy is 0%-2%, short-term follow-up of 3 to 6 months; Class 4: Suspected malignancy, the possibility of malignancy is 3%-94%, biopsy is recommended; Class 5: Highly suggestive of malignancy, malignant probability ≥ 95%, recommended biopsy and active treatment. BI-RADS class 4 lesions were further classified into 4a, 4b, and 4c subcategories (18)(19)(20), as follows: 4a -low suspicion for malignancy, with a 3-10% likelihood of malignancy; 4b -intermediate suspicion for malignancy, with a 10-50% likelihood of malignancy; 4c -high suspicion for malignancy, with a 51-94% likelihood of malignancy. Malignant signs, such as microcalcifications, irregular shape, spiculated margins, were categorized, round shape, microlobulated/ indistinct/angular margins, duct extension, complex echogenicity and posterior acoustic shadowing, non-parallel growth, were categorized (19,20).…”

Section: Classification Criteria and Imaging Observation Indicatorsmentioning

confidence: 99%

“…BI-RADS class 4 lesions were further classified into 4a, 4b, and 4c subcategories (18)(19)(20), as follows: 4a -low suspicion for malignancy, with a 3-10% likelihood of malignancy; 4b -intermediate suspicion for malignancy, with a 10-50% likelihood of malignancy; 4c -high suspicion for malignancy, with a 51-94% likelihood of malignancy. Malignant signs, such as microcalcifications, irregular shape, spiculated margins, were categorized, round shape, microlobulated/ indistinct/angular margins, duct extension, complex echogenicity and posterior acoustic shadowing, non-parallel growth, were categorized (19,20). SMI: The number, course, and distribution of microvessels inside and around the lesion are observed using CDFI and mSMI technology after determining the location of the breast lesion using routine ultrasound.…”

Section: Classification Criteria and Imaging Observation Indicatorsmentioning

confidence: 99%

Diagnostic value of shear wave elastography combined with super microvascular imaging for BI-RADS 3-5 nodules

Wang,

He,

Wang

2023

Front. Oncol.

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BackgroundTo investigate the diagnostic value of shear wave elastography (SWE) and super microvascular imaging (SMI) integrated with the traditional ultrasound breast imaging reporting and data system (BI-RADS) classification in differentiating between benign and malignant breast nodules.MethodsFor analysis, 88 patients with 110 breast nodules assessed as BI-RADS 3-5 by conventional ultrasound were selected. SWE and SMI evaluations were conducted separately, and all nodules were verified as benign or malignant ones by pathology. Receiver operating characteristic (ROC) curves were plotted after obtaining quantitative parameters of different shear waves of nodules, including maximum (Emax), mean (Emean), minimum (Emin) Young’s modulus, modulus standard deviation (SD), and modulus ratio (Eratio). The best cut-off value, specificity, sensitivity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) for diagnosing malignant nodules employing Emax were obtained, and the diagnostic value of combining Emax and BI-RADS classification was compared. SMI graded nodule based on the Alder blood flow grading standard, whereas the BI-RADS classification was based on microvascular morphology. We assessed the diagnostic value of SMI for breast nodules and investigated the diagnostic efficacy of SWE combined with SMI in differentiating benign and malignant breast nodules with BI-RADS classification 3–5.ResultsThe adjusted the BI-RADS classification using SMI and SWE technologies promoted the sensitivity, specificity, and accuracy of discriminating benign and malignant breast nodules (P < 0.05). The combination of traditional ultrasound BI-RADS classification with SWE and SMI technologies offered high sensitivity, specificity, accuracy, PPV, and NPV for identifying benign and malignant breast lesions. Moreover, combining SWE and SMI technologies with the adjusted BI-RADS classificationhad the best diagnostic efficacy for distinguishing benign and malignant breast nodules with BI-RADS 3–5.ConclusionThe combination of SWE and SMI with the adjusted BI-RADS classification is a promising diagnostic method for differentiating benign and malignant breast nodules.

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