Diversity and compounding for enhanced discrimination of breast masses in ultrasonic B-scan images

2021 ◽  
Author(s):  
Vishruta Ajitkumar Dumane
Keyword(s):  
Breast Masses ◽  
B Scan Images

Clinical Application of Computer-Aided Diagnosis According to S-Detect in Category 4 Breast Lesions: Prospective Single-Center Study (Preprint)

2020 ◽  
Author(s):  
Pengfei Sun ◽  
Chen Chen ◽  
Weiqi Wang ◽  
Lei Liang ◽  
Dan Luo ◽  
...  
Keyword(s):  
Clinical Management ◽  
Diagnostic Performance ◽  
Breast Biopsy ◽  
Computer Aided Diagnosis ◽  
Breast Lesions ◽  
Breast Masses ◽  
Computer Aided ◽  
Conventional Ultrasound ◽  
Us Findings ◽  
Aided Diagnosis

BACKGROUND Computer-aided diagnosis (CAD) is a useful tool that can provide a reference for the differential diagnosis of benign and malignant breast lesion. Previous studies have demonstrated that CAD can improve the diagnostic performance. However, conventional ultrasound (US) combined with CAD were used to adjust the classification of category 4 lesions has been few assessed. OBJECTIVE The objective of our study was to evaluate the diagnosis performance of conventional ultrasound combined with a CAD system S-Detect in the category of BI-RADS 4 breast lesions. METHODS Between December 2018 and May 2020, we enrolled patients in this study who received conventional ultrasound and S-Detect before US-guided biopsy or surgical excision. The diagnostic performance was compared between US findings only and the combined use of US findings with S-Detect, which were correlated with pathology results. RESULTS A total of 98 patients (mean age 51.06 ±16.25 years, range 22-81) with 110 breast masses (mean size1.97±1.38cm, range0.6-8.5) were included in this study. Of the 110 breast masses, 64/110 (58.18%) were benign, 46/110 (41.82%) were malignant. Compared with conventional ultrasound, a significant increase in specificity (0% to 53.12%, P<.001), accuracy (41.81% to70.19%, P<.001) were noted, with no statistically significant decrease on sensitivity(100% to 95.65% ,P=.48). According to S-Detect-guided US BI-RADS re-classification, 30 out of 110 (27.27%) breast lesions underwent a correct change in clinical management, 74of 110 (67.27%) breast lesions underwent no change and 6 of 110 (5.45%) breast lesions underwent an incorrect change in clinical management. The biopsy rate decreased from 100% to 67.27 % (P<.001).Benign masses among subcategory 4a had higher rates of possibly benign assessment on S-Detect for the US only (60% to 0%, P<.001). CONCLUSIONS S-Detect can be used as an additional diagnostic tool to improve the specificity and accuracy in clinical practice. S-Detect have the potential to be used in downgrading benign masses misclassified as BI-RADS category 4 on US by radiologist, and may reduce unnecessary breast biopsy. CLINICALTRIAL none

scholarly journals Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning

2021 ◽  
Vol 11 (7) ◽  
pp. 3119
Author(s):  
Cristina L. Saratxaga ◽  
Jorge Bote ◽  
Juan F. Ortega-Morán ◽  
Artzai Picón ◽  
Elena Terradillos ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Data Augmentation ◽  
Optical Biopsy ◽  
Colon Polyps ◽  
Optical Coherence ◽  
Colon Lesion ◽  
Patient Prognosis ◽  
B Scan Images

(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (±0.0141) sensitivity and 0.8094 (±0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (±0.0197) sensitivity and 0.7865 (±0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.

scholarly journals Imaging Evaluation of Male Breast Masses with Histopathologic Correlation: A Case Series

2021 ◽  
Author(s):  
Tanvi P. Vaidya ◽  
Subhash K. Ramani
Keyword(s):  
Magnetic Resonance Imaging ◽  
Wide Spectrum ◽  
Case Series ◽  
Male Breast ◽  
Radiological Evaluation ◽  
Resonance Imaging ◽  
Imaging Evaluation ◽  
Breast Masses ◽  
Female Breast

AbstractThe male breast can be afflicted with a wide spectrum of benign and malignant masses, similar to the female breast. A systematic radiological evaluation using mammography, ultrasonography, and when appropriate, magnetic resonance imaging, could aid this differentiation and provide clues to the diagnosis. In this article, we present six cases of male breast masses with an emphasis on the role of imaging in characterization and diagnosis.

scholarly journals Breast involvement of hematological malignancies: imaging and clinical findings

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Gamze Durhan ◽  
Figen Demirkazık
Keyword(s):  
Hematological Malignancies ◽  
Clinical Findings ◽  
Accurate Diagnosis ◽  
Radiological Findings ◽  
Breast Masses ◽  
Appropriate Treatment ◽  
Magnetic Resonance Imaging Mri ◽  
Type 3 ◽  
And Diffusion

Abstract Background Breast involvement of hematological malignancies is a very rare entity. Accurate diagnosis is essential for appropriate treatment. The aim of this study was to clarify the clinical and radiological findings of hematological malignancy breast involvement and to describe possible pitfalls in diagnosis. Results The images of 20 patients with breast involvement of hematological malignancies were retrospectively evaluated on ultrasonography, mammography, and magnetic resonance imaging (MRI) and the findings were reported. Bilaterality was seen only in cases with secondary involvement, and there was no marked difference between primary and secondary breast involvement of hematological malignancies. All patients underwent ultrasonography examination. According to ultrasonography, breast masses were most frequently irregular in shape (11/20, 55%) with non-circumscribed margins (11/20, 55%). Posterior acoustic enhancement was noted in 14 cases (70%). Posterior shadowing was not observed in any of the patients. Mammography was available in 10 patients. Microcalcification was not observed in any patient on mammography. MRI was available in four patients. Hyperintensity in T2-weighted images, type 2 or type 3 dynamic curve, and diffusion restriction were observed in all cases. Conclusions Hematological malignancies may mimic both benign breast lesions and breast carcinoma. Familiarity with the radiological features of hematological malignancies can help accurate diagnosis.

Specificity of ultrasonography in the diagnosis of benign breast masses.

1984 ◽  
Vol 3 (10) ◽  
pp. 453-461 ◽  
Author(s):  
S H Heywang ◽  
E R Lipsit ◽  
L M Glassman ◽  
M A Thomas
Keyword(s):  
Breast Masses
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