Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study

Radiologists’ diagnostic capabilities for breast mass lesions depend on their experience. Junior radiologists may underestimate or overestimate Breast Imaging Reporting and Data System (BI-RADS) categories of mass lesions owing to a lack of diagnostic experience. The computer-aided diagnosis (CAD) method assists in improving diagnostic performance by providing a breast mass classification reference to radiologists. This study aims to evaluate the impact of a CAD method based on perceptive features learned from quantitative BI-RADS descriptions on breast mass diagnosis performance. We conducted a retrospective multi-reader multi-case (MRMC) study to assess the perceptive feature-based CAD method. A total of 416 digital mammograms of patients with breast masses were obtained from 2014 through 2017, including 231 benign and 185 malignant masses, from which we randomly selected 214 cases (109 benign, 105 malignant) to train the CAD model for perceptive feature extraction and classification. The remaining 202 cases were enrolled as the test set for evaluation, of which 51 patients (29 benign and 22 malignant) participated in the MRMC study. In the MRMC study, we categorized six radiologists into three groups: junior, middle-senior, and senior. They diagnosed 51 patients with and without support from the CAD model. The BI-RADS category, benign or malignant diagnosis, malignancy probability, and diagnosis time during the two evaluation sessions were recorded. In the MRMC evaluation, the average area under the curve (AUC) of the six radiologists with CAD support was slightly higher than that without support (0.896 vs. 0.850, p = 0.0209). Both average sensitivity and specificity increased (p = 0.0253). Under CAD assistance, junior and middle-senior radiologists adjusted the assessment categories of more BI-RADS 4 cases. The diagnosis time with and without CAD support was comparable for five radiologists. The CAD model improved the radiologists’ diagnostic performance for breast masses without prolonging the diagnosis time and assisted in a better BI-RADS assessment, especially for junior radiologists.

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

10.2196/preprints.20875 ◽

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

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A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

BioMed Research International ◽

10.1155/2020/7695207 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Said Boumaraf ◽

Xiabi Liu ◽

Chokri Ferkous ◽

Xiaohong Ma

Keyword(s):

Feature Selection ◽

Predictive Value ◽

Breast Imaging ◽

Feature Selection Method ◽

Computer Aided Diagnosis ◽

Screening Mammography ◽

Breast Masses ◽

Computer Aided ◽

Aided Diagnosis

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient’s age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

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Comprehensive Computer-Aided Decision Support Framework to Diagnose Tuberculosis From Chest X-Ray Images: Data Mining Study (Preprint)

10.2196/preprints.21790 ◽

2020 ◽

Author(s):

Muhammad Owais ◽

Muhammad Arsalan ◽

Tahir Mahmood ◽

Yu Hwan Kim ◽

Kang Ryoung Park

Keyword(s):

Diagnostic Performance ◽

State Of The Art ◽

Medical Condition ◽

Area Under The Curve ◽

Classification Model ◽

Cad Tools ◽

Descriptive Information ◽

Computer Aided ◽

Art Methods ◽

Effective Diagnosis

BACKGROUND Tuberculosis (TB) is one of the most infectious diseases that can be fatal. Its early diagnosis and treatment can significantly reduce the mortality rate. In the literature, several computer-aided diagnosis (CAD) tools have been proposed for the efficient diagnosis of TB from chest radiograph (CXR) images. However, the majority of previous studies adopted conventional handcrafted feature-based algorithms. In addition, some recent CAD tools utilized the strength of deep learning methods to further enhance diagnostic performance. Nevertheless, all these existing methods can only classify a given CXR image into binary class (either TB positive or TB negative) without providing further descriptive information. OBJECTIVE The main objective of this study is to propose a comprehensive CAD framework for the effective diagnosis of TB by providing visual as well as descriptive information from the previous patients’ database. METHODS To accomplish our objective, first we propose a fusion-based deep classification network for the CAD decision that exhibits promising performance over the various state-of-the-art methods. Furthermore, a multilevel similarity measure algorithm is devised based on multiscale information fusion to retrieve the best-matched cases from the previous database. RESULTS The performance of the framework was evaluated based on 2 well-known CXR data sets made available by the US National Library of Medicine and the National Institutes of Health. Our classification model exhibited the best diagnostic performance (0.929, 0.937, 0.921, 0.928, and 0.965 for F1 score, average precision, average recall, accuracy, and area under the curve, respectively) and outperforms the performance of various state-of-the-art methods. CONCLUSIONS This paper presents a comprehensive CAD framework to diagnose TB from CXR images by retrieving the relevant cases and their clinical observations from the previous patients’ database. These retrieval results assist the radiologist in making an effective diagnostic decision related to the current medical condition of a patient. Moreover, the retrieval results can facilitate the radiologists in subjectively validating the CAD decision.

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Comprehensive Computer-Aided Decision Support Framework to Diagnose Tuberculosis From Chest X-Ray Images: Data Mining Study

JMIR Medical Informatics ◽

10.2196/21790 ◽

2020 ◽

Vol 8 (12) ◽

pp. e21790

Author(s):

Muhammad Owais ◽

Muhammad Arsalan ◽

Tahir Mahmood ◽

Yu Hwan Kim ◽

Kang Ryoung Park

Keyword(s):

Diagnostic Performance ◽

State Of The Art ◽

Medical Condition ◽

Area Under The Curve ◽

Classification Model ◽

Cad Tools ◽

Descriptive Information ◽

Computer Aided ◽

Art Methods ◽

Effective Diagnosis

Background Tuberculosis (TB) is one of the most infectious diseases that can be fatal. Its early diagnosis and treatment can significantly reduce the mortality rate. In the literature, several computer-aided diagnosis (CAD) tools have been proposed for the efficient diagnosis of TB from chest radiograph (CXR) images. However, the majority of previous studies adopted conventional handcrafted feature-based algorithms. In addition, some recent CAD tools utilized the strength of deep learning methods to further enhance diagnostic performance. Nevertheless, all these existing methods can only classify a given CXR image into binary class (either TB positive or TB negative) without providing further descriptive information. Objective The main objective of this study is to propose a comprehensive CAD framework for the effective diagnosis of TB by providing visual as well as descriptive information from the previous patients’ database. Methods To accomplish our objective, first we propose a fusion-based deep classification network for the CAD decision that exhibits promising performance over the various state-of-the-art methods. Furthermore, a multilevel similarity measure algorithm is devised based on multiscale information fusion to retrieve the best-matched cases from the previous database. Results The performance of the framework was evaluated based on 2 well-known CXR data sets made available by the US National Library of Medicine and the National Institutes of Health. Our classification model exhibited the best diagnostic performance (0.929, 0.937, 0.921, 0.928, and 0.965 for F1 score, average precision, average recall, accuracy, and area under the curve, respectively) and outperforms the performance of various state-of-the-art methods. Conclusions This paper presents a comprehensive CAD framework to diagnose TB from CXR images by retrieving the relevant cases and their clinical observations from the previous patients’ database. These retrieval results assist the radiologist in making an effective diagnostic decision related to the current medical condition of a patient. Moreover, the retrieval results can facilitate the radiologists in subjectively validating the CAD decision.

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ROLE OF ULTRASONOGRAPHIC ELASTOGRAPHY IN THE DIFFERENTIAL DIAGNOSIS OF BREAST MASSES AND ITS CONTRIBUTION TO CLASSICAL ULTRASONOGRAPHIC EVALUATION

10.36106/ijsr/0733120 ◽

2021 ◽

pp. 48-50

Author(s):

Ashok Kumar Verma ◽

Rashmi Rashmi ◽

Rakesh Kumar Verma ◽

Mahendra Kumar Pandey

Keyword(s):

Breast Cancer ◽

Breast Imaging ◽

Diagnostic Performance ◽

Breast Biopsy ◽

Strain Ratio ◽

Breast Lesions ◽

Breast Masses ◽

Screening Programs ◽

Imaging Department ◽

Malignant Breast

Introduction: India is experiencing an unprecedented rise in the number of breast cancer cases across all sections of society. Breast cancer is now the most common malignancy in women and the second leading cause of cancer- related mortality. Breast cancer is quite easily and effectively treated, provided it is detected in it's early stages. There is a drastic drop in the survival rates when women present with advanced stage of breast cancer, regardless of the setting. Unfortunately, women in resource-poor and developing countries, like India, generally present at a later stage of disease than women elsewhere, partly due to the absence of effective awareness programs and partly due to the lack of proper mass screening programs Aims And Objectives: The diagnostic performance of elastography in differentiating benign from malignant breast lesions. To assess whether elastography has the potential to reduce the need for breast biopsy /FNAC. Cut off value of Strain Ratio for benign versus malignant breast lesions. Further characterize BI-RADS 3 lesions using elastography Materials And Methods: The study was approved by the GSVM MEDICAL COLLEGE AND LLR HOSPITAL Ethics Committee. All patients that presented to the Radiology and Imaging Department of LLR HOSPITAL for diagnostic work up for breast pathology were included in the study. After obtaining a written and signed informed consent from all patients, they were subjected to conventional B-Mode ultrasonography followed by elastography. All diagnostic breast imaging was done with Samsung RS80A ultrasound machine using linear array transducer of frequency 5-12MHz.Observations & Results: The elastography patterns for each lesion were assessed and documented in color scale. Color images were constructed automatically and displayed as a color-overlay on the B-mode image. The color pattern of each lesion was then evaluated on a scale of 1-5 according to the Tsukuba elasticity scoring system. Conclusion: Strain Ratio cutoff of 3.3 is a sensitive parameter to differentiate benign and malignant breast lesions. Elastography is a specic test for differentiating benign and malignant breast lesions. The combined use of elasticity score, strain ratio and B- Mode sonographyincreases the diagnostic performance in distinguishing benign from malignant breast masses.

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Breast ultrasound diagnostic performance and outcomes for mass lesions using Breast Imaging Reporting and Data System category 0 mammogram

Clinics ◽

10.1590/s1807-59322011000300014 ◽

2011 ◽

Vol 66 (3) ◽

pp. 443-448 ◽

Cited By ~ 13

Author(s):

Paulo Almazy Zanello ◽

Andre Felipe Cica Robim ◽

Tatiane Mendes Gonçalves de Oliveira ◽

Jorge Elias Junior ◽

Jurandyr Moreira de Andrade ◽

...

Keyword(s):

Breast Imaging ◽

Diagnostic Performance ◽

Breast Ultrasound ◽

Data System ◽

Mass Lesions

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The Establishment of Transformer-Based Computer-Aided Diagnosis Model to Improve the Classification Consistency of BI-RADS-US3–5 Nodules Among Radiologists: A Multi-Center Study

10.21203/rs.3.rs-1179922/v1 ◽

2021 ◽

Author(s):

Hongtao Ji ◽

Qiang Zhu ◽

Teng Ma ◽

Yun Cheng ◽

Shuai Zhou ◽

...

Keyword(s):

Breast Imaging ◽

Computer Aided Diagnosis ◽

Image Features ◽

Cad Model ◽

Data Systems ◽

Test Set ◽

Classification Consistency ◽

Computer Aided ◽

Sensitivity Specificity ◽

Aided Diagnosis

Abstract Background: Significant differences exist in classification outcomes for radiologists using ultrasonography-based breast imaging-reporting and data systems for diagnosing category 3–5 (BI-RADS-US 3–5) breast nodules, due to a lack of clear and distinguishing image features. As such, this study investigates the use of a transformer-based computer-aided diagnosis (CAD) model for improved BI-RADS-US 3–5 classification consistency.Methods: Five radiologists independently performed BI-RADS-US annotations on a breast ultrasonography image set collected from 20 hospitals in China. The data were divided into training, validation, testing, and sampling sets. The trained transformer-based CAD model was then used to classify test images, for which sensitivity, specificity, and accuracy were calculated. Variations in these metrics among the 5 radiologists were analyzed by referencing BI-RADS-US classification results for the sampling test set, provided by CAD, to determine whether classification consistency (the kappa value)，sensitivity, specificity, and accuracy had improved.Results: Classification accuracy for the CAD model applied to the test set was 95.7% for category 3 nodules, 97.6% for category 4A nodules, 95.60% for category 4B nodules, 94.2% for category 4C nodules, and 97.5% for category 5 nodules. Adjustments were made to 1,583 nodules, as 905 were classified to a higher category and 678 to a lower category in the sampling test set. As a result, the accuracy, sensitivity, and specificity of classification by each radiologist improved, with the consistency (kappa values) for all radiologists increasing to >0.60.Conclusions: The proposed transformer-based CAD model improved BI-RADS-US 3–5 nodule classification by individual radiologists and increased diagnostic consistency.

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Evaluation of Manual and Electronic Health-care associated infections (HAIs) Surveillance: a prospective multi-center study (Preprint)

10.2196/preprints.14831 ◽

2019 ◽

Author(s):

Wensen Chen ◽

Weihong Zhang ◽

Yue Yang ◽

Yun Liu

Keyword(s):

Health Care ◽

Diagnostic Performance ◽

Area Under The Curve ◽

Significant Heterogeneity ◽

General Hospitals ◽

Health Care Associated Infections ◽

Multi Center Study ◽

Platform System ◽

The Impact ◽

Center Study

UNSTRUCTURED A large multi-center study including 21 tertiary general hospitals and 63 wards were recruited to evaluate the impact of electronic surveillance and interaction platform system (SIPS) for health-care associated infections (HAIs). We collected 4098 consecutive patients and found that the hospitals installed with SIPS significantly increased work efficiency of infection control practitioners (ICPs) achieving satisfactory diagnostic performance of HAIs with 0.73 for sensitivity, 0.81 for specificity and 0.81 area under the curve (AUC). However, there were significant heterogeneity own to regions, time of SIPS installation, departments and sample size.

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Breast Mass Lesions: Computer-aided Diagnosis Models with Mammographic and Sonographic Descriptors

Radiology ◽

10.1148/radiol.2442060712 ◽

2007 ◽

Vol 244 (2) ◽

pp. 390-398 ◽

Cited By ~ 59

Author(s):

Jonathan L. Jesneck ◽

Joseph Y. Lo ◽

Jay A. Baker

Keyword(s):

Computer Aided Diagnosis ◽

Breast Mass ◽

Computer Aided ◽

Mass Lesions ◽

Aided Diagnosis

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A qualitative and quantitative assessment of simultaneous strain, shear wave, and point shear wave elastography to distinguish malignant and benign breast lesions

Acta Radiologica ◽

10.1177/0284185120961422 ◽

2020 ◽

pp. 028418512096142

Author(s):

Yasemin Altıntas ◽

Mehmet Bayrak ◽

Ömer Alabaz ◽

Medih Celiktas

Keyword(s):

Shear Wave ◽

Diagnostic Performance ◽

Area Under The Curve ◽

Shear Wave Elastography ◽

Strain Ratio ◽

Breast Lesions ◽

Breast Masses ◽

Malignant Breast ◽

The Mean ◽

Us Elastography

Background Ultrasound (US) elastography has become a routine instrument in ultrasonographic diagnosis that measures the consistency and stiffness of tissues. Purpose To distinguish benign and malignant breast masses using a single US system by comparing the diagnostic parameters of three kinds of breast elastography simultaneously added to B-mode ultrasonography. Material and Methods A total of 163 breast lesions in 159 consecutive women who underwent US-guided core needle biopsy were included in this prospective study. Before the biopsy, the lesions were examined with B-mode ultrasonography and strain (SE), shear wave (SWE), and point shear wave (STQ) elastography. The strain ratio was computed and the Tsukuba score determined. The mean elasticity values using SWE and STQ were computed and converted to Young’s modulus E (kPa). Results All SE, SWE, and STQ parameters showed similar diagnostic performance. The SE score, SE ratio, SWEmean, SWEmax, STQmean, and STQmax yielded higher specificity than B-mode US alone to differentiate benign and malignant masses. The sensitivity of B-mode US, SWE, and STQ was slightly higher than that of the SE score and SE ratio. The SE score, SE ratio, SWEmean, SWEmax, STQmean, and STQmax had significantly higher positive predictive value and diagnostic accuracy than B-mode US alone. The area under the curve for each of these elastography methods in differentiating benign and malignant breast lesions was 0.93, 0.93, 0.98, 0.97, 0.98, and 0.96, respectively; P<0.001 for all measurements. Conclusion SE (ratio and score), SWE, and STQ had higher diagnostic performance individually than B-mode US alone in distinguishing between malignant and benign breast masses.

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