Effect of contrast dose on diagnostic performance in DCE‐MR breast imaging

ObjectiveThe aim of the study is to explore the potential value of S-Detect for residents-in-training, a computer-assisted diagnosis system based on deep learning (DL) algorithm.MethodsThe study was designed as a cross-sectional study. Routine breast ultrasound examinations were conducted by an experienced radiologist. The ultrasonic images of the lesions were retrospectively assessed by five residents-in-training according to the Breast Imaging Report and Data System (BI-RADS) lexicon, and a dichotomic classification of the lesions was provided by S-Detect. The diagnostic performances of S-Detect and the five residents were measured and compared using the pathological results as the gold standard. The category 4a lesions assessed by the residents were downgraded to possibly benign as classified by S-Detect. The diagnostic performance of the integrated results was compared with the original results of the residents.ParticipantsA total of 195 focal breast lesions were consecutively enrolled, including 82 malignant lesions and 113 benign lesions.ResultsS-Detect presented higher specificity (77.88%) and area under the curve (AUC) (0.82) than the residents (specificity: 19.47%–48.67%, AUC: 0.62–0.74). A total of 24, 31, 38, 32 and 42 identified as BI-RADS 4a lesions by residents 1, 2, 3, 4 and 5 were downgraded to possibly benign lesions by S-Detect, respectively. Among these downgraded lesions, 24, 28, 35, 30 and 40 lesions were proven to be pathologically benign, respectively. After combining the residents' results with the results of the software in category 4a lesions, the specificity and AUC of the five residents significantly improved (specificity: 46.02%–76.11%, AUC: 0.71–0.85, p<0.001). The intraclass correlation coefficient of the five residents also increased after integration (from 0.480 to 0.643).ConclusionsWith the help of the DL software, the specificity, overall diagnostic performance and interobserver agreement of the residents greatly improved. The software can be used as adjunctive tool for residents-in-training, downgrading 4a lesions to possibly benign and reducing unnecessary biopsies.

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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

Frontiers in Oncology ◽

10.3389/fonc.2021.773389 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zilong He ◽

Yue Li ◽

Weixiong Zeng ◽

Weimin Xu ◽

Jialing Liu ◽

...

Keyword(s):

Breast Imaging ◽

Diagnostic Performance ◽

Area Under The Curve ◽

Breast Mass ◽

Cad Model ◽

Breast Masses ◽

Radiology Reports ◽

Computer Aided ◽

Mass Lesions ◽

The Impact

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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Multiparameter MRI Model With DCE-MRI, DWI, and Synthetic MRI Improves the Diagnostic Performance of BI-RADS 4 Lesions

Frontiers in Oncology ◽

10.3389/fonc.2021.699127 ◽

2021 ◽

Vol 11 ◽

Author(s):

Shi Yun Sun ◽

Yingying Ding ◽

Zhuolin Li ◽

Lisha Nie ◽

Chengde Liao ◽

...

Keyword(s):

Relaxation Time ◽

Clinical Features ◽

Breast Imaging ◽

Diagnostic Performance ◽

Rate Of Change ◽

Enhancement Pattern ◽

Post Contrast ◽

Dce Mri ◽

Conventional Mri ◽

Mri Features

ObjectivesTo evaluate the value of synthetic magnetic resonance imaging (syMRI), diffusion-weighted imaging (DWI), DCE-MRI, and clinical features in breast imaging–reporting and data system (BI-RADS) 4 lesions, and develop an efficient method to help patients avoid unnecessary biopsy.MethodsA total of 75 patients with breast diseases classified as BI-RADS 4 (45 with malignant lesions and 30 with benign lesions) were prospectively enrolled in this study. T1-weighted imaging (T1WI), T2WI, DWI, and syMRI were performed at 3.0 T. Relaxation time (T1 and T2), apparent diffusion coefficient (ADC), conventional MRI features, and clinical features were assessed. “T” represents the relaxation time value of the region of interest pre-contrast scanning, and “T+” represents the value post-contrast scanning. The rate of change in the T value between pre- and post-contrast scanning was represented by ΔT%.ResultsΔT1%, T2, ADC, age, body mass index (BMI), menopause, irregular margins, and heterogeneous internal enhancement pattern were significantly associated with a breast cancer diagnosis in the multivariable logistic regression analysis. Based on the above parameters, four models were established: model 1 (BI-RADS model, including all conventional MRI features recommended by BI-RADS lexicon), model 2 (relaxation time model, including ΔT1% and T2), model 3 [multi-parameter (mp)MRI model, including ΔT1%, T2, ADC, margin, and internal enhancement pattern], and model 4 (combined image and clinical model, including ΔT1%, T2, ADC, margin, internal enhancement pattern, age, BMI, and menopausal state). Among these, model 4 has the best diagnostic performance, followed by models 3, 2, and 1.ConclusionsThe mpMRI model with DCE-MRI, DWI, and syMRI is a robust tool for evaluating the malignancies in BI-RADS 4 lesions. The clinical features could further improve the diagnostic performance of the model.

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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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Diagnostic Performance of Low Radiation Dose and Contrast-Dose Coronary Computed Tomography Angiography in the Detection of Coronary In-Stent Restenosis

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2018.2508 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1566-1572

Author(s):

Wang Du ◽

Zhanming Fan ◽

Rui Wang ◽

Lei Xu ◽

Jianying Li

Keyword(s):

Computed Tomography ◽

Radiation Dose ◽

Computed Tomography Angiography ◽

Diagnostic Performance ◽

Coronary Computed Tomography Angiography ◽

Low Radiation Dose ◽

Coronary Computed Tomography ◽

Contrast Dose ◽

Stent Restenosis ◽

In Stent Restenosis

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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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Section Discrepancy and Diagnostic Performance of Breast Lesions in Two-dimensional Ultrasound by Dynamic Videos versus Static Images

BIO Integration ◽

10.15212/bioi-2021-0021 ◽

2021 ◽

Author(s):

Dinghong Yang ◽

Xiaoyun Xiao ◽

Haohu Wang ◽

Huan Wu ◽

Wei Qin ◽

...

Keyword(s):

Breast Imaging ◽

Diagnostic Performance ◽

Architectural Distortion ◽

Breast Lesions ◽

Two Dimensional ◽

Benign Lesions ◽

Static Image ◽

Malignant Breast ◽

Static Images ◽

Malignant Lesions

Background: Benign or malignant breast lesions with typical ultrasonic characteristics could be easily and correctly diagnosed with two-dimensional ultrasound (2D US). However, diagnosis of atypical lesions remains a challenge. Most atypical lesions have different ultrasonographic features with probe direction variation. Thus, the interpretation of ultrasonographic features based on static images empirically collected by sonographers might be inaccurate. We aimed to investigate the section discrepancy and diagnostic performance of breast lesions in 2D US by dynamic videos versus static images.Methods: Static images and dynamic videos based on two perpendicular planes of 468 breast lesions were collected and evaluated. The Breast Imaging and Reporting Data System (BI-RADS®) US lexicon was used. Category 3 was used as the cut-off point, and section discrepancy was defined as two perpendicular planes showing different BI-RADS categories (3 versus 4A, 4B, 4C, and 5).Results: This retrospective study included 315 benign and 153 malignant lesions. There were 53 and 50 lesions with section discrepancy during static and dynamic observations, respectively. The proportion of benign lesions with section discrepancy was significantly higher than that of malignant lesions (P < 0.05) either in dynamic or static observation, and the contingency coefficient was 0.2 between section discrepancy and histopathology. Duct changes were more clearly depicted in dynamic videos than in static images (P < 0.05) both in malignant and benign lesions. Calcification and architectural distortion were more sensitively detected by dynamic videos than with static images (P < 0.05) in malignant lesions. The interpretation of “margin” significantly differed in benign lesions between static images and dynamic videos (P < 0.05). The areas under the curve of static image-horizontal, static image-sagittal, dynamic video-horizontal, and dynamic video-sagittal were 0.807, 0.820, 0.837, and 0.846, respectively. The specificities of dynamic videos were higher than those of static images (P < 0.05).Conclusion: Breast lesions have section discrepancy in 2D US. Observations based on dynamic videos could more accurately reflect lesion features and increase the specificity of US in the differentiation of atypical breast lesions.

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Additive Value of Texture Analysis Based on Breast MRI for Distinguishing Between Benign and Malignant Non-mass Enhancement in Premenopausal Women

10.21203/rs.3.rs-92781/v1 ◽

2020 ◽

Author(s):

Yu Tan ◽

Hui Mai ◽

Zhiqing Huang ◽

Li Zhang ◽

Chengwei Li ◽

...

Keyword(s):

Texture Analysis ◽

Breast Imaging ◽

Diagnostic Performance ◽

Premenopausal Women ◽

Growth Patterns ◽

Categorical Variables ◽

Validation Group ◽

Post Contrast ◽

Combination Model ◽

Mass Enhancement

Abstract Background: Non-mass enhancement (NME) is a diagnostic dilemma. Texture analysis (TA) could serve as an objective method to quantify tumor characteristics and growth patterns. However, there are few reports about TA use in NME diagnosis. To our knowledge, NME diagnosis based on the combination of the features noted on routine MRI and TA has not been reported.. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women. Methods: Women in whom NME was histologically proven (n = 147) were enrolled (benign: 58; malignant: 89) was retrospective. Then, 102 and 45 patients were classified as the training and validation groups, respectively. Scanning sequences included Fat-suppressed T2-weighted and fat-suppressed contrast-enhanced T1-weighted which were acquired on a 1.5T MRI system. Clinical and routine MR characteristics (CRMC) were evaluated by two radiologists according to the Breast Imaging and Reporting and Data system (2013). Texture features were extracted from all post-contrast sequences in the training group. The combination model was built and then assessed in the validation group. Pearson’s chi-square test and Mann-Whitney U test were used to compare categorical variables and continuous variables, respectively. Logistic regression analysis and receiver operating characteristic curve were employed to assess the diagnostic performance of CRMC, TA, and their combination model in NME diagnosis.Results: The combination model showed a superior diagnostic performance in differentiating between benign and malignant NME compared to that of CRMC or TA alone (AUC, 0.887 vs 0.832 vs 0.74). Moreover, compared to CRMC, the model showed high specificity (72.5% vs 80%). The results obtained in the validation group confirmed the model was promising.Conclusion: The combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME.

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Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women

BMC Medical Imaging ◽

10.1186/s12880-021-00571-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yu Tan ◽

Hui Mai ◽

Zhiqing Huang ◽

Li Zhang ◽

Chengwei Li ◽

...

Keyword(s):

Texture Analysis ◽

Breast Imaging ◽

Diagnostic Performance ◽

Characteristic Curve ◽

Premenopausal Women ◽

Categorical Variables ◽

Validation Group ◽

Post Contrast ◽

Combination Model ◽

Mass Enhancement

Abstract Background Non-mass enhancement (NME) is a diagnostic dilemma and highly reliant on the experience of the radiologists. Texture analysis (TA) could serve as an objective method to quantify lesion characteristics. However, it remains unclear what role TA plays in a predictive model based on routine MRI characteristics. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women. Methods Women in whom NME was histologically proven (n = 147) were enrolled (benign: 58; malignant: 89) was retrospective. Then, 102 and 45 patients were classified as the training and validation groups, respectively. Scanning sequences included Fat-suppressed T2-weighted and fat-suppressed contrast-enhanced T1-weighted which were acquired on a 1.5T MRI system. Clinical and routine MR characteristics (CRMC) were evaluated by two radiologists according to the Breast Imaging and Reporting and Data system (2013). Texture features were extracted from all post-contrast sequences in the training group. The combination model was built and then assessed in the validation group. Pearson’s chi-square test and Mann–Whitney U test were used to compare categorical variables and continuous variables, respectively. Logistic regression analysis and receiver operating characteristic curve were employed to assess the diagnostic performance of CRMC, TA, and their combination model in NME diagnosis. Results The combination model showed superior diagnostic performance in differentiating between benign and malignant NME compared to that of CRMC or TA alone (AUC, 0.887 vs 0.832 vs 0.74). Moreover, compared to CRMC, the model showed high specificity (72.5% vs 80%). The results obtained in the validation group confirmed the model was promising. Conclusions With the combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME.

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