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Mannina D, Kulkarni A, van der Pol CB, Al Mazroui R, Abdullah P, Joshi S, Alabousi A. Utilization of Texture Analysis in Differentiating Benign and Malignant Breast Masses: Comparison of Grayscale Ultrasound, Shear Wave Elastography, and Radiomic Features. JOURNAL OF BREAST IMAGING 2024:wbae037. [PMID: 39027926 DOI: 10.1093/jbi/wbae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Indexed: 07/20/2024]
Abstract
OBJECTIVE This study aims to determine which qualitative and quantitative US features are independently associated with malignancy, including those derived from grayscale imaging morphology, shear wave elastography (SWE), and texture analysis. METHODS This single-center retrospective study was approved by the institutional research ethics board. Consecutive breast US studies performed between January and December 2020 were included. Images were acquired using a Canon Aplio i800 US unit (Canon Medical Systems, Inc., CA) and i18LX5 wideband linear matrix transducer. Grayscale US features, SWE mean, and median elasticity were obtained. Single representative grayscale images were analyzed using dedicated software (LIFEx, version 6.30). First-order and gray-level co-occurrence matrix second-order texture features were extracted. Multivariate logistic regression was performed to assess for predictors of malignancy (STATA v16.1). RESULTS One hundred forty-seven cases with complete SWE data were selected for analysis (mean age 54.3, range 21-92). The following variables were found to be independently associated with malignancy: age (P <.001), family history (P = .013), irregular mass shape (P = .024), and stiffness on SWE (mean SWE ≥40 kPa; P <.001). Remaining variables (including texture features) were not found to be independently associated with malignancy (P >.05). CONCLUSION US texture analysis features were not associated with malignancy independent of other qualitative and quantitative US characteristics currently utilized in clinical practice. This suggests texture analysis may not be warranted when differentiating benign and malignant breast masses on US. In contrast, irregular mass shape on grayscale imaging and increased stiffness on SWE were found to be independent predictors of malignancy.
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Affiliation(s)
- Daniel Mannina
- Department of Radiology, McMaster University, Hamilton, ON, Canada
| | - Ameya Kulkarni
- Department of Radiology, McMaster University, Juravinski Hospital, Hamilton, ON, Canada
| | | | - Reem Al Mazroui
- Department of Radiology, Sultan Qaboos Comprehensive Cancer Care and Research Center, Muscat, Oman
| | - Peri Abdullah
- Department of Kinesiology, York University, Toronto, ON, Canada
| | - Sayali Joshi
- Hospital for Sick Children, IMS-University of Toronto, Toronto, ON, Canada
| | - Abdullah Alabousi
- Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, ON, Canada
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Eremici I, Borlea A, Dumitru C, Stoian D. Factors Associated with False Positive Breast Cancer Results in the Real-Time Sonoelastography Evaluation of Solid Breast Lesions. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1023. [PMID: 39064452 PMCID: PMC11279031 DOI: 10.3390/medicina60071023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/07/2024] [Accepted: 06/20/2024] [Indexed: 07/28/2024]
Abstract
Background and Objectives: Breast cancer is one of the most widespread cancers among the female population around the world and is curable if diagnosed in an early stage. Consequently, breast cancer screening imaging techniques have greatly evolved and adjusted over the last decades. Alongside mammography, sonoelastography became an important tool for breast cancer detection. However, sonoelastography still has its limitations, namely, there is still a high occurrence of false positive results in the BIRADS 4 category. The aim of our study is to identify potential false positive predictors and to ascertain the factors influencing the quality of strain ultrasound elastography for the evaluation of suspicious solid breast lesions categorized as BIRADS 4B, 4C, and 5. Materials and Methods: We conducted a retrospective study in a single private medical center in Timisoara between January 2017 and January 2022 analyzing 1625 solid breast lesions by the sonoelastography strain using a standardized BIRADS-US lexicon. Results: Our study showed that most sonoelastography factors linked to incorrect and overdiagnosis were due to a nodule dimension (OR = 1.02 per unit increase), posterior acoustic shadowing (OR = 12.26), reactive adenopathy (OR = 6.35), and an increased TES score (TES3 OR = 6.60; TES4 OR = 23.02; TES5 OR = 108.24). Regarding patient characteristics, age (OR = 1.09 per unit increase), BMI, (OR = 1.09 per unit increase), and breastfeeding history (OR = 3.00) were observed to increase the likelihood of false positive results. On the other hand, the nodules less likely to be part of the false positive group exhibited the following characteristics: a regular shape (OR = 0.27), homogenous consistency (OR = 0.42), and avascularity (OR = 0.22). Conclusions: Older age, high BMI, patients with a breastfeeding history, and those who exhibit the following specific nodule characteristics were most often linked to false positive results: large tumors with posterior acoustic shadowing and high elasticity scores, accompanied by reactive adenopathy. On the other hand, homogenous, avascular nodules with regular shapes were less likely to be misdiagnosed.
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Affiliation(s)
- Ivana Eremici
- PhD School, Victor Babes University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Andreea Borlea
- Department of Internal Medicine II, Victor Babes University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Catalin Dumitru
- Obstetrics and Gynecology Department, Victor Babes University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Dana Stoian
- Department of Internal Medicine II, Victor Babes University of Medicine and Pharmacy, 300041 Timisoara, Romania
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Yang Y, Long H, Feng Y, Tian S, Chen H, Zhou P. A multi-omics method for breast cancer diagnosis based on metabolites in exhaled breath, ultrasound imaging, and basic clinical information. Heliyon 2024; 10:e32115. [PMID: 38947468 PMCID: PMC11214460 DOI: 10.1016/j.heliyon.2024.e32115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 05/27/2024] [Accepted: 05/28/2024] [Indexed: 07/02/2024] Open
Abstract
Background and aims Through a nested cohort study, we evaluated the diagnostic performance of breath-omics in differentiating between benign and malignant breast lesions, and assessed the diagnostic performance of a multi-omics approach that combines breath-omics, ultrasound radiomics, and clinic-omics in distinguishing between benign and malignant breast lesions. Materials and methods We recruited 1,723 consecutive patients who underwent an automated breast volume scanner (ABVS) examination. Breath samples were collected and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOF-MS) to obtain breath-omics features. 238 of 1,723 enrolled participants have received pathological confirmation of breast nodules finally. The breast lesions of the 238 participants were contoured manually based on ABVS images for ultrasound radiomics feature calculation. Then, single- and multi-omics models were constructed and evaluated for breast nodules diagnosis via five-fold cross-validation. Results The area under the curve (AUC) of the breath-omics model was 0.855. In comparison, the multi-omics model demonstrated superior diagnostic performance for breast cancer, with sensitivity, specificity, and AUC of 84.1 %, 89.9 %, and 0.946, respectively. The multi-omics performance was comparable to that of the Breast Imaging Reporting and Data System (BI-RADS) classification via senior ultrasound physician evaluation. Conclusion The multi-omics approach combining metabolites in exhaled breath, ultrasound imaging, and basic clinical information exhibits superior diagnostic performance and promises to be a non-invasive and reliable tool for breast cancer diagnosis.
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Affiliation(s)
- Yuan Yang
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Huiling Long
- Hunan Drug Evaluation and Adverse Reaction Monitoring Center
| | - Yong Feng
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100071, China
| | - Shuangming Tian
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Haibin Chen
- Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100071, China
- Digital Medicine Division, Guangzhou Sinohealth Digital Technology Co., Ltd., Guangzhou, 510000, China
| | - Ping Zhou
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
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Xu P, Zhao J, Wan M, Song Q, Su Q, Wang D. Classification of multi-feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks. Med Phys 2024; 51:4243-4257. [PMID: 38436433 DOI: 10.1002/mp.16946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/03/2024] [Accepted: 01/09/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND Breast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI-RADS) category 4 has the highest false-positive value of about 30% among five categories. The classification task in BI-RADS category 4 is challenging and has not been fully studied. PURPOSE This work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B-mode images in category 4 to overcome the dependence on operator and artifacts. Additionally, this work intends to take full advantage of morphological and textural features in breast tumor US images to improve classification accuracy. METHODS First, original US images coming directly from the hospital were cropped and resized. In 1385 B-mode US BI-RADS category 4 images, the biopsy eliminated 503 samples of benign tumor and left 882 of malignant. Then, K-means clustering algorithm and entropy of sliding windows of US images were conducted. Considering the diversity of different characteristic information of malignant and benign represented by original B-mode images, K-means clustering images and entropy images, they are fused in a three-channel form multi-feature fusion images dataset. The training, validation, and test sets are 969, 277, and 139. With transfer learning, 11 CNN models including DenseNet and ResNet were investigated. Finally, by comparing accuracy, precision, recall, F1-score, and area under curve (AUC) of the results, models which had better performance were selected. The normality of data was assessed by Shapiro-Wilk test. DeLong test and independent t-test were used to evaluate the significant difference of AUC and other values. False discovery rate was utilized to ultimately evaluate the advantages of CNN with highest evaluation metrics. In addition, the study of anti-log compression was conducted but no improvement has shown in CNNs classification results. RESULTS With multi-feature fusion images, DenseNet121 has highest accuracy of 80.22 ± 1.45% compared to other CNNs, precision of 77.97 ± 2.89% and AUC of 0.82 ± 0.01. Multi-feature fusion improved accuracy of DenseNet121 by 1.87% from classification of original B-mode images (p < 0.05). CONCLUSION The CNNs with multi-feature fusion show a good potential of reducing the false-positive rate within category 4. The work illustrated that CNNs and fusion images have the potential to reduce false-positive rate in breast tumor within US BI-RADS category 4, and make the diagnosis of category 4 breast tumors to be more accurate and precise.
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Affiliation(s)
- Pengfei Xu
- Department of Biomedical Engineering, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Jing Zhao
- The Second Hospital of Jilin University, Changchun, China
| | - Mingxi Wan
- Department of Biomedical Engineering, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Qing Song
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Qiang Su
- Department of Oncology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Diya Wang
- Department of Biomedical Engineering, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
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Wang H, Hu Y, Tan C, Gu R, Li Y, Jin L, Jiang X, Mei J, Liu Q, Gong C. Differential diagnosis of breast mucinous carcinoma with an oval shape from fibroadenoma based on ultrasonographic features. BMC Womens Health 2024; 24:87. [PMID: 38310239 PMCID: PMC10838407 DOI: 10.1186/s12905-024-02910-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 01/16/2024] [Indexed: 02/05/2024] Open
Abstract
BACKGROUND Approximately 50% of breast mucinous carcinomas (MCs) are oval and have the possibility of being misdiagnosed as fibroadenomas (FAs). We aimed to identify the key features that can help differentiate breast MC with an oval shape from FA on ultrasonography (US). METHODS Seventy-six MCs from 71 consecutive patients and 50 FAs with an oval shape from 50 consecutive patients were included in our study. All lesions pathologically diagnosed. According to the Breast Imaging Reporting and Data System (BI-RADS), first, the ultrasonographic features of the MCs and FAs were recorded and a final category was assessed. Then, the differences in ultrasonographic characteristics between category 4 A (low-risk group) and category 4B-5 (medium-high- risk group) MCs were identified. Finally, other ultrasonographic features of MC and FA both with an oval shape were compared to determine the key factors for differential diagnosis. The Mann-Whitney test, χ2 test or Fisher's exact test was used to compare data between groups. RESULTS MCs with an oval shape (81.2%) and a circumscribed margin (25%) on US were more commonly assessed in the low-risk group (BI-RADS 4 A) than in the medium-high-risk group (BI-RADS 4B-5) (20%, p < 0.001 and 0%, p = 0.001, respectively). Compared with those with FA, patients with MC were older, and tended to have masses with non-hypoechoic patterns, not circumscribed margins, and a posterior echo enhancement on US (p < 0.001, p < 0.001, and p = 0.003, respectively). CONCLUSION The oval shape was the main reason for the underestimation of MCs. On US, an oval mass found in the breast of women of older age with non-hypoechoic patterns, not circumscribed margins, and a posterior echo enhancement was associated with an increased risk of being an MC, and should be subjected to active biopsy.
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Affiliation(s)
- Hongli Wang
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Yue Hu
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Cui Tan
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Department of Pathology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Ran Gu
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Yudong Li
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Liang Jin
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Xiaofang Jiang
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Jingsi Mei
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Qiang Liu
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
| | - Chang Gong
- Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
- Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
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Du Y, Yi CB, Du LW, Gong HY, Ling LJ, Ye XH, Zong M, Li CY. Combining primary tumor features derived from conventional and contrast-enhanced ultrasound facilitates the prediction of positive axillary lymph nodes in Breast Imaging Reporting and Data System category 4 malignant breast lesions. Diagn Interv Radiol 2023; 29:469-477. [PMID: 36994900 PMCID: PMC10679605 DOI: 10.4274/dir.2022.22534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 07/30/2022] [Indexed: 01/15/2023]
Abstract
PURPOSE To determine whether the primary tumor features derived from conventional ultrasound (US) and contrast-enhanced US (CEUS) facilitate the prediction of positive axillary lymph nodes (ALNs) in breast cancer diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4. METHODS A total of 240 women with breast cancer who underwent preoperative conventional US, strain elastography, and CEUS between September 2016 and December 2019 were included. The multiple parameters of the primary tumor were obtained, and univariate and multivariate analyses were performed to predict positive ALNs. Then three prediction models (conventional US features, CEUS features, and the combined features) were developed, and the diagnostic performance was evaluated with receiver operating characteristic curves. RESULTS On conventional US, the traits of large size and the non-circumscribed margin of the primary tumor were marked as two independent predictors. On CEUS, the features of vessel perforation or distortion and the enhanced range of the primary tumor were marked as two independent predictors for positive ALNs. Three prediction models were then developed: model A (conventional US features), model B (CEUS features), and model C (model A plus B). Model C yielded the highest area under the curve (AUC) of 0.82 [95% confidence interval (CI), 0.75-0.88] compared with model A (AUC 0.74; 95% CI, 0.68-0.81; P = 0.008) and model B (AUC 0.72; 95% CI, 0.65-0.80; P < 0.001) as per the DeLong test. CONCLUSION CEUS, as a non-invasive examination technique, can be used to predict ALN metastasis. Combining conventional US and CEUS may produce favorable predictive accuracy for positive ALNs in BI-RADS category 4 breast cancer.
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Affiliation(s)
- Yu Du
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Chun-Bei Yi
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Li-Wen Du
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hai-Yan Gong
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Li-Jun Ling
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xin-Hua Ye
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Min Zong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Cui-Ying Li
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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The Potential of Adding Mammography to Handheld Ultrasound or Automated Breast Ultrasound to Reduce Unnecessary Biopsies in BI-RADS Ultrasound Category 4a: A Multicenter Hospital-Based Study in China. Curr Oncol 2023; 30:3301-3314. [PMID: 36975464 PMCID: PMC10047589 DOI: 10.3390/curroncol30030251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/04/2023] [Accepted: 03/09/2023] [Indexed: 03/14/2023] Open
Abstract
The appropriate management strategies for BI-RADS category 4a lesions among handheld ultrasound (HHUS) remain a matter of debate. We aimed to explore the role of automated breast ultrasound (ABUS) or the second-look mammography (MAM) adjunct to ultrasound (US) of 4a masses to reduce unnecessary biopsies. Women aged 30 to 69 underwent HHUS and ABUS from 2016 to 2017 at five high-level hospitals in China, with those aged 40 or older also accepting MAM. Logistic regression analysis assessed image variables correlated with false-positive lesions in US category 4a. Unnecessary biopsies, invasive cancer (IC) yields, and diagnostic performance among different biopsy thresholds were compared. A total of 1946 women (44.9 ± 9.8 years) were eligible for analysis. The false-positive rate of category 4a in ABUS was almost 65.81% (77/117), which was similar to HHUS (67.55%; 127/188). Orientation, architectural distortion, and duct change were independent factors associated with the false-positive lesions in 4a of HHUS, whereas postmenopausal, calcification, and architectural distortion were significant features of ABUS (all p < 0.05). For HHUS, both unnecessary biopsy rate and IC yields were significantly reduced when changing biopsy thresholds by adding MAM for US 4a in the total population (scenario #1:BI-RADS 3, 4, and 5; scenario #2: BI-RADS 4 and 5) compared with the current scenario (all p < 0.05). Notably, scenario #1 reduced false-positive biopsies without affecting IC yields when compared to the current scenario for ABUS (p < 0.001; p = 0.125). The higher unnecessary biopsy rate of category 4a by ABUS was similar to HHUS. However, the second-look MAM adjunct to ABUS has the potential to safely reduce false-positive biopsies compared with HHUS.
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A Novel Nomogram Based on Imaging Biomarkers of Shear Wave Elastography, Angio Planewave Ultrasensitive Imaging, and Conventional Ultrasound for Preoperative Prediction of Malignancy in Patients with Breast Lesions. Diagnostics (Basel) 2023; 13:diagnostics13030540. [PMID: 36766645 PMCID: PMC9914566 DOI: 10.3390/diagnostics13030540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/18/2023] [Accepted: 01/28/2023] [Indexed: 02/05/2023] Open
Abstract
Several studies have demonstrated the difficulties in distinguishing malignant lesions of the breast from benign lesions owing to overlapping morphological features on ultrasound. Consequently, we aimed to develop a nomogram based on shear wave elastography (SWE), Angio Planewave Ultrasensitive imaging (Angio PLUS (AP)), and conventional ultrasound imaging biomarkers to predict malignancy in patients with breast lesions. This prospective study included 117 female patients with suspicious lesions of the breast. Features of lesions were extracted from SWE, AP, and conventional ultrasound images. The least absolute shrinkage and selection operator (Lasso) algorithms were used to select breast cancer-related imaging biomarkers, and a nomogram was developed based on six of the 16 imaging biomarkers. This model exhibited good discrimination (area under the receiver operating characteristic curve (AUC): 0.969; 95% confidence interval (CI): 0.928, 0.989) between malignant and benign breast lesions. Moreover, the nomogram also showed demonstrated good calibration and clinical usefulness. In conclusion, our nomogram can be a potentially useful tool for individually-tailored diagnosis of breast tumors in clinical practice.
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Liu M, He F, Xiao J. Application of S-detect combined with virtual touch imaging quantification in ultrasound for diagnosis of breast mass. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2022; 47:1089-1098. [PMID: 36097777 PMCID: PMC10950111 DOI: 10.11817/j.issn.1672-7347.2022.220078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Ultrasound is a safe and timely diagnosis method commonly used for breast lesion, however it depends on the operator to a certain degree. As an emerging technology, artificial intelligence can be combined with ultrasound in depth to improve the intelligence and precision of ultrasound diagnosis and avoid diagnostic errors caused by subjectivity of radiologists. This paper aims to investigate the value of artificial intelligence S-detect system combined with virtual touch imaging quantification (VTIQ) technique in the differential diagnosis of benign and malignant breast masses by conventional ultrasound (CUS). respectively, and AUCs for them were 0.74, 0.86, 0.79, and 0.94, respectively. The diagnostic accuracy of CUS+S-detect was higher than that of CUS (P<0.05). The diagnostic accuracy of CUS+S-detect was higher than that of CUS (P<0.05). The diagnostic specificity of CUS+VTIQ was higher than that of CUS (P<0.05). The diagnostic accuracy and AUC of CUS+S-detect+VTIQ were higher than those of S-detect or VTIQ applied to CUS alone (P<0.05). The sensitivities of CUS for senior radiologists, CUS for junior radiologists, CUS+S-detect+VTIQ for senior radiologists, and CUS+S-detect+VTIQ for junior radiologists were 60.00%, 80.00%, 72.73%, and 90.00%, respectively, when diagnosing benign and malignant breast masses in 50 randomly selected cases. The specificities for them was 66.67%, 76.67%, 80.00%, and 81.25%, respectively. The accuracies for them was 64.00%, 78.00%, 80.00%, and 88.00%, respectively. The AUCs for them were 0.63, 0.78, 0.88, and 0.80, respectively. Compared with the CUS for junior radiologists, the CUS+S-detect+VTIQ for junior radiologists had higher sensitivity, specificity, and accuracy (all P<0.05). The consistency of the combined application of S-detect and VTIQ for diagnosing breast masses at 2 different times was good among junior radiologists (Kappa=0.800). METHODS CUS, S-detects, and VTIQ were used to differentially diagnose benign and malignant breast masses in 108 cases, and the final pathological results were referred to as the gold standard for classifying breast masses. The diagnostic efficacy were evaluated and compared, among the 3 methods and among S-detect applied to CUS (CUS+S-detect), VTIQ applied to CUS (CUS+VTIQ), and S-detect combined with VTIQ applied to CUS (CUS+S-detect+VTIQ). Fifty cases were acquired randomly from the collected breast masses, and 2 radiologists with different years of experience (2 and 8 years) used S-detect combined with VTIQ for the ultrasonic differential diagnosis of benign and malignant breast masses. RESULTS The differences in sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) of the 3 diagnostic methods of CUS, S-detect, and VTIQ were not statistically significant (all P>0.05). The sensitivities of CUS, CUS+Sdetect, CUS+VTIQ, and CUS+S-detect+VTIQ were 78.57%, 92.86%, 69.05%, and 95.24%, respectively, the specificities for them were 69.70%, 78.79%, 87.88%, and 92.42%, respectively, the accuracies for them were 73.15%, 84.26%, 80.56%, and 93.52%. CONCLUSIONS S-detect combined with VTIQ when applied to CUS can overcome the shortcomings of separate applications and complement each other, especially for junior radiologists, and can more effectively improve the diagnostic efficacy of ultrasound for benign and malignant breast masses.
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Affiliation(s)
- Menghan Liu
- Department of Ultrasound, Third Xiangya Hospital, Central South University, Changsha 410013.
| | - Fang He
- Department of Ultrasound, Third Xiangya Hospital, Central South University, Changsha 410013
- Department of Ultrasound, Third Hospital of Changsha, Changsha 410035, China
| | - Jidong Xiao
- Department of Ultrasound, Third Xiangya Hospital, Central South University, Changsha 410013.
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程 扬, 夏 群, 王 俊, 解 红, 余 奕, 刘 海, 姚 志, 胡 金. [Value of ultrasonic S-Detect technique in diagnosis of breast masses]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2022; 42:1044-1049. [PMID: 35869768 PMCID: PMC9308870 DOI: 10.12122/j.issn.1673-4254.2022.07.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To evaluate the value of ultrasound S-Detect in the diagnosis of breast masses. METHODS A total of 85 breast masses in 62 female patients were diagnosed by S-Detect technique and conventional ultrasound. The diagnostic efficacy of conventional ultrasound and S-Detect technique was analyzed and compared with postoperative pathological results as the gold standard. RESULTS When operated by junior physicians, the diagnostic efficacy of conventional ultrasound was significantly lower than that of S-Detect technique (P < 0.05), but this difference was not observed in moderately experienced and senior physicians (P>0.05). S-Detect technique was positively correlated with the diagnostic results of senior physicians (r=0.97). Using S-Detect technique, the diagnostic efficacy did not differ significantly between the long axis section and its vertical section (P>0.05). Routine ultrasound showed a better diagnostic efficacy than S-Detect for breast masses with a diameter below 20 mm (P < 0.05), but for larger breast masses, its diagnostic efficacy was significantly lower than that of SDetect (P < 0.05). CONCLUSION S-Detect can be used in differential diagnosis of benign and malignant breast masses, and its diagnostic efficiency can be comparable with that of BI-RADS classification for moderately experienced and senior physicians, but its diagnostic efficacy can be low for breast masses less than 20 mm in diameter.
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Affiliation(s)
- 扬眉 程
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| | - 群 夏
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| | - 俊 王
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| | - 红娟 解
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| | - 奕 余
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| | - 海华 刘
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| | - 志正 姚
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
| | - 金花 胡
- />安徽医科大学附属安庆第一人民医院超声科,安徽 安庆 246001Department of Ultrasound, Anqing First People's Hospital Affiliated to Anhui Medical University, Anqing 246001, China
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Zhang H, Zhang XY, Wang Y. Value of magnetic resonance diffusion combined with perfusion imaging techniques for diagnosing potentially malignant breast lesions. World J Clin Cases 2022; 10:6021-6031. [PMID: 35949832 PMCID: PMC9254209 DOI: 10.12998/wjcc.v10.i18.6021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/23/2022] [Accepted: 04/21/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Lesions of breast imaging reporting and data system (BI-RADS) 4 at mammography vary from benign to malignant, leading to difficulties for clinicians to distinguish between them. The specificity of magnetic resonance imaging (MRI) in detecting breast is relatively low, leading to many false-positive results and high rates of re-examination or biopsy. Diffusion-weighted imaging (DWI), combined with perfusion-weighted imaging (PWI), might help to distinguish between benign and malignant BI-RADS 4 breast lesions at mammography.
AIM To evaluate the value of DWI and PWI in diagnosing BI-RADS 4 breast lesions.
METHODS This is a retrospective study which included patients who underwent breast MRI between May 2017 and May 2019 in the hospital. The lesions were divided into benign and malignant groups according to the classification of histopathological results. The diagnostic efficacy of DWI and PWI were analyzed respectively and combinedly. The 95 lesions were divided according to histopathological diagnosis, with 46 benign and 49 malignant. The main statistical methods used included the Student t-test, the Mann-Whitney U-test, the chi-square test or Fisher’s exact test.
RESULTS The mean apparent diffusion coefficient (ADC) values in the parenchyma and lesion area of the normal mammary gland were 1.82 ± 0.22 × 10-3 mm2/s and 1.24 ± 0.16 × 10-3 mm2/s, respectively (P = 0.021). The mean ADC value of the malignant group was 1.09 ± 0.23 × 10-3 mm2/s, which was lower than that of the benign group (1.42 ± 0.68 × 10-3 mm2/s) (P = 0.016). The volume transfer constant (Ktrans) and rate constant (Kep) values were higher in malignant lesions than in benign ones (all P < 0.001), but there were no significant statistical differences regarding volume fraction (Ve) (P = 0.866). The sensitivity and specificity of PWI combined with DWI (91.7% and 89.3%, respectively) were higher than that of PWI or DWI alone. The accuracy of PWI combined with DWI in predicting pathological results was significantly higher than that predicted by PWI or DWI alone.
CONCLUSION DWI, combined with PWI, might possibly distinguish between benign and malignant BI-RADS 4 breast lesions at mammography.
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Affiliation(s)
- Hui Zhang
- Department of Radiology, Hebei General Hospital, Shijiazhuang 050000, Hebei Province, China
| | - Xin-Yi Zhang
- Department of Radiology, the First Hospital of Hebei Medical University, Shijiazhuang 050000, Hebei Province, China
| | - Yong Wang
- Department of Radiology, the First Hospital of Hebei Medical University, Shijiazhuang 050000, Hebei Province, China
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12
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Wang B, Chen YY, Yang S, Chen ZW, Luo J, Cui XW, Dietrich CF, Yi AJ. Combined Use of Shear Wave Elastography, Microvascular Doppler Ultrasound Technique, and BI-RADS for the Differentiation of Benign and Malignant Breast Masses. Front Oncol 2022; 12:906501. [PMID: 35686093 PMCID: PMC9171023 DOI: 10.3389/fonc.2022.906501] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 04/25/2022] [Indexed: 12/07/2022] Open
Abstract
Objective To evaluate the value of the combined use of Breast Imaging Reporting and Data System (BI-RADS), qualitative shear wave elastography (SWE), and AngioPLUS microvascular Doppler ultrasound technique (AP) for distinguishing benign and malignant breast masses. Materials and Methods A total of 210 pathologically confirmed breast lesions in 210 patients were reviewed using BI-RADS, qualitative SWE, and AP. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), accuracy, and area under the receiver operating characteristic curve (AUC) of BI-RADS and the combination of qualitative SWE and/or AP with BI-RADS were compared, respectively. Results Compared with using BI-RADS alone, the use of combined qualitative SWE and/or AP with BI-RADS had higher AUC values (P < 0.001). Besides this, the combination of qualitative SWE and AP with BI-RADS had the best diagnostic performance for differentiating between benign and malignant masses. When AP and SWE were combined with BI-RADS, 49/76 benign masses were downgraded from BI-RADS category 4a into BI-RADS category 3, while no benign masses were upgraded from BI-RADS category 3 into BI-RADS category 4a. Three sub-centimeter malignant masses were downgraded from BI-RADS category 4a into BI-RADS category 3, while three malignant masses remain in BI-RADS category 3 due to a benign manifestation in both AP and qualitative SWE. Moreover, 5/6 of them were sub-centimeter masses, and 4/6 of them were intraductal carcinoma. The sensitivity, specificity, PPV, NPV, accuracy, and AUC were 91.0%, 81.1%, 69.3%, 95.1%, 84.3%, and 0.861 (95% confidence interval, 0.806–0.916; P < 0.001), respectively. Compared with BI-RADS alone, the sensitivity slightly decreased, while the specificity, PPV, NPV, and accuracy were significantly improved. Conclusion Combination of qualitative SWE and AP with BI-RADS improved the diagnostic performance in differentiating benign from malignant breast lesions, which is helpful for avoiding unnecessary biopsies. However, we should be careful about the downgrading of sub-centimeter BI-RADS 4a category lesions.
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Affiliation(s)
- Bin Wang
- Department of Medical Ultrasound, Yueyang Central Hospital, Yueyang, China
| | - Yu-Yuan Chen
- Department of Medical Ultrasound, Yueyang Central Hospital, Yueyang, China
| | - Si Yang
- Department of Medical Ultrasound, Yueyang Central Hospital, Yueyang, China
| | - Zhen-Wen Chen
- Department of Medical Ultrasound, Yueyang Central Hospital, Yueyang, China
| | - Jia Luo
- Department of Medical Ultrasound, Yueyang Central Hospital, Yueyang, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin, Kliniken Hirslanden Beau Site, Salem und Permanence, Bern, Switzerland
| | - Ai-Jiao Yi
- Department of Medical Ultrasound, Yueyang Central Hospital, Yueyang, China
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13
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The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study. Eur Radiol 2022; 32:4046-4055. [PMID: 35066633 DOI: 10.1007/s00330-021-08452-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/11/2021] [Accepted: 10/31/2021] [Indexed: 12/31/2022]
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Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions. Diagnostics (Basel) 2022; 12:diagnostics12010172. [PMID: 35054339 PMCID: PMC8774686 DOI: 10.3390/diagnostics12010172] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/07/2022] [Accepted: 01/10/2022] [Indexed: 01/22/2023] Open
Abstract
Improving the assessment of breast imaging reporting and data system (BI-RADS) 4 lesions and reducing unnecessary biopsies are urgent clinical issues. In this prospective study, a radiomic nomogram based on the automated breast volume scanner (ABVS) was constructed to identify benign and malignant BI-RADS 4 lesions and evaluate its value in reducing unnecessary biopsies. A total of 223 histologically confirmed BI-RADS 4 lesions were enrolled and assigned to the training and validation cohorts. A radiomic score was generated from the axial, sagittal, and coronal ABVS images. Combining the radiomic score and clinical-ultrasound factors, a radiomic nomogram was developed by multivariate logistic regression analysis. The nomogram integrating the radiomic score, lesion size, and BI-RADS 4 subcategories showed good discrimination between malignant and benign BI-RADS 4 lesions in the training (AUC, 0.959) and validation (AUC, 0.925) cohorts. Moreover, 42.5% of unnecessary biopsies would be reduced by using the nomogram, but nine (4%) malignant BI-RADS 4 lesions were unfortunately missed, of which 4A (77.8%) and small-sized (<10 mm) lesions (66.7%) accounted for the majority. The ABVS radiomics nomogram may be a potential tool to reduce unnecessary biopsies of BI-RADS 4 lesions, but its ability to detect small BI-RADS 4A lesions needs to be improved.
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15
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Hai L, Feng Y, Zhao J, Tang Q, Wang X, Cao X, Xiao C. An Improved Nomogram to Reduce False-Positive Biopsy Rates of Breast Imaging Reporting and Data System Ultrasonography Category 4A Lesions. Cancer Control 2022; 29:10732748221122703. [PMID: 37735939 PMCID: PMC9478716 DOI: 10.1177/10732748221122703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/25/2022] [Accepted: 08/08/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND The NCCN clinical guidelines recommended core needle biopsy for breast lesions classified as Breast Imaging Reporting and Data System (BI-RADS) 4, while category 4A lesions are only 2-10% likely to be malignant. Thus, a large number of biopsies of BI-RADS 4A lesions were ultimately determined to be benign, and those unnecessary biopsies may incur additional costs and pains. However, it is important to emphasize that the current risk prediction model focuses primarily on the details and complex risk features of US or MG findings, which may be difficult to apply in order to benefit from the model. To stratify and manage BI-RADS 4A lesions effectively and efficiently, a more effective and practical predictive model must be developed. METHODS We retrospectively analyzed 465 patients with BI-RADS ultrasonography (US) category 4A lesions, diagnosed between January 2019 and July 2019 in Tianjin Medical University Cancer Institute and Hospital and National Clinical Research Center for Cancer. Univariate and multivariate logistic regression analyses were conducted to identify risk factors. To stratify and predict the malignancy of BI-RADS 4A lesions, a nomogram combining the risk factors was constructed based on the multivariate logistic regression results. In order to determine the predictive performance of our predictive model, we used the concordance index (C-index), calibration curve, and receiver operating characteristic (ROC), and the decision curve analysis (DCA) to assess the clinical benefits. RESULTS Based on our analysis, 16.3% (76 out of 465) of patients were pathologically diagnosed with malignant lesions, while 83.6% (389 out of 465) were diagnosed with benign lesions. According to univariate and multivariate logistic regression analysis, age (OR = 3.414, 95%CI:1.849-6.303), nipple discharge (OR = .326, 95%CI:0.157-.835), palpable lesions (OR = 1.907, 95%CI:1.004-3.621), uncircumscribed margin (US) (OR = 1.732, 95%CI:1.033-2.905), calcification (mammography, MG) (OR = 2.384, 95%CI:1.366-4.161), BI-RADS(MG) (OR = 5.345, 95%CI:2.934-9.736) were incorporated into the predictive nomogram (C-index = .773). There was good agreement between the predicted risk and the observed probability of recurrence. Furthermore, we determined that 153 was the best cutoff score for distinguishing between patients in the low- and high-risk groups. Malignant lesions were significantly more prevalent in high-risk patients than in low-risk patients. CONCLUSION Based on clinical, US, and MG features, we present a predictive nomogram to reliably predict the malignancy risk of BI-RADS(US) 4A lesions, which may assist clinicians in the selection of patients at low risk of malignancy and reduce the number of false-positive biopsies.
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Affiliation(s)
- Linyue Hai
- The First Department of Breast
Cancer, Tianjin Medical University Cancer Institute &
Hospital, National Clinical Research Center for Cancer, Tianjin,
China
- Key Laboratory of Cancer Prevention
and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center
for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer
Prevention and Therapy, Tianjin Medical
University, Ministry of Education, Tianjin, China
| | - Youqin Feng
- The First Department of Breast
Cancer, Tianjin Medical University Cancer Institute &
Hospital, National Clinical Research Center for Cancer, Tianjin,
China
- Key Laboratory of Cancer Prevention
and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center
for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer
Prevention and Therapy, Tianjin Medical
University, Ministry of Education, Tianjin, China
| | - Jingjing Zhao
- The First Department of Breast
Cancer, Tianjin Medical University Cancer Institute &
Hospital, National Clinical Research Center for Cancer, Tianjin,
China
- Key Laboratory of Cancer Prevention
and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center
for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer
Prevention and Therapy, Tianjin Medical
University, Ministry of Education, Tianjin, China
| | - Qiang Tang
- The First Department of Breast
Cancer, Tianjin Medical University Cancer Institute &
Hospital, National Clinical Research Center for Cancer, Tianjin,
China
- Key Laboratory of Cancer Prevention
and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center
for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer
Prevention and Therapy, Tianjin Medical
University, Ministry of Education, Tianjin, China
| | - Xuefei Wang
- The First Department of Breast
Cancer, Tianjin Medical University Cancer Institute &
Hospital, National Clinical Research Center for Cancer, Tianjin,
China
- Key Laboratory of Cancer Prevention
and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center
for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer
Prevention and Therapy, Tianjin Medical
University, Ministry of Education, Tianjin, China
| | - Xuchen Cao
- The First Department of Breast
Cancer, Tianjin Medical University Cancer Institute &
Hospital, National Clinical Research Center for Cancer, Tianjin,
China
- Key Laboratory of Cancer Prevention
and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center
for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer
Prevention and Therapy, Tianjin Medical
University, Ministry of Education, Tianjin, China
| | - Chunhua Xiao
- The First Department of Breast
Cancer, Tianjin Medical University Cancer Institute &
Hospital, National Clinical Research Center for Cancer, Tianjin,
China
- Key Laboratory of Cancer Prevention
and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center
for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer
Prevention and Therapy, Tianjin Medical
University, Ministry of Education, Tianjin, China
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The Utility of the Fifth Edition of the BI-RADS Ultrasound Lexicon in Category 4 Breast Lesions: A Prospective Multicenter Study in China. Acad Radiol 2022; 29 Suppl 1:S26-S34. [PMID: 32768352 DOI: 10.1016/j.acra.2020.06.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 06/17/2020] [Accepted: 06/24/2020] [Indexed: 12/21/2022]
Abstract
RATIONALE AND OBJECTIVES The objective of this study was to evaluate the utility of the fifth edition of the Breast Imaging-Reporting and Data System (BI-RADS) in clinical breast radiology by using prospective multicenter real-time analyses of ultrasound (US) images. MATERIALS AND METHODS We prospectively studied 2049 female patients (age range, 19-86 years; mean age 46.88 years) with BI-RADS category 4 breast masses in 32 tertiary hospitals. All the patients underwent B-mode, color Doppler US, and US elastography examination. US features of the mass and associated features were described and categorized according to the fifth edition of the BI-RADS US lexicon. The pathological results were used as the reference standard. The positive predictive values (PPVs) of subcategories 4a-4c were calculated. RESULTS A total of 2094 masses were obtained, including 1124 benign masses (54.9%) and 925 malignant masses (45.1%). For BI-RADS US features of mass shape, orientation, margin, posterior features, calcifications, architectural distortion, edema, skin changes, vascularity, and elasticity assessment were significantly different for benign and malignant masses (p< 0.05). Typical signs of malignancy were irregular shape (PPV, 57.2%), spiculated margin (PPV, 83.7%), nonparallel orientation (PPV, 63.9%), and combined pattern of posterior features (PPV, 60.6%). For the changed or newly added US features, the PPVs for intraductal calcifications were 80%, 56.4% for internal vascularity, and 80% for a hard pattern on elastography. The associated features such as architectural distortion (PPV, 89.3%), edema (PPV, 69.2%), and skin changes (PPV, 76.2%) displayed high predictive value for malignancy. The rate of malignant was 7.4% (72/975) in category 4a, 61.4% (283/461) in category 4b, and 93.0% (570/613) in category 4c. The PPV for category 4b was higher than the likelihood ranges specified in BI-RADS and the PPVs for categories 4a and 4c were within the acceptable performance ranges specified in the fifth edition of BI-RADS in our study. CONCLUSION Not only the US features of the breast mass, but also associated features, including vascularity and elasticity assessment, have become an indispensable part of the fifth edition of BI-RADS US lexicon to distinguish benign and malignant breast lesions. The subdivision of category 4 lesions into categories 4a, 4b, and 4c for US findings is helpful for further assessment of the likelihood of malignancy of breast lesions.
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Zhao F, Cai C, Liu M, Xiao J. Identification of the lymph node metastasis-related automated breast volume scanning features for predicting axillary lymph node tumor burden of invasive breast cancer via a clinical prediction model. Front Endocrinol (Lausanne) 2022; 13:881761. [PMID: 35992122 PMCID: PMC9388849 DOI: 10.3389/fendo.2022.881761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/13/2022] [Indexed: 11/13/2022] Open
Abstract
Breast cancer has become the malignant tumor with the highest incidence in women. Axillary lymph node dissection (ALND) is an effective method of maintaining regional control; however, it is associated with a significant risk of complications. Meanwhile, whether the patients need ALND or not is according to sentinel lymph node biopsy (SLNB). However, the false-negative results of SLNB had been reported. Automated breast volume scanning (ABVS) is a routine examination in breast cancer. A real-world cohort consisting of 245 breast cancer patients who underwent ABVS examination were enrolled, including 251 tumor lesions. The ABVS manifestations were analyzed with the SLNB results, and the ALND results for selecting the lymph node metastasis were related to ABVS features. Finally, a nomogram was used to construct a breast cancer axillary lymph node tumor burden prediction model. Breast cancer patients with a molecular subtype of luminal B type, a maximum lesion diameter of ≥5 cm, tumor invasion of the Cooper's ligament, and tumor invasion of the nipple had heavy lymph node tumor burden. Molecular classification, tumor size, and Cooper's ligament status were used to construct a clinical prediction model of axillary lymph node tumor burden. The consistency indexes (or AUC) of the training cohort and the validation cohort were 0.743 and 0.711, respectively, which was close to SLNB (0.768). The best cutoff value of the ABVS nomogram was 81.146 points. After combination with ABVS features and SLNB, the AUC of the prediction model was 0.889, and the best cutoff value was 178.965 points. The calibration curve showed that the constructed nomogram clinical prediction model and the real results were highly consistent. The clinical prediction model constructed using molecular classification, tumor size, and Cooper's ligament status can effectively predict the probability of heavy axillary lymph node tumor burden, which can be the significant supplement to the SLNB. Therefore, this model may be used for individual decision-making in the diagnosis and treatments of breast cancer.
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Affiliation(s)
- Feng Zhao
- Department of Cardiovascular Surgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Department of Ultrasound, Third Xiangya Hospital, Central South University, Changsha, China
| | - Changjing Cai
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Menghan Liu
- Department of Ultrasound, Third Xiangya Hospital, Central South University, Changsha, China
| | - Jidong Xiao
- Department of Ultrasound, Third Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Jidong Xiao,
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Correlation Between Ultrasound BI-RADS 4 Breast Lesions and Fine Needle Cytology Categories in a Sample of Iraqi Female Patients. SERBIAN JOURNAL OF EXPERIMENTAL AND CLINICAL RESEARCH 2021. [DOI: 10.2478/sjecr-2021-0048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Abstract
Background: Breast cancer is the most common malignancy in female and the most registered cause of women’s mortality worldwide. BI-RADS 4 breast lesions are associated with an exceptionally high rate of benign breast pathology and breast cancer, so BI-RADS 4 is subdivided into 4A, 4B and 4C to standardize the risk estimation of breast lesions.
The aim of the study: to evaluate the correlation between BI-RADS 4 subdivisions 4A, 4B & 4C and the categories of reporting FNA cytology results.
Patients and Methods: A case series study was conducted in the Oncology Teaching Hospital in Baghdad from September 2018 to September 2019. Included patients had suspicious breast findings and given BI-RADS 4 (4A, 4B, or 4C) in the radiological report accordingly. Fine needle aspiration was performed under the ultrasound guide and the results were classified into five categories. The biopsy was performed for suspicious, malignant or equivocal FNA findings.
Results: This study included 158 women with BIRADS 4 breast lesions with the mean age of (44.6 years); There was a highly significant association between BI-RADS 4 breast lesion and FNA results (p<0.001); 51.9% of BI-RADS IV-C had C5 FNA results. There was a highly significant association between BI-RADS 4 lesion and the final diagnosis (p<0.001); 41.2% of BI-RADS 4 B had a malignant breast lesion, while 37.3% of BIRADS 4 C had a malignant lesion.
Conclusion: A clear relationship was observed between BI-RADS 4 subcategories and the fine needle aspiration cytology subgroups. BI-RADS 4-B is helpful in the discrimination between benign and malignant breast lesions; furthermore BI-RADS 4C has more acceptable validity in the diagnosis of breast malignancy. Therefore, BI-RADS subcategories are encouraged to be included and mentioned in the ultrasound report for more accurate estimation of the lesion nature.
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Prospective assessment of adjunctive ultrasound-guided diffuse optical tomography in women undergoing breast biopsy: Impact on BI-RADS assessments. Eur J Radiol 2021; 145:110029. [PMID: 34801874 DOI: 10.1016/j.ejrad.2021.110029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 10/22/2021] [Accepted: 11/09/2021] [Indexed: 11/23/2022]
Abstract
PURPOSE To assess the impact of adjunctive ultrasound guided diffuse optical tomography (US-guided DOT) on BI-RADS assessment in women undergoing US-guided breast biopsy. METHOD This prospective study enrolled women referred for US-guided breast biopsy between 3/5/2019 and 3/19/2020. Participants underwent US-guided DOT immediately before biopsy. The US-guided DOT acquisition generated average maximum total hemoglobin (HbT) spatial maps and quantitative HbT values. Four radiologists blinded to histopathology assessed conventional imaging (CI) to assign a CI BI-RADS assessment and then integrated DOT information in assigning a CI&DOT BI-RADS assessment. HbT was compared between benign and malignant lesions using an ANOVA test and Tukey's test. Benign biopsies were tabulated, deeming BI-RADS ≥ 4A as positive. Reader agreement was assessed. RESULTS Among 61 included women (mean age 48 years), biopsy demonstrated 15 (24.6%) malignant and 46 (75.4%) benign lesions. Mean HbT was 55.3 ± 22.6 µM in benign lesions versus 85.4 ± 15.6 µM in cancers (p < .001). HbT threshold of 78.5 µM achieved sensitivity 80% (12/15) and specificity 89% (41/46) for malignancy. Across readers and patients, 197 pairs of CI BI-RADS and CI&DOT BI-RADS assessments were assigned. Adjunctive US-guided DOT achieved a net decrease in 23.5% (31/132) of suspicious (CI BI-RADS ≥ 4A) assessments of benign lesions (34 correct downgrades and 3 incorrect upgrades). 38.3% (31/81) of 4A assessments were appropriately downgraded. No cancer was downgraded to a non-actionable assessment. Interreader agreement analysis demonstrated kappa = 0.48-0.53 for CI BI-RADS and kappa = 0.28-0.44 for CI&DOT BI-RADS. CONCLUSIONS Integration of US-guided DOT information achieved a 23.5% reduction in suspicious BI-RADS assessments for benign lesions. Larger studies are warranted, with attention to improved reader agreement.
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Berg WA. BI-RADS 3 on Screening Breast Ultrasound: What Is It and What Is the Appropriate Management? JOURNAL OF BREAST IMAGING 2021; 3:527-538. [PMID: 34545351 PMCID: PMC8445238 DOI: 10.1093/jbi/wbab060] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Indexed: 12/24/2022]
Abstract
US is widely used in breast imaging for diagnostic purposes and is also used increasingly for supplemental screening in women with dense breasts. US frequently depicts masses that are occult on mammography, even after tomosynthesis, and the vast majority of such masses are benign. Many masses seen only on screening US are easily recognized as benign simple cysts. Probably benign, BI-RADS 3, or low suspicion, BI-RADS 4A masses are also common and often prompt short-interval follow-up or biopsy, respectively, yet the vast majority of these are benign. This review details appropriate characterization, classification, and new approaches to the management of probably benign masses seen on screening US that can reduce false positives and, thereby, reduce costs and patient anxiety.
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Affiliation(s)
- Wendie A Berg
- University of Pittsburgh School of Medicine, Department of Radiology, Magee-Womens Hospital of UPMC, Pittsburgh, PA, USA
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21
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Yang Y, Hu Y, Shen S, Jiang X, Gu R, Wang H, Liu F, Mei J, Liang J, Jia H, Liu Q, Gong C. A new nomogram for predicting the malignant diagnosis of Breast Imaging Reporting and Data System (BI-RADS) ultrasonography category 4A lesions in women with dense breast tissue in the diagnostic setting. Quant Imaging Med Surg 2021; 11:3005-3017. [PMID: 34249630 DOI: 10.21037/qims-20-1203] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 03/05/2021] [Indexed: 11/06/2022]
Abstract
Background Biopsy has been recommended for Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. However, the malignancy rate of category 4A lesions is very low (2-10%). Therefore, most biopsies of category 4A lesions are benign, and the results will generally cause additional health care costs and patient anxiety. Methods A prediction model was developed based on an analysis of 418 BI-RADS ultrasonography (US) category 4A patients at Sun Yat-sen Memorial Hospital. Univariate and multivariate logistic regression analyses were applied to identify significant variables for inclusion in the final nomogram. The predictive accuracy and discriminative ability were evaluated using the concordance index (C-index) and calibration curves. An independent cohort of 97 patients from the Second Affiliated Hospital of Guangzhou Medical University was used for external validation. Results The independent risk factors from the multivariate analysis for the training cohort were family history of breast cancer (OR =4.588, P=0.004), US features [margin (OR =2.916, P=0.019), shape (irregular vs. oval, OR =2.474, P=0.044; round vs. oval, OR =1.935, P=0.276), parallel orientation vs. not parallel (OR =2.204, P=0.040)], low suspicious lymph nodes (OR =7.664, P=0.019), and suspicious calcifications on mammography (MG) (OR =6.736, P=0.001). The C-index was good in the training [0.813, 95% confidence interval (95% CI), 0.733 to 0.893] and validation cohorts (0.765, 95% CI, 0.584 to 0.946). The calibration curves showed optimal agreement between the nomogram prediction and actual observations for the probability of malignancy. Also, the cutoff score was set to 100 for discriminating high and low risk. The model performed well in discerning different risk groups. Conclusions We developed a well-discriminated and calibrated nomogram to predict the malignancy of BI-RADS US category 4A lesions in dense breast tissue, which may help clinicians identify patients at lower or higher risk.
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Affiliation(s)
- Yaping Yang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yue Hu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shiyu Shen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaofang Jiang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ran Gu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hongli Wang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Fengtao Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jingsi Mei
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jing Liang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Haixia Jia
- Department of Breast Surgery, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qiang Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chang Gong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
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22
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Cai Y, Zhu C, Chen Q, Zhao F, Guo S. Application of a second opinion ultrasound in Breast Imaging Reporting and Data System 4A cases: can immediate biopsy be avoided? J Int Med Res 2021; 49:3000605211024452. [PMID: 34162260 PMCID: PMC8236802 DOI: 10.1177/03000605211024452] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Objective The probability of malignancy in women who are diagnosed with a Breast Imaging Reporting and Data System (BI-RADS) 4A score is low. Application of a second opinion ultrasound (SOUS), which is low in cost and minimally invasive, may lower the biopsy rate for patients who fall into this category. This study aimed to apply SOUS to patients with a BI-RADS score of 4A and predict the pathological results of a biopsy. Methods One hundred seventy-eight patients were analyzed. Univariate and multivariate analyses were performed to screen for predictive factors that are associated with malignancy. Categorical alteration of downgraded, unchanged, or upgraded was made after SOUS results. Changes in category were compared with biopsies to determine their predictive value of benignancy or malignancy. Results Independent factors associated with malignancy were age (>50 years), tumor size (≥20 mm), margin (not circumscribed), orientation (not parallel), and peripheral location, and an upgraded categorical alteration from SOUS. Downgraded categorical alterations were associated with benignancy. Conclusions In BI-RADS 4A cases, a biopsy is recommended when independent factors are associated with malignancy. A downgraded result from an SOUS examination is a protective factor, supporting the likelihood of benignancy in these patients.
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Affiliation(s)
- Yantao Cai
- Department of General Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Chenfang Zhu
- Department of General Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Qianqian Chen
- Department of Ultrasound, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Feng Zhao
- Department of General Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Shanyu Guo
- Department of General Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
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Li WM, Sun QW, Fan XF, Zhang JC, Xu T, Shen QQ, Jia L. Mammography breast density: an effective supplemental modality for the precise grading of ultrasound BI-RADS 4 categories. Gland Surg 2021; 10:2010-2018. [PMID: 34268085 DOI: 10.21037/gs-21-313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/17/2021] [Indexed: 11/06/2022]
Abstract
Background High breast density is significantly associated with an increased risk of breast diseases. Presently, suspected breast masses assessed as Breast Imaging-Reporting and Data System (BI-RADS) grade 4 provide a wide range of positive predictive values. Moreover, subcategories (4a, 4b, and 4c) are still under consideration as the diagnostic criteria are neither comprehensive nor objective. However, whether mammography breast density (MBD) has any impact on the accurate grading of BI-RADS 4 assessed by ultrasound (US) remains unknown. Methods A total of 1,086 women with 1,293 breast masses were included and assessed as BI-RADS 3-5 by US. The subcategories of MBD (from the ACR-a to the ACR-d group) were assessed by mammography according to the criteria of the American College of Radiology (ACR). The clinicopathological characteristics of these patients were reviewed retrospectively. The malignancy rates of breast masses among different subgroups assessed by BI-RADS were re-estimated with MBD. Results Almost all BI-RADS 3 masses were classified as benign and nearly all BI-RADS 5 masses were identified as malignant. Significant inverse associations between MBD and malignancy rates were detected between the BI-RADS 4a and BI-RADS 4b groups. Moreover, malignancy rates decreased significantly from ACR-a to ACR-d for BI-RADS 4a and 4b breast lesions (P<0.001). However, this trend was not observed in BI-RADS 4c breast lesions. Conclusions MBD could serve as a crucial factor for the accurate grading of BI-RADS 4 lesions assessed by US. We strongly recommend the adoption of the MBD as a possible supplemental screening modality for US. Furthermore, it is equally beneficial for accurate risk assessment and screening recommendations based on MBD.
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Affiliation(s)
- Wei-Min Li
- Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Qiu-Wei Sun
- Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiao-Fang Fan
- Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Jun-Chao Zhang
- Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Ting Xu
- Department of Clinical and Research, Shenzhen Mindray Biomedical Electronics Co., Ltd, Shenzhen, China
| | - Qi-Qi Shen
- Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Lei Jia
- Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, China
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Zhang F, Jin L, Li G, Jia C, Shi Q, Du L, Wu R. The role of contrast-enhanced ultrasound in the diagnosis of malignant non-mass breast lesions and exploration of diagnostic criteria. Br J Radiol 2021; 94:20200880. [PMID: 33560894 DOI: 10.1259/bjr.20200880] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES To assess the value of contrast-enhanced ultrasound (CEUS) for diagnosing malignant non-mass breast lesions (NMLs) and to explore the CEUS diagnostic criteria. METHODS A total of 116 patients with 119 NMLs detected by conventional US were enrolled. Histopathological results were used as the reference standard. The enhancement characteristics of NMLs in CEUS were compared between malignant and benign NMLs. The CEUS diagnostic criteria for malignant NMLs were established using independent diagnostic indicators identified by binary logistic regression analysis. The diagnostic performance of Breast Imaging Reporting and Data System-US (BI-RADS-US), CEUS, and BI-RADS-US combined with CEUS was evaluated and compared. RESULTS Histopathological results showed 63 and 56 benign and malignant NMLs. Enhancement degree (OR = 5.75, p = 0.003), enhancement area (OR = 4.25, p = 0.005), and radial or penetrating vessels (OR = 7.54, p = 0.003) were independent diagnostic indicators included to establish the CEUS diagnostic criteria. The sensitivity and specificity of BI-RADS-US, CEUS, and BI-RADS-US combined with CEUS were 100 and 30.2%, 80.4 and 74.6%, and 94.6 and 77.8%, respectively; the corresponding areas under the receiver operating characteristic curve (AUC) were 0.819, 0.775, and 0.885, respectively. CONCLUSIONS CEUS has a high specificity in malignant NML diagnosis based on the diagnostic criteria including enhancement degree, enhancement area, and radial or penetrating vessels, but with lower sensitivity than BI-RADS-US. The combination of CEUS and BI-RADS-US is an effective diagnostic tool with both high sensitivity and specificity for the diagnosis of malignant NMLs. ADVANCES IN KNOWLEDGE In this study, we assessed the diagnostic value of CEUS for malignant NMLs and constructed a feasible diagnostic criterion. We further revealed that the combination of CEUS and BI-RADS-US has a high diagnostic value for malignant NMLs.
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Affiliation(s)
- Fan Zhang
- Department of Ultrasound, Shanghai General Hospital of Nanjing Medical University, Shanghai, China.,Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lifang Jin
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Gang Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chao Jia
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiusheng Shi
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lianfang Du
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rong Wu
- Department of Ultrasound, Shanghai General Hospital of Nanjing Medical University, Shanghai, China.,Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Kim SY, Choi Y, Kim EK, Han BK, Yoon JH, Choi JS, Chang JM. Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses. Sci Rep 2021; 11:395. [PMID: 33432076 PMCID: PMC7801712 DOI: 10.1038/s41598-020-79880-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 12/09/2020] [Indexed: 01/31/2023] Open
Abstract
A major limitation of screening breast ultrasound (US) is a substantial number of false-positive biopsy. This study aimed to develop a deep learning-based computer-aided diagnosis (DL-CAD)-based diagnostic model to improve the differential diagnosis of screening US-detected breast masses and reduce false-positive diagnoses. In this multicenter retrospective study, a diagnostic model was developed based on US images combined with information obtained from the DL-CAD software for patients with breast masses detected using screening US; the data were obtained from two hospitals (development set: 299 imaging studies in 2015). Quantitative morphologic features were obtained from the DL-CAD software, and the clinical findings were collected. Multivariable logistic regression analysis was performed to establish a DL-CAD-based nomogram, and the model was externally validated using data collected from 164 imaging studies conducted between 2018 and 2019 at another hospital. Among the quantitative morphologic features extracted from DL-CAD, a higher irregular shape score (P = .018) and lower parallel orientation score (P = .007) were associated with malignancy. The nomogram incorporating the DL-CAD-based quantitative features, radiologists' Breast Imaging Reporting and Data Systems (BI-RADS) final assessment (P = .014), and patient age (P < .001) exhibited good discrimination in both the development and validation cohorts (area under the receiver operating characteristic curve, 0.89 and 0.87). Compared with the radiologists' BI-RADS final assessment, the DL-CAD-based nomogram lowered the false-positive rate (68% vs. 31%, P < .001 in the development cohort; 97% vs. 45% P < .001 in the validation cohort) without affecting the sensitivity (98% vs. 93%, P = .317 in the development cohort; each 100% in the validation cohort). In conclusion, the proposed model showed good performance for differentiating screening US-detected breast masses, thus demonstrating a potential to reduce unnecessary biopsies.
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Affiliation(s)
- Soo -Yeon Kim
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Yunhee Choi
- Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea
| | - Eun -Kyung Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Boo-Kyung Han
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jung Hyun Yoon
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ji Soo Choi
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jung Min Chang
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
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Ghaemian N, Haji Ghazi Tehrani N, Nabahati M. Accuracy of mammography and ultrasonography and their BI-RADS in detection of breast malignancy. CASPIAN JOURNAL OF INTERNAL MEDICINE 2021; 12:573-579. [PMID: 34820065 PMCID: PMC8590403 DOI: 10.22088/cjim.12.4.573] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 02/02/2021] [Accepted: 03/13/2021] [Indexed: 11/27/2022]
Abstract
BACKGROUND We aimed to compare the diagnostic accuracy of mammography and ultrasonography and their breast imaging-reporting and data system (BI-RADS) classification versus breast core needle biopsy (CNB) findings in distinguishing the breast masses. METHODS This cross-sectional study was conducted during 2016-2018 on female patients who were referred to a radiology center in Babol, northern Iran, for routine screening and/or for CNB. Patients underwent sonography and mammography by a senior radiologist. The breast lesions were also evaluated according to BI-RADS classification. CNB was performed on the breast masses by the same radiologist and pathological procedures were performed by an expert pathologist. Descriptive statistics were used to analyze the data. RESULTS In total, 213 breast masses were finally assessed, of which 107 (50.2 %) masses were benign and 106 (49.8 %) masses were malignant. The sensitivity for mammography and ultrasound alone was 72.6% and 68.9%, respectively. This rate for combined mammography and ultrasound was 84.9%. About BI-RADS classification, 28 masses were classified as BI-RADS 3, 99 as BI-RADS 4A, 4 as BI-RADS 4B, 18 as BI-RADS 4C, and 64 as BI-RADS 5. BI-RADS 4A had the highest sensitivity (70.1%) among BI-RADS categories. The highest specificity pertained to BI-RADS 3 and 5 (100%) among BI-RADS categories. Also, the highest accuracy was related to BI-RADS 5 (80.3%). CONCLUSION The results of the present study showed that combined mammography and ultrasound had a higher rate of accuracy than mammography or ultrasound alone. Furthermore, the imaging methods BI-RADS classification had an acceptable positive predictive value.
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Affiliation(s)
- Naser Ghaemian
- Department of Radiology, Shahid Beheshti Hospital, Babol University of Medical Sciences, Babol, Iran
| | | | - Mehrdad Nabahati
- Department of Radiology, Shahid Beheshti Hospital, Babol University of Medical Sciences, Babol, Iran,Correspondence: Mehrdad Nabahati, Department of Radiology, Shahid Beheshti Hospital, Babol University of Medical Sciences, Ganjafrooz Street, Babol, 47176-47745, Iran. E-mail: , Tel: 0098 1132252071, Fax: 0098 1132252071
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Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A. BMC Cancer 2020; 20:959. [PMID: 33008320 PMCID: PMC7532640 DOI: 10.1186/s12885-020-07413-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 09/15/2020] [Indexed: 12/14/2022] Open
Abstract
Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.
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Cai SM, Wang HY, Zhang XY, Zhang L, Zhu QL, Li JC, Sun Q, Jiang YX. The Vascular Index of Superb Microvascular Imaging Can Improve the Diagnostic Accuracy for Breast Imaging Reporting and Data System Category 4 Breast Lesions. Cancer Manag Res 2020; 12:1819-1826. [PMID: 32210624 PMCID: PMC7073432 DOI: 10.2147/cmar.s242101] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 02/27/2020] [Indexed: 02/05/2023] Open
Abstract
PURPOSE To investigate whether the vascular index (VI) of superb microvascular imaging (SMI) could improve the diagnostic efficiency for BI-RADS 4 breast lesions and reduce the number of unnecessary biopsies. PATIENTS AND METHODS For this study, we selected 222 consecutive BI-RADS 4 breast lesions detected by ultrasound and confirmed by pathology from January 2016 to October 2018. A VI of 4.0 was set as the cutoff value to degrade BI-RADS classification. We calculated the accuracy, sensitivity and PPV of a BI-RADS diagnosis alone and the combination of BI-RADS and the VI. RESULTS Pathologically, of the 222 lesions, 129 were confirmed to be benign, and 93 were found to be malignant. A VI of 4.0 was set as the cutoff value; when the VI≤4.0, those BI-RADS 4 masses were downgraded one level (4C-4B, 4B-4A, 4A-3) to an integral BI-RADS grade, while the others maintained the conventional grade. A total of 54 BI-RADS 4 lesions were degraded to BI-RADS 3, including 53 benign lesions and 1 malignant lesion. The diagnostic accuracy (65.3% vs 41.9%) and PPV (54.8% vs 41.9%) were significantly improved. The sensitivity decreased slightly (98.9% vs 100%) because 1 of the 54 downgraded BI-RADS 4 lesions, which had a pathological type of invasive ductal carcinoma, was incorrectly downgraded. CONCLUSION SMI is a noninvasive tool for visualizing the vascular structure with high-resolution microvascular images. As a quantitative index, the VI can be used to appropriately downgrade benign lesions classified as BI-RADS 4, which can improve the diagnostic accuracy and PPV and reduce unnecessary biopsies.
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Affiliation(s)
- Si-Man Cai
- Department of Medical Ultrasound, Peking Union Medical College Hospital and Chinese Academy Medical Sciences, Beijing100730, People’s Republic of China
| | - Hong-Yan Wang
- Department of Medical Ultrasound, Peking Union Medical College Hospital and Chinese Academy Medical Sciences, Beijing100730, People’s Republic of China
| | - Xiao-Yan Zhang
- Department of Medical Ultrasound, Peking Union Medical College Hospital and Chinese Academy Medical Sciences, Beijing100730, People’s Republic of China
| | - Li Zhang
- Department of Medical Ultrasound, Peking Union Medical College Hospital and Chinese Academy Medical Sciences, Beijing100730, People’s Republic of China
| | - Qing-Li Zhu
- Department of Medical Ultrasound, Peking Union Medical College Hospital and Chinese Academy Medical Sciences, Beijing100730, People’s Republic of China
| | - Jian-Chu Li
- Department of Medical Ultrasound, Peking Union Medical College Hospital and Chinese Academy Medical Sciences, Beijing100730, People’s Republic of China
| | - Qiang Sun
- Department of Breast Surgery, Peking Union Medical College Hospital and Chinese Academy Medical Sciences, Beijing100730, People’s Republic of China
| | - Yu-Xin Jiang
- Department of Medical Ultrasound, Peking Union Medical College Hospital and Chinese Academy Medical Sciences, Beijing100730, People’s Republic of China
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Liu G, Zhang MK, He Y, Liu Y, Li XR, Wang ZL. BI-RADS 4 breast lesions: could multi-mode ultrasound be helpful for their diagnosis? Gland Surg 2019; 8:258-270. [PMID: 31328105 DOI: 10.21037/gs.2019.05.01] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background The malignant probability of Breast Imaging Reporting and Data System (BI-RADS) 4 breast lesions is 3-94%, which is a very large span, and thus leads to a high rate of unnecessary biopsy. Therefore, the differential diagnosis of benign and malignant BI-RADS 4 breast lesions has become extremely important. Thus, in this paper, we investigated the diagnostic value of conventional ultrasonography (US), contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) for BI-RADS 4 breast lesions, and tried to figure out a multi-mode ultrasonic method for them. Methods From March 2016 to May 2017, 118 breast lesions that were categorized as BI-RADS 4 lesions by US were studied with CEUS and SWE. All the lesions were confirmed by pathology via surgery or vacuum-assisted biopsy. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of US, CEUS and SWE were analyzed. Then the diagnostic efficacies of US, CEUS, SWE and the combination of these modalities were compared. Logistic regression analysis was performed to identify the independent risk factors. A multi-mode method to evaluate BI-RADS 4 lesions based on the logistic regression was developed. Results Of the 118 BI-RADS 4 lesions, 74 lesions (62.7%) were benign and 44 lesions (37.3%) were malignant. The diagnostic sensitivity and specificity for US, US + CEUS, US + SWE, US + CEUS + SWE were 88.6% and 75.7%, 86.4% and 94.6%, 88.6% and 90.5%, 97.7% and 93.2%, respectively. The area under the ROC curve (AUC) of US + SWE + CEUS was significantly higher than that of US (P<0.0001), US + CEUS (P=0.020), but there was no significant difference between the AUC of US + SWE + CEUS and the AUC of US + SWE. Conclusions US + CEUS + SWE and US + SWE could significantly improve the diagnostic efficiency and accuracy of US in the diagnosis of BI-RADS 4 breast lesions.
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Affiliation(s)
- Gang Liu
- Department of Radiology, Chinese People's Liberation Army General Hospital, Beijing 100853, China
| | - Meng-Ke Zhang
- Department of Ultrasound, Chinese People's Liberation Army General Hospital, Beijing 100853, China
| | - Yan He
- Department of Ultrasound, Chinese People's Liberation Army General Hospital, Beijing 100853, China
| | - Yuan Liu
- Department of Ultrasound, Chinese People's Liberation Army General Hospital, Beijing 100853, China
| | - Xi-Ru Li
- General Surgery Department, Chinese People's Liberation Army General Hospital, Beijing 100853, China
| | - Zhi-Li Wang
- Department of Ultrasound, Chinese People's Liberation Army General Hospital, Beijing 100853, China
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He P, Cui LG, Chen W, Yang RL. Subcategorization of Ultrasonographic BI-RADS Category 4: Assessment of Diagnostic Accuracy in Diagnosing Breast Lesions and Influence of Clinical Factors on Positive Predictive Value. ULTRASOUND IN MEDICINE & BIOLOGY 2019; 45:1253-1258. [PMID: 30799123 DOI: 10.1016/j.ultrasmedbio.2018.12.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 12/13/2018] [Accepted: 12/17/2018] [Indexed: 05/14/2023]
Abstract
To evaluate the diagnostic accuracy of subcategories 4a-4c of the second edition of the Breast Imaging Reporting and Data System (BI-RADS) ultrasonography (US) lexicon, and to investigate whether clinical factors influence the positive predictive values (PPVs). Overall, 1240 breast lesions in 1227 women diagnosed on ultrasound as category 4 and with pathology were included. The PPV with 95% confidence interval (CI) was 13.6% (95% CI: 11%, 16%) in BI-RADS 4a, 50.0% (95% CI: 44%, 56%) in BI-RADS 4b and 86.0% (95% CI: 82%, 90%) in BI-RADS 4c. Patients' age significantly affected PPVs for subcategories 4a-4c, whereas radiologists' experience and application time had little influence on PPVs for subcategories 4a-4c. In conclusion, the diagnostic accuracies of subcategories 4b and 4c were superior to subcategory 4a. Patients' age significantly affected PPVs for subcategories 4a-4c. Utilizing the subcategories of category 4 was a feasible method regardless of radiologists' experience and application time.
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Affiliation(s)
- Ping He
- Department of Ultrasound, Peking University Third Hospital, Beijing, China
| | - Li-Gang Cui
- Department of Ultrasound, Peking University Third Hospital, Beijing, China.
| | - Wen Chen
- Department of Ultrasound, Peking University Third Hospital, Beijing, China
| | - Ruo-Lin Yang
- Department of Ultrasound, Peking University Third Hospital, Beijing, China
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Dedicated breast PET value to evaluate BI-RADS 4 breast lesions. Eur J Radiol 2018; 108:201-207. [DOI: 10.1016/j.ejrad.2018.10.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 09/06/2018] [Accepted: 10/01/2018] [Indexed: 11/18/2022]
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Stember JN, Liu M, Poliak D, Hecht E, Laifer-Narin S. The Distal Acoustic Spotlight: a novel method to visualize the distal acoustic space on ultrasound. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aab336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Choi EJ, Choi H, Park EH, Song JS, Youk JH. Evaluation of an automated breast volume scanner according to the fifth edition of BI-RADS for breast ultrasound compared with hand-held ultrasound. Eur J Radiol 2018; 99:138-145. [PMID: 29362145 DOI: 10.1016/j.ejrad.2018.01.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2017] [Revised: 12/26/2017] [Accepted: 01/02/2018] [Indexed: 12/25/2022]
Abstract
OBJECTIVES To investigate the automated breast volume scanner (ABVS) in comparison with hand-held ultrasound (HHUS) according to the fifth edition of BI-RADS ultrasound. MATERIAL AND METHODS A total of 831 lesions in 786 patients who underwent both HHUS and ABVS were included. Three radiologists independently evaluated the sonographic features of each lesion according to the fifth BI-RADS edition. The kappa coefficient (κ) was calculated for each BI-RADS descriptor and final assessment category. The accuracy of malignancy prediction and diagnostic performance of the BI-RADS descriptors were assessed using multivariate logistic regression and area under the receiver operator characteristic curve (AUC), respectively. RESULTS ABVS and HHUS showed moderate to good interobserver agreement (κ = 0.53-0.67 and 0.55-0.70, respectively) except in associated features (κ = 0.31 and 0.36, respectively) for BI-RADS lexicons. Irregular shape, a non-circumscribed margin, and posterior features (combined or shadowing) were independently associated with malignancy in both ABVS and HHUS. Calcification presence on ABVS (odds ratio [OR], 95% confidence interval [CI]: 2.09, 1.11-3.94) and non-parallel orientation on HHUS (OR, 95% CI: 2.04, 1.10-3.78) were independently associated with malignancy. There were no significant differences between ABVS and HHUS in sensitivity (84.2% vs. 84.2%), specificity (80.5% vs. 83.9%), or AUC (0.88 vs. 0.90). CONCLUSIONS According to the fifth BI-RADS edition, ABVS is not statistically significantly different from HHUS with regard to interobserver variability and diagnostic performance.
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Affiliation(s)
- Eun Jung Choi
- Department of Radiology, Chonbuk National University Medical School and Hospital, Institute of Medical Science, Research Institute of Clinical Medicine, 20 Geonji-ro, Deokjin-gu, Jeonju City, Jeollabuk-Do 54907, South Korea.
| | - Hyemi Choi
- Department of Statistics, Chonbuk National University, Research Institute of Applied Statistics, 567 Baekje-daero, Deokjin-gu, Jeonju City, Jeollabuk-Do 54896, South Korea.
| | - Eun Hae Park
- Department of Radiology, Chonbuk National University Medical School and Hospital, Institute of Medical Science, Research Institute of Clinical Medicine, 20 Geonji-ro, Deokjin-gu, Jeonju City, Jeollabuk-Do 54907, South Korea.
| | - Ji Soo Song
- Department of Radiology, Chonbuk National University Medical School and Hospital, Institute of Medical Science, Research Institute of Clinical Medicine, 20 Geonji-ro, Deokjin-gu, Jeonju City, Jeollabuk-Do 54907, South Korea.
| | - Ji Hyun Youk
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-Gu, Seoul 06273, South Korea.
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Hu Y, Yang Y, Gu R, Jin L, Shen S, Liu F, Wang H, Mei J, Jiang X, Liu Q, Su F. Does patient age affect the PPV 3 of ACR BI-RADS Ultrasound categories 4 and 5 in the diagnostic setting? Eur Radiol 2018; 28:2492-2498. [PMID: 29302783 DOI: 10.1007/s00330-017-5203-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 11/12/2017] [Accepted: 11/22/2017] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To calculate the positive predictive value of biopsies performed (PPV3) of the Ultrasound section of the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS US) atlas categories 4 and 5 in different age groups and to determine whether patient age influences the PPV3 of each category in the diagnosis of breast lesions. METHODS We identified 2,433 ACR BI-RADS US categories 4 and 5 lesions with a known pathological diagnosis in 2,433 women. The patients were classified into three age groups (<35, 35-50, and >50 years). The age-related PPV3 of each category in the three age groups were calculated based on the pathological diagnoses and compared using the chi-squared test. RESULTS The overall PPV3 of each category was within the reference range provided by the ACR in 2013. PPV3 gradually increased with increasing age in patients with category 4 lesions. PPV3 in the oldest group with subcategories 4A and 4B lesions were close to or exceeded the reference values. CONCLUSIONS PPV3 and age were significantly associated in patients with category 4 lesions according to the newest edition of ACR BI-RADS US in the diagnostic setting. Closer attention should be given to older patients when assigning a final assessment category. KEY POINTS • In patients with category 4 lesions , the likelihood of malignancy is associated with age. • In patients with category 5 lesions, the association is not definite. • Closer attention should be given to older patients in applying the ACR BI-RADS US.
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Affiliation(s)
- Yue Hu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.,Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Yingfeng Road No. 33, 510260, Haizhu district, Guangzhou, Guangdong, China
| | - Yaping Yang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.,Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Yingfeng Road No. 33, 510260, Haizhu district, Guangzhou, Guangdong, China
| | - Ran Gu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.,Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Yingfeng Road No. 33, 510260, Haizhu district, Guangzhou, Guangdong, China
| | - Liang Jin
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.,Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Yingfeng Road No. 33, 510260, Haizhu district, Guangzhou, Guangdong, China
| | - Shiyu Shen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.,Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Yingfeng Road No. 33, 510260, Haizhu district, Guangzhou, Guangdong, China
| | - Fengtao Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.,Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Yingfeng Road No. 33, 510260, Haizhu district, Guangzhou, Guangdong, China
| | - Hongli Wang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.,Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Yingfeng Road No. 33, 510260, Haizhu district, Guangzhou, Guangdong, China
| | - Jingsi Mei
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.,Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Yingfeng Road No. 33, 510260, Haizhu district, Guangzhou, Guangdong, China
| | - Xiaofang Jiang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.,Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Yingfeng Road No. 33, 510260, Haizhu district, Guangzhou, Guangdong, China
| | - Qiang Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China. .,Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Yingfeng Road No. 33, 510260, Haizhu district, Guangzhou, Guangdong, China.
| | - Fengxi Su
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China. .,Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Yingfeng Road No. 33, 510260, Haizhu district, Guangzhou, Guangdong, China.
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Longo M, Bavcar S, Handel I, Smith S, Liuti T. Real-time elastosonography of lipomatous vs. malignant subcutaneous neoplasms in dogs: Preliminary results. Vet Radiol Ultrasound 2017; 59:198-202. [DOI: 10.1111/vru.12588] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 10/16/2017] [Accepted: 10/19/2017] [Indexed: 12/21/2022] Open
Affiliation(s)
- Maurizio Longo
- Royal (Dick) School of Veterinary Studies and Roslin Institute; The University of Edinburgh; Roslin EH25 9RG UK
| | - Spela Bavcar
- Royal (Dick) School of Veterinary Studies and Roslin Institute; The University of Edinburgh; Roslin EH25 9RG UK
| | - Ian Handel
- Royal (Dick) School of Veterinary Studies and Roslin Institute; The University of Edinburgh; Roslin EH25 9RG UK
| | - Sionagh Smith
- Royal (Dick) School of Veterinary Studies and Roslin Institute; The University of Edinburgh; Roslin EH25 9RG UK
| | - Tiziana Liuti
- Royal (Dick) School of Veterinary Studies and Roslin Institute; The University of Edinburgh; Roslin EH25 9RG UK
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Percutaneous Needle Biopsies of the Breast in Women Younger than 35 Years: Minimally or Excessively Invasive? Am Surg 2017. [DOI: 10.1177/000313481708301001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Percutaneous needle biopsy (PNB) of the breast is commonly used for diagnosis of breast pathology, but has been less studied in young women. We sought to determine the effectiveness and necessity of PNB in patients younger than 35 years of age. The charts of sequential patients <35 years who underwent PNB between February 2013 and May 2016 were reviewed; 181 PNB were performed in 127 patients. Median age was 30 years (13–34). Indications for PNB were Breast Imaging Reporting and Data System (BIRADS) ≥4 in 137 (75.7%) cases, with mass on imaging in 139 (76.8%). Carcinoma was diagnosed in 12 (6.6%), PNB in eight unique patients (6.3%). Other PNB pathology included atypia in four (2.2%) patients; papillary lesion, five (2.8%); benign lymph node, 10 (5.5%); fibroepithelial lesion, 15 (8.3%); benign breast tissue, 63 (34.8%); and fibroadenoma, 72 (39.8%). Women with atypia or malignancy were older than those with benign findings (30.9 vs 28.0 years, P = 0.002). No other patient or imaging factors were significantly associated with pathologic diagnosis on PNB. Routine PNB for all BIRADS 4 findings may be over-used in young women as most results are benign and subsequent surgical findings are concordant. Improved diagnostic accuracy of breast imaging is warranted to reduce unnecessary procedures.
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Spinelli Varella MA, Teixeira da Cruz J, Rauber A, Varella IS, Fleck JF, Moreira LF. Role of BI-RADS Ultrasound Subcategories 4A to 4C in Predicting Breast Cancer. Clin Breast Cancer 2017; 18:e507-e511. [PMID: 29066139 DOI: 10.1016/j.clbc.2017.09.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Revised: 09/01/2017] [Accepted: 09/07/2017] [Indexed: 11/26/2022]
Abstract
BACKGROUND The Breast Imaging Reporting and Data System (BI-RADS) ultrasound (US) categorization revised in 2013 by the American College of Radiology resulted in unquestionable standardization of reports and confirmed category 3 and 5 as benign and malignant lesions, respectively. In contrast, suspected images (category 4) have subcategorization criteria, although theses have been detailed difficult to apply. The aim of the present study was to determine the role of the US 4A to 4C BI-RADS subcategories in predicting malignancy. PATIENTS AND METHODS We performed a cross-sectional study of diagnostic tests to estimate the performance of the US BI-RADS categorization to clearly differentiate benign from malignant lesions. A total of 975 US examinations performed at the Hospital Femina, Grupo Hospitalar Conceição teaching hospitals from January 2012 through March 2015 were included in the present study. The US BI-RADS lexicon was used to classify the examination findings. Suspicious lesions underwent core needle biopsy, and the US and histology reports were compared to determine the performance using receiver operating characteristic curves. RESULTS Overall, the BI-RADS US categorization showed good discriminating accuracy with a receiver operating characteristic curve of 91% (95% confidence interval [CI], 88%-93%). However, BI-RADS subcategory 4b had a positive predictive value of 25% (95% CI, 20%-31%) and subcategory 4A had a positive predictive value of only 6% (95% CI, 3.5%-9.8%). CONCLUSION Our results have shown that US BI-RADS subcategories 4A and 4B are clearly unfit for use in screening tests, because they cannot rule out the need for biopsy. Therefore, management will not be improved by subcategorizing category 4, because all suspicious lesions will still require definite biopsy.
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Affiliation(s)
- Miguel Angelo Spinelli Varella
- Postgraduate Programme of Surgery, Faculty of Medicine, Rio Grande do Sul Federal University; Department of General and Breast Surgery, Hospital Femina, Grupo Hospitalar Conceição, Ministério da Saúde, Porto Alegre, Brazil.
| | - Jackson Teixeira da Cruz
- Department of Radiology, Ultrasonography Section, Hospital Femina, Grupo Hospitalar Conceição, Ministério da Saúde, Porto Alegre, Brazil
| | - Andrea Rauber
- Department of Gynecology and Obstetrics, Hospital Femina, Grupo Hospitalar Conceição, Ministério da Saúde, Porto Alegre, Brazil
| | - Ivana Santos Varella
- Postgraduate Programme of Epidemiology, Faculty of Medicine, Rio Grande do Sul Federal University; Núcleo de Epidemiologia Hospitalar do Grupo Hospitalar Conceição, Ministério da Saúde, Porto Alegre, Brazil
| | - James Freitas Fleck
- Department of Clinical Oncology, Rio Grande do Sul Federal University; Brazilian Research Council (Conselho Nacional de Pesquisa), Porto Alegre, Brazil
| | - Luis Fernando Moreira
- Department of Surgery, Postgraduate Programme of Surgery, Faculty of Medicine, Rio Grande do Sul Federal University and Hospital de Clínicas de Porto Alegre University Attached Hospital, Porto Alegre, Brazil
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Zou X, Wang J, Lan X, Lin Q, Han F, Liu L, Li A. Assessment of Diagnostic Accuracy and Efficiency of Categories 4 and 5 of the Second Edition of the BI-RADS Ultrasound Lexicon in Diagnosing Breast Lesions. ULTRASOUND IN MEDICINE & BIOLOGY 2016; 42:2065-71. [PMID: 27262521 DOI: 10.1016/j.ultrasmedbio.2016.04.020] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Revised: 04/14/2016] [Accepted: 04/27/2016] [Indexed: 05/14/2023]
Abstract
The purpose of this study was to evaluate the diagnostic accuracy and efficiency of categories 4 and 5 of the second edition of the Breast Imaging Reporting and Data System (BI-RADS) ultrasound (US) lexicon in diagnosing breast lesions. In our retrospective study, 579 lesions in 544 patients were assessed by US as the preliminary diagnosis and classified in subcategories 4a-4c and category 5 based on the second edition of the BI-RADS US lexicon with some obvious changes, such as the redefined margin, new calcification type, associated features and some special cases. Inter-observer agreement was determined. Ultrasound results were compared with the pathologic results for confirmation. Positive predictive values (PPVs) of subcategories 4a-4c were compared with theoretical values using the χ(2) test; the binomial test was used for category 5 lesions. Of the 579 lesions, 212 were confirmed as benign (36.61%), and the remaining 367 lesions were confirmed as borderline/malignant (63.39%). Inter-observer agreement was moderate for subcategories 4a-4c (κ = 0.52), moderate for subcategories 4a-4c and category 5 (κ = 0.56) and substantial for categories 4 and 5 (κ = 0.67). The PPVs for subcategories 4a-4c were 23.74%, 70.67% and 81.25%, respectively. In addition, the total PPV for category 4 was 46.92% (183/390), and the total PPV for category 5 was 97.35% (184/189). Statistical results revealed that the PPVs of subcategories 4a and 4b differed significantly from the theoretical values (p < 0.05); the PPVs of subcategory 4c and category 5 were significantly correlated with the theoretical PPVs (p > 0.05). In conclusion, subcategories 4a and 4b have lower diagnostic efficiency than subcategory 4c and category 5. Inter-observer agreement for subcategories 4a-4c remains to be improved. The most common features of subcategories 4a-4c differ, but overlap. It is recommended that inexperienced doctors in primary hospitals not classify lesions into subcategories in clinical practice.
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Affiliation(s)
- Xuebin Zou
- Department of Ultrasound, Cancer Center of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
| | - Jianwei Wang
- Department of Ultrasound, Cancer Center of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
| | - Xiaowen Lan
- Department of Radiation Oncology, Cancer Center of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
| | - Qingguang Lin
- Department of Ultrasound, Cancer Center of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
| | - Feng Han
- Department of Ultrasound, Cancer Center of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
| | - Longzhong Liu
- Department of Ultrasound, Cancer Center of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
| | - Anhua Li
- Department of Ultrasound, Cancer Center of Sun Yat-Sen University, Guangzhou, Guangdong Province, China.
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