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Golatta M, Pfob A, Büsch C, Bruckner T, Alwafai Z, Balleyguier C, Clevert DA, Duda V, Goncalo M, Gruber I, Hahn M, Kapetas P, Ohlinger R, Rutten M, Togawa R, Tozaki M, Wojcinski S, Rauch G, Heil J, Barr RG. The potential of combined shear wave and strain elastography to reduce unnecessary biopsies in breast cancer diagnostics - An international, multicentre trial. Eur J Cancer 2021; 161:1-9. [PMID: 34879299 DOI: 10.1016/j.ejca.2021.11.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Shear wave elastography (SWE) and strain elastography (SE) have shown promising potential in breast cancer diagnostics by evaluating the stiffness of a lesion. Combining these two techniques could further improve the diagnostic performance. We aimed to exploratorily define the cut-offs at which adding combined SWE and SE to B-mode breast ultrasound could help reclassify Breast Imaging Reporting and Data System (BI-RADS) 3-4 lesions to reduce the number of unnecessary breast biopsies. METHODS We report the secondary results of a prospective, multicentre, international trial (NCT02638935). The trial enrolled 1288 women with BI-RADS 3 to 4c breast masses on conventional B-mode breast ultrasound. All patients underwent SWE and SE (index test) and histopathologic evaluation (reference standard). Reduction of unnecessary biopsies (biopsies in benign lesions) and missed malignancies after recategorising with SWE and SE were the outcome measures. RESULTS On performing histopathologic evaluation, 368 of 1288 breast masses were malignant. Following the routine B-mode breast ultrasound assessment, 53.80% (495 of 920 patients) underwent an unnecessary biopsy. After recategorising BI-RADS 4a lesions (SWE cut-off ≥3.70 m/s, SE cut-off ≥1.0), 34.78% (320 of 920 patients) underwent an unnecessary biopsy corresponding to a 35.35% (320 versus 495) reduction of unnecessary biopsies. Malignancies in the new BI-RADS 3 cohort were missed in 1.96% (12 of 612 patients). CONCLUSION Adding combined SWE and SE to routine B-mode breast ultrasound to recategorise BI-RADS 4a patients could help reduce the number of unnecessary biopsies in breast diagnostics by about 35% while keeping the rate of undetected malignancies below the 2% ACR BI-RADS 3 definition.
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Combination of shear wave elastography and BI-RADS in identification of solid breast masses. BMC Med Imaging 2021; 21:183. [PMID: 34852775 PMCID: PMC8638471 DOI: 10.1186/s12880-021-00702-4] [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: 10/18/2021] [Accepted: 11/08/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To explore the value of quantitative shear wave elastography (SWE) plus the Breast Imaging Reporting and Data System (BI-RADS) in the identification of solid breast masses. METHODS A total of 108 patients with 120 solid breast masses admitted to our hospital from January 2019 to January 2020 were enrolled in this study. The pathological examination served as the gold standard for definitive diagnosis. Both SWE and BI-RADS grading were performed. RESULTS Out of the 120 solid breast masses in 108 patients, 75 benign and 45 malignant masses were pathologically confirmed. The size, shape, margin, internal echo, microcalcification, lateral acoustic shadow, and posterior acoustic enhancement of benign and malignant masses were significantly different (all P < 0.05). The E mean, E max, SD, and E ratio of benign and malignant masses were significantly different (all P < 0.05). The E min was similar between benign and malignant masses (P > 0.05). The percentage of Adler grade II-III of the benign masses was lower than that of the malignant masses (P < 0.05). BI-RADS plus SWE yielded higher diagnostic specificity and positive predictive value than either BI-RADS or SWE; BI-RADS plus SWE yielded the highest diagnostic accuracy among the three methods (all P < 0.05). CONCLUSION SWE plus routine ultrasonography BI-RADS has a higher value in differentiating benign from malignant breast masses than color doppler or SWE alone, which should be further promoted in clinical practice.
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Ma M, Liu R, Wen C, Xu W, Xu Z, Wang S, Wu J, Pan D, Zheng B, Qin G, Chen W. Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms. Eur Radiol 2021; 32:1652-1662. [PMID: 34647174 DOI: 10.1007/s00330-021-08271-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 06/25/2021] [Accepted: 08/12/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVES To evaluate the performance of interpretable machine learning models in predicting breast cancer molecular subtypes. METHODS We retrospectively enrolled 600 patients with invasive breast carcinoma between 2012 and 2019. The patients were randomly divided into a training (n = 450) and a testing (n = 150) set. The five constructed models were trained based on clinical characteristics and imaging features (mammography and ultrasonography). The model classification performances were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity. Shapley additive explanation (SHAP) technique was used to interpret the optimal model output. Then we choose the optimal model as the assisted model to evaluate the performance of another four radiologists in predicting the molecular subtype of breast cancer with or without model assistance, according to mammography and ultrasound images. RESULTS The decision tree (DT) model performed the best in distinguishing triple-negative breast cancer (TNBC) from other breast cancer subtypes, yielding an AUC of 0.971; accuracy, 0.947; sensitivity, 0.905; and specificity, 0.941. The accuracy, sensitivity, and specificity of all radiologists in distinguishing TNBC from other molecular subtypes and Luminal breast cancer from other molecular subtypes have significantly improved with the assistance of DT model. In the diagnosis of TNBC versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.090, 0.125, 0.114, and 0.060, 0.090, 0.083, respectively. In the diagnosis of Luminal versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.084, 0.152, 0.159, and 0.020, 0.100, 0.048. CONCLUSIONS This study established an interpretable machine learning model to differentiate between breast cancer molecular subtypes, providing additional values for radiologists. KEY POINTS • Interpretable machine learning model (MLM) could help clinicians and radiologists differentiate between breast cancer molecular subtypes. • The Shapley additive explanations (SHAP) technique can select important features for predicting the molecular subtypes of breast cancer from a large number of imaging signs. • Machine learning model can assist radiologists to evaluate the molecular subtype of breast cancer to some extent.
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Vassiou K, Fanariotis M, Tsougos I, Fezoulidis I. Incorporating diffusion-weighted imaging in a diagnostic algorithm for multiparametric MR mammography. Acta Radiol 2021; 63:1332-1343. [PMID: 34605311 DOI: 10.1177/02841851211041822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Apparent diffusion coefficient (ADC) measurements are not incorporated in BI-RADS classification. PURPOSE To assess the probability of malignancy of breast lesions at magnetic resonance mammography (MRM) at 3 T, by combining ADC measurements with the BI-RADS score, in order to improve the specificity of MRM. MATERIAL AND METHODS A total of 296 biopsy-proven breast lesions were included in this prospective study. MRM was performed at 3 T, using a standard protocol with dynamic sequence (DCE-MRI) and an extra echo-planar diffusion-weighted sequence. A freehand region of interest was drawn inside the lesion, and ADC values were calculated. Each lesion was categorized according to the BI-RADS classification. Logistic regression analysis was employed to predict the probability of malignancy of a lesion. The model combined the BI-RADS classification and the ADC value. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were calculated. RESULTS In total, 153 malignant and 143 benign lesions were analyzed; 257 lesions were masses and 39 lesions were non-mass-like enhancements. The sensitivity and specificity of the combined method were 96% and 86%, respectively, in contrast to 95% and 81% with BI-RADS classification alone. CONCLUSION We propose a method of assessing the probability of malignancy in breast lesions by combining BI-RADS score and ADC values into a single formula, increasing sensitivity and specificity compared to BI-RADS classification alone.
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Zhang B, Vakanski A, Xian M. BI-RADS-NET: AN EXPLAINABLE MULTITASK LEARNING APPROACH FOR CANCER DIAGNOSIS IN BREAST ULTRASOUND IMAGES. IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING : [PROCEEDINGS]. IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING 2021; 2021:10.1109/mlsp52302.2021.9596314. [PMID: 35509454 PMCID: PMC9063460 DOI: 10.1109/mlsp52302.2021.9596314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In healthcare, it is essential to explain the decision-making process of machine learning models to establish the trustworthiness of clinicians. This paper introduces BI-RADS-Net, a novel explainable deep learning approach for cancer detection in breast ultrasound images. The proposed approach incorporates tasks for explaining and classifying breast tumors, by learning feature representations relevant to clinical diagnosis. Explanations of the predictions (benign or malignant) are provided in terms of morphological features that are used by clinicians for diagnosis and reporting in medical practice. The employed features include the BI-RADS descriptors of shape, orientation, margin, echo pattern, and posterior features. Additionally, our approach predicts the likelihood of malignancy of the findings, which relates to the BI-RADS assessment category reported by clinicians. Experimental validation on a dataset consisting of 1,192 images indicates improved model accuracy, supported by explanations in clinical terms using the BI-RADS lexicon.
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Smith E, Moore DA, Jordan SG. You'll see it when you know it: granulomatous mastitis. Emerg Radiol 2021; 28:1213-1223. [PMID: 34292441 DOI: 10.1007/s10140-021-01931-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 04/07/2021] [Indexed: 01/24/2023]
Abstract
Granulomatous mastitis (GM) is an under-recognized and under-diagnosed disease. Patients with GM often present to the emergency room with a painful breast mass, nipple retraction, and skin changes. This pictorial essay will review the clinical presentation and imaging appearance of GM, BI-RADS reporting parameters, differential diagnoses, and diagnostic challenges posed by this disease. Early and accurate diagnosis is essential, as misdiagnosis can result in repeated core biopsies, leading to fistulae and sinus tract formation. A classic history and typical sonographic appearance allow the emergency radiologist to confidently make this diagnosis.
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Liu A, Yin L, Ma Y, Han P, Wu Y, Wu Y, Ye Z. Quantitative breast density measurement based on three-dimensional images: a study on cone-beam breast computed tomography. Acta Radiol 2021; 63:1023-1031. [PMID: 34259021 DOI: 10.1177/02841851211027386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Breast density is an independent predictor of breast cancer risk. Quantitative volumetric breast density (QVBD) is expected to provide more information on the prediction of breast cancer risk. PURPOSE To evaluate the reliability of QVBD measurements based on cone-beam breast computed tomography (CBBCT) images. MATERIAL AND METHODS A total of 216 breasts were used to evaluate the stability of QVBD measurements based on CBBCT images and the correlations between this volumetric measurement and visual and area-based measurement methods. The intra- and inter-observer consistency of QVBD measurements were compared. Visual breast density (VBD) was evaluated with Breast Imaging Reporting and Data System (BI-RADS) standard on CBBCT images. The correlation between QVBD and VBD was evaluated by Spearman correlation coefficient. Receiver operating characteristic (ROC) curve was used to assess the sensitivity and specificity of the volumetric method in distinguishing dense and non-dense breasts. The correlation between QVBD and quantitative area-based breast density (QABD) was determined with Pearson correlation coefficient. Then, the breast volume measured with CBBCT images was compared with the breast specimen obtained during nipple-sparing mastectomy (NSM) by Pearson correlation coefficient and linear regression. RESULTS Excellent intra- and inter-observer consistency was found from QVBD measurements. The volumetric method distinguished dense and non-dense breasts at a cutoff value of 9.5%, with 94.5% sensitivity and 77.1% specificity. Positive correlations were found between QVBD and QABD (r=0.890; P<0.001) and between the volume measured with CBBCT images and Archimedes method (r=0.969; P<0.001). CONCLUSION CBBCT images can evaluate breast density reliably on a continuous scale.
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Lin ZM, Chen JF, Xu FT, Liu CM, Chen JS, Wang Y, Zhang C, Huang PT. Principal component regression-based contrast-enhanced ultrasound evaluation system for the management of BI-RADS US 4A breast masses: objective assistance for radiologists. ULTRASOUND IN MEDICINE & BIOLOGY 2021; 47:1737-1746. [PMID: 33838937 DOI: 10.1016/j.ultrasmedbio.2021.02.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 02/23/2021] [Accepted: 02/26/2021] [Indexed: 06/12/2023]
Abstract
A portion of detected breast masses might be overrated by using the Breast Imaging-Reporting and Data System ultrasonography (BI-RADS US) lexicon. A principal component regression-based contrast-enhanced ultrasound (PCR-CEUS) evaluation system was built to quantitatively illustrate whether CEUS could help radiologists to differentiate 4A masses. The PCR-CEUS evaluation system, based on principal component analysis (PCA) and logistic regression, was verified by random assignment into training and test sets and shown to reduce the data dimension and avoid collinearity in CEUS variables. This prospective study consecutively collected 238 patients with 238 4A masses confirmed pathologically. All enrolled patients accepted CEUS examination. The diagnostic performance of senior and junior radiologists, PCR-CEUS and combined methods was compared. The PCR-CEUS system had consistent diagnostic performance in both the training and test sets, with an area under the curve (AUC) of 0.831 (0.765-0.897), 0.798 (0.7034-0.892) and 0.854 (0.765-0.943) (all P > 0.05). The AUC of the combined diagnostic model (PCR-CEUS + Senior radiologists) was higher than that of senior radiologists, and the combined model had higher sensitivity (0.875 (0.781-0.969) vs. 0.729 (0.603-0.855)) without compromising specificity. Furthermore, the AUC and specificity of the combined model (PCR-CEUS + Junior radiologists) (0.852 (0.787-0.916)) was higher than that of junior radiologists (0.665 (0.592-0.737) (P < 0.00001)). PCR-CEUS demonstrated good ability in differentiating malignant BI-RADS-US 4A masses and was helpful for both senior and junior radiologists.
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Cai S, Wang H, Zhang X, Zhang L, Zhu Q, Sun Q, Li J, Jiang Y. Superb Microvascular Imaging Technology Can Improve the Diagnostic Efficiency of the BI-RADS System. Front Oncol 2021; 11:634752. [PMID: 34249681 PMCID: PMC8263934 DOI: 10.3389/fonc.2021.634752] [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] [Received: 11/28/2020] [Accepted: 06/08/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND To explore whether superb microvascular imaging (SMI)SMI can improve the diagnostic efficiency by evaluating the vascular index (VI) and vascular architecture (VA) in breast lesions. METHODS This is a retrospective study of data collected prospectively for research use. Taking 225 consecutive cases of breast lesions from November 2016 to December 2017 as a training set, the VI values and VA types of benign and malignant lesions were calculated based on the pathological results. Taking 238 consecutive cases of breast lesions from January 2018 to October 2018 as the verification set, the diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were calculated to compare the diagnostic efficacy. RESULTS The training set included 225 breast lesions and the validation set 238 breast lesions. The VI value in the malignant group (10.3 ± 8.0) was significantly higher than that in the benign group (4.3 ± 5.0)(P<0.001). A VI value of 4.05 was used as the diagnostic threshold for differentiating benign from malignant lesions, with a sensitivity of 80.5%, a specificity of 61.9%, an accuracy of 71.1%, a PPV of 62.9%, a NPV of 76.9%, and an area under the curve of 0.758 (0.696-0.819). There was a significant difference in the types of benign and malignant VA (P < 0.001), and the PPV of the root hair-like and crab claw-like VAs were 93.9% and 100.0%, respectively. The diagnostic sensitivity, specificity, accuracy, PPV, NPV and area under the AUC curve were 58.0%, 98.2%, 97.0%, 70.3% and 0.781, respectively (95%CI: 0.719-0.844). SMI combined with conventional ultrasound improved the diagnostic specificity (70.0% vs. 90.0%), accuracy (87.4% vs. 96.6%), and PPV (82.5% vs. 93.2%) without decreasing the diagnostic sensitivity (99.3%), yielded higher diagnostic performance with the area under the ROC curve was 0.941 (95%CI: 0904-0.979) compared with conventional US alone (P < 0.001). CONCLUSION A VI value 4.05 is a cut-off value with good diagnostic efficacy. The residual root-like and crab claw-like VAs are the characteristic VAs of malignant lesions. Conventional ultrasound combined with the VI and VA can improve the diagnostic specificity, accuracy and PPV without reducing the diagnostic sensitivity.
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Spatial Distribution and Quantification of Mammographic Breast Density, and Its Correlation with BI-RADS Using an Image Segmentation Method. Life (Basel) 2021; 11:life11060516. [PMID: 34204876 PMCID: PMC8228882 DOI: 10.3390/life11060516] [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: 05/07/2021] [Revised: 05/28/2021] [Accepted: 06/01/2021] [Indexed: 11/17/2022] Open
Abstract
(1) Background: Mammographic breast density (MBD) and older age are classical breast cancer risk factors. Normally, MBDs are not evenly distributed in the breast, with different women having different spatial distribution and clustering patterns. The presence of MBDs makes tumors and other lesions challenging to be identified in mammograms. The objectives of this study were: (i) to quantify the amount of MBDs—in the whole (overall), different sub-regions, and different zones of the breast using an image segmentation method; (ii) to investigate the spatial distribution patterns of MBD in different sub-regions of the breast. (2) Methods: The image segmentation method was used to quantify the overall amount of MBDs in the whole breast (overall percentage density (PD)), in 48 sub-regions (regional PDs), and three different zones (zonal PDs) of the whole breast, and the results of the amount of MBDs in 48 sub-regional PDs were further analyzed to determine its spatial distribution pattern in the breast using Moran’s I values (spatial autocorrelation). (3) Results: The overall PD showed a negative correlation with age (p = 0.008); the younger women tended to have denser breasts (higher overall PD in breasts). We also found a higher proportion (p < 0.001) of positive autocorrelation pattern in the less dense breast group than in the denser breast group, suggesting that MBDs in the less dense breasts tend to be clustered together. Moreover, we also observed that MBDs in the mature women (<65 years old) tended to be clustered in the middle zone, while in older women (>64 years old) they tended to be clustered in both the posterior and middle zones. (4) Conclusions: There is an inverse relationship between the amount of MBD (overall PD in the breast) and age, and a different clustering pattern of MBDs between the older and mature women.
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Davis J, Liang J, Roh A, Kittrell L, Petterson M, Winton L, Connell M, Viscusi R, Komenaka I, Jamshidi R. Use of breast imaging-reporting and data system ( BI-RADS) ultrasound classification in pediatric and adolescent patients overestimates likelihood of malignancy. J Pediatr Surg 2021; 56:1000-1003. [PMID: 33494944 DOI: 10.1016/j.jpedsurg.2020.12.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 12/21/2020] [Accepted: 12/28/2020] [Indexed: 12/26/2022]
Abstract
BACKGROUND/PURPOSE Breast masses in the pediatric population cause patient and family concern, partially driven by public awareness of adult breast cancer. However, the spectrum of breast masses in children differs greatly from that in adults, and malignancy is exceedingly rare. The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) ultrasound-based classification system is the diagnostic standard, yet no study has validated BI-RADS in pediatric patients. This study compares BI-RADS classification with histologic diagnoses to evaluate BI-RADS validity in pediatric patients. METHODS Multicenter retrospective evaluation of breast masses in patients under 21 years. Ultrasound reports were compared with histologic diagnoses. RESULTS There were 283 patients with breast pathology results after excluding clinical diagnoses of gynecomastia. Mean age was 16.9 (SD 2.3), ranging 10-20 years. 227 had pre-operative ultrasounds, and 84% (191/227) were assigned a BI-RADS category. BI-RADS 4 was the most frequent category (55%, n = 124), by definition predicting 2 - 95% likelihood of malignancy. However, pathology was benign in all patients. CONCLUSIONS The current BI-RADS categorization system overestimates cancer risk when applied to pediatric patients. BI-RADS scores should not be assigned to pediatric patients, and BIRADS-defined recommendations for biopsy should be disregarded. A pediatric-specific classification system could be useful.
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Management of Pediatric Breast Masses: A Multi-institutional Retrospective Cohort Study. J Surg Res 2021; 264:309-315. [PMID: 33845414 DOI: 10.1016/j.jss.2021.01.041] [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: 10/05/2020] [Revised: 12/18/2020] [Accepted: 01/18/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND The objective of our study was to describe the workup, management, and outcomes of pediatric patients with breast masses undergoing operative intervention. MATERIALS AND METHODS A retrospective cohort study was conducted of girls 10-21 y of age who underwent surgery for a breast mass across 11 children's hospitals from 2011 to 2016. Demographic and clinical characteristics were summarized. RESULTS Four hundred and fifty-three female patients with a median age of 16 y (IQR: 3) underwent surgery for a breast mass during the study period. The most common preoperative imaging was breast ultrasound (95%); 28% reported the Breast Imaging Reporting and Data System (BI-RADS) classification. Preoperative core biopsy was performed in 12%. All patients underwent lumpectomy, most commonly due to mass size (45%) or growth (29%). The median maximum dimension of a mass on preoperative ultrasound was 2.8 cm (IQR: 1.9). Most operations were performed by pediatric surgeons (65%) and breast surgeons (25%). The most frequent pathology was fibroadenoma (75%); 3% were phyllodes. BI-RADS scoring ≥4 on breast ultrasound had a sensitivity of 0% and a negative predictive value of 93% for identifying phyllodes tumors. CONCLUSIONS Most pediatric breast masses are self-identified and benign. BI-RADS classification based on ultrasound was not consistently assigned and had little clinical utility for identifying phyllodes.
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Berg WA, Bandos AI, Zuley ML, Waheed UX. Training Radiologists to Interpret Contrast-enhanced Mammography: Toward a Standardized Lexicon. JOURNAL OF BREAST IMAGING 2021; 3:176-189. [PMID: 38424825 DOI: 10.1093/jbi/wbaa115] [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: 10/09/2020] [Accepted: 12/05/2020] [Indexed: 03/02/2024]
Abstract
OBJECTIVE Using terms adapted from the BI-RADS Mammography and MRI lexicons, we trained radiologists to interpret contrast-enhanced mammography (CEM) and assessed reliability of their description and assessment. METHODS A 60-minute presentation on CEM and terminology was reviewed independently by 21 breast imaging radiologist observers. For 21 CEM exams with 31 marked findings, observers recorded background parenchymal enhancement (BPE) (minimal, mild, moderate, marked), lesion type (oval/round or irregular mass, or non-mass enhancement), intensity of enhancement (none, weak, medium, strong), enhancement quality (none, homogeneous, heterogeneous, rim), and BI-RADS assessment category (2, 3, 4A, 4B, 4C, 5). "Expert" consensus of 3 other radiologists experienced in CEM was developed. Kappa statistic was used to assess agreement between radiologists and expert consensus, and between radiologists themselves, on imaging feature categories and final assessments. Reproducibility of specific feature descriptors was assessed as fraction of consensus-concordant responses. RESULTS Radiologists demonstrated moderate agreement for BPE, (mean kappa, 0.43; range, 0.05-0.69), and lowest reproducibility for "minimal." Agreement was substantial for lesion type (mean kappa, 0.70; range, 0.47-0.93), moderate for intensity of enhancement (mean kappa, 0.57; range, 0.44-0.76), and moderate for enhancement quality (mean kappa, 0.59; range, 0.20-0.78). Agreement on final assessment was fair (mean kappa, 0.26; range, 0.09-0.44), with BI-RADS category 3 the least reproducible. Decision to biopsy (BI-RADS 2-3 vs 4-5) showed moderate agreement with consensus (mean kappa, 0.54; range, -0.06-0.87). CONCLUSION With minimal training, agreement for description of CEM findings by breast imaging radiologists was comparable to other BI-RADS lexicons.
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Istomin A, Masarwah A, Vanninen R, Okuma H, Sudah M. Diagnostic performance of the Kaiser score for characterizing lesions on breast MRI with comparison to a multiparametric classification system. Eur J Radiol 2021; 138:109659. [PMID: 33752000 DOI: 10.1016/j.ejrad.2021.109659] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 03/05/2021] [Accepted: 03/15/2021] [Indexed: 12/20/2022]
Abstract
PURPOSE To determine the diagnostic performance of the Kaiser score and to compare it with the BI-RADS-based multiparametric classification system (MCS). METHOD Two breast radiologists, blinded to the clinical and pathological information, separately evaluated a database of 499 consecutive patients with structural 3.0 T breast MRI and 697 histopathologically verified lesions. The Kaiser scores and corresponding MCS categories were recorded. The sensitivity and specificity of the Kaiser score and the MCS categories to differentiate benign from malignant lesions were calculated. The interobserver reproducibility and receiver operating characteristic (ROC) parameters were analysed. RESULTS The sensitivity and specificity of the MCS were 100 % and 12 %, respectively, and those of the Kaiser score were 98.5 % and 34.8 % for reader 1 and 98.7 % and 47.5 % for reader 2. The area under the ROC-curve was 85.9 and 87.6 for readers 1 and 2. The interobserver intraclass correlation coefficient was excellent at 0.882. Reader 1 upgraded six lesions from BI-RADS 3 to a Kaiser score of >4, and reader 2 upgraded seven lesions. When applying the Kaiser score to 158 benign lesions readers 1 and 2 would have reduced the biopsy rate by 22.8 % and 35.4 %, respectively. CONCLUSIONS The Kaiser score showed high diagnostic accuracy with excellent interobserver reproducibility. The MCS had perfect sensitivity but low specificity. Although the Kaiser score had slightly lower sensitivity, its specificity was 3-4 times greater than that of the MCS. Thus, the Kaiser score has the potential to considerably reduce the biopsy rate for true negative lesions.
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Valencia-Hernandez I, Peregrina-Barreto H, Reyes-Garcia CA, Lopez-Armas GC. Density map and fuzzy classification for breast density by using BI-RADS. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105825. [PMID: 33190944 DOI: 10.1016/j.cmpb.2020.105825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 10/29/2020] [Indexed: 06/11/2023]
Abstract
Mammographic density (MD) is conformed by a different percentage of stromal, epithelial, and adipose tissue within the breast. One of the most critical findings in mammographic patterns for establishing a diagnosis of breast cancer is high breast tissue density. There is a wide variety of works focused on the study and automatic calculation of general breast density; however, they do not provide more detailed information about the changes that may occur within the breast tissue. This work proposes to generate a breast density map based on a texture analysis to identify the internal composition and distribution of the breast tissue through the diffuse division technique of the different densities inside the breast. Therefore, it is possible to obtain a density map associated with the breast that allows us to distinguish and quantify the different types of breast densities and their distribution according to the Breast Imaging Reporting and Data System (BI-RADS Breast Density Category). The proposed methodology was tested with mammograms from the BCDR and InBreast databases, demonstrating consistency in results and reaching an accuracy of 84.2% and 81.3%, respectively. Finally, the information obtained from the density map and its analysis could be a support tool for the specialist physician to monitor changes in breast density over time, since the fuzzy classification carried out allows quantifying the degree of membership in the BI-RADS breast density classes.
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Tozaki M, Yabuuchi H, Goto M, Sasaki M, Kubota K, Nakahara H. Effects of gadobutrol on background parenchymal enhancement and differential diagnosis between benign and malignant lesions in dynamic magnetic resonance imaging of the breast. Breast Cancer 2021; 28:927-936. [PMID: 33625722 DOI: 10.1007/s12282-021-01229-w] [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: 07/04/2020] [Accepted: 02/18/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND The high concentration of gadolinium in gadobutrol, which is widely used in Japan, helps visualize signal enhancement of neoplastic lesions, however, there was concern that high T1 relaxivity could decrease the contrast between the lesion and the background mammary gland. We evaluate the effect of gadobutrol on background parenchymal enhancement (BPE) and differential diagnosis between benign and malignant lesions in dynamic MRI of the breast. METHODS Ninety-nine patients were enrolled prospectively. Measurements of the following signal intensities (SIs) were obtained: breast tissue on a pre-contrast image (SIpre) and an early-phase image (SIearly); and the SIs of breast cancer on a pre-contrast image (SIpre-cancer) and an early-phase image (SIearly-cancer). We calculated the BPE ratio, i.e., (SIearly - SIpre)/SIpre and the cancer/BPE ratio, i.e., (SIearly-cancer - SIpre-cancer)/(SIearly on the affected side - SIpre on the affected side). These quantitative assessments were compared with the data from the recently published multicenter study (reference study without use of gadobutrol). In addition, two radiologists reinterpreted each of the MR images, and a third radiologist set the ROIs in the lesions and performed kinetic analysis as a Reader 3. RESULTS While there was no significant difference in the SI of breast cancer in the premenopausal patients between the two studies, that in postmenopausal patients was significantly higher in the present study than in the reference study (p = 0.002). Although there was no significant difference in the cancer/BPE ratio in the postmenopausal patients between the two studies, the cancer/BPE ratio in the premenopausal patients was significantly higher in the reference study than in the present study (p = 0.028). For differentiation between benign and malignant masses, the mass margin was found to be the most important term (p < 0.001). According to the data of Reader 3, visual washout was observed in all 18 patients in whom the interpretation was changed from "plateau" to "washout". CONCLUSIONS Gadobutrol may decrease the contrast between breast cancer and background parenchyma in premenopausal patients, and it may have a characteristic that "washout" does not easily occur, leading to "plateau" in patients with breast cancer.
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Qualitative characterization of breast tumors with diffusion-weighted imaging has comparable accuracy to quantitative analysis. Clin Imaging 2021; 77:17-24. [PMID: 33639496 DOI: 10.1016/j.clinimag.2021.02.025] [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: 11/09/2020] [Revised: 01/22/2021] [Accepted: 02/10/2021] [Indexed: 11/20/2022]
Abstract
PURPOSE To evaluate the applicability and accuracy of a new qualitative diffusion-weighted imaging (DWI) assessment method in the characterization of breast tumors compared to quantitative ADC measurement and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS After review board approval, MRIs of 216 consecutive women with final diagnoses (131 malignant, 85 benign) were retrospectively analyzed. Two radiologists independently scored DWI and dynamic contrast-enhanced MRI (DCE-MRI) according to malignancy probability. Qualitative assessments were performed by combined analysis of tumor morphology and diffusion signal. Quantitative data was obtained from apparent diffusion coefficient (ADC) measurements. Lastly, descriptive DWI features were evaluated and recorded. Cohen's kappa, receiver operating characteristic and multivariate analyzes were applied. RESULTS Of malignant tumors, 97% were visible on DWI. Qualitative and quantitative DWI assessments provided comparable sensitivities of 89-94% and 88-92% and specificities of 51-61% and 59-67%, respectively. There was no statistical difference between the accuracies of qualitative and quantitative DWI (p ≥ 0.105). Best diagnostic values were obtained with DCE-MRI (sensitivity, 99-100%; specificity, 69-71%). Inter-reader agreement was moderate (kappa = 0.597) for qualitative DWI and substantial (kappa = 0.689) for DCE-MRI (p < 0.001). Agreement between qualitative DWI and DCE-MRI scores was moderate (kappa = 0.536 and 0.442). Visual diffusion signal, mass margin and shape were the most predictive features of malignancy on multivariate analysis of qualitative assessment. CONCLUSION Qualitative characterization of breast tumors on DWI has comparable accuracy to quantitative ADC analysis. This method might be used to make DWI more widely available with eliminating the need to a predetermined ADC threshold in tumor characterization. However, lower accuracy and inter-reader agreement of it compared to DCE-MRI should be considered.
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Nykänen A, Okuma H, Sutela A, Masarwah A, Vanninen R, Sudah M. The mammographic breast density distribution of Finnish women with breast cancer and comparison of breast density reporting using the 4 th and 5 th editions of the Breast Imaging-Reporting and Data System. Eur J Radiol 2021; 137:109585. [PMID: 33607373 DOI: 10.1016/j.ejrad.2021.109585] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/24/2021] [Accepted: 02/03/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE To examine the breast density distribution in patients diagnosed with breast cancer in an eastern Finnish population and to examine the changes in breast density reporting patterns between the 4th and 5th editions of the Breast Imaging-Reporting and Data System (BI-RADS). METHOD 821 women (mean age 62.8 ± 12.2 years, range 28-94 years) with breast cancer were included in this retrospective study and their digital mammographic examinations were assessed semi-automatically and then visually by two radiologists in accordance with the 4th and 5th editions of the BI-RADS. Intraclass correlation coefficients (ICCs) were used to evaluate interobserver reproducibility. Chi-square tests were used to examine the associations between the breast density distribution and age or body mass index (BMI). RESULTS Interobserver reproducibility of the visual assessment was excellent, with an ICCr = 0.93. The majority of breast cancers occurred in fatty breasts (93.8 %) when density was assessed according to the 4th edition of the BI-RADS. The distributions remained constant after correction for age and BMI. Using the 5th edition, there was an overall 50.2 % decrease in almost entirely fatty (p < 0.001), 19.4 % increase in scattered fibroglandular (p < 0.001), 28.7 % increase in heterogeneously dense (p < 0.001), and 2.1 % increase in extremely dense (p < 0.001) categories. CONCLUSIONS Most breast cancers in eastern Finland occur in fatty breasts with an area density of < 50 %. Assessing breast density using the 5th edition of the BI-RADS greatly increased denser assessments.
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Kim S, Park B. Association between changes in mammographic density category and the risk of breast cancer: A nationwide cohort study in East-Asian women. Int J Cancer 2021; 148:2674-2684. [PMID: 33368233 DOI: 10.1002/ijc.33455] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 12/01/2020] [Accepted: 12/18/2020] [Indexed: 12/09/2022]
Abstract
Breast density is strongly associated with breast cancer risk; however, studies on the association between density changes and breast cancer risk have controversial results. The aim of our study was to determine the association between breast density changes and breast cancer risk in East-Asian women. We included 3 301 279 women aged ≥40 years screened for breast cancer twice during 2009 to 2010 and 2011 to 2012. Data were obtained from the National Health Insurance Service (NHIS) database. Breast density was evaluated using the Breast Imaging-Reporting and Data System (BI-RADS). Relative risk (RR) and 5-year risk of developing breast cancer according to density category changes were calculated. Overall, 23.0% of the women had a higher breast density and 22.2% of the women had a lower breast density in second screening compared to the first. An increase in the BI-RADS density category between two subsequent mammographic screenings was associated with an increase in breast cancer risk and vice versa in terms of RR. The 5-year breast cancer risk was affected by the initial BI-RADS density category, changes in density category and patients' characteristics such as age, menopausal status and family history of breast cancer. In patients with breast cancer family history, the 5-year breast cancer risk was prominent, at a maximum of 2.39% (95% CI = 1.23-3.55) in women with breast density category of 2 to 4. Changes in the BI-RADS density category were associated with breast cancer risk. Longitudinal measures of BI-RADS density may be helpful in identifying high-risk women, especially those with a breast cancer family history.
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Xu YJ, Gong HL, Hu B, Hu B. Role of "Stiff Rim" sign obtained by shear wave elastography in diagnosis and guiding therapy of breast cancer. Int J Med Sci 2021; 18:3615-3623. [PMID: 34522189 PMCID: PMC8436109 DOI: 10.7150/ijms.64243] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/18/2021] [Indexed: 11/12/2022] Open
Abstract
Background: Because the halo around the tumor in shear wave elastography (SWE) is defined as the "stiff rim" sign, the diagnosis of breast lesions with the stiff rim sign is popular. However, only a few studies have described the stiff rim sign quantitatively. Objective: This study aimed to investigate the usefulness of the stiff rim sign in the diagnosis and tumor, node, metastasis stage of breast cancer. Methods: Two hundred and ten breast lesions were analyzed retrospectively. The maximum, mean, minimum Young's modulus (YM), and the YM standard deviation in the lesion, the peritumoral stiffness (shell), and the region containing lesion and shell were obtained. The suspicious SWE feature with the best diagnostic performance was chosen to downgrade or upgrade the Breast Imaging Reporting and Data System (BI-RADS) classification. The coincidence rates of SWE and B-mode ultrasound in T staging and their positive predictive value (PPV) for T staging were compared. Results: The presence of "stiff rim" sign was selected to upgrade or downgrade the BI-RADS classification because of its best performance. In pathological benign lesions, 18.9% (25 of 132) of lesions should undergo biopsy if BI-RADS combined with the stiff rim sign were referred while it was 57.6% (76 of 132) if BI-RADS alone was referred. The coincidence rate of T2 staging evaluated by SWE was significantly higher than B-mode ultrasound (about 30% increase, P < 0.001). The PPVs of SWE for T1 and T2 staging were higher than B-mode ultrasound (P < 0.05). Conclusions: BI-RADS combined with "stiff rim" sign is expected to improve the diagnostic performance of breast lesions to avoid unnecessary biopsy. The maximum diameter of the lesion measured in SWE is more accurate than B-mode ultrasound in the estimation of T staging, which is beneficial to the treatment and prognosis of breast cancer.
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Sravani N, Ramesh A, Sureshkumar S, Vijayakumar C, Abdulbasith KM, Balasubramanian G, Ch Toi P. Diagnostic role of shear wave elastography for differentiating benign and malignant breast masses. SA J Radiol 2020; 24:1999. [PMID: 33391842 PMCID: PMC7756970 DOI: 10.4102/sajr.v24i1.1999] [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] [Received: 09/19/2020] [Accepted: 10/23/2020] [Indexed: 11/12/2022] Open
Abstract
Background Use of B-mode ultrasound (US) may not obviate the need for diagnosis by histopathology, which is an invasive technique and remains the gold standard. These limitations are being overcome with the advent of shear wave elastography (SWE). Objectives To assess the diagnostic role of SWE parameters and combined SWE and B-mode US in diagnosing malignant breast lesions. Method This cross-sectional study included all patients with a breast mass on clinical examination. A B-mode US with a Breast Imaging Reporting and Data System (BI-RADS) assessment and SWE evaluation (distance ratio [DR], area ratio [AR] and shear wave velocity [SWV]) in the lesion and healthy breast tissue of all recruited patients was performed. Cut-offs for SWE parameters were derived by receiver operating characteristic (ROC) analysis. The diagnostic performance of the B-mode US, the SWE parameters and the combined imaging in diagnosing malignancy was assessed. Results This study included a total of 175 breast masses. The median values of the SWE parameters were significantly higher (p < 0.001) in the malignant breast masses (DR, 1.29 vs. 1.03; AR, 1.69 vs. 1.06; and SWV, 9.1 metre per second [m/s] vs. 2.1 m/s). The ROC cut-off for malignancy was derived at 1.135 m/s, 1.18 m/s and 3.18 m/s, respectively, for DR, AR and SWV. The area under the ROC curve was highest for the DR (0.930), whilst this value was 0.914 and 0.901 for the SWV and AR, respectively. Amongst the respective sensitivities and specificities of the B-mode US (90.6% and 90%), SWE (97.6% and 61.1%), SWE (excluding AR) (96.5% and 77.8%) and combined imaging (100% and 72.2%), the highest sensitivity was noted for the combined method. Conclusion All the SWE parameters were significantly higher in the malignant breast masses, compared to the benign lesions. On combining SWE and B-mode US, there was a significant increase in sensitivity but a decrease in specificity.
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Current Status and Future of BI-RADS in Multimodality Imaging, From the AJR Special Series on Radiology Reporting and Data Systems. AJR Am J Roentgenol 2020; 216:860-873. [PMID: 33295802 DOI: 10.2214/ajr.20.24894] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BI-RADS is a communication and data tracking system that has evolved since its inception as a brief mammography lexicon and reporting guide into a robust structured reporting platform and comprehensive quality assurance tool for mammography, ultrasound, and MRI. Consistent and appropriate use of the BI-RADS lexicon terminology and assessment categories effectively communicates findings, estimates the risk of malignancy, and provides management recommendations to patients and referring clinicians. The impact of BI-RADS currently extends internationally through six language translations. A condensed version has been proposed to facilitate a phased implementation of BI-RADS in resource-constrained regions. The primary advance of the 5th edition of BI-RADS is harmonization of the lexicon terms across mammography, ultrasound, and MRI. Harmonization has also been achieved across these modalities for the reporting structure, assessment categories, management recommendations, and data tracking system. Areas for improvement relate to certain common findings that lack lexicon descriptors and a need for further clarification of proper use of category 3. BI-RADS is anticipated to continue to evolve for application to a range of emerging breast imaging modalities.
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Shankar PR, Davenport MS, Helvie MA. Prostate MRI and quality: lessons learned from breast imaging rad-path correlation. Abdom Radiol (NY) 2020; 45:4028-4030. [PMID: 31820045 DOI: 10.1007/s00261-019-02343-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Radiologic-pathologic correlation of Prostate Imaging Reporting & Data System (PI-RADS) scores ensures local quality and offers opportunities to improve future iterations of the reporting system. Tracking positive predictive values of lesion-targeted biopsies helps provide generalizable population-level risks associated with each PI-RADS category and can highlight the sources of variation. While this process of formalized pathologic correlation is somewhat new to abdominal radiology, we are fortunate to have a model to follow which was developed by our colleagues in breast imaging. If the success and multi-national adoption of BI-RADS is an indicator, building a scoring system anchored on a histologic reference is an important step to ensuring diagnostic quality and reliability.
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Pape R, Spuur KM, Wilkinson JM, Umo P. Correlation of the BI-RADS assessment categories of Papua New Guinean women with mammographic parenchymal patterns, age and diagnosis. J Med Radiat Sci 2020; 67:269-276. [PMID: 32936540 PMCID: PMC7754014 DOI: 10.1002/jmrs.422] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 07/31/2020] [Indexed: 12/24/2022] Open
Abstract
INTRODUCTION Women with increased breast density are at increased risk of breast cancer. The aim of this research is to evidence for the first time the mammographic breast findings of Papua New Guinean (PNG) women and the relationship between Breast Imaging-Reporting and Data System (BI-RADS) assessment, mammographic parenchymal patterns (MPPs) and age. METHODS A retrospective analysis of 1357 mammograms of women imaged at the Pacific International Hospital (PIH) from August 2006 to July 2010 was undertaken. Mammographic findings were categorised using the BI-RADS Atlas® 5th Edition. MPPs were recorded for each woman using the Tabár Pattern I-V classification system. Age was recorded in years. Statistical analysis was by descriptive analysis and Kruskal-Wallis with Dunn's post-test and Spearman's rho correlation for inferential analysis. RESULTS True pathological findings (benign and malignant); BI-RADS 2-5 were noted in 111 women (8.2%); 1242 (91.5%) were negative. BI-RADS categories for malignancy were reported in 16 (88.9%) of women aged 30 to 60 years. The lower risk Tabár Type I, II and III MPPs were associated with 94.4% (n = 17) of malignancies. Linear correlations between variables were weak and not statistically significant: age and Tabár pattern r = 0.031, P = 0.0261; age and BI-RADS r = 0.018, P = 0.517; Tabár pattern and BI-RADS r = 0.020, P = 0.459 (n = 1357). CONCLUSION There was no correlation demonstrated between BI-RADS category, age and MPP. Importantly, there was no correlation demonstrated between BI-RADS categories 4 and 5 for breast malignancy and high-risk Tabár Type IV and V MPPs. The results of this study again reflect that the incidence of breast cancer in PNG cannot be explained by breast density and suggest that any formalised screening program in PNG has a target age group aimed at women younger than that of Western screening programs.
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Esmaeili M, Ayyoubzadeh SM, Ahmadinejad N, Ghazisaeedi M, Nahvijou A, Maghooli K. A decision support system for mammography reports interpretation. Health Inf Sci Syst 2020; 8:17. [PMID: 32257128 PMCID: PMC7113352 DOI: 10.1007/s13755-020-00109-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 03/30/2020] [Indexed: 02/06/2023] Open
Abstract
PURPOSE Mammography plays a key role in the diagnosis of breast cancer; however, decision-making based on mammography reports is still challenging. This paper aims to addresses the challenges regarding decision-making based on mammography reports and propose a Clinical Decision Support System (CDSS) using data mining methods to help clinicians to interpret mammography reports. METHODS For this purpose, 2441 mammography reports were collected from Imam Khomeini Hospital from March 21, 2018, to March 20, 2019. In the first step, these mammography reports are analyzed and program code is developed to transform the reports into a dataset. Then, the weight of every feature of the dataset is calculated. Random Forest, Naïve Bayes, K-nearest neighbor (K-NN), Deep Learning classifiers are applied to the dataset to build a model capable of predicting the need for referral to biopsy. Afterward, the models are evaluated using cross-validation with measuring Area Under Curve (AUC), accuracy, sensitivity, specificity indices. RESULTS The mammography type (diagnostic or screening), mass and calcification features mentioned in the reports are the most important features for decision-making. Results reveal that the K-NN model is the most accurate and specific classifier with the accuracy and specificity values of 84.06% and 84.72% respectively. The Random Forest classifier has the best sensitivity and AUC with the sensitivity and AUC values of 87.74% and 0.905 respectively. CONCLUSIONS Accordingly, data mining approaches are proved to be a helpful tool to make the final decision as to whether patients should be referred to biopsy or not based on mammography reports. The developed CDSS may also be helpful especially for less experienced radiologists.
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