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Liu W, Wang D, Liu L, Zhou Z. Assessing the Influence of B-US, CDFI, SE, and Patient Age on Predicting Molecular Subtypes in Breast Lesions Using Deep Learning Algorithms. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:1375-1388. [PMID: 38581195 DOI: 10.1002/jum.16460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/01/2024] [Accepted: 03/25/2024] [Indexed: 04/08/2024]
Abstract
OBJECTIVES Our study aims to investigate the impact of B-mode ultrasound (B-US) imaging, color Doppler flow imaging (CDFI), strain elastography (SE), and patient age on the prediction of molecular subtypes in breast lesions. METHODS Totally 2272 multimodal ultrasound imaging was collected from 198 patients. The ResNet-18 network was employed to predict four molecular subtypes from B-US imaging, CDFI, and SE of patients with different ages. All the images were split into training and testing datasets by the ratio of 80%:20%. The predictive performance on testing dataset was evaluated through 5 metrics including mean accuracy, precision, recall, F1-scores, and confusion matrix. RESULTS Based on B-US imaging, the test mean accuracy is 74.50%, the precision is 74.84%, the recall is 72.48%, and the F1-scores is 0.73. By combining B-US imaging with CDFI, the results were increased to 85.41%, 85.03%, 85.05%, and 0.84, respectively. With the integration of B-US imaging and SE, the results were changed to 75.64%, 74.69%, 73.86%, and 0.74, respectively. Using images from patients under 40 years old, the results were 90.48%, 90.88%, 88.47%, and 0.89. When images from patients who are above 40 years old, they were changed to 81.96%, 83.12%, 80.5%, and 0.81, respectively. CONCLUSION Multimodal ultrasound imaging can be used to accurately predict the molecular subtypes of breast lesions. In addition to B-US imaging, CDFI rather than SE contribute further to improve predictive performance. The predictive performance is notably better for patients under 40 years old compared with those who are 40 years old and above.
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Affiliation(s)
- Weiyong Liu
- Department of Ultrasound, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Dongyue Wang
- School of Management, Hefei University of Technology, Hefei, China
- Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, China
- Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies, Hefei, China
| | - Le Liu
- Department of Ultrasound, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Zhiguo Zhou
- Reliable Intelligence and Medical Innovation Laboratory (RIMI Lab), Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
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Shen Y, He J, Liu M, Hu J, Wan Y, Zhang T, Ding J, Dong J, Fu X. Diagnostic value of contrast-enhanced ultrasound and shear-wave elastography for small breast nodules. PeerJ 2024; 12:e17677. [PMID: 38974410 PMCID: PMC11227273 DOI: 10.7717/peerj.17677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 06/12/2024] [Indexed: 07/09/2024] Open
Abstract
Background The study aims to evaluate the diagnostic efficacy of contrast-enhanced ultrasound (CEUS) and shear-wave elastography (SWE) in detecting small malignant breast nodules in an effort to inform further refinements of the Breast Imaging Reporting and Data System (BI-RADS) classification system. Methods This study retrospectively analyzed patients with breast nodules who underwent conventional ultrasound, CEUS, and SWE at Gongli Hospital from November 2015 to December 2019. The inclusion criteria were nodules ≤ 2 cm in diameter with pathological outcomes determined by biopsy, no prior treatments, and solid or predominantly solid nodules. The exclusion criteria included pregnancy or lactation and low-quality images. Imaging features were detailed and classified per BI-RADS. Diagnostic accuracy was assessed using receiver operating characteristic curves. Results The study included 302 patients with 305 breast nodules, 113 of which were malignant. The diagnostic accuracy was significantly improved by combining the BI-RADS classification with CEUS and SWE. The combined approach yielded a sensitivity of 88.5%, specificity of 87.0%, positive predictive value of 80.0%, negative predictive value of 92.8%, and accuracy of 87.5% with an area under the curve of 0.877. Notably, 55.8% of BI-RADS 4A nodules were downgraded to BI-RADS 3 and confirmed as benign after pathological examination, suggesting the potential to avoid unnecessary biopsies. Conclusion The integrated use of the BI-RADS classification, CEUS, and SWE enhances the accuracy of differentiating benign and malignant small breast nodule, potentially reducing the need for unnecessary biopsies.
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Affiliation(s)
- Yan Shen
- Department of Medical Ultrasound, Gongli Hospital, Shanghai, China
| | - Jie He
- Department of Medical Ultrasound, Gongli Hospital, Shanghai, China
| | - Miao Liu
- Department of Medical Ultrasound, Gongli Hospital, Shanghai, China
| | - Jiaojiao Hu
- Department of Medical Ultrasound, Gongli Hospital, Shanghai, China
| | - Yonglin Wan
- Department of Medical Ultrasound, Gongli Hospital, Shanghai, China
| | - Tingting Zhang
- Department of Medical Ultrasound, Gongli Hospital, Shanghai, China
| | - Jun Ding
- Department of Pathology, Gongli Hospital, Shanghai, China
| | - Jiangnan Dong
- Department of Surgery, Gongli Hospital, Shanghai, China
| | - Xiaohong Fu
- Department of Medical Ultrasound, Gongli Hospital, Shanghai, China
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Sandoval-Ato R, Coral-Gonzales P, Coronel-Arias S, Espinoza-Mantilla L, Terrones-Chaparro G, Serna-Alarcón V. Clinical and radiological manifestations associated with triple-negative breast cancer in women from northern Peru. A case-control study. Ecancermedicalscience 2024; 18:1720. [PMID: 39021537 PMCID: PMC11254400 DOI: 10.3332/ecancer.2024.1720] [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: 02/23/2024] [Indexed: 07/20/2024] Open
Abstract
Objective Triple-negative breast cancer (TNBC) has an aggressive clinical behaviour, with advanced stages at initial diagnostic evaluation, early recurrences and poor survival, so the purpose was to determine the clinical and radiological manifestations associated with TNBC. Materials and methods A case-control study in women diagnosed with breast cancer from January 2015 to August 2022 at the 'Instituto Regional de Enfermedades Neoplásicas del Norte'. We classified cases (Triple Negative subtype) and controls (Luminal A, Luminal B and HER2) according to immunohistochemistry ical analysis. Bivariate and multivariate logistic regression models were used to calculate the odds ratio (OR) with their respective 95% confidence intervals (CIs). Results The medical reports of 88 cases and 236 controls were reviewed. Cases were more likely to report pain (p = 0.001), nodules on ultrasound (p = 0.01) and mammography (p = 0.003), superior median size (p < 0.05), posterior enhancement (p = 0.001) and moderate density (p = 0.003). Multivariate analysis identified that TNBC was more likely to have a nodular type lesion by ultrasound (OR: 9.73, 95% CI: 1.10-86.16; p = 0.04), ultrasound lesion larger than 36 mm (OR: 4.99, 95% CI: 1.75-14.17; p = 0.003) and moderate density (OR: 3.83, 95% CI: 1.44-10.14; p = 0.007). Conclusion There are particular clinical and imaging manifestations of TNBC, showing that radiological lesions that presented characteristics in ultrasound as nodular type lesions larger than 36 mm and in mammography moderate grade density, were associated with this subtype of breast tumours in a Peruvian population.
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Affiliation(s)
- Raúl Sandoval-Ato
- Escuela de Posgrado, Facultad de Medicina, Universidad Privada Antenor Orrego, Trujillo 13008, Perú
- Unidad de Investigación Clínica, Scientia Clinical and Epidemiological Research Institute, Trujillo 13001, Perú
- https://orcid.org/0000-0001-8666-7188
| | - Patricia Coral-Gonzales
- Servicio de Radiodiagnóstico, Instituto Regional de Enfermedades Neoplásicas Norte, Trujillo 13008, Perú
- Escuela Profesional de Medicina, Facultad de Medicina, Universidad Privada Antenor Orrego, Trujillo 13008, Perú
- https://orcid.org/0000-0002-8734-4687
| | - Sebastian Coronel-Arias
- Servicio de Radiodiagnóstico, Instituto Regional de Enfermedades Neoplásicas Norte, Trujillo 13008, Perú
- https://orcid.org/0000-0002-2607-7191
| | - Luisa Espinoza-Mantilla
- Servicio de Radiodiagnóstico, Instituto Regional de Enfermedades Neoplásicas Norte, Trujillo 13008, Perú
- https://orcid.org/0000-0002-5465-7775
| | - Grace Terrones-Chaparro
- Servicio de Radiodiagnóstico, Instituto Regional de Enfermedades Neoplásicas Norte, Trujillo 13008, Perú
- https://orcid.org/0000-0001-6938-1401
| | - Victor Serna-Alarcón
- Escuela Profesional de Medicina, Facultad de Medicina, Universidad Privada Antenor Orrego, Trujillo 13008, Perú
- https://orcid.org/0000-0002-9803-6217
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Cai L, Sidey-Gibbons C, Nees J, Riedel F, Schäfgen B, Togawa R, Killinger K, Heil J, Pfob A, Golatta M. Can multi-modal radiomics using pretreatment ultrasound and tomosynthesis predict response to neoadjuvant systemic treatment in breast cancer? Eur Radiol 2024; 34:2560-2573. [PMID: 37707548 PMCID: PMC10957593 DOI: 10.1007/s00330-023-10238-6] [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: 03/10/2023] [Revised: 07/17/2023] [Accepted: 08/01/2023] [Indexed: 09/15/2023]
Abstract
OBJECTIVES Response assessment to neoadjuvant systemic treatment (NAST) to guide individualized treatment in breast cancer is a clinical research priority. We aimed to develop an intelligent algorithm using multi-modal pretreatment ultrasound and tomosynthesis radiomics features in addition to clinical variables to predict pathologic complete response (pCR) prior to the initiation of therapy. METHODS We used retrospective data on patients who underwent ultrasound and tomosynthesis before starting NAST. We developed a support vector machine algorithm using pretreatment ultrasound and tomosynthesis radiomics features in addition to patient and tumor variables to predict pCR status (ypT0 and ypN0). Findings were compared to the histopathologic evaluation of the surgical specimen. The main outcome measures were area under the curve (AUC) and false-negative rate (FNR). RESULTS We included 720 patients, 504 in the development set and 216 in the validation set. Median age was 51.6 years and 33.6% (242 of 720) achieved pCR. The addition of radiomics features significantly improved the performance of the algorithm (AUC 0.72 to 0.81; p = 0.007). The FNR of the multi-modal radiomics and clinical algorithm was 6.7% (10 of 150 with missed residual cancer). Surface/volume ratio at tomosynthesis and peritumoral entropy characteristics at ultrasound were the most relevant radiomics. Hormonal receptors and HER-2 status were the most important clinical predictors. CONCLUSION A multi-modal machine learning algorithm with pretreatment clinical, ultrasound, and tomosynthesis radiomics features may aid in predicting residual cancer after NAST. Pending prospective validation, this may facilitate individually tailored NAST regimens. CLINICAL RELEVANCE STATEMENT Multi-modal radiomics using pretreatment ultrasound and tomosynthesis showed significant improvement in assessing response to NAST compared to an algorithm using clinical variables only. Further prospective validation of our findings seems warranted to enable individualized predictions of NAST outcomes. KEY POINTS • We proposed a multi-modal machine learning algorithm with pretreatment clinical, ultrasound, and tomosynthesis radiomics features to predict response to neoadjuvant breast cancer treatment. • Compared with the clinical algorithm, the AUC of this integrative algorithm is significantly higher. • Used prior to the initiative of therapy, our algorithm can identify patients who will experience pathologic complete response following neoadjuvant therapy with a high negative predictive value.
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Affiliation(s)
- Lie Cai
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany
| | - Chris Sidey-Gibbons
- Department of Symptom Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Juliane Nees
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany
| | - Fabian Riedel
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany
| | - Benedikt Schäfgen
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany
| | - Riku Togawa
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany
| | - Kristina Killinger
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany
| | - Joerg Heil
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany
| | - André Pfob
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany.
- MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, USA.
- National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Michael Golatta
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany.
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Cai L, Sidey-Gibbons C, Nees J, Riedel F, Schaefgen B, Togawa R, Killinger K, Heil J, Pfob A, Golatta M. Ultrasound Radiomics Features to Identify Patients With Triple-Negative Breast Cancer: A Retrospective, Single-Center Study. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:467-478. [PMID: 38069582 DOI: 10.1002/jum.16377] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/04/2023] [Indexed: 02/08/2024]
Abstract
OBJECTIVES Patients with triple-negative breast cancer (TNBC) exhibit a fast tumor growth rate and poor survival outcomes. In this study, we aimed to develop and compare intelligent algorithms using ultrasound radiomics features in addition to clinical variables to identify patients with TNBC prior to histopathologic diagnosis. METHODS We used single-center, retrospective data of patients who underwent ultrasound before histopathologic verification and subsequent neoadjuvant systemic treatment (NAST). We developed a logistic regression with an elastic net penalty algorithm using pretreatment ultrasound radiomics features in addition to patient and tumor variables to identify patients with TNBC. Findings were compared to the histopathologic evaluation of the biopsy specimen. The main outcome measure was the area under the curve (AUC). RESULTS We included 1161 patients, 813 in the development set and 348 in the validation set. Median age was 50.1 years and 24.4% (283 of 1161) had TNBC. The integrative model using radiomics and clinical information showed significantly better performance in identifying TNBC compared to the radiomics model (AUC: 0.71, 95% confidence interval [CI]: 0.65-0.76 versus 0.64, 95% CI: 0.57-0.71, P = .004). The five most important variables were cN status, shape surface volume ratio (SA:V), gray level co-occurrence matrix (GLCM) correlation, gray level dependence matrix (GLDM) dependence nonuniformity normalized, and age. Patients with TNBC were more often categorized as BI-RADS 4 than BI-RADS 5 compared to non-TNBC patients (P = .002). CONCLUSION A machine learning algorithm showed promising potential to identify patients with TNBC using ultrasound radiomics features and clinical information prior to histopathologic evaluation.
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Affiliation(s)
- Lie Cai
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Chris Sidey-Gibbons
- MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Symptom Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Juliane Nees
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Riedel
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Benedikt Schaefgen
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Riku Togawa
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Kristina Killinger
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Joerg Heil
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - André Pfob
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
- MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Golatta
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
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Coffey K, Aukland B, Amir T, Sevilimedu V, Saphier NB, Mango VL. Artificial Intelligence Decision Support for Triple-Negative Breast Cancers on Ultrasound. JOURNAL OF BREAST IMAGING 2024; 6:33-44. [PMID: 38243859 PMCID: PMC11296726 DOI: 10.1093/jbi/wbad080] [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: 05/10/2023] [Indexed: 01/22/2024]
Abstract
OBJECTIVE To assess performance of an artificial intelligence (AI) decision support software in assessing and recommending biopsy of triple-negative breast cancers (TNBCs) on US. METHODS Retrospective institutional review board-approved review identified patients diagnosed with TNBC after US-guided biopsy between 2009 and 2019. Artificial intelligence output for TNBCs on diagnostic US included lesion features (shape, orientation) and likelihood of malignancy category (benign, probably benign, suspicious, and probably malignant). Artificial intelligence true positive was defined as suspicious or probably malignant and AI false negative (FN) as benign or probably benign. Artificial intelligence and radiologist lesion feature agreement, AI and radiologist sensitivity and FN rate (FNR), and features associated with AI FNs were determined using Wilcoxon rank-sum test, Fisher's exact test, chi-square test of independence, and kappa statistics. RESULTS The study included 332 patients with 345 TNBCs. Artificial intelligence and radiologists demonstrated moderate agreement for lesion shape and orientation (k = 0.48 and k = 0.47, each P <.001). On the set of examinations using 6 earlier diagnostic US, radiologists recommended biopsy of 339/345 lesions (sensitivity 98.3%, FNR 1.7%), and AI recommended biopsy of 333/345 lesions (sensitivity 96.5%, FNR 3.5%), including 6/6 radiologist FNs. On the set of examinations using immediate prebiopsy diagnostic US, AI recommended biopsy of 331/345 lesions (sensitivity 95.9%, FNR 4.1%). Artificial intelligence FNs were more frequently oval (q < 0.001), parallel (q < 0.001), circumscribed (q = 0.04), and complex cystic and solid (q = 0.006). CONCLUSION Artificial intelligence accurately recommended biopsies for 96% to 97% of TNBCs on US and may assist radiologists in classifying these lesions, which often demonstrate benign sonographic features.
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Affiliation(s)
- Kristen Coffey
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Brianna Aukland
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tali Amir
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Varadan Sevilimedu
- Department of Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicole B. Saphier
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Victoria L. Mango
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Schopp JG, Polat DS, Arjmandi F, Hayes JC, Ahn RW, Sullivan K, Sahoo S, Porembka JH. Imaging Challenges in Diagnosing Triple-Negative Breast Cancer. Radiographics 2023; 43:e230027. [PMID: 37708071 DOI: 10.1148/rg.230027] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
Triple-negative breast cancer (TNBC) refers to a heterogeneous group of carcinomas that have more aggressive biologic features, faster growth, and a propensity for early distant metastasis and recurrence compared with other breast cancer subtypes. Due to the aggressiveness and rapid growth of TNBCs, there are specific imaging challenges associated with their timely and accurate diagnosis. TNBCs commonly manifest initially as circumscribed masses and therefore lack the typical features of a primary breast malignancy, such as irregular shape, spiculated margins, and desmoplastic reaction. Given the potential for misinterpretation, review of the multimodality imaging appearances of TNBCs is important for guiding the radiologist in distinguishing TNBCs from benign conditions. Rather than manifesting as a screening-detected cancer, TNBC typically appears clinically as a palpable area of concern that most commonly corresponds to a discrete mass at mammography, US, and MRI. The combination of circumscribed margins and hypoechoic to anechoic echogenicity may lead to TNBC being misinterpreted as a benign fibroadenoma or cyst. Therefore, careful mammographic and sonographic evaluation with US image optimization can help avoid misinterpretation. Radiologists should recognize the characteristics of TNBCs that can mimic benign entities, as well as the subtle features of TNBCs that should raise concern for malignancy and aid in timely and accurate diagnosis. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
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Affiliation(s)
- Jennifer G Schopp
- From the Departments of Radiology (J.G.S., D.S.P., F.A., J.C.H, R.W.A., K.S., J.H.P.) and Pathology (S.S.), University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, MC 8896, Dallas, TX 75390-8896
| | - Dogan S Polat
- From the Departments of Radiology (J.G.S., D.S.P., F.A., J.C.H, R.W.A., K.S., J.H.P.) and Pathology (S.S.), University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, MC 8896, Dallas, TX 75390-8896
| | - Firouzeh Arjmandi
- From the Departments of Radiology (J.G.S., D.S.P., F.A., J.C.H, R.W.A., K.S., J.H.P.) and Pathology (S.S.), University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, MC 8896, Dallas, TX 75390-8896
| | - Jody C Hayes
- From the Departments of Radiology (J.G.S., D.S.P., F.A., J.C.H, R.W.A., K.S., J.H.P.) and Pathology (S.S.), University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, MC 8896, Dallas, TX 75390-8896
| | - Richard W Ahn
- From the Departments of Radiology (J.G.S., D.S.P., F.A., J.C.H, R.W.A., K.S., J.H.P.) and Pathology (S.S.), University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, MC 8896, Dallas, TX 75390-8896
| | - Kirbi Sullivan
- From the Departments of Radiology (J.G.S., D.S.P., F.A., J.C.H, R.W.A., K.S., J.H.P.) and Pathology (S.S.), University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, MC 8896, Dallas, TX 75390-8896
| | - Sunati Sahoo
- From the Departments of Radiology (J.G.S., D.S.P., F.A., J.C.H, R.W.A., K.S., J.H.P.) and Pathology (S.S.), University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, MC 8896, Dallas, TX 75390-8896
| | - Jessica H Porembka
- From the Departments of Radiology (J.G.S., D.S.P., F.A., J.C.H, R.W.A., K.S., J.H.P.) and Pathology (S.S.), University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, MC 8896, Dallas, TX 75390-8896
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Adrada BE, Moseley TW, Kapoor MM, Scoggins ME, Patel MM, Perez F, Nia ES, Khazai L, Arribas E, Rauch GM, Guirguis MS. Triple-Negative Breast Cancer: Histopathologic Features, Genomics, and Treatment. Radiographics 2023; 43:e230034. [PMID: 37792593 PMCID: PMC10560981 DOI: 10.1148/rg.230034] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/09/2023] [Accepted: 06/01/2023] [Indexed: 10/06/2023]
Abstract
Triple-negative breast cancer (TNBC) is a heterogeneous and aggressive group of tumors that are defined by the absence of estrogen and progesterone receptors and lack of ERBB2 (formerly HER2 or HER2/neu) overexpression. TNBC accounts for 8%-13% of breast cancers. In addition, it accounts for a higher proportion of breast cancers in younger women compared with those in older women, and it disproportionately affects non-Hispanic Black women. TNBC has high metastatic potential, and the risk of recurrence is highest during the 5 years after it is diagnosed. TNBC exhibits benign morphologic imaging features more frequently than do other breast cancer subtypes. Mammography can be suboptimal for early detection of TNBC owing to factors that include the fast growth of this cancer, increased mammographic density in young women, and lack of the typical features of malignancy at imaging. US is superior to mammography for TNBC detection, but benign-appearing features can lead to misdiagnosis. Breast MRI is the most sensitive modality for TNBC detection. Most cases of TNBC are treated with neoadjuvant chemotherapy, followed by surgery and radiation. MRI is the modality of choice for evaluating the response to neoadjuvant chemotherapy. Survival rates for individuals with TNBC are lower than those for persons with hormone receptor-positive and human epidermal growth factor receptor 2-positive cancers. The 5-year survival rates for patients with localized, regional, and distant disease at diagnosis are 91.3%, 65.8%, and 12.0%, respectively. The early success of immunotherapy has raised hope regarding the development of personalized strategies to treat TNBC. Imaging and tumor biomarkers are likely to play a crucial role in the prediction of TNBC treatment response and TNBC patient survival in the future. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
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Affiliation(s)
- Beatriz E. Adrada
- From the Departments of Breast Imaging (B.E.A., T.W.M., M.M.K.,
M.E.S., M.M.P., F.P., E.S.N., E.A., G.M.R., M.S.G.), Breast Surgical Oncology
(T.W.M.), Pathology-Anatomical (L.K.), and Abdominal Imaging (G.M.R.), The
University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 1350,
Houston, TX 77030
| | - Tanya W. Moseley
- From the Departments of Breast Imaging (B.E.A., T.W.M., M.M.K.,
M.E.S., M.M.P., F.P., E.S.N., E.A., G.M.R., M.S.G.), Breast Surgical Oncology
(T.W.M.), Pathology-Anatomical (L.K.), and Abdominal Imaging (G.M.R.), The
University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 1350,
Houston, TX 77030
| | - Megha M. Kapoor
- From the Departments of Breast Imaging (B.E.A., T.W.M., M.M.K.,
M.E.S., M.M.P., F.P., E.S.N., E.A., G.M.R., M.S.G.), Breast Surgical Oncology
(T.W.M.), Pathology-Anatomical (L.K.), and Abdominal Imaging (G.M.R.), The
University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 1350,
Houston, TX 77030
| | - Marion E. Scoggins
- From the Departments of Breast Imaging (B.E.A., T.W.M., M.M.K.,
M.E.S., M.M.P., F.P., E.S.N., E.A., G.M.R., M.S.G.), Breast Surgical Oncology
(T.W.M.), Pathology-Anatomical (L.K.), and Abdominal Imaging (G.M.R.), The
University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 1350,
Houston, TX 77030
| | - Miral M. Patel
- From the Departments of Breast Imaging (B.E.A., T.W.M., M.M.K.,
M.E.S., M.M.P., F.P., E.S.N., E.A., G.M.R., M.S.G.), Breast Surgical Oncology
(T.W.M.), Pathology-Anatomical (L.K.), and Abdominal Imaging (G.M.R.), The
University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 1350,
Houston, TX 77030
| | - Frances Perez
- From the Departments of Breast Imaging (B.E.A., T.W.M., M.M.K.,
M.E.S., M.M.P., F.P., E.S.N., E.A., G.M.R., M.S.G.), Breast Surgical Oncology
(T.W.M.), Pathology-Anatomical (L.K.), and Abdominal Imaging (G.M.R.), The
University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 1350,
Houston, TX 77030
| | - Emily S. Nia
- From the Departments of Breast Imaging (B.E.A., T.W.M., M.M.K.,
M.E.S., M.M.P., F.P., E.S.N., E.A., G.M.R., M.S.G.), Breast Surgical Oncology
(T.W.M.), Pathology-Anatomical (L.K.), and Abdominal Imaging (G.M.R.), The
University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 1350,
Houston, TX 77030
| | - Laila Khazai
- From the Departments of Breast Imaging (B.E.A., T.W.M., M.M.K.,
M.E.S., M.M.P., F.P., E.S.N., E.A., G.M.R., M.S.G.), Breast Surgical Oncology
(T.W.M.), Pathology-Anatomical (L.K.), and Abdominal Imaging (G.M.R.), The
University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 1350,
Houston, TX 77030
| | - Elsa Arribas
- From the Departments of Breast Imaging (B.E.A., T.W.M., M.M.K.,
M.E.S., M.M.P., F.P., E.S.N., E.A., G.M.R., M.S.G.), Breast Surgical Oncology
(T.W.M.), Pathology-Anatomical (L.K.), and Abdominal Imaging (G.M.R.), The
University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 1350,
Houston, TX 77030
| | - Gaiane M. Rauch
- From the Departments of Breast Imaging (B.E.A., T.W.M., M.M.K.,
M.E.S., M.M.P., F.P., E.S.N., E.A., G.M.R., M.S.G.), Breast Surgical Oncology
(T.W.M.), Pathology-Anatomical (L.K.), and Abdominal Imaging (G.M.R.), The
University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 1350,
Houston, TX 77030
| | - Mary S. Guirguis
- From the Departments of Breast Imaging (B.E.A., T.W.M., M.M.K.,
M.E.S., M.M.P., F.P., E.S.N., E.A., G.M.R., M.S.G.), Breast Surgical Oncology
(T.W.M.), Pathology-Anatomical (L.K.), and Abdominal Imaging (G.M.R.), The
University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 1350,
Houston, TX 77030
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Huang Y, Guo Y, Xiao Q, Liang S, Yu Q, Qian L, Zhou J, Le J, Pei Y, Wang L, Chang C, Chen S, Zhou S. Unraveling the Pivotal Network of Ultrasound and Somatic Mutations in Triple-Negative and Non-Triple-Negative Breast Cancer. BREAST CANCER (DOVE MEDICAL PRESS) 2023; 15:461-472. [PMID: 37456987 PMCID: PMC10349575 DOI: 10.2147/bctt.s408997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 07/01/2023] [Indexed: 07/18/2023]
Abstract
Purpose The emergence of genomic targeted therapy has improved the prospects of treatment for breast cancer (BC). However, genetic testing relies on invasive and sophisticated procedures. Patients and Methods Here, we performed ultrasound (US) and target sequencing to unravel the possible association between US radiomics features and somatic mutations in TNBC (n=83) and non-TNBC (n=83) patients. Least absolute shrinkage and selection operator (Lasso) were utilized to perform radiomic feature selection. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was utilized to identify the signaling pathways associated with radiomic features. Results Thirteen differently represented radiomic features were identified in TNBC and non-TNBC, including tumor shape, textual, and intensity features. The US radiomic-gene pairs were differently exhibited between TNBC and non-TNBC. Further investigation with KEGG verified radiomic-pathway (ie, JAK-STAT, MAPK, Ras, Wnt, microRNAs in cancer, PI3K-Akt) associations in TNBC and non-TNBC. Conclusion The pivotal network provided the connections of US radiogenomic signature and target sequencing for non-invasive genetic assessment of precise BC treatment.
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Affiliation(s)
- Yunxia Huang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Yi Guo
- Department of Radiology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Qin Xiao
- Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, People’s Republic of China
| | - Shuyu Liang
- Department of Radiology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Qiang Yu
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Lang Qian
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Jin Zhou
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Jian Le
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Yuchen Pei
- Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Lei Wang
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Cai Chang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
| | - Sheng Chen
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, People’s Republic of China
| | - Shichong Zhou
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China
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10
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Lin CJ, Xiao WX, Fu T, Jin X, Shao ZM, Di GH. Calcifications in triple-negative breast cancer: Molecular features and treatment strategies. NPJ Breast Cancer 2023; 9:26. [PMID: 37061514 PMCID: PMC10105779 DOI: 10.1038/s41523-023-00531-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 03/30/2023] [Indexed: 04/17/2023] Open
Abstract
Despite the high prevalence of mammographic calcifications, our understanding remains limited regarding the clinical and molecular features of calcifications within triple-negative breast cancer (TNBC). To investigate the clinical relevance and biological basis of TNBC with calcifications of high suspicion for malignancy, we established a study cohort (N = 312) by integrating mammographic records with clinical data and genomic, transcriptomic, and metabolomic profiling. Despite similar clinicopathological features, patients with highly suspicious calcifications exhibited a worse overall survival than those without. In addition, TNBC with highly suspicious calcifications was characterized by a higher frequency of PIK3CA mutation, lower infiltration of immune cells, and increased abnormality of lipid metabolism. Overall, our study systematically revealed clinical and molecular heterogeneity between TNBC with or without calcifications of high suspicion for malignancy. These data might help to understand the clinical relevance and biological basis of mammographic calcifications.
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Affiliation(s)
- Cai-Jin Lin
- Department of Breast Surgery, Fudan University Shanghai Cancer Center; Key Laboratory of Breast Cancer in Shanghai, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Wen-Xuan Xiao
- Department of Breast Surgery, Fudan University Shanghai Cancer Center; Key Laboratory of Breast Cancer in Shanghai, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Tong Fu
- Department of Breast Surgery, Fudan University Shanghai Cancer Center; Key Laboratory of Breast Cancer in Shanghai, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Xi Jin
- Department of Breast Surgery, Fudan University Shanghai Cancer Center; Key Laboratory of Breast Cancer in Shanghai, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| | - Zhi-Ming Shao
- Department of Breast Surgery, Fudan University Shanghai Cancer Center; Key Laboratory of Breast Cancer in Shanghai, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| | - Gen-Hong Di
- Department of Breast Surgery, Fudan University Shanghai Cancer Center; Key Laboratory of Breast Cancer in Shanghai, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
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11
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Zhang X, Cui H, Hu N, Han P, Fan W, Wang P, Zuo X, Zhao D, Huang H, Li S, Kong H, Peng F, Tian J, Zhang L. Correlation of androgen receptor with ultrasound, clinicopathological features and clinical outcomes in breast cancer. Insights Imaging 2023; 14:46. [PMID: 36929229 PMCID: PMC10020396 DOI: 10.1186/s13244-023-01387-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 02/04/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND This study aimed to explore whether there is an association between androgen receptor (AR) expression and ultrasound, clinicopathological features and prognosis of breast cancer. METHODS A total of 141 breast cancer patients were included in this retrospective study. AR expression was analyzed by immunohistochemistry. The images of B-mode, color Doppler and strain elastography from 104 patients were collected continuously, and the corresponding ultrasound characteristics were obtained. The differences in ultrasound and clinicopathological features in different AR status were analyzed. Progression-free survival (PFS) of patients was obtained through up to 90 months of follow-up; then, the effect of AR on PFS was analyzed. Subsequently, a nomogram was constructed to predict the AR status. The predictive accuracy was calculated using C-index. RESULTS The positive expression of AR (AR +) was associated with lower histological grade (p = 0.034) and lower Ki-67 level (p = 0.029). Triple-negative breast cancer (TNBC) had the lowest probability of AR + (p < 0.001). The AR + group mostly showed unsmooth margin (p < 0.001), posterior acoustic shadowing (p = 0.002) and higher elasticity score (p = 0.022) on ultrasound. The echo pattern of most tumors with AR + was heterogeneous (p = 0.024) in Luminal A subtype. AR + could be a sign of a better prognosis in overall breast cancer (p < 0.001), as well as in human epidermal growth factor receptor 2 (HER2) overexpression and Luminal B subtypes (p = 0.001 and 0.025). The nomogram showed relatively reliable performance with a C-index of 0.799. CONCLUSION Our research demonstrated that AR expression was closely related to ultrasound, clinicopathological features and prognosis of breast cancer.
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Affiliation(s)
- Xudong Zhang
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China, 150086
| | - Hao Cui
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China, 150086
| | - Nana Hu
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China, 150086
| | - Peng Han
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China, 150086
| | - Wei Fan
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China, 150086
| | - Panting Wang
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China, 150086
| | - Xiaoxuan Zuo
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China, 150086
| | - Dantong Zhao
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China, 150086
| | - He Huang
- Department of Clinical Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China, 150086
| | - Shuo Li
- Department of Clinical Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China, 150086
| | - Hanqing Kong
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China, 150086
| | - Fuhui Peng
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China, 150086
| | - Jiawei Tian
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China, 150086
| | - Lei Zhang
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China, 150086.
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12
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Lohitvisate W, Pummee N, Kwankua A. Mammographic and ultrasonographic features of triple-negative breast cancer compared with non-triple-negative breast cancer. J Ultrasound 2023; 26:193-200. [PMID: 35976611 PMCID: PMC10063690 DOI: 10.1007/s40477-022-00709-9] [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: 06/19/2022] [Accepted: 07/05/2022] [Indexed: 10/15/2022] Open
Abstract
OBJECTIVE To evaluate and compare the mammographic and ultrasonographic features of TNBC with non-TNBC. METHODS A retrospective review of 193 invasive breast cancer patients (TNBC = 32 and non-TNBC = 161) was collected from January 2014 to June 2019. The imaging features were reviewed according to the 5th edition of the American College of Radiology Breast Imaging Reporting and Data System lexicon. We used the student t-test, Mann-Whitney U test, and Fisher's exact test for statistical analyses. RESULTS Mass without calcifications was the most mammographic feature of TNBC (22 of 32, 68.8%) and more commonly found in TNBC than in non-TNBC (p = 0.007). The irregular shape (19 of 28, 67.9%) and indistinct margin (10 of 28, 35.7%) were the most common findings in the TNBC group. However, TNBC lesions appeared as round or oval shape and microlobulated margin more frequently than non-TNBC lesions (p < 0.001). Additionally, the tumor size and histological grade of TNBC were significantly higher than non-TNBC (p < 0.001). CONCLUSION TNBC has distinct imaging features compared to non-TNBC. The imaging features on mammography combined with ultrasonography can be used to detect and differentiate this subtype from other breast cancers.
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Affiliation(s)
- Wanrudee Lohitvisate
- Department of Radiology, Faculty of Medicine, Thammasat University, 95 M.8 Paholyothin Rd., Klongluang, Pathumthani, 12120 Thailand
| | - Natthiya Pummee
- Department of Radiology, Faculty of Medicine, Thammasat University, 95 M.8 Paholyothin Rd., Klongluang, Pathumthani, 12120 Thailand
| | - Amolchaya Kwankua
- Department of Radiology, Faculty of Medicine, Thammasat University, 95 M.8 Paholyothin Rd., Klongluang, Pathumthani, 12120 Thailand
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13
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Cui H, Sun Y, Zhao D, Zhang X, Kong H, Hu N, Wang P, Zuo X, Fan W, Yao Y, Fu B, Tian J, Wu M, Gao Y, Ning S, Zhang L. Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions. J Transl Med 2023; 21:44. [PMID: 36694240 PMCID: PMC9875533 DOI: 10.1186/s12967-022-03840-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 12/19/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Human epidermal growth factor receptor 2 (HER2) overexpressed associated with poor prognosis in breast cancer and HER2 has been defined as a therapeutic target for breast cancer treatment. We aimed to explore the molecular biological information in ultrasound radiomic features (URFs) of HER2-positive breast cancer using radiogenomic analysis. Moreover, a radiomics model was developed to predict the status of HER2 in breast cancer. METHODS This retrospective study included 489 patients who were diagnosed with breast cancer. URFs were extracted from a radiomics analysis set using PyRadiomics. The correlations between differential URFs and HER2-related genes were calculated using Pearson correlation analysis. Functional enrichment of the identified URFs-correlated HER2 positive-specific genes was performed. Lastly, the radiomics model was developed based on the URF-module mined from auxiliary differential URFs to assess the HER2 status of breast cancer. RESULTS Eight differential URFs (p < 0.05) were identified among the 86 URFs extracted by Pyradiomics. 25 genes that were found to be the most closely associated with URFs. Then, the relevant biological functions of each differential URF were obtained through functional enrichment analysis. Among them, Zone Entropy is related to immune cell activity, which regulate the generation of calcification in breast cancer. The radiomics model based on the Logistic classifier and URF-module showed good discriminative ability (AUC = 0.80, 95% CI). CONCLUSION We searched for the URFs of HER2-positive breast cancer, and explored the underlying genes and biological functions of these URFs. Furthermore, the radiomics model based on the Logistic classifier and URF-module relatively accurately predicted the HER2 status in breast cancer.
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Affiliation(s)
- Hao Cui
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Yue Sun
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 China
| | - Dantong Zhao
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Xudong Zhang
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Hanqing Kong
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Nana Hu
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Panting Wang
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Xiaoxuan Zuo
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Wei Fan
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Yuan Yao
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Baiyang Fu
- grid.412463.60000 0004 1762 6325Department of Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Jiawei Tian
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
| | - Meixin Wu
- grid.412463.60000 0004 1762 6325Department of Clinical Medicine, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang, 150086 China
| | - Yue Gao
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 China
| | - Shangwei Ning
- grid.410736.70000 0001 2204 9268College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 China
| | - Lei Zhang
- grid.412463.60000 0004 1762 6325Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086 Heilongjiang China
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14
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Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images. Med Biol Eng Comput 2023; 61:567-578. [PMID: 36542320 PMCID: PMC9852203 DOI: 10.1007/s11517-022-02728-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022]
Abstract
To develop a deep-learning system for the automatic identification of triple-negative breast cancer (TNBC) solely from ultrasound images. A total of 145 patients and 831 images were retrospectively enrolled at Peking Union College Hospital from April 2018 to March 2019. Ultrasound images and clinical information were collected accordingly. Molecular subtypes were determined from immunohistochemical (IHC) results. A CNN with VGG-based architecture was then used to predict TNBC. The model's performance was evaluated using randomized k-fold stratified cross-validation. A t-SNE analysis and saliency maps were used for model visualization. TNBC was identified in 16 of 145 (11.03%) patients. One hundred fifteen (80%) patients, 15 (10%) patients, and 15 (10%) patients formed the train, validation, and test set respectively. The deep learning system exhibits good efficacy, with an AUC of 0.86 (95% CI: 0.64, 0.95), an accuracy of 85%, a sensitivity of 86%, a specificity of 86%, and an F1-score of 0.74. In addition, the internal representation features learned by the model showed clear differentiation across molecular subtype groups. Such a deep learning system can automatically predict triple-negative breast cancer preoperatively and accurately. It may help to get to more precise and comprehensive management.
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15
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Calcification, Posterior Acoustic, and Blood Flow: Ultrasonic Characteristics of Triple-Negative Breast Cancer. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:9336185. [PMID: 36199374 PMCID: PMC9529478 DOI: 10.1155/2022/9336185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 07/07/2022] [Accepted: 08/27/2022] [Indexed: 11/18/2022]
Abstract
Previous studies suggest that triple-negative breast cancer (TNBC) may have unique imaging characteristics, however, studies focused on the imaging characteristics of TNBC are still limited. The aim of the present study is to analyze the ultrasonic characteristics of TNBC and to provide more reliable information on imaging diagnosis of TNBC. This retrospective study was performed including 162 TNBC patients with 184 TNBC lesions. 174 non-TNBC cases with 196 lesions were used as the control group. The median size of TNBC lesions and non-TNBC lesions were 23 mm × 16 mm and 21 mm × 15 mm, respectively. The shape of most breast cancer lesions was irregular. However, 15.30% (28/183) TNBC lesions and 16.84% (33/196) non-TNBC lesions were oval-shaped. Most breast cancer lesions (79.78% TNBC & 85.71% non-TNBC) were ill-defined. In comparison to non-TNBC, the distinctive ultrasonic characteristics of TNBC were summarized as three features: calcifications, posterior acoustic, and blood flow. Microcalcifications was less common in non-TNBC. The remarkable posterior acoustic characteristics on TNBC were no posterior acoustic features (136, 73.91%). Avascular pattern (21.74%) was also more common in TNBC. The other feature of TNBC was markedly hypoechoic lesions (23.91%). The above-mentioned differences between TNBC and non-TNBC were significant. 93.48% TBNC and 94.39% non–TNBC lesions were in BI-RADS-US category of 4A-5. The results indicate that TNBC has some distinctive ultrasound characteristics. Ultrasound is a useful adjunct in early detection of breast cancer. A combination of ultrasound with mammography is excellent for detecting breast cancer.
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16
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An Integrative Clinical Model for the Prediction of Pathological Complete Response in Patients with Operable Stage II and Stage III Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy. Cancers (Basel) 2022; 14:cancers14174170. [PMID: 36077706 PMCID: PMC9454735 DOI: 10.3390/cancers14174170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 08/20/2022] [Accepted: 08/23/2022] [Indexed: 11/26/2022] Open
Abstract
Simple Summary Neoadjuvant chemotherapy (NAC) is widely used to treat stage II and III primary, operable triple-negative breast cancer (TNBC). The response to NAC critically affects the subsequent treatment plan, including not only curative surgical planning but also adjuvant therapy. There is no standard prediction model that accurately predicts NAC response. Therefore, the development of an easy-to-apply and cost-effective clinical prediction model for NAC treatment response would improve clinical practice. We propose an integrative clinical prediction model for the prediction of pathologically complete response in patients with operable stage II and stage III TNBC receiving NAC based on findings from tumor ultrasound and blood tests. All included parameters were readily available during and before NAC. This clinical prediction model could provide a reference to guide clinicians’ decisions in planning a patient’s NAC treatment as early as after the first cycle of NAC. Abstract Triple-negative breast cancer (TNBC) is treated with neoadjuvant chemotherapy (NAC). The response to NAC, particularly the probability of a complete pathological response (pCR), guides the surgical approach and adjuvant therapy. We developed a prediction model using a nomogram integrating blood tests and pre-treatment ultrasound findings for predicting pCR in patients with stage II or III operable TNBC receiving NAC. Clinical data before and after the first cycle of NAC collected from patients between 2012 and 2019 were analyzed using univariate and multivariate analyses to identify correlations with pCR. The coefficients of the significant parameters were calculated using logistic regression, and a nomogram was developed based on the logistic model to predict the probability of pCR. Eighty-eight patients were included. Five parameters correlated with the probability of pCR, including the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte (PLR) ratio, percentage change in PLR, presence of echogenic halo, and tumor height-to-width ratio. The discrimination performance of the nomogram was indicated by an area under the curve of 87.7%, and internal validation showed that the chi-square value of the Hosmer–Lemeshow test was 7.67 (p = 0.363). Thus, the integrative prediction model using clinical data can predict the probability of pCR in patients with TNBC receiving NAC.
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Dołęga-Kozierowski B, Lis M, Marszalska-Jacak H, Koziej M, Celer M, Bandyk M, Kasprzak P, Szynglarewicz B, Matkowski R. Multimodality imaging in lobular breast cancer: Differences in mammography, ultrasound, and MRI in the assessment of local tumor extent and correlation with molecular characteristics. Front Oncol 2022; 12:855519. [PMID: 36072800 PMCID: PMC9441946 DOI: 10.3389/fonc.2022.855519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 07/11/2022] [Indexed: 11/28/2022] Open
Abstract
Introduction Invasive lobular breast cancer (ILC) is a diagnostic challenge due to the diversity of morphological features. The objective of the study was to investigate the presentation and local extent of ILC using various imaging techniques and to assess the correlation between imaging and molecular profile. Materials and methods We reviewed 162 consecutive patients with ILC found on vacuum-assisted biopsy, who underwent evaluation of the lesion morphology and extent using ultrasound (US), mammography (MMG), and magnetic resonance imaging (MRI). Radiographic features were compared with ILC intrinsic subtype based on the expression of Ki-67 and estrogen, progesterone, and HER2 receptors. Results A total of 113 mass lesions and 49 non-mass enhancements (NMEs) were found in MRI. Masses were typically irregular and spiculated, showing heterogeneous contrast enhancement, diffusion restriction, and type III enhancement curve. NMEs presented mainly as the area of focal or multiregional distribution with heterogeneous or clumped contrast enhancement, diffusion restriction, and type III enhancement curve. Lesion extent significantly varied between MRI and MMG/ultrasonography (USG) (P < 0.001) but did not differ between MGF and ultrasonography (USG). The larger the ILC, the higher the disproportion when lesion extent in MRI was compared with MMG (P < 0.001) and ultrasonography (USG) (P < 0.001). In the study group, there were 97 cases of luminal A subtype (59.9%), 54 cases of luminal B HER2− (33.3%), nine cases of luminal B HER2+ (5.5%), and two cases of triple negative (1.2%). The HER2 type was not found in the study group. We did not observe any significant correlation between molecular profile and imaging. Conclusion MRI is the most effective technique for the assessment of ILC local extent, which is important for optimal treatment planning. Further studies are needed to investigate if the intrinsic subtype of ILC can be predicted by imaging features on MRI.
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Affiliation(s)
- Bartosz Dołęga-Kozierowski
- Breast Unit, Department of Breast Imaging, Lower Silesian Oncology, Pulmonology and Hematology Center, Wroclaw, Poland
| | - Michał Lis
- Burn and Plastic Surgery Department, Ludwik Rydygier Memorial Specialized Hospital in Krakow, Krakow, Poland
- *Correspondence: Michał Lis,
| | - Hanna Marszalska-Jacak
- Breast Unit, Department of Breast Imaging, Lower Silesian Oncology, Pulmonology and Hematology Center, Wroclaw, Poland
| | - Mateusz Koziej
- Department of Anatomy, Jagiellonian University Medical College, Krakow, Poland
| | - Marcin Celer
- Breast Unit, Department of Breast Imaging, Lower Silesian Oncology, Pulmonology and Hematology Center, Wroclaw, Poland
| | - Małgorzata Bandyk
- Breast Unit, Department of Breast Imaging, Lower Silesian Oncology, Pulmonology and Hematology Center, Wroclaw, Poland
| | - Piotr Kasprzak
- Breast Unit, Department of Breast Imaging, Lower Silesian Oncology, Pulmonology and Hematology Center, Wroclaw, Poland
| | - Bartłomiej Szynglarewicz
- Breast Unit, Department of Breast Surgery, Lower Silesian Oncology, Pulmonology and Hematology Center, Wroclaw, Poland
- Department of Oncology, Faculty of Medicine, Wroclaw Medical University, Wroclaw, Poland
| | - Rafał Matkowski
- Breast Unit, Department of Breast Surgery, Lower Silesian Oncology, Pulmonology and Hematology Center, Wroclaw, Poland
- Department of Oncology, Faculty of Medicine, Wroclaw Medical University, Wroclaw, Poland
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Du Y, Zha HL, Wang H, Liu XP, Pan JZ, Du LW, Cai MJ, Zong M, Li CY. Ultrasound-based radiomics nomogram for differentiation of triple-negative breast cancer from fibroadenoma. Br J Radiol 2022; 95:20210598. [PMID: 35138938 PMCID: PMC10993963 DOI: 10.1259/bjr.20210598] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 01/13/2022] [Accepted: 01/20/2022] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE This study aimed to develop a radiomics nomogram that incorporates radiomics, conventional ultrasound (US) and clinical features in order to differentiate triple-negative breast cancer (TNBC) from fibroadenoma. METHODS A total of 182 pathology-proven fibroadenomas and 178 pathology-proven TNBCs, which underwent preoperative US examination, were involved and randomly divided into training (n = 253) and validation cohorts (n = 107). The radiomics features were extracted from the regions of interest of all lesions, which were delineated on the basis of preoperative US examination. The least absolute shrinkage and selection operator model and the maximum relevance minimum redundancy algorithm were established for the selection of tumor status-related features and construction of radiomics signature (Rad-score). Then, multivariate logistic regression analyses were utilized to develop a radiomics model by incorporating the radiomics signature and clinical findings. Finally, the usefulness of the combined nomogram was assessed by using the receiver operator characteristic curve, calibration curve, and decision curve analysis (DCA). RESULTS The radiomics signature, composed of 12 selected features, achieved good diagnostic performance. The nomogram incorporated with radiomics signature and clinical data showed favorable diagnostic efficacy in the training cohort (AUC 0.986, 95% CI, 0.975-0.997) and validation cohort (AUC 0.977, 95% CI, 0.953-1.000). The radiomics nomogram outperformed the Rad-score and clinical models (p < 0.05). The calibration curve and DCA demonstrated the good clinical utility of the combined radiomics nomogram. CONCLUSION The radiomics signature is a potential predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models. ADVANCES IN KNOWLEDGE Recent advances in radiomics-based US are increasingly showing potential for improved diagnosis, assessment of therapeutic response and disease prediction in oncology. Rad-score is an independent predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models.
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Affiliation(s)
- Yu Du
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Hai-Ling Zha
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Hui Wang
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Xin-Pei Liu
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Jia-Zhen Pan
- 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
| | - Meng-Jun Cai
- 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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Elfgen C, Baumgartner S, Varga Z, Reeve K, Tausch CJ, Bjelic-Radisic V, Fleisch M, Güth U. Diagnostic delay in moderately/poorly differentiated breast cancer types. Eur J Cancer Prev 2022; 31:152-157. [PMID: 33899749 DOI: 10.1097/cej.0000000000000681] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
BACKGROUND Diagnostic delay of breast cancer related to the false-negative assessment of the healthcare provider leads to tumor progression and might worsen the outcome. Previous studies found some factors associated with provider-related diagnostic delay; however, tumor biology has tended not to be considered. The aim of our study was to find differences in diagnostic delay of poorly differentiated breast cancer types. METHODS Data of 970 patients with newly diagnosed moderately/poorly differentiated (G2/3) breast cancer at the age ≥40 years was retrospectively analyzed regarding breast cancer type, diagnostic delay and its consequence, clinical factors and physician's assessment. Multivariate analysis was used to evaluate associated factors with diagnostic delay. RESULTS We observed a diagnostic delay in 3.8% (n = 37) of all patients. Mean delay time was 128 days, and clinically relevant tumor growth was observed in 43.2% of these cases. Delay was significantly higher in the group of triple-negative breast cancer (9.9% versus 2.7, 5.3 and 1.8% in hormonal receptor (HR)+/human epidermal growth factor receptor 2 (HER2)-, HR-/Her2+ and HR+/Her2+, respectively; P value <0.001). Age, breast density and reason for presentation were not correlated to diagnostic delay. CONCLUSION Patients with triple-negative breast cancer are at higher risk of receiving a false-negative assessment and experiencing a diagnostic delay. Our results emphasize the importance of a detailed consideration of clinical risk factors and provider training and suggest a broad indication for a core needle biopsy.
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Affiliation(s)
- Constanze Elfgen
- Department of Breast Surgery, Breast-Center Zurich, Zurich, Switzerland
- Faculty of Medicine, University of Witten-Herdecke, Witten-Herdecke, Germany
| | | | - Zsuzsanna Varga
- Institute of Pathology and Molecular Pathology, University Hospital of Zurich
| | - Kelly Reeve
- Department of Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Christoph J Tausch
- Department of Breast Surgery, Breast-Center Zurich, Zurich, Switzerland
- Department of Gynecology and Obstetrics, Landesfrauenklinik Wuppertal, Wuppertal, Germany
| | - Vesna Bjelic-Radisic
- Faculty of Medicine, University of Witten-Herdecke, Witten-Herdecke, Germany
- Department of Gynecology and Obstetrics, Landesfrauenklinik Wuppertal, Wuppertal, Germany
| | - Markus Fleisch
- Faculty of Medicine, University of Witten-Herdecke, Witten-Herdecke, Germany
- Department of Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Uwe Güth
- Department of Breast Surgery, Breast-Center Zurich, Zurich, Switzerland
- Department of Gynecology and Obstetrics, Landesfrauenklinik Wuppertal, Wuppertal, Germany
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Chen C, Ding Y, Liu H, Sun M, Wang H, Wu D. Flubendazole Plays an Important Anti-Tumor Role in Different Types of Cancers. Int J Mol Sci 2022; 23:ijms23010519. [PMID: 35008943 PMCID: PMC8745596 DOI: 10.3390/ijms23010519] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/30/2021] [Accepted: 01/01/2022] [Indexed: 02/01/2023] Open
Abstract
Flubendazole, belonging to benzimidazole, is a broad-spectrum insect repellent and has been repurposed as a promising anticancer drug. In recent years, many studies have shown that flubendazole plays an anti-tumor role in different types of cancers, including breast cancer, melanoma, prostate cancer, colorectal cancer, and lung cancer. Although the anti-tumor mechanism of flubendazole has been studied, it has not been fully understood. In this review, we summarized the recent studies regarding the anti-tumor effects of flubendazole in different types of cancers and analyzed the related mechanisms, in order to provide the theoretical reference for further studies in the future.
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Affiliation(s)
- Chaoran Chen
- Institute of Nursing and Health, School of Nursing and Health, Henan University, Jinming Avenue, Kaifeng 475004, China; (C.C.); (Y.D.)
| | - Yueming Ding
- Institute of Nursing and Health, School of Nursing and Health, Henan University, Jinming Avenue, Kaifeng 475004, China; (C.C.); (Y.D.)
| | - Huiyang Liu
- Henan International Joint Laboratory of Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China;
| | - Mengyao Sun
- School of Clinical Medicine, Henan University, Kaifeng 475004, China;
| | - Honggang Wang
- Henan International Joint Laboratory of Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China;
- Correspondence: (H.W.); (D.W.)
| | - Dongdong Wu
- Henan International Joint Laboratory of Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China;
- School of Stomatology, Henan University, Kaifeng 475004, China
- Correspondence: (H.W.); (D.W.)
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Is There a Correlation between Multiparametric Assessment in Ultrasound and Intrinsic Subtype of Breast Cancer? J Clin Med 2021; 10:jcm10225394. [PMID: 34830676 PMCID: PMC8618837 DOI: 10.3390/jcm10225394] [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: 10/10/2021] [Revised: 11/05/2021] [Accepted: 11/16/2021] [Indexed: 11/17/2022] Open
Abstract
Molecular profile of breast cancer provides information about its biological activity, prognosis and treatment strategies. The purpose of our study was to investigate the correlation between ultrasound features and molecular subtypes of breast cancer. From June 2019 to December 2019, 86 patients (median age 57 years; range 32–88) with 102 breast cancer tumors were included in the study. The molecular subtypes were classified into five types: luminal A (LA), luminal B without HER2 overexpression (LB HER2−), luminal B with HER2 overexpression (LB HER2+), human epidermal growth factor receptor 2 positive (HER2+) and triple negative breast cancer (TNBC). Histopathological verification was obtained in core biopsy or/and post-surgery specimens in all cases. Univariate logistic regression analysis was performed to assess the association between the subtypes and ultrasound imaging features. Experienced radiologists assessed lesions according to the BIRADS-US lexicon. The ultrasound scans were performed with a Supersonic Aixplorer and Supersonix. Based on histopathological verification, the rates of LA, LB HER2−, LB HER2+, HER2+, and TNBC were 33, 17, 17, 16, 19, respectively. Both LB HER2+ and HER2+ subtypes presented higher incidence of calcification (OR = 3.125, p = 0.02, CI 0.0917–5.87) and HER2+ subtype presented a higher incidence of posterior enhancement (OR = 5.75, p = 0.03, CI 1.2257–32.8005), compared to other subtypes. The calcifications were less common in TNBC (OR = 0.176, p = 0.0041, CI 0.0469–0.5335) compared to other subtypes. There were no differences with regard to margin, shape, orientation, elasticity values and vascularity among five molecular subtypes. Our results suggest that there is a correlation between ultrasonographic features assessed according to BIRADS-US lexicon and BC subtypes with HER2 overexpression (both LB HER2+ and HER2+). It may be useful for identification of these aggressive subtypes of breast cancer.
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22
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Decoding the molecular subtypes of breast cancer seen on multimodal ultrasound images using an assembled convolutional neural network model: A prospective and multicentre study. EBioMedicine 2021; 74:103684. [PMID: 34773890 PMCID: PMC8599999 DOI: 10.1016/j.ebiom.2021.103684] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 10/17/2021] [Accepted: 10/25/2021] [Indexed: 02/06/2023] Open
Abstract
Background Preoperative determination of breast cancer molecular subtypes facilitates individualized treatment plan-making and improves patient prognosis. We aimed to develop an assembled convolutional neural network (ACNN) model for the preoperative prediction of molecular subtypes using multimodal ultrasound (US) images. Methods This multicentre study prospectively evaluated a dataset of greyscale US, colour Doppler flow imaging (CDFI), and shear-wave elastography (SWE) images in 807 patients with 818 breast cancers from November 2016 to February 2021. The St. Gallen molecular subtypes of breast cancer were confirmed by postoperative immunohistochemical examination. The monomodal ACNN model based on greyscale US images, the dual-modal ACNN model based on greyscale US and CDFI images, and the multimodal ACNN model based on greyscale US and CDFI as well as SWE images were constructed in the training cohort. The performances of three ACNN models in predicting four- and five-classification molecular subtypes and identifying triple negative from non-triple negative subtypes were assessed and compared. The performance of the multimodal ACNN was also compared with preoperative core needle biopsy (CNB). Finding The performance of the multimodal ACNN model (macroaverage area under the curve [AUC]: 0.89–0.96) was superior to that of the dual-modal ACNN model (macroaverage AUC: 0.81–0.84) and the monomodal ACNN model (macroaverage AUC: 0.73–0.75) in predicting four-classification breast cancer molecular subtypes, which was also better than that of preoperative CNB (AUC: 0.89–0.99 vs. 0.67–0.82, p < 0.05). In addition, the multimodal ACNN model outperformed the other two ACNN models in predicting five-classification molecular subtypes (AUC: 0.87–0.94 vs. 0.78-0.81 vs. 0.71–0.78) and identifying triple negative from non-triple negative breast cancers (AUC: 0.934–0.970 vs. 0.688–0.830 vs. 0.536–0.650, p < 0.05). Moreover, the multimodal ACNN model obtained satisfactory prediction performance for both T1 and non-T1 lesions (AUC: 0.957–0.958 and 0.932–0.985). Interpretation The multimodal US-based ACNN model is a potential noninvasive decision-making method for the management of patients with breast cancer in clinical practice. Funding This work was supported in part by the National Natural Science Foundation of China (Grants 81725008 and 81927801), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), and the Science and Technology Commission of Shanghai Municipality (Grants 19441903200, 19DZ2251100, and 21Y11910800).
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Ye H, Hang J, Zhang M, Chen X, Ye X, Chen J, Zhang W, Xu D, Zhang D. Automatic identification of triple negative breast cancer in ultrasonography using a deep convolutional neural network. Sci Rep 2021; 11:20474. [PMID: 34650065 PMCID: PMC8517009 DOI: 10.1038/s41598-021-00018-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 09/27/2021] [Indexed: 11/08/2022] Open
Abstract
Triple negative (TN) breast cancer is a subtype of breast cancer which is difficult for early detection and the prognosis is poor. In this paper, 910 benign and 934 malignant (110 TN and 824 NTN) B-mode breast ultrasound images were collected. A Resnet50 deep convolutional neural network was fine-tuned. The results showed that the averaged area under the receiver operating characteristic curve (AUC) of discriminating malignant from benign ones were 0.9789 (benign vs. TN), 0.9689 (benign vs. NTN). To discriminate TN from NTN breast cancer, the AUC was 0.9000, the accuracy was 88.89%, the sensitivity was 87.5%, and the specificity was 90.00%. It showed that the computer-aided system based on DCNN is expected to be a promising noninvasive clinical tool for ultrasound diagnosis of TN breast cancer.
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Affiliation(s)
- Heng Ye
- The MOE Key Laboratory of Modern Acoustics, Department of Physics, Nanjing University, Nanjing, 210093, China
| | - Jing Hang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Meimei Zhang
- The MOE Key Laboratory of Modern Acoustics, Department of Physics, Nanjing University, Nanjing, 210093, China
| | - Xiaowei Chen
- The MOE Key Laboratory of Modern Acoustics, Department of Physics, Nanjing University, Nanjing, 210093, China
| | - Xinhua Ye
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Jie Chen
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Weixin Zhang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Di Xu
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Dong Zhang
- The MOE Key Laboratory of Modern Acoustics, Department of Physics, Nanjing University, Nanjing, 210093, China.
- The State Key Laboratory of Acoustics, Chinese Academy of Science, Beijing, 10080, China.
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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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Affiliation(s)
- Mengwei Ma
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Renyi Liu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Chanjuan Wen
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Weimin Xu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Zeyuan Xu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Sina Wang
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Jiefang Wu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Derun Pan
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Bowen Zheng
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Genggeng Qin
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Weiguo Chen
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
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Yu F, Hang J, Deng J, Yang B, Wang J, Ye X, Liu Y. Radiomics features on ultrasound imaging for the prediction of disease-free survival in triple negative breast cancer: a multi-institutional study. Br J Radiol 2021; 94:20210188. [PMID: 34478336 DOI: 10.1259/bjr.20210188] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVES To explore the predictive value of radiomics nomogram using pretreatment ultrasound for disease-free survival (DFS) after resection of triple negative breast cancer (TNBC). METHODS AND MATERIALS A total of 486 TNBC patients from 3 different institutions were consecutively recruited for this study. They were categorized into the primary cohort (n = 216), as well as the internal validation cohort (n = 108) and external validation cohort (n = 162). In primary cohort, least absolute shrinkage and selection operator logistic regression algorithm was used to select recurrence-related radiomics features extracted from the breast tumor and peritumor regions, and a radiomics signature was constructed derived from the grayscale ultrasound images. A radiomic nomogram integrating independent clinicopathological variables and radiomic signature was established with uni- and multivariate cox regressions. The predictive nomogram was validated using an internal cohort and an independent external cohort regarding abilities of discrimination, calibration and clinical usefulness. RESULTS The patients with higher Rad-score had a worse prognostic outcome than those with lower Rad-score in primary cohort and two validation cohorts (All p < 0.05).The radiomics nomogram indicated more effective prognostic performance compared with the clinicopathological model and tumor node metastasis staging system (p < 0.01), with a training C-index of 0.75 (95% confidence interval (CI), 0.71-0.80), an internal validation C-index of 0.73 (95% CI, 0.69-0.78) and an external validation 0.71 (95% CI,0.66-0.76). Moreover, the calibration curves revealed a good consistency for survival prediction of the radiomics model. CONCLUSIONS The ultrasound-based radiomics signature was a promising biomarker for risk stratification for TNBC patients. Furthermore, the proposed radiomics modal integrating the optimal radiomics features and clinical data provided individual relapse risk accurately. ADVANCES IN KNOWLEDGE The radiomics model integrating radiomic signature and independent clinicopathological variables could improve individual prognostic evaluation and facilitate therapeutic decision-making, which demonstrated the incremental value of the radiomics signature for prognostic prediction in TNBC.
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Affiliation(s)
- Feihong Yu
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jing Hang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jing Deng
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Bin Yang
- Department of Ultrasound, Jinling Clinical Medical College, Nanjing Medical University, Nanjing, China
| | - Jianxiang Wang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xinhua Ye
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yun Liu
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Screening mammography mitigates breast cancer disparities through early detection of triple negative breast cancer. Clin Imaging 2021; 80:430-437. [PMID: 34543867 DOI: 10.1016/j.clinimag.2021.08.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/12/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE Screening mammography improves breast cancer survival through early detection, but Triple Negative Breast Cancer (TNBC) is more difficult to detect on mammography and has lower survival compared to non-TNBC, even when detected at early stages. TNBC is twice as common among African American (AA) compared to White American (WA) women, thereby contributing to the 40% higher breast cancer mortality rates observed in AA women. The role of screening mammography in addressing breast cancer disparities is therefore worthy of study. METHODS Outcomes were evaluated for TNBC patients treated in the prospectively-maintained databases of academic cancer programs in two metropolitan cities of the Northeast and Midwest, 1998-2018. RESULTS Of 756 TNBC cases, 301 (39.8%) were mammographically screen-detected. 46% of 189 AA and 38.5% of 460 WA patients had screen-detected TNBC (p = 0.16). 25.3% of 257 TNBC cases ≤50 years old had screen-detected disease compared to 47.3% of 499 TNBC cases >50 years old (p < 0.0001). 220/301 (73.1%) screen-detected TNBC cases were T1 lesions versus 118/359 (32.9%) non-screen-detected cases (p < 0.0001). Screen-detected TNBC was more likely to be node-negative (51.9% v. 40.4%; p < 0.0001). Five-year overall survival was better in screen-detected TNBC compared to nonscreen-detected TNBC (92.8% v. 81.5%; p < 0.0001) in the entire cohort. The magnitude of this effect was most significant among AA patients (Fig. 1). Screening-related survival patterns were similar among AA and WA patients in both cities. CONCLUSION Data from two different cities demonstrates the value of screening mammography to mitigate breast cancer disparities in AA women through the early detection of TNBC.
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Abstract
Patients may present to the emergency department with breast complaints due to traumatic or nontraumatic changes in the breast. Benign and malignant breast pathologies may mimic each other both in clinical presentation and imaging appearance. A complex cystic and solid mass seen on ultrasound in a patient with a palpable mass can represent breast cancer, abscess, or hematoma. A unilateral swollen breast may result from inflammatory breast cancer, mastitis, or other benign etiologies; correlation with clinical history, physical exam, and close follow-up are required to ensure complete resolution of symptoms. Uncommon breast entities such as granulomatous mastitis and breast implant-associated anaplastic large-cell lymphoma may cause changes in the appearance of the breast that prompt a patient to seek initial evaluation in the emergency department. Imaging evaluation of the breast in the emergency department is limited, and it is important that patients with a breast complaint be referred to a dedicated breast center for complete evaluation at an appropriate time interval after their discharge from the emergency department.
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Affiliation(s)
- Deanna L Lane
- University of Texas MD Anderson Cancer Center, Department of Breast Imaging, Houston, TX,USA
| | - Jay R Parikh
- University of Texas MD Anderson Cancer Center, Department of Breast Imaging, Houston, TX,USA
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Characterization of breast cancer subtypes based on quantitative assessment of intratumoral heterogeneity using dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging. Eur Radiol 2021; 32:822-833. [PMID: 34345946 DOI: 10.1007/s00330-021-08166-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/18/2021] [Accepted: 06/19/2021] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To investigate whether intratumoral heterogeneity, assessed via dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI), reflects the molecular subtypes of invasive breast cancers. MATERIAL AND METHODS We retrospectively evaluated data from 248 consecutive women (mean age ± standard deviation, 54.6 ± 12.2 years) with invasive breast cancer who underwent preoperative DCE-MRI and DWI between 2019 and 2020. To evaluate intratumoral heterogeneity, kinetic heterogeneity (a measure of heterogeneity in the proportions of tumor pixels with delayed washout, plateau, and persistent components within a tumor) was assessed with DCE-MRI using a commercially available computer-aided diagnosis system. Apparent diffusion coefficients (ADCs) were obtained using a region-of-interest technique, and ADC heterogeneity was calculated using the following formula: (ADCmax-ADCmin)/ADCmean. Possible associations between imaging-based heterogeneity values and breast cancer subtypes were analyzed. RESULTS Of the 248 invasive breast cancers, 61 (24.6%) were classified as luminal A, 130 (52.4%) as luminal B, 25 (10.1%) as HER2-enriched, and 32 (12.9%) as triple-negative breast cancer (TNBC). There were significant differences in the kinetic and ADC heterogeneity values among tumor subtypes (p < 0.001 and p = 0.023, respectively). The TNBC showed higher kinetic and ADC heterogeneity values, whereas the HER2-enriched subtype showed higher kinetic heterogeneity values compared to the luminal subtypes. Multivariate linear analysis showed that the HER2-enriched (p < 0.001) and TNBC subtypes (p < 0.001) were significantly associated with higher kinetic heterogeneity values. The TNBC subtype (p = 0.042) was also significantly associated with higher ADC heterogeneity values. CONCLUSIONS Quantitative assessments of heterogeneity in enhancement kinetics and ADC values may provide biological clues regarding the molecular subtypes of breast cancer. KEY POINTS • Higher kinetic heterogeneity was associated with HER2-enriched and triple-negative breast cancer. • Higher ADC heterogeneity was associated with triple-negative breast cancer. • Aggressive breast cancer subtypes exhibited higher intratumoral heterogeneity based on MRI.
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Evans A, Sim YT, Whelehan P, Savaridas S, Jordan L, Thompson A. Are baseline mammographic and ultrasound features associated with metastasis free survival in women receiving neoadjuvant chemotherapy for invasive breast cancer? Eur J Radiol 2021; 141:109790. [PMID: 34091135 DOI: 10.1016/j.ejrad.2021.109790] [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: 03/10/2021] [Revised: 05/17/2021] [Accepted: 05/20/2021] [Indexed: 11/18/2022]
Abstract
OBJECTIVES To identify associations between baseline ultrasound (US) and mammographic features and metastasis free survival (MFS) in women receiving neo-adjuvant chemotherapy (NACT) for breast cancer. METHODS The data were collected as part of an ethically approved prospective study. Women with invasive breast cancer receiving NACT who were metastasis free at diagnosis were included. Baseline US and mammography were performed. Imaging was assessed by an experienced breast radiologist who was blinded to outcomes. US imaging features documented included posterior effect, skin thickening, size and stiffness using shear wave elastography (SWE). The mammographic features documented were spiculation and microcalcification. The development of metastatic disease was ascertained from computer records. Statistical analysis was performed using Kaplan Meier survival curves and Receiver Operator Characteristic (ROC) analysis. RESULTS 171 women with 172 cancers were included in the study and 55 developed metastatic disease. Mean follow-up was 6.0 years. Women with mammographic calcification had significantly poorer metastasis free survival (MFS) compared to women without calcification (p = 0.043, 6 yr MFS 50 % vs 69 %). Women bearing cancer with distal shadowing had poorer MFS than women without shadowing (p = 0.025, 6 yr MFS 47 % vs. 73 %). Women with US skin thickening had poorer MFS compared to women without skin thickening (p = 0.032, 6 yr MFS 52 % vs. 68 %). Mammographic spiculation, US size and stiffness at SWE had no significant association with MFS. CONCLUSION We have identified mammographic and US features associated with MFS in women receiving NACT. Such information may be useful when counselling patients about the benefits and risks of NACT.
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Affiliation(s)
- Andy Evans
- Mail Box 4, Ninewells Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom.
| | - Yee Ting Sim
- Breast Unit, Ninewells Hospital, Dundee, DD1 9SY, United Kingdom
| | - Patsy Whelehan
- Breast Unit, Ninewells Hospital, Dundee, DD1 9SY, United Kingdom
| | - Sarah Savaridas
- Breast Unit, Ninewells Hospital, Dundee, DD1 9SY, United Kingdom
| | - Lee Jordan
- Breast Unit, Ninewells Hospital, Dundee, DD1 9SY, United Kingdom
| | - Alastair Thompson
- Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, United States
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Bansal GJ, Santosh D. Accuracy of MRI for prediction of response to neo-adjuvant chemotherapy in triple negative breast cancer compared to other subtypes of breast cancer. Indian J Radiol Imaging 2021; 26:475-481. [PMID: 28104942 PMCID: PMC5201078 DOI: 10.4103/0971-3026.195793] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Purpose: The aim of this study was to compare the accuracy of magnetic resonance imaging (MRI) for the prediction of response to neo-adjuvant chemotherapy in triple negative (TN) breast cancer, with respect to other subtypes. Materials and Methods: There were a total of 1610 breast cancers diagnosed between March 2009 and August 2014, out of which 82 patients underwent MRI before and after neo-adjuvant chemotherapy but just before surgery. TN cancers were analyzed with respect to others subtypes. Accuracy of MRI for prediction of pathological complete response was compared between different subtypes by obtaining receiver operating characteristic (ROC) curves. The Statistical Package for the Social Sciences version 21 was used for all data analysis, with P value of 0.05 as statistically significant. Results: Out of 82 patients, 29 were luminal (HR+/HER2−), 23 were TN (HR−, HER2−), 11 were HER2 positive (HR−, HER2+), and 19 were of hybrid subtype (HR+/HER2+). TN cancers presented as masses on the pre-chemotherapy MRI scan, were grade 3 on histopathology, and showed concentric shrinkage following chemotherapy. TN cancers were more likely to have both imaging and pathological complete response following chemotherapy (P = 0.055) in contrast to luminal cancers, which show residual cancer. ROC curves were constructed for the prediction of pathological complete response with MRI. For the TN subgroup, MR had a sensitivity of 0.745 and specificity of 0.700 (P = 0.035), with an area under curve of 0.745 (95% confidence interval: 0.526–0.965), which was significantly better compared to other subtypes. Conclusion: TN breast cancers present as masses and show concentric shrinkage following chemotherapy. MRI is most accurate in predicting response to chemotherapy in the TN group, compared to others subtypes. MRI underestimates residual disease in luminal cancers.
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Affiliation(s)
- Gaurav J Bansal
- The Breast Centre, University Hospital of Llandough, Penarth, United Kingdom
| | - Divya Santosh
- The Breast Centre, University Hospital of Llandough, Penarth, United Kingdom
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Sheng C, Gao S, Yan L, Yin H, Hu J, Ye Z, Wei X. Application value of conventional ultrasound combined with shear wave elastography in diagnosing triple negative breast cancer. Gland Surg 2021; 10:1980-1988. [PMID: 34268082 DOI: 10.21037/gs-21-320] [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: 03/24/2021] [Accepted: 06/18/2021] [Indexed: 11/06/2022]
Abstract
Background At present, most ultrasound (US) studies on triple negative breast cancer (TNBC) are limited to conventional US features, so it is necessary to develop new joint diagnostic methods. The study aims to explore the values of conventional US and shear wave elastography (SWE) in differential diagnoses of TNBC and non-TNBC. Methods A total of 120 breast cancer (BC) patients involving 120 lesions that were pathologically verified were retrospectively analyzed in this study. All participants had received both conventional US and SWE before surgery. Meanwhile, the participants were divided into a TNBC group or a non-TNBC group according to their immunohistochemical (IHC) results. The differences between the conventional US features (including lesion shape, growth location, margin, boundary, internal echo, micro-calcification, posterior echo, and internal blood supply) and the SWE image features [including mean lesion hardness (Emean), maximum (Emax), minimum (Emin), standard deviation (SD), and ratio to normal gland (Eratio)] of 2 groups were compared. The receiver operating characteristic (ROC) curve of the diagnosed lesion was calculated by the area under the curve (AUC). Results According to the findings of conventional US, the TNBC group mostly manifested as a micro-lobulated margin, with a clear boundary and no internal micro-calcification; the non-TNBC group mainly manifested as marginal angulation or burr, and hyper-echo halo in the boundary, accompanied with internal micro-calcification, and the difference was statistically significant (P<0.05); the internal thrombolysis in myocardial infarction (TIMI) and resistance index between the TNBC group and non-TNBC group were similar, and the differences were not statistically significant (P>0.05). The findings of SWE were as follows: differences in Emax, Emean, and Eratio values between 2 groups were statistically significant (P<0.05); and the areas under the ROC curve (AUC) of these three in diagnosing the lesions were 0.811, 0.781 and 0.770, respectively. Conclusions Conventional US combined with SWE can comprehensively analyze the morphological, blood supply, and hardness features of breast lesions, and provide more reliable information for the differential diagnosis between TNBC and non-TNBC.
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Affiliation(s)
- Changrui Sheng
- Department of Ultrasound, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Shanshan Gao
- Department of Ultrasound, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Liming Yan
- Department of Ultrasound, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Hua Yin
- Department of Ultrasound, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Jingjing Hu
- Department of Ultrasound, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Zhiying Ye
- Department of Ultrasound, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Xiuzhi Wei
- Department of Ultrasound, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
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Zhang X, Li H, Wang C, Cheng W, Zhu Y, Li D, Jing H, Li S, Hou J, Li J, Li Y, Zhao Y, Mo H, Pang D. Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model. Front Oncol 2021; 11:623506. [PMID: 33747937 PMCID: PMC7973262 DOI: 10.3389/fonc.2021.623506] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 02/15/2021] [Indexed: 12/24/2022] Open
Abstract
Background: Breast ultrasound is the first choice for breast tumor diagnosis in China, but the Breast Imaging Reporting and Data System (BI-RADS) categorization routinely used in the clinic often leads to unnecessary biopsy. Radiologists have no ability to predict molecular subtypes with important pathological information that can guide clinical treatment. Materials and Methods: This retrospective study collected breast ultrasound images from two hospitals and formed training, test and external test sets after strict selection, which included 2,822, 707, and 210 ultrasound images, respectively. An optimized deep learning model (DLM) was constructed with the training set, and the performance was verified in both the test set and the external test set. Diagnostic results were compared with the BI-RADS categorization determined by radiologists. We divided breast cancer into different molecular subtypes according to hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression. The ability to predict molecular subtypes using the DLM was confirmed in the test set. Results: In the test set, with pathological results as the gold standard, the accuracy, sensitivity and specificity were 85.6, 98.7, and 63.1%, respectively, according to the BI-RADS categorization. The same set achieved an accuracy, sensitivity, and specificity of 89.7, 91.3, and 86.9%, respectively, when using the DLM. For the test set, the area under the curve (AUC) was 0.96. For the external test set, the AUC was 0.90. The diagnostic accuracy was 92.86% with the DLM in BI-RADS 4a patients. Approximately 70.76% of the cases were judged as benign tumors. Unnecessary biopsy was theoretically reduced by 67.86%. However, the false negative rate was 10.4%. A good prediction effect was shown for the molecular subtypes of breast cancer with the DLM. The AUC were 0.864, 0.811, and 0.837 for the triple-negative subtype, HER2 (+) subtype and HR (+) subtype predictions, respectively. Conclusion: This study showed that the DLM was highly accurate in recognizing breast tumors from ultrasound images. Thus, the DLM can greatly reduce the incidence of unnecessary biopsy, especially for patients with BI-RADS 4a. In addition, the predictive ability of this model for molecular subtypes was satisfactory,which has specific clinical application value.
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Affiliation(s)
- Xianyu Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Hui Li
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Chaoyun Wang
- Harbin Engineering University Automation College, Harbin, China
| | - Wen Cheng
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yuntao Zhu
- Harbin Engineering University Automation College, Harbin, China
| | - Dapeng Li
- Department of Epidemiology, Harbin Medical University, Harbin, China
| | - Hui Jing
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China
| | - Shu Li
- Prenatal Diagnosis Center, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jiahui Hou
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China
| | - Jiaying Li
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yingpu Li
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yashuang Zhao
- Department of Epidemiology, Harbin Medical University, Harbin, China
| | - Hongwei Mo
- Harbin Engineering University Automation College, Harbin, China
| | - Da Pang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China
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Wang H, Li CY, Zha HL, Xu D, Hu ZB. Diagnostic and Predictive Values of Strain Ratios in the Regions of Interests in Reference Tissue for Breast Tumor. Cancer Manag Res 2021; 13:1017-1028. [PMID: 33574701 PMCID: PMC7871176 DOI: 10.2147/cmar.s292944] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 01/08/2021] [Indexed: 12/23/2022] Open
Abstract
PURPOSE To investigate the diagnostic and predictive value of strain ratios in the regions of interests (ROIs) in reference tissue for breast tumor. PATIENTS AND METHODS A total of 707 lesions in 665 consecutive patients were examined with B-mode Breast Imaging-Reporting and Data System (BI-RADS) and Ultrasonic elastography (UE). Elasticity score (ES) and strain ratio (SR) in each lesion were calculated. Receiver operating characteristic (ROC) curves were used to assess the diagnostic value of BI-RADS, ES, SR1, SR2, BI-RADS combined with ES (BI-RADS+ES), BI-RADS combined with SR1 (BI-RADS+SR1), and BI-RADS combined with SR2 (BI-RADS+SR2). The sensitivity, specificity, and areas under the ROC curves (Az) were obtained. Scatter plots were generated to demonstrate the correlation between SR1 and SR2. Kruskal-Walls H-test, Mann-Whitney U-test and one-way ANOVA were performed to evaluate SRs and tumor-related variables. Multiple linear regression analysis was carried out to determine variables independently associated with SRs. RESULTS BI-RADS had high sensitivity and low specificity in the diagnosis of breast tumor. The specificity of BI-BADS combined with ES or SR was even higher. The Az value of BI-RADS+ES or BI-RADS+SRs was higher than that of BI-RADS (P < 0.001). The Az value of ES was higher than those of SR1 and SR2 (P < 0.001), and those of SR1 and SR2 were similar. SR1 and SR2 were highly positively correlated. There was no statistical difference between Az values of BI-RADS+ES, BI-RADS+SR1, and BI-RADS+SR2. Indistinct margin, high histologic grade, histological type, and negative human epidermal growth factor receptor (Her-2) were associated with SR1 and SR2. Progesterone receptor (PR) status and molecular subtype were associated with SR2. Histologic grade and tumor margin were significantly associated with SR1, and tumor margin was associated with SR2. CONCLUSION SRs in different ROIs in the reference tissue at the same depth showed no different diagnostic value for breast tumor. Both SR1 and SR2 could be useful in assessing the biological characteristics of invasive breast carcinoma.
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Affiliation(s)
- Hui Wang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, People’s Republic of China
| | - Cui-Ying Li
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, People’s Republic of China
| | - Hai-Ling Zha
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, People’s Republic of China
| | - Di Xu
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, People’s Republic of China
| | - Zhi-Bin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, People’s Republic of China
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Son J, Lee SE, Kim EK, Kim S. Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis. Sci Rep 2020; 10:21566. [PMID: 33299040 PMCID: PMC7726048 DOI: 10.1038/s41598-020-78681-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 11/26/2020] [Indexed: 12/23/2022] Open
Abstract
We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three different molecular subtypes (luminal A + B, luminal; HER2-positive, HER2; triple-negative, TN) were assigned to the training set and temporally independent validation cohort. A total of 129 radiomics features were extracted from synthetic mammograms. The radiomics signature was built using the elastic-net approach. Clinical features included patient age, lesion size and image features assessed by radiologists. In the validation cohort, the radiomics signature yielded an AUC of 0.838, 0.556, and 0.645 for the TN, HER2 and luminal subtypes, respectively. In a multivariate analysis, the radiomics signature was the only independent predictor of the molecular subtype. The combination of the radiomics signature and clinical features showed significantly higher AUC values than clinical features only for distinguishing the TN subtype. In conclusion, the radiomics signature showed high performance for distinguishing TN breast cancer. Radiomics signatures may serve as biomarkers for TN breast cancer and may help to determine the direction of treatment for these patients.
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Affiliation(s)
- Jinwoo Son
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Si Eun Lee
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Eun-Kyung Kim
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
| | - Sungwon Kim
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
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Khalaf LMR, Herdan RA. Role of ultrasound in predicting the molecular subtypes of invasive breast ductal carcinoma. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [DOI: 10.1186/s43055-020-00240-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Breast cancer is a heterogeneous disease that was explained recently by the presence of multiple molecular subtypes. These subtypes are the luminal A (LA), luminal B (LB), human epidermal growth factor receptor 2 (HER2), and triple-negative (TN). In this study, we aim to investigate whether ultrasound imaging features can play a role in predicting the molecular subtypes of invasive ductal breast cancer (IDC) and to assess whether the nodal metastasis is an independent predictor for each subtype.
Results
The predictive sonographic signs for each subtype are as follows: echogenic halo, posterior shadowing, angular or spiculated margin, and unifocal mass for LA subtype; irregular shape for LB subtype; unifocal mass, abrupt interface of the tumor boundary, and posterior enhancement or no posterior change for HER2 subtype; and circumscribed or lobulated margin, oval or rounded shape, posterior enhancement or no posterior change, abrupt interface of the tumor boundary, and parallel orientation of the mass for TN. By multivariate logistic regression, presence of nodal metastasis is the strongest independent predictor for HER2 subtype, and its absence is the strongest independent predictor for LA subtype.
Conclusions
Certain sonographic signs are predictors for each molecular subtype of IDC. Nodal metastasis is an independent predictor for HER2 subtype when present and for LA subtype when absent.
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Ko KH, Jung HK, Park AY, Koh JE, Jang H, Kim Y. Accuracy of tumor size measurement on shear wave elastography (SWE): Correlation with histopathologic factors of invasive breast cancer. Medicine (Baltimore) 2020; 99:e23023. [PMID: 33126387 PMCID: PMC7598781 DOI: 10.1097/md.0000000000023023] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
The aim of this study is to investigate the accuracy of tumor size assessment by shear wave elastography (SWE) in invasive breast cancer and also evaluated histopathologic factors influencing the accuracy.A total of 102 lesions of 102 women with breast cancers of which the size was 3 cm or smaller were included and retrospectively analyzed. Tumor size on B-mode ultrasound (US) and SWE were recorded and compared with the pathologic tumor size. If tumor size measurements compared to pathological size were within ±3 mm, they were considered as accurate. The relationship between the accuracy and histopathologic characteristics were evaluated.The mean pathologic tumor size was 16.60 ± 6.12 mm. Tumor sizes on SWE were significantly different from pathologic sizes (18.00 ± 6.71 mm, P < 0.001). The accuracy of SWE (69.6%) was lower than that by B-mode US (74.5%). There was more size overestimation than underestimation (23.5% vs 6.9%) using SWE. Conversely, there was more size underestimation than overestimation (18.6% vs 6.9%) using B-mode US. The accuracy of SWE was associated with ER positivity (P = .004), PR positivity (P = .02), molecular subtype (P = .02), and histologic grade (P = .03). In the multivariate analysis, ER positivity (P = .002) and molecular subtype (P = .027) significantly influenced the accuracy of tumor size measurement by SWE.In conclusion, the accuracy of the tumor size measured with SWE was lower than that measured with B-mode US and SWE tends to overestimate the size. ER positivity and molecular subtype are significantly associated with the accuracy of SWE in tumor size assessment.
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Tian L, Wang L, Qin Y, Cai J. Systematic Review and Meta-analysis of the Malignant Ultrasound Features of Triple-Negative Breast Cancer. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2020; 39:2013-2025. [PMID: 32339328 DOI: 10.1002/jum.15309] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 03/11/2020] [Accepted: 04/08/2020] [Indexed: 06/11/2023]
Abstract
OBJECTIVES The malignant ultrasound (US) features of breast cancer are known to include an irregular shape, a noncircumscribed margin, an echogenic halo, a nonparallel orientation, posterior acoustic attenuation, microcalcification, and others. However, these US features are uncertain and controversial for the diagnosis of triple-negative breast cancer (TNBC). This study aimed to analyze the diagnostic value of malignant US features for TNBC by a systematic review and meta-analysis, analyze the US characteristics of TNBC, and provide US evidence for clinical diagnosis. METHODS A comprehensive search was performed to identify relevant English articles concerning the US diagnosis of TNBC (from the date of database establishment to November 2019). The pooled sensitivity, pooled specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio with 95% confidence interval, summary receiver operating characteristic curve, and area under the curve for the different malignant US features were calculated. RESULTS Ten studies (620 patients) met the eligibility criteria. The sensitivity (range, 0.14-0.68) and specificity (range, 0.19-0.66) of the malignant US features were not high. Summary receiver operating characteristic curves showed that the area under the curve (range, 0.25-0.47) of the malignant US features was low, demonstrating that these features have poor diagnostic value for TNBC. The positive likelihood ratio (range, 0.4-to 0.9) of the malignant US features was low, and the negative likelihood ratio (range, 1.09-2.02) was not low, revealing that these features had a poor ability to confirm or exclude TNBC. CONCLUSIONS Triple-negative breast cancer lacks the typical malignant US features of breast cancer and has its own US features.
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Affiliation(s)
- Lu Tian
- Department of Radiology, Children's Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing International Science and Technology Cooperation Center for Child Development and Disorders, Key Laboratory of Pediatrics in Chongqing, Chongqing, China
| | - Longlun Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing International Science and Technology Cooperation Center for Child Development and Disorders, Key Laboratory of Pediatrics in Chongqing, Chongqing, China
| | - Yong Qin
- Department of Radiology, Children's Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing International Science and Technology Cooperation Center for Child Development and Disorders, Key Laboratory of Pediatrics in Chongqing, Chongqing, China
| | - Jinhua Cai
- Department of Radiology, Children's Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing International Science and Technology Cooperation Center for Child Development and Disorders, Key Laboratory of Pediatrics in Chongqing, Chongqing, China
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Yin R, Jiang M, Lv WZ, Jiang F, Li J, Hu B, Cui XW, Dietrich CF. Study Processes and Applications of Ultrasomics in Precision Medicine. Front Oncol 2020; 10:1736. [PMID: 33014858 PMCID: PMC7494734 DOI: 10.3389/fonc.2020.01736] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 08/04/2020] [Indexed: 12/12/2022] Open
Abstract
Ultrasomics is the science of transforming digitally encrypted medical ultrasound images that hold information related to tumor pathophysiology into mineable high-dimensional data. Ultrasomics data have the potential to uncover disease characteristics that are not found with the naked eye. The task of ultrasomics is to quantify the state of diseases using distinctive imaging algorithms and thereby provide valuable information for personalized medicine. Ultrasomics is a powerful tool in oncology but can also be applied to other medical problems for which a disease is imaged. To date there is no comprehensive review focusing on ultrasomics. Here, we describe how ultrasomics works and its capability in diagnosing disease in different organs, including breast, liver, and thyroid. Its pitfalls, challenges and opportunities are also discussed.
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Affiliation(s)
- Rui Yin
- Department of Ultrasound, Affiliated Renhe Hospital of China Three Gorges University, Yichang, China
| | - Meng Jiang
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wen-Zhi Lv
- Department of Artificial Intelligence, Julei Technology, Wuhan, China
| | - Fan Jiang
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jun Li
- Department of Ultrasound, The First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Bing Hu
- Department of Ultrasound, Affiliated Renhe Hospital of China Three Gorges University, Yichang, China
| | - Xin-Wu Cui
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Cui H, Zhang D, Peng F, Kong H, Guo Q, Wu T, Wen X, Zhang L, Tian J. Identifying ultrasound features of positive expression of Ki67 and P53 in breast cancer using radiomics. Asia Pac J Clin Oncol 2020; 17:e176-e184. [PMID: 32779399 DOI: 10.1111/ajco.13397] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 05/21/2020] [Indexed: 12/19/2022]
Abstract
PURPOSE To examine the relationship between ultrasonic findings and positive expression of Ki67 and P53 in breast cancer. MATERIAL AND METHODS Surgical resection specimens of 263 breast cancer lesions were examined. Ultrasound examination and pathological examination were performed on each lesion for retrospective analysis. We applied regression analysis to the ultrasonic features related to the positive expression of Ki67 and P53 and obtained the corresponding models. To analyze diagnostic efficiency, we calculated the area under the curve (AUC). Additionally, we created a heat map to show the results of the cluster analysis. RESULTS Lesions with higher Ki67 expression were associated with posterior acoustic enhancement, absence of an echo halo and a higher Breast Imaging Reporting and Data System (BI-RADS) category. P53-positive cancer were associated with an absence of an echo halo and a higher BI-RADS category. The AUC of the regression models of Ki67 and P53 was 0.78 and 0.71, respectively. CONCLUSIONS Our study revealed that breast cancer ultrasonic findings were closely related to expression of molecular indicators, suggesting that ultrasound can be used to provide useful information to clinicians.
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Affiliation(s)
- Hao Cui
- Department of Ultrasound Medicine, the Second Affiliated Hospital of Harbin Medical University, Heilongjiang, China
| | - Dandan Zhang
- Department of Ultrasound Medicine, Heilongjiang provincial hospital, Heilongjiang, China
| | - Fuhui Peng
- Department of Ultrasound Medicine, the Second Affiliated Hospital of Harbin Medical University, Heilongjiang, China
| | - Hanqing Kong
- Department of Ultrasound Medicine, the Second Affiliated Hospital of Harbin Medical University, Heilongjiang, China
| | - Qiang Guo
- Department of Ultrasound Medicine, Jinshan Branch of Shanghai Sixth People's Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Tong Wu
- Department of Ultrasound Medicine, the Second Affiliated Hospital of Harbin Medical University, Heilongjiang, China
| | - Xin Wen
- Department of Ultrasound Medicine, the Third Affiliated Hospital of Harbin Medical University, Heilongjiang, China
| | - Lei Zhang
- Department of Ultrasound Medicine, the Second Affiliated Hospital of Harbin Medical University, Heilongjiang, China
| | - Jiawei Tian
- Department of Ultrasound Medicine, the Second Affiliated Hospital of Harbin Medical University, Heilongjiang, China
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Fang K, Wang L, Huang H, Lan M, Shen D, Dong S, Guo Y. Construction of Nucleolin-Targeted Lipid Nanobubbles and Contrast-Enhanced Ultrasound Molecular Imaging in Triple-Negative Breast Cancer. Pharm Res 2020; 37:145. [PMID: 32666304 DOI: 10.1007/s11095-020-02873-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 07/02/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE To construct aptamer AS1411-functionalized targeted lipid nanobubbles that could simultaneously target abnormally highly expressed nucleolin (NCL) on tumor tissue and neovasculature. Additionally, the study of their contrast-enhanced ultrasound molecular imaging capabilities in vitro and in vivo to explore new methods and approaches for the early and accurate diagnosis of triple-negative breast cancer (TNBC). METHODS First, the targeted lipid-nucleic acid molecules were constructed by an amide reaction. Then, the targeted lipid nanobubbles (AS1411-NBs) and nontargeted lipid nanobubbles (NBs) were prepared by membrane hydration, mechanical vibration and centrifugal floatation. The physicochemical characteristics and contrast-enhanced ultrasound imaging capabilities of AS1411-NBs and NBs were compared and analyzed in vitro and in vivo. RESULTS There were no significant differences between the AS1411-NBs and NBs in their concentration, average particle size or ultrasound imaging capabilities in vitro (P > 0.05). However, AS1411-NBs could simultaneously target NCL in tumor tissue and neovasculature to effectively prolong the duration of contrast-enhanced ultrasound imaging compared to NBs in vivo. The area under the time-intensity curve was significantly different between AS1411-NBs and NBs (P < 0.001), and the drug loading capacity of the AS1411-NBs was also significantly higher than that of the NBs (P < 0.05). CONCLUSIONS Aptamer AS1411-functionalized targeted lipid nanobubbles could significantly prolong the duration of contrast-enhanced ultrasound imaging to achieve dual-targeted ultrasound molecular imaging of tumor tissue and neovasculature. AS1411-NBs also have higher drug loading and targeted drug delivery capabilities compared with NBs, which can provide new methods and approaches for the early accurate diagnosis and effective treatment of TNBC.
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Affiliation(s)
- Kejing Fang
- Department of Ultrasound, Southwest Hospital, Army Medical University, Chongqing, 400038, China
- Department of Biomedical Materials Science, Army Medical University, Chongqing, 400038, China
| | - Luofu Wang
- Department of Urology, Army Characteristic Medical Center, Chongqing, 400042, China
| | - Haiyun Huang
- Department of Ultrasound, Southwest Hospital, Army Medical University, Chongqing, 400038, China
| | - Minmin Lan
- Department of Ultrasound, Southwest Hospital, Army Medical University, Chongqing, 400038, China
| | - Daijia Shen
- Department of Ultrasound, Southwest Hospital, Army Medical University, Chongqing, 400038, China
| | - Shiwu Dong
- Department of Biomedical Materials Science, Army Medical University, Chongqing, 400038, China.
| | - Yanli Guo
- Department of Ultrasound, Southwest Hospital, Army Medical University, Chongqing, 400038, China.
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Algazzar MAA, Elsayed EEM, Alhanafy AM, Mousa WA. Breast cancer imaging features as a predictor of the hormonal receptor status, HER2neu expression and molecular subtype. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [DOI: 10.1186/s43055-020-00210-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Abstract
Background
Determination of the hormonal receptor (HR) status, HER2neu expression, and the molecular subtype has valuable diagnostic, therapeutic, and prognostic implications for breast cancer as breast cancer stratification during the last two decades has become dependent upon the underlying biology. The aim of this study is to assess the correlation between imaging features of breast cancer and the HR status, HER2neu expression, and the molecular subtype. Sixty breast cancer patients underwent breast ultrasound, mammography, and MRI evaluation. Pathological evaluation using immunohistochemistry and FISH was used to detect the HR status, HER2/neu expression, and the molecular subtype. Those findings were then correlated with the radiologic data.
Results
HR-positive tumors were associated with posterior acoustic shadowing (34/44, 77.3%; p = 0.004). Hormonal-negative tumors presenting as masses were more likely circumscribed on US and MRI compared to hormonal positive mass tumors (6/14, 42.9% vs 3/36, 7.7%; p = 0.003 on US and 6/13, 46.3% vs 3/36, 8.3%; P = 0.007 on MRI) and had malignant DCE kinetics with washout curves compared to the hormonal positive group (10/16, 62.5% vs 4/44, 9.1%; P < 0.001). HER2neu-positive tumors were significantly associated with calcifications and multifocality on mammography compared to HER2neu-negative group (9/13, 69% vs 12/34, 25.5%; P = 0.007) and (7/13, 53% vs 3/47, 6%; P < 0.001). TNBC and HER2neu-enriched were associated with washout kinetic curve pattern (57.1% and 66.7%, respectively). TNBCs were associated with circumscribed margins on US and MRI (6/9, 66.7%; P < 0.001).
Conclusion
Microcalcifications, margins, posterior acoustic features, and malignant washout kinetics strongly correlate with the hormonal receptor status, HER2neu status, and molecular subtype of breast cancer. These findings may suggest the molecular subtype of breast cancer and further expand the role of imaging.
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Formes précoces des cancers du sein en fonction des différents sous-types moléculaires: présentations en imagerie. IMAGERIE DE LA FEMME 2020. [DOI: 10.1016/j.femme.2020.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Eghlimi R, Shi X, Hrovat J, Xi B, Gu H. Triple Negative Breast Cancer Detection Using LC-MS/MS Lipidomic Profiling. J Proteome Res 2020; 19:2367-2378. [PMID: 32397718 DOI: 10.1021/acs.jproteome.0c00038] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Breast cancer (BC) is a heterogeneous malignancy that is responsible for a great portion of female cancer cases and cancer-related deaths in the United States. In comparison to other major BC subtypes, triple negative breast cancer (TNBC) presents with a relatively low survival rate and a high rate of metastasis. This has led to a strong, though largely unmet, need for more sensitive and specific methods of early-stage TNBC (ES-TNBC) detection to combat its high-grade pathology and relatively low survival rate. The current study employs a liquid chromatography-tandem mass spectrometry assay capable of targeted, highly specific, and sensitive detection of lipids to propose two diagnostic biomarker panels for TNBC/ES-TNBC. Using this approach, 110 lipids were reliably detected in 166 human plasma samples, 45 controls, and 121 BC (96 non-TNBC and 25 TNBC) subjects. Univariate and multivariate analyses allowed the construction and application of a 19-lipid biomarker panel capable of distinguishing TNBC (and ES-TNBC) from controls, as well as a 5-lipid biomarker panel capable of differentiating TNBC from non-TNBC and ES-TNBC from ES-non-TNBC. Receiver operating characteristic curves with notable classification performances were generated from the biomarker panels according to their orthogonal partial least-squares discrimination analysis models. TNBC was distinguished from controls with an area under the receiving operating characteristic curve (AUROC) = 0.93, sensitivity = 0.96, and specificity = 0.76 and ES-TNBC from controls with an AUROC = 0.96, sensitivity = 0.95, and specificity = 0.89. TNBC was differentiated from non-TNBC with an AUROC = 0.88, sensitivity = 0.88, and specificity = 0.79 and ES-TNBC from ES-non-TNBC with an AUROC = 0.95, sensitivity = 0.95, and specificity = 0.87. A pathway enrichment analysis between TNBC and controls also revealed significant disturbances in choline metabolism, sphingolipid signaling, and glycerophospholipid metabolism. To the best of our knowledge, this is the first study to propose a diagnostic lipid biomarker panel for TNBC detection. All raw mass spectrometry data have been deposited to MassIVE (dataset identifier MSV000085324).
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Affiliation(s)
- Ryan Eghlimi
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
| | - Xiaojian Shi
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
| | - Jonathan Hrovat
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
| | - Bowei Xi
- Department of Statistics, Purdue University, West Lafayette, Indiana 47907, United States
| | - Haiwei Gu
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
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Zhu K, Wang D, Li Z, Du G, Guo Q, Wu T, Li J. Heterogeneity of Triple-Negative Breast Cancer: Differences in Clinicopathologic and Ultrasound Features Between Premenopausal and Postmenopausal Patients. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2020; 39:919-927. [PMID: 31737929 DOI: 10.1002/jum.15174] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 10/10/2019] [Accepted: 10/11/2019] [Indexed: 06/10/2023]
Abstract
OBJECTIVES The aim of this study was to determine whether any salient differences exist in triple-negative breast cancer (TNBC) between premenopausal and postmenopausal patients by comprehensively comparing the ultrasound (US) and clinicopathologic features of these patients. METHODS A detailed evaluation of the US and clinicopathologic features of 60 tumors from 60 patients with a diagnosis of TNBC (36 premenopausal and 24 postmenopausal women) was conducted in this retrospective study. RESULTS Triple-negative breast cancer in premenopausal patients was more likely to show a round or oval shape and microlobulated margins, more commonly exerted effects on surrounding tissue, and showed grade 3 blood flow according to the method of Adler et al (Ultrasound Med Biol 1990; 16:553-559) compared with tumors in postmenopausal women. Triple-negative breast cancer in postmenopausal patients showed some similar US features as those observed in estrogen receptor-positive tumors, including irregular shapes, angular or spiculated margins, and nonparallel growth to the skin (P < .05 for all). CONCLUSIONS Although US cannot be used to unequivocally identify TNBC, it can distinguish some salient US features regarding TNBC between premenopausal and postmenopausal women. Such information can then provide some valuable predictive information on the diagnosis of TNBC for clinicians.
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Affiliation(s)
- Kai Zhu
- First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Dongmo Wang
- Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ziyao Li
- Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Guoqing Du
- Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qiang Guo
- Jinshan Branch of Shanghai Sixth People's Hospital, affiliated with Shanghai Jiaotong University, Shanghai, China
| | - Tong Wu
- Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Juan Li
- Second Affiliated Hospital of Harbin Medical University, Harbin, China
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Immunohistochemical index prediction of breast tumor based on multi-dimension features in contrast-enhanced ultrasound. Med Biol Eng Comput 2020; 58:1285-1295. [PMID: 32232794 DOI: 10.1007/s11517-020-02164-2] [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] [Received: 08/17/2019] [Accepted: 03/13/2020] [Indexed: 10/24/2022]
Abstract
Breast cancer is the leading killer of Chinese women. Immunohistochemistry index has great significance in the treatment strategy selection and prognosis analysis for breast cancer patients. Currently, histopathological examination of the tumor tissue through surgical biopsy is the gold standard to determine immunohistochemistry index. However, this examination is invasive and commonly causes discomfort in patients. There has been a lack of noninvasive method capable of predicting immunohistochemistry index for breast cancer patients. This paper proposes a machine learning method to predict the immunohistochemical index of breast cancer patients by using noninvasive contrast-enhanced ultrasound. A total of 119 breast cancer patients were included in this retrospective study. Each patient implemented the pathological examination of immunohistochemical expression and underwent contrast-enhanced ultrasound imaging of breast tumor. The multi-dimension features including 266 three-dimension features and 837 two-dimension dynamic features were extracted from the contrast-enhanced ultrasound sequences. Using the machine learning prediction method, 21 selected multi-dimension features were integrated to generate a model for predicting the immunohistochemistry index noninvasively. The immunohistochemical index of human epidermal growth factor receptor-2 (HER2) was predicted based on multi-dimension features in contrast-enhanced ultrasound sequence with the sensitivity of 71%, and the specificity of 79% in the testing cohort. Therefore, the noninvasive contrast-enhanced ultrasound can be used to predict the immunohistochemical index. To our best knowledge, no studies have been reported about predicting immunohistochemical index by using contrast-enhanced ultrasound sequences for breast cancer patients. Our proposed method is noninvasive and can predict immunohistochemical index by using contrast-enhanced ultrasound in several minutes, instead of relying totally on the invasive and biopsy-based histopathological examination. Graphical abstract Immunohistochemical index prediction of breast tumor based on multi-dimension features in contrast-enhanced ultrasound.
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Karbasian N, Sohrabi S, Omofoye TS, Le-Petross H, Arun BK, Albarracin CT, Hess KR, Gutierrez-Barrera AM, Whitman GJ. Imaging Features of Triple Negative Breast Cancer and the Effect of BRCA Mutations. Curr Probl Diagn Radiol 2020; 50:303-307. [PMID: 32033821 DOI: 10.1067/j.cpradiol.2020.01.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 12/01/2019] [Accepted: 01/05/2020] [Indexed: 11/22/2022]
Abstract
OBJECTIVE The purpose of this study is to review the mammographic and the ultrasound features of triple negative breast cancer (TNBC) patients and to investigate the potential effect of BRCA mutations on the imaging features of these patients. METHODS One hundred and seven patients with TNBC were enrolled in a retrospective study following IRB approval and approval of waiver of informed consent. BRCA mutations were assessed using genetic testing. Imaging features on mammography and ultrasound (US) as well as pathology and clinical information were retrospectively reviewed and characterized according to the BI-RADS lexicon (fifth edition). The relationships between BRCA mutations and the imaging findings were examined. RESULTS TNBC commonly presented as an irregular mass with obscured margins on mammography and as an irregular hypoechoic mass with microlobulated or angular margins on US. Approximately two thirds of TNBC cases had a parallel orientation and approximately one third had posterior enhancement, features often associated with benign masses. There was no statistically significant difference in the mammographic and the US features of BRCA positive and BRCA negative triple negative tumors. CONCLUSION TNBC may have a parallel orientation and posterior enhancement, which are features often seen with benign masses. BRCA mutations do not affect the imaging features of triple negative breast tumors.
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Affiliation(s)
- Niloofar Karbasian
- Department of Interventional Radiology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Sahand Sohrabi
- Department of Radiology, Kaiser Permanente of Northern California, Sacramento, CA
| | - Toma S Omofoye
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Huong Le-Petross
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Banu K Arun
- Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Constance T Albarracin
- Department of Anatomical Pathology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Kenneth R Hess
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Gary J Whitman
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX.
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Multiple Bilateral Circumscribed Breast Masses Detected at Imaging: Review of Evidence for Management Recommendations. AJR Am J Roentgenol 2019; 214:276-281. [PMID: 31825259 DOI: 10.2214/ajr.19.22061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE. Historically, management recommendations for multiple bilateral circumscribed breast masses encountered with breast imaging have varied. This article reviews the evidence and provides best-practice recommendations for managing these masses. CONCLUSION. Meticulous imaging technique and interpretation are required to correctly diagnose multiple bilateral circumscribed breast masses. Radiologists should classify such masses identified at mammography, digital breast tomosynthesis, or bilateral whole-breast sonography as benign and recommend annual follow-up. Elucidating the significance of these masses on MRI, contrast-enhanced mammography, or nuclear breast imaging requires further study.
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Savaridas SL, Sim YT, Vinnicombe SJ, Purdie CA, Thompson AM, Evans A. Are baseline ultrasound and mammographic features associated with rates of pathological completes response in patients receiving neoadjuvant chemotherapy for breast cancer? Cancer Imaging 2019; 19:67. [PMID: 31639053 PMCID: PMC6802305 DOI: 10.1186/s40644-019-0251-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 09/10/2019] [Indexed: 12/23/2022] Open
Abstract
Background Increasing numbers of breast cancer patients receive neoadjuvant chemotherapy (NACT). We seek to investigate whether baseline mammographic and ultrasound features are associated with complete pathological response (pCR) after NACT. Methods A database of NACT patients was reviewed. Baseline imaging parameters assessed were ultrasound: posterior effect; echo pattern; margin and lesion diameter; mammography: spiculation and microcalcification. Core biopsy grade and immunophenotype were documented. Data were analysed for the whole study group and by immunophenotype. Results Of the 222 cancers, 83 (37%) were triple negative (TN), 61 (27%) ER positive/HER-2 negative and 78 (35%) HER-2 positive. A pCR occurred in 46 of 222 cancers (21%). For the whole group, response was associated with high core biopsy grade (grade 3 vs. grade 1 or 2) (26% vs. 9%, p = 0.0044), absence of posterior shadowing on ultrasound (26% vs. 10%, p < 0.001) and the absence of mammographic spiculation (26 vs. 6%, p < 0.001). Within the HER-2 positive group; the absence of shadowing and spiculation remained highly associated with pCR, in addition to small ultrasound size (AUC = 0.71, p < 0.001) and the absence of microcalcification (39% vs. 21%, p < 0.02). On multivariable analysis absence of spiculation and core grade remained significant for the whole cohort, size and absence of spiculation remained significant for HER-2 positive tumours. No feature predicted pCR in TN tumours. Conclusion A pCR is less likely when there is mammographic spiculation. Small ultrasound size is associated with pCR in HER-2 positive tumours. These findings may be helpful when discussing NACT and surgical options with patients. Trial registration UK Clinical Trials Gateway: registration number 16712.
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Affiliation(s)
| | | | | | | | | | - Andy Evans
- University of Dundee, Dundee, UK.,Ninewells Hospital, Dundee, UK
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Au FWF, Ghai S, Lu FI, Lu H. Clinical Value of Shear Wave Elastography Added to Targeted Ultrasound (Second-Look Ultrasound) in the Evaluation of Breast Lesions Suspicious of Malignancy Detected on Magnetic Resonance Imaging. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2019; 38:2395-2406. [PMID: 30666681 DOI: 10.1002/jum.14936] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 12/07/2018] [Accepted: 12/20/2018] [Indexed: 06/09/2023]
Abstract
OBJECTIVES To determine the value of shear wave elastography (SWE) added to targeted ultrasound (US) after breast magnetic resonance imaging (MRI). METHODS From July 2015 to October 2017, 40 patients who underwent targeted US evaluations of suspicious MRI-detected American College of Radiology Breast Imaging Reporting and Data System category 4 lesions (mass or nonmass enhancement) were enrolled in this prospective study. B-mode US and SWE examinations were performed to detect US correlates to MRI-detected lesions; their Breast Imaging Reporting and Data System categories were recorded; lesions that were dark blue on a 6-point color scale or had maximum elasticity of 30 kPa or less were categorized as soft. Biopsy was performed with US or MRI guidance, with the pathologic findings correlated with MRI, US, and SWE findings. The value of SWE for lesion detection and identification of benign lesions was determined. RESULTS The mean age of the 40 patients was 51.1 years. There were 48 MRI-detected lesions (20 cancers, 3 high-risk lesions, and 25 benign lesions). Ultrasound correlates (8 category 3 and 25 category 4) were shown for 33 lesions (69%; P < .0001), with 16 cancers (80%; P < .0001) and 17 benign lesions. Shear wave elastography assisted detection of 3 (19%) cancers on US imaging. All 7 soft US category 3 lesions were benign (7 of 33 [21%]; P = .0014). CONCLUSIONS Shear wave elastography was useful with targeted US after breast MRI to increase cancer detection by US. A significant number of US correlates to MRI-detected lesions could have been identified as benign (category 3 and soft) before biopsy, with the potential of short-interval follow-up of MRI-detected lesions with benign US correlates instead of biopsy.
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Affiliation(s)
- Frederick Wing-Fai Au
- Joint Department of Medical Imaging, Toronto General Hospital, Toronto, Ontario, Canada
| | - Sandeep Ghai
- Joint Department of Medical Imaging, Women's College Hospital, Toronto, Ontario, Canada
| | - Fang-I Lu
- Department of Pathology, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Hua Lu
- University of Toronto, Toronto, Ontario, Canada
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Tsukada H, Tsukada J, Schrading S, Strobel K, Okamoto T, Kuhl CK. Accuracy of multi-parametric breast MR imaging for predicting pathological complete response of operable breast cancer prior to neoadjuvant systemic therapy. Magn Reson Imaging 2019; 62:242-248. [PMID: 31352016 DOI: 10.1016/j.mri.2019.07.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/12/2019] [Accepted: 07/13/2019] [Indexed: 10/26/2022]
Abstract
OBJECTIVES To evaluate whether multiparametric breast-MRI, obtained before the initiation of neoadjuvant systemic therapy (NST) for operable breast cancer, predicts which cancer will achieve a pathological complete response (pCR) after the completion of NST. METHODS This was an IRB-approved retrospective study on 31 consecutive patients (median age, 56 years) with operable invasive breast cancer (median size: 22 mm; triple-negative: 11/31 [35%], HER2-positive: 7/31 [23%], triple-positive: 13/31 [42%]) who underwent multiparametric DCE-MRI before the initiation of NST. The MRI protocol consisted of high-resolution dynamic contrast-enhanced MRI (DCE-MRI), T2-TSE, and DWI (b-values 0, 100, 800 s/mm2). The results of surgical pathology after the completion of NST served as a standard of reference. Patient characteristics (age and menopausal status), pathological tumor characteristics (type, stage, nuclear grade, ER/PR and HER2 receptor status, and Ki-67 staining), and MRI characteristics (size, morphology, T2 signal intensity, enhancement kinetics, and ADC values) before NST were evaluated and compared between patients achieving pCR vs. non-pCR. RESULTS Among 31 patients, 17 achieved pCR (55%) and 14 non-pCR (45%). No correlation was observed between patient- or tumor pathology-derived characteristics and pCR vs. non-pCR. Among MRI-derived tumor characteristics, tumor growth orientation parallel to Cooper's ligaments (p = 0.002) and wash-out rates (p = 0.019) correlated with pCR. Pre-NST ADC values were lower in patients achieving pCR (P = 0.086). CONCLUSIONS A tumor growth pattern parallel with Cooper's ligaments and a fast wash-out rate on pre-treatment multiparametric MRI are predictive of pCR and more closely associated with pCR than ADC values.
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Affiliation(s)
- Hiroko Tsukada
- Department of Diagnostic and Interventional Radiology, Hospital of the University of Aachen, RWTH, Pauwelsstrasse 30, 52074 Aachen, Germany; Department of Surgery II, School of Medicine, Tokyo Women's Medical University, 8-1, Kawada-cho, Shinjuku-ku, 162-8666 Tokyo, Japan.
| | - Jitsuro Tsukada
- Department of Radiology, Nihon University School of Medicine, 30-1, Oyaguchi Kami-Cho, Itabashi-ku, 173-8610 Tokyo, Japan
| | - Simone Schrading
- Department of Diagnostic and Interventional Radiology, Hospital of the University of Aachen, RWTH, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Kevin Strobel
- Department of Diagnostic and Interventional Radiology, Hospital of the University of Aachen, RWTH, Pauwelsstrasse 30, 52074 Aachen, Germany
| | - Takahiro Okamoto
- Department of Surgery II, School of Medicine, Tokyo Women's Medical University, 8-1, Kawada-cho, Shinjuku-ku, 162-8666 Tokyo, Japan
| | - Christiane K Kuhl
- Department of Diagnostic and Interventional Radiology, Hospital of the University of Aachen, RWTH, Pauwelsstrasse 30, 52074 Aachen, Germany
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