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Montella L, Di Marino L, Marino MA, Riccio V, Del Gaudio N, Altucci L, Berretta M, Facchini G. Case report: An ultrasound-based approach as an easy tool to evaluate hormone receptor-positive HER-2-negative breast cancer in advanced/metastatic settings: preliminary data of the Plus-ENDO study. Front Oncol 2024; 14:1295772. [PMID: 38690171 PMCID: PMC11058846 DOI: 10.3389/fonc.2024.1295772] [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: 09/17/2023] [Accepted: 03/11/2024] [Indexed: 05/02/2024] Open
Abstract
Background Hormone receptor-positive tumors are unlikely to exhibit a complete pathological tumor response. The association of CDK 4/6 inhibitor plus hormone therapy has changed this perspective. Case presentation In this study, we retrospectively reviewed the charts of patients with a diagnosis of luminal A/B advanced/metastatic tumors treated with a CDK 4/6 inhibitor-based therapy. In this part of the study, we present clinical and ultrasound evaluation. Eight female patients were considered eligible for the study aims. Three complete and five partial responses were reported, including a clinical tumor response of 50% or more in five out of nine assessed lesions (55%). All patients showed a response on ultrasound. The mean lesion size measured by ultrasound was 27.1 ± 15.02 mm (range, 6-47 mm) at the baseline; 16.08 ± 14.6 mm (range, 0-40 mm) after 4 months (T1); and 11.7 ± 12.9 mm (range, 0-30 mm) at the 6 months follow-up (T2). Two patients underwent surgery. The radiological complete response found confirmation in a pathological complete response, while the partial response matched a moderate residual disease. Conclusion The evaluation of breast cancer by ultrasound is basically informative of response and may be an easy and practical tool to monitor advanced tumors, especially in advanced/unfit patients who are reluctant to invasive exams.
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Affiliation(s)
- Liliana Montella
- Oncology Operative Unit, “Santa Maria delle Grazie” Hospital, ASL Napoli 2 NORD, Pozzuoli, Italy
| | | | | | | | - Nunzio Del Gaudio
- Department of Precision Medicine, “Luigi Vanvitelli” University of Campania, Napoli, Italy
| | - Lucia Altucci
- Department of Precision Medicine, “Luigi Vanvitelli” University of Campania, Napoli, Italy
- Molecular Biology and Genetics Research Institute, Biogem, Ariano Irpino, Italy
| | - Massimiliano Berretta
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Gaetano Facchini
- Oncology Operative Unit, “Santa Maria delle Grazie” Hospital, ASL Napoli 2 NORD, Pozzuoli, Italy
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Sui L, Yan Y, Jiang T, Ou D, Chen C, Lai M, Ni C, Zhu X, Wang L, Yang C, Li W, Yao J, Xu D. Ultrasound and clinicopathological characteristics-based model for prediction of pathologic response to neoadjuvant chemotherapy in HER2-positive breast cancer: a case-control study. Breast Cancer Res Treat 2023; 202:45-55. [PMID: 37639063 PMCID: PMC10504141 DOI: 10.1007/s10549-023-07057-0] [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: 05/23/2023] [Accepted: 07/14/2023] [Indexed: 08/29/2023]
Abstract
BACKGROUND The objective of this study was to develop a model combining ultrasound (US) and clinicopathological characteristics to predict the pathologic response to neoadjuvant chemotherapy (NACT) in human epidermal growth factor receptor 2 (HER2)-positive breast cancer. MATERIALS AND METHODS This is a retrospective study that included 248 patients with HER2-positive breast cancer who underwent NACT from March 2018 to March 2022. US and clinicopathological characteristics were collected from all patients in this study, and characteristics obtained using univariate analysis at p < 0.1 were subjected to multivariate analysis and then the conventional US and clinicopathological characteristics independently associated with pathologic complete response (pCR) from the analysis were used to develop US models, clinicopathological models, and their combined models by the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity to assess their predictive efficacy. RESULTS The combined model had an AUC of 0.808, a sensitivity of 88.72%, a specificity of 60.87%, and an accuracy of 75.81% in predicting pCR of HER2-positive breast cancer after NACT, which was significantly better than the clinicopathological model (AUC = 0.656) and the US model (AUC = 0.769). In addition, six characteristics were screened as independent predictors, namely the Clinical T stage, Clinical N stage, PR status, posterior acoustic, margin, and calcification. CONCLUSION The conventional US combined with clinicopathological characteristics to construct a combined model has a good diagnostic effect in predicting pCR in HER2-positive breast cancer and is expected to be a useful tool to assist clinicians in effectively determining the efficacy of NACT in HER2-positive breast cancer patients.
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Affiliation(s)
- Lin Sui
- Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, China
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial IntelligenceTaizhou Branch of Zhejiang Cancer Hospital(Taizhou Cancer Hospital), Taizhou, China
| | - Yuqi Yan
- Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, China
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial IntelligenceTaizhou Branch of Zhejiang Cancer Hospital(Taizhou Cancer Hospital), Taizhou, China
| | - Tian Jiang
- Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, China
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Di Ou
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Chen Chen
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial IntelligenceTaizhou Branch of Zhejiang Cancer Hospital(Taizhou Cancer Hospital), Taizhou, China
- Graduate School, Wannan Medical College, Wuhu, China
| | - Min Lai
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- The Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Chen Ni
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Xi Zhu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial IntelligenceTaizhou Branch of Zhejiang Cancer Hospital(Taizhou Cancer Hospital), Taizhou, China
| | - Liping Wang
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Chen Yang
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Wei Li
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Jincao Yao
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, China
| | - Dong Xu
- Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, China
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, China
- Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou, China
- Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial IntelligenceTaizhou Branch of Zhejiang Cancer Hospital(Taizhou Cancer Hospital), Taizhou, China
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Byra M, Dobruch-Sobczak K, Piotrzkowska-Wroblewska H, Klimonda Z, Litniewski J. Prediction of response to neoadjuvant chemotherapy in breast cancer with recurrent neural networks and raw ultrasound signals. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac8c82] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 08/24/2022] [Indexed: 12/07/2022]
Abstract
Abstract
Objective. Prediction of the response to neoadjuvant chemotherapy (NAC) in breast cancer is important for patient outcomes. In this work, we propose a deep learning based approach to NAC response prediction in ultrasound (US) imaging. Approach. We develop recurrent neural networks that can process serial US imaging data to predict chemotherapy outcomes. We present models that can process either raw radio-frequency (RF) US data or regular US images. The proposed approach is evaluated based on 204 sequences of US data from 51 breast cancers. Each sequence included US data collected before the chemotherapy and after each subsequent dose, up to the 4th course. We investigate three pre-trained convolutional neural networks (CNNs) as back-bone feature extractors for the recurrent network. The CNNs were pre-trained using raw US RF data, US b-mode images and RGB images from the ImageNet dataset. The first two networks were developed using US data collected from malignant and benign breast masses. Main results. For the pre-treatment data, the better performing network, with back-bone CNN pre-trained on US images, achieved area under the receiver operating curve (AUC) of 0.81 (±0.04). Performance of the recurrent networks improved with each course of the chemotherapy. For the 4th course, the better performing model, based on the CNN pre-trained with RGB images, achieved AUC value of 0.93 (±0.03). Statistical analysis based on the DeLong test presented that there were no significant differences in AUC values between the pre-trained networks at each stage of the chemotherapy (p-values > 0.05). Significance. Our study demonstrates the feasibility of using recurrent neural networks for the NAC response prediction in breast cancer US.
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Ma JJ, Meng S, Dang SJ, Wang JZ, Yuan Q, Yang Q, Song CX. Evaluation of a new method of calculating breast tumor volume based on automated breast ultrasound. Front Oncol 2022; 12:895575. [PMID: 36176389 PMCID: PMC9513394 DOI: 10.3389/fonc.2022.895575] [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: 03/14/2022] [Accepted: 08/26/2022] [Indexed: 11/13/2022] Open
Abstract
Objective To evaluate the effectiveness and advantages of a new method for calculating breast tumor volume based on an automated breast ultrasound system (ABUS). Methods A total of 42 patients (18–70 years old) with breast lesions were selected for this study. The Ivenia ABUS 2.0 (General Electric Company, USA) was used, with a probe frequency of 6–15 MHz. Adobe Photoshop CS6 software was used to calculate the pixel ratio of each ABUS image, and to draw an outline of the tumor cross-section. The resulting area (in pixels) was multiplied by the pixel ratio to yield the area of the tumor cross-section. The Wilcoxon signed rank test and Bland-Altman plot were used to compare mean differences and mean values, respectively, between the two methods. Results There was no significant difference between the tumor volumes calculated by pixel method as compared to the traditional method (P>0.05). Repeated measurements of the same tumor volume were more consistent with the pixel method. Conclusion The new pixel method is feasible for measuring breast tumor volume and has good validity and measurement stability.
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Affiliation(s)
- Jing-Jing Ma
- Department of Internal Medicine, Xi’an Fifth Hospital, Xi’an, China
| | - Shan Meng
- Department of Hematology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Sha-Jie Dang
- Department of Anesthesia, Shaanxi Provincial Cancer Hospital, Affiliated to Xi’an Jiaotong University, Xi’an, China
| | - Jia-Zhong Wang
- Department of General Surgery, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Quan Yuan
- Department of Ultrasound, Shaanxi Provincial Cancer Hospital, Affiliated to Xi’an Jiaotong University, Xi’an, China
| | - Qi Yang
- Department of Surgery, Shaanxi Provincial Cancer Hospital, Affiliated to Xi’an Jiaotong University, Xi’an, China
| | - Can-Xu Song
- Department of Ultrasound, Shaanxi Provincial Cancer Hospital, Affiliated to Xi’an Jiaotong University, Xi’an, China
- *Correspondence: Can-Xu Song,
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Hu Z, Lu M, Wang X, Yang W, Fan Y, Li T, Wang L, Wei T. Diagnostic Value of Different 3-D Shear Wave Elastography Sections in the Diagnosis of Thyroid Nodules. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:1957-1965. [PMID: 35853762 DOI: 10.1016/j.ultrasmedbio.2022.05.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 05/23/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
The aim of the study was to explore the value of 3-D shear wave elastography (SWE) in differentiating malignant from benign thyroid nodules. A total of 188 patients with 216 nodules who underwent conventional ultrasound, 2-D SWE and 3-D SWE were included in this study. All patients underwent surgical excision, and the pathological results were the gold standard. Receiver operating characteristic (ROC) curves of the American College of Radiology's Thyroid Imaging Reporting and Data System (ACR TI-RADS), 2-D SWE and 3-D SWE were plotted, and the areas under the curves (AUCs) were compared using a Z-test. There were 62 benign thyroid nodules and 154 malignant thyroid nodules in this study. Young's modulus (Emin, Emean, Emax, Esd) values of thyroid malignant nodules in different sections of 2-D SWE and 3-D SWE were significantly higher than those of thyroid benign nodules (p < 0.001). The AUC of Emax in 2-D SWE transverse sections was significantly lower than that in 3-D SWE transverse sections and 3-D SWE sagittal sections (0.768 vs. 0.831 and 0.844, p < 0.05). The AUC of 3-D S-Emax combined with ACR TI-RADS was 0.859; the specificity increased from 54.84% to 85.71%, and the diagnostic accuracy increased from 74.54% to 85.19%, compared with ACR TI-RADS. The difference was statistically significant (p < 0.05). Three-dimensional SWE combined with ACR TI-RADS for the diagnosis of thyroid nodules significantly improved the diagnostic ability of ACR TI-RADS, and was significantly better than 2-D SWE combined with ACR TI-RADS.
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Affiliation(s)
- Ziyue Hu
- Department of Ultrasound, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Man Lu
- Department of Ultrasound, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
| | - Xu Wang
- Department of Head and Neck Surgery, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Yang
- Department of Ultrasound, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuting Fan
- Department of Ultrasound, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Tingting Li
- Department of Ultrasound, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Lu Wang
- Department of Ultrasound, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Ting Wei
- Department of Ultrasound, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Park SY, Park JY, Park JW, Kim WH, Park JY, Kim HJ. Unexpected hyperechoic lesions of the breast and their correlations with pathology: a pictorial essay. Ultrasonography 2022; 41:597-609. [PMID: 35462528 PMCID: PMC9262659 DOI: 10.14366/usg.21243] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/12/2022] [Indexed: 12/04/2022] Open
Abstract
Hyperechoic lesions of the breast encompass a wide range of conditions that are occasionally encountered during breast ultrasonography. Although typical hyperechoic lesions with a distinct fat component on imaging are well known, some hyperechoic lesions are diagnosed as unexpected pathology, making the radiology-pathology correlation difficult. Therefore, understanding the pathology of these lesions and how it correlates with imaging findings can help radiologists accurately diagnose and properly manage a range of related conditions. This article presents a pictorial review of unexpected hyperechoic benign and malignant breast lesions, with a focus on the pathological conditions that give rise to the hyperechoic pattern.
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Affiliation(s)
- Seo Young Park
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Korea.,Department of Radiology, Fatima Hospital, Daegu, Korea
| | - Jee Young Park
- Department of Pathology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Korea
| | - Ji Won Park
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Korea.,Department of Radiology, Chamteunteun Hospital, Daegu, Korea
| | - Won Hwa Kim
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Korea
| | - Ji Young Park
- Department of Pathology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Korea
| | - Hye Jung Kim
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Korea
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Liu F, Li G, Lin L. A novel method for selecting the set optimal wavelength combination in multi-spectral transmission image. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 261:120080. [PMID: 34147734 DOI: 10.1016/j.saa.2021.120080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 05/24/2021] [Accepted: 06/09/2021] [Indexed: 06/12/2023]
Abstract
In the process of detecting heterogeneity in breast tissue based on multi-spectral transmission imaging, the detection accuracy will be affected due to the high redundancy degree of information between bands. In order to select the reasonable wavelength combination, this paper uses various nonlinear transformations to convert the multi-spectral images into spectral data for the first time, so as to select the set optimal wavelength combination based on the successive projections algorithm (SPA). Firstly, we design the collection experiment of 4-wavelength multi-spectral image. And then, K-SVD dictionary learning method, texture extraction method and gray correlation analysis method are used to obtain the feature spectral information. Finally, the set optimal wavelength combination is selected based on SPA. The experimental results show that random forest (RF) classification model and Faster-RCNN recognition models effectively verify that the combination of wavelengths 1,2,4 selected has the highest accuracy in the heterogeneous detection. In conclusion, this paper uses modulation-frame accumulation technique to improve the quality of multi-spectral transmission images. And based on the RF and Faster-RCNN models, the effectiveness of SPA-based optimal wavelength combination method proposed is verified, which will provide a new idea of feature wavelength selection for screening early breast masses through multi-spectral transmission imaging.
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Affiliation(s)
- Fulong Liu
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin 300072, China
| | - Gang Li
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin 300072, China
| | - Ling Lin
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin 300072, China.
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Wang J, Chu Y, Wang B, Jiang T. A Narrative Review of Ultrasound Technologies for the Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer. Cancer Manag Res 2021; 13:7885-7895. [PMID: 34703310 PMCID: PMC8523361 DOI: 10.2147/cmar.s331665] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 09/29/2021] [Indexed: 12/21/2022] Open
Abstract
The incidence and mortality rate of breast cancer (BC) in women currently ranks first worldwide, and neoadjuvant chemotherapy (NAC) is widely used in patients with BC. A variety of imaging assessment methods have been used to predict and evaluate the response to NAC. Ultrasound (US) has many advantages, such as being inexpensive and offering a convenient modality for follow-up detection without radiation emission. Although conventional grayscale US is typically used to predict the response to NAC, this approach is limited in its ability to distinguish viable tumor tissue from fibrotic scar tissue. Contrast-enhanced ultrasound (CEUS) combined with a time-intensity curve (TIC) not only provides information on blood perfusion but also reveals a variety of quantitative parameters; elastography has the potential capacity to predict NAC efficiency by evaluating tissue stiffness. Both CEUS and elastography can greatly improve the accuracy of predicting NAC responses. Other US techniques, including three-dimensional (3D) techniques, quantitative ultrasound (QUS) and US-guided near-infrared (NIR) diffuse optical tomography (DOT) systems, also have advantages in assessing NAC response. This paper reviews the different US technologies used for predicting NAC response in BC patients based on the previous literature.
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Affiliation(s)
- Jing Wang
- Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People's Republic of China
| | - Yanhua Chu
- Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People's Republic of China
| | - Baohua Wang
- Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People's Republic of China
| | - Tianan Jiang
- Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People's Republic of China
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9
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Dobruch-Sobczak KS, Piotrzkowska-Wróblewska H, Karwat P, Klimonda Z, Markiewicz-Grodzicka E, Litniewski J. Quantitative Assessment of the Echogenicity of a Breast Tumor Predicts the Response to Neoadjuvant Chemotherapy. Cancers (Basel) 2021; 13:3546. [PMID: 34298759 PMCID: PMC8307405 DOI: 10.3390/cancers13143546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/25/2021] [Accepted: 07/12/2021] [Indexed: 11/16/2022] Open
Abstract
The aim of the study was to improve monitoring the treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Ultrasound examinations were performed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was the standard of reference. Alteration in B-mode ultrasound (tumor echogenicity and volume) and the Kullback-Leibler divergence (kld), as a quantitative measure of amplitude difference, were used. Correlations of these parameters with RMC were assessed and Receiver Operating Characteristic curve (ROC) analysis was performed. Thirty-nine patients (mean age 57 y.) with 50 tumors were included. There was a significant correlation between RMC and changes in quantitative parameters (KLD) after the second, third and fourth course of NAC, and alteration in echogenicity after the third and fourth course. Multivariate analysis of the echogenicity and KLD after the third NAC course revealed a sensitivity of 91%, specificity of 92%, PPV = 77%, NPV = 97%, accuracy = 91%, and AUC of 0.92 for non-responding tumors (RMC ≥ 70%). In conclusion, monitoring the echogenicity and KLD parameters made it possible to accurately predict the treatment response from the second course of NAC.
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Affiliation(s)
- Katarzyna Sylwia Dobruch-Sobczak
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland; (H.P.-W.); (P.K.); (Z.K.); (J.L.)
- Radiology Department II, Maria Sklodowska-Curie National Research Institute of Oncology, 15 Wawelska St., 02-034 Warsaw, Poland
| | - Hanna Piotrzkowska-Wróblewska
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland; (H.P.-W.); (P.K.); (Z.K.); (J.L.)
| | - Piotr Karwat
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland; (H.P.-W.); (P.K.); (Z.K.); (J.L.)
| | - Ziemowit Klimonda
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland; (H.P.-W.); (P.K.); (Z.K.); (J.L.)
| | - Ewa Markiewicz-Grodzicka
- Department of Oncology and Radiotherapy, Maria Sklodowska-Curie National Research Institute of Oncology, 15 Wawelska St., 02-034 Warsaw, Poland;
| | - Jerzy Litniewski
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland; (H.P.-W.); (P.K.); (Z.K.); (J.L.)
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10
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Romeo V, Accardo G, Perillo T, Basso L, Garbino N, Nicolai E, Maurea S, Salvatore M. Assessment and Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparison of Imaging Modalities and Future Perspectives. Cancers (Basel) 2021; 13:cancers13143521. [PMID: 34298733 PMCID: PMC8303777 DOI: 10.3390/cancers13143521] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 06/30/2021] [Indexed: 02/06/2023] Open
Abstract
Neoadjuvant chemotherapy (NAC) is becoming the standard of care for locally advanced breast cancer, aiming to reduce tumor size before surgery. Unfortunately, less than 30% of patients generally achieve a pathological complete response and approximately 5% of patients show disease progression while receiving NAC. Accurate assessment of the response to NAC is crucial for subsequent surgical planning. Furthermore, early prediction of tumor response could avoid patients being overtreated with useless chemotherapy sections, which are not free from side effects and psychological implications. In this review, we first analyze and compare the accuracy of conventional and advanced imaging techniques as well as discuss the application of artificial intelligence tools in the assessment of tumor response after NAC. Thereafter, the role of advanced imaging techniques, such as MRI, nuclear medicine, and new hybrid PET/MRI imaging in the prediction of the response to NAC is described in the second part of the review. Finally, future perspectives in NAC response prediction, represented by AI applications, are discussed.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (T.P.); (S.M.)
- Correspondence: ; Tel.: +39-3930426928; Fax: +39-081-746356
| | - Giuseppe Accardo
- Department of Breast Surgery, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), Rionero in Vulture, 85028 Potenza, Italy;
| | - Teresa Perillo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (T.P.); (S.M.)
| | - Luca Basso
- IRCCS SDN, 80143 Naples, Italy; (L.B.); (N.G.); (E.N.); (M.S.)
| | - Nunzia Garbino
- IRCCS SDN, 80143 Naples, Italy; (L.B.); (N.G.); (E.N.); (M.S.)
| | | | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (T.P.); (S.M.)
| | - Marco Salvatore
- IRCCS SDN, 80143 Naples, Italy; (L.B.); (N.G.); (E.N.); (M.S.)
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11
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Zhu Q, Ademuyiwa FO, Young C, Appleton C, Covington MF, Ma C, Sanati S, Hagemann IS, Mostafa A, Uddin KMS, Grigsby I, Frith AE, Hernandez-Aya LF, Poplack SS. Early Assessment Window for Predicting Breast Cancer Neoadjuvant Therapy using Biomarkers, Ultrasound, and Diffuse Optical Tomography. Breast Cancer Res Treat 2021; 188:615-630. [PMID: 33970392 DOI: 10.1007/s10549-021-06239-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 04/20/2021] [Indexed: 12/28/2022]
Abstract
PURPOSE The purpose of the study was to assess the utility of tumor biomarkers, ultrasound (US) and US-guided diffuse optical tomography (DOT) in early prediction of breast cancer response to neoadjuvant therapy (NAT). METHODS This prospective HIPAA compliant study was approved by the institutional review board. Forty one patients were imaged with US and US-guided DOT prior to NAT, at completion of the first three treatment cycles, and prior to definitive surgery from February 2017 to January 2020. Miller-Payne grading was used to assess pathologic response. Receiver operating characteristic curves (ROCs) were derived from logistic regression using independent variables, including: tumor biomarkers, US maximum diameter, percentage reduction of the diameter (%US), pretreatment maximum total hemoglobin concentration (HbT) and percentage reduction in HbT (%HbT) at different treatment time points. Resulting ROCs were compared using area under the curve (AUC). Statistical significance was tested using two-sided two-sample student t-test with P < 0.05 considered statistically significant. Logistic regression was used for ROC analysis. RESULTS Thirty-eight patients (mean age = 47, range 24-71 years) successfully completed the study, including 15 HER2 + of which 11 were ER + ; 12 ER + or PR + /HER2-, and 11 triple negative. The combination of HER2 and ER biomarkers, %HbT at the end of cycle 1 (EOC1) and %US (EOC1) provided the best early prediction, AUC = 0.941 (95% CI 0.869-1.0). Similarly an AUC of 0.910 (95% CI 0.810-1.0) with %US (EOC1) and %HbT (EOC1) can be achieved independent of HER2 and ER status. The most accurate prediction, AUC = 0.974 (95% CI 0.933-1.0), was achieved with %US at EOC1 and %HbT (EOC3) independent of biomarker status. CONCLUSION The combined use of tumor HER2 and ER status, US, and US-guided DOT may provide accurate prediction of NAT response as early as the completion of the first treatment cycle. CLINICAL TRIAL REGISTRATION NUMBER NCT02891681. https://clinicaltrials.gov/ct2/show/NCT02891681 , Registration time: September 7, 2016.
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Affiliation(s)
- Quing Zhu
- Biomedical Engineering and Radiology, Washington University in St Louis, One Brookings Drive, Mail Box 1097, Whitaker Hall 200F, St. Louis, MO, 63130, USA. .,Washington University School of Medicine in St Louis, St. Louis, USA.
| | - Foluso O Ademuyiwa
- Medical Oncology, Washington University School of Medicine in St Louis, St. Louis, USA
| | - Catherine Young
- Washington Baylor Scott & White Health, Medical Center, Texas, Dallas, USA
| | - Catherine Appleton
- Diagnostic Imaging Associates, Ltd. St. Luke's Hospital, Chesterfield, USA
| | - Matthew F Covington
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, USA
| | - Cynthia Ma
- Medical Oncology, Washington University School of Medicine in St Louis, St. Louis, USA
| | - Souzan Sanati
- Pathology, Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Ian S Hagemann
- Washington University School of Medicine in St Louis, St. Louis, USA
| | - Atahar Mostafa
- Biomedical Engineering and Radiology, Washington University in St Louis, One Brookings Drive, Mail Box 1097, Whitaker Hall 200F, St. Louis, MO, 63130, USA
| | - K M Shihab Uddin
- Biomedical Engineering and Radiology, Washington University in St Louis, One Brookings Drive, Mail Box 1097, Whitaker Hall 200F, St. Louis, MO, 63130, USA
| | - Isabella Grigsby
- Medical Oncology, Washington University School of Medicine in St Louis, St. Louis, USA
| | - Ashley E Frith
- Medical Oncology, Washington University School of Medicine in St Louis, St. Louis, USA
| | | | - Steven S Poplack
- Washington University School of Medicine in St Louis, St. Louis, USA.,Radiology, Stanford University, Stanford, USA
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12
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Adrada BE, Candelaria R, Moulder S, Thompson A, Wei P, Whitman GJ, Valero V, Litton JK, Santiago L, Scoggins ME, Moseley TW, White JB, Ravenberg EE, Yang WT, Rauch GM. Early ultrasound evaluation identifies excellent responders to neoadjuvant systemic therapy among patients with triple-negative breast cancer. Cancer 2021; 127:2880-2887. [PMID: 33878210 DOI: 10.1002/cncr.33604] [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: 12/14/2020] [Revised: 03/06/2021] [Accepted: 03/18/2021] [Indexed: 01/12/2023]
Abstract
BACKGROUND Heterogeneity exists in the response of triple-negative breast cancer (TNBC) to standard anthracycline (AC)/taxane-based neoadjuvant systemic therapy (NAST), with 40% to 50% of patients having a pathologic complete response (pCR) to therapy. Early assessment of the imaging response during NAST may identify a subset of TNBCs that are likely to have a pCR upon completion of treatment. The authors aimed to evaluate the performance of early ultrasound (US) after 2 cycles of neoadjuvant NAST in identifying excellent responders to NAST among patients with TNBC. METHODS Two hundred fifteen patients with TNBC were enrolled in the ongoing ARTEMIS (A Robust TNBC Evaluation Framework to Improve Survival) clinical trial. The patients were divided into a discovery cohort (n = 107) and a validation cohort (n = 108). A receiver operating characteristic analysis with 95% confidence intervals (CIs) and a multivariate logistic regression analysis were performed to model the probability of a pCR on the basis of the tumor volume reduction (TVR) percentage by US from the baseline to after 2 cycles of AC. RESULTS Overall, 39.3% of the patients (42 of 107) achieved a pCR. A positive predictive value (PPV) analysis identified a cutoff point of 80% TVR after 2 cycles; the pCR rate was 77% (17 of 22) in patients with a TVR ≥ 80%, and the area under the curve (AUC) was 0.84 (95% CI, 0.77-0.92; P < .0001). In the validation cohort, the pCR rate was 44%. The PPV for pCR with a TVR ≥ 80% after 2 cycles was 76% (95% CI, 55%-91%), and the AUC was 0.79 (95% CI, 0.70-0.87; P < .0001). CONCLUSIONS The TVR percentage by US evaluation after 2 cycles of NAST may be a cost-effective early imaging biomarker for a pCR to AC/taxane-based NAST.
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Affiliation(s)
- Beatriz E Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Rosalind Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Stacy Moulder
- Department of Breast Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alastair Thompson
- Department of Breast Surgery, University of Baylor College of Medicine, Houston, Texas.,Lester and Sue Smith Breast Cancer, University of Baylor College of Medicine, Houston, Texas
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Gary J Whitman
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Vicente Valero
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jennifer K Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lumarie Santiago
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Marion E Scoggins
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Tanya W Moseley
- Department of Breast Imaging and Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jason B White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Elizabeth E Ravenberg
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Wei T Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Gaiane M Rauch
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas
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13
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Huang Y, Le J, Miao A, Zhi W, Wang F, Chen Y, Zhou S, Chang C. Prediction of treatment responses to neoadjuvant chemotherapy in breast cancer using contrast-enhanced ultrasound. Gland Surg 2021; 10:1280-1290. [PMID: 33968680 DOI: 10.21037/gs-20-836] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Background Elucidation the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer is important for informing therapeutic decisions. This study aimed at evaluating the potential value of contrast-enhanced ultrasound (CEUS) parameters in predicting breast cancer responses to NAC. Methods We performed CEUS examinations before and after two cycles of NAC. Quantitative CEUS parameters [maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (mTT)], tumor diameter, and their changes were measured and compared to histopathological responses, according to the Miller-Payne Grading (MPG) system (score 1, 2, or 3: minor response; score 4 or 5: good response). Prediction models for good response were developed by multiple logistic regression analysis and internally validated through bootstrap analysis. The receiver operating characteristic (ROC) curve was used to evaluate the performance of prediction models. Results A total of 143 patients were enrolled in this study among whom 98 (68.5%) achieved a good response and while 45 (31.5%) exhibited a minor response. Several imaging variables including diameter, IMAX, changes in diameter (Δdiameter), IMAX (ΔIMAX) and TTP (ΔTTP) were found to be significantly associated with good therapeutic responses (P<0.05). The areas under the curve (AUC) increased from 0.748 to 0.841 in the multivariate model that combined CEUS parameters and molecular subtypes with a sensitivity and specificity of 0.786, 0.745, respectively. Tumor molecular subtype was the primary predictor of primary endpoint. Conclusions CEUS is a potential tool for predicting responses to NAC in locally advanced breast cancer patients. Compared to the other molecular subtypes, triple negative and HER2+/ER- subtypes are more likely to exhibit a good response to NAC.
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Affiliation(s)
- Yunxia Huang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian Le
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Aiyu Miao
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wenxiang Zhi
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Fen Wang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yaling Chen
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shichong Zhou
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Cai Chang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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14
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Byra M, Dobruch-Sobczak K, Klimonda Z, Piotrzkowska-Wroblewska H, Litniewski J. Early Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer Sonography Using Siamese Convolutional Neural Networks. IEEE J Biomed Health Inform 2021; 25:797-805. [PMID: 32749986 DOI: 10.1109/jbhi.2020.3008040] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer is crucial for guiding therapy decisions. In this work, we propose a deep learning based approach for the early NAC response prediction in ultrasound (US) imaging. We used transfer learning with deep convolutional neural networks (CNNs) to develop the response prediction models. The usefulness of two transfer learning techniques was examined. First, a CNN pre-trained on the ImageNet dataset was utilized. Second, we applied double transfer learning, the CNN pre-trained on the ImageNet dataset was additionally fine-tuned with breast mass US images to differentiate malignant and benign lesions. Two prediction tasks were investigated. First, a L1 regularized logistic regression prediction model was developed based on generic neural features extracted from US images collected before the chemotherapy (a priori prediction). Second, Siamese CNNs were used to quantify differences between US images collected before the treatment and after the first and second course of NAC. The proposed methods were evaluated using US data collected from 39 tumors. The better performing deep learning models achieved areas under the receiver operating characteristic curve of 0.797 and 0.847 in the case of the a priori prediction and the Siamese model, respectively. The proposed approach was compared with a method based on handcrafted morphological features. Our study presents the feasibility of using transfer learning with CNNs for the NAC response prediction in US imaging.
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15
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Multiparametric ultrasound examination for response assessment in breast cancer patients undergoing neoadjuvant therapy. Sci Rep 2021; 11:2501. [PMID: 33510306 PMCID: PMC7844231 DOI: 10.1038/s41598-021-82141-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 01/06/2021] [Indexed: 02/07/2023] Open
Abstract
To investigate the performance of multiparametric ultrasound for the evaluation of treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Breast cancer patients who were scheduled to undergo NAC were invited to participate in this study. Changes in tumour echogenicity, stiffness, maximum diameter, vascularity and integrated backscatter coefficient (IBC) were assessed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was considered as standard of reference. RMC < 30% was considered a good response and > 70% a poor response. The correlation coefficients of these parameters were compared with RMC from post-operative histology. Linear Discriminant Analysis (LDA), cross-validation and Receiver Operating Characteristic curve (ROC) analysis were performed. Thirty patients (mean age 56.4 year) with 42 lesions were included. There was a significant correlation between RMC and echogenicity and tumour diameter after the 3rd course of NAC and average stiffness after the 2nd course. The correlation coefficient for IBC and echogenicity calculated after the first four doses of NAC were 0.27, 0.35, 0.41 and 0.30, respectively. Multivariate analysis of the echogenicity and stiffness after the third NAC revealed a sensitivity of 82%, specificity of 90%, PPV = 75%, NPV = 93%, accuracy = 88% and AUC of 0.88 for non-responding tumours (RMC > 70%). High tumour stiffness and persistent hypoechogenicity after the third NAC course allowed to accurately predict a group of non-responding tumours. A correlation between echogenicity and IBC was demonstrated as well.
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