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Sarfraz Z, Sarfraz A, Mehak O, Akhund R, Bano S, Aftab H. Racial and socioeconomic disparities in triple-negative breast cancer treatment. Expert Rev Anticancer Ther 2024; 24:107-116. [PMID: 38436305 DOI: 10.1080/14737140.2024.2326575] [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: 08/09/2023] [Accepted: 02/29/2024] [Indexed: 03/05/2024]
Abstract
INTRODUCTION Triple-negative breast cancer (TNBC) continues to be a significant concern, especially among minority populations, where treatment disparities are notably pronounced. Addressing these disparities, especially among African American women and other minorities, is crucial for ensuring equitable healthcare. AREAS COVERED This review delves into the continuum of TNBC treatment, noting that the standard of care, previously restricted to chemotherapy, has now expanded due to emerging clinical trial results. With advances like PARP inhibitors, immunotherapy, and antibody-drug conjugates, a more personalized treatment approach is on the horizon. The review highlights innovative interventions tailored for minorities, such as utilizing technology like text messaging, smartphone apps, and targeted radio programming, coupled with church-based behavioral interventions. EXPERT OPINION Addressing TNBC treatment disparities demands a multifaceted approach, blending advanced medical treatments with culturally sensitive community outreach. The potential of technology, especially in the realm of promoting health awareness, is yet to be fully harnessed. As the field progresses, understanding and integrating the socio-economic, biological, and access-related challenges faced by minorities will be pivotal for achieving health equity in TNBC care.
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Affiliation(s)
- Zouina Sarfraz
- Department of Medicine, Fatima Jinnah Medical University, Lahore, Pakistan
| | - Azza Sarfraz
- Department of Pediatrics, Aga Khan University, Karachi, Pakistan
| | - Onaiza Mehak
- Department of Medicine, Aziz Fatimah Medical and Dental College, Faisalabad, Pakistan
| | - Ramsha Akhund
- Department of Surgery, University of Alabama at Birmingham, Tuscaloosa, AL, USA
| | - Shehar Bano
- Department of Medicine, Fatima Jinnah Medical University, Lahore, Pakistan
| | - Hinna Aftab
- Department of Medicine, CMH Lahore Medical College, Lahore, Pakistan
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Cao Y, Wang X, Li L, Shi J, Zeng X, Huang Y, Chen H, Jiang F, Yin T, Nickel D, Zhang J. Early prediction of pathologic complete response of breast cancer after neoadjuvant chemotherapy using longitudinal ultrafast dynamic contrast-enhanced MRI. Diagn Interv Imaging 2023; 104:605-614. [PMID: 37543490 DOI: 10.1016/j.diii.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/07/2023]
Abstract
PURPOSE The purpose of this study was to evaluate the temporal trends of ultrafast dynamic contrast-enhanced (DCE)-MRI during neoadjuvant chemotherapy (NAC) and to investigate whether the changes in DCE-MRI parameters could early predict pathologic complete response (pCR) of breast cancer. MATERIALS AND METHODS This longitudinal study prospectively recruited consecutive participants with breast cancer who underwent ultrafast DCE-MRI examinations before treatment and after two, four, and six NAC cycles between February 2021 and February 2022. Five ultrafast DCE-MRI parameters (maximum slope [MS], time-to-peak [TTP], time-to-enhancement [TTE], peak enhancement intensity [PEI], and initial area under the curve in 60 s [iAUC]) and tumor size were measured at each timepoint. The changes in parameters between each pair of adjacent timepoints were additionally measured and compared between the pCR and non-pCR groups. Longitudinal data were analyzed using generalized estimating equations. The performance for predicting pCR was assessed using area under the receiver operating characteristic curve (AUC). RESULTS Sixty-seven women (mean age, 50 ± 8 [standard deviation] years; age range: 25-69 years) were included, 19 of whom achieved pCR. MS, PEI, iAUC, and tumor size decreased, while TTP increased during NAC (all P < 0.001). The AUC (0.92; 95% confidence interval [CI]: 0.83-0.97) of the model incorporating ultrafast DCE-MRI parameter change values (from timepoints 1 to 2) and clinicopathologic characteristics was greater than that of the clinical model (AUC, 0.79; 95% CI: 0.68-0.88) and ultrafast DCE-MRI parameter model at timepoint 2 when combined with clinicopathologic characteristics (AUC, 0.82; 95% CI: 0.71-0.90) (P = 0.01 and 0.02). CONCLUSION Early changes in ultrafast DCE-MRI parameters after NAC combined with clinicopathologic characteristics could serve as predictive markers of pCR of breast cancer.
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Affiliation(s)
- Ying Cao
- School of Medicine, Chongqing University, Chongqing, 400030, Chongqing, China
| | - Xiaoxia Wang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Lan Li
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Jinfang Shi
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Xiangfei Zeng
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Yao Huang
- School of Medicine, Chongqing University, Chongqing, 400030, Chongqing, China
| | - Huifang Chen
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Fujie Jiang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Ting Yin
- MR Collaborations, Siemens Healthineers Ltd., 610065 Chengdu, China
| | | | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China.
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Elsayed B, Alksas A, Shehata M, Mahmoud A, Zaky M, Alghandour R, Abdelwahab K, Abdelkhalek M, Ghazal M, Contractor S, El-Din Moustafa H, El-Baz A. Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review. Cancers (Basel) 2023; 15:5288. [PMID: 37958461 PMCID: PMC10648987 DOI: 10.3390/cancers15215288] [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: 09/02/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023] Open
Abstract
Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.
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Affiliation(s)
- Basma Elsayed
- Biomedical Engineering Program, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt;
| | - Ahmed Alksas
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Mohamed Shehata
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Ali Mahmoud
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Mona Zaky
- Diagnostic Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt;
| | - Reham Alghandour
- Medical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt;
| | - Khaled Abdelwahab
- Surgical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt; (K.A.); (M.A.)
| | - Mohamed Abdelkhalek
- Surgical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt; (K.A.); (M.A.)
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA;
| | | | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
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Fanizzi A, Latorre A, Bavaro DA, Bove S, Comes MC, Di Benedetto EF, Fadda F, La Forgia D, Giotta F, Palmiotti G, Petruzzellis N, Rinaldi L, Rizzo A, Lorusso V, Massafra R. Prognostic power assessment of clinical parameters to predict neoadjuvant response therapy in HER2-positive breast cancer patients: A machine learning approach. Cancer Med 2023; 12:20663-20669. [PMID: 37905688 PMCID: PMC10709715 DOI: 10.1002/cam4.6512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 07/27/2023] [Accepted: 08/29/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND About 15%-20% of breast cancer (BC) cases is classified as Human Epidermal growth factor Receptor type 2 (HER2) positive. The Neoadjuvant chemotherapy (NAC) was initially introduced for locally advanced and inflammatory BC patients to allow a less extensive surgical resection, whereas now it represents the current standard for early-stage and operable BC. However, only 20%-40% of patients achieve pathologic complete response (pCR). According to the results of practice-changing clinical trials, the addition of trastuzumab to NAC brings improvements to pCR, and recently, the use of pertuzumab plus trastuzumab has registered further statistically significant and clinically meaningful improvements in terms of pCR. The goal of our work is to propose a machine learning model to predict the pCR to NAC in HER2-positive patients based on a subset of clinical features. METHOD First, we evaluated the significant association of clinical features with pCR on the retrospectively collected data referred to 67 patients afferent to Istituto Tumori "Giovanni Paolo II." Then, we performed a feature selection procedure to identify a subset of features to be used for training a machine learning-based classification algorithm. As a result, pCR to NAC was associated with ER status, Pgr status, and HER2 score. RESULTS The machine learning model trained on a subgroup of essential features reached an AUC of 73.27% (72.44%-73.66%) and an accuracy of 71.67% (71.64%-73.13%). According to our results, the clinical features alone are not enough to define a support system useful for clinical pathway. CONCLUSION Our results seem worthy of further investigation in large validation studies and this work could be the basis of future study that will also involve radiomics analysis of biomedical images.
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Affiliation(s)
| | | | | | - Samantha Bove
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”BariItaly
| | | | | | | | | | | | | | | | - Lucia Rinaldi
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”BariItaly
| | | | - Vito Lorusso
- I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”BariItaly
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Civil YA, Jonker LW, Groot Koerkamp MPM, Duvivier KM, de Vries R, Oei AL, Slotman BJ, van der Velde S, van den Bongard HJGD. Preoperative Partial Breast Irradiation in Patients with Low-Risk Breast Cancer: A Systematic Review of Literature. Ann Surg Oncol 2023; 30:3263-3279. [PMID: 36869253 PMCID: PMC10175515 DOI: 10.1245/s10434-023-13233-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 01/29/2023] [Indexed: 03/05/2023]
Abstract
BACKGROUND Preoperative instead of standard postoperative partial breast irradiation (PBI) after breast-conserving surgery (BCS) has the advantage of reducing the irradiated breast volume, toxicity, and number of radiotherapy sessions and can allow tumor downstaging. In this review, we assessed tumor response and clinical outcomes after preoperative PBI. PATIENTS AND METHODS We conducted a systematic review of studies on preoperative PBI in patients with low-risk breast cancer using the databases Ovid Medline, Embase.com, Web of Science (Core Collection), and Scopus (PROSPERO registration CRD42022301435). References of eligible manuscripts were checked for other relevant manuscripts. The primary outcome measure was pathologic complete response (pCR). RESULTS A total of eight prospective and one retrospective cohort study were identified (n = 359). In up to 42% of the patients, pCR was obtained and this increased after a longer interval between radiotherapy and BCS (0.5-8 months). After a maximum median follow-up of 5.0 years, three studies on external beam radiotherapy reported low local recurrence rates (0-3%) and overall survival of 97-100%. Acute toxicity consisted mainly of grade 1 skin toxicity (0-34%) and seroma (0-31%). Late toxicity was predominantly fibrosis grade 1 (46-100%) and grade 2 (10-11%). Cosmetic outcome was good to excellent in 78-100% of the patients. CONCLUSIONS Preoperative PBI showed a higher pCR rate after a longer interval between radiotherapy and BCS. Mild late toxicity and good oncological and cosmetic outcomes were reported. In the ongoing ABLATIVE-2 trial, BCS is performed at a longer interval of 12 months after preoperative PBI aiming to achieve a higher pCR rate.
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Affiliation(s)
- Yasmin A Civil
- Department of Radiation Oncology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands.
| | - Lysanne W Jonker
- Department of Radiation Oncology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Maartje P M Groot Koerkamp
- Department of Radiation Oncology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Katya M Duvivier
- Department of Radiology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ralph de Vries
- Medical Library, Vrije Universiteit, Amsterdam, The Netherlands
| | - Arlene L Oei
- Department of Radiation Oncology, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands
- Laboratory for Experimental Oncology and Radiobiology, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands
- Center for Experimental Molecular Medicine, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, The Netherlands
| | - Berend J Slotman
- Department of Radiation Oncology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
| | - Susanne van der Velde
- Department of Surgery, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - H J G Desirée van den Bongard
- Department of Radiation Oncology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Biology and Immunology, Amsterdam, The Netherlands
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Li Y, Fan Y, Xu D, Li Y, Zhong Z, Pan H, Huang B, Xie X, Yang Y, Liu B. Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer. Front Oncol 2023; 12:1041142. [PMID: 36686755 PMCID: PMC9850142 DOI: 10.3389/fonc.2022.1041142] [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: 09/10/2022] [Accepted: 12/13/2022] [Indexed: 01/07/2023] Open
Abstract
Objective The aim of this study was to develop and validate a deep learning-based radiomic (DLR) model combined with clinical characteristics for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. For early prediction of pCR, the DLR model was based on pre-treatment and early treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. Materials and methods This retrospective study included 95 women (mean age, 48.1 years; range, 29-77 years) who underwent DCE-MRI before (pre-treatment) and after two or three cycles of NAC (early treatment) from 2018 to 2021. The patients in this study were randomly divided into a training cohort (n=67) and a validation cohort (n=28) at a ratio of 7:3. Deep learning and handcrafted features were extracted from pre- and early treatment DCE-MRI contoured lesions. These features contribute to the construction of radiomic signature RS1 and RS2 representing information from different periods. Mutual information and least absolute shrinkage and selection operator regression were used for feature selection. A combined model was then developed based on the DCE-MRI features and clinical characteristics. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. Results The overall pCR rate was 25.3% (24/95). One radiomic feature and three deep learning features in RS1, five radiomic features and 11 deep learning features in RS2, and five clinical characteristics remained in the feature selection. The performance of the DLR model combining pre- and early treatment information (AUC=0.900) was better than that of RS1 (AUC=0.644, P=0.068) and slightly higher that of RS2 (AUC=0.888, P=0.604) in the validation cohort. The combined model including pre- and early treatment information and clinical characteristics showed the best ability with an AUC of 0.925 in the validation cohort. Conclusion The combined model integrating pre-treatment, early treatment DCE-MRI data, and clinical characteristics showed good performance in predicting pCR to NAC in patients with breast cancer. Early treatment DCE-MRI and clinical characteristics may play an important role in evaluating the outcomes of NAC by predicting pCR.
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Affiliation(s)
- Yuting Li
- The First Clinical Medical College, Guangdong Medical University, Zhanjiang, China,Department of Radiology, Dongguan People’s Hospital, Dongguan, China
| | - Yaheng Fan
- Medical Artificial Intelligence Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Dinghua Xu
- Department of Radiology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
| | - Yan Li
- Department of Radiology, Dongguan People’s Hospital, Dongguan, China
| | - Zhangnan Zhong
- Medical Artificial Intelligence Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Haoyu Pan
- Medical Artificial Intelligence Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Bingsheng Huang
- Medical Artificial Intelligence Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xiaotong Xie
- Department of Minimally Invasive Interventional Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Yang Yang
- Department of Radiology, Suining Central Hospital, Suining, China,*Correspondence: Yang Yang, ; Bihua Liu,
| | - Bihua Liu
- The First Clinical Medical College, Guangdong Medical University, Zhanjiang, China,Department of Radiology, Dongguan People’s Hospital, Dongguan, China,*Correspondence: Yang Yang, ; Bihua Liu,
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Matsuda M, Fukuyama N, Matsuda T, Kikuchi S, Shiraishi Y, Takimoto Y, Kamei Y, Kurata M, Kitazawa R, Kido T. Utility of synthetic MRI in predicting pathological complete response of various breast cancer subtypes prior to neoadjuvant chemotherapy. Clin Radiol 2022; 77:855-863. [DOI: 10.1016/j.crad.2022.06.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 06/24/2022] [Accepted: 06/27/2022] [Indexed: 11/25/2022]
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Ramtohul T, Tescher C, Vaflard P, Cyrta J, Girard N, Malhaire C, Tardivon A. Prospective Evaluation of Ultrafast Breast MRI for Predicting Pathologic Response after Neoadjuvant Therapies. Radiology 2022; 305:565-574. [PMID: 35880977 DOI: 10.1148/radiol.220389] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Ultrafast dynamic contrast-enhanced (DCE) MRI parameters are associated with breast cancer aggressiveness. However, the role of these parameters as predictive biomarkers for pathologic response after neoadjuvant chemotherapy (NAC) has been poorly investigated. Purpose To assess whether semiquantitative perfusion parameters calculated at initial ultrafast DCE MRI are associated with early prediction for pathologic response after NAC in participants with breast cancer. Materials and Methods This prospective single-center study included consecutive women with nonmetastatic invasive breast cancer treated with NAC followed by surgery who underwent initial ultrafast DCE MRI between December 2020 and August 2021. Six semiquantitative ultrafast DCE MRI parameters were calculated for each participant from the fitted time-signal intensity curve. Multivariable logistic regression was used to identify independent predictors of pathologic complete response (pCR) and residual cancer burden (RCB). Results Fifty women (mean age, 49 years ± 12 [SD]) were included in the study; 20 achieved pCR and 25 achieved low RCB (RCB-0 and I). A wash-in slope (WIS) cutoff value of 1.6% per second had a sensitivity of 94% (17 of 18 participants) and a specificity of 59% (19 of 32 participants) for pCR. A WIS of more than 1.6% per second (odds ratio [OR], 8.4 [95% CI: 1.5, 48.2]; P = .02), human epidermal growth factor receptor 2 (HER2) positivity (OR, 6.3 [95% CI: 1.5, 27.4]; P = .01), and tumor-infiltrating lymphocytes of more than 10% (OR, 6.9 [95% CI: 1.3, 37.7]; P = .03) were independent predictive factors of pCR. The area under the receiver operating characteristic curve of the three-component model, which included WIS, tumor-infiltrating lymphocytes, and HER2 positivity, was 0.92 (95% CI: 0.84, 0.99). A WIS of more than 1.6% per second was associated with higher pCR rates in the HER2-positive (OR, 21.7 [95% CI: 1.8, 260.6]; P = .02) breast cancer subgroup. For luminal HER2-negative and triple-negative breast cancers, a WIS of more than 1.6% per second was associated with low RCB (OR, 11.0 [95% CI: 1.1, 106.4]; P = .04). Conclusion The wash-in slope (WIS) assessment at initial ultrafast dynamic contrast-enhanced MRI may be used to predict pathologic complete response (pCR) in participants with breast cancer. The WIS value was used to identify two subsets of human epidermal growth factor receptor 2-positive cancers with distinct pCR rates. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Moy in this issue.
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Affiliation(s)
- Toulsie Ramtohul
- From the Departments of Radiology (T.R., C.T., C.M., A.T.), Medical Oncology (P.V.), Diagnostic and Theranostic Medicine - Pathology (J.C.), and Surgical Oncology (N.G.), Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France
| | - Clara Tescher
- From the Departments of Radiology (T.R., C.T., C.M., A.T.), Medical Oncology (P.V.), Diagnostic and Theranostic Medicine - Pathology (J.C.), and Surgical Oncology (N.G.), Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France
| | - Pauline Vaflard
- From the Departments of Radiology (T.R., C.T., C.M., A.T.), Medical Oncology (P.V.), Diagnostic and Theranostic Medicine - Pathology (J.C.), and Surgical Oncology (N.G.), Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France
| | - Joanna Cyrta
- From the Departments of Radiology (T.R., C.T., C.M., A.T.), Medical Oncology (P.V.), Diagnostic and Theranostic Medicine - Pathology (J.C.), and Surgical Oncology (N.G.), Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France
| | - Noémie Girard
- From the Departments of Radiology (T.R., C.T., C.M., A.T.), Medical Oncology (P.V.), Diagnostic and Theranostic Medicine - Pathology (J.C.), and Surgical Oncology (N.G.), Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France
| | - Caroline Malhaire
- From the Departments of Radiology (T.R., C.T., C.M., A.T.), Medical Oncology (P.V.), Diagnostic and Theranostic Medicine - Pathology (J.C.), and Surgical Oncology (N.G.), Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France
| | - Anne Tardivon
- From the Departments of Radiology (T.R., C.T., C.M., A.T.), Medical Oncology (P.V.), Diagnostic and Theranostic Medicine - Pathology (J.C.), and Surgical Oncology (N.G.), Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France
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Massafra R, Comes MC, Bove S, Didonna V, Gatta G, Giotta F, Fanizzi A, La Forgia D, Latorre A, Pastena MI, Pomarico D, Rinaldi L, Tamborra P, Zito A, Lorusso V, Paradiso AV. Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy. J Pers Med 2022; 12:jpm12060953. [PMID: 35743737 PMCID: PMC9225219 DOI: 10.3390/jpm12060953] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/24/2022] [Accepted: 06/07/2022] [Indexed: 02/04/2023] Open
Abstract
To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to identify finer tumor properties as potential earlier indicators of pathological Complete Response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). However, they work either for sagittal or axial MRI protocols. More flexible AI tools, to be used easily in clinical practice across various institutions in accordance with its own imaging acquisition protocol, are required. Here, we addressed this topic by developing an AI method based on deep learning in giving an early prediction of pCR at various DCE-MRI protocols (axial and sagittal). Sagittal DCE-MRIs refer to 151 patients (42 pCR; 109 non-pCR) from the public I-SPY1 TRIAL database (DB); axial DCE-MRIs are related to 74 patients (22 pCR; 52 non-pCR) from a private DB provided by Istituto Tumori “Giovanni Paolo II” in Bari (Italy). By merging the features extracted from baseline MRIs with some pre-treatment clinical variables, accuracies of 84.4% and 77.3% and AUC values of 80.3% and 78.0% were achieved on the independent tests related to the public DB and the private DB, respectively. Overall, the presented method has shown to be robust regardless of the specific MRI protocol.
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Affiliation(s)
- Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Samantha Bove
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Gianluca Gatta
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (G.G.); (A.L.)
| | - Francesco Giotta
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (V.L.)
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
- Correspondence: (A.F.); (D.L.F.)
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
- Correspondence: (A.F.); (D.L.F.)
| | - Agnese Latorre
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (G.G.); (A.L.)
| | - Maria Irene Pastena
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.I.P.); (A.Z.)
| | - Domenico Pomarico
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale di Oncologia Per la Presa in Carico Globale del Paziente, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.I.P.); (A.Z.)
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (V.L.)
| | - Angelo Virgilio Paradiso
- Oncologia Sperimentale e Biobanca, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
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10
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Gong C, Cheng Z, Yang Y, Shen J, Zhu Y, Ling L, Lin W, Yu Z, Li Z, Tan W, Zheng C, Zheng W, Zhong J, Zhang X, Zeng Y, Liu Q, Huang RS, Komorowski AL, Yang ES, Bertucci F, Ricci F, Orlandi A, Franceschini G, Takabe K, Klimberg S, Ishii N, Toss A, Tan MP, Cherian MA, Song E. A 10-miRNA risk score-based prediction model for pathological complete response to neoadjuvant chemotherapy in hormone receptor-positive breast cancer. SCIENCE CHINA. LIFE SCIENCES 2022; 65:2205-2217. [PMID: 35579777 DOI: 10.1007/s11427-022-2104-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 02/23/2022] [Indexed: 12/21/2022]
Abstract
Patients with hormone receptor (HR)-positive tumors breast cancer usually experience a relatively low pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Here, we derived a 10-microRNA risk score (10-miRNA RS)-based model with better performance in the prediction of pCR and validated its relation with the disease-free survival (DFS) in 755 HR-positive breast cancer patients (273, 265, and 217 in the training, internal, and external validation sets, respectively). This model, presented as a nomogram, included four parameters: the 10-miRNA RS found in our previous study, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) status, and volume transfer constant (Ktrans). Favorable calibration and discrimination of 10-miRNA RS-based model with areas under the curve (AUC) of 0.865, 0.811, and 0.804 were shown in the training, internal, and external validation sets, respectively. Patients who have higher nomogram score (>92.2) with NAC treatment would have longer DFS (hazard ratio=0.57; 95%CI: 0.39-0.83; P=0.004). In summary, our data showed the 10-miRNA RS-based model could precisely identify more patients who can attain pCR to NAC, which may help clinicians formulate the personalized initial treatment strategy and consequently achieves better clinical prognosis for patients with HR-positive breast cancer.
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Affiliation(s)
- Chang Gong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Ziliang Cheng
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Yaping Yang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Jun Shen
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Yingying Zhu
- Clinical Research Design Division, Clinical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Li Ling
- Clinical Research Design Division, Clinical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.,Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Wanyi Lin
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Zhigang Yu
- Department of Breast Surgery, the Second Affiliated Hospital, Shandong University, Jinan, 250033, China
| | - Zhihua Li
- Department of Breast Surgery, Key Laboratory of Breast Diseases, Third Hospital of Nanchang, Nanchang, 330009, China
| | - Weige Tan
- Department of Breast Surgery, the First Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510120, China
| | - Chushan Zheng
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Wenbo Zheng
- Department of Breast Surgery, the First Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510120, China
| | - Jiajie Zhong
- Department of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Xiang Zhang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Yunjie Zeng
- Department of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Qiang Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - R Stephanie Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Andrzej L Komorowski
- Department of Surgery, College of Medicine, University of Rzeszów, Rzeszów, 35-959, Poland
| | - Eddy S Yang
- Department of Radiation Oncology, O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham School of Medicine, Birmingham, AL, USA
| | - François Bertucci
- Laboratoty of Predictive Oncology, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, INSERM UMR1068, CNRS UMR725, Marseille, France
| | - Francesco Ricci
- Department of Drug Development and Innovation(D3i), Institut Curie, Paris, 75005, France
| | - Armando Orlandi
- Comprehensive Cancer Center, UOC di Oncologia Medica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, 00168, Italy
| | - Gianluca Franceschini
- Multidisciplinary Breast Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, 00168, Italy
| | - Kazuaki Takabe
- Breast Surgery, Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Suzanne Klimberg
- Department of Surgery, MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Naohiro Ishii
- Department of Plastic and Reconstructive Surgery, International University of Health and Welfare Hospital, Nasushiobara City, Tochigi, 329-2763, Japan
| | - Angela Toss
- Department of Oncology and Hematology, University Hospital of Modena, Modena, 41124, Italy
| | - Mona P Tan
- MammoCare: Breast Clinic and Surgery in Singapore, Singapore, 228510, Singapore
| | - Mathew A Cherian
- The Ohio State University Comprehensive Cancer Center, Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, Columbus, OH, 43210, USA
| | - Erwei Song
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
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11
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Assessment of Cone-Beam Breast Computed Tomography for Predicting Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer: A Prospective Study. JOURNAL OF ONCOLOGY 2022; 2022:9321763. [PMID: 35528237 PMCID: PMC9076291 DOI: 10.1155/2022/9321763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 03/17/2022] [Accepted: 04/05/2022] [Indexed: 11/18/2022]
Abstract
Background Response surveillance of neoadjuvant chemotherapy is needed to facilitate treatment decisions. We aimed to assess the imaging features of cone-beam breast computed tomography (CBBCT) for predicting the pathologic response of breast cancer after neoadjuvant chemotherapy. Methods This prospective study included 81 women with locally advanced breast cancer who underwent neoadjuvant chemotherapy from August 2017 to January 2021. All patients underwent CBBCT before treatment, and 55 and 65 patients underwent CT examinations during the midtreatment (3 cycles) and late-treatment phases (7 cycles), respectively. Clinical information and quantitative parameters such as the diameter, volume, surface area, and CT density were compared between pathologic responders and nonresponders using the T–test and the Mann–Whitney U test. The performance of meaningful parameters was evaluated with the receiver operating characteristic curve, sensitivity, and specificity. Results The quantitative results for the segmented volume, segmented surface area, segmented volume reduction, maximum enhancement ratio, wash-in rate and two-minute enhancement value in the mid- and late-treatment periods had predictive value for pathologic complete response. The area under the curve for the prediction model after multivariate regression analysis was 0.874. Conclusion After comparing the outcomes of each timepoint, mid- and late-treatment parameters can be used to predict pathologic outcome. The late-treatment parameters showed significant value with a predictive model.
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12
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Peng Y, Cheng Z, Gong C, Zheng C, Zhang X, Wu Z, Yang Y, Yang X, Zheng J, Shen J. Pretreatment DCE-MRI-Based Deep Learning Outperforms Radiomics Analysis in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer. Front Oncol 2022; 12:846775. [PMID: 35359387 PMCID: PMC8960929 DOI: 10.3389/fonc.2022.846775] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 01/26/2022] [Indexed: 01/02/2023] Open
Abstract
PurposeTo compare the performances of deep learning (DL) to radiomics analysis (RA) in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) based on pretreatment dynamic contrast-enhanced MRI (DCE-MRI) in breast cancer.Materials and MethodsThis retrospective study included 356 breast cancer patients who underwent DCE-MRI before NAC and underwent surgery after NAC. Image features and kinetic parameters of tumors were derived from DCE-MRI. Molecular information was assessed based on immunohistochemistry results. The image-based RA and DL models were constructed by adding kinetic parameters or molecular information to image-only linear discriminant analysis (LDA) and convolutional neural network (CNN) models. The predictive performances of developed models were assessed by receiver operating characteristic (ROC) curve analysis and compared with the DeLong method.ResultsThe overall pCR rate was 23.3% (83/356). The area under the ROC (AUROC) of the image-kinetic-molecular RA model was 0.781 [95% confidence interval (CI): 0.735, 0.828], which was higher than that of the image-kinetic RA model (0.629, 95% CI: 0.595, 0.663; P < 0.001) and comparable to that of the image-molecular RA model (0.755, 95% CI: 0.708, 0.802; P = 0.133). The AUROC of the image-kinetic-molecular DL model was 0.83 (95% CI: 0.816, 0.847), which was higher than that of the image-kinetic and image-molecular DL models (0.707, 95% CI: 0.654, 0.761; 0.79, 95% CI: 0.768, 0.812; P < 0.001) and higher than that of the image-kinetic-molecular RA model (0.778, 95% CI: 0.735, 0.828; P < 0.001).ConclusionsThe pretreatment DCE-MRI-based DL model is superior to the RA model in predicting pCR to NAC in breast cancer patients. The image-kinetic-molecular DL model has the best prediction performance.
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Affiliation(s)
- Yunsong Peng
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Ziliang Cheng
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chang Gong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chushan Zheng
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiang Zhang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhuo Wu
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yaping Yang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaodong Yang
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Jian Zheng
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- *Correspondence: Jian Zheng, ; Jun Shen,
| | - Jun Shen
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Jian Zheng, ; Jun Shen,
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13
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Xu H, Liu J, Chen Z, Wang C, Liu Y, Wang M, Zhou P, Luo H, Ren J. Intratumoral and peritumoral radiomics based on dynamic contrast-enhanced MRI for preoperative prediction of intraductal component in invasive breast cancer. Eur Radiol 2022; 32:4845-4856. [DOI: 10.1007/s00330-022-08539-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 12/01/2021] [Accepted: 12/22/2021] [Indexed: 12/11/2022]
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14
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Ya G, Wen F, Xing-ru L, Zhuan-zhuan G, Jun-qiang L. Difference of DCE-MRI Parameters at Different Time Points and Their Predictive Value for Axillary Lymph Node Metastasis of Breast Cancer. Acad Radiol 2022; 29 Suppl 1:S79-S86. [PMID: 33504446 DOI: 10.1016/j.acra.2021.01.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/09/2021] [Accepted: 01/11/2021] [Indexed: 12/26/2022]
Abstract
RATIONALE AND OBJECTIVES To assess differences of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) parameters at different postcontrast time points (TPs), and to explore the predictive value of DCE-MRI parameters for axillary lymph node (ALN) metastasis of breast cancer. MATERIALS AND METHODS A total of 107 breast cancer patients were included retrospectively, and 50 phases were collected on DCE-MRI for each patient. DCE-MRI parameters Ktrans, Kep, Ve, TTP, Peak, Washin, Washout, and AUC were extracted from the images at 67.8 seconds, 128.5 seconds, 189.2 seconds, 249.9 seconds, and 310.5 seconds (regard as TP1, 2, 3, 4, and 5). Wilcoxon signed rank test was used to compare DCE-MRI parameters at different postcontrast TPs. Logistic regression was performed to analyze the predictive value of DCE-MRI parameters for ALN metastasis of breast cancer, and receiver operating characteristic (ROC) curve was constructed to evaluate the predictive performance. RESULTS The difference of DCE-MRI parameters between TP1, 2, 3, 4, and 5 was statistically significant (p < 0.01) in breast cancer. The TPs are considered as the optimal TPs when DCE-MRI parameters values reach the maximum. The optimal TPs of Ktrans, Kep, and Ve were respectively at TP2, TP2, and TP4 (Ktrans2, Kep2, and Ve4). The optimal TPs of TTP, Peak, and AUC were at TP5 (TTP5, Peak5, and AUC5). AUC5 showed the ability to predict ALN metastasis of breast cancer (area under ROC curve = 0.656, p < 0.05). CONCLUSIONS DCE-MRI parameters values were different at different postcontrast TPs. AUC5 may be an independent predictor of ALN metastasis in breast cancer.
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15
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Virostko J, Sorace AG, Slavkova KP, Kazerouni AS, Jarrett AM, DiCarlo JC, Woodard S, Avery S, Goodgame B, Patt D, Yankeelov TE. Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting. Breast Cancer Res 2021; 23:110. [PMID: 34838096 PMCID: PMC8627106 DOI: 10.1186/s13058-021-01489-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 11/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer. METHODS Patients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (Ktrans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with Ktrans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves. RESULTS Tumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p < 0.05). At the third and fourth MRI, changes in tumor volume, Ktrans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p < 0.05) in the cohort who achieved pCR compared to those patients with non-pCR. CONCLUSIONS Quantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI.
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Affiliation(s)
- John Virostko
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX, 78712, USA
- Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX, USA
- Department of Oncology, University of Texas at Austin, Austin, TX, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
| | - Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Kalina P Slavkova
- Department of Physics, University of Texas at Austin, Austin, TX, USA
| | - Anum S Kazerouni
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
| | - Julie C DiCarlo
- Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
| | - Stefanie Woodard
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sarah Avery
- Austin Radiological Association, Austin, TX, USA
| | - Boone Goodgame
- Dell Seton Medical Center at the University of Texas, Austin, USA
| | | | - Thomas E Yankeelov
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX, 78712, USA.
- Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX, USA.
- Department of Oncology, University of Texas at Austin, Austin, TX, USA.
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA.
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA.
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA.
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16
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Wang C, Chen X, Luo H, Liu Y, Meng R, Wang M, Liu S, Xu G, Ren J, Zhou P. Development and Internal Validation of a Preoperative Prediction Model for Sentinel Lymph Node Status in Breast Cancer: Combining Radiomics Signature and Clinical Factors. Front Oncol 2021; 11:754843. [PMID: 34820327 PMCID: PMC8606782 DOI: 10.3389/fonc.2021.754843] [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: 08/07/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose To develop and internally validate a nomogram combining radiomics signature of primary tumor and fibroglandular tissue (FGT) based on pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical factors for preoperative prediction of sentinel lymph node (SLN) status in breast cancer patients. Methods This study retrospectively enrolled 186 breast cancer patients who underwent pretreatment pharmacokinetic DCE-MRI with positive (n = 93) and negative (n = 93) SLN. Logistic regression models and radiomics signatures of tumor and FGT were constructed after feature extraction and selection. The radiomics signatures were further combined with independent predictors of clinical factors for constructing a combined model. Prediction performance was assessed by receiver operating characteristic (ROC), calibration, and decision curve analysis. The areas under the ROC curve (AUCs) of models were corrected by 1,000-times bootstrapping method and compared by Delong's test. The added value of each independent model or their combinations was also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. This report referred to the "Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis" (TRIPOD) statement. Results The AUCs of the tumor radiomic model (eight features) and the FGT radiomic model (three features) were 0.783 (95% confidence interval [CI], 0.717-0.849) and 0.680 (95% CI, 0.604-0.757), respectively. A higher AUC of 0.799 (95% CI, 0.737-0.862) was obtained by combining tumor and FGT radiomics signatures. By further combining tumor and FGT radiomics signatures with progesterone receptor (PR) status, a nomogram was developed and showed better discriminative ability for SLN status [AUC 0.839 (95% CI, 0.783-0.895)]. The IDI and NRI indices also showed significant improvement when combining tumor, FGT, and PR compared with each independent model or a combination of any two of them (all p < 0.05). Conclusion FGT and clinical factors improved the prediction performance of SLN status in breast cancer. A nomogram integrating the DCE-MRI radiomics signature of tumor and FGT and PR expression achieved good performance for the prediction of SLN status, which provides a potential biomarker for clinical treatment decision-making.
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Affiliation(s)
- Chunhua Wang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoyu Chen
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongbing Luo
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanyuan Liu
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Ruirui Meng
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Wang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Siyun Liu
- Pharmaceutical Diagnostics, General Electric (GE) Company (Healthcare), Beijing, China
| | - Guohui Xu
- Department of Interventional Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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17
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Yang Z, Chen X, Zhang T, Cheng F, Liao Y, Chen X, Dai Z, Fan W. Quantitative Multiparametric MRI as an Imaging Biomarker for the Prediction of Breast Cancer Receptor Status and Molecular Subtypes. Front Oncol 2021; 11:628824. [PMID: 34604024 PMCID: PMC8481692 DOI: 10.3389/fonc.2021.628824] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 08/30/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives To assess breast cancer receptor status and molecular subtypes by using the CAIPIRINHA-Dixon-TWIST-VIBE and readout-segmented echo-planar diffusion weighted imaging techniques. Methods A total of 165 breast cancer patients were retrospectively recruited. Patient age, estrogen receptor, progesterone receptor, human epidermal growth factorreceptor-2 (HER-2) status, and the Ki-67 proliferation index were collected for analysis. Quantitative parameters (Ktrans, Ve, Kep), semiquantitative parameters (W-in, W-out, TTP), and apparent diffusion coefficient (ADC) values were compared in relation to breast cancer receptor status and molecular subtypes. Statistical analysis were performed to compare the parameters in the receptor status and molecular subtype groups.Multivariate analysis was performed to explore confounder-adjusted associations, and receiver operating characteristic curve analysis was used to assess the classification performance and calculate thresholds. Results Younger age (<49.5 years, odds ratio (OR) =0.95, P=0.004), lower Kep (<0.704,OR=0.14, P=0.044),and higher TTP (>0.629 min, OR=24.65, P=0.011) were independently associated with progesterone receptor positivity. A higher TTP (>0.585 min, OR=28.19, P=0.01) was independently associated with estrogen receptor positivity. Higher Kep (>0.892, OR=11.6, P=0.047), lower TTP (<0.582 min, OR<0.001, P=0.004), and lower ADC (<0.719 ×10-3 mm2/s, OR<0.001, P=0.048) had stronger independent associations with triple-negative breast cancer (TNBC) compared to luminal A, and those parameters could differentiate TNBC from luminal A with the highest AUC of 0.811. Conclusions Kep and TTP were independently associated with hormone receptor status. In addition, the Kep, TTP, and ADC values had stronger independent associations with TNBC than with luminal A and could be used as imaging biomarkers for differentiate TNBC from Luminal A.
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Affiliation(s)
- Zhiqi Yang
- Department of Radiology, Meizhou People's Hospital, Meizhou, China.,Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, China
| | - Xiaofeng Chen
- Department of Radiology, Meizhou People's Hospital, Meizhou, China.,Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, China
| | - Tianhui Zhang
- Department of Radiology, Meizhou People's Hospital, Meizhou, China
| | - Fengyan Cheng
- Department of Radiology, Meizhou People's Hospital, Meizhou, China
| | - Yuting Liao
- Pharmaceutical Diagnostics, GE Healthcare, Guangzhou, China
| | - Xiangguan Chen
- Department of Radiology, Meizhou People's Hospital, Meizhou, China.,Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, China
| | - Zhuozhi Dai
- Department of Radiology, Shantou Central Hospital, Shantou, China
| | - Weixiong Fan
- Department of Radiology, Meizhou People's Hospital, Meizhou, China
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18
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Ragusi MA, Winter-Warnars GA, Wesseling J, Linn SC, Beets-Tan RG, van der Velden BH, Elias SG, Gilhuijs KG, Loo CE. Prognostic value of breast MRI characteristics before and during neoadjuvant endocrine therapy in patients with ER+/HER2- breast cancer. Br J Radiol 2021; 94:20201125. [PMID: 34142870 PMCID: PMC8248214 DOI: 10.1259/bjr.20201125] [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] [Indexed: 11/24/2022] Open
Abstract
Objective: To investigate whether BIRADS MRI characteristics before or during neoadjuvant endocrine therapy (NET) are associated with the preoperative endocrine prognostic index (PEPI) in ER+/HER2- breast cancer patients. Methods: This retrospective observational cohort study included 35 ER+/HER2- patients with 38 tumors (3 bilateral cases) treated with NET. The pre- and midtreatment (after 3 months) MRIs were evaluated by two breast radiologists for BIRADS imaging characteristics, shrinkage pattern, and radiologic response. PEPI was used as end point. PEPI is based on the post-treatment surgical specimen’s pT- and pN-stage, Ki67, and ER-status. Tumors were assigned PEPI-1 (good prognosis) or PEPI-2/3 (poor prognosis). We investigated whether pre- and midtreatment BIRADS characteristics were associated with PEPI. Results: Median patient age was 65 years (interquartile interval [IQI]: 53, 70). 17 tumors (44.7%) were associated with good prognosis (PEPI-1), and 21 tumors (55.3%) with poor prognosis (PEPI-2/3). A larger reduction in tumor size after 3 months of NET was significantly associated with PEPI; 10 mm (IQI: 5, 13.5) in PEPI-1 tumors vs 4.5 mm (IQI: 3, 7; p = .045) in PEPI-2/3 tumors. Other BIRADS characteristics, shrinkage pattern or radiologic response were not associated with PEPI. Conclusion: Only a larger reduction in tumor size on MRI after 3 months of NET was associated with PEPI-1 (good prognosis) in ER+/HER2- breast cancer patients. Advances in knowledge: MRI characteristics previously reported to be associated with prognosis during neoadjuvant chemotherapy are not necessarily associated with prognosis during NET in ER+/HER2- breast cancer patients.
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Affiliation(s)
- Max Aa Ragusi
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.,Department of Radiology/Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gonneke Ao Winter-Warnars
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Jelle Wesseling
- Department of Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Sabine C Linn
- Department of Medical Oncology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Regina G Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Bas Hm van der Velden
- Department of Radiology/Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Sjoerd G Elias
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Kenneth Ga Gilhuijs
- Department of Radiology/Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Claudette E Loo
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
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19
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Wang PN, Velikina JV, Strigel RM, Henze Bancroft LC, Samsonov AA, Cashen TA, Wang K, Kelcz F, Johnson KM, Korosec FR, Ersoz A, Holmes JH. Comparison of data-driven and general temporal constraints on compressed sensing for breast DCE MRI. Magn Reson Med 2021; 85:3071-3084. [PMID: 33306217 DOI: 10.1002/mrm.28628] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 11/10/2020] [Accepted: 11/11/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE Current breast DCE-MRI strategies provide high sensitivity for cancer detection but are known to be insufficient in fully capturing rapidly changing contrast kinetics at high spatial resolution across both breasts. Advanced acquisition and reconstruction strategies aim to improve spatial and temporal resolution and increase specificity for disease characterization. In this work, we evaluate the spatial and temporal fidelity of a modified data-driven low-rank-based model (known as MOCCO, model consistency condition) compressed-sensing (CS) reconstruction compared to CS with temporal total variation with radial acquisition for high spatial-temporal breast DCE MRI. METHODS Reconstruction performance was characterized using numerical simulations of a golden-angle stack-of-stars breast DCE-MRI acquisition at 5-second temporal resolution. Specifically, MOCCO was compared with CS total variation and conventional SENSE reconstructions. The temporal model for MOCCO was prelearned over the source data, whereas CS total variation was performed using a first-order temporal gradient sparsity transform. RESULTS The MOCCO reconstruction was able to capture rapid lesion kinetics while providing high image quality across a range of optimal regularization values. It also recovered kinetics in small lesions (1.5 mm) in line-profile analysis and error images, whereas g-factor maps showed relatively low and constant values with no significant artifacts. The CS-TV method demonstrated either recovery of high spatial resolution with reduced temporal accuracy using large regularization values, or recovery of rapid lesion kinetics with reduced image quality using low regularization values. CONCLUSION Simulations demonstrated that MOCCO with radial acquisition provides a robust imaging technique for improving temporal fidelity, while maintaining high spatial resolution and image quality in the setting of bilateral breast DCE MRI.
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Affiliation(s)
- Ping N Wang
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Julia V Velikina
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Roberta M Strigel
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Leah C Henze Bancroft
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Alexey A Samsonov
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Ty A Cashen
- Global MR Applications & Workflow, GE Healthcare, Madison, Wisconsin, USA
| | - Kang Wang
- Global MR Applications & Workflow, GE Healthcare, Madison, Wisconsin, USA
| | - Frederick Kelcz
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Kevin M Johnson
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Frank R Korosec
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Ali Ersoz
- MR Engineering, GE Healthcare, Waukesha, Wisconsin, USA
| | - James H Holmes
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
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20
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Halligan S, Menu Y, Mallett S. Why did European Radiology reject my radiomic biomarker paper? How to correctly evaluate imaging biomarkers in a clinical setting. Eur Radiol 2021; 31:9361-9368. [PMID: 34003349 PMCID: PMC8589811 DOI: 10.1007/s00330-021-07971-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/06/2021] [Accepted: 03/31/2021] [Indexed: 12/23/2022]
Abstract
This review explains in simple terms, accessible to the non-statistician, general principles regarding the correct research methods to develop and then evaluate imaging biomarkers in a clinical setting, including radiomic biomarkers. The distinction between diagnostic and prognostic biomarkers is made and emphasis placed on the need to assess clinical utility within the context of a multivariable model. Such models should not be restricted to imaging biomarkers and must include relevant disease and patient characteristics likely to be clinically useful. Biomarker utility is based on whether its addition to the basic clinical model improves diagnosis or prediction. Approaches to both model development and evaluation are explained and the need for adequate amounts of representative data stressed so as to avoid underpowering and overfitting. Advice is provided regarding how to report the research correctly. KEY POINTS: • Imaging biomarker research is common but methodological errors are encountered frequently that may mean the research is not clinically useful. • The clinical utility of imaging biomarkers is best assessed by their additive effect on multivariable models based on clinical factors known to be important. • The data used to develop such models should be sufficient for the number of variables investigated and the model should be evaluated, preferably using data unrelated to development.
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Affiliation(s)
- Steve Halligan
- Centre for Medical Imaging, University College London UCL, 43-45 Foley Street, London, W1W 7TS, UK.
| | - Yves Menu
- Department of Diagnostic and Interventional Radiology, Saint Antoine Hospital, APHP-Sorbonne University, Paris, France
| | - Sue Mallett
- Centre for Medical Imaging, University College London UCL, 43-45 Foley Street, London, W1W 7TS, UK
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21
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Fan M, Chen H, You C, Liu L, Gu Y, Peng W, Gao X, Li L. Radiomics of Tumor Heterogeneity in Longitudinal Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer. Front Mol Biosci 2021; 8:622219. [PMID: 33869279 PMCID: PMC8044916 DOI: 10.3389/fmolb.2021.622219] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 02/15/2021] [Indexed: 01/23/2023] Open
Abstract
Breast tumor morphological and vascular characteristics can be changed during neoadjuvant chemotherapy (NACT). The early changes in tumor heterogeneity can be quantitatively modeled by longitudinal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is useful in predicting responses to NACT in breast cancer. In this retrospective analysis, 114 female patients with unilateral unifocal primary breast cancer who received NACT were included in a development (n = 61) dataset and a testing dataset (n = 53). DCE-MRI was performed for each patient before and after treatment (two cycles of NACT) to generate baseline and early follow-up images, respectively. Feature-level changes (delta) of the entire tumor were evaluated by calculating the relative net feature change (deltaRAD) between baseline and follow-up images. The voxel-level change inside the tumor was evaluated, which yielded a Jacobian map by registering the follow-up image to the baseline image. Clinical information and the radiomic features were fused to enhance the predictive performance. The area under the curve (AUC) values were assessed to evaluate the prediction performance. Predictive models using radiomics based on pre- and post-treatment images, Jacobian maps and deltaRAD showed AUC values of 0.568, 0.767, 0.630 and 0.726, respectively. When features from these images were fused, the predictive model generated an AUC value of 0.771. After adding the molecular subtype information in the fused model, the performance was increased to an AUC of 0.809 (sensitivity of 0.826 and specificity of 0.800), which is significantly higher than that of the baseline imaging- and Jacobian map-based predictive models (p = 0.028 and 0.019, respectively). The level of tumor heterogeneity reduction (evaluated by texture feature) is higher in the NACT responders than in the nonresponders. The results suggested that changes in DCE-MRI features that reflect a reduction in tumor heterogeneity following NACT could provide early prediction of breast tumor response. The prediction was improved when the molecular subtype information was combined into the model.
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Affiliation(s)
- Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Hang Chen
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Li Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
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22
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Wang N, Xie Y, Fan Z, Ma S, Saouaf R, Guo Y, Shiao SL, Christodoulou AG, Li D. Five-dimensional quantitative low-dose Multitasking dynamic contrast- enhanced MRI: Preliminary study on breast cancer. Magn Reson Med 2021; 85:3096-3111. [PMID: 33427334 DOI: 10.1002/mrm.28633] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/17/2020] [Accepted: 11/13/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE To develop a low-dose Multitasking DCE technique (LD-MT-DCE) for breast imaging, enabling dynamic T1 mapping-based quantitative characterization of tumor blood flow and vascular properties with whole-breast coverage, a spatial resolution of 0.9 × 0.9 × 1.1 mm3 , and a temporal resolution of 1.4 seconds using a 20% gadolinium dose (0.02 mmol/kg). METHODS Magnetic resonance Multitasking was used to reconstruct 5D images with three spatial dimensions, one T1 recovery dimension for dynamic T1 quantification, and one DCE dimension for contrast kinetics. Kinetic parameters F p , v p , K trans , and v e were estimated from dynamic T1 maps using the two-compartment exchange model. The LD-MT-DCE repeatability and agreement against standard-dose MT-DCE were evaluated in 20 healthy subjects. In 7 patients with triple-negative breast cancer, LD-MT-DCE image quality and diagnostic results were compared with that of standard-dose clinical DCE in the same imaging session. One-way unbalanced analysis of variance with Tukey test was performed to evaluate the statistical significance of the kinetic parameters between control and patient groups. RESULTS The LD-MT-DCE technique was repeatable, agreed with standard-dose MT-DCE, and showed excellent image quality. The diagnosis using LD-MT-DCE matched well with clinical results. The values of F p , v p , and K trans were significantly different between malignant tumors and normal breast tissue (P < .001, < .001, and < .001, respectively), and between malignant and benign tumors (P = .020, .003, and < .001, respectively). CONCLUSION The LD-MT-DCE technique was repeatable and showed excellent image quality and equivalent diagnosis compared with standard-dose clinical DCE. The estimated kinetic parameters were capable of differentiating between normal breast tissue and benign and malignant tumors.
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Affiliation(s)
- Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Zhaoyang Fan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Sen Ma
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Rola Saouaf
- Department of Imaging, Cedars Sinai Medical Center, Los Angeles, California, USA
| | - Yu Guo
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - Stephen L Shiao
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Biomedical Sciences, Division of Immunology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Anthony G Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
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23
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Murphy LC, Quinn EM, Razzaq Z, Brady C, Livingstone V, Duddy L, Barry J, Redmond HP, Corrigan MA. Assessing the accuracy of conventional gadolinium-enhanced breast MRI in measuring the nodal response to neoadjuvant chemotherapy (NAC) in breast cancer. Breast J 2020; 26:2151-2156. [PMID: 33176396 DOI: 10.1111/tbj.14065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 09/08/2020] [Accepted: 09/09/2020] [Indexed: 11/30/2022]
Abstract
Management of the axilla in the era of neoadjuvant chemotherapy for breast cancer is evolving. The aim of this study is to determine if conventional gadolinium-enhanced breast MRI can aid in evaluation of the response to neoadjuvant chemotherapy in the axilla. A retrospective review of a prospectively maintained database of patients undergoing neoadjuvant chemotherapy for breast cancer was performed. Pre and post-neoadjuvant chemotherapy MRI reports for node-positive patients were examined in conjunction with demographic data, treatment type, and final histopathology reports. One-hundred and fourteen patients with breast cancer undergoing neoadjuvant chemotherapy were included in the study. The sensitivity of magnetic resonance imaging in detecting nodal response post-neoadjuvant chemotherapy was 33.93% and the specificity was 82.76%. Magnetic resonance imaging had a positive predictive value of 65.52% and a negative predictive value of 56.47%. MRI was found to be most specific in the detection of triple-negative cancer response. Specificity was 100% in this group and sensitivity was 75%. Magnetic resonance imaging has a relatively high specificity in detecting nodal response post-neoadjuvant chemotherapy but has a low sensitivity. Alone it cannot be relied upon to identify active axillary malignancy post-neoadjuvant chemotherapy. However, given its increased specificity among certain subgroups, it may have a role in super-selecting patients suitable for sentinel lymph node biopsy post-neoadjuvant chemotherapy.
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Affiliation(s)
| | - Edel Marie Quinn
- Cork Breast Research Centre, University College Cork, Cork, Ireland
| | - Zeeshan Razzaq
- Cork Breast Research Centre, University College Cork, Cork, Ireland
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24
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Incoronato M, Mirabelli P, Grimaldi AM, Soricelli A, Salvatore M. Correlating imaging parameters with molecular data: An integrated approach to improve the management of breast cancer patients. Int J Biol Markers 2020; 35:47-50. [PMID: 32079469 DOI: 10.1177/1724600819899665] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The goal of this review is to provide an overview of the studies aimed at integrating imaging parameters with molecular biomarkers for improving breast cancer patient's diagnosis and prognosis. The use of diagnostic imaging to extract quantitative parameters related to the morphology, metabolism, and functionality of tumors, as well as their correlation with cancer tissue biomarkers is an emerging research topic. Thanks to the development of imaging biobanks and the technological tools required for extraction of imaging parameters including radiomic features, it is possible to integrate imaging markers with genetic data. This new field of study represents the evolution of radiology-pathology correlation from an anatomic-histologic level to a genetic level, which paves new interesting perspectives for breast cancer management.
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Affiliation(s)
| | | | | | - Andrea Soricelli
- IRCCS SDN, Naples, Italy.,Department of Motor Sciences & Healthiness, University of Naples Parthenope, Naples, Italy
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25
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Wahab RA, Lewis K, Vijapura C, Zhang B, Lee SJ, Brown A, Mahoney MC. Textural Characteristics of Biopsy-proven Metastatic Axillary Nodes on Preoperative Breast MRI in Breast Cancer Patients: A Feasibility Study. JOURNAL OF BREAST IMAGING 2020; 2:361-371. [PMID: 38424965 DOI: 10.1093/jbi/wbaa038] [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: 11/23/2019] [Indexed: 03/02/2024]
Abstract
OBJECTIVE To determine the diagnostic accuracy of MRI textural analysis (TA) to differentiate malignant from benign axillary lymph nodes in patients with breast cancer. METHODS This was an institutional review board-approved retrospective study of axillary lymph nodes in women with breast cancer that underwent ultrasound-guided biopsy and contrast-enhanced (CE) breast MRI from January 2015 to December 2018. TA of axillary lymph nodes was performed on 3D dynamic CE T1-weighted fat-suppressed, 3D delayed CE T1-weighted fat-suppressed, and T2-weighted fat-suppressed MRI sequences. Quantitative parameters used to measure TA were compared with pathologic diagnoses. Areas under the curve (AUC) were calculated using receiver operating characteristic curve analysis to distinguish between malignant and benign lymph nodes. RESULTS Twenty-three biopsy-proven malignant lymph nodes and 24 benign lymph nodes were analyzed. The delayed CE T1-weighted fat-suppressed sequence had the greatest ability to differentiate malignant from benign outcome at all spatial scaling factors, with the highest AUC (0.84-0.93), sensitivity (0.78 [18/23] to 0.87 [20/23]), and specificity (0.76 [18/24] to 0.88 [21/24]). Kurtosis on the 3D delayed CE T1-weighted fat-suppressed sequence was the most prominent TA parameter differentiating malignant from benign lymph nodes (P < 0.0001). CONCLUSION This study suggests that MRI TA could be helpful in distinguishing malignant from benign axillary lymph nodes. Kurtosis has the greatest potential on 3D delayed CE T1-weighted fat-suppressed sequences to distinguish malignant and benign lymph nodes.
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Affiliation(s)
- Rifat A Wahab
- University of Cincinnati Medical Center, Department of Radiology, Cincinnati, OH
| | - Kyle Lewis
- University of Cincinnati Medical Center, Department of Radiology, Cincinnati, OH
| | - Charmi Vijapura
- University of Cincinnati Medical Center, Department of Radiology, Cincinnati, OH
| | - Bin Zhang
- Cincinnati Children's Hospital Medical Center, Division of Biostatistics and Epidemiology, Cincinnati, OH
| | - Su-Ju Lee
- University of Cincinnati Medical Center, Department of Radiology, Cincinnati, OH
| | - Ann Brown
- University of Cincinnati Medical Center, Department of Radiology, Cincinnati, OH
| | - Mary C Mahoney
- University of Cincinnati Medical Center, Department of Radiology, Cincinnati, OH
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26
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Contralateral parenchymal enhancement on breast MRI before and during neoadjuvant endocrine therapy in relation to the preoperative endocrine prognostic index. Eur Radiol 2020; 30:6740-6748. [PMID: 32691100 PMCID: PMC7599193 DOI: 10.1007/s00330-020-07058-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 04/30/2020] [Accepted: 07/01/2020] [Indexed: 02/07/2023]
Abstract
Objectives To investigate whether contralateral parenchymal enhancement (CPE) on MRI during neoadjuvant endocrine therapy (NET) is associated with the preoperative endocrine prognostic index (PEPI) of ER+/HER2− breast cancer. Methods This retrospective observational cohort study included 40 unilateral ER+/HER2− breast cancer patients treated with NET. Patients received NET for 6 to 9 months with MRI response monitoring after 3 and/or 6 months. PEPI was used as endpoint. PEPI is based on surgery-derived pathology (pT- and pN-stage, Ki67, and ER-status) and stratifies patients in three groups with distinct prognoses. Mixed effects and ROC analysis were performed to investigate whether CPE was associated with PEPI and to assess discriminatory ability. Results The median patient age was 61 (interquartile interval: 52, 69). Twelve patients had PEPI-1 (good prognosis), 15 PEPI-2 (intermediate), and 13 PEPI-3 (poor). High pretreatment CPE was associated with PEPI-3: pretreatment CPE was 39.4% higher on average (95% CI = 1.3, 91.9%; p = .047) compared with PEPI-1. CPE decreased after 3 months in PEPI-2 and PEPI-3. The average reduction was 24.4% (95% CI = 2.6, 41.3%; p = .032) in PEPI-2 and 29.2% (95% CI = 7.8, 45.6%; p = .011) in PEPI-3 compared with baseline. Change in CPE was predictive of PEPI-1 vs PEPI-2+3 (AUC = 0.77; 95% CI = 0.57, 0.96). Conclusions CPE during NET is associated with PEPI-group in ER+/HER2− breast cancer: a high pretreatment CPE and a decrease in CPE during NET were associated with a poor prognosis after NET on the basis of PEPI. Key Points • Change in contralateral breast parenchymal enhancement on MRI during neoadjuvant endocrine therapy distinguished between patients with a good and intermediate/poor prognosis at final pathology. • Patients with a poor prognosis at final pathology showed higher baseline parenchymal enhancement on average compared to patients with a good prognosis. • Patients with an intermediate/poor prognosis at final pathology showed a higher average reduction in parenchymal enhancement after 3 months of neoadjuvant endocrine therapy. Electronic supplementary material The online version of this article (10.1007/s00330-020-07058-3) contains supplementary material, which is available to authorized users.
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27
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Chronaiou I, Giskeødegård GF, Goa PE, Teruel J, Hedayati R, Lundgren S, Huuse EM, Pickles MD, Gibbs P, Sitter B, Bathen TF. Feasibility of contrast-enhanced MRI derived textural features to predict overall survival in locally advanced breast cancer. Acta Radiol 2020; 61:875-884. [PMID: 31744303 DOI: 10.1177/0284185119885116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The prognosis for women with locally advanced breast cancer (LABC) is poor and there is a need for better treatment stratification. Gray-level co-occurrence matrix (GLCM) texture analysis of magnetic resonance (MR) images has been shown to predict pathological response and could become useful in stratifying patients to more targeted treatments. PURPOSE To evaluate the ability of GLCM textural features obtained before neoadjuvant chemotherapy to predict overall survival (OS) seven years after diagnosis of patients with LABC. MATERIAL AND METHODS This retrospective study includes data from 55 patients with LABC. GLCM textural features were extracted from segmented tumors in pre-treatment dynamic contrast-enhanced 3-T MR images. Prediction of OS by GLCM textural features was assessed and compared to predictions using traditional clinical variables. RESULTS Linear mixed-effect models showed significant differences in five GLCM features (f1, f2, f5, f10, f11) between survivors and non-survivors. Using discriminant analysis for prediction of survival, GLCM features from 2 min post-contrast images achieved a classification accuracy of 73% (P < 0.001), whereas traditional prognostic factors resulted in a classification accuracy of 67% (P = 0.005). Using a combination of both yielded the highest classification accuracy (78%, P < 0.001). Median values for features f1, f2, f10, and f11 provided significantly different survival curves in Kaplan-Meier analysis. CONCLUSION This study shows a clear association between textural features from post-contrast images obtained before neoadjuvant chemotherapy and OS seven years after diagnosis. Further studies in larger cohorts should be undertaken to investigate how this prognostic information can be used to benefit treatment stratification.
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Affiliation(s)
- Ioanna Chronaiou
- Department of Circulation and Medical Imaging, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Guro Fanneløb Giskeødegård
- Department of Circulation and Medical Imaging, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Pål Erik Goa
- Department of Physics, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Jose Teruel
- Department of Radiation Oncology, NYU Langone Health, New York, NY, USA
| | - Roja Hedayati
- Cancer clinic, St. Olavs University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Steinar Lundgren
- Cancer clinic, St. Olavs University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Else Marie Huuse
- Clinic of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway
| | - Martin D Pickles
- Radiology Department, Hull University Teaching Hospitals NHS Trust, Hull, UK
| | - Peter Gibbs
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Beathe Sitter
- Department of Circulation and Medical Imaging, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
| | - Tone Frost Bathen
- Department of Circulation and Medical Imaging, NTNU – Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Radiology and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway
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El Adoui M, Drisis S, Benjelloun M. Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images. Int J Comput Assist Radiol Surg 2020; 15:1491-1500. [DOI: 10.1007/s11548-020-02209-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 06/01/2020] [Indexed: 12/13/2022]
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Jones EF, Hathi DK, Freimanis R, Mukhtar RA, Chien AJ, Esserman LJ, van’t Veer LJ, Joe BN, Hylton NM. Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy. Cancers (Basel) 2020; 12:E1511. [PMID: 32527022 PMCID: PMC7352259 DOI: 10.3390/cancers12061511] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/03/2020] [Accepted: 06/05/2020] [Indexed: 12/24/2022] Open
Abstract
In recent years, neoadjuvant treatment trials have shown that breast cancer subtypes identified on the basis of genomic and/or molecular signatures exhibit different response rates and recurrence outcomes, with the implication that subtype-specific treatment approaches are needed. Estrogen receptor-positive (ER+) breast cancers present a unique set of challenges for determining optimal neoadjuvant treatment approaches. There is increased recognition that not all ER+ breast cancers benefit from chemotherapy, and that there may be a subset of ER+ breast cancers that can be treated effectively using endocrine therapies alone. With this uncertainty, there is a need to improve the assessment and to optimize the treatment of ER+ breast cancers. While pathology-based markers offer a snapshot of tumor response to neoadjuvant therapy, non-invasive imaging of the ER disease in response to treatment would provide broader insights into tumor heterogeneity, ER biology, and the timing of surrogate endpoint measurements. In this review, we provide an overview of the current landscape of breast imaging in neoadjuvant studies and highlight the technological advances in each imaging modality. We then further examine some potential imaging markers for neoadjuvant treatment response in ER+ breast cancers.
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Affiliation(s)
- Ella F. Jones
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
| | - Deep K. Hathi
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
| | - Rita Freimanis
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
| | - Rita A. Mukhtar
- Department of Surgery, University of California, San Francisco, CA 94115, USA;
| | - A. Jo Chien
- School of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94115, USA; (A.J.C.); (L.J.v.V.)
| | - Laura J. Esserman
- Department of Surgery, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94115, USA;
| | - Laura J. van’t Veer
- School of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94115, USA; (A.J.C.); (L.J.v.V.)
| | - Bonnie N. Joe
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
| | - Nola M. Hylton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94115, USA; (D.K.H.); (R.F.); (B.N.J.); (N.M.H.)
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Cheng Q, Huang J, Liang J, Ma M, Ye K, Shi C, Luo L. The Diagnostic Performance of DCE-MRI in Evaluating the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer: A Meta-Analysis. Front Oncol 2020; 10:93. [PMID: 32117747 PMCID: PMC7028702 DOI: 10.3389/fonc.2020.00093] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 01/17/2020] [Indexed: 12/23/2022] Open
Abstract
Background: Neoadjuvant chemotherapy (NAC) is commonly utilized in preoperative treatment for local breast cancer, and it gives high clinical response rates and can result in pathologic complete response (pCR) in 6–25% of patients. In recent years, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been increasingly used to assess the pathological response of breast cancer to NAC. In present analysis, we assess the diagnostic performance of DCE-MRI in evaluating the pathological response of breast cancer to NAC. Materials and Methods: A systematic search in PubMed, the Cochrane Library, and Web of Science for original studies was performed. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the methodological quality of the included studies. Patient, study, and imaging characteristics were extracted, and sufficient data to reconstruct 2 × 2 tables were obtained. Data pooling, heterogeneity testing, forest plot construction, meta-regression analysis and sensitivity analysis were performed using Stata version 12.0 (StataCorp LP, College Station, TX). Results: Eighteen studies (969 patients with breast cancer) were included in the present meta-analysis. The pooled sensitivity and specificity of DCE-MRI were 0.80 (95% confidence interval [CI]: 0.70, 0.88) and 0.84 (95% [CI]: 0.79, 0.88), respectively. Meta-regression analysis found no significant factors affecting heterogeneity. Sensitivity analysis showed that studies that set pathological complete response (pCR) (n = 14) as a responder showed a tendency for higher sensitivity compared with those that set pCR and near pCR together (n = 5) as a responder (0.83 vs. 0.72), and studies (n = 14) that used DCE-MRI to early predict the pathological response of breast cancer had a higher sensitivity (0.83 vs. 0.71) and equivalent specificity (0.80 vs. 0.86) compared to studies (n = 5) that assessed the response after NAC completion. Conclusion: Our results indicated that DCE-MRI could be considered an important auxiliary method for evaluating the pathological response of breast cancer to NAC and used as an effective method for dynamically monitoring the efficacy during NAC. DCE-MRI also performed well in predicting the pCR of breast cancer to NAC. However, due to the heterogeneity of the included studies, caution should be exercised in applying our results.
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Affiliation(s)
- Qingqing Cheng
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jiaxi Huang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jianye Liang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Mengjie Ma
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Kunlin Ye
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Changzheng Shi
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.,Engineering Research Center of Medical Imaging Artificial Intelligence for Precision Diagnosis and Treatment, Guangzhou, China
| | - Liangping Luo
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.,Engineering Research Center of Medical Imaging Artificial Intelligence for Precision Diagnosis and Treatment, Guangzhou, China
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Gallagher FA, Woitek R, McLean MA, Gill AB, Manzano Garcia R, Provenzano E, Riemer F, Kaggie J, Chhabra A, Ursprung S, Grist JT, Daniels CJ, Zaccagna F, Laurent MC, Locke M, Hilborne S, Frary A, Torheim T, Boursnell C, Schiller A, Patterson I, Slough R, Carmo B, Kane J, Biggs H, Harrison E, Deen SS, Patterson A, Lanz T, Kingsbury Z, Ross M, Basu B, Baird R, Lomas DJ, Sala E, Wason J, Rueda OM, Chin SF, Wilkinson IB, Graves MJ, Abraham JE, Gilbert FJ, Caldas C, Brindle KM. Imaging breast cancer using hyperpolarized carbon-13 MRI. Proc Natl Acad Sci U S A 2020; 117:2092-2098. [PMID: 31964840 PMCID: PMC6995024 DOI: 10.1073/pnas.1913841117] [Citation(s) in RCA: 111] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Our purpose is to investigate the feasibility of imaging tumor metabolism in breast cancer patients using 13C magnetic resonance spectroscopic imaging (MRSI) of hyperpolarized 13C label exchange between injected [1-13C]pyruvate and the endogenous tumor lactate pool. Treatment-naïve breast cancer patients were recruited: four triple-negative grade 3 cancers; two invasive ductal carcinomas that were estrogen and progesterone receptor-positive (ER/PR+) and HER2/neu-negative (HER2-), one grade 2 and one grade 3; and one grade 2 ER/PR+ HER2- invasive lobular carcinoma (ILC). Dynamic 13C MRSI was performed following injection of hyperpolarized [1-13C]pyruvate. Expression of lactate dehydrogenase A (LDHA), which catalyzes 13C label exchange between pyruvate and lactate, hypoxia-inducible factor-1 (HIF1α), and the monocarboxylate transporters MCT1 and MCT4 were quantified using immunohistochemistry and RNA sequencing. We have demonstrated the feasibility and safety of hyperpolarized 13C MRI in early breast cancer. Both intertumoral and intratumoral heterogeneity of the hyperpolarized pyruvate and lactate signals were observed. The lactate-to-pyruvate signal ratio (LAC/PYR) ranged from 0.021 to 0.473 across the tumor subtypes (mean ± SD: 0.145 ± 0.164), and a lactate signal was observed in all of the grade 3 tumors. The LAC/PYR was significantly correlated with tumor volume (R = 0.903, P = 0.005) and MCT 1 (R = 0.85, P = 0.032) and HIF1α expression (R = 0.83, P = 0.043). Imaging of hyperpolarized [1-13C]pyruvate metabolism in breast cancer is feasible and demonstrated significant intertumoral and intratumoral metabolic heterogeneity, where lactate labeling correlated with MCT1 expression and hypoxia.
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Affiliation(s)
- Ferdia A Gallagher
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom;
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Mary A McLean
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Andrew B Gill
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Raquel Manzano Garcia
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Elena Provenzano
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cambridge Breast Cancer Research Unit, Addenbrooke's Hospital, Cambridge University Hospital National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
- Department of Histopathology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Frank Riemer
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Joshua Kaggie
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Anita Chhabra
- Pharmacy Department, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, United Kingdom
| | - Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - James T Grist
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Charlie J Daniels
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Fulvio Zaccagna
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | | | - Matthew Locke
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Sarah Hilborne
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Amy Frary
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Turid Torheim
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Chris Boursnell
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Amy Schiller
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Ilse Patterson
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Rhys Slough
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Bruno Carmo
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Justine Kane
- Cambridge Breast Cancer Research Unit, Addenbrooke's Hospital, Cambridge University Hospital National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Heather Biggs
- Cambridge Breast Cancer Research Unit, Addenbrooke's Hospital, Cambridge University Hospital National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Emma Harrison
- Cambridge Breast Cancer Research Unit, Addenbrooke's Hospital, Cambridge University Hospital National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Surrin S Deen
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Andrew Patterson
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Titus Lanz
- RAPID Biomedical GmbH, 97222 Rimpar, Germany
| | - Zoya Kingsbury
- Medical Genomics Research, Illumina, Great Abington, Cambridge CB21 6DF, United Kingdom
| | - Mark Ross
- Medical Genomics Research, Illumina, Great Abington, Cambridge CB21 6DF, United Kingdom
| | - Bristi Basu
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Richard Baird
- Cambridge Breast Cancer Research Unit, Addenbrooke's Hospital, Cambridge University Hospital National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - David J Lomas
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - James Wason
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Institute of Health and Society, Newcastle University, Newcastle-upon-Tyne NE2 4AX, United Kingdom
| | - Oscar M Rueda
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Suet-Feung Chin
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Ian B Wilkinson
- Department of Experimental Medicine and Immunotherapeutics, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Martin J Graves
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
| | - Jean E Abraham
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cambridge Breast Cancer Research Unit, Addenbrooke's Hospital, Cambridge University Hospital National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
| | - Carlos Caldas
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cambridge Breast Cancer Research Unit, Addenbrooke's Hospital, Cambridge University Hospital National Health Service Foundation Trust, Cambridge CB2 0QQ, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Kevin M Brindle
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom
- Department of Biochemistry, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
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Qu YH, Zhu HT, Cao K, Li XT, Ye M, Sun YS. Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method. Thorac Cancer 2020; 11:651-658. [PMID: 31944571 PMCID: PMC7049483 DOI: 10.1111/1759-7714.13309] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/26/2019] [Accepted: 12/27/2019] [Indexed: 01/25/2023] Open
Abstract
Background The aim of the study was to develop a deep learning (DL) algorithm to evaluate the pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer. Methods A total of 302 breast cancer patients in this retrospective study were randomly divided into a training set (n = 244) and a validation set (n = 58). Tumor regions were manually delineated on each slice by two expert radiologists on enhanced T1‐weighted images. Pathological results were used as ground truth. Deep learning network contained five repetitions of convolution and max‐pooling layers and ended with three dense layers. The pre‐NAC model and post‐NAC model inputted six phases of pre‐NAC and post‐NAC images, respectively. The combined model used 12 channels from six phases of pre‐NAC and six phases of post‐NAC images. All models above included three indexes of molecular type as one additional input channel. Results The training set contained 137 non‐pCR and 107 pCR participants. The validation set contained 33 non‐pCR and 25 pCR participants. The area under the receiver operating characteristic (ROC) curve (AUC) of three models was 0.553 for pre‐NAC, 0.968 for post‐NAC and 0.970 for the combined data, respectively. A significant difference was found in AUC between using pre‐NAC data alone and combined data (P < 0.001). The positive predictive value of the combined model was greater than that of the post‐NAC model (100% vs. 82.8%, P = 0.033). Conclusion This study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre‐NAC and post‐NAC MRI data. The model performed better than using pre‐NAC data only, and also performed better than using post‐NAC data only. Key points Significant findings of the study. It achieved an AUC of 0.968 for pCR prediction. It showed a significantly greater AUC than using pre‐NAC data only. What this study adds This study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre‐NAC and post‐NAC MRI data.
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Affiliation(s)
- Yu-Hong Qu
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Hai-Tao Zhu
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Kun Cao
- Radiology, Peking University Cancer Hospital & Institute, Beijing, China
| | | | - Meng Ye
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Ying-Shi Sun
- Radiology, Peking University Cancer Hospital & Institute, Beijing, China
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Whole-lesion histogram and texture analyses of breast lesions on inline quantitative DCE mapping with CAIPIRINHA-Dixon-TWIST-VIBE. Eur Radiol 2019; 30:57-65. [PMID: 31372782 DOI: 10.1007/s00330-019-06365-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 06/17/2019] [Accepted: 07/10/2019] [Indexed: 01/01/2023]
Abstract
PURPOSE To investigate the diagnostic capability of whole-lesion (WL) histogram and texture analysis of dynamic contrast-enhanced (DCE) MRI inline-generated quantitative parametric maps using CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) to differentiate malignant from benign breast lesions and breast cancer subtypes. MATERIALS AND METHODS From February 2018 to November 2018, DCE MRI using CDTV was performed on 211 patients. The inline-generated parametric maps included Ktrans, kep, Ve, and IAUGC60. Histogram and texture features were extracted from the above parametric maps respectively based on a WL analysis. Student's t tests, one-way ANOVAs, Mann-Whitney U tests, Jonckheere-Terpstra tests, and ROC curves were used for statistical analysis. RESULTS Compared with benign breast lesions, malignant breast lesions showed significantly higher Ktrans_median, 5th percentile, entropy, and diff-entropy, IAUGC60_median, 5th percentile, entropy, and diff-entropy, kep_mean, median, 5th percentile, entropy, and diff-entropy, and Ve_95th percentile, diff-variance, and contrast, and significantly lower kep_skewness and Ve_SD, entropy, diff-entropy, and skewness (all p ≤ 0.011). The combination of all the extracted parameters yielded an AUC of 0.85 (sensitivity 76%, specificity 86%). kep_contrast showed a significant difference among different subtypes of breast cancer (p = 0.006). kep_skewness showed a significant difference between lymph node-positive and lymph node-negative breast cancer (p = 0.007). The IAGC60_5th percentile had an AUC of 0.71 (sensitivity 50%, specificity 91%) for differentiating between high- and low-proliferation groups of breast cancer. CONCLUSIONS The WL histogram and texture analyses of CDTV-DCE-derived parameters may give additional information for further evaluation of breast cancer. KEY POINTS • Inline DCE mapping with CDTV is effective and time-saving. • WL histogram and texture-extracted features could distinguish breast cancer from benign lesions accurately. • kep_contrast, kep_skewness, and IAUGC60_5th percentile could predict breast cancer subtypes, lymph node metastasis, and proliferation abilities, respectively.
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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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Ianniello C, Madelin G, Moy L, Brown R. A dual-tuned multichannel bilateral RF coil for 1 H/ 23 Na breast MRI at 7 T. Magn Reson Med 2019; 82:1566-1575. [PMID: 31148249 DOI: 10.1002/mrm.27829] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 05/06/2019] [Accepted: 05/07/2019] [Indexed: 12/25/2022]
Abstract
PURPOSE Sodium MRI has shown promise for monitoring neoadjuvant chemotherapy response in breast cancer. The purpose of this work was to build a dual-tuned bilateral proton/sodium breast coil for 7T MRI that provides sufficient SNR to enable sodium breast imaging in less than 10 minutes. METHODS The proton/sodium coil consists of 2 shielded unilateral units: 1 for each breast. Each unit consists of 3 nested layers: (1) a 3-loop solenoid for sodium excitation, (2) a 3-loop solenoid for proton excitation and signal reception, and (3) a 4-channel receive array for sodium signal reception. Benchmark measurements were performed in phantoms with and without the sodium receive array insert. In vivo images were acquired on a healthy volunteer. RESULTS The sodium receive array boosted 1.5 to 3 times the SNR compared with the solenoid. Proton SNR loss due to residual interaction with the sodium array was less than 10%. The coil enabled sodium imaging in vivo with 2.8-mm isotropic nominal resolution (~5-mm real resolution) in 9:36 minutes. CONCLUSION The coil design that we propose addresses challenges associated with sodium's low SNR from a hardware perspective and offers the opportunity to investigate noninvasively breast tumor metabolism as a function of sodium concentration in patients undergoing neoadjuvant chemotherapy.
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Affiliation(s)
- Carlotta Ianniello
- Center for Advanced Imaging Innovation and Research (CAI2R) and Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York.,The Sackler Institute of Graduate Biomedical Science, New York University School of Medicine, New York, New York
| | - Guillaume Madelin
- Center for Advanced Imaging Innovation and Research (CAI2R) and Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York.,The Sackler Institute of Graduate Biomedical Science, New York University School of Medicine, New York, New York
| | - Linda Moy
- Center for Advanced Imaging Innovation and Research (CAI2R) and Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York.,The Sackler Institute of Graduate Biomedical Science, New York University School of Medicine, New York, New York
| | - Ryan Brown
- Center for Advanced Imaging Innovation and Research (CAI2R) and Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York.,The Sackler Institute of Graduate Biomedical Science, New York University School of Medicine, New York, New York
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Wu C, Pineda F, Hormuth DA, Karczmar GS, Yankeelov TE. Quantitative analysis of vascular properties derived from ultrafast DCE-MRI to discriminate malignant and benign breast tumors. Magn Reson Med 2019; 81:2147-2160. [PMID: 30368906 PMCID: PMC6347496 DOI: 10.1002/mrm.27529] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2018] [Revised: 08/22/2018] [Accepted: 08/22/2018] [Indexed: 12/30/2022]
Abstract
PURPOSE We propose a novel methodology to integrate morphological and functional information of tumor-associated vessels to assist in the diagnosis of suspicious breast lesions. THEORY AND METHODS Ultrafast, fast, and high spatial resolution DCE-MRI data were acquired on 15 patients with suspicious breast lesions. Segmentation of the vasculature from the surrounding tissue was performed by applying a Hessian filter to the enhanced image to generate a map of the probability for each voxel to belong to a vessel. Summary measures were generated for vascular morphology, as well as the inputs and outputs of vessels physically connected to the tumor. The ultrafast DCE-MRI data was analyzed by a modified Tofts model to estimate the bolus arrival time, Ktrans (volume transfer coefficient), and vp (plasma volume fraction). The measures were compared between malignant and benign lesions via the Wilcoxon test, and then incorporated into a logistic ridge regression model to assess their combined diagnostic ability. RESULTS A total of 24 lesions were included in the study (13 malignant and 11 benign). The vessel count, Ktrans , and vp showed significant difference between malignant and benign lesions (P = 0.009, 0.034, and 0.010, area under curve [AUC] = 0.76, 0.63, and 0.70, respectively). The best multivariate logistic regression model for differentiation included the vessel count and bolus arrival time (AUC = 0.91). CONCLUSION This study provides preliminary evidence that combining quantitative characterization of morphological and functional features of breast vasculature may provide an accurate means to diagnose breast cancer.
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Affiliation(s)
- Chengyue Wu
- Department of Biomedical Engineering, The University of Texas at Austin, Texas 78712
| | - Federico Pineda
- Department of Radiology The University of Chicago, Chicago, Illinois 60637
| | - David A. Hormuth
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Texas 78712
| | | | - Thomas E. Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Texas 78712,Department of Diagnostic Medicine, The University of Texas at Austin, Texas 78712,Department of Oncology The University of Texas at Austin, Texas 78712,Institute for Computational and Engineering Sciences, The University of Texas at Austin, Texas 78712
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Enhancement of breast cancer on pre-treatment dynamic contrast-enhanced MRI using computer-aided detection is associated with response to neo-adjuvant chemotherapy. Diagn Interv Imaging 2018; 99:773-781. [DOI: 10.1016/j.diii.2018.09.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 09/18/2018] [Accepted: 09/25/2018] [Indexed: 12/14/2022]
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38
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Sharma U, Agarwal K, Sah RG, Parshad R, Seenu V, Mathur S, Gupta SD, Jagannathan NR. Can Multi-Parametric MR Based Approach Improve the Predictive Value of Pathological and Clinical Therapeutic Response in Breast Cancer Patients? Front Oncol 2018; 8:319. [PMID: 30159254 PMCID: PMC6104482 DOI: 10.3389/fonc.2018.00319] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 07/26/2018] [Indexed: 11/13/2022] Open
Abstract
The potential of total choline (tCho), apparent diffusion coefficient (ADC) and tumor volume, both individually and in combination of all these three parameters (multi-parametric approach), was evaluated in predicting both pathological and clinical responses in 42 patients with locally advanced breast cancer (LABC) enrolled for neoadjuvant chemotherapy (NACT). Patients were sequentially examined by conventional MRI; diffusion weighted imaging and in vivo proton MR spectroscopy at 4 time points (pre-therapy, after I, II, and III NACT) at 1.5 T. Miller Payne grading system was used for pathological assessment of response. Of the 42 patients, 24 were pathological responders (pR) while 18 were pathological non-responders (pNR). Clinical response determination classified 26 patients as responders (cR) while 16 as non-responders (cNR). tCho and ADC showed significant changes after I NACT, however, MR measured tumor volume showed reduction only after II NACT both in pR and cR. After III NACT, the sensitivity to detect responders was highest for MR volume (83.3% for pR and 96.2% for cR) while the specificity was highest for ADC (76.5% for pR and 100% for cR). Combination of all three parameters exhibited lower sensitivity (66.7%) than MR volume for pR prediction, however, a moderate improvement was seen in specificity (58.8%). For the prediction of clinical response, multi-parametric approach showed 84.6% sensitivity with 100% specificity compared to MR volume (sensitivity 96.2%; specificity 80%). Kappa statistics demonstrated substantial agreement of clinical response with MR volume (k = 0.78) and with multi-parametric approach (k = 0.80) while moderate agreement was seen for tCho (k = 0.48) and ADC (k = 0.46). The values of k for tCho, MR volume and ADC were 0.31, 0.38, and 0.18 indicating fair, moderate, and slight agreement, respectively with pathological response. Moderate agreement (k = 0.44) was observed between clinical and pathological responses. Our study demonstrated that both tCho and ADC are strong predictors of assessment of early pathological and clinical responses. Multi-parametric approach yielded 100% specificity in predicting clinical response. Following III NACT, MR volume emerged as highly suitable predictor for both clinical and pathological assessments. PCA demonstrated separate clusters of pR vs. pNR and cR vs. cNR at post-therapy while with some overlap at pre-therapy.
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Affiliation(s)
- Uma Sharma
- Department of NMR and MRI Facility, All India Institute of Medical Sciences, New Delhi, India
| | - Khushbu Agarwal
- Department of NMR and MRI Facility, All India Institute of Medical Sciences, New Delhi, India
| | - Rani G Sah
- Department of NMR and MRI Facility, All India Institute of Medical Sciences, New Delhi, India
| | - Rajinder Parshad
- Department of Surgical Disciplines, All India Institute of Medical Sciences, New Delhi, India
| | - Vurthaluru Seenu
- Department of Surgical Disciplines, All India Institute of Medical Sciences, New Delhi, India
| | - Sandeep Mathur
- Department of Pathology, All India Institute of Medical Sciences, New Delhi, India
| | - Siddhartha D Gupta
- Department of Pathology, All India Institute of Medical Sciences, New Delhi, India
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Pop CF, Stanciu-Pop C, Drisis S, Radermeker M, Vandemerckt C, Noterman D, Moreau M, Larsimont D, Nogaret JM, Veys I. The impact of breast MRI workup on tumor size assessment and surgical planning in patients with early breast cancer. Breast J 2018; 24:927-933. [DOI: 10.1111/tbj.13104] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 11/15/2017] [Accepted: 11/16/2017] [Indexed: 12/31/2022]
Affiliation(s)
- Catalin-Florin Pop
- Service of Surgery, Institut Jules Bordet; Université Libre de Bruxelles; Brussels Belgium
| | - Claudia Stanciu-Pop
- Department of Pathology, CHU UCL Namur; Université catholique de Louvain; Yvoir Belgium
| | - Stylianos Drisis
- Service of Radiology, Institut Jules Bordet; Université Libre de Bruxelles; Brussels Belgium
| | - Magali Radermeker
- Service of Radiology, Institut Jules Bordet; Université Libre de Bruxelles; Brussels Belgium
| | - Carine Vandemerckt
- Service of Radiology, Institut Jules Bordet; Université Libre de Bruxelles; Brussels Belgium
| | - Danielle Noterman
- Service of Surgery, Institut Jules Bordet; Université Libre de Bruxelles; Brussels Belgium
| | - Michel Moreau
- Statistics Department, Institut Jules Bordet; Université Libre de Bruxelles; Brussels Belgium
| | - Denis Larsimont
- Department of Pathology, Institut Jules Bordet; Université Libre de Bruxelles; Brussels Belgium
| | - Jean-Marie Nogaret
- Service of Surgery, Institut Jules Bordet; Université Libre de Bruxelles; Brussels Belgium
| | - Isabelle Veys
- Service of Surgery, Institut Jules Bordet; Université Libre de Bruxelles; Brussels Belgium
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Nagasaka K, Satake H, Ishigaki S, Kawai H, Naganawa S. Histogram analysis of quantitative pharmacokinetic parameters on DCE-MRI: correlations with prognostic factors and molecular subtypes in breast cancer. Breast Cancer 2018; 26:113-124. [PMID: 30069785 DOI: 10.1007/s12282-018-0899-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 07/26/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND Breast cancer heterogeneity influences poor prognoses thorough therapy resistance. This study quantitatively evaluated intratumoral heterogeneity through a histogram analysis of dynamic contrast-enhanced MRI (DCE-MRI) pharmacokinetic parameters, and determined correlations with prognostic factors and molecular subtypes. METHODS We retrospectively investigated 101 invasive ductal breast cancers from 99 women who underwent preoperative DCE-MRI between July 2012 and November 2014. Pharmacokinetic parameters (Ktrans, kep, and ve) were obtained by the Tofts model. For each parameter, the mean, standard deviation, coefficient of variation, skewness, and kurtosis values of tumor were calculated, and prognostic factors and subtypes associations were assessed. RESULTS The mean of ve was lower in cancers with high Ki-67 than in cancers with low Ki-67 (P = 0.002). The coefficient of variation of ve was higher in cancers with estrogen receptor negativity than in cancers with estrogen receptor positivity (P < 0.001). The coefficient of variation of ve was also higher in cancers with high Ki-67 than in cancers with low Ki-67 (P < 0.001). The skewness of ve was higher in cancers with high nuclear grade than in cancers with low nuclear grade (P = 0.006). Triple-negative cancers showed higher ve coefficient of variation than did those with luminal A (P < 0.001) and B (P = 0.006). CONCLUSIONS Various ve parameters correlated with breast cancer prognostic factors and molecular subtypes.
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Affiliation(s)
- Ken Nagasaka
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan.
| | - Hiroko Satake
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
| | - Satoko Ishigaki
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
| | - Hisashi Kawai
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
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Lee J, Kim SH, Kang BJ. Pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: Perfusion metrics of dynamic contrast enhanced MRI. Sci Rep 2018; 8:9490. [PMID: 29934524 PMCID: PMC6014994 DOI: 10.1038/s41598-018-27764-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 05/14/2018] [Indexed: 02/03/2023] Open
Abstract
The purpose of this study was to investigate imaging parameters predicting pathologic complete response (pCR) in pretreatment dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) in breast cancer patients who were treated with neoadjuvant chemotherapy (NAC). A total of 74 patients who received NAC followed by surgery were retrospectively reviewed. All patients underwent breast MRI before NAC. Perfusion parameters including Ktrans, Kep and Ve of tumor were measured three-dimensionally. These perfusion parameters of background parenchyma of contralateral breasts were analyzed two-dimensionally. Receiver-operating characteristic (ROC) analysis and multivariable logistic regression analysis were performed to compare the ability of perfusion parameters to predict pCR. Of 74 patients, 13 achieved pCR in final pathology. The fiftieth percentile and skewness of each perfusion parameter - Ktrans, Kep, and Ve of tumor were associated with pCR. Perfusion parameters of contralateral breast parenchyma in 2D analysis also showed predictive ability for pCR. The model combining perfusion parameters of contralateral breast background parenchyma and those of the tumor had higher predictive value than each single parameter. Thus, perfusion parameters of tumor, background parenchyma of contralateral breast and their combinations in pretreatment breast MRI allow early prediction for pCR of breast cancer.
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Affiliation(s)
- Jeongmin Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sung Hun Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
| | - Bong Joo Kang
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Imbriaco M, Cuocolo R. Does Texture Analysis of MR Images of Breast Tumors Help Predict Response to Treatment? Radiology 2018; 286:421-423. [PMID: 29356631 DOI: 10.1148/radiol.2017172454] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Massimo Imbriaco
- From the Department of Advanced Biomedical Sciences, University of Naples "Federico II," Via Pansini 5, 80131 Naples, Italy
| | - Renato Cuocolo
- From the Department of Advanced Biomedical Sciences, University of Naples "Federico II," Via Pansini 5, 80131 Naples, Italy
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Drisis S, Flamen P, Ignatiadis M, Metens T, Chao SL, Chintinne M, Lemort M. Total choline quantification measured by 1H MR spectroscopy as early predictor of response after neoadjuvant treatment for locally advanced breast cancer: The impact of immunohistochemical status. J Magn Reson Imaging 2018; 48:982-993. [PMID: 29659077 DOI: 10.1002/jmri.26042] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 03/21/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Validation of new biomarkers is essential for the early evaluation of neoadjuvant treatments. PURPOSE To determine whether measurements of total choline (tCho) by 1H spectroscopy could predict morphological or pathological complete response (pCR) of neoadjuvant treatment and whether breast cancer subgroups are related to prediction accuracy. STUDY TYPE Prospective, nonrandomized, monocentric, diagnostic study. POPULATION Sixty patients were initially included with 39 women participating in the final cohort. FIELD STRENGTH/SEQUENCE A 1.5T scanner was used for acquisition and MRS was performed using the syngo GRACE sequence. ASSESSMENT MRS and MRI examinations were performed at baseline (TP1), 24-72 hours after first chemotherapy (TP2), after the end of anthracycline treatment (TP3), and MRI only after the end of taxane treatment (TP4). Early (EMR) and late (LMR) morphological response were defined as %ΔDmax13 or %ΔDmax14, respectively. Responders were patients with %ΔDmax >30. Pathological complete response (pCR) patients achieved a residual cancer burden score of 0. STATISTICAL TESTS T-test, receiver operating characteristic (ROC) curves, multiple regression, logistic regression, one-way analysis of variance (ANOVA) analysis were used for the analysis. RESULTS At TP1 there was a significant difference between response groups for tCho1 concerning EMR prediction (P = 0.05) and pCR (P < 0.05) and for Kep 1 (P = 0.03) concerning LMR prediction. At TP2, no modification of tCho and other parameters could predict response. At TP3, ΔtCho, ΔDmax, and ΔVol could predict LMR (P < 0.05 for all parameters), pCR (P < 0.05 for all parameters), and ΔKtrans could predict only pCR (P = 0.04). Logistic regression at baseline showed the highest area under the curve (AUC) of 0.9 for prediction of pCR. The triple negative (TN) subgroup showed significantly higher tCho at baseline (P = 0.02) and higher ΔtCho levels at TP3 (P < 0.05). DATA CONCLUSION Baseline measurements of tCho in combination with clinicopathological criteria could predict non-pCR with a high AUC. Furthermore, tCho quantification for prediction of pCR was more sensitive for TN tumors. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2018;48:982-993.
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Affiliation(s)
| | - Patrick Flamen
- Nuclear Department, Institute Jules Bordet, Brussels, Belgium
| | | | - Thierry Metens
- Radiology Department, Erasme University Hospital, Brussels, Belgium
| | - Shih-Li Chao
- Radiology Department, Institute Jules Bordet, Brussels, Belgium
| | - Marie Chintinne
- Pathology Department, Institute Jules Bordet, Brussels, Belgium
| | - Marc Lemort
- Radiology Department, Institute Jules Bordet, Brussels, Belgium
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Banaie M, Soltanian-Zadeh H, Saligheh-Rad HR, Gity M. Spatiotemporal features of DCE-MRI for breast cancer diagnosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 155:153-164. [PMID: 29512495 DOI: 10.1016/j.cmpb.2017.12.015] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 11/09/2017] [Accepted: 12/12/2017] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Breast cancer is a major cause of mortality among women if not treated in early stages. Previous works developed non-invasive diagnosis methods using imaging data, focusing on specific sets of features that can be called spatial features or temporal features. However, limited set of features carry limited information, requiring complex classification methods to diagnose the disease. For non-invasive diagnosis, different imaging modalities can be used. DCE-MRI is one of the best imaging techniques that provides temporal information about the kinetics of the contrast agent in suspicious lesions along with acceptable spatial resolution. METHODS We have extracted and studied a comprehensive set of features from spatiotemporal space to obtain maximum available information from the DCE-MRI data. Then, we have applied a feature fusion technique to remove common information and extract a feature set with maximum information to be used by a simple classification method. We have also implemented conventional feature selection and classification methods and compared them with our proposed approach. RESULTS Experimental results obtained from DCE-MRI data of 26 biopsy or short-term follow-up proven patients illustrate that the proposed method outperforms alternative methods. The proposed method achieves a classification accuracy of 99% without missing any of the malignant cases. CONCLUSIONS The proposed method may help physicians determine the likelihood of malignancy in breast cancer using DCE-MRI without biopsy.
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Affiliation(s)
- Masood Banaie
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Hamid Soltanian-Zadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, USA.
| | - Hamid-Reza Saligheh-Rad
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Science, Tehran, Iran
| | - Masoumeh Gity
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Medical Imaging Center, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
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Jiang J, Xiao Z, Tang Z, Zhong Y, Qiang J. Differentiating between benign and malignant sinonasal lesions using dynamic contrast-enhanced MRI and intravoxel incoherent motion. Eur J Radiol 2017; 98:7-13. [PMID: 29279173 DOI: 10.1016/j.ejrad.2017.10.028] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 10/04/2017] [Accepted: 10/29/2017] [Indexed: 11/30/2022]
Abstract
PURPOSE To explore the value of dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion (IVIM) for distinguishing between benign and malignant sinonasal lesions and investigate the correlations between the two methods. METHODS AND MATERIALS Patients with sinonasal lesions (42 benign and 31 malignant) who underwent DCE-MRI and IVIM before confirmation by histopathology were enrolled in this prospective study. Parameters derived from DCE-MRI and IVIM were measured, the optimal cut-off values for differential diagnosis were determined, and the correlations between the two methods were evaluated. Statistical analyses were performed using the Wilcoxon rank sum test, receiver operating characteristic (ROC) curve analysis, and Spearman's rank correlation. RESULTS Significantly higher Ktrans and Kep values but lower D and f values were found in malignant lesions than in benign lesions (all p<0.001). There were no significant differences in the Ve and D* values between the two groups. The area under the curve (AUC) of Ktrans was significantly higher than those of other parameters. There was no significant difference between the AUCs of DCE-MRI and IVIM with parameters combined (p=0.86). Significant inverse but weak correlations were found between D and Ktrans (r=-0.46, p<0.001), f and Ktrans (r=-0.41, p<0.001), D and Kep (r=-0.37, p=0.008), and f and Kep (r=-0.33, p=0.004). CONCLUSIONS DCE-MRI and IVIM can effectively differentiate between benign and malignant sinonasal lesions. IVIM findings correlate with DCE-MRI results and may represent an alternative to DCE-MRI.
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Affiliation(s)
- Jingxuan Jiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai 201508, China; Department of Radiology, Eye and ENT Hospital, Fudan University, Shanghai 200031, China; Department of Radiology, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - Zebin Xiao
- Department of Radiology, Eye and ENT Hospital, Fudan University, Shanghai 200031, China
| | - Zuohua Tang
- Department of Radiology, Eye and ENT Hospital, Fudan University, Shanghai 200031, China.
| | - Yufeng Zhong
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai 201508, China; Department of Radiology, Eye and ENT Hospital, Fudan University, Shanghai 200031, China
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai 201508, China.
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Barnes SL, Sorace AG, Whisenant JG, McIntyre JO, Kang H, Yankeelov TE. DCE- and DW-MRI as early imaging biomarkers of treatment response in a preclinical model of triple negative breast cancer. NMR IN BIOMEDICINE 2017; 30:e3799. [PMID: 28915312 DOI: 10.1002/nbm.3799] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Revised: 08/02/2017] [Accepted: 08/03/2017] [Indexed: 06/07/2023]
Abstract
This work evaluates quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) parameters as early biomarkers of response in a preclinical model of triple negative breast cancer (TNBC). The standard Tofts' model of DCE-MRI returns estimates of the volume transfer constant (Ktrans ) and the extravascular extracellular volume fraction (ve ). DW-MRI returns estimates of the apparent diffusion coefficient (ADC). Mice (n = 38) were injected subcutaneously with MDA-MB-231. Tumors were grown to approximately 275 mm3 and sorted into the following groups: saline controls, low-dose Abraxane (15 mg/kg) and high-dose Abraxane (25 mg/kg). Animals were imaged at days zero, one and three. On day three, tumors were extracted for immunohistochemistry. The positive percentage change in ADC on day one was significantly higher in both treatment groups relative to the control group (p < 0.05). In addition, the positive percentage change in Ktrans was significantly higher than controls (p < 0.05) on day one for the high-dose group and on days one and three for the low-dose group. The percentage change in tumor volume was significantly different between the high-dose and control groups on day three (p = 0.006). Histology confirmed differences at day three through reduced numbers of proliferating cells (Ki67 staining) in the high-dose group (p = 0.03) and low-dose group (p = 0.052) compared with the control group. Co-immunofluorescent staining of vascular maturity [using von Willebrand Factor (vWF) and α-smooth muscle actin (α-SMA)] indicated significantly higher vascular maturation in the low-dose group compared with the controls on day three (p = 0.03), and trending towards significance in the high-dose group compared with controls on day three (p = 0.052). These results from quantitative imaging with histological validation indicate that ADC and Ktrans have the potential to serve as early biomarkers of treatment response in murine studies of TNBC.
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Affiliation(s)
- Stephanie L Barnes
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Anna G Sorace
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA
| | - Jennifer G Whisenant
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - J Oliver McIntyre
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Thomas E Yankeelov
- Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA
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Chamming's F, Ueno Y, Ferré R, Kao E, Jannot AS, Chong J, Omeroglu A, Mesurolle B, Reinhold C, Gallix B. Features from Computerized Texture Analysis of Breast Cancers at Pretreatment MR Imaging Are Associated with Response to Neoadjuvant Chemotherapy. Radiology 2017; 286:412-420. [PMID: 28980886 DOI: 10.1148/radiol.2017170143] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Purpose To evaluate whether features from texture analysis of breast cancers were associated with pathologic complete response (pCR) after neoadjuvant chemotherapy and to explore the association between texture features and tumor subtypes at pretreatment magnetic resonance (MR) imaging. Materials and Methods Institutional review board approval was obtained. This retrospective study included 85 patients with 85 breast cancers who underwent breast MR imaging before neoadjuvant chemotherapy between April 10, 2008, and March 12, 2015. Two-dimensional texture analysis was performed by using software at T2-weighted MR imaging and contrast material-enhanced T1-weighted MR imaging. Quantitative parameters were compared between patients with pCR and those with non-pCR and between patients with triple-negative breast cancer and those with non-triple-negative cancer. Multiple logistic regression analysis was used to determine independent parameters. Results Eighteen tumors (22%) were triple-negative breast cancers. pCR was achieved in 30 of the 85 tumors (35%). At univariate analysis, mean pixel intensity with spatial scaling factor (SSF) of 2 and 4 on T2-weighted images and kurtosis on contrast-enhanced T1-weighted images showed a significant difference between triple-negative breast cancer and non-triple-negative breast cancer (P = .009, .003, and .001, respectively). Kurtosis (SSF, 2) on T2-weighted images showed a significant difference between pCR and non-pCR (P = .015). At multiple logistic regression, kurtosis on T2-weighted images was independently associated with pCR in non-triple-negative breast cancer (P = .033). A multivariate model incorporating T2-weighted and contrast-enhanced T1-weighted kurtosis showed good performance for the identification of triple-negative breast cancer (area under the receiver operating characteristic curve, 0.834). Conclusion At pretreatment MR imaging, kurtosis appears to be associated with pCR to neoadjuvant chemotherapy in non-triple-negative breast cancer and may be a promising biomarker for the identification of triple-negative breast cancer. © RSNA, 2017.
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Affiliation(s)
- Foucauld Chamming's
- From the Departments of Radiology (F.C., Y.U., R.F., E.K., J.C., B.M., C.R., B.G.) and Pathology (A.O.), McGill University Health Centre, Montréal, QC, Canada; and Departments of Radiology (F.C.) and Data Processing and Statistics (A.S.J.), Université Paris Descartes Sorbonne Paris Cité, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, 20 rue Leblanc, 75908 Paris, France
| | - Yoshiko Ueno
- From the Departments of Radiology (F.C., Y.U., R.F., E.K., J.C., B.M., C.R., B.G.) and Pathology (A.O.), McGill University Health Centre, Montréal, QC, Canada; and Departments of Radiology (F.C.) and Data Processing and Statistics (A.S.J.), Université Paris Descartes Sorbonne Paris Cité, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, 20 rue Leblanc, 75908 Paris, France
| | - Romuald Ferré
- From the Departments of Radiology (F.C., Y.U., R.F., E.K., J.C., B.M., C.R., B.G.) and Pathology (A.O.), McGill University Health Centre, Montréal, QC, Canada; and Departments of Radiology (F.C.) and Data Processing and Statistics (A.S.J.), Université Paris Descartes Sorbonne Paris Cité, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, 20 rue Leblanc, 75908 Paris, France
| | - Ellen Kao
- From the Departments of Radiology (F.C., Y.U., R.F., E.K., J.C., B.M., C.R., B.G.) and Pathology (A.O.), McGill University Health Centre, Montréal, QC, Canada; and Departments of Radiology (F.C.) and Data Processing and Statistics (A.S.J.), Université Paris Descartes Sorbonne Paris Cité, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, 20 rue Leblanc, 75908 Paris, France
| | - Anne-Sophie Jannot
- From the Departments of Radiology (F.C., Y.U., R.F., E.K., J.C., B.M., C.R., B.G.) and Pathology (A.O.), McGill University Health Centre, Montréal, QC, Canada; and Departments of Radiology (F.C.) and Data Processing and Statistics (A.S.J.), Université Paris Descartes Sorbonne Paris Cité, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, 20 rue Leblanc, 75908 Paris, France
| | - Jaron Chong
- From the Departments of Radiology (F.C., Y.U., R.F., E.K., J.C., B.M., C.R., B.G.) and Pathology (A.O.), McGill University Health Centre, Montréal, QC, Canada; and Departments of Radiology (F.C.) and Data Processing and Statistics (A.S.J.), Université Paris Descartes Sorbonne Paris Cité, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, 20 rue Leblanc, 75908 Paris, France
| | - Atilla Omeroglu
- From the Departments of Radiology (F.C., Y.U., R.F., E.K., J.C., B.M., C.R., B.G.) and Pathology (A.O.), McGill University Health Centre, Montréal, QC, Canada; and Departments of Radiology (F.C.) and Data Processing and Statistics (A.S.J.), Université Paris Descartes Sorbonne Paris Cité, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, 20 rue Leblanc, 75908 Paris, France
| | - Benoît Mesurolle
- From the Departments of Radiology (F.C., Y.U., R.F., E.K., J.C., B.M., C.R., B.G.) and Pathology (A.O.), McGill University Health Centre, Montréal, QC, Canada; and Departments of Radiology (F.C.) and Data Processing and Statistics (A.S.J.), Université Paris Descartes Sorbonne Paris Cité, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, 20 rue Leblanc, 75908 Paris, France
| | - Caroline Reinhold
- From the Departments of Radiology (F.C., Y.U., R.F., E.K., J.C., B.M., C.R., B.G.) and Pathology (A.O.), McGill University Health Centre, Montréal, QC, Canada; and Departments of Radiology (F.C.) and Data Processing and Statistics (A.S.J.), Université Paris Descartes Sorbonne Paris Cité, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, 20 rue Leblanc, 75908 Paris, France
| | - Benoit Gallix
- From the Departments of Radiology (F.C., Y.U., R.F., E.K., J.C., B.M., C.R., B.G.) and Pathology (A.O.), McGill University Health Centre, Montréal, QC, Canada; and Departments of Radiology (F.C.) and Data Processing and Statistics (A.S.J.), Université Paris Descartes Sorbonne Paris Cité, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, 20 rue Leblanc, 75908 Paris, France
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Zhang Q, Yuan C, Dai W, Tang L, Shi J, Li Z, Chen M. Evaluating pathologic response of breast cancer to neoadjuvant chemotherapy with computer-extracted features from contrast-enhanced ultrasound videos. Phys Med 2017; 39:156-163. [PMID: 28690116 DOI: 10.1016/j.ejmp.2017.06.023] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 06/26/2017] [Accepted: 06/28/2017] [Indexed: 01/30/2023] Open
Abstract
PURPOSE To extract quantitative perfusion and texture features with computer assistance from contrast-enhanced ultrasound (CEUS) videos of breast cancer before and after neoadjuvant chemotherapy (NAC), and to evaluate pathologic response to NAC with these features. METHODS Forty-two CEUS videos with 140,484 images were acquired from 21 breast cancer patients pre- and post-NAC. Time-intensity curve (TIC) features were calculated including the difference between area under TIC within a tumor and that within a computer-detected reference region (AUT_T-R). Four texture features were extracted including Homogeneity and Contrast. All patients were identified as pathologic responders by Miller and Payne criteria. The features between pre- and post-treatment in these responders were statistically compared, and the discrimination between pre- and post-treatment cancers was assessed with a receiver operating characteristic (ROC) curve. RESULTS Compared with the pre-treatment cancers, the post-treatment cancers had significantly lower Homogeneity (p<0.001) and AUT_T-R (p=0.014), as well as higher Contrast (p<0.001), indicating the intratumoral contrast enhancement decreased and became more heterogeneous after NAC in responders. The combination of Homogeneity and AUT_T-R achieved an accuracy of 90.5% and area under ROC curve of 0.946 for discrimination between pre- and post-chemotherapy cancers without cross validation. The accuracy still reached as high as 85.7% under leave-one-out cross validation. CONCLUSIONS The computer-extracted CEUS features show reduced and more heterogeneous neovascularization of cancer after NAC. The features achieve high accuracy for discriminating between pre- and post-chemotherapy cancers in responders and thus are potentially valuable for tumor response evaluation in clinical practice.
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Affiliation(s)
- Qi Zhang
- Institute of Biomedical Engineering, Shanghai University, Shanghai, China; Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University), Fuzhou, China.
| | - Congcong Yuan
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Wei Dai
- Institute of Biomedical Engineering, Shanghai University, Shanghai, China
| | - Lei Tang
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Jun Shi
- Institute of Biomedical Engineering, Shanghai University, Shanghai, China
| | - Zuoyong Li
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University), Fuzhou, China
| | - Man Chen
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
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Kuchcinski G, Le Rhun E, Cortot AB, Drumez E, Duhal R, Lalisse M, Dumont J, Lopes R, Pruvo JP, Leclerc X, Delmaire C. Dynamic contrast-enhanced MR imaging pharmacokinetic parameters as predictors of treatment response of brain metastases in patients with lung cancer. Eur Radiol 2017; 27:3733-3743. [PMID: 28210799 DOI: 10.1007/s00330-017-4756-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Revised: 01/04/2017] [Accepted: 01/19/2017] [Indexed: 11/30/2022]
Abstract
OBJECTIVES To determine the diagnostic accuracy of pharmacokinetic parameters measured by dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in predicting the response of brain metastases to antineoplastic therapy in patients with lung cancer. METHODS Forty-four consecutive patients with lung cancer, harbouring 123 newly diagnosed brain metastases prospectively underwent conventional 3-T MRI at baseline (within 1 month before treatment), during the early (7-10 weeks) and midterm (5-7 months) post-treatment period. An additional DCE MRI sequence was performed during baseline and early post-treatment MRI to evaluate baseline pharmacokinetic parameters (K trans, k ep, v e, v p) and their early variation (∆K trans, ∆k ep, ∆v e, ∆v p). The objective response was judged by the volume variation of each metastasis from baseline to midterm MRI. ROC curve analysis determined the best DCE MRI parameter to predict the objective response. RESULTS Baseline DCE MRI parameters were not associated with the objective response. Early ∆K trans, ∆v e and ∆v p were significantly associated with the objective response (p = 0.02, p = 0.001 and p = 0.02, respectively). The best predictor of objective response was ∆v e with an area under the curve of 0.93 [95% CI = 0.87, 0.99]. CONCLUSIONS DCE MRI and early ∆v e may be a useful tool to predict the objective response of brain metastases in patients with lung cancer. KEY POINTS • DCE MRI could predict the response of brain metastases from lung cancer • ∆v e was the best predictor of response • DCE MRI could be used to individualize patients' follow-up.
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Affiliation(s)
- Grégory Kuchcinski
- Department of Neuroradiology, University of Lille, CHU Lille, Rue Emile Laine, F-59000, Lille, France.
| | - Emilie Le Rhun
- Department of Neurosurgery, University of Lille, CHU Lille, F-59000, Lille, France.,Department of Medical Oncology, Oscar Lambret Center, F-59000, Lille, France.,Inserm U1192-PRISM-Laboratoire de Protéomique, Réponse Inflammatoire, Spectrométrie de Masse, F-59000, Lille, France
| | - Alexis B Cortot
- Department of Thoracic Oncology, University of Lille, CHU Lille, F-59000, Lille, France
| | - Elodie Drumez
- Department of Biostatistics, University of Lille, CHU Lille, EA 2694-Santé publique: épidémiologie et qualité des soins, F-59000, Lille, France
| | - Romain Duhal
- Department of Neuroradiology, University of Lille, CHU Lille, Rue Emile Laine, F-59000, Lille, France
| | - Maxime Lalisse
- Department of Neuroradiology, University of Lille, CHU Lille, Rue Emile Laine, F-59000, Lille, France
| | - Julien Dumont
- Department of Neuroradiology, University of Lille, CHU Lille, Rue Emile Laine, F-59000, Lille, France
| | - Renaud Lopes
- Department of Neuroradiology, University of Lille, CHU Lille, Rue Emile Laine, F-59000, Lille, France
| | - Jean-Pierre Pruvo
- Department of Neuroradiology, University of Lille, CHU Lille, Rue Emile Laine, F-59000, Lille, France
| | - Xavier Leclerc
- Department of Neuroradiology, University of Lille, CHU Lille, Rue Emile Laine, F-59000, Lille, France
| | - Christine Delmaire
- Department of Neuroradiology, University of Lille, CHU Lille, Rue Emile Laine, F-59000, Lille, France
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