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Dietrich CF, Correas JM, Cui XW, Dong Y, Havre RF, Jenssen C, Jung EM, Krix M, Lim A, Lassau N, Piscaglia F. EFSUMB Technical Review - Update 2023: Dynamic Contrast-Enhanced Ultrasound (DCE-CEUS) for the Quantification of Tumor Perfusion. ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2024; 45:36-46. [PMID: 37748503 DOI: 10.1055/a-2157-2587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
Dynamic contrast-enhanced ultrasound (DCE-US) is a technique to quantify tissue perfusion based on phase-specific enhancement after the injection of microbubble contrast agents for diagnostic ultrasound. The guidelines of the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) published in 2004 and updated in 2008, 2011, and 2020 focused on the use of contrast-enhanced ultrasound (CEUS), including essential technical requirements, training, investigational procedures and steps, guidance regarding image interpretation, established and recommended clinical indications, and safety considerations. However, the quantification of phase-specific enhancement patterns acquired with ultrasound contrast agents (UCAs) is not discussed here. The purpose of this EFSUMB Technical Review is to further establish a basis for the standardization of DCE-US focusing on treatment monitoring in oncology. It provides some recommendations and descriptions as to how to quantify dynamic ultrasound contrast enhancement, and technical explanations for the analysis of time-intensity curves (TICs). This update of the 2012 EFSUMB introduction to DCE-US includes clinical aspects for data collection, analysis, and interpretation that have emerged from recent studies. The current study not only aims to support future work in this research field but also to facilitate a transition to clinical routine use of DCE-US.
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Affiliation(s)
- Christoph F Dietrich
- Department General Internal Medicine, Kliniken Hirslanden Beau Site, Salem und Permanence, Bern, Switzerland
- Zentrum der Inneren Medizin, Johann Wolfgang Goethe Universitätsklinik Frankfurt, Frankfurt, Germany
| | - Jean-Michel Correas
- Department of Adult Radiology, Assistance Publique Hôpitaux de Paris, Necker University Hospital, Paris, France
- Paris Cité University, Paris, France
- CNRS, INSERM Laboratoire d'Imagerie Biomédicale, Sorbonne Université, Paris, France
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Dong
- Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Roald Flesland Havre
- Department of Medicine, National Centre for Ultrasound in Gastroenterology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Christian Jenssen
- Department of Internal Medicine, Krankenhaus Märkisch Oderland Strausberg/ Wriezen, Wriezen, Germany
- Brandenburg Institute for Clinical Ultrasound (BICUS), Medical University Brandenburg, Neuruppin, Brandenburg, Germany
| | - Ernst Michael Jung
- Institute of Diagnostic Radiology, Interdisciplinary Ultrasound Department, University Hospital Regensburg, Regensburg, Germany
| | - Martin Krix
- Global Medical & Regulatory Affairs, Bracco Imaging, Konstanz, Germany
| | - Adrian Lim
- Department of Imaging, Imperial College London and Healthcare NHS Trust, Charing Cross Hospital Campus, London, United Kingdom of Great Britain and Northern Ireland
| | - Nathalie Lassau
- Imaging Department. Gustave Roussy cancer Campus. Villejuif, France. BIOMAPS. UMR 1281. CEA. CNRS. INSERM, Université Paris-Saclay, France
| | - Fabio Piscaglia
- Division of Internal Medicine, Hepatobiliary and Immunoallergic Diseases, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Dept of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
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Yan X, Fu X, Gui Y, Chen X, Cheng Y, Dai M, Wang W, Xiao M, Tan L, Zhang J, Shao Y, Wang H, Chang X, Lv K. Development and validation of a nomogram model based on pretreatment ultrasound and contrast-enhanced ultrasound to predict the efficacy of neoadjuvant chemotherapy in patients with borderline resectable or locally advanced pancreatic cancer. Cancer Imaging 2024; 24:13. [PMID: 38245789 PMCID: PMC10800053 DOI: 10.1186/s40644-024-00662-2] [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: 05/29/2023] [Accepted: 01/11/2024] [Indexed: 01/22/2024] Open
Abstract
OBJECTIVES To develop a nomogram using pretreatment ultrasound (US) and contrast-enhanced ultrasound (CEUS) to predict the clinical response of neoadjuvant chemotherapy (NAC) in patients with borderline resectable pancreatic cancer (BRPC) or locally advanced pancreatic cancer (LAPC). METHODS A total of 111 patients with pancreatic ductal adenocarcinoma (PDAC) treated with NAC between October 2017 and February 2022 were retrospectively enrolled. The patients were randomly divided (7:3) into training and validation cohorts. The pretreatment US and CEUS features were reviewed. Univariate and multivariate logistic regression analyses were used to determine the independent predictors of clinical response in the training cohort. Then a prediction nomogram model based on the independent predictors was constructed. The area under the curve (AUC), calibration plot, C-index and decision curve analysis (DCA) were used to assess the nomogram's performance, calibration, discrimination and clinical benefit. RESULTS The multivariate logistic regression analysis showed that the taller-than-wide shape in the longitudinal plane (odds ratio [OR]:0.20, p = 0.01), time from injection of contrast agent to peak enhancement (OR:3.64; p = 0.05) and Peaktumor/ Peaknormal (OR:1.51; p = 0.03) were independent predictors of clinical response to NAC. The predictive nomogram developed based on the above imaging features showed AUCs were 0.852 and 0.854 in the primary and validation cohorts, respectively. Good calibration was achieved in the training datasets, with C-index of 0.852. DCA verified the clinical usefulness of the nomogram. CONCLUSIONS The nomogram based on pretreatment US and CEUS can effectively predict the clinical response of NAC in patients with BRPC and LAPC; it may help guide personalized treatment.
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Affiliation(s)
- Xiaoyi Yan
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Xianshui Fu
- Department of Ultrasound, No.304 Hospital of Chinese PLA, Beijing, 100037, China
| | - Yang Gui
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Xueqi Chen
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Yuejuan Cheng
- Department of Medical Oncology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Menghua Dai
- Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Weibin Wang
- Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Mengsu Xiao
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Li Tan
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Jing Zhang
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Yuming Shao
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Huanyu Wang
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Xiaoyan Chang
- Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Ke Lv
- Department of Ultrasound, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
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3
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Panthi B, Mohamed RM, Adrada BE, Boge M, Candelaria RP, Chen H, Hunt KK, Huo L, Hwang KP, Korkut A, Lane DL, Le-Petross HC, Leung JWT, Litton JK, Pashapoor S, Perez F, Son JB, Sun J, Thompson A, Tripathy D, Valero V, Wei P, White J, Xu Z, Yang W, Zhou Z, Yam C, Rauch GM, Ma J. Longitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer. Front Oncol 2023; 13:1264259. [PMID: 37941561 PMCID: PMC10628525 DOI: 10.3389/fonc.2023.1264259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/09/2023] [Indexed: 11/10/2023] Open
Abstract
Early prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST.
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Affiliation(s)
- Bikash Panthi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Rania M. Mohamed
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Beatriz E. Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Medine Boge
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Koc University Hospital, Istanbul, Türkiye
| | - Rosalind P. Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Huiqin Chen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kelly K. Hunt
- Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Lei Huo
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Ken-Pin Hwang
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Anil Korkut
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Deanna L. Lane
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Huong C. Le-Petross
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jessica W. T. Leung
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jennifer K. Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Sanaz Pashapoor
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Frances Perez
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jia Sun
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Alastair Thompson
- Department of Surgery, Baylor College of Medicine, Houston, TX, United States
| | - Debu Tripathy
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Vicente Valero
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jason White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Zhan Xu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Wei Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Zijian Zhou
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Clinton Yam
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Gaiane M. Rauch
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Choi S, Cho SI, Jung W, Lee T, Choi SJ, Song S, Park G, Park S, Ma M, Pereira S, Yoo D, Shin S, Ock CY, Kim S. Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer. NPJ Breast Cancer 2023; 9:71. [PMID: 37648694 PMCID: PMC10469174 DOI: 10.1038/s41523-023-00577-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 08/17/2023] [Indexed: 09/01/2023] Open
Abstract
Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693-0.805) in comparison to the pathologists' scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01-1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.
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Affiliation(s)
- Sangjoon Choi
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | | | | | | | - Su Jin Choi
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea
| | | | | | | | - Minuk Ma
- Lunit Inc, Seoul, Republic of Korea
| | | | | | | | | | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea.
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea.
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5
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Yu FH, Miao SM, Li CY, Hang J, Deng J, Ye XH, Liu Y. Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. Eur Radiol 2023; 33:5634-5644. [PMID: 36976336 DOI: 10.1007/s00330-023-09555-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 02/09/2023] [Accepted: 02/19/2023] [Indexed: 03/29/2023]
Abstract
OBJECTIVES To investigate the predictive performance of the deep learning radiomics (DLR) model integrating pretreatment ultrasound imaging features and clinical characteristics for evaluating therapeutic response after neoadjuvant chemotherapy (NAC) in patients with breast cancer. METHODS A total of 603 patients who underwent NAC were retrospectively included between January 2018 and June 2021 from three different institutions. Four different deep convolutional neural networks (DCNNs) were trained by pretreatment ultrasound images using annotated training dataset (n = 420) and validated in a testing cohort (n = 183). Comparing the predictive performance of these models, the best one was selected for image-only model structure. Furthermore, the integrated DLR model was constructed based on the image-only model combined with independent clinical-pathologic variables. Areas under the curve (AUCs) of these models and two radiologists were compared by using the DeLong method. RESULTS As the optimal basic model, Resnet50 achieved an AUC and accuracy of 0.879 and 82.5% in the validation set. The integrated DLR model, yielding the highest classification performance in predicting response to NAC (AUC 0.962 and 0.939 in the training and validation cohort), outperformed the image-only model and the clinical model and also performed better than two radiologists' prediction (all p < 0.05). In addition, predictive efficacy of the radiologists was improved under the assistance of the DLR model significantly. CONCLUSION The pretreatment US-based DLR model could hold promise as a clinical guidance for predicting NAC response of patients with breast cancer, thereby providing benefit of timely treatment strategy adjustment to potential poor NAC responders. KEY POINTS • Multicenter retrospective study showed that deep learning radiomics (DLR) model based on pretreatment ultrasound image and clinical parameter achieved satisfactory prediction of tumor response to neoadjuvant chemotherapy (NAC) in breast cancer. • The integrated DLR model could become an effective tool to guide clinicians in identifying potential poor pathological responders before chemotherapy. • The predictive efficacy of the radiologists was improved under the assistance of the DLR model.
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Affiliation(s)
- Fei-Hong Yu
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shu-Mei Miao
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Cui-Ying Li
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jing Hang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jing Deng
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xin-Hua Ye
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yun Liu
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
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Rink M, Jung EM, Künzel J. The Use of Contrast-Enhanced Sonography for Therapy Monitoring of Metastatic Lymph Nodes: A Systematic Review. Curr Oncol 2023; 30:6734-6743. [PMID: 37504354 PMCID: PMC10378161 DOI: 10.3390/curroncol30070494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/06/2023] [Accepted: 07/13/2023] [Indexed: 07/29/2023] Open
Abstract
Metastatic cervical lymph nodes are a frequent finding in head and neck squamous cell carcinoma (HNSCC). If a non-surgical approach is primarily chosen, a therapy response evaluation of the primary tumor and the affected lymph nodes is necessary in the follow-up. Supplementary contrast-enhanced ultrasound (CEUS) can be used to precisely visualize the microcirculation of the target lesion in the neck, whereby malignant and benign findings differ in their uptake behavior. The same applies to many other solid tumors. For various tumor entities, it has already been shown that therapy monitoring is possible through regular contrast-enhanced sonography of the primary tumor or the affected lymph nodes. Thus, in some cases, maybe in the future, a change in therapy strategy can be achieved at an early stage in the case of non-response or, in the case of therapy success, a de-escalation of subsequent (surgical) measures can be achieved. In this paper, a systematic review of the available studies and a discussion of the potential of therapy monitoring by means of CEUS in HNSCC are presented.
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Affiliation(s)
- Maximilian Rink
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital of Regensburg, 93053 Regensburg, Germany
| | - Ernst-Michael Jung
- Department of Radiology, University Hospital of Regensburg, 93053 Regensburg, Germany
| | - Julian Künzel
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital of Regensburg, 93053 Regensburg, Germany
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7
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Hwang KP, Elshafeey NA, Kotrotsou A, Chen H, Son JB, Boge M, Mohamed RM, Abdelhafez AH, Adrada BE, Panthi B, Sun J, Musall BC, Zhang S, Candelaria RP, White JB, Ravenberg EE, Tripathy D, Yam C, Litton JK, Huo L, Thompson AM, Wei P, Yang WT, Pagel MD, Ma J, Rauch GM. A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer. Radiol Imaging Cancer 2023; 5:e230009. [PMID: 37505106 PMCID: PMC10413296 DOI: 10.1148/rycan.230009] [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: 02/08/2023] [Revised: 04/18/2023] [Accepted: 06/03/2023] [Indexed: 07/29/2023]
Abstract
Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. Keywords: MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.
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Affiliation(s)
- Ken-Pin Hwang
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Nabil A. Elshafeey
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Aikaterini Kotrotsou
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Huiqin Chen
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jong Bum Son
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Medine Boge
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Rania M. Mohamed
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Abeer H. Abdelhafez
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Beatriz E. Adrada
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Bikash Panthi
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jia Sun
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Benjamin C. Musall
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Shu Zhang
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Rosalind P. Candelaria
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jason B. White
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Elizabeth E. Ravenberg
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Debu Tripathy
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Clinton Yam
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jennifer K. Litton
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Lei Huo
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Alastair M. Thompson
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Peng Wei
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Wei T. Yang
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Mark D. Pagel
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Jingfei Ma
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
| | - Gaiane M. Rauch
- From the Departments of Imaging Physics (K.P.H., A.K., J.B.S., B.P.,
B.C.M., J.M.), Breast Imaging (N.A.E., M.B., R.M.M., A.H.A., B.E.A., R.P.C.,
W.T.Y., G.M.R.), Biostatistics (H.C., J.S., P.W.), Cancer Systems Imaging (S.Z.,
M.D.P.), Moon Shots Operations (J.B.W.), Breast Medical Oncology (E.E.R., D.T.,
C.Y.), Clinical Research (J.K.L.), Pathology (L.H.), and Abdominal Imaging
(G.M.R.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd,
Houston, TX 77030; and Division of Surgical Oncology, Baylor College of
Medicine, Houston, Tex (A.M.T.)
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8
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Early Assessment of Neoadjuvant Chemotherapy Response Using Multiparametric Magnetic Resonance Imaging in Luminal B-like Subtype of Breast Cancer Patients: A Single-Center Prospective Study. Diagnostics (Basel) 2023; 13:diagnostics13040694. [PMID: 36832182 PMCID: PMC9955433 DOI: 10.3390/diagnostics13040694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/05/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023] Open
Abstract
This study aimed to evaluate the performance of multiparametric breast magnetic resonance imaging (mpMRI) for predicting response to neoadjuvant chemotherapy (NAC) in patients with luminal B subtype breast cancer. The prospective study included thirty-five patients treated with NAC for both early and locally advanced breast cancer of the luminal B subtype at the University Hospital Centre Zagreb between January 2015 and December 2018. All patients underwent breast mpMRI before and after two cycles of NAC. Evaluation of mpMRI examinations included analysis of both morphological (shape, margins, and pattern of enhancement) and kinetic characteristics (initial signal increase and post-initial behavior of the time-signal intensity curve), which were additionally interpreted with a Göttingen score (GS). Histopathological analysis of surgical specimens included grading the tumor response based on the residual cancer burden (RCB) grading system and revealed 29 NAC responders (RCB-0 (pCR), I, II) and 6 NAC non-responders (RCB-III). Changes in GS were compared with RCB classes. A lack of GS decrease after the second cycle of NAC is associated with RCB class and non-responders to NAC.
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9
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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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10
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Guo J, Wang BH, He M, Fu P, Yao M, Jiang T. Contrast-enhanced ultrasonography for early prediction of response of neoadjuvant chemotherapy in breast cancer. Front Oncol 2022; 12:1026647. [PMID: 36531048 PMCID: PMC9753903 DOI: 10.3389/fonc.2022.1026647] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 10/18/2022] [Indexed: 07/29/2023] Open
Abstract
Neoadjuvant chemotherapy (NAC) is widely accepted as a primary treatment for inoperable or locally advanced breast cancer before definitive surgery. However, not all advanced breast cancers are sensitive to NAC. Contrast-enhanced ultrasonography (CEUS) has been considered to assess tumor response to NAC as it can effectively reflect the condition of blood perfusion and lesion size. Therefore, this study aimed to evaluate the diagnostic performance of CEUS to predict early response in different regions of interest in breast tumors under NAC treatment. This prospective study included 82 patients with advanced breast cancer. Parameters of TIC (time-intensive curve) between baseline and after the first cycle of NAC were calculated for the rate of relative change (Δ), including Δpeak, ΔTTP (time to peak), ΔRBV (regional blood volume), ΔRBF (regional blood flow) and ΔMTT (mean transit time). The responders and non-responders were distinguished by the Miller-Payne Grading (MPG) system and parameters from different regions of tumors were compared in these two groups. For ROI 1(the greatest enhancement area in the central region of the tumor), there were significant differences in Δpeak1, ΔRBV1 and ΔRBF1 between responders and non-responders. For ROI 2 (the greatest enhancement area on edge of the tumor), there were significant differences in Δpeak2 and ΔRBF2 between the groups. The Δpeak1 and ΔRBF2 showed good prediction (AUC 0.798-0.820, p ≤ 0.02) after the first cycle of NAC. When the cut-off value was 0.115, the ΔRBF2 had the highest diagnostic accuracy and the maximum NPV. Quantitative TIC parameters could be effectively used to evaluate early response to NAC in advanced breast cancer.
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Affiliation(s)
- Jiabao Guo
- Department of Ultrasound Medicine, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Bao-Hua Wang
- Department of Ultrasound Medicine, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Mengna He
- Department of Ultrasound Medicine, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Peifen Fu
- Department of Breast Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Minya Yao
- Department of Breast Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tian’an Jiang
- Department of Ultrasound Medicine, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
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11
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Effect of Neoadjuvant Chemotherapy on Angiogenesis and Cell Proliferation of Breast Cancer Evaluated by Dynamic Enhanced Magnetic Resonance Imaging. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3156093. [PMID: 35915805 PMCID: PMC9338867 DOI: 10.1155/2022/3156093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/19/2022] [Accepted: 06/18/2022] [Indexed: 11/17/2022]
Abstract
Background. Breast cancer is the uncontrolled proliferation of breast epithelial cells under the action of various carcinogenic factors. The evaluation of early efficacy of neoadjuvant chemotherapy for breast cancer is helpful to change the treatment plan in time. On this basis, dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) was used to evaluate the effects of neoadjuvant chemotherapy on angiogenesis and cell proliferation in breast cancer. Objective. To evaluate the effect of neoadjuvant chemotherapy on angiogenesis and cell proliferation of breast cancer by dynamic enhanced DCE-MRI. Method. 80 breast cancer patients were divided into the routine chemotherapy group (3 cycles) and neoadjuvant chemotherapy groups (3 cycles) of 40 cases each from January 2018 to June 2021. Based on conventional imaging, DCE-MRI was performed with Intera Achieva 3.0 TMR superconducting MR scanner before and after treatment. The quantitative indexes, MRI parameters, cell proliferation expression, and DCE-MRI angiogenesis were compared between the two groups. Result. The inhibition rate, Vepost, Ktranspre, ADC, Bax, Alexi, and Aurora in the neoadjuvant chemotherapy group were significantly higher than those in the conventional chemotherapy group (
), while Kep, Ktrans, and Nek2 were significantly lower than those in the conventional chemotherapy group (
). Vepre (cm3), Ktranspre (ml/min/100 cm3), and Ve had no significant difference (
). Conclusion. The quantitative parameters, MRI parameters, proliferation, and expression of DCE-MRI in breast cancer patients with different chemotherapy regimens are quite different. They can be applied to the diagnosis of neoadjuvant chemotherapy in breast cancer patients with angiogenesis and cell proliferation.
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Han X, Yang H, Jin S, Sun Y, Zhang H, Shan M, Cheng W. Prediction of pathological complete response to neoadjuvant chemotherapy in patients with breast cancer using a combination of contrast-enhanced ultrasound and dynamic contrast-enhanced magnetic resonance imaging. Cancer Med 2022; 12:1389-1398. [PMID: 35822639 PMCID: PMC9883403 DOI: 10.1002/cam4.5019] [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: 03/28/2022] [Revised: 06/20/2022] [Accepted: 06/29/2022] [Indexed: 02/01/2023] Open
Abstract
This study aimed to evaluate the value of dynamic contrast-enhanced ultrasound (CEUS) combined with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting pathological complete response (pCR) in patients with breast cancer receiving neoadjuvant chemotherapy (NAC). Fifty-seven female patients with breast cancer (mean age, 50.46 years; range, 32-66 years) scheduled for NAC were recruited. CEUS and DCE-MRI were performed before and after NAC. Imaging features and their changes were compared with postoperative pathological results. After the clinical differences were balanced using propensity score matching, univariate and multiple logistic regression analyses were used to derive the characteristics independently associated with pCR. Receiver operating characteristic curve analysis was performed to assess diagnostic performance. After six to eight cycles of NAC, 24 (42.1%) patients achieved pCR, while 33 (57.9%) did not. Multivariate analysis showed that enhancement order on CEUS and DCE-MRI before NAC, reduction in diameter and enhancement shape on CEUS, maximum diameter on DCE-MRI, and the type of progressive dynamic contrast enhancement after NAC were independently associated with pCR after NAC. The area under the receiver operating characteristic curve for CEUS+DCE-MRI was 0.911 (95% confidence interval, 0.826-0.997), and the specificity and positive predictive values were 87.0% and 87.5%. CEUS and DCE-MRI have the potential for assessing the pathological response to NAC in patients with breast cancer; their combination showed the best diagnostic performance. CEUS+DCE-MRI has proved beneficial for comprehensive assessment and personalizing treatment strategies for patients with breast cancer.
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Affiliation(s)
- Xue Han
- Department of UltrasoundHarbin Medical University Cancer HospitalHarbinChina
| | - Huajing Yang
- Department of UltrasoundHarbin Medical University Cancer HospitalHarbinChina
| | - Shiyang Jin
- Department of Breast SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Yunfeng Sun
- Imaging CenterHarbin Medical University Cancer HospitalHarbinChina
| | - Hongxia Zhang
- Imaging CenterHarbin Medical University Cancer HospitalHarbinChina
| | - Ming Shan
- Department of Breast SurgeryHarbin Medical University Cancer HospitalHarbinChina
| | - Wen Cheng
- Department of UltrasoundHarbin Medical University Cancer HospitalHarbinChina
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13
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Murakami W, Won Choi H, Joines MM, Hoyt A, Doepke L, McCann KE, Salamon N, Sayre J, Lee-Felker S. Quantitative Predictors of Response to Neoadjuvant Chemotherapy on Dynamic Contrast-enhanced 3T Breast MRI. JOURNAL OF BREAST IMAGING 2022; 4:168-176. [PMID: 38422427 DOI: 10.1093/jbi/wbab095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Indexed: 03/02/2024]
Abstract
OBJECTIVE To assess whether changes in quantitative parameters on breast MRI better predict pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer than change in volume. METHODS This IRB-approved retrospective study included women with newly diagnosed breast cancer who underwent 3T MRI before and during NAC from January 2013 to December 2019 and underwent surgery at our institution. Clinical data such as age, histologic diagnosis and grade, biomarker status, clinical stage, maximum index cancer dimension and volume, and surgical pathology (presence or absence of in-breast pCR) were collected. Quantitative parameters were calculated using software. Correlations between clinical features and MRI quantitative measures in pCR and non-pCR groups were assessed using univariate and multivariate logistic regression. RESULTS A total of 182 women with a mean age of 52 years (range, 26-79 years) and 187 cancers were included. Approximately 45% (85/182) of women had pCR at surgery. Stepwise multivariate regression analysis showed statistical significance for changes in quantitative parameters (increase in time to peak and decreases in peak enhancement, wash out, and Kep [efflux rate constant]) for predicting pCR. These variables in combination predicted pCR with 81.2% accuracy and an area under the curve (AUC) of 0.878. The AUCs of change in index cancer volume and maximum dimension were 0.767 and 0.613, respectively. CONCLUSION Absolute changes in quantitative MRI parameters between pre-NAC MRI and intra-NAC MRI could help predict pCR with excellent accuracy, which was greater than changes in index cancer volume and maximum dimension.
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Affiliation(s)
- Wakana Murakami
- University of California at Los Angeles David Geffen School of Medicine, Department of Radiological Sciences, Los Angeles, CA, USA
- Showa University Graduate School of Medicine, Department of Radiology, Shinagawa-ku, Tokyo, Japan
| | - Hyung Won Choi
- University of California at Los Angeles David Geffen School of Medicine, Department of Radiological Sciences, Los Angeles, CA, USA
| | - Melissa M Joines
- University of California at Los Angeles David Geffen School of Medicine, Department of Radiological Sciences, Los Angeles, CA, USA
| | - Anne Hoyt
- University of California at Los Angeles David Geffen School of Medicine, Department of Radiological Sciences, Los Angeles, CA, USA
| | - Laura Doepke
- University of California at Los Angeles David Geffen School of Medicine, Department of Radiological Sciences, Los Angeles, CA, USA
| | - Kelly E McCann
- University of California at Los Angeles David Geffen School of Medicine, Department of Medicine, Los Angeles, CA, USA
| | - Noriko Salamon
- University of California at Los Angeles David Geffen School of Medicine, Department of Radiological Sciences, Los Angeles, CA, USA
| | - James Sayre
- University of California at Los Angeles Fielding School of Public Health, Department of Biostatistics, Los Angeles, CA, USA
| | - Stephanie Lee-Felker
- University of California at Los Angeles David Geffen School of Medicine, Department of Radiological Sciences, Los Angeles, CA, USA
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14
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Correlation of breast cancer microcirculation construction with tumor stem cells (CSCs) and epithelial-mesenchymal transition (EMT) based on contrast-enhanced ultrasound (CEUS). PLoS One 2021; 16:e0261138. [PMID: 34932597 PMCID: PMC8691655 DOI: 10.1371/journal.pone.0261138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 11/24/2021] [Indexed: 01/17/2023] Open
Abstract
Objective This study is to explore the correlation between the contrast-enhanced ultrasound (CEUS) characteristics of breast cancer and the epithelial-mesenchyme transformation (EMT). Methods Totally 119 patients of breast cancer underwent CEUS. Tissues in the active area were collected and subjected to the immunohistochemical detection, PT-PCR and Western blot. Correlation analysis was conducted between the clinical pathological parameters and the CEUS indicators. Results The expression levels of CD44, N-cadherin, and β-catenin in breast cancer tissues were higher than those in adjacent tissues (P<0.05). However, the expression levels of CD24 and E-cadherin in breast cancer tissues were lower than those in adjacent tissues (P<0.05). There was no significant difference in E-cadherin mRNA and Vimentin levels between cancer and adjacent tissues (P>0.05). The expressions were up-regulated in the CSCs, with higher histological grade, lymph node metastasis, and negative estrogen receptor (ER) expression. Smaller breast tumors, with no lymph node metastasis, lower clinical stage, and positive ER expression, tended to exhibit the up-regulated epithelial phenotype. Breast tumors, with high histological grade, lymph node metastasis, high clinical staging grade, and negative ER expression, tended to exhibit the up-regulated interstitial phenotype. The peak intensity of the time-intensity curve (TIC) for the CEUS was positively correlated with the CSC marker CD44 and the interstitial phenotype marker N-cadherin. The starting time of enhancement was negatively correlated with the N-cadherin. Area under the curve was positively correlated with the expression of CD44 and N-cadherin, while negatively correlated with the epithelial phenotype marker β-catenin. The time to peak was negatively correlated with the interstitial phenotypes Vimentin and N-cadherin, with no correlation with the E-cadherin or β-catenin. Conclusion Breast cancers show the enlarged lesions after enlargement and perfusion defect for the CEUS. The fast-in pattern, high enhancement, and high perfusion in the TIC are correlated with the CSCs and EMT expressions, suggesting poor disease prognosis.
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15
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Gu L, Xie X, Guo Z, Shen W, Qian P, Jiang N, Fan Y. Dynamic contrast-enhanced magnetic resonance imaging: A novel approach to assessing treatment in locally advanced esophageal cancer patients. Niger J Clin Pract 2021; 24:1800-1807. [PMID: 34889788 DOI: 10.4103/njcp.njcp_78_21] [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: 11/04/2022]
Abstract
Aims This study aims to investigate the potential application of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict concurrent chemoradiation (CRT) in locally advanced esophageal carcinoma. Patients and Methods This study involved 33 patients with locally advanced esophageal cancer and treated with CRT. The patients underwent DCE-MRI before CRT (pre) and 3 weeks after starting CRT (mid). The patients were categorized into two groups: complete response (CR) and non-complete response (non-CR) after 3 months of treatment. The quantitative parameters of DCE-MRI (Ktrans, Kep, and Ve), the changes and ratios of parameters (ΔKtrans, ΔKep, ΔVe, rΔKtrans, rΔKep, and rΔVe), and the relative ratio in the tumor area and a normal tube wall (rKtrans, rKep, and rVe) were calculated and compared between two timeframes in two groups, respectively. Moreover, the receiver operating characteristics (ROC) statistical analysis was used to assess the above parameters. Results We divided 33 patients into two groups: 22 in the CR group and 11 in the non-CR group. During the mid-CRT phase in the CR group, both Ktrans and Kep rapidly decreased, while only Kep decreased in the non-CR group. The pre-Ktrans and pre-Kep in the CR group were substantially higher compared to the non-CR group. Moreover, the rKtrans was also apparently observed as higher at pre-CRT in the CR group compared to the non-CR group. The ROC analysis demonstrated that the pre-Ktrans could be the best parameter to evaluate the treatment performance (AUC = 0.74). Conclusion Pre-Ktrans could be a promising parameter to forecast how patients with locally advanced esophageal cancer will respond to CRT.
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Affiliation(s)
- L Gu
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University and Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Baiziting Road, Nanjing, P. R. China
| | - X Xie
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University and Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Baiziting Road, Nanjing, P. R. China
| | - Z Guo
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University and Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Baiziting Road, Nanjing, P. R. China
| | - W Shen
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University and Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Baiziting Road, Nanjing, P. R. China
| | - P Qian
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Nanjing Medical University and Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Baiziting Road, Nanjing, P. R. China
| | - N Jiang
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Nanjing Medical University and Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Baiziting Road, Nanjing, P. R. China
| | - Y Fan
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Nanjing Medical University and Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Baiziting Road, Nanjing, P. R. China
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16
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Zhao DW, Fan WJ, Meng LL, Luo YR, Wei J, Liu K, Liu G, Li JF, Zang X, Li M, Zhang XX, Ma L. Comparison of the pre-treatment functional MRI metrics' efficacy in predicting Locoregionally advanced nasopharyngeal carcinoma response to induction chemotherapy. Cancer Imaging 2021; 21:59. [PMID: 34758876 PMCID: PMC8579637 DOI: 10.1186/s40644-021-00428-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 09/10/2021] [Indexed: 02/07/2023] Open
Abstract
Background Functional MRI (fMRI) parameters analysis has been proven to be a promising tool of predicting therapeutic response to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC). The study was designed to identify and compare the value of fMRI parameters in predicting early response to IC in patients with NPC. Methods This prospective study enrolled fifty-six consecutively NPC patients treated with IC from January 2021 to May 2021. Conventional diffusion weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) protocols were performed before and after IC. Parameters maps (ADC, MD, MK, Dslow, Dfast, PF, Ktrans, Ve and Kep) of the primary tumor were calculated by the Functool post-processing software. The participants were classified as responding group (RG) and non-responding group (NRG) according to Response Evaluation Criteria in Solid Tumors 1.1. The fMRI parameters were compared before and after IC and between RG with NRG. Logistic regression analysis and ROC were performed to further identify and compare the efficacy of the parameters. Results After IC, the mean values of ADC(p < 0.001), MD(p < 0.001), Dslow(p = 0.001), PF(p = 0.030) and Ve(p = 0.003) significantly increased, while MK(p < 0.001), Dfast(p = 0.009) and Kep(p = 0.003) values decreased dramatically, while no significant difference was detected in Ktrans(p = 0.130). Compared with NRG, ADC-pre(p < 0.001), MD-pre(p < 0.001) and Dslow-pre(p = 0.002) values in RG were lower, while MK-pre(p = 0.017) values were higher. The areas under the ROC curves for the ADC-pre, MD-pre, MK-pre, Dslow-pre and PRE were 0.885, 0.855, 0.809, 0.742 and 0.912, with the optimal cutoff value of 1210 × 10− 6 mm2/s, 1010 × 10− 6 mm2/s, 832 × 10− 6, 835 × 10− 6 mm2/s and 0.799 respectively. Conclusions The pretreatment conventional DWI (ADC), DKI (MD and MK), and IVIM (Dslow) values derived from fMRI showed a promising potential in predicting the response of the primary tumor to IC in NPC patients. Trial registration This study was approved by ethics board of the Chinese PLA General Hospital, and registered on January 30, 2021, in Chinese Clinical Trial Registry (ChiCTR2100042863). Supplementary Information The online version contains supplementary material available at 10.1186/s40644-021-00428-0.
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Affiliation(s)
- Da-Wei Zhao
- Medical School of Chinese PLA, No.28 Fuxing Road, Beijing, 100853, China.,Department of Radiology, Pingjin Hospital, Characteristic Medical center of Chinese People's Armed Police Force, Tianjin, China.,Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Wen-Jun Fan
- Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China.,Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University, Foshan, China.,Armed Police Forces Corps Hospital of Henan Province, No.1 Kangfu Road, Zhengzhou, 450052, China
| | - Ling-Ling Meng
- Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yan-Rong Luo
- Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jian Wei
- Department of Otolaryngology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Kun Liu
- Department of Otolaryngology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Gang Liu
- Department of Radiology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jin-Feng Li
- Department of Radiology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiao Zang
- Department of Radiology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Meng Li
- Department of Radiology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xin-Xin Zhang
- Department of Otolaryngology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Lin Ma
- Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China.
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17
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Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nat Protoc 2021; 16:5309-5338. [PMID: 34552262 PMCID: PMC9753909 DOI: 10.1038/s41596-021-00617-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 08/12/2021] [Indexed: 02/07/2023]
Abstract
This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2-3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction-diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor's size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient's tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.
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18
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Wang J, Chu Y, Wang B, Jiang T. A Narrative Review of Ultrasound Technologies for the Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer. Cancer Manag Res 2021; 13:7885-7895. [PMID: 34703310 PMCID: PMC8523361 DOI: 10.2147/cmar.s331665] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 09/29/2021] [Indexed: 12/21/2022] Open
Abstract
The incidence and mortality rate of breast cancer (BC) in women currently ranks first worldwide, and neoadjuvant chemotherapy (NAC) is widely used in patients with BC. A variety of imaging assessment methods have been used to predict and evaluate the response to NAC. Ultrasound (US) has many advantages, such as being inexpensive and offering a convenient modality for follow-up detection without radiation emission. Although conventional grayscale US is typically used to predict the response to NAC, this approach is limited in its ability to distinguish viable tumor tissue from fibrotic scar tissue. Contrast-enhanced ultrasound (CEUS) combined with a time-intensity curve (TIC) not only provides information on blood perfusion but also reveals a variety of quantitative parameters; elastography has the potential capacity to predict NAC efficiency by evaluating tissue stiffness. Both CEUS and elastography can greatly improve the accuracy of predicting NAC responses. Other US techniques, including three-dimensional (3D) techniques, quantitative ultrasound (QUS) and US-guided near-infrared (NIR) diffuse optical tomography (DOT) systems, also have advantages in assessing NAC response. This paper reviews the different US technologies used for predicting NAC response in BC patients based on the previous literature.
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Affiliation(s)
- Jing Wang
- Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People's Republic of China
| | - Yanhua Chu
- Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People's Republic of China
| | - Baohua Wang
- Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People's Republic of China
| | - Tianan Jiang
- Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People's Republic of China
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Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11101796. [PMID: 34679494 PMCID: PMC8534713 DOI: 10.3390/diagnostics11101796] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/15/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.
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20
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Li Z, Li J, Lu X, Qu M, Tian J, Lei J. The diagnostic performance of diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging in evaluating the pathological response of breast cancer to neoadjuvant chemotherapy: A meta-analysis. Eur J Radiol 2021; 143:109931. [PMID: 34492627 DOI: 10.1016/j.ejrad.2021.109931] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/10/2021] [Accepted: 08/18/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE To evaluate and compare the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the pathological response of breast cancer to neoadjuvant chemotherapy (NAC). METHODS We searched PubMed, EMBASE, Cochrane Library, and Web of Science systematically to identify relevant studies from inception to December 2020. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess the methodological quality of the included studies. We extracted sufficient data to construct 2 × 2 tables and then used STATA 12.0 to perform data pooling, heterogeneity testing, meta-regression analysis and subgroup analysis. RESULTS A total of 41 articles were enrolled in this study, including 27 studies (2107 patients) on DCE-MRI and 23 studies (1321 patients) on DWI. The pooled sensitivity and specificity of DCE-MRI were 0.75 and 0.79, and the pooled sensitivity and specificity of DWI were 0.77 and 0.75. There was no significant difference in sensitivity (P = 0.598) and specificity (P = 0.218) between DCE-MRI and DWI. And meta-regression analysis showed that both magnetic field strength and the time of examination had significant effects on heterogeneity. CONCLUSIONS DWI might be a potential substitute for DCE-MRI in predicting the pathological response of breast cancer to NAC as there was no significant difference in the diagnostic performance between the two. However, considering that not all included studies directly compared the diagnostic performance of DWI and DCE-MRI in the same patients and the heterogeneity of the included studies, caution should be exercised in applying our results.
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Affiliation(s)
- Zhifan Li
- The first Clinical Medical College of Lanzhou University, Lanzhou 730000, China; First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Jinkui Li
- The first Clinical Medical College of Lanzhou University, Lanzhou 730000, China; First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Xingru Lu
- First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Mengmeng Qu
- The first Clinical Medical College of Lanzhou University, Lanzhou 730000, China; First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Jinhui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China; Key Laboratory of Evidence-based Medicine and Knowledge Translation of Gansu Province, Lanzhou University, Lanzhou 730000, China.
| | - Junqiang Lei
- First Hospital of Lanzhou University, Lanzhou 730000, China.
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Quantification of dynamic contrast-enhanced ultrasound (CEUS) in non-cystic breast lesions using external perfusion software. Sci Rep 2021; 11:17677. [PMID: 34480040 PMCID: PMC8417292 DOI: 10.1038/s41598-021-96137-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 08/05/2021] [Indexed: 12/14/2022] Open
Abstract
The aim of this present clinical pilot study is the display of typical perfusion results in patients with solid, non-cystic breast lesions. The lesions were characterized using contrast enhanced ultrasound (CEUS) with (i) time intensity curve analyses (TIC) and (ii) parametric color maps. The 24 asymptomatic patients included were genetically tested for having an elevated risk for breast cancer. At a center of early detection of familial ovary and breast cancer, those patients received annual MRI and grey-scale ultrasound. If lesions remained unclear or appeared even suspicious, those patients also received CEUS. CEUS was performed after intravenous application of sulfur hexafluoride microbubbles. Digital DICOM cine loops were continuously stored for one minute in PACS (picture archiving and communication system). Perfusion images and TIC analyses were calculated off-line with external perfusion software (VueBox). The lesion diameter ranged between 7 and 15 mm (mean 11 ± 3 mm). Five hypoechoic irregular lesions were scars, 6 lesions were benign and 12 lesions were highly suspicious for breast cancer with irregular enhancement at the margins and a partial wash out. In those 12 cases, histopathology confirmed breast cancer. All the suspicious lesions were correctly identified visually. For the perfusion analysis only Peak Enhancement (PE) and Area Under the Curve (AUC) added more information for correctly identifying the lesions. Typical for benign lesions is a prolonged contrast agent enhancement with lower PE and prolonged wash out, while scars are characterized typically by a reduced enhancement in the center. No differences (p = 0.428) were found in PE in the center of benign lesions (64.2 ± 28.9 dB), malignant lesions (88.1 ± 93.6 dB) and a scar (40.0 ± 17.0 dB). No significant differences (p = 0.174) were found for PE values at the margin of benign lesions (96.4 ± 144.9 dB), malignant lesions (54.3 ± 86.2 dB) or scar tissue (203.8 ± 218.9 dB). Significant differences (p < 0.001) were found in PE of the surrounding tissue when comparing benign lesions (33.6 ± 25.2 dB) to malignant lesions (15.7 ± 36.3 dB) and scars (277.2 ± 199.9 dB). No differences (p = 0.821) were found in AUC in the center of benign lesions (391.3 ± 213.7), malignant lesions (314.7 ± 643.9) and a scar (213.1 ± 124.5). No differences (p = 0.601) were found in AUC values of the margin of benign lesions (313.3 ± 372.8), malignant lesions (272.6 ± 566.4) or scar tissue (695.0 ± 360.6). Significant differences (p < 0.01) were found in AUC of the surrounding tissue for benign lesions (151.7 ± 127.8), malignant lesions (177.9 ± 1345.6) and scars (1091 ± 693.3). There were no differences in perfusion evaluation for mean transit time (mTT), rise time (RT) and time to peak (TTP) when comparing the center to the margins and the surrounding tissue. The CEUS perfusion parameters PE and AUC allow a very good assessment of the risk of malignant breast lesions and thus a downgrading of BI-RADS 4 lesions. The use of the external perfusion software (VueBox, Bracco, Milan, Italy) did not lead to any further improvement in the diagnosis of suspicious breast lesions and does appears not to have any additional diagnostic value in breast lesions.
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Han X, Jin S, Yang H, Zhang J, Huang Z, Han J, He C, Guo H, Yang Y, Shan M, Zhang G. Application of conventional ultrasonography combined with contrast-enhanced ultrasonography in the axillary lymph nodes and evaluation of the efficacy of neoadjuvant chemotherapy in breast cancer patients. Br J Radiol 2021; 94:20210520. [PMID: 34415197 PMCID: PMC9327747 DOI: 10.1259/bjr.20210520] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Objective: Axillary lymph node status assessment has always been an important issue in clinical treatment of breast cancer. However, there has been no effective method to accurately predict the pathological complete response (pCR) of axillary lymph node after neoadjuvant chemotherapy (NAC). The objective of our study was to investigate whether conventional ultrasonography combined with contrast-enhanced ultrasonography (CEUS) can be used to evaluate axillary lymph node status of breast cancer patients after NAC. Methods: A total of 74 patients who underwent NAC were recruited for the present study. Prior to and after NAC, examinations of conventional ultrasonography and CEUS were performed. After evaluating the images of conventional ultrasonography, four characteristics were recorded: lymph node medulla boundary, cortex of lymph node, lymph node hilus, and lymph node aspect ratio. Two additional imaging characteristics of CEUS were analyzed: CEUS way and CEUS pattern. Receiver operating characteristiccurve analysis was applied to evaluate their diagnostic performance. Results: After 6~8 cycles of NAC, 46 (71.9%) patients had negative axillary lymph node, and 18 (28.1%) patients turned out non-pCR. According to statistical analysis, lymph node medulla, lymph node aspect ratio and CEUS way were independently associated with pCR of axillary lymph node after NAC. The area under the curve of the prediction model with three imaging characteristics was 0.882 (95% confidence interval: 0.608–0.958), and the accuracy to predict the patients’ lymph node status was 78.1% (p < 0.01). Conclusions: Conventional ultrasonography combined with CEUS technology can accurately predict axillary lymph nodes status of breast cancer patients after NAC. Advances in knowledge: The usefulness of CEUS technology in predicting pCR after neoadjuvant chemotherapy is highlighted.
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Affiliation(s)
- Xue Han
- Department of Ultrasound, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Harbin, China
| | - Shiyang Jin
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Harbin, China
| | - Huajing Yang
- Department of Ultrasound, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Harbin, China
| | - Jinxing Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Harbin, China
| | - Zhenfeng Huang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Harbin, China
| | - Jiguang Han
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Harbin, China
| | - Chuan He
- Department of Orthopedics, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Harbin, China
| | - Hongyan Guo
- Department of Biochemistry, Qiqihar Medical University, No. 333 Bukui North Road, Qiqihar, China
| | - Yue Yang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Harbin, China
| | - Ming Shan
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Harbin, China
| | - Guoqiang Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Harbin, China
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23
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Brloznik M, Boc N, Cemazar M, Sersa G, Bosnjak M, Brezar SK, Pavlin D. Tumor perfusion evaluation using dynamic contrast-enhanced ultrasound after electrochemotherapy and IL-12 plasmid electrotransfer in murine melanoma. Sci Rep 2021; 11:13446. [PMID: 34188103 PMCID: PMC8242003 DOI: 10.1038/s41598-021-92820-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 05/24/2021] [Indexed: 11/09/2022] Open
Abstract
Electrochemotherapy with bleomycin (ECT BLM) is an effective antitumor treatment already used in clinical oncology. However, ECT alone is still considered a local antitumor therapy because it cannot induce systemic immunity. When combined with adjuvant gene electrotransfer of plasmid DNA encoding IL-12 (GET pIL-12), the combined therapy leads to a systemic effect on untreated tumors and distant metastases. Although the antitumor efficacy of both therapies alone or in combination has been demonstrated at both preclinical and clinical levels, data on the predictors of efficacy of the treatments are still lacking. Herein, we evaluated the results of dynamic contrast-enhanced ultrasound (DCE-US) as a predictive factor for ECT BLM and GET pIL-12 in murine melanoma. Melanoma B16F10 tumors grown in female C57Bl/6NCrl mice were treated with GET pIL-12 and ECT BLM. Immediately after therapy, 6 h and 1, 3, 7 and 10 days later, tumors were examined by DCE-US. Statistical analysis was performed to inspect the correlation between tumor doubling time (DT) and DCE-US measurements using semilinear regression models and Bland-Altman plots. Therapeutic groups in which DCE-US showed reduced tumor perfusion had longer tumor DTs. It was confirmed that the DCE-US parameter peak enhancement (PE), reflecting relative blood volume, had predictive value for the outcome of therapy: larger PE correlated with shorter DT. In addition, perfusion heterogeneity was also associated with outcome: tumors that had more heterogeneous perfusion had faster growth, i.e., shorter DTs. This study demonstrates that DCE-US can be used as a method to predict the efficacy of electroporation-based treatment.
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Affiliation(s)
- Maja Brloznik
- Clinic for Small Animals, Veterinary Faculty, University of Ljubljana, Gerbičeva 60, Ljubljana, Slovenia
| | - Nina Boc
- Institute of Oncology Ljubljana, Zaloška 2, Ljubljana, Slovenia
| | - Maja Cemazar
- Institute of Oncology Ljubljana, Zaloška 2, Ljubljana, Slovenia.,Faculty of Health Sciences, University of Primorska, Polje 42, Izola, Slovenia
| | - Gregor Sersa
- Institute of Oncology Ljubljana, Zaloška 2, Ljubljana, Slovenia.,Faculty of Health Sciences, University of Ljubljana, Zdravstvena 5, Ljubljana, Slovenia
| | - Masa Bosnjak
- Institute of Oncology Ljubljana, Zaloška 2, Ljubljana, Slovenia
| | - Simona Kranjc Brezar
- Institute of Oncology Ljubljana, Zaloška 2, Ljubljana, Slovenia. .,Faculty of Medicine, University of Ljubljana, Vrazov trg 2, Ljubljana, Slovenia.
| | - Darja Pavlin
- Clinic for Small Animals, Veterinary Faculty, University of Ljubljana, Gerbičeva 60, Ljubljana, Slovenia.
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24
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Brloznik M, Kranjc Brezar S, Boc N, Knific T, Cemazar M, Milevoj N, Sersa G, Tozon N, Pavlin D. Results of Dynamic Contrast-Enhanced Ultrasound Correlate With Treatment Outcome in Canine Neoplasia Treated With Electrochemotherapy and Interleukin-12 Plasmid Electrotransfer. Front Vet Sci 2021; 8:679073. [PMID: 34095282 PMCID: PMC8173043 DOI: 10.3389/fvets.2021.679073] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 04/09/2021] [Indexed: 12/21/2022] Open
Abstract
Electrochemotherapy (ECT) and/or gene electrotransfer of plasmid DNA encoding interleukin-12 (GET pIL-12) are effective treatments for canine cutaneous, subcutaneous, and maxillofacial tumors. Despite the clinical efficacy of the combined treatments of ECT and GET, data on parameters that might predict the outcome of the treatments are still lacking. This study aimed to investigate whether dynamic contrast-enhanced ultrasound (DCE-US) results of subcutaneous tumors differ between tumors with complete response (CR) and tumors without complete response (non-CR) in dogs treated with ECT and GET pIL-12. Eight dogs with a total of 12 tumor nodules treated with ECT and GET pIL-12 were included. DCE-US examinations were performed in all animals before and immediately after therapy as well as 8 h and 1, 3, and 7 days later. Clinical follow-up examinations were performed 7 and 14 days, 1 and 6 months, and 1 year after treatment. Numerous significant differences in DCE-US parameters were noted between tumors with CR and non-CR tumors; perfusion and perfusion heterogeneity were lower in CR tumors than in non-CR tumors. Therefore, studies with larger numbers of patients are needed to investigate whether DCE-US results can be used to predict treatment outcomes and to make effective decisions about the need for repeated therapy or different treatment combinations in individual patients.
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Affiliation(s)
- Maja Brloznik
- Veterinary Faculty, Small Animal Clinic, University of Ljubljana, Ljubljana, Slovenia
| | - Simona Kranjc Brezar
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia.,Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Nina Boc
- Department of Radiology, Institute of Oncology Ljubljana, Ljubljana, Slovenia
| | - Tanja Knific
- Institute of Food Safety, Feed and Environment, Veterinary Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Maja Cemazar
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia.,Faculty of Health Sciences, University of Primorska, Izola, Slovenia
| | - Nina Milevoj
- Veterinary Faculty, Small Animal Clinic, University of Ljubljana, Ljubljana, Slovenia
| | - Gregor Sersa
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia.,Faculty of Health Sciences, University of Ljubljana, Ljubljana, Slovenia
| | - Natasa Tozon
- Veterinary Faculty, Small Animal Clinic, University of Ljubljana, Ljubljana, Slovenia
| | - Darja Pavlin
- Veterinary Faculty, Small Animal Clinic, University of Ljubljana, Ljubljana, Slovenia
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25
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Peng J, Pu H, Jia Y, Chen C, Ke XK, Zhou Q. Early prediction of response to neoadjuvant chemotherapy using contrast-enhanced ultrasound in breast cancer. Medicine (Baltimore) 2021; 100:e25908. [PMID: 34106653 PMCID: PMC8133101 DOI: 10.1097/md.0000000000025908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 04/22/2021] [Indexed: 11/26/2022] Open
Abstract
Early prediction of non-response is essential in order to avoid inefficient treatments. The objective of this study was to determine the contrast-enhanced ultrasound (CEUS) for early predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients.Between March 2018 and October 2019, 93 consecutive patients with histologically proven breast cancer scheduled for NAC were enrolled. Conventional ultrasound and CEUS imaging were performed before NAC and after two cycles of NAC. CEUS parameters were compared with pathologic response. Multiple logistic regression analyses were utilized to explore CEUS parameters to predict pCR, and receiver operating characteristic analysis was used to evaluate the predictive ability.Therapeutic response was obtained from 25 (27%) patients with pCR and 68 (73%) with non-pCR. Compared to non-pCR, pCR cases have a significantly higher proportion of homogeneous enhancement feature (56% vs 14%, P < .001) and centripetal enhancement (52% vs 23%, P = .012). A significant decrease in peak intensity (PI) was observed after two cycles of NAC. Compared with non-pCR patients, the kinetic parameters PI change (PI%) was higher in pCR patients (P < .001). Multiple logistic regression demonstrated two independent predictors of pCR: internal homogeneity (odds ratio, 4.85; 95% confidence interval: 1.20-19.65; P = .027) and PI% (odds ratio, 1.08; 95% confidence interval: 1.02-1.15; P = .007). In receiver operating characteristic curve analysis, internal homogeneity and PI%, with area under curve of 0.71 and 0.84, predicted pCR with sensitivity (56%, 95%) and specificity (85%, 70%), respectively.Internal homogeneity and PI% of CEUS may be useful in the noninvasive early prediction of pCR in patients with breast cancer.
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Affiliation(s)
| | - Huan Pu
- Department of Medical Ultrasound
| | - Yan Jia
- Department of Medical Ultrasound
| | | | - Xiao-Kang Ke
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China
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26
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Dong B, Hu Q, He H, Liu Y. Prediction model that combines with multidisciplinary analysis for clinical evaluation of malignancy risk of solid breast nodules. J Int Med Res 2021; 49:3000605211004681. [PMID: 33845599 PMCID: PMC8047088 DOI: 10.1177/03000605211004681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Objective Few studies have systematically developed predictive models for clinical evaluation of the malignancy risk of solid breast nodules. We performed a retrospective review of female patients who underwent breast surgery or puncture, aiming to establish a predictive model for evaluating the clinical malignancy risk of solid breast nodules. Method Multivariable logistic regression was used to identify independent variables and establish a predictive model based on a model group (207 nodules). The regression model was further validated using a validation group (112 nodules). Results We identified six independent risk factors (X3, boundary; X4, margin; X6, resistive index; X7, S/L ratio; X9, increase of maximum sectional area; and X14, microcalcification) using multivariate analysis. The combined predictive formula for our model was: Z=−5.937 + 1.435X3 + 1.820X4 + 1.760X6 + 2.312X7 + 3.018X9 + 2.494X14. The accuracy, sensitivity, specificity, missed diagnosis rate, misdiagnosis rate, negative likelihood ratio, and positive likelihood ratio of the model were 88.39%, 90.00%, 87.80%, 10.00%, 12.20%, 7.38, and 0.11, respectively. Conclusion This predictive model is simple, practical, and effective for evaluation of the malignancy risk of solid breast nodules in clinical settings.
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Affiliation(s)
- Bin Dong
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Qiaohong Hu
- Department of ultrasonography, Zhejiang Provincial People's Hospital & People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Hongfeng He
- Department of ultrasonography, Zhejiang Provincial People's Hospital & People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Ying Liu
- Department of ultrasonography, Zhejiang Provincial People's Hospital & People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China
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27
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Huang Y, Le J, Miao A, Zhi W, Wang F, Chen Y, Zhou S, Chang C. Prediction of treatment responses to neoadjuvant chemotherapy in breast cancer using contrast-enhanced ultrasound. Gland Surg 2021; 10:1280-1290. [PMID: 33968680 DOI: 10.21037/gs-20-836] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Background Elucidation the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer is important for informing therapeutic decisions. This study aimed at evaluating the potential value of contrast-enhanced ultrasound (CEUS) parameters in predicting breast cancer responses to NAC. Methods We performed CEUS examinations before and after two cycles of NAC. Quantitative CEUS parameters [maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (mTT)], tumor diameter, and their changes were measured and compared to histopathological responses, according to the Miller-Payne Grading (MPG) system (score 1, 2, or 3: minor response; score 4 or 5: good response). Prediction models for good response were developed by multiple logistic regression analysis and internally validated through bootstrap analysis. The receiver operating characteristic (ROC) curve was used to evaluate the performance of prediction models. Results A total of 143 patients were enrolled in this study among whom 98 (68.5%) achieved a good response and while 45 (31.5%) exhibited a minor response. Several imaging variables including diameter, IMAX, changes in diameter (Δdiameter), IMAX (ΔIMAX) and TTP (ΔTTP) were found to be significantly associated with good therapeutic responses (P<0.05). The areas under the curve (AUC) increased from 0.748 to 0.841 in the multivariate model that combined CEUS parameters and molecular subtypes with a sensitivity and specificity of 0.786, 0.745, respectively. Tumor molecular subtype was the primary predictor of primary endpoint. Conclusions CEUS is a potential tool for predicting responses to NAC in locally advanced breast cancer patients. Compared to the other molecular subtypes, triple negative and HER2+/ER- subtypes are more likely to exhibit a good response to NAC.
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Affiliation(s)
- Yunxia Huang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian Le
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Aiyu Miao
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wenxiang Zhi
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Fen Wang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yaling Chen
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shichong Zhou
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Cai Chang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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28
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Shan Q, Li Z, Liu R. Pattern recognition of breast tumor based on image dynamic enhancement technique. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
At present, the diagnosis of breast tumors is affected by many factors, which leads to certain errors in the diagnosis results. Therefore, it is necessary to improve the diagnosis in combination with the actual situation. This study used the whole tumor ADC histogram to identify the heterogeneous features of benign and malignant breast lesions and used the diffusion characteristics of the whole tumor to construct a diagnostic model suitable for breast tumor image feature recognition. Simultaneously, this study combined the actual situation to construct a system framework of image enhancement algorithm based on Retinex theory, and combined image processing algorithms to improve the model. In addition, this study converted the pixel data type of the grayscale image of each color channel into a double type and converted each color channel image into a logarithmic domain. Finally, in order to study the performance of the algorithm, this study designed a comparative test for performance analysis. The research shows that the algorithm has certain clinical effects and can provide theoretical reference for subsequent related research.
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Affiliation(s)
- Qinxing Shan
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhiwei Li
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Rong Liu
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
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29
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Zhang J, Gao S, Zheng Q, Kang Y, Li J, Zhang S, Shang C, Tan X, Ren W, Ma Y. A Novel Model Incorporating Tumor Stiffness, Blood Flow Characteristics, and Ki-67 Expression to Predict Responses After Neoadjuvant Chemotherapy in Breast Cancer. Front Oncol 2020; 10:603574. [PMID: 33364197 PMCID: PMC7753215 DOI: 10.3389/fonc.2020.603574] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 11/09/2020] [Indexed: 01/04/2023] Open
Abstract
Objective To investigate the ability of tumor stiffness, tumor blood flow, and Ki-67 expression alone or in combination in predicting the pathological response to neoadjuvant chemotherapy (NACT) in breast cancer. Patients and Methods This prospective cohort study included 145 breast cancer patients treated with NACT. Tumor stiffness (maximum stiffness (Emax), mean stiffness (Emean)), blood score (BS), and their relative changes, were evaluated before (t0), during (t1-t5), and at the end of NACT (t6) by shear-wave elastography and optical imaging. Ki-67 expression was quantitatively evaluated by immunohistochemistry using core biopsy specimens obtained before NACT. Pathological responses were evaluated by residual cancer burden. The ability of tumor stiffness, BS, Ki-67, and predRCB-which combined ΔEmean (t2) (the relative changes in Emean after the second NACT cycle), BS2 (BS after the second NACT cycle), and Ki-67-in predicting tumor responses was compared using receiver operating characteristic curves and the Z-test. Results Tumor stiffness and BS decreased during NACT. ΔEmean (t2), BS2, and Ki-67 had better predictive performance than other indexes in identifying a favorable response (AUC = 0.82, 0.81, and 0.80) and resistance responses (AUC = 0.85, 0.79, and 0.84), with no significant differences between the three (p > 0.05). PredRCB had better predictive performance than any parameter alone for a favorable response (AUC = 0.90) and resistance (AUC = 0.93). Conclusion Tumor stiffness, BS, and Ki-67 expression showed good and similar abilities for predicting the pathological response to NACT, and predRCB was a significantly better predictor than each index alone. These results may help design therapeutic strategies for breast cancer patients undergoing NACT.
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Affiliation(s)
- Jing Zhang
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Song Gao
- Department of Clinical Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qiaojin Zheng
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ye Kang
- Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jianyi Li
- Department of Breast Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Shuo Zhang
- Department of Neurology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Cong Shang
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xueying Tan
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Weidong Ren
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yan Ma
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
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30
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Liu B, Sun Z, Ma WL, Ren J, Zhang GW, Wei MQ, Hou WH, Hou BX, Wei LC, Huan Y, Zheng MW. DCE-MRI Quantitative Parameters as Predictors of Treatment Response in Patients With Locally Advanced Cervical Squamous Cell Carcinoma Underwent CCRT. Front Oncol 2020; 10:585738. [PMID: 33194734 PMCID: PMC7658627 DOI: 10.3389/fonc.2020.585738] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 09/22/2020] [Indexed: 12/21/2022] Open
Abstract
Purpose To evaluate the predictive value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters in treatment response to concurrent chemoradiotherapy (CCRT) for locally advanced cervical squamous cell carcinoma (LACSC). Methods and materials LACSC patients underwent CCRT had DCE-MRI before (e0) and after 3 days of treatment (e3). Extended Tofts Linear model with a user arterial input function was adopted to generate quantitative measurements. Endothelial transfer constant (Ktrans), reflux rate (Kep), fractional extravascular extracellular space volume (Ve), and fractional plasma volume (Vp) were calculated, and percentage changes ΔKtrans, ΔKep, ΔVe, and ΔVp were computed. The correlations of these measurements with the tumor regression rate were analyzed. The predictive value of these parameters on treatment outcome was generated by the receiver operating characteristic (ROC) curve. Univariate and multivariate logistic regression analyses were conducted to find the independent variables. Results Ktrans-e0, Kep -e0, ΔKtrans, and ΔVe were positively correlated with the tumor regression rate. Mean values of Ktrans-e0, Ktrans-e3, ΔKtrans, and ΔVe were higher in the non-residual tumor group than residual tumor group and were independent prognostic factors for predicting residual tumor occurrence. Ktrans-e3 showed the highest area under the curve (AUC) for treatment response prediction. Conclusions Quantitative parameters at e0 and e3 from DCE-MRI could be used as potential indicators for predicting treatment response of LACSC.
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Affiliation(s)
- Bing Liu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhen Sun
- Department of Orthopedic, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Wan-Ling Ma
- Department of Radiology, Longgang District People's Hospital, Shenzhen, China
| | - Jing Ren
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Guang-Wen Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Meng-Qi Wei
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Wei-Huan Hou
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Bing-Xin Hou
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Li-Chun Wei
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yi Huan
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Min-Wen Zheng
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
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31
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Kim SY, Cho N, Choi Y, Shin SU, Kim ES, Lee SH, Chang JM, Moon WK. Ultrafast Dynamic Contrast-Enhanced Breast MRI: Lesion Conspicuity and Size Assessment according to Background Parenchymal Enhancement. Korean J Radiol 2020; 21:561-571. [PMID: 32323501 PMCID: PMC7183839 DOI: 10.3348/kjr.2019.0567] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 01/13/2020] [Indexed: 12/12/2022] Open
Abstract
Objective To evaluate the clinical utility of ultrafast dynamic contrast-enhanced (DCE)-MRI compared to conventional DCE-MRI by studying lesion conspicuity and size according to the level of background parenchymal enhancement (BPE). Materials and Methods This study included 360 women (median age, 54 years; range, 26–82 years) with 361 who had undergone breast MRI, including both ultrafast and conventional DCE-MRI before surgery, between January and December 2017. Conspicuity was evaluated using a five-point score. Size was measured as the single maximal diameter. The Wilcoxon signed-rank test was used to compare median conspicuity score. To identify factors associated with conspicuity, multivariable logistic regression was performed. Absolute agreement between size at MRI and histopathologic examination was assessed using the intraclass correlation coefficient (ICC). Results The median conspicuity scores were 5 at both scans, but the interquartile ranges were significantly different (5-5 at ultrafast vs. 4-5 at conventional, p < 0.001). Premenopausal status (odds ratio [OR] = 2.2, p = 0.048), non-mass enhancement (OR = 4.1, p = 0.001), moderate to marked BPE (OR = 7.5, p < 0.001), and shorter time to enhancement (OR = 0.9, p = 0.043) were independently associated with better conspicuity at ultrafast scans. Tumor size agreement between MRI and histopathologic examination was similar for both scans (ICC = 0.66 for ultrafast vs. 0.63 for conventional). Conclusion Ultrafast DCE-MRI could improve lesion conspicuity compared to conventional DCE-MRI, especially in women with premenopausal status, non-mass enhancement, moderate to marked BPE or short time to enhancement.
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Affiliation(s)
- Soo Yeon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Nariya Cho
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
| | - Yunhee Choi
- Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea
| | - Sung Ui Shin
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.,Department of Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Eun Sil Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Su Hyun Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Jung Min Chang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| | - Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
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32
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Zhang J, Tan X, Zhang X, Kang Y, Li J, Ren W, Ma Y. Efficacy of shear-wave elastography versus dynamic optical breast imaging for predicting the pathological response to neoadjuvant chemotherapy in breast cancer. Eur J Radiol 2020; 129:109098. [PMID: 32559591 DOI: 10.1016/j.ejrad.2020.109098] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 05/21/2020] [Accepted: 05/25/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE Explore the value of shear-wave elastography (SWE) parameters and dynamic optical breast imaging features for predicting pathological responses to neoadjuvant chemotherapy (NACT) in breast cancer (BC). METHOD This prospective cohort study included 91 BC patients receiving NACT. Tumor size, SWE (maximum stiffness [Emax] and mean stiffness [Emean]), blood score (BS), and oxygen score (OS) and their relative changes were collected before (t0), during (t1-t5), and after NACT (t6). The pathological response was classified according to the residual cancer burden. Relationships between tumor size, SWE stiffness, BS, and OS at t0-t6 were analyzed, and their predictive power was compared. RESULTS During six NACT cycles, tumor size, tumor stiffness, and BS decreased, and tumor OS increased. ΔEmean (t2), E2mean, BS2, and OS2 had a greater power than other indexes for predicting a favorable response (AUC = 0.79, 0.71, 0.77, 0.78) and a resistance response (0.86, 0.74, 0.71, 0.71). For the favorable response, predictive power did not differ significantly between ΔEmean (t2), E2mean, BS2, and OS2, whereas for the resistance response, ΔEmean (t2) showed better prediction than E2mean, BS2, and OS2. CONCLUSIONS SWE stiffness, BS, and OS exhibited good and similar performances in predicting a NACT favorable response, and SWE stiffness showed better performance than BS and OS in predicting NACT resistance. These results may provide an important reference for individualized treatment in BC patients receiving NACT.
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Affiliation(s)
- Jing Zhang
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, China
| | - Xueying Tan
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, China
| | - Xintong Zhang
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, China
| | - Ye Kang
- Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, China
| | - Jianyi Li
- Department of Breast Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, China
| | - Weidong Ren
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, China.
| | - Yan Ma
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, China.
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Conti A, Duggento A, Indovina I, Guerrisi M, Toschi N. Radiomics in breast cancer classification and prediction. Semin Cancer Biol 2020; 72:238-250. [PMID: 32371013 DOI: 10.1016/j.semcancer.2020.04.002] [Citation(s) in RCA: 135] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 03/30/2020] [Accepted: 04/01/2020] [Indexed: 12/15/2022]
Abstract
Breast Cancer (BC) is the common form of cancer in women. Its diagnosis and screening are usually performed through different imaging modalities such as mammography, magnetic resonance imaging and ultrasound. However, mammography and ultrasound-imaging techniques have limited sensitivity and specificity both in identifying lesions and in differentiating malign from benign lesions, especially in presence of dense breast parenchyma. Due to the higher resolution of magnetic resonance images, MRI represents the method with the higher specificity and sensitivity among all the available tools, in both lesions' identification and diagnosis. However, especially for diagnosis, even MRI has limitations that are only partially solved if combined with mammography. Unfortunately, due to the limits of all these imaging tools, in order to have a certain diagnosis, patients often receive painful and costly bioptics procedures. In this context, several computational approaches have been developed to increase sensitivity, while maintaining the same specificity, in BC diagnosis and screening. Amongst these, radiomics has been increasingly gaining ground in oncology to improve cancer diagnosis, prognosis and treatment. Radiomics derives multiple quantitative features from single or multiple medical imaging modalities, highlighting image traits which are not visible to the naked eye and hence significantly augmenting the discriminatory and predictive potential of medical imaging. This review article aims to summarize the state of the art in radiomics-based BC research. The dominating evidence extracted from the literature points towards a high potential of radiomics in disentangling malignant from benign breast lesions, classifying BC types and grades and also in predicting treatment response and recurrence risk. In the era of personalized medicine, radiomics has the potential to improve diagnosis, prognosis, prediction, monitoring, image-based intervention, and assessment of therapeutic response in BC.
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Affiliation(s)
- Allegra Conti
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Via Ardeatina, 306, 00179, Rome, Italy; Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
| | - Andrea Duggento
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
| | - Iole Indovina
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Via Ardeatina, 306, 00179, Rome, Italy; Department of Medicine and Surgery, Saint Camillus International University of Health and Medical Sciences, Rome, Italy
| | - Maria Guerrisi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States.
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Comparison of Model-Free and Model-Based Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Parameters for Predicting Breast Cancers' Response to Neoadjuvant Chemotherapy. J Comput Assist Tomogr 2020; 44:269-274. [PMID: 32195807 DOI: 10.1097/rct.0000000000001001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To prospectively compare the performance of model-based and model-free dynamic contrast-enhanced (DCE) pharmacokinetic parameters in monitoring breast cancers' early response to neoadjuvant chemotherapy (NACT). METHODS Sixty patients, with 61 pathology-proven breast cancers, were examined using DCE magnetic resonance imaging before, after the first cycle, and after full cycles of NACT. Both model-based (Ktrans and others) and model-free parameters, mainly time-intensity curve (TIC), were measured. According to Miller-Payne grading, patients were divided into response and nonresponse group. Mann-Whitney U test, Fisher exact test, multivariate logistic regression, and receiver operating characteristic curve were used in analysis. RESULTS After the first cycle, among all the parameters, Ktrans and TIC were strongly associated with tumors' early response. There was no significant difference between the areas under receiver operating characteristic curve of Ktrans and TIC (0.768, 0.852, respectively). CONCLUSIONS Model-based and model-free DCE parameters, especially Ktrans and TIC, have similar performance in predicting the efficacy of NACT for breast cancers.
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35
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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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Choe YH. Characteristics of Recent Articles Published in the Korean Journal of Radiology Based on the Citation Frequency. Korean J Radiol 2020; 21:1284. [PMID: 33236548 PMCID: PMC7689137 DOI: 10.3348/kjr.2020.1322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 11/05/2020] [Accepted: 11/05/2020] [Indexed: 11/24/2022] Open
Affiliation(s)
- Yeon Hyeon Choe
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- HVSI Imaging Center, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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Song SE, Cho KR, Seo BK, Woo OH, Jung SP, Sung DJ. Kinetic Features of Invasive Breast Cancers on Computer-Aided Diagnosis Using 3T MRI Data: Correlation with Clinical and Pathologic Prognostic Factors. Korean J Radiol 2019; 20:411-421. [PMID: 30799572 PMCID: PMC6389817 DOI: 10.3348/kjr.2018.0587] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 11/30/2018] [Indexed: 11/15/2022] Open
Abstract
Objective To investigate the correlation of kinetic features of breast cancers on computer-aided diagnosis (CAD) of preoperative 3T magnetic resonance imaging (MRI) data and clinical-pathologic factors in breast cancer patients. Materials and Methods Between July 2016 and March 2017, 85 patients (mean age, 54 years; age range, 35–81 years) with invasive breast cancers (mean, 1.8 cm; range, 0.8–4.8 cm) who had undergone MRI and surgery were retrospectively enrolled. All magnetic resonance images were processed using CAD, and kinetic features of tumors were acquired. The relationships between kinetic features and clinical-pathologic factors were assessed using Spearman correlation test and binary logistic regression analysis. Results Peak enhancement and angio-volume were significantly correlated with histologic grade, Ki-67 index, and tumor size: r = 0.355 (p = 0.001), r = 0.330 (p = 0.002), and r = 0.231 (p = 0.033) for peak enhancement, r = 0.410 (p = 0.005), r = 0.341 (p < 0.001), and r = 0.505 (p < 0.001) for angio-volume. Delayed-plateau component was correlated with Ki-67 (r = 0.255 [p = 0.019]). In regression analysis, higher peak enhancement was associated with higher histologic grade (odds ratio [OR] = 1.004; 95% confidence interval [CI]: 1.001–1.008; p = 0.024), and higher delayed-plateau component and angio-volume were associated with higher Ki-67 (OR = 1.051; 95% CI: 1.011–1.094; p = 0.013 for delayed-plateau component, OR = 1.178; 95% CI: 1.023–1.356; p = 0.023 for angio-volume). Conclusion Of the CAD-assessed kinetic features, higher peak enhancement may correlate with higher histologic grade, and higher delayed-plateau component and angio-volume correlate with higher Ki-67 index. These results support the clinical application of kinetic features in prognosis assessment.
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Affiliation(s)
- Sung Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Kyu Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea.
| | - Bo Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan, Korea
| | - Ok Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Seung Pil Jung
- Department of Surgery, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Deuk Jae Sung
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
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Emerging Functional Imaging Biomarkers of Tumour Responses to Radiotherapy. Cancers (Basel) 2019; 11:cancers11020131. [PMID: 30678055 PMCID: PMC6407112 DOI: 10.3390/cancers11020131] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 01/11/2019] [Accepted: 01/13/2019] [Indexed: 12/11/2022] Open
Abstract
Tumour responses to radiotherapy are currently primarily assessed by changes in size. Imaging permits non-invasive, whole-body assessment of tumour burden and guides treatment options for most tumours. However, in most tumours, changes in size are slow to manifest and can sometimes be difficult to interpret or misleading, potentially leading to prolonged durations of ineffective treatment and delays in changing therapy. Functional imaging techniques that monitor biological processes have the potential to detect tumour responses to treatment earlier and refine treatment options based on tumour biology rather than solely on size and staging. By considering the biological effects of radiotherapy, this review focusses on emerging functional imaging techniques with the potential to augment morphological imaging and serve as biomarkers of early response to radiotherapy.
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Choe YH. A Glimpse on Trends and Characteristics of Recent Articles Published in the Korean Journal of Radiology. Korean J Radiol 2019; 20:1555-1561. [PMID: 31854145 PMCID: PMC6923209 DOI: 10.3348/kjr.2019.0928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- Yeon Hyeon Choe
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea; Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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