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Zheng L, Yang LX, Liu JY, Jiang Z, Li XW, Pu PP. Correlation and predictive value of pathological complete response and ultrasound characteristic parameters in neoadjuvant chemotherapy for breast. World J Clin Cases 2024; 12:5320-5328. [DOI: 10.12998/wjcc.v12.i23.5320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 05/12/2024] [Accepted: 06/11/2024] [Indexed: 07/05/2024] Open
Abstract
BACKGROUND Breast cancer ranks as one of the most prevalent malignant tumors among women, significantly endangering their health and lives. While radical surgery has been a pivotal method for halting disease progression, it alone is insufficient for enhancing the quality of life for patients.
AIM To investigate the correlation between ultrasound characteristic parameters of breast cancer lesions and clinical efficacy in patients undergoing neoadjuvant chemotherapy (NAC).
METHODS Employing a case-control study design, this research involved 178 breast cancer patients treated with NAC at our hospital from July 2019 to June 2022. According to the Miller-Payne grading system, the pathological response, i.e. efficacy, of the NAC in the initial breast lesion after NAC was evaluated. Of these, 59 patients achieved a pathological complete response (PCR), while 119 did not (non-PCR group). Ultrasound characteristics prior to NAC were compared between these groups, and the association of various factors with NAC efficacy was analyzed using univariate and multivariate approaches.
RESULTS In the PCR group, the incidence of posterior echo attenuation, lesion diameter ≥ 2.0 cm, and Alder blood flow grade ≥ II were significantly lower compared to the non-PCR group (P < 0.05). The area under the curve values for predicting NAC efficacy using posterior echo attenuation, lesion diameter, and Alder grade were 0.604, 0.603, and 0.583, respectively. Also, rates of pathological stage II, lymph node metastasis, vascular invasion, and positive Ki-67 expression were significantly lower in the PCR group (P < 0.05). Logistic regression analysis identified posterior echo attenuation, lesion diameter ≥ 2.0 cm, Alder blood flow grade ≥ II, pathological stage III, vascular invasion, and positive Ki-67 expression as independent predictors of poor response to NAC in breast cancer patients (P < 0.05).
CONCLUSION While ultrasound characteristics such as posterior echo attenuation, lesion diameter ≥ 2.0 cm, and Alder blood flow grade ≥ II exhibit limited predictive value for NAC efficacy, they are significantly associated with poor response to NAC in breast cancer patients.
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Affiliation(s)
- Lei Zheng
- Department of Breast Surgery, Xingtai People's Hospital, Xingtai 054001, Hebei Province, China
| | - Li-Xian Yang
- Department of Breast Surgery, Xingtai People's Hospital, Xingtai 054001, Hebei Province, China
| | - Jing-Yi Liu
- Department of Breast Surgery, Xingtai People's Hospital, Xingtai 054001, Hebei Province, China
| | - Zhe Jiang
- Department of Medical Imaging, Xingtai People´s Hospital, Xingtai 054001, Hebei Province, China
| | - Xiao-Wei Li
- Department of Breast Surgery, Xingtai People's Hospital, Xingtai 054001, Hebei Province, China
| | - Peng-Peng Pu
- Department of Breast Surgery, Xingtai People's Hospital, Xingtai 054001, Hebei Province, China
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Yao Y, Mou F, Kong J, Liu X. Kinetic Heterogeneity Improves the Specificity of Dynamic Enhanced MRI in Differentiating Benign and Malignant Breast Tumours. Acad Radiol 2024; 31:812-821. [PMID: 37980221 DOI: 10.1016/j.acra.2023.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/27/2023] [Accepted: 10/03/2023] [Indexed: 11/20/2023]
Abstract
RATIONALE AND OBJECTIVES To investigate whether kinetic heterogeneity in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) improves the specificity of breast cancer (BC) diagnosis. MATERIALS AND METHODS The DCE-MRI data of patients with benign breast tumours and BC from June 2020 to July 2022 were retrospectively evaluated. MATLAB and SPM were used to determine six major kinetic parameters: peak, enhancement volume, heterogeneity, as well as persistent, plateau, and washout proportions. Continuous variables were compared using the Student's t-test or Mann-Whitney U tests, and categorical variables were compared using the chi-square or Fisher's exact tests. Receiver operating characteristic curves were plotted. The intraclass correlation coefficient (ICC) was used to evaluate agreement between the two observers. Multivariate logistic regression analysis was conducted to calculate the odds ratios (ORs) with 95% confidence intervals (CIs) for the association between benign and malignant breast tumours. RESULTS In total, 147 patients (mean age, 47 years old) were included in the study, 76 of whom had BC. Data analysis by the two observers showed good consistency in the peak, enhancement volume, persistent proportion, plateau proportion, washout proportion, and heterogeneity, with ICCs of 0.865, 0.988, 0.906, 0.940, 0.740, and 0.867, respectively (p < 0.001). In the DCE kinetic analysis, differences in all the six kinetic parameters were statistically significant (p < 0.05). The area under the curve for heterogeneity was 0.92 (95% CI:0.88,0.97), and the sensitivity and specificity were 0.895 and 0.845, respectively. Multivariate logistic regression analysis showed that heterogeneity was an independent predictor of BC compared to benign breast tumours (OR=2.020; 95% CI:1.316, 3.100; p = 0.001). CONCLUSION The kinetic heterogeneity of DCE-MRI can effectively distinguish between benign and malignant breast tumours and improve the specificity of BC diagnosis.
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Affiliation(s)
- Yiming Yao
- Department of Radiology, Chongqing University Three Gorges Hospital, 165 Xincheng Road, Chongqing, Wanzhou 404000, China (Y,Y., F.M., J.K., X.L.)
| | - Fangsheng Mou
- Department of Radiology, Chongqing University Three Gorges Hospital, 165 Xincheng Road, Chongqing, Wanzhou 404000, China (Y,Y., F.M., J.K., X.L.)
| | - Junfeng Kong
- Department of Radiology, Chongqing University Three Gorges Hospital, 165 Xincheng Road, Chongqing, Wanzhou 404000, China (Y,Y., F.M., J.K., X.L.)
| | - Xinghua Liu
- Department of Radiology, Chongqing University Three Gorges Hospital, 165 Xincheng Road, Chongqing, Wanzhou 404000, China (Y,Y., F.M., J.K., X.L.).
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Elsayed B, Alksas A, Shehata M, Mahmoud A, Zaky M, Alghandour R, Abdelwahab K, Abdelkhalek M, Ghazal M, Contractor S, El-Din Moustafa H, El-Baz A. Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review. Cancers (Basel) 2023; 15:5288. [PMID: 37958461 PMCID: PMC10648987 DOI: 10.3390/cancers15215288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023] Open
Abstract
Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.
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Affiliation(s)
- Basma Elsayed
- Biomedical Engineering Program, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt;
| | - Ahmed Alksas
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Mohamed Shehata
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Ali Mahmoud
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Mona Zaky
- Diagnostic Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt;
| | - Reham Alghandour
- Medical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt;
| | - Khaled Abdelwahab
- Surgical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt; (K.A.); (M.A.)
| | - Mohamed Abdelkhalek
- Surgical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt; (K.A.); (M.A.)
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA;
| | | | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
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Mattusch C, Bick U, Michallek F. Development and validation of a four-dimensional registration technique for DCE breast MRI. Insights Imaging 2023; 14:17. [PMID: 36701001 PMCID: PMC9880129 DOI: 10.1186/s13244-022-01362-w] [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: 07/27/2022] [Accepted: 12/19/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Patient motion can degrade image quality of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) due to subtraction artifacts. By objectively and subjectively assessing the impact of principal component analysis (PCA)-based registration on pretreatment DCE-MRIs of breast cancer patients, we aim to validate four-dimensional registration for DCE breast MRI. RESULTS After applying a four-dimensional, PCA-based registration algorithm to 154 pretreatment DCE-MRIs of histopathologically well-described breast cancer patients, we quantitatively determined image quality in unregistered and registered images. For subjective assessment, we ranked motion severity in a clinical reading setting according to four motion categories (0: no motion, 1: mild motion, 2: moderate motion, 3: severe motion with nondiagnostic image quality). The median of images with either moderate or severe motion (median category 2, IQR 0) was reassigned to motion category 1 (IQR 0) after registration. Motion category and motion reduction by registration were correlated (Spearman's rho: 0.83, p < 0.001). For objective assessment, we performed perfusion model fitting using the extended Tofts model and calculated its volume transfer coefficient Ktrans as surrogate parameter for motion artifacts. Mean Ktrans decreased from 0.103 (± 0.077) before registration to 0.097 (± 0.070) after registration (p < 0.001). Uncertainty in perfusion quantification was reduced by 7.4% after registration (± 15.5, p < 0.001). CONCLUSIONS Four-dimensional, PCA-based image registration improves image quality of breast DCE-MRI by correcting for motion artifacts in subtraction images and reduces uncertainty in quantitative perfusion modeling. The improvement is most pronounced when moderate-to-severe motion artifacts are present.
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Affiliation(s)
- Chiara Mattusch
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Ulrich Bick
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Florian Michallek
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Charitéplatz 1, 10117 Berlin, Germany ,grid.260026.00000 0004 0372 555XDepartment of Radiology, Mie University Graduate School of Medicine, Tsu, Japan
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Caballo M, Sanderink WBG, Han L, Gao Y, Athanasiou A, Mann RM. Four-Dimensional Machine Learning Radiomics for the Pretreatment Assessment of Breast Cancer Pathologic Complete Response to Neoadjuvant Chemotherapy in Dynamic Contrast-Enhanced MRI. J Magn Reson Imaging 2023; 57:97-110. [PMID: 35633290 PMCID: PMC10083908 DOI: 10.1002/jmri.28273] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/13/2022] [Accepted: 05/13/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Breast cancer response to neoadjuvant chemotherapy (NAC) is typically evaluated through the assessment of tumor size reduction after a few cycles of NAC. In case of treatment ineffectiveness, this results in the patient suffering potentially severe secondary effects without achieving any actual benefit. PURPOSE To identify patients achieving pathologic complete response (pCR) after NAC by spatio-temporal radiomic analysis of dynamic contrast-enhanced (DCE) MRI images acquired before treatment. STUDY TYPE Single-center, retrospective. POPULATION A total of 251 DCE-MRI pretreatment images of breast cancer patients. FIELD STRENGTH/SEQUENCE 1.5 T/3 T, T1-weighted DCE-MRI. ASSESSMENT Tumor and peritumoral regions were segmented, and 348 radiomic features that quantify texture temporal variation, enhancement kinetics heterogeneity, and morphology were extracted. Based on subsets of features identified through forward selection, machine learning (ML) logistic regression models were trained separately with all images and stratifying on cancer molecular subtype and validated with leave-one-out cross-validation. STATISTICAL TESTS Feature significance was assessed using the Mann-Whitney U-test. Significance of the area under the receiver operating characteristics (ROC) curve (AUC) of the ML models was assessed using the associated 95% confidence interval (CI). Significance threshold was set to 0.05, adjusted with Bonferroni correction. RESULTS Nine features related to texture temporal variation and enhancement kinetics heterogeneity were significant in the discrimination of cases achieving pCR vs. non-pCR. The ML models achieved significant AUC of 0.707 (all cancers, n = 251, 59 pCR), 0.824 (luminal A, n = 107, 14 pCR), 0.823 (luminal B, n = 47, 15 pCR), 0.844 (HER2 enriched, n = 25, 11 pCR), 0.803 (triple negative, n = 72, 19 pCR). DATA CONCLUSIONS Differences in imaging phenotypes were found between complete and noncomplete responders. Furthermore, ML models trained per cancer subtype achieved high performance in classifying pCR vs. non-pCR cases. They may, therefore, have potential to help stratify patients according to the level of response predicted before treatment, pending further validation with larger prospective cohorts. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 4.
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Affiliation(s)
- Marco Caballo
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Luyi Han
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Yuan Gao
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | | | - Ritse M Mann
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
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Muacevic A, Adler JR. Mammographic and Ultrasonographic Imaging Analysis for Neoadjuvant Chemotherapy Evaluation: Volume Reduction Indexes That Correlate With Pathological Complete Response. Cureus 2022; 14:e29960. [PMID: 36225243 PMCID: PMC9534532 DOI: 10.7759/cureus.29960] [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] [Accepted: 10/05/2022] [Indexed: 12/03/2022] Open
Abstract
INTRODUCTION We aimed to evaluate volume reduction in digital mammography (DM) and ultrasound (US) for neoadjuvant chemotherapy (NAC) evaluation, with breast cancer-specific survival and pathological complete response (pCR) associations. METHODS This is a retrospective observational cohort study analyzing recorded images in 122 selected subjects out of which 569 patients presented with advanced breast cancers. Spearman's correlation and generalized estimating equations (GEE) compared volume reduction on DM and US between pCR and non-pCR after NAC with post-surgical anatomopathology. Cox regression and Kaplan-Meier curves analyzed associations between cancer-specific survival, pCR, and volume reductions. RESULTS A total of 34.4% (N=42) obtained pCR and 65.6% (N=80) did not. Minimum percentage indexes needed to correlate with pCR over time were, at least, 28.9% for DM (p=0.006) and 10.36% for US (p=0.046), with high specificity (US=98%, DM=93%) but low sensitivity (US=7%, DM=18%). Positive predictive values were 82% (DM) and 86% (US) and negative predictive values were 37% (DM) and 36% (US). Cox regression and Kaplan-Meier curves demonstrated associations of breast cancer-specific survival with pCR (Cox regression coefficient {B}=0.209, CI 95%=0.048-0.914, p=0.038). CONCLUSIONS At least 28.9% of volume reduction on DM and 10.36% of volume reduction on US are correlated with pCR. Furthermore, pCR was associated with breast cancer-specific survival after NAC in volumetric morphological imaging analysis.
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Ross BD, Chenevert TL, Meyer CR. Retrospective Registration in Molecular Imaging. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00080-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Whisenant JG, Williams JM, Kang H, Arlinghaus LR, Abramson RG, Abramson VG, Fakhoury K, Chakravarthy AB, Yankeelov TE. Quantitative Comparison of Prone and Supine PERCIST Measurements in Breast Cancer. ACTA ACUST UNITED AC 2020; 6:170-176. [PMID: 32548293 PMCID: PMC7289244 DOI: 10.18383/j.tom.2020.00002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Positron emission tomography (PET) is typically performed in the supine position. However, breast magnetic resonance imaging (MRI) is performed in prone, as this improves visibility of deep breast tissues. With the emergence of hybrid scanners that integrate molecular information from PET and functional information from MRI, it is of great interest to determine if the prognostic utility of prone PET is equivalent to supine. We compared PERCIST (PET Response Criteria in Solid Tumors) measurements between prone and supine FDG-PET in patients with breast cancer and the effect of orientation on predicting pathologic complete response (pCR). In total, 47 patients were enrolled and received up to 6 cycles of neoadjuvant therapy. Prone and supine FDG-PET were performed at baseline (t0; n = 46), after cycle 1 (t1; n = 1) or 2 (t2; n = 10), or after all neoadjuvant therapy (t3; n = 19). FDG uptake was quantified by maximum and peak standardized uptake value (SUV) with and without normalization to lean body mass; that is, SUVmax, SUVpeak, SULmax, and SULpeak. PERCIST measurements were performed for each paired baseline and post-treatment scan. Receiver operating characteristic analysis for the prediction of pCR was performed using logistic regression that included age and tumor size as covariates. SUV and SUL metrics were significantly different between orientation (P < .001), but were highly correlated (P > .98). Importantly, no differences were observed with the PERCIST measurements (P > .6). Overlapping 95% confidence intervals for the receiver operating characteristic analysis suggested no difference at predicting pCR. Therefore, prone and supine PERCIST in this data set were not statistically different.
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Affiliation(s)
- Jennifer G Whisenant
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.,Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Jason M Williams
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
| | - Hakmook Kang
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN.,Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Lori R Arlinghaus
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
| | - Richard G Abramson
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Vandana G Abramson
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.,Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Kareem Fakhoury
- Department of Radiation Oncology, University of Colorado Cancer Center-Anschutz Medical Campus, Aurora, CO
| | - A Bapsi Chakravarthy
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.,Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN; and
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences; Livestrong Cancer Institutes; Department of Biomedical Engineering; Department of Diagnostic Medicine; and Department of Oncology, The University of Texas, Austin, TX
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El Adoui M, Drisis S, Benjelloun M. Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images. Int J Comput Assist Radiol Surg 2020; 15:1491-1500. [DOI: 10.1007/s11548-020-02209-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 06/01/2020] [Indexed: 12/13/2022]
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