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Chikarmane SA, Smith S. Background Parenchymal Enhancement: A Comprehensive Update. Radiol Clin North Am 2024; 62:607-617. [PMID: 38777537 DOI: 10.1016/j.rcl.2023.12.013] [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] [Indexed: 05/25/2024]
Abstract
Breast MR imaging is a complementary screening tool for patients at high risk for breast cancer and has been used in the diagnostic setting. Normal enhancement of breast tissue on MR imaging is called breast parenchymal enhancement (BPE), which occurs after administration of an intravenous contrast agent. BPE varies widely due to menopausal status, use of exogenous hormones, and breast cancer treatment. Degree of BPE has also been shown to influence breast cancer risk and may predict treatment outcomes. The authors provide a comprehensive update on BPE with review of the recent literature.
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Affiliation(s)
- Sona A Chikarmane
- Breast Imaging Division, Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA.
| | - Sharon Smith
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
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Chen K, Yu C, Pan J, Xu Y, Luo Y, Yang T, Yang X, Xie L, Zhang J, Zhuo R. Prediction of the Nottingham prognostic index and molecular subtypes of breast cancer through multimodal magnetic resonance imaging. Magn Reson Imaging 2024; 108:168-175. [PMID: 38408689 DOI: 10.1016/j.mri.2024.02.012] [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: 07/23/2023] [Revised: 02/19/2024] [Accepted: 02/21/2024] [Indexed: 02/28/2024]
Abstract
PURPOSE To explore the ability of intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and background parenchyma enhancement (BPE) to predict the Nottingham prognostic index (NPI) and molecular subtypes of breast cancer (BC). MATERIALS AND METHODS In this study, 93 patients with BC were included, and they all underwent DKI, IVIM and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) examinations. The corresponding mean kurtosis value (MK), pure diffusion (MD), perfusion fraction (f), pseudo diffusion coefficient (D*), true diffusion coefficient (D), and BPE were measured. We used logistic regression analysis to investigate the relevance between the NPI, molecular subtypes and variables. The diagnostic efficacy was analyzed using receiver operating characteristic curves (ROC). RESULTS The MD and D values of the high-level NPI group were significantly lower than those of the low-level NPI group (p < 0.01), and the f value of the high-level NPI group was obviously higher than that of low-level NPI group (p < 0.001). The area under curve (AUC) of the combined model (f + D) was 0.824. Comparing with non-Luminal subtypes, the Luminal subtypes showed obviously lower MK, f and D*, and the AUC of the combined model (MK + f + D*) was 0.785. In comparison to other subtypes, the MK and D* values of triple-negative subtype were higher than other subtypes, and the combined model (MK + D*) represented an AUC of 0.865. CONCLUSION The quantitative parameters of DKI and IVIM have vital value in predicting the NPI and molecular subtypes of BC, while BPE could not provide additional information. Besides, these combined models can obviously improve the prediction performance.
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Affiliation(s)
- Kewei Chen
- Department of Radiology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, China; Department of Radiology, Women and Children's Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Chengxin Yu
- Department of Radiology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, China.
| | - Junlong Pan
- Department of Radiology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, China
| | - Yaqia Xu
- Department of Radiology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, China
| | - Yuqing Luo
- Department of Radiology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, China
| | - Ting Yang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Xiaoling Yang
- Department of Radiology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, China
| | - Lisi Xie
- Department of Radiology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, China
| | - Jing Zhang
- Department of Radiology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, China
| | - Renfeng Zhuo
- Department of Radiology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, China
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Li X, Yan F. Predictive value of background parenchymal enhancement on breast magnetic resonance imaging for pathological tumor response to neoadjuvant chemotherapy in breast cancers: a systematic review. Cancer Imaging 2024; 24:35. [PMID: 38462607 PMCID: PMC10926651 DOI: 10.1186/s40644-024-00672-0] [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: 07/19/2023] [Accepted: 02/09/2024] [Indexed: 03/12/2024] Open
Abstract
OBJECTIVES This review aimed to assess the predictive value of background parenchymal enhancement (BPE) on breast magnetic resonance imaging (MRI) as an imaging biomarker for pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT). METHODS Two reviewers independently performed a systemic literature search using the PubMed, MEDLINE, and Embase databases for studies published up to 11 June 2022. Data from relevant articles were extracted to assess the relationship between BPE and pCR. RESULTS This systematic review included 13 studies with extensive heterogeneity in population characteristics, MRI follow-up points, MRI protocol, NACT protocol, pCR definition, and BPE assessment. Baseline BPE levels were not associated with pCR, except in 1 study that reported higher baseline BPE of the younger participants (< 55 years) in the pCR group than the non-pCR group. A total of 5 studies qualitatively assessed BPE levels and indicated a correlation between reduced BPE after NACT and pCR; however, among the studies that quantitatively measured BPE, the same association was observed only in the subgroup analysis of 2 articles that assessed the status of hormone receptor and human epidermal growth factor receptor 2. In addition, the predictive ability of early BPE changes for pCR was reported in several articles and remains controversial. CONCLUSIONS Changes in BPE may be a promising imaging biomarker for predicting pCR in breast cancer. Because current studies remain insufficient, particularly those that quantitatively measure BPE, prospective and multicenter large-sample studies are needed to confirm this relationship.
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Affiliation(s)
- Xue Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, 100730, PR China
- Graduate School of Peking, Union Medical College, Beijing, PR China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China.
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Musall BC, Rauch DE, Mohamed RMM, Panthi B, Boge M, Candelaria RP, Chen H, Guirguis MS, Hunt KK, Huo L, Hwang KP, Korkut A, Litton JK, Moseley TW, Pashapoor S, Patel MM, Reed BJ, Scoggins ME, Son JB, Tripathy D, Valero V, Wei P, White JB, Whitman GJ, Xu Z, Yang WT, Yam C, Adrada BE, Ma J. Diffusion Tensor Imaging for Characterizing Changes in Triple-Negative Breast Cancer During Neoadjuvant Systemic Therapy. J Magn Reson Imaging 2024. [PMID: 38294179 DOI: 10.1002/jmri.29267] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 01/18/2024] [Accepted: 01/18/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND Assessment of treatment response in triple-negative breast cancer (TNBC) may guide individualized care for improved patient outcomes. Diffusion tensor imaging (DTI) measures tissue anisotropy and could be useful for characterizing changes in the tumors and adjacent fibroglandular tissue (FGT) of TNBC patients undergoing neoadjuvant systemic treatment (NAST). PURPOSE To evaluate the potential of DTI parameters for prediction of treatment response in TNBC patients undergoing NAST. STUDY TYPE Prospective. POPULATION Eighty-six women (average age: 51 ± 11 years) with biopsy-proven clinical stage I-III TNBC who underwent NAST followed by definitive surgery. 47% of patients (40/86) had pathologic complete response (pCR). FIELD STRENGTH/SEQUENCE 3.0 T/reduced field of view single-shot echo-planar DTI sequence. ASSESSMENT Three MRI scans were acquired longitudinally (pre-treatment, after 2 cycles of NAST, and after 4 cycles of NAST). Eleven histogram features were extracted from DTI parameter maps of tumors, a peritumoral region (PTR), and FGT in the ipsilateral breast. DTI parameters included apparent diffusion coefficients and relative diffusion anisotropies. pCR status was determined at surgery. STATISTICAL TESTS Longitudinal changes of DTI features were tested for discrimination of pCR using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC). A P value <0.05 was considered statistically significant. RESULTS 47% of patients (40/86) had pCR. DTI parameters assessed after 2 and 4 cycles of NAST were significantly different between pCR and non-pCR patients when compared between tumors, PTRs, and FGTs. The median surface/average anisotropy of the PTR, measured after 2 and 4 cycles of NAST, increased in pCR patients and decreased in non-pCR patients (AUC: 0.78; 0.027 ± 0.043 vs. -0.017 ± 0.042 mm2 /s). DATA CONCLUSION Quantitative DTI features from breast tumors and the peritumoral tissue may be useful for predicting the response to NAST in TNBC. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 4.
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Affiliation(s)
- Benjamin C Musall
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - David E Rauch
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Rania M M Mohamed
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Bikash Panthi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Medine Boge
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Rosalind P Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Huiqin Chen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mary S Guirguis
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kelly K Hunt
- Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Lei Huo
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Anil Korkut
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jennifer K Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Tanya W Moseley
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Sanaz Pashapoor
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Miral M Patel
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Brandy J Reed
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Marion E Scoggins
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Debu Tripathy
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Vicente Valero
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jason B White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Gary J Whitman
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Zhan Xu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Wei T Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Clinton Yam
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Beatriz E Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Zheng G, Hou J, Shu Z, Peng J, Han L, Yuan Z, He X, Gong X. Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue. BMC Med Imaging 2024; 24:22. [PMID: 38245712 PMCID: PMC10800060 DOI: 10.1186/s12880-024-01198-4] [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: 06/07/2023] [Accepted: 01/10/2024] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Non-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide the resection range of tumor. This study aimed to examine the effectiveness of a nomogram created by combining radiomics signatures from both intratumoral and derived tissues with clinical characteristics for predicting pCR after NACT. METHODS The clinical data of 133 BCa patients were analyzed retrospectively and divided into training and validation sets. The radiomics features for Intratumoral, peritumoral, and background parenchymal enhancement (BPE) in the training set were dimensionalized. Logistic regression analysis was used to select the optimal feature set, and a radiomics signature was constructed using a decision tree. The signature was combined with clinical features to build joint models and generate nomograms. The area under curve (AUC) value of receiver operating characteristic (ROC) curve was then used to assess the performance of the nomogram and independent predictors. RESULTS Among single region, intratumoral had the best predictive value. The diagnostic performance of the intratumoral improved after adding the BPE features. The AUC values of the radiomics signature were 0.822 and 0.82 in the training and validation sets. Multivariate logistic regression analysis revealed that age, ER, PR, Ki-67, and radiomics signature were independent predictors of pCR in constructing a nomogram. The AUC of the nomogram in the training and validation sets were 0.947 and 0.933. The DeLong test showed that the nomogram had statistically significant differences compared to other independent predictors in both the training and validation sets (P < 0.05). CONCLUSION BPE has value in predicting the efficacy of neoadjuvant chemotherapy, thereby revealing the potential impact of tumor growth environment on the efficacy of neoadjuvant chemotherapy.
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Affiliation(s)
- Guangying Zheng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Jie Hou
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhenyu Shu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China
| | - Jiaxuan Peng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Lu Han
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhongyu Yuan
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Xiaodong He
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China
| | - Xiangyang Gong
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China.
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Zhang N, Song Q, Liang H, Wang Z, Wu Q, Zhang H, Zhang L, Liu A, Wang H, Wang J, Lin L. Early prediction of pathological response to neoadjuvant chemotherapy of breast tumors: a comparative study using amide proton transfer-weighted, diffusion weighted and dynamic contrast enhanced MRI. Front Med (Lausanne) 2024; 11:1295478. [PMID: 38298813 PMCID: PMC10827983 DOI: 10.3389/fmed.2024.1295478] [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: 09/16/2023] [Accepted: 01/05/2024] [Indexed: 02/02/2024] Open
Abstract
Objective To examine amide proton transfer-weighted (APTw) combined with diffusion weighed (DWI) and dynamic contrast enhanced (DCE) MRI for early prediction of pathological response to neoadjuvant chemotherapy in invasive breast cancer. Materials In this prospective study, 50 female breast cancer patients (49.58 ± 10.62 years old) administered neoadjuvant chemotherapy (NAC) were enrolled with MRI carried out both before NAC (T0) and at the end of the second cycle of NAC (T1). The patients were divided into 2 groups based on tumor response according to the Miller-Payne Grading (MPG) system. Group 1 included patients with a greater degree of decrease in major histologic responder (MHR, Miller-Payne G4-5), while group 2 included non-MHR cases (Miller-Payne G1-3). Traditional imaging protocols (T1 weighted, T2 weighted, diffusion weighted, and DCE-MRI) and APTw imaging were scanned for each subject before and after treatment. APTw value (APTw0 and APTw1), Dmax (maximum diameter, Dmax0 and Dmax1), V (3D tumor volume, V0 and V1), and ADC (apparent diffusion coefficient, ADC0 and ADC1) before and after treatment, as well as changes between the two times points (ΔAPT, ΔDmax, ΔV, ΔADC) for breast tumors were compared between the two groups. Results APT0 and APT1 values significantly differed between the two groups (p = 0.034 and 0.01). ΔAPTw values were significantly lower in non-MHR tumors compared with MHR tumors (p = 0.015). ΔDmax values were significantly higher in MHR tumors compared with non-MHR tumors (p = 0.005). ADC0 and ADC1 values were significantly higher in MHR tumors than in non-MHR tumors (p = 0.038 and 0.035). AUC (Dmax+DWI + APTw) = AUC (Dmax+APTw) > AUC (APTw) > AUC (Dmax+DWI) > AUC (Dmax). Conclusion APTw imaging along with change of tumor size showed a significant potential in early prediction of MHR for NAC treatment in breast cancer, which might allow timely regimen refinement before definitive surgical treatment.
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Affiliation(s)
- Nan Zhang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qingwei Song
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Hongbing Liang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Zhuo Wang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Qi Wu
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Haonan Zhang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Lina Zhang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Ailian Liu
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Huali Wang
- Department of Pathology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Jiazheng Wang
- MSC Clinical and Technical Solutions, Philips Healthcare, Beijing, China
| | - Liangjie Lin
- MSC Clinical and Technical Solutions, Philips Healthcare, Beijing, China
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Onishi N, Bareng TJ, Gibbs J, Li W, Price ER, Joe BN, Kornak J, Esserman LJ, Newitt DC, Hylton NM. Effect of Longitudinal Variation in Tumor Volume Estimation for MRI-guided Personalization of Breast Cancer Neoadjuvant Treatment. Radiol Imaging Cancer 2023; 5:e220126. [PMID: 37505107 PMCID: PMC10413289 DOI: 10.1148/rycan.220126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 05/02/2023] [Accepted: 06/03/2023] [Indexed: 07/29/2023]
Abstract
Purpose To investigate the impact of longitudinal variation in functional tumor volume (FTV) underestimation and overestimation in predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Materials and Methods Women with breast cancer who were enrolled in the prospective I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2) from May 2010 to November 2016 were eligible for this retrospective analysis. Participants underwent four MRI examinations during NAC treatment. FTV was calculated based on automated segmentation. Baseline FTV before treatment (FTV0) and the percentage of FTV change at early treatment and inter-regimen time points relative to baseline (∆FTV1 and ∆FTV2, respectively) were classified into high-standard or standard groups based on visual assessment of FTV under- and overestimation. Logistic regression models predicting pCR using single predictors (FTV0, ∆FTV1, and ∆FTV2) and multiple predictors (all three) were developed using bootstrap resampling with out-of-sample data evaluation with the area under the receiver operating characteristic curve (AUC) independently in each group. Results This study included 432 women (mean age, 49.0 years ± 10.6 [SD]). In the FTV0 model, the high-standard and standard groups showed similar AUCs (0.61 vs 0.62). The high-standard group had a higher estimated AUC compared with the standard group in the ∆FTV1 (0.74 vs 0.63), ∆FTV2 (0.79 vs 0.62), and multiple predictor models (0.85 vs 0.64), with a statistically significant difference for the latter two models (P = .03 and P = .01, respectively). Conclusion The findings in this study suggest that longitudinal variation in FTV estimation needs to be considered when using early FTV change as an MRI-based criterion for breast cancer treatment personalization. Keywords: Breast, Cancer, Dynamic Contrast-enhanced, MRI, Tumor Response ClinicalTrials.gov registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Ram in this issue.
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Affiliation(s)
| | | | - Jessica Gibbs
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
| | - Wen Li
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
| | - Elissa R. Price
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
| | - Bonnie N. Joe
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
| | - John Kornak
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
| | - Laura J. Esserman
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
| | - David C. Newitt
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
| | - Nola M. Hylton
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
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Acciavatti RJ, Lee SH, Reig B, Moy L, Conant EF, Kontos D, Moon WK. Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities. Radiology 2023; 306:e222575. [PMID: 36749212 PMCID: PMC9968778 DOI: 10.1148/radiol.222575] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/23/2022] [Accepted: 12/05/2022] [Indexed: 02/08/2023]
Abstract
Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.
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Affiliation(s)
| | | | - Beatriu Reig
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
| | - Linda Moy
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
| | - Emily F. Conant
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
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9
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Dagıstanli S, Sonmez S, Bulut N, Kose AM. Evaluation of treatment responses among subgroups of breast cancer patients receiving neoadjuvant chemotherapy. J Cancer Res Ther 2023; 19:S821-S826. [PMID: 38384061 DOI: 10.4103/jcrt.jcrt_1409_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 02/06/2023] [Indexed: 02/23/2024]
Abstract
BACKGROUND Breast MRIs are helpful for determining treatment plans, responses, and prospective survival analyses. In this retrospective cross-sectional study, we compared the preoperative MRI treatment response to neoadjuvant chemotherapy (NAC) administration with the postoperative pathological response in breast cancer patients. MATERIALS AND METHODS We analyzed data from 108 hospitalized patients receiving NAC between 2020 and 2022. We used MRI to evaluate the treatment response to NAC in patients with locally advanced breast cancers who had not received any prior treatment. We recorded the longest diameter of the primary tumor and the numbers of secondary tumors and axillary lymph nodes. In addition, we examined the correlation between the MRI response rate and pathological specimen results. RESULTS In our subgroup analyses, we found the best pathological response in patients with luminal B (Ki-67 index >14%) breast cancer and positivity for both hormone receptor and HER-2 markers. After comparing the pathological and radiological treatment responses in tumors and lymph nodes, the sensitivities were 90.3% for the pathological assessment and 42.8% for the radiological assessment, while the accuracies were 84.2% for the pathological assessment and 61.1% for the radiological assessment. CONCLUSION Using MRI techniques and sequence intervals and examining the histopathological characteristics of tumors may help increase the accuracy of the pathological complete response.
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Affiliation(s)
- Sevinc Dagıstanli
- Department of General Surgery, Kanuni Sultan Suleyman Research and Training Hospital, Istanbul, Turkey
| | - Suleyman Sonmez
- Department of Radiology, Kanuni Sultan Suleyman Research and Training Hospital, Istanbul, Turkey
| | - Nilufer Bulut
- Department of Medical Oncology, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Ali Mertcan Kose
- Department of Computer Programming, Vocational School, Istanbul Ticaret University, Istanbul, Turkey
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10
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Wang KN, Meng YJ, Yu Y, Cai WR, Wang X, Cao XC, Ge J. Predicting pathological complete response after neoadjuvant chemotherapy: A nomogram combining clinical features and ultrasound semantics in patients with invasive breast cancer. Front Oncol 2023; 13:1117538. [PMID: 37035201 PMCID: PMC10075137 DOI: 10.3389/fonc.2023.1117538] [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: 12/06/2022] [Accepted: 03/03/2023] [Indexed: 04/11/2023] Open
Abstract
Background Early identification of response to neoadjuvant chemotherapy (NAC) is instrumental in predicting patients prognosis. However, since a fixed criterion with high accuracy cannot be generalized to molecular subtypes, our study first aimed to redefine grades of clinical response to NAC in invasive breast cancer patients (IBC). And then developed a prognostic model based on clinical features and ultrasound semantics. Methods A total of 480 IBC patients were enrolled who underwent anthracycline and taxane-based NAC between 2018 and 2020. The decrease rate of the largest diameter was calculated by ultrasound after NAC and their cut-off points were determined among subtypes. Thereafter, a nomogram was constructed based on clinicopathological and ultrasound-related data, and validated using the calibration curve, receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results The optimal cut-off points for predicting pCR were 53.23%, 51.56%, 41.89%, and 53.52% in luminal B-like (HER2 negative), luminal B-like (HER2 positive), HER2 positive, and triple-negative, respectively. In addition, time interval, tumor size, molecular subtypes, largest diameter decrease rate, and change of blood perfusion were significantly associated with pCR (all p < 0.05). The prediction model based on the above variables has great predictive power and clinical value. Conclusion Taken together, our data demonstrated that calculated cut-off points of tumor reduction rates could be reliable in predicting pathological response to NAC and developed nomogram predicting prognosis would help tailor systematic regimens with high precision.
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Affiliation(s)
- Ke-Nie Wang
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Ya-Jiao Meng
- Department of Obstetrics & Gynecology, Tianjin 4th Centre Hospital, Tianjin, China
| | - Yue Yu
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Wen-Run Cai
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Xin Wang
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Xu-Chen Cao
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Jie Ge
- The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
- *Correspondence: Jie Ge,
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11
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Lin P, Wan WJ, Kang T, Qin LF, Meng QX, Wu XX, Qin HY, Lin YQ, He Y, Yang H. Molecular hallmarks of breast multiparametric magnetic resonance imaging during neoadjuvant chemotherapy. LA RADIOLOGIA MEDICA 2023; 128:171-183. [PMID: 36680710 PMCID: PMC9860227 DOI: 10.1007/s11547-023-01595-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 01/10/2023] [Indexed: 01/22/2023]
Abstract
PURPOSE To identify molecular basis of four parameters obtained from dynamic contrast-enhanced magnetic resonance imaging, including functional tumor volume (FTV), longest diameter (LD), sphericity, and contralateral background parenchymal enhancement (BPE). MATERIAL AND METHODS Pretreatment-available gene expression profiling and different treatment timepoints MRI features were integrated for Spearman correlation analysis. MRI feature-related genes were submitted to hypergeometric distribution-based gene functional enrichment analysis to identify related Kyoto Encyclopedia of Genes and Genomes annotation. Gene set variation analysis was utilized to assess the infiltration of distinct immune cells, which were used to determine relationships between immune phenotypes and medical imaging phenotypes. The clinical significance of MRI and relevant molecular features were analyzed to identify their prediction performance of neoadjuvant chemotherapy (NAC) and prognostic impact. RESULTS Three hundred and eighty-three patients were included for integrative analysis of MRI features and molecular information. FTV, LD, and sphericity measurements were most positively significantly correlated with proliferation-, signal transmission-, and immune-related pathways, respectively. However, BPE did not show marked correlation relationships with gene expression alteration status. FTV, LD and sphericity all showed significant positively or negatively correlated with some immune-related processes and immune cell infiltration levels. Sphericity decreased at 3 cycles after treatment initiation was also markedly negatively related to baseline sphericity measurements and immune signatures. Its decreased status could act as a predictor for prediction of response to NAC. CONCLUSION Different MRI features capture different tumor molecular characteristics that could explain their corresponding clinical significance.
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Affiliation(s)
- Peng Lin
- grid.412594.f0000 0004 1757 2961Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi People’s Republic of China ,Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Image, Nanning, Guangxi People’s Republic of China
| | - Wei-Jun Wan
- grid.412594.f0000 0004 1757 2961Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi People’s Republic of China
| | - Tong Kang
- grid.412594.f0000 0004 1757 2961Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi People’s Republic of China
| | - Lian-feng Qin
- grid.256607.00000 0004 1798 2653Department of Medical Imaging, Guangxi Medical University, Nanning, Guangxi People’s Republic of China
| | - Qiu-xue Meng
- grid.256607.00000 0004 1798 2653Department of Medical Imaging, Guangxi Medical University, Nanning, Guangxi People’s Republic of China
| | - Xiao-xin Wu
- grid.256607.00000 0004 1798 2653Department of Medical Imaging, Guangxi Medical University, Nanning, Guangxi People’s Republic of China
| | - Hong-yan Qin
- grid.256607.00000 0004 1798 2653Department of Medical Imaging, Guangxi Medical University, Nanning, Guangxi People’s Republic of China
| | - Yi-qun Lin
- grid.12955.3a0000 0001 2264 7233Department of Radiology, Dongnan Hospital of Ximen University, School of Medicine, Xiamen University, Xiamen, Fujian People’s Republic of China
| | - Yun He
- grid.412594.f0000 0004 1757 2961Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi People’s Republic of China ,Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Image, Nanning, Guangxi People’s Republic of China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China. .,Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Image, Nanning, Guangxi, People's Republic of China.
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12
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Kataoka M, Iima M, Miyake KK, Matsumoto Y. Multiparametric imaging of breast cancer: An update of current applications. Diagn Interv Imaging 2022; 103:574-583. [DOI: 10.1016/j.diii.2022.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 10/26/2022] [Indexed: 11/21/2022]
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13
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Nguyen AAT, Onishi N, Carmona-Bozo J, Li W, Kornak J, Newitt DC, Hylton NM. Post-Processing Bias Field Inhomogeneity Correction for Assessing Background Parenchymal Enhancement on Breast MRI as a Quantitative Marker of Treatment Response. Tomography 2022; 8:891-904. [PMID: 35448706 PMCID: PMC9027600 DOI: 10.3390/tomography8020072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 11/16/2022] Open
Abstract
Background parenchymal enhancement (BPE) of breast fibroglandular tissue (FGT) in dynamic contrast-enhanced breast magnetic resonance imaging (MRI) has shown an association with response to neoadjuvant chemotherapy (NAC) in patients with breast cancer. Fully automated segmentation of FGT for BPE calculation is a challenge when image artifacts are present. Low spatial frequency intensity nonuniformity due to coil sensitivity variations is known as bias or inhomogeneity and can affect FGT segmentation and subsequent BPE measurement. In this study, we utilized the N4ITK algorithm for bias correction over a restricted bilateral breast volume and compared the contralateral FGT segmentations based on uncorrected and bias-corrected images in three MRI examinations at pre-treatment, early treatment and inter-regimen timepoints during NAC. A retrospective analysis of 2 cohorts was performed: one with 735 patients enrolled in the multi-center I-SPY 2 TRIAL and the sub-cohort of 340 patients meeting a high-quality benchmark for segmentation. Bias correction substantially increased the FGT segmentation quality for 6.3–8.0% of examinations, while it substantially decreased the quality for no examination. Our results showed improvement in segmentation quality and a small but statistically significant increase in the resulting BPE measurement after bias correction at all timepoints in both cohorts. Continuing studies are examining the effects on pCR prediction.
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Affiliation(s)
- Alex Anh-Tu Nguyen
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.A.-T.N.); (J.C.-B.); (W.L.); (D.C.N.); (N.M.H.)
| | - Natsuko Onishi
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.A.-T.N.); (J.C.-B.); (W.L.); (D.C.N.); (N.M.H.)
- Correspondence: ; Tel.: +1-415-885-7511
| | - Julia Carmona-Bozo
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.A.-T.N.); (J.C.-B.); (W.L.); (D.C.N.); (N.M.H.)
| | - Wen Li
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.A.-T.N.); (J.C.-B.); (W.L.); (D.C.N.); (N.M.H.)
| | - John Kornak
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94143, USA;
| | - David C. Newitt
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.A.-T.N.); (J.C.-B.); (W.L.); (D.C.N.); (N.M.H.)
| | - Nola M. Hylton
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.A.-T.N.); (J.C.-B.); (W.L.); (D.C.N.); (N.M.H.)
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14
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McDonald ES, Rosen MA. Editorial for "Evaluation of Monoexponential, Stretched Exponential and Intravoxel Incoherent Motion MRI Diffusion Models in Early Response Monitoring to Neoadjuvant Chemotherapy in Patients With Breast Cancer-A Preliminary Study.". J Magn Reson Imaging 2022; 56:1089-1090. [PMID: 35179266 DOI: 10.1002/jmri.28118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 11/06/2022] Open
Affiliation(s)
- Elizabeth S McDonald
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Mark A Rosen
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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15
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Philpotts LE. MRI Background Parenchymal Enhancement and Neoadjuvant Chemotherapy Response. Radiology 2021; 301:309-310. [PMID: 34427469 DOI: 10.1148/radiol.2021210967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Liane E Philpotts
- From the Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar St, PO Box 208042, New Haven, CT 06520-8042
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