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Yin H, Bai L, Jia H, Lin G. Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning. Thorac Cancer 2022; 13:3183-3191. [PMID: 36203226 PMCID: PMC9663668 DOI: 10.1111/1759-7714.14673] [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] [Received: 07/13/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND To evaluate the performances of multiparametric MRI-based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes. METHODS A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast-enhanced T1 -weighted imaging (T1 C), Apparent diffusion coefficient (ADC), and T2 -weighted imaging (T2 W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance. RESULTS For the separation of each subtype from other subtypes on the testing set, the T1 C-based models yielded AUCs from 0.762 to 0.920; the ADC-based models yielded AUCs from 0.686 to 0.851; and the T2 W-based models achieved AUCs from 0.639 to 0.697. CONCLUSION T1 C-based models performed better than ADC-based models and T2 W-based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2-enriched subtypes were better than that of luminal A and luminal B subtypes.
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Affiliation(s)
- Haolin Yin
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Lutian Bai
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Huihui Jia
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
| | - Guangwu Lin
- Department of RadiologyHuadong Hospital Affiliated to Fudan UniversityShanghaiChina
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Ren Z, Pineda FD, Howard FM, Hill E, Szasz T, Safi R, Medved M, Nanda R, Yankeelov TE, Abe H, Karczmar GS. Differences Between Ipsilateral and Contralateral Early Parenchymal Enhancement Kinetics Predict Response of Breast Cancer to Neoadjuvant Therapy. Acad Radiol 2022; 29:1469-1479. [PMID: 35351365 DOI: 10.1016/j.acra.2022.02.008] [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: 10/25/2021] [Revised: 02/02/2022] [Accepted: 02/08/2022] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES To determine whether kinetics measured with ultrafast dynamic contrast-enhanced magnetic resonance imaging in tumor and normal parenchyma pre- and post-neoadjuvant therapy (NAT) can predict the response of breast cancer to NAT. MATERIALS AND METHODS Twenty-four patients with histologically confirmed invasive breast cancer were enrolled. They were scanned with ultrafast dynamic contrast-enhanced magnetic resonance imaging (3-7 seconds/frame) pre- and post-NAT. Four kinetic parameters were calculated in the segmented tumors, and ipsi- and contra-lateral normal parenchyma: (1) tumor (tSE30) or background parenchymal relative enhancement at 30 seconds (BPE30), (2) maximum relative enhancement slope (MaxSlope), (3) bolus arrival time (BAT), and (4) area under relative signal enhancement curve for the initial 30 seconds (AUC30). The tumor kinetics and the differences between ipsi- and contra-lateral parenchymal kinetics were compared for patients achieving pathologic complete response (pCR) vs those who had residual disease after NAT. The chi-squared test and two-sided t-test were used for baseline demographics. The Wilcoxon rank sum test and one-way analysis of variance were used for differential responses to therapy. RESULTS Patients with similar pre-NAT mean BPE30, median BAT and mean AUC30 in the ipsi- and contralateral normal parenchyma were more likely to achieve pCR following NAT (p < 0.02). Patients classified as having residual cancer burden (RCB) II after NAT showed higher post-NAT tSE30 and tumor AUC30 and higher post-NAT MaxSlope in ipsilateral normal parenchyma compared to those classified as RCB I or pCR (p < 0.05). CONCLUSION Bilateral asymmetry in normal parenchyma could predict treatment outcome prior to NAT. Post-NAT tumor kinetics could evaluate the aggressiveness of residual tumor.
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Affiliation(s)
- Zhen Ren
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637
| | - Federico D Pineda
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637
| | - Frederick M Howard
- Section of Hematology and Oncology, Department of Medicine, The University of Chicago, Chicago, Illinois
| | - Elle Hill
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637
| | - Teodora Szasz
- Research Computing Center, The University of Chicago, Chicago, Illinois
| | - Rabia Safi
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637
| | - Milica Medved
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637
| | - Rita Nanda
- Section of Hematology and Oncology, Department of Medicine, The University of Chicago, Chicago, Illinois
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, Texas; Department of Oncology, The University of Texas at Austin, Austin, Texas; Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas; Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Texas; Department of Imaging Physics, MD Anderson Cancer Center, Houston, Texas
| | - Hiroyuki Abe
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637
| | - Gregory S Karczmar
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637.
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Bae J, Huang Z, Knoll F, Geras K, Sood TP, Feng L, Heacock L, Moy L, Kim SG. Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach. Magn Reson Med 2022; 87:2536-2550. [PMID: 35001423 PMCID: PMC8852816 DOI: 10.1002/mrm.29148] [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: 01/24/2021] [Revised: 12/09/2021] [Accepted: 12/16/2021] [Indexed: 11/06/2022]
Abstract
PURPOSE To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI. METHODS A deep convolutional network was trained with numerically simulated data to estimate the CIF. The trained network was tested using simulated lesion data and used to estimate voxel-wise CIF for pharmacokinetic model analysis of breast DCE-MRI data using an abbreviated protocol from women with malignant (n = 25) and benign (n = 28) lesions. The estimated parameters were used to build a logistic regression model to detect the malignancy. RESULT The pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for all parameters. Testing the diagnostic performance with the estimated parameters, the conventional approach with arterial input function (AIF) showed an area under the curve (AUC) between 0.76 and 0.87, and the proposed approach with CIF demonstrated similar performance with an AUC between 0.79 and 0.81. CONCLUSION This study shows the feasibility of estimating voxel-wise CIF using a deep neural network. The proposed approach could eliminate the need to measure AIF manually without compromising the diagnostic performance to detect the malignancy in the clinical setting.
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Affiliation(s)
- Jonghyun Bae
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
- Department of Radiology, Weill Cornell Medical College
| | - Zhengnan Huang
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
| | - Florian Knoll
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
| | - Krzysztof Geras
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
- Center for Data Science, New York University
| | - Terlika Pandit Sood
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
| | - Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai
| | - Laura Heacock
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
| | - Linda Moy
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
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Kazama T, Takahara T, Hashimoto J. Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review. Life (Basel) 2022; 12:life12040490. [PMID: 35454981 PMCID: PMC9028183 DOI: 10.3390/life12040490] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 02/20/2022] [Accepted: 03/08/2022] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.
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Affiliation(s)
- Toshiki Kazama
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan;
- Correspondence: ; Tel.: +81-463-93-1121
| | - Taro Takahara
- Department of Biomedical Engineering, Tokai University School of Engineering, Hiratsuka 259-1207, Japan;
| | - Jun Hashimoto
- Department of Diagnostic Radiology, Tokai University School of Medicine, Isehara 259-1193, Japan;
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Characterization of breast cancer subtypes based on quantitative assessment of intratumoral heterogeneity using dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging. Eur Radiol 2021; 32:822-833. [PMID: 34345946 DOI: 10.1007/s00330-021-08166-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/18/2021] [Accepted: 06/19/2021] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To investigate whether intratumoral heterogeneity, assessed via dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI), reflects the molecular subtypes of invasive breast cancers. MATERIAL AND METHODS We retrospectively evaluated data from 248 consecutive women (mean age ± standard deviation, 54.6 ± 12.2 years) with invasive breast cancer who underwent preoperative DCE-MRI and DWI between 2019 and 2020. To evaluate intratumoral heterogeneity, kinetic heterogeneity (a measure of heterogeneity in the proportions of tumor pixels with delayed washout, plateau, and persistent components within a tumor) was assessed with DCE-MRI using a commercially available computer-aided diagnosis system. Apparent diffusion coefficients (ADCs) were obtained using a region-of-interest technique, and ADC heterogeneity was calculated using the following formula: (ADCmax-ADCmin)/ADCmean. Possible associations between imaging-based heterogeneity values and breast cancer subtypes were analyzed. RESULTS Of the 248 invasive breast cancers, 61 (24.6%) were classified as luminal A, 130 (52.4%) as luminal B, 25 (10.1%) as HER2-enriched, and 32 (12.9%) as triple-negative breast cancer (TNBC). There were significant differences in the kinetic and ADC heterogeneity values among tumor subtypes (p < 0.001 and p = 0.023, respectively). The TNBC showed higher kinetic and ADC heterogeneity values, whereas the HER2-enriched subtype showed higher kinetic heterogeneity values compared to the luminal subtypes. Multivariate linear analysis showed that the HER2-enriched (p < 0.001) and TNBC subtypes (p < 0.001) were significantly associated with higher kinetic heterogeneity values. The TNBC subtype (p = 0.042) was also significantly associated with higher ADC heterogeneity values. CONCLUSIONS Quantitative assessments of heterogeneity in enhancement kinetics and ADC values may provide biological clues regarding the molecular subtypes of breast cancer. KEY POINTS • Higher kinetic heterogeneity was associated with HER2-enriched and triple-negative breast cancer. • Higher ADC heterogeneity was associated with triple-negative breast cancer. • Aggressive breast cancer subtypes exhibited higher intratumoral heterogeneity based on MRI.
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Molecular subtypes of invasive breast cancer: correlation between PET/computed tomography and MRI findings. Nucl Med Commun 2021; 41:810-816. [PMID: 32427700 DOI: 10.1097/mnm.0000000000001220] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aim of the study was to investigate the diagnostic value of fluorodeoxyglucose-18 (FDG)-PET/computed tomography (CT) and MRI parameters in determining the molecular subtypes of invasive breast cancer. METHODS Data from 55 primary invasive breast cancer masses in 51 female patients who underwent pre-treatment PET/CT and MRI scans, and histopathological diagnosis at the authors' center were retrospectively reviewed. The relationship between FDG-PET/CT and MRI parameters, including maximum and mean standard uptake values (SUVmax and SUVmean, respectively), mean metabolic index (MImean) and metabolic tumor volume (MTV) values obtained from FDG-PET, and shape, margin, internal contrast-enhancement characteristics, kinetic curve types, functional tumor volume (FTV), apparent diffusion coefficient (ADC) values obtained from MRI was evaluated. Subsequently, differences among molecular subtypes (i.e. luminal A, luminal B, c-erbB-2 positive, and triple-negative) in terms of PET/CT and MRI parameters were evaluated. RESULTS The luminal B subtype of invasive breast cancer had higher SUVmax and SUVmean (P = 0.002 and P = 0.017, respectively) values than the luminal A subtype. In addition, the triple-negative subtype had a higher SUVmax (P = 0.028) than the luminal A subtype. There was a statistically significant positive correlation between pathological tumor volume (PTV) and SUVmean (P = 0.019, r = 0.720). SUVmax and ADC were negatively correlated (P = 0.001; r = -0.384). A very strong positive correlation was detected between MTV and FTV (P = 0.000; r = 0.857), and between MTV and PTV (P = 0.006, r = 0.796), and between FTV and PTV (P = 0.006, r = 0.921). CONCLUSION Results of the present study suggest that SUVmax was superior to MRI findings in predicting molecular subtypes and that MRI was superior to PET/CT in predicting PTV.
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Formes précoces des cancers du sein en fonction des différents sous-types moléculaires: présentations en imagerie. IMAGERIE DE LA FEMME 2020. [DOI: 10.1016/j.femme.2020.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Shin SU, Cho N, Kim SY, Lee SH, Chang JM, Moon WK. Time-to-enhancement at ultrafast breast DCE-MRI: potential imaging biomarker of tumour aggressiveness. Eur Radiol 2020; 30:4058-4068. [PMID: 32144456 DOI: 10.1007/s00330-020-06693-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 01/20/2020] [Accepted: 01/30/2020] [Indexed: 11/26/2022]
Abstract
OBJECTIVES This study was conducted in order to investigate whether there is a correlation between the time-to-enhancement (TTE) in ultrafast MRI and histopathological characteristics of breast cancers. METHODS Between January and August 2017, 274 consecutive breast cancer patients (mean age, 53.5 years; range, 25-80 years) who underwent ultrafast MRI and subsequent surgery were included for analysis. Ultrafast MRI scans were acquired using TWIST-VIBE or 4D TRAK-3D TFE sequences. TTE and maximum slope (MS) were derived from the ultrafast MRI. The repeated measures ANOVA, Mann-Whitney U test and Kruskal-Wallis H test were performed to compare the median TTE, MS and SER according to histologic type, histologic grade, ER/PR/HER2 positivity, level of Ki-67 and tumour subtype. For TTE calculation, intraclass correlation coefficient (ICC) was used to evaluate interobserver variability. RESULTS The median TTE of invasive cancers was shorter than that of in situ cancers (p < 0.001). In invasive cancers, large tumours showed shorter TTE than small tumours (p = 0.001). High histologic/nuclear grade cancers had shorter TTE than low to intermediate grade cancers (p < 0.001 and p < 0.001). HER2-positive cancers showed shorter TTE than HER2-negative cancers (p = 0.001). The median TTE of cancers with high Ki-67 was shorter than that of cancers with low Ki-67 (p < 0.001). ICC between two readers showed moderate agreement (0.516). No difference was found in the median MS or SER values according to the clinicopathologic features. CONCLUSIONS The median TTE of breast cancer in ultrafast MRI was shorter in invasive or aggressive tumours than in in situ cancer or less aggressive tumours, respectively. KEY POINTS • Invasive breast tumours show a shorter TTE in ultrafast DCE-MRI than in situ cancers. • A shorter TTE in ultrafast DCE-MRI is associated with breast tumours of a large size, high histologic or nuclear grade, PR negativity, HER2 positivity and high Ki-67 level.
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Affiliation(s)
- Sung Ui Shin
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea
| | - Nariya Cho
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea.
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
| | - Soo-Yeon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Su Hyun Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Jung Min Chang
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
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Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer. Sci Rep 2020; 10:3664. [PMID: 32111898 PMCID: PMC7048934 DOI: 10.1038/s41598-020-60393-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 02/04/2020] [Indexed: 12/23/2022] Open
Abstract
To investigate whether automated volumetric radiomic analysis of breast cancer vascularization (VAV) can improve survival prediction in primary breast cancer. 314 consecutive patients with primary invasive breast cancer received standard clinical MRI before the initiation of treatment according to international recommendations. Diagnostic work-up, treatment, and follow-up was done at one tertiary care, academic breast-center (outcome: disease specific survival/DSS vs. disease specific death/DSD). The Nottingham Prognostic Index (NPI) was used as the reference method with which to predict survival of breast cancer. Based on the MRI scans, VAV was accomplished by commercially available, FDA-cleared software. DSD served as endpoint. Integration of VAV into the NPI gave NPIVAV. Prediction of DSD by NPIVAV compared to standard NPI alone was investigated (Cox regression, likelihood-test, predictive accuracy: Harrell's C, Kaplan Meier statistics and corresponding hazard ratios/HR, confidence intervals/CI). DSD occurred in 35 and DSS in 279 patients. Prognostication of the survival outcome by NPI (Harrell's C = 75.3%) was enhanced by VAV (NPIVAV: Harrell's C = 81.0%). Most of all, the NPIVAV identified patients with unfavourable outcome more reliably than NPI alone (hazard ratio/HR = 4.5; confidence interval/CI = 2.14-9.58; P = 0.0001). Automated volumetric radiomic analysis of breast cancer vascularization improved survival prediction in primary breast cancer. Most of all, it optimized the identification of patients at higher risk of an unfavorable outcome. Future studies should integrate MRI as a "gate keeper" in the management of breast cancer patients. Such a "gate keeper" could assist in selecting patients benefitting from more advanced diagnostic procedures (genetic profiling etc.) in order to decide whether are a more aggressive therapy (chemotherapy) is warranted.
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Milon A, Vande Perre S, Poujol J, Kermarrec É, Pottier E, Abdel-Wahab C, Bekhouche A, Thomassin-Naggara I. Protocoles abrégés en IRM mammaire : où en sommes-nous ? IMAGERIE DE LA FEMME 2019. [DOI: 10.1016/j.femme.2019.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Xie T, Zhao Q, Fu C, Bai Q, Zhou X, Li L, Grimm R, Liu L, Gu Y, Peng W. Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging. Eur Radiol 2018; 29:2535-2544. [PMID: 30402704 DOI: 10.1007/s00330-018-5804-5] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 08/31/2018] [Accepted: 09/25/2018] [Indexed: 12/27/2022]
Abstract
PURPOSE To identify triple-negative (TN) breast cancer imaging biomarkers in comparison to other molecular subtypes using multiparametric MR imaging maps and whole-tumor histogram analysis. MATERIALS AND METHODS This retrospective study included 134 patients with invasive ductal carcinoma. Whole-tumor histogram-based texture features were extracted from a quantitative ADC map and DCE semi-quantitative maps (washin and washout). Univariate analysis using the Student's t test or Mann-Whitney U test was performed to identify significant variables for differentiating TN cancer from other subtypes. The ROC curves were generated based on the significant variables identified from the univariate analysis. The AUC, sensitivity, and specificity for subtype differentiation were reported. RESULTS The significant parameters on the univariate analysis achieved an AUC of 0.710 (95% confidence interval [CI] 0.562, 0.858) with a sensitivity of 63.6% and a specificity of 73.1% at the best cutoff point for differentiating TN cancers from Luminal A cancers. An AUC of 0.763 (95% CI 0.608, 0.917) with a sensitivity of 86.4% and a specificity of 72.2% was achieved for differentiating TN cancers from human epidermal growth factor receptor 2 (HER2) positive cancers. Also, an AUC of 0.683 (95% CI 0.556, 0.809) with a sensitivity of 54.5% and a specificity of 83.9% was achieved for differentiating TN cancers from non-TN cancers. There was no significant feature on the univariate analysis for TN cancers versus Luminal B cancers. CONCLUSIONS Whole-tumor histogram-based imaging features derived from ADC, along with washin and washout maps, provide a non-invasive analytical approach for discriminating TN cancers from other subtypes. KEY POINTS • Whole-tumor histogram-based features on MR multiparametric maps can help to assess biological characterization of breast cancer. • Histogram-based texture analysis may predict the molecular subtypes of breast cancer. • Combined DWI and DCE evaluation helps to identify triple-negative breast cancer.
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Affiliation(s)
- Tianwen Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China
| | - Qiufeng Zhao
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
| | - Caixia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, People's Republic of China
| | - Qianming Bai
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Xiaoyan Zhou
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, People's Republic of China
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthineers, Erlangen, Germany
| | - Li Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China.
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Choi JH, Lim I, Noh WC, Kim HA, Seong MK, Jang S, Seol H, Moon H, Byun BH, Kim BI, Choi CW, Lim SM. Prediction of tumor differentiation using sequential PET/CT and MRI in patients with breast cancer. Ann Nucl Med 2018; 32:389-397. [PMID: 29797002 DOI: 10.1007/s12149-018-1259-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 04/08/2018] [Indexed: 11/28/2022]
Abstract
OBJECTIVE The aim of this study is to assess tumor differentiation using parameters from sequential positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) in patients with breast cancer. METHODS This retrospective study included 78 patients with breast cancer. All patients underwent sequential PET/CT and MRI. For fluorodeoxyglucose (FDG)-PET image analysis, the maximum standardized uptake value (SUVmax) of FDG was assessed at both 1 and 2 h and metabolic tumor volume (MTV) and total lesion glycolysis (TLG). The kinetic analysis of dynamic contrast-enhanced MRI parameters was performed using dynamic enhancement curves. We assessed diffusion-weighted imaging (DWI)-MRI parameters regarding apparent diffusion coefficient (ADC) values. Histologic grades 1 and 2 were classified as low-grade, and grade 3 as high-grade tumor. RESULTS Forty-five lesions of 78 patients were classified as histologic grade 3, while 26 and 7 lesions were grade 2 and grade 1, respectively. Patients with high-grade tumors showed significantly lower ADC-mean values than patients with low-grade tumors (0.99 ± 0.19 vs.1.12 ± 0.32, p = 0.007). With respect to SUVmax1, MTV2.5, and TLG2.5, patients with high-grade tumors showed higher values than patients with low-grade tumors: SUVmax1 (7.92 ± 4.5 vs.6.19 ± 3.05, p = 0.099), MTV2.5 (7.90 ± 9.32 vs.4.38 ± 5.10, p = 0.095), and TLG2.5 (40.83 ± 59.17 vs.19.66 ± 26.08, p = 0.082). However, other parameters did not reveal significant differences between low-grade and high-grade malignancies. In receiver-operating characteristic (ROC) curve analysis, ADC-mean values showed the highest area under the curve of 0.681 (95%CI 0.566-0.782) for assessing high-grade malignancy. CONCLUSIONS Lower ADC-mean values may predict the poor differentiation of breast cancer among diverse PET-MRI functional parameters.
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Affiliation(s)
- Joon Ho Choi
- Department of Nuclear Medicine, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, Republic of Korea
| | - Ilhan Lim
- Department of Nuclear Medicine, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, Republic of Korea
| | - Woo Chul Noh
- Department of Surgery, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, Republic of Korea
| | - Hyun-Ah Kim
- Department of Surgery, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, Republic of Korea
| | - Min-Ki Seong
- Department of Surgery, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, Republic of Korea
| | - Seonah Jang
- Department of Radiology, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, Republic of Korea
| | - Hyesil Seol
- Department of Pathology, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, Republic of Korea
| | - Hansol Moon
- Department of Nuclear Medicine, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, Republic of Korea
| | - Byung Hyun Byun
- Department of Nuclear Medicine, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, Republic of Korea
| | - Byung Il Kim
- Department of Nuclear Medicine, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, Republic of Korea
| | - Chang Woon Choi
- Department of Nuclear Medicine, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, Republic of Korea
| | - Sang Moo Lim
- Department of Nuclear Medicine, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul, Republic of Korea.
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Nam SY, Ko ES, Lim Y, Han BK, Ko EY, Choi JS, Lee JE. Preoperative dynamic breast magnetic resonance imaging kinetic features using computer-aided diagnosis: Association with survival outcome and tumor aggressiveness in patients with invasive breast cancer. PLoS One 2018; 13:e0195756. [PMID: 29649266 PMCID: PMC5896992 DOI: 10.1371/journal.pone.0195756] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 03/28/2018] [Indexed: 02/08/2023] Open
Abstract
Objectives To evaluate whether preoperative breast dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging kinetic features, assessed using computer-aided diagnosis (CAD), can predict survival outcome and tumor aggressiveness in patients with invasive breast cancer. Materials and methods Between March and December 2011, 301 women who underwent preoperative DCE MR imaging for invasive breast cancer, with CAD data, were identified. All MR images were retrospectively evaluated using a commercially available CAD system. The following kinetic parameters were prospectively recorded for each lesion: initial peak enhancement, the proportion of early phase medium and rapid enhancement, and the proportion of delayed phase persistent, plateau, and washout enhancement. The Cox proportional hazards model was used to determine the association between the kinetic features assessed by CAD and disease-free survival (DFS). The peak signal intensity and kinetic enhancement profiles were compared with the clinical-pathological variables. Results There were 32 recurrences during a mean follow-up time of 55.2 months (range, 5–72 months). Multivariate analysis revealed that a higher peak enhancement (DFS hazard ratio, 1.004 [95% confidence interval (CI): 1.001, 1.006]; P = .013) on DCE MR imaging and a triple-negative subtype (DFS hazard ratio, 21.060 [95% CI: 2.675, 165.780]; P = .004) were associated with a poorer DFS. Higher peak enhancement was significantly associated with a higher tumor stage, clinical stage, and histologic grade. Conclusions Patients with breast cancer who showed higher CAD-derived peak enhancement on breast MR imaging had worse DFS. Peak enhancement and volumetric analysis of kinetic patterns were useful for predicting tumor aggressiveness.
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Affiliation(s)
- Sang Yu Nam
- Department of Radiology, Gachon University Gil Medical Center, Gachon University of Medicine and Science, Incheon, Korea
| | - Eun Sook Ko
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, Korea
- * E-mail:
| | - Yaeji Lim
- Department of Applied Statistics, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, Korea
| | - Boo-Kyung Han
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, Korea
| | - Eun Young Ko
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, Korea
| | - Ji Soo Choi
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, Korea
| | - Jeong Eon Lee
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, Korea
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Derakhshan JJ, McDonald ES, Siegelman ES, Schnall MD, Wehrli FW. Characterizing and eliminating errors in enhancement and subtraction artifacts in dynamic contrast-enhanced breast MRI: Chemical shift artifact of the third kind. Magn Reson Med 2018; 79:2277-2289. [PMID: 28840613 PMCID: PMC5811365 DOI: 10.1002/mrm.26879] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Revised: 07/27/2017] [Accepted: 07/30/2017] [Indexed: 12/29/2022]
Abstract
PURPOSE To characterize errors in enhancement in breast dynamic contrast-enhanced (DCE) MRI studies as a function of echo time and determine the source of dark band artifacts in clinical subtraction images. METHODS Computer simulations, oil and water substitute (methylene chloride), as well as an American College of Radiology quality control phantom were tested. Routine clinical DCE breast MRI study was bracketed with (accelerated) in-phase DCE acquisitions in five patients. RESULTS Simulation results demonstrated up to -160% suppression of the expected enhancement caused by differential enhancement of fat and water. Two-dimensional gradient-recalled echo and fat-suppressed 3D GRE phantom imaging confirmed the simulation results and showed that fat suppression does not eliminate the artifact. In vivo in-phase DCE images showed increased enhancement consistent with predictions and also confirmed increased spatial blurring on in-phase 3D gradient-recalled echo images. Combined multi-dimensional partial Fourier and parallel imaging provided a time-equivalent in-phase DCE MRI acquisition. CONCLUSION Errors in expected enhancement occur in DCE breast MRI subtraction images because of differential enhancement of fat and water and incomplete fat signal suppression. These errors can lead to artificial suppression of enhancement as well as dark band artifacts on subtraction images. These artifacts can be eliminated with a time-equivalent in-phase fat-suppressed 3D gradient-recalled echo sequence. Understanding chemical shift artifact of the third kind, a unique artifact of artificial enhancement suppression in the presence of intravoxel fat and water signal, will aid DCE breast MRI image interpretation. In-phase acquisitions (combined with simultaneous minimum echo time or opposed-phase echoes) may facilitate qualitative, quantitative and longitudinal analysis of contrast enhancement. Magn Reson Med 79:2277-2289, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Jamal J Derakhshan
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Elizabeth S McDonald
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Evan S Siegelman
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Mitchell D Schnall
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Felix W Wehrli
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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A comparison of computer-assisted detection (CAD) programs for the identification of colorectal polyps: performance and sensitivity analysis, current limitations and practical tips for radiologists. Clin Radiol 2018; 73:593.e11-593.e18. [PMID: 29602538 DOI: 10.1016/j.crad.2018.02.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 02/13/2018] [Indexed: 01/27/2023]
Abstract
AIM To directly compare the accuracy and speed of analysis of two commercially available computer-assisted detection (CAD) programs in detecting colorectal polyps. MATERIALS AND METHOD In this retrospective single-centre study, patients who had colorectal polyps identified on computed tomography colonography (CTC) and subsequent lower gastrointestinal endoscopy, were analysed using two commercially available CAD programs (CAD1 and CAD2). Results were compared against endoscopy to ascertain sensitivity and positive predictive value (PPV) for colorectal polyps. Time taken for CAD analysis was also calculated. RESULTS CAD1 demonstrated a sensitivity of 89.8%, PPV of 17.6% and mean analysis time of 125.8 seconds. CAD2 demonstrated a sensitivity of 75.5%, PPV of 44.0% and mean analysis time of 84.6 seconds. CONCLUSION The sensitivity and PPV for colorectal polyps and CAD analysis times can vary widely between current commercially available CAD programs. There is still room for improvement. Generally, there is a trade-off between sensitivity and PPV, and so further developments should aim to optimise both. Information on these factors should be made routinely available, so that an informed choice on their use can be made. This information could also potentially influence the radiologist's use of CAD results.
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16
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Heacock L, Lewin AA, Gao Y, Babb JS, Heller SL, Melsaether AN, Bagadiya N, Kim SG, Moy L. Feasibility analysis of early temporal kinetics as a surrogate marker for breast tumor type, grade, and aggressiveness. J Magn Reson Imaging 2017; 47:1692-1700. [PMID: 29178258 DOI: 10.1002/jmri.25897] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 10/30/2017] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Screening breast MRI has been shown to preferentially detect high-grade ductal carcinoma in situ (DCIS) and invasive carcinoma, likely due to increased angiogenesis resulting in early initial uptake of contrast. As interest grows in abbreviated screening breast MRI (AB-MRI), markers of early contrast washin that can predict tumor grade and potential aggressiveness are of clinical interest. PURPOSE To evaluate the feasibility of using the initial enhancement ratio (IER) as a surrogate marker for tumor grade, hormone receptor status, and prognostic markers, as an initial step to being incorporated into AB-MRI. STUDY TYPE Retrospective. SUBJECTS In all, 162 women (mean 55.0 years, range 32.8-87.7 years) with 187 malignancies imaged January 2012-November 2015. FIELD STRENGTH/SEQUENCE Images were acquired at 3.0T with a T1 -weighted gradient echo fat-suppressed-volume interpolated breath-hold sequence. ASSESSMENT Subjects underwent dynamic contrast-enhanced breast MRI with a 7-channel breast coil. IER (% signal increase over baseline at the first postcontrast acquisition) was assessed and correlated with background parenchymal enhancement, washout curves, stage, and final pathology. STATISTICAL TESTS Chi-square test, Spearman rank correlation, Mann-Whitney U-tests, Bland-Altman analysis, and receiver operating characteristic curve analysis. RESULTS IER was higher for invasive cancer than for DCIS (R1/R2, P < 0.001). IER increased with tumor grade (R1: r = 0.56, P < 0.001, R2: r = 0.50, P < 0.001), as ki-67 increased (R1: r = 0.35, P < 0.001; R2 r = 0.35, P < 0.001), and for node-positive disease (R1/R2, P = 0.001). IER was higher for human epidermal growth factor receptor two-positive and triple negative cancers than for estrogen receptor-positive / progesterone receptor-positive tumors (R1 P < 0.001-0.002; R2 P = 0.0.001-0.011). IER had higher sensitivity (80.6% vs. 75.5%) and specificity (55.8% vs. 48.1%) than washout curves for positive nodes, higher specificity (48.1% vs. 36.5%) and positive predictive value (70.2% vs. 66.7%) for high ki-67, and excellent interobserver agreement (intraclass correlation coefficient = 0.82). DATA CONCLUSION IER, a measurement of early contrast washin, is associated with higher-grade malignancies and tumor aggressiveness and might be potentially incorporated into an AB-MRI protocol. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1692-1700.
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Affiliation(s)
- Laura Heacock
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Alana A Lewin
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Yiming Gao
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - James S Babb
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Samantha L Heller
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Amy N Melsaether
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2 R), New York University School of Medicine, New York, New York, USA
| | - Neeti Bagadiya
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Sungheon G Kim
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2 R), New York University School of Medicine, New York, New York, USA
| | - Linda Moy
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2 R), New York University School of Medicine, New York, New York, USA
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Association of DW/DCE-MRI features with prognostic factors in breast cancer. Int J Biol Markers 2017; 32:e118-e125. [PMID: 27646773 DOI: 10.5301/jbm.5000230] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2016] [Indexed: 01/03/2023]
Abstract
BACKGROUND Through analyzing apparent diffusion coefficient (ADC) values and morphological evaluations, this research aimed to study how magnetic resonance imaging (MRI)-based breast lesion characteristics can enhance the diagnosis and prognosis of breast cancer. METHODS: A total of 118 breast lesions, including 50 benign and 68 malignant lesions, from 106 patients were analyzed. All lesions were measured with both diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI. The average ADC of breast lesions was analyzed at b values of 600, 800 and 1,000 s/mm2. Lesion margins, lesion enhancement patterns, and dynamic curves were also investigated. The relations between MRI-based features and molecular prognostic factors were evaluated using Spearman's rank correlation analysis. RESULTS: A b value of 800 s/mm2 was used to distinguish malignant from benign breast lesions, with an ADC cutoff value of 1.365 × 10-3 mm2/s. The average ADC value between invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS) was significantly different. Malignant lesions were more likely to have spiculated margins, heterogeneous enhancement and washout curves. On the other hand, DCIS was more likely to have spiculated margins, heterogeneous/rim enhancement and plateau/washout dynamic curves. A significant negative correlation was found between progesterone receptor (PR) status and dynamic imaging (p = 0.027), while a significant positive correlation was found between Ki-67 status and lesion enhancement (p = 0.045). CONCLUSIONS: Both ADC values and MRI morphological assessment could be used to distinguish malignant breast lesions from benign ones.
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Heacock L, Gao Y, Heller SL, Melsaether AN, Babb JS, Block TK, Otazo R, Kim SG, Moy L. Comparison of conventional DCE-MRI and a novel golden-angle radial multicoil compressed sensing method for the evaluation of breast lesion conspicuity. J Magn Reson Imaging 2016; 45:1746-1752. [PMID: 27859874 DOI: 10.1002/jmri.25530] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 10/10/2016] [Indexed: 12/25/2022] Open
Abstract
PURPOSE To compare a novel multicoil compressed sensing technique with flexible temporal resolution, golden-angle radial sparse parallel (GRASP), to conventional fat-suppressed spoiled three-dimensional (3D) gradient-echo (volumetric interpolated breath-hold examination, VIBE) MRI in evaluating the conspicuity of benign and malignant breast lesions. MATERIALS AND METHODS Between March and August 2015, 121 women (24-84 years; mean, 49.7 years) with 180 biopsy-proven benign and malignant lesions were imaged consecutively at 3.0 Tesla in a dynamic contrast-enhanced (DCE) MRI exam using sagittal T1-weighted fat-suppressed 3D VIBE in this Health Insurance Portability and Accountability Act-compliant, retrospective study. Subjects underwent MRI-guided breast biopsy (mean, 13 days [1-95 days]) using GRASP DCE-MRI, a fat-suppressed radial "stack-of-stars" 3D FLASH sequence with golden-angle ordering. Three readers independently evaluated breast lesions on both sequences. Statistical analysis included mixed models with generalized estimating equations, kappa-weighted coefficients and Fisher's exact test. RESULTS All lesions demonstrated good conspicuity on VIBE and GRASP sequences (4.28 ± 0.81 versus 3.65 ± 1.22), with no significant difference in lesion detection (P = 0.248). VIBE had slightly higher lesion conspicuity than GRASP for all lesions, with VIBE 12.6% (0.63/5.0) more conspicuous (P < 0.001). Masses and nonmass enhancement (NME) were more conspicuous on VIBE (P < 0.001), with a larger difference for NME (14.2% versus 9.4% more conspicuous). Malignant lesions were more conspicuous than benign lesions (P < 0.001) on both sequences. CONCLUSION GRASP DCE-MRI, a multicoil compressed sensing technique with high spatial resolution and flexible temporal resolution, has near-comparable performance to conventional VIBE imaging for breast lesion evaluation. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;45:1746-1752.
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Affiliation(s)
- Laura Heacock
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Yiming Gao
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Samantha L Heller
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Amy N Melsaether
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - James S Babb
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Tobias K Block
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Ricardo Otazo
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Sungheon G Kim
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Linda Moy
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
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Kim JH, Ko ES, Lim Y, Lee KS, Han BK, Ko EY, Hahn SY, Nam SJ. Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival Outcomes. Radiology 2016; 282:665-675. [PMID: 27700229 DOI: 10.1148/radiol.2016160261] [Citation(s) in RCA: 171] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Purpose To determine the relationship between tumor heterogeneity assessed by means of magnetic resonance (MR) imaging texture analysis and survival outcomes in patients with primary breast cancer. Materials and Methods Between January and August 2010, texture analysis of the entire primary breast tumor in 203 patients was performed with T2-weighted and contrast material-enhanced T1-weighted subtraction MR imaging for preoperative staging. Histogram-based uniformity and entropy were calculated. To dichotomize texture parameters for survival analysis, the 10-fold cross-validation method was used to determine cutoff points in the receiver operating characteristic curve analysis. The Cox proportional hazards model and Kaplan-Meier analysis were used to determine the association of texture parameters and morphologic or volumetric information obtained at MR imaging or clinical-pathologic variables with recurrence-free survival (RFS). Results There were 26 events, including 22 recurrences (10 local-regional and 12 distant) and four deaths, with a mean follow-up time of 56.2 months. In multivariate analysis, a higher N stage (RFS hazard ratio, 11.15 [N3 stage]; P = .002, Bonferroni-adjusted α = .0167), triple-negative subtype (RFS hazard ratio, 16.91; P < .001, Bonferroni-adjusted α = .0167), high risk of T1 entropy (less than the cutoff values [mean, 5.057; range, 5.022-5.167], RFS hazard ratio, 4.55; P = .018), and T2 entropy (equal to or higher than the cutoff values [mean, 6.013; range, 6.004-6.035], RFS hazard ratio = 9.84; P = .001) were associated with worse outcomes. Conclusion Patients with breast cancers that appeared more heterogeneous on T2-weighted images (higher entropy) and those that appeared less heterogeneous on contrast-enhanced T1-weighted subtraction images (lower entropy) exhibited poorer RFS. © RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Jae-Hun Kim
- From the Departments of Radiology (J.H.K., E.S.K., K.S.L., B.K.H., E.Y.K., S.Y.H.) and Surgery (S.J.N.), Samsung Medical Center Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 135-710, Korea; and Department of Statistics, Pukyong National University, Busan, Korea (Y.L.)
| | - Eun Sook Ko
- From the Departments of Radiology (J.H.K., E.S.K., K.S.L., B.K.H., E.Y.K., S.Y.H.) and Surgery (S.J.N.), Samsung Medical Center Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 135-710, Korea; and Department of Statistics, Pukyong National University, Busan, Korea (Y.L.)
| | - Yaeji Lim
- From the Departments of Radiology (J.H.K., E.S.K., K.S.L., B.K.H., E.Y.K., S.Y.H.) and Surgery (S.J.N.), Samsung Medical Center Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 135-710, Korea; and Department of Statistics, Pukyong National University, Busan, Korea (Y.L.)
| | - Kyung Soo Lee
- From the Departments of Radiology (J.H.K., E.S.K., K.S.L., B.K.H., E.Y.K., S.Y.H.) and Surgery (S.J.N.), Samsung Medical Center Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 135-710, Korea; and Department of Statistics, Pukyong National University, Busan, Korea (Y.L.)
| | - Boo-Kyung Han
- From the Departments of Radiology (J.H.K., E.S.K., K.S.L., B.K.H., E.Y.K., S.Y.H.) and Surgery (S.J.N.), Samsung Medical Center Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 135-710, Korea; and Department of Statistics, Pukyong National University, Busan, Korea (Y.L.)
| | - Eun Young Ko
- From the Departments of Radiology (J.H.K., E.S.K., K.S.L., B.K.H., E.Y.K., S.Y.H.) and Surgery (S.J.N.), Samsung Medical Center Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 135-710, Korea; and Department of Statistics, Pukyong National University, Busan, Korea (Y.L.)
| | - Soo Yeon Hahn
- From the Departments of Radiology (J.H.K., E.S.K., K.S.L., B.K.H., E.Y.K., S.Y.H.) and Surgery (S.J.N.), Samsung Medical Center Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 135-710, Korea; and Department of Statistics, Pukyong National University, Busan, Korea (Y.L.)
| | - Seok Jin Nam
- From the Departments of Radiology (J.H.K., E.S.K., K.S.L., B.K.H., E.Y.K., S.Y.H.) and Surgery (S.J.N.), Samsung Medical Center Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 135-710, Korea; and Department of Statistics, Pukyong National University, Busan, Korea (Y.L.)
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Heacock L, Melsaether AN, Heller SL, Gao Y, Pysarenko KM, Babb JS, Kim SG, Moy L. Evaluation of a known breast cancer using an abbreviated breast MRI protocol: Correlation of imaging characteristics and pathology with lesion detection and conspicuity. Eur J Radiol 2016; 85:815-23. [DOI: 10.1016/j.ejrad.2016.01.005] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Revised: 12/18/2015] [Accepted: 01/13/2016] [Indexed: 11/25/2022]
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Chang RF, Chen HH, Chang YC, Huang CS, Chen JH, Lo CM. Quantification of breast tumor heterogeneity for ER status, HER2 status, and TN molecular subtype evaluation on DCE-MRI. Magn Reson Imaging 2016; 34:809-819. [PMID: 26968141 DOI: 10.1016/j.mri.2016.03.001] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Revised: 02/25/2016] [Accepted: 03/03/2016] [Indexed: 01/07/2023]
Abstract
PURPOSE Recognizing molecular markers is helpful for guiding treatment plans for breast cancer. This study correlated estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), and triple-negative breast cancer (TNBC) statuses to the degree of heterogeneity on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS A total of 102 biopsy-proven cancers from 102 patients between October 2010 and December 2012 were used in this study, including ER (59 positive, 43 negative), HER2 (47 positive, 55 negative), and TNBC (22 TNBC, 80 non-TNBC). At first, the tumor region was segmented by using a region growing method. Then, the region-based features were extracted by the proposed regionalization method to quantify intra-tumoral heterogeneity on breast DCE-MRI. The three-dimensional morphological features (texture features and shape feature) and the pharmacokinetic model were also extracted from the segmented tumor region. After feature extraction, a logistic regression was used to classify ER, HER2, and TNBC statuses respectively. The performances were evaluated by using receiver operating characteristic (ROC) curve analysis. RESULTS The proposed region-based features achieved the accuracy of 73.53%, 82.35%, and 77.45% for ER, HER2, and TNBC classifications. The corresponding area under the ROC curves (Az) achieves 0.7320, 0.8458, and 0.8328 that were better than those of texture features, shape features, and Tofts pharmacokinetic model. CONCLUSION The intra-tumoral heterogeneity quantified by the region-based features can be used to reflect the vasculature complexity of different molecular markers and to provide prediction information of cell surface receptors on clinical examination.
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Affiliation(s)
- Ruey-Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Hong-Hao Chen
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
| | - Chiun-Sheng Huang
- Department of Surgery, National Taiwan University Hospital and Nation Taiwan University College of Medicine, Taipei, Taiwan
| | - Jeon-Hor Chen
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, United States; Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan
| | - Chung-Ming Lo
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
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Lin D, Moy L, Axelrod D, Smith J. Utilization of magnetic resonance imaging in breast cancer screening. Curr Oncol 2015; 22:e332-5. [PMID: 26628872 PMCID: PMC4608405 DOI: 10.3747/co.22.2882] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Early detection of malignancy through breast cancer screening has contributed significantly to the decline in cancer-related mortality. [...]
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Affiliation(s)
- D. Lin
- NYU Langone Medical Center, Laura and Issac Perlmutter Cancer Center, New York, NY, U.S.A
| | - L. Moy
- NYU Langone Medical Center, Laura and Issac Perlmutter Cancer Center, New York, NY, U.S.A
| | - D. Axelrod
- NYU Langone Medical Center, Laura and Issac Perlmutter Cancer Center, New York, NY, U.S.A
| | - J. Smith
- NYU Langone Medical Center, Laura and Issac Perlmutter Cancer Center, New York, NY, U.S.A
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