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Harada TL, Uematsu T, Nakashima K, Sugino T, Nishimura S, Takahashi K, Hayashi T, Tadokoro Y. Non-contrast-enhanced breast MRI for evaluation of tumor volume change after neoadjuvant chemotherapy. Eur J Radiol 2024; 177:111555. [PMID: 38880053 DOI: 10.1016/j.ejrad.2024.111555] [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: 01/23/2024] [Revised: 05/06/2024] [Accepted: 06/05/2024] [Indexed: 06/18/2024]
Abstract
PURPOSE Three-dimensional contrast-enhanced magnetic resonance imaging (3D-Ce-MRI) is a most powerful tool for evaluation of neoadjuvant chemotherapy (NAC). However, the use of contrast agent is invasive, expensive, and time consuming, Thus, contrast agent-free imaging is preferable. We aimed to investigate the tumor volume change after NAC using maximum intensity projection diffusion-weighted image (MIP-DWI) and 3D-Ce-MRI. METHOD We finally enrolled 55 breast cancer patients who underwent NAC in 2018. All MRI analyses were performed using SYNAPSE VINCENT® medical imaging system (Fujifilm Medical, Tokyo, Japan). We evaluated the tumor volumes before, during, and after NAC. Tumor volume before NAC on 3D-Ce-MRI was termed Pre-CE and those during and after NAC were termed Post-CE. The observer raised the lower end of the window width until the tumor was clearly visible and then manually deleted the non-tumor tissues. A month thereafter, the same observer who was blinded to the 3D-Ce-MRI results randomly evaluated the tumor volumes (Pre-DWI and Post-DWI) using MIP-DWI with the same method. Tumor volume change between ΔCE (Pre-CE - Post-CE/Pre-CE) and ΔDWI (Pre-DWI - Post-DWI/Pre-DWI) and the processing time for both methods (Time-DWI and Time-CE) were compared. RESULTS We enrolled 55 patients. Spearman's rho between ΔDWI and ΔCE for pure mass lesions, and non-mass enhancement (NME) was 0.89 (p < 0.01), 0.63(p < 0.01) respectively. Time-DWI was significantly shorter than Time-CE (41.3 ± 21.2 and 199.5 ± 98.3 respectively, p < 0.01). CONCLUSIONS Non-contrast-enhanced Breast MRI enables appropriate and faster evaluation of tumor volume change after NAC than 3D-Ce-MRI especially for mass lesions.
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Affiliation(s)
- Taiyo L Harada
- Division of Breast Imaging and Breast Interventional Radiology, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Takayoshi Uematsu
- Division of Breast Imaging and Breast Interventional Radiology, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan.
| | - Kazuaki Nakashima
- Division of Breast Imaging and Breast Interventional Radiology, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Takashi Sugino
- Division of Pathology, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Seiichirou Nishimura
- Division of Breast Surgery, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Kaoru Takahashi
- Division of Breast Surgery, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Tomomi Hayashi
- Division of Breast Surgery, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
| | - Yukiko Tadokoro
- Division of Breast Surgery, Shizuoka Cancer Center Hospital, Shizuoka 411-8777, Japan
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Ge W, Fan X, Zeng Y, Yang X, Zhou L, Zuo Z. Exploring habitats-based spatial distributions: improving predictions of lymphovascular invasion in invasive breast cancer. Acad Radiol 2024:S1076-6332(24)00355-6. [PMID: 38876841 DOI: 10.1016/j.acra.2024.05.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/12/2024] [Accepted: 05/24/2024] [Indexed: 06/16/2024]
Abstract
RATIONALE AND OBJECTIVES Accurate assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) plays a pivotal role in tailoring personalized treatment plans. This study aimed to investigate habitats-based spatial distributions to quantitatively measure tumor heterogeneity on multiparametric magnetic resonance imaging (MRI) scans and assess their predictive capability for LVI in patients with IBC. MATERIALS AND METHODS In this retrospective cohort study, we consecutively enrolled 241 women diagnosed with IBC between July 2020 and July 2023 and who had 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, and dynamic contrast-enhanced MRI. Habitats-based spatial distributions were derived from the gross tumor volume (GTV) and gross tumor volume plus peritumoral volume (GPTV). GTV_habitats and GPTV_habitats were generated through sub-region segmentation, and their performances were compared. Subsequently, a combined nomogram was developed by integrating relevant spatial distributions with the identified MR morphological characteristics. Diagnostic performance was compared using receiver operating characteristic curve analysis and decision curve analysis. Statistical significance was set at p < 0.05. RESULTS GPTV_habitats exhibited superior performance compared to GTV_habitats. Consequently, the GPTV_habitats, diffusion-weighted imaging rim signs, and peritumoral edema were integrated to formulate the combined nomogram. This combined nomogram outperformed individual MR morphological characteristics and the GPTV_habitats index, achieving area under the curve values of 0.903 (0.847 -0.959), 0.770 (0.689 -0.852), and 0.843 (0.776 -0.910) in the training set and 0.931 (0.863 -0.999), 0.747 (0.613 -0.880), and 0.849 (0.759 -0.938) in the validation set. CONCLUSION The combined nomogram incorporating the GPTV_habitats and identified MR morphological characteristics can effectively predict LVI in patients with IBC.
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Affiliation(s)
- Wu Ge
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan province 411000, PR China (W.G., Y.Z., X.Y., L.Z.).
| | - Xiaohong Fan
- School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, Hunan province, PR China (X.F., Z.Z.).
| | - Ying Zeng
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan province 411000, PR China (W.G., Y.Z., X.Y., L.Z.).
| | - Xiuqi Yang
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan province 411000, PR China (W.G., Y.Z., X.Y., L.Z.).
| | - Lu Zhou
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan province 411000, PR China (W.G., Y.Z., X.Y., L.Z.).
| | - Zhichao Zuo
- School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, Hunan province, PR China (X.F., Z.Z.).
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Jiang Y, Zeng Y, Zuo Z, Yang X, Liu H, Zhou Y, Fan X. Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer. Heliyon 2024; 10:e23916. [PMID: 38192872 PMCID: PMC10772250 DOI: 10.1016/j.heliyon.2023.e23916] [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: 09/27/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 01/10/2024] Open
Abstract
Objective This study aimed to investigate and validate the effectiveness of diverse radiomics models for preoperatively differentiating lymphovascular invasion (LVI) in clinically node-negative breast cancer (BC). Methods This study included 198 patients diagnosed with clinically node-negative bc and pathologically confirmed LVI status from January 2018-July 2023. The training dataset consisted of 138 patients, while the validation dataset included 60. Radiomics features were extracted from multimodal magnetic resonance imaging obtained from T1WI, T2WI, DCE, DWI, and ADC sequences. Dimensionality reduction and feature selection techniques were applied to the extracted features. Subsequently, machine learning approaches, including logistic regression, support vector machine, classification and regression trees, k-nearest neighbors, and gradient boosting machine models (GBM), were constructed using the radiomics features. The best-performing radiomic model was selected based on its performance using the confusion matrix. Univariate and multivariable logistic regression analyses were conducted to identify variables for developing a clinical-radiological (Clin-Rad) model. Finally, a combined model incorporating both radiomics and clinical-radiological model features was created. Results A total of 6195 radiomic features were extracted from multimodal magnetic resonance imaging. After applying dimensionality reduction and feature selection, seven valuable radiomics features were identified. Among the radiomics models, the GBM model demonstrated superior predictive efficiency and robustness, achieving area under the curve values (AUC) of 0.881 (0.823,0.940) and 0.820 (0.693,0.947) in the training and validation datasets, respectively. The Clin-Rad model was developed based on the peritumoral edema and DWI rim sign. In the training dataset, it achieved an AUC of 0.767 (0.681, 0.854), while in the validation dataset, it achieved an AUC of 0.734 (0.555-0.913). The combined model, which incorporated radiomics and the Clin-Rad model, showed the highest discriminatory capability. In the training dataset, it had an AUC value of 0.936 (0.892, 0.981), and in the validation dataset, it had an AUC value of 0.876 (0.757, 0.995). Additionally, decision curve analysis of the combined model revealed its optimal clinical efficacy. Conclusion The combined model, integrating radiomics and clinical-radiological features, exhibited excellent performance in distinguishing LVI status. This non-invasive and efficient approach holds promise for aiding clinical decision-making in the context of clinically node-negative BC.
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Affiliation(s)
- Yihong Jiang
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China
| | - Ying Zeng
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China
| | - Zhichao Zuo
- The School of Mathematics and Computational Science, Xiangtan University, Xiangtan, Hunan, 411105, China
| | - Xiuqi Yang
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China
| | - Haibo Liu
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China
| | - Yingjun Zhou
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China
| | - Xiaohong Fan
- The School of Mathematics and Computational Science, Xiangtan University, Xiangtan, Hunan, 411105, China
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Xu WJ, Zheng BJ, Lu J, Liu SY, Li HL. Identification of triple-negative breast cancer and androgen receptor expression based on histogram and texture analysis of dynamic contrast-enhanced MRI. BMC Med Imaging 2023; 23:70. [PMID: 37264313 DOI: 10.1186/s12880-023-01022-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 05/23/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Triple-negative breast cancer (TNBC) is highly malignant and has a poor prognosis due to the lack of effective therapeutic targets. Androgen receptor (AR) has been investigated as a possible therapeutic target. This study quantitatively assessed intratumor heterogeneity by histogram analysis of pharmacokinetic parameters and texture analysis on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to discriminate TNBC from non-triple-negative breast cancer (non-TNBC) and to identify AR expression in TNBC. METHODS This retrospective study included 99 patients with histopathologically proven breast cancer (TNBC: 36, non-TNBC: 63) who underwent breast DCE-MRI before surgery. The pharmacokinetic parameters of DCE-MRI (Ktrans, Kep and Ve) and their corresponding texture parameters were calculated. The independent t-test, or Mann-Whitney U-test was used to compare quantitative parameters between TNBC and non-TNBC groups, and AR-positive (AR+) and AR-negative (AR-) TNBC groups. The parameters with significant difference between two groups were further involved in logistic regression analysis to build a prediction model for TNBC. The ROC analysis was conducted on each independent parameter and the TNBC predicting model for evaluating the discrimination performance. The area under the ROC curve (AUC), sensitivity and specificity were derived. RESULTS The binary logistic regression analysis revealed that Kep_Range (p = 0.032) and Ve_SumVariance (p = 0.005) were significantly higher in TNBC than in non-TNBC. The AUC of the combined model for identifying TNBC was 0.735 (p < 0.001) with a cut-off value of 0.268, and its sensitivity and specificity were 88.89% and 52.38%, respectively. The value of Kep_Compactness2 (p = 0.049), Kep_SphericalDisproportion (p = 0.049), and Ve_GlcmEntropy (p = 0.008) were higher in AR + TNBC group than in AR-TNBC group. CONCLUSION Histogram and texture analysis of breast lesions on DCE-MRI showed potential to identify TNBC, and the specific features can be possible predictors of AR expression, enhancing the ability to individualize the treatment of patients with TNBC.
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Affiliation(s)
- Wen-Juan Xu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Bing-Jie Zheng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Jun Lu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Si-Yun Liu
- GE healthcare (China), Beijing, 100176, China
| | - Hai-Liang Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China.
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Tanişman Ö, Kiziltepe FT, Yildirim Ç, Coşar ZS. Prediction of prognostic factors in breast cancer: A noninvasive method utilizing histogram parameters derived from Adc maps. Heliyon 2023; 9:e16282. [PMID: 37251865 PMCID: PMC10208937 DOI: 10.1016/j.heliyon.2023.e16282] [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: 11/03/2022] [Revised: 05/04/2023] [Accepted: 05/11/2023] [Indexed: 05/31/2023] Open
Abstract
Objective The aim of this study is to investigate the relationship between histogram parameters and prognostic factors of breast cancer and to reveal the diagnostic performance of histogram parameters in predicting prognostic factors status. Materials and methods Ninety-two patients with a confirmed histopathological diagnosis of breast cancer were included in the study. Magnetic resonance imaging (MRI) was performed using a 1.5T scanner and two different b values were used for diffusion-weighted imaging (DWI) (b values: 0 s/mm2, b: 800 s/mm2). For 3D histogram analysis, regions of interest (ROI) were drawn each slice of the lesion on apparent diffusion coefficient (ADC) maps. The following data were derived from the histogram analysis data: percentiles, skewness, kurtosis, and entropy. The relationship between prognostic factors and histogram analysis data was investigated using the Kolmogorov-Smirnov test, Shapiro-Wilk test, skewness-kurtosis test, independent t-test, Mann-Whitney U test, and Kruskal-Wallis test. Receiver operator characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the histogram parameters. Results ADCmax, kurtosis, and entropy parameters were statistically significantly correlated with tumor diameter (p = 0.002, p = 0.008, and p = 0.001, respectively). There was a significant difference in ADC90% and ADCmax values, depending on estrogen receptor (ER) and progesterone receptor (PR) status. These values were lower in ER- and PR-positive than ER- and PR-negative patients (p = 0.02 and p = 0.001 vs. p = 0.018, p = 0.008). All ADC percentage values were lower in patients with a positive Ki-67 proliferation index as compared with those with a negative Ki-67 proliferation index (all p = 0.001). The entropy value was high in high-grade lesions and lesions with axillary involvement (p = 0.039 and p = 0.048, respectively). The highest area under the curve (AUC) for ER and PR status was calculated for the ADC90% value with ROC curve analysis. The highest AUC for Ki-67 proliferation index was found for the ADC50%. Conclusion Histogram analysis parameters derived from of ADC maps of whole lesions can reflect histopathological features of the tumors. Based on our study, it was concluded that histogram analysis parameters were related to the prognostic factors of the tumor.
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Affiliation(s)
- Özge Tanişman
- Deparment of Radiology, Oltu State Hospital, Erzurum, Turkey
| | - Fatma Tuba Kiziltepe
- Deparment of Radiology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Çiğdem Yildirim
- Department of Pathology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Zehra Sumru Coşar
- Deparment of Radiology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
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Qin Y, Wu F, Hu Q, He L, Huo M, Tang C, Yi J, Zhang H, Yin T, Ai T. Histogram analysis of multi-model high-resolution diffusion-weighted MRI in breast cancer: correlations with molecular prognostic factors and subtypes. Front Oncol 2023; 13:1139189. [PMID: 37188173 PMCID: PMC10175778 DOI: 10.3389/fonc.2023.1139189] [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: 01/06/2023] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
Objective To investigate the correlations between quantitative diffusion parameters and prognostic factors and molecular subtypes of breast cancer, based on a single fast high-resolution diffusion-weighted imaging (DWI) sequence with mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) models. Materials and Methods A total of 143 patients with histopathologically verified breast cancer were included in this retrospective study. The multi-model DWI-derived parameters were quantitatively measured, including Mono-ADC, IVIM-D, IVIM-D*, IVIM-f, DKI-Dapp, and DKI-Kapp. In addition, the morphologic characteristics of the lesions (shape, margin, and internal signal characteristics) were visually assessed on DWI images. Next, Kolmogorov-Smirnov test, Mann-Whitney U test, Spearman's rank correlation, logistic regression, receiver operating characteristic (ROC) curve, and Chi-squared test were utilized for statistical evaluations. Results The histogram metrics of Mono-ADC, IVIM-D, DKI-Dapp, and DKI-Kapp were significantly different between estrogen receptor (ER)-positive vs. ER-negative groups, progesterone receptor (PR)-positive vs. PR-negative groups, Luminal vs. non-Luminal subtypes, and human epidermal receptor factor-2 (HER2)-positive vs. non-HER2-positive subtypes. The histogram metrics of Mono-ADC, DKI-Dapp, and DKI-Kapp were also significantly different between triple-negative (TN) vs. non-TN subtypes. The ROC analysis revealed that the area under the curve considerably improved when the three diffusion models were combined compared with every single model, except for distinguishing lymph node metastasis (LNM) status. For the morphologic characteristics of the tumor, the margin showed substantial differences between ER-positive and ER-negative groups. Conclusions Quantitative multi-model analysis of DWI showed improved diagnostic performance for determining the prognostic factors and molecular subtypes of breast lesions. The morphologic characteristics obtained from high-resolution DWI can be identifying ER statuses of breast cancer.
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Affiliation(s)
- Yanjin Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Wu
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Qilan Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Litong He
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Min Huo
- Department of Radiology, Xiantao First People’s Hospital Affiliated to Yangtze University, Xiantao, China
| | - Caili Tang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jingru Yi
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huiting Zhang
- Magnetic Resonance (MR) Scientific Marketing, Siemens Healthineers Ltd., Wuhan, China
| | - Ting Yin
- Magnetic Resonance (MR) Collaborations, Siemens Healthineers Ltd., Chengdu, China
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Tao Ai,
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Gerwing M, Hoffmann E, Kronenberg K, Hansen U, Masthoff M, Helfen A, Geyer C, Wachsmuth L, Höltke C, Maus B, Hoerr V, Krähling T, Hiddeßen L, Heindel W, Karst U, Kimm MA, Schinner R, Eisenblätter M, Faber C, Wildgruber M. Multiparametric MRI enables for differentiation of different degrees of malignancy in two murine models of breast cancer. Front Oncol 2022; 12:1000036. [PMID: 36408159 PMCID: PMC9667047 DOI: 10.3389/fonc.2022.1000036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/11/2022] [Indexed: 11/07/2022] Open
Abstract
Objective The objective of this study was to non-invasively differentiate the degree of malignancy in two murine breast cancer models based on identification of distinct tissue characteristics in a metastatic and non-metastatic tumor model using a multiparametric Magnetic Resonance Imaging (MRI) approach. Methods The highly metastatic 4T1 breast cancer model was compared to the non-metastatic 67NR model. Imaging was conducted on a 9.4 T small animal MRI. The protocol was used to characterize tumors regarding their structural composition, including heterogeneity, intratumoral edema and hemorrhage, as well as endothelial permeability using apparent diffusion coefficient (ADC), T1/T2 mapping and dynamic contrast-enhanced (DCE) imaging. Mice were assessed on either day three, six or nine, with an i.v. injection of the albumin-binding contrast agent gadofosveset. Ex vivo validation of the results was performed with laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), histology, immunhistochemistry and electron microscopy. Results Significant differences in tumor composition were observed over time and between 4T1 and 67NR tumors. 4T1 tumors showed distorted blood vessels with a thin endothelial layer, resulting in a slower increase in signal intensity after injection of the contrast agent. Higher permeability was further reflected in higher Ktrans values, with consecutive retention of gadolinium in the tumor interstitium visible in MRI. 67NR tumors exhibited blood vessels with a thicker and more intact endothelial layer, resulting in higher peak enhancement, as well as higher maximum slope and area under the curve, but also a visible wash-out of the contrast agent and thus lower Ktrans values. A decreasing accumulation of gadolinium during tumor progression was also visible in both models in LA-ICP-MS. Tissue composition of 4T1 tumors was more heterogeneous, with intratumoral hemorrhage and necrosis and corresponding higher T1 and T2 relaxation times, while 67NR tumors mainly consisted of densely packed tumor cells. Histogram analysis of ADC showed higher values of mean ADC, histogram kurtosis, range and the 90th percentile (p90), as markers for the heterogenous structural composition of 4T1 tumors. Principal component analysis (PCA) discriminated well between the two tumor models. Conclusions Multiparametric MRI as presented in this study enables for the estimation of malignant potential in the two studied tumor models via the assessment of certain tumor features over time.
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Affiliation(s)
- Mirjam Gerwing
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
- *Correspondence: Mirjam Gerwing,
| | - Emily Hoffmann
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Katharina Kronenberg
- Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany
| | - Uwe Hansen
- Institute for Musculoskeletal Medicine, University of Münster, Münster, Germany
| | - Max Masthoff
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Anne Helfen
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Christiane Geyer
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Lydia Wachsmuth
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Carsten Höltke
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Bastian Maus
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Verena Hoerr
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
- Heart Center Bonn, Department of Internal Medicine II, University of Bonn, Bonn, Germany
| | - Tobias Krähling
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Lena Hiddeßen
- Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany
| | - Walter Heindel
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Uwe Karst
- Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany
| | - Melanie A. Kimm
- Department of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Regina Schinner
- Department of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Michel Eisenblätter
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
- Department of Diagnostic and Interventional Radiology, University of Freiburg, Freiburg, Germany
| | - Cornelius Faber
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
| | - Moritz Wildgruber
- Clinic of Radiology, University of Münster, Münster, Germany
- Translational Research Imaging Center, University of Münster, Münster, Germany
- Department of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany
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T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma. Eur Radiol 2022; 33:258-269. [PMID: 35953734 DOI: 10.1007/s00330-022-09026-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/05/2022] [Accepted: 07/09/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma. METHODS This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman's rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression. RESULTS High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 (p = 0.001-0.009), lower minimum, and C10 of ADC (p = 0.013-0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance). CONCLUSION T1 and ADC histogram parameters may represent an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma. KEY POINTS • The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma. • The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas.
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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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10
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Meyer HJ, Wienke A, Surov A. Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis. Breast Care (Basel) 2022; 17:47-54. [PMID: 35355697 PMCID: PMC8914237 DOI: 10.1159/000514407] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 01/08/2021] [Indexed: 02/03/2023] Open
Abstract
Background Magnetic resonance imaging can be used to diagnose breast cancer (BC). Diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure. Objectives This analysis aimed to compare ADC values between molecular subtypes of BC based on a large sample of patients. Method The MEDLINE library and Scopus database were screened for the associations between ADC and molecular subtypes of BC up to April 2020. The primary end point of the systematic review was the ADC value in different BC subtypes. Overall, 28 studies were included. Results The included studies comprised a total of 2,990 tumors. Luminal A type was diagnosed in 865 cases (28.9%), luminal B in 899 (30.1%), human epidermal growth factor receptor (Her2)-enriched in 597 (20.0%), and triple-negative in 629 (21.0%). The mean ADC values of the subtypes were as follows: luminal A: 0.99 × 10-3 mm2/s (95% CI 0.94-1.04), luminal B: 0.97 × 10-3 mm2/s (95% CI 0.89-1.05), Her2-enriched: 1.02 × 10-3 mm2/s (95% CI 0.95-1.08), and triple-negative: 0.99 × 10-3 mm2/s (95% CI 0.91-1.07). Conclusions ADC values cannot be used to discriminate between molecular subtypes of BC.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany,*Hans-Jonas Meyer, Department of Diagnostic and Interventional Radiology, University of Leipzig, DE–04103 Leipzig (Germany),
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Alexey Surov
- Department of Radiology and Nuclear Medicine, University of Magdeburg, Magdeburg, Germany
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11
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Davey MG, Davey MS, Boland MR, Ryan ÉJ, Lowery AJ, Kerin MJ. Radiomic differentiation of breast cancer molecular subtypes using pre-operative breast imaging - A systematic review and meta-analysis. Eur J Radiol 2021; 144:109996. [PMID: 34624649 DOI: 10.1016/j.ejrad.2021.109996] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 09/17/2021] [Accepted: 09/30/2021] [Indexed: 01/12/2023]
Abstract
INTRODUCTION Breast cancer has four distinct molecular subtypes which are discriminated using gene expression profiling following biopsy. Radiogenomics is an emerging field which utilises diagnostic imaging to reveal genomic properties of disease. We aimed to perform a systematic review of the current literature to evaluate the value radiomics in differentiating breast cancers into their molecular subtypes using diagnostic imaging. METHODS A systematic review was performed as per PRISMA guidelines. Studies assessing radiomictumour analysis in differentiatingbreast cancer molecular subtypeswere included. Quality was assessed using the radiomics quality score (RQS). Diagnostic sensitivity and specificity of radiomic analyses were included for meta-analysis; Study specific sensitivity and specificity were retrieved and summary ROC analysis were performed to compile pooled sensitivities and specificities. RESULTS Forty-one studies were included. Overall, there were 10,090 female patients (mean age of 47.6 ± 11.7 years, range: 21-93) and molecular subtypewas reported in 7,693 of cases, with Luminal A (LABC), Luminal B (LBBC), Human Epidermal Growth Factor Receptor-2 overexpressing (HER2+), and Triple Negative (TNBC) breast cancers representing 51.3%, 19.9%, 12.3% and 16.3% of tumour respectively. Seven studies provided radiomic analysis to determine molecular subtypes using mammography to differentiateTNBCvs.others (sensitivity: 0.82,specificity:0.79). Thirty-five studies reported on radiomic analysis of magnetic resonance imaging (MRI); LABC versus others(sensitivity:0.78,specificity:0.83),HER2+versusothers(sensitivity:0.87,specificity:0.88), andLBBCversusTNBC (sensitivity: 0.79,specificity:0.88) respectively. CONCLUSION Radiomic tumour assessment of contemporary breast imaging provide a novel option in determining breast cancer molecular subtypes. However, amelioration of such techniques are required and genetic expression assessment will remain the gold standard.
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Affiliation(s)
- Matthew G Davey
- The Lambe Institute for Translational Research, National University of Ireland, Galway H91 YR91, Ireland.
| | - Martin S Davey
- The Lambe Institute for Translational Research, National University of Ireland, Galway H91 YR91, Ireland
| | - Michael R Boland
- The Lambe Institute for Translational Research, National University of Ireland, Galway H91 YR91, Ireland
| | - Éanna J Ryan
- The Lambe Institute for Translational Research, National University of Ireland, Galway H91 YR91, Ireland
| | - Aoife J Lowery
- The Lambe Institute for Translational Research, National University of Ireland, Galway H91 YR91, Ireland
| | - Michael J Kerin
- The Lambe Institute for Translational Research, National University of Ireland, Galway H91 YR91, Ireland
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12
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Yang F, Wan Y, Xu L, Wu Y, Shen X, Wang J, Lu D, Shao C, Zheng S, Niu T, Xu X. MRI-Radiomics Prediction for Cytokeratin 19-Positive Hepatocellular Carcinoma: A Multicenter Study. Front Oncol 2021; 11:672126. [PMID: 34476208 PMCID: PMC8406635 DOI: 10.3389/fonc.2021.672126] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 07/20/2021] [Indexed: 12/13/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and has poor prognosis. Cytokeratin (CK)19-positive (CK19+) HCC is especially aggressive; early identification of this subtype and timely intervention can potentially improve clinical outcomes. In the present study, we developed a preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI)-based radiomics model for noninvasive and accurate classification of CK19+ HCC. A multicenter and time-independent cohort of 257 patients were retrospectively enrolled (training cohort, n = 143; validation cohort A, n = 75; validation cohort B, n = 39). A total of 968 radiomics features were extracted from preoperative multisequence MR images. The maximum relevance minimum redundancy algorithm was applied for feature selection. Multiple logistic regression, support vector machine, random forest, and artificial neural network (ANN) algorithms were used to construct the radiomics model, and the area under the receiver operating characteristic (AUROC) curve was used to evaluate the diagnostic performance of corresponding classifiers. The incidence of CK19+ HCC was significantly higher in male patients. The ANN-derived combined classifier comprising 12 optimal radiomics features showed the best diagnostic performance, with AUROCs of 0.857, 0.726, and 0.790 in the training cohort and validation cohorts A and B, respectively. The combined model based on multisequence MRI radiomics features can be used for preoperative noninvasive and accurate classification of CK19+ HCC, so that personalized management strategies can be developed.
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Affiliation(s)
- Fan Yang
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yidong Wan
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China.,Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lei Xu
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China.,Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yichao Wu
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoyong Shen
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianguo Wang
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Di Lu
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chuxiao Shao
- Department of General Surgery, Lishui Central Hospital, Lishui, China
| | - Shusen Zheng
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, Shulan Health Hangzhou Hospital, Hangzhou, China
| | - Tianye Niu
- Nucelar & Radiological Engineering and Medical Physics Programs, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Xiao Xu
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Zhejiang University Cancer Center, Hangzhou, China.,NHC Key Laboratory of Combined Multi-Organ Transplantation, Hangzhou, China.,Institute of Organ Transplantation, Zhejiang University, Hangzhou, China
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13
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Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis: Significance for Discriminating Lung Cancer from Pulmonary Abscess and Mycobacterial Infection. Cancers (Basel) 2021; 13:cancers13112720. [PMID: 34072867 PMCID: PMC8198705 DOI: 10.3390/cancers13112720] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/01/2021] [Accepted: 05/28/2021] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Diffusion-weighted magnetic resonance imaging (DWI) can differentiate malignant from benign pulmonary nodules and masses. However, it is difficult to differentiate pulmonary abscesses and mycobacterium infections (PAMIs) from lung cancers because PAMIs show restricted diffusion in DWI. The purpose of this study was to establish the role of ADC histogram for differentiating lung cancer from PAMI. There were 41 lung cancers and 19 PAMIs. Parameters more than 60% of AUC were ADC, maximal ADC, mean ADC, median ADC, most frequency ADC, kurtosis of ADC, and volume of lesion. There were significant differences between lung cancer and PAMI in ADC, mean ADC, median ADC, and most frequency ADC. ADC histogram has the potential to be a valuable tool to differentiate PAMI from lung cancer. Abstract Diffusion-weighted magnetic resonance imaging (DWI) can differentiate malignant from benign pulmonary nodules. However, it is difficult to differentiate pulmonary abscesses and mycobacterial infections (PAMIs) from lung cancers because PAMIs show restricted diffusion in DWI. The study purpose is to establish the role of ADC histogram for differentiating lung cancer from PAMI. There were 41 lung cancers (25 adenocarcinomas, 16 squamous cell carcinomas), and 19 PAMIs (9 pulmonary abscesses, 10 mycobacterial infections). Parameters more than 60% of the area under the ROC curve (AUC) were ADC, maximal ADC, mean ADC, median ADC, most frequency ADC, kurtosis of ADC, and volume of lesion. There were significant differences between lung cancer and PAMI in ADC, mean ADC, median ADC, and most frequency ADC. The ADC (1.19 ± 0.29 × 10−3 mm2/s) of lung cancer obtained from a single slice was significantly lower than that (1.44 ± 0.54) of PAMI (p = 0.0262). In contrast, mean, median, or most frequency ADC of lung cancer which was obtained in the ADC histogram was significantly higher than the value of each parameter of PAMI. ADC histogram could discriminate PAMIs from lung cancers by showing that AUCs of several parameters were more than 60%, and that several parameters of ADC of PAMI were significantly lower than those of lung cancer. ADC histogram has the potential to be a valuable tool to differentiate PAMI from lung cancer.
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14
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Uslu H, Önal T, Tosun M, Arslan AS, Ciftci E, Utkan NZ. Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with molecular subtypes and histological grades. Magn Reson Imaging 2021; 78:35-41. [PMID: 33556485 DOI: 10.1016/j.mri.2021.02.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 01/09/2021] [Accepted: 02/03/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE The purpose of this paper is to investigate whether the IVIM parameters (D, D *, f) helps to determine the molecular subtypes and histological grades of breast cancer. METHODS Fifty-one patients with breast cancer were included in the study. All subjects were examined by 3 T Magnetic Resonance Imaging (MRI). Diffusion-weighted imaging (DWI) was undertaken with 16 b-values. IVIM parameters [D (true diffusion coefficient), D* (pseudo-diffusion coefficient), f (perfusion fraction)] were calculated. Histopathological reports were reviewed to histological grade, histological type, and immunohistochemistry. IVIM parameters of tumors with different histological grades and molecular subtypes were compared. RESULTS D* and f were significantly different between molecular subtypes (p = 0.019, p = 0.03 respectively). D* and f were higher in the HER-2 group and lower in Triple negative (-) group (D*:36.8 × 10-3 ± 5.3 × 10-3 mm2/s, f:29.5%, D*:29.8 × 10-3 ± 5.6 × 10-3 mm2/s, f:21.5% respectively). There was a significant difference in D* and f between HER-2 and Triple (-) subgroups (p = 0,028, p = 0.024, respectively). D* was also significantly different between the HER-2 group and the Luminal group (p = 0,041). While histological grades increase, D and f values tend to decrease, and D* tends to increase. While the Ki-67 index increases, D* and f values tend to increase, and D tend to decrease. CONCLUSION D* and f values measured with IVIM imaging were useful for assessing breast cancer molecular subtyping. IVIM imaging may be an alternative to breast biopsy for sub-typing of breast cancer with further research.
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Affiliation(s)
- Hande Uslu
- Department of Radiology, School of Medicine, Kocaeli University, Kocaeli, Turkey.
| | | | - Mesude Tosun
- Department of Radiology, School of Medicine, Kocaeli University, Kocaeli, Turkey
| | - Arzu S Arslan
- Department of Radiology, School of Medicine, Kocaeli University, Kocaeli, Turkey
| | - Ercument Ciftci
- Department of Radiology, School of Medicine, Kocaeli University, Kocaeli, Turkey
| | - Nihat Zafer Utkan
- Department of General Surgery, School of Medicine, Kocaeli University, Kocaeli, Turkey
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15
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Choi BB. Dynamic contrast enhanced-MRI and diffusion-weighted image as predictors of lymphovascular invasion in node-negative invasive breast cancer. World J Surg Oncol 2021; 19:76. [PMID: 33722246 PMCID: PMC7962354 DOI: 10.1186/s12957-021-02189-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 03/09/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Lymphovascular invasion (LVI) is an important risk factor for prognosis of breast cancer and an unfavorable prognostic factor in node-negative invasive breast cancer patients. The purpose of this study was to evaluate the association between LVI and pre-operative features of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in node-negative invasive breast cancer. METHODS Data were collected retrospectively from 132 cases who had undergone pre-operative MRI and had invasive breast carcinoma confirmed on the last surgical pathology report. MRI and DWI data were analyzed for the size of tumor, mass shape, margin, internal enhancement pattern, kinetic enhancement curve, high intratumoral T2-weighted signal intensity, peritumoral edema, DWI rim sign, and apparent diffusion coefficient (ADC) values. We calculated the relationship between presence of LVI and various prognostic factors and MRI features. RESULTS Pathologic tumor size, mass margin, internal enhancement pattern, kinetic enhancement curve, DWI rim sign, and the difference between maximum and minimum ADC were significantly correlated with LVI (p < 0.05). CONCLUSIONS We suggest that DCE-MRI with DWI would assist in predicting LVI status in node-negative invasive breast cancer patients.
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Affiliation(s)
- Bo Bae Choi
- Department of Radiology, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon, 35015, Republic of Korea.
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16
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Tang WJ, Jin Z, Zhang YL, Liang YS, Cheng ZX, Chen LX, Liang YY, Wei XH, Kong QC, Guo Y, Jiang XQ. Whole-Lesion Histogram Analysis of the Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker for Assessing the Level of Tumor-Infiltrating Lymphocytes: Value in Molecular Subtypes of Breast Cancer. Front Oncol 2021; 10:611571. [PMID: 33489920 PMCID: PMC7820903 DOI: 10.3389/fonc.2020.611571] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 11/19/2020] [Indexed: 12/18/2022] Open
Abstract
Purpose To assess whether apparent diffusion coefficient (ADC) metrics can be used to assess tumor-infiltrating lymphocyte (TIL) levels in breast cancer, particularly in the molecular subtypes of breast cancer. Methods In total, 114 patients with breast cancer met the inclusion criteria (mean age: 52 years; range: 29–85 years) and underwent multi-parametric breast magnetic resonance imaging (MRI). The patients were imaged by diffusion-weighted (DW)-MRI (1.5 T) using a single-shot spin-echo echo-planar imaging sequence. Two readers independently drew a region of interest (ROI) on the ADC maps of the whole tumor. The mean ADC and histogram parameters (10th, 25th, 50th, 75th, and 90th percentiles of ADC, skewness, entropy, and kurtosis) were used as features to analyze associations with the TIL levels in breast cancer. Additionally, the correlation between the ADC values and Ki-67 expression were analyzed. Continuous variables were compared with Student’s t-test or Mann-Whitney U test if the variables were not normally distributed. Categorical variables were compared using Pearson’s chi-square test or Fisher’s exact test. Associations between TIL levels and imaging features were evaluated by the Mann-Whitney U and Kruskal-Wallis tests. Results A statistically significant difference existed in the 10th and 25th percentile ADC values between the low and high TIL groups in breast cancer (P=0.012 and 0.027). For the luminal subtype of breast cancer, the 10th percentile ADC value was significantly lower in the low TIL group (P=0.041); for the non-luminal subtype of breast cancer, the kurtosis was significantly lower in the low TIL group (P=0.023). The Ki-67 index showed statistical significance for evaluating the TIL levels in breast cancer (P=0.007). Additionally, the skewness was significantly higher for samples with high Ki-67 levels in breast cancer (P=0.029). Conclusions Our findings suggest that whole-lesion ADC histogram parameters can be used as surrogate biomarkers to evaluate TIL levels in molecular subtypes of breast cancer.
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Affiliation(s)
- Wen-Jie Tang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Zhe Jin
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Yan-Ling Zhang
- Department of Ultrasound, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yun-Shi Liang
- Department of Pathology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Zi-Xuan Cheng
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Lei-Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Ying-Ying Liang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xin-Hua Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Qing-Cong Kong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yuan Guo
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xin-Qing Jiang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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17
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Yuen S, Monzawa S, Yanai S, Matsumoto H, Yata Y, Ichinose Y, Deai T, Hashimoto T, Tashiro T, Yamagami K. The association between MRI findings and breast cancer subtypes: focused on the combination patterns on diffusion-weighted and T2-weighted images. Breast Cancer 2020; 27:1029-1037. [PMID: 32377938 DOI: 10.1007/s12282-020-01105-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 04/28/2020] [Indexed: 01/06/2023]
Abstract
PURPOSE To assess morphology on diffusion-weighted imaging (DWI) and intratumoral signal intensity (SI) on T2-weighted images (T2WI) of breast carcinomas, and to evaluate the association between the combined DWI and T2WI findings and breast cancer subtypes. METHODS Two hundred and eighty breast cancer patients who underwent breast MRI prior to therapy were included in this retrospective study. All had invasive carcinomas, which were classified into five subtypes: Luminal A-like (n = 149), Luminal B-like (n = 63), Hormone receptor-positive HER2 (n = 31), Hormone receptor-negative HER2 (n = 13), or Triple-negative (TN) (n = 24). Based on the morphology on DWI, the tumors were classified into two patterns: DWI-homogeneous or DWI-heterogeneous. If DWI-heterogeneous, an assessment of intratumoral SI on T2WI was performed: tumors with intratumoral high/low SI on T2WI were classified as Hete-H/Hete-L, respectively. The associations between (1) the morphological patterns on DWI and the five subtypes, and (2) the intratumoral SI patterns on T2WI and the five subtypes in DWI-heterogeneous were evaluated. RESULTS There was a significant association between (1) the morphological patterns on DWI and the five subtypes (p < 0.0001), and (2) the intratumoral SI patterns on T2WI and the five subtypes in DWI-heterogeneous (p < 0.0001). DWI-homogeneous was dominant in Luminal A-like (67.1%), and Hete-H was dominant in TN type (75%). Hete-H, suggesting the presence of intratumoral necrosis, included high proliferative and/or aggressive subtypes more frequently (80%) than Hete-L, suggesting the presence of fibrotic focus. Fibrotic focus was seen more commonly in the luminal subtypes. CONCLUSION The combined findings on DWI and T2WI revealed breast carcinomas that were associated with particular subtypes.
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Affiliation(s)
- Sachiko Yuen
- Breast Research Center, The Shinko Institution for Medical Research, Shinko Hospital, 1-4-47, Wakinohama Chuo, Kobe, 651-0072, Japan. .,Department of Breast Surgery and Oncology, Shinko Hospital, Kobe, Japan.
| | - Shuichi Monzawa
- Department of Diagnostic Radiology, Shinko Hospital, Kobe, Japan
| | - Seiji Yanai
- Breast Research Center, The Shinko Institution for Medical Research, Shinko Hospital, 1-4-47, Wakinohama Chuo, Kobe, 651-0072, Japan.,Department of Breast Surgery and Oncology, Shinko Hospital, Kobe, Japan
| | - Hajime Matsumoto
- Breast Research Center, The Shinko Institution for Medical Research, Shinko Hospital, 1-4-47, Wakinohama Chuo, Kobe, 651-0072, Japan.,Department of Breast Surgery and Oncology, Shinko Hospital, Kobe, Japan
| | - Yoshihiro Yata
- Breast Research Center, The Shinko Institution for Medical Research, Shinko Hospital, 1-4-47, Wakinohama Chuo, Kobe, 651-0072, Japan.,Department of Breast Surgery and Oncology, Shinko Hospital, Kobe, Japan
| | - You Ichinose
- Department of Breast Surgery and Oncology, Shinko Hospital, Kobe, Japan
| | - Teruyuki Deai
- Department of Breast Surgery and Oncology, Shinko Hospital, Kobe, Japan
| | - Takashi Hashimoto
- Department of Breast Surgery and Oncology, Shinko Hospital, Kobe, Japan
| | | | - Kazuhiko Yamagami
- Breast Research Center, The Shinko Institution for Medical Research, Shinko Hospital, 1-4-47, Wakinohama Chuo, Kobe, 651-0072, Japan.,Department of Breast Surgery and Oncology, Shinko Hospital, Kobe, Japan
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18
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Hirata A, Hayano K, Ohira G, Imanishi S, Hanaoka T, Toyozumi T, Murakami K, Aoyagi T, Shuto K, Matsubara H. Volumetric Histogram Analysis of Apparent Diffusion Coefficient as a Biomarker to Predict Survival of Esophageal Cancer Patients. Ann Surg Oncol 2020; 27:3083-3089. [PMID: 32100222 DOI: 10.1245/s10434-020-08270-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Indexed: 12/29/2022]
Abstract
BACKGROUND The purpose of this study was to investigate whether histogram analysis of an apparent diffusion coefficient (ADC) can serve as a prognostic biomarker for esophageal squamous cell carcinoma (ESCC). METHODS This retrospective study enrolled 116 patients with ESCC who received curative surgery from 2006 to 2015 (including 70 patients who received neoadjuvant chemotherapy). Diffusion-weighted magnetic resonance imaging (DWI) was performed prior to treatment. The ADC maps were generated by DWIs at b = 0 and 1000 (s/mm2), and analyzed to obtain ADC histogram-derived parameters (mean ADC, kurtosis, and skewness) of the primary tumor. Associations of these parameters with pathological features were analyzed, and Cox regression and Kaplan-Meier analyses were performed to compare these parameters with recurrence-free survival (RFS) and disease-specific survival (DSS). RESULTS Kurtosis was significantly higher in tumors with lymphatic invasion (p = 0.005) with respect to the associations with pathological features. In univariate Cox regression analysis, tumor depth, lymph node status, mean ADC, and kurtosis were significantly correlated with RFS (p = 0.047, p < 0.001, p = 0.037, and p < 0.001, respectively), while lymph node status and kurtosis were also correlated with DSS (p = 0.002 and p = 0.017, respectively). Furthermore, multivariate analysis demonstrated that kurtosis was the independent prognostic factor for both RFS and DSS (p < 0.001 and p = 0.015, respectively). In Kaplan-Meier analysis, patients with higher kurtosis tumors (> 3.24) showed a significantly worse RFS and DFS (p < 0.001 and p = 0.006, respectively). CONCLUSIONS Histogram analysis of ADC may serve as a useful biomarker for ESCC, reflecting pathological features and prognosis.
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Affiliation(s)
- Atsushi Hirata
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Koichi Hayano
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Chiba, Japan.
| | - Gaku Ohira
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Shunsuke Imanishi
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Toshiharu Hanaoka
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Takeshi Toyozumi
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Kentaro Murakami
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Tomoyoshi Aoyagi
- Department of Surgery, Funabashi Municipal Medical Center, Chiba, Japan
| | - Kiyohiko Shuto
- Department of Surgery, Teikyo University Chiba Medical Center, Chiba, Japan
| | - Hisahiro Matsubara
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
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Chen J, Wu Z, Xia C, Jiang H, Liu X, Duan T, Cao L, Ye Z, Zhang Z, Ma L, Song B, Shi Y. Noninvasive prediction of HCC with progenitor phenotype based on gadoxetic acid-enhanced MRI. Eur Radiol 2020; 30:1232-1242. [PMID: 31529254 DOI: 10.1007/s00330-019-06414-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 07/26/2019] [Accepted: 08/07/2019] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To explore the noninvasive prediction of hepatocellular carcinoma (HCC) with progenitor phenotype based on gadoxetic acid-enhanced magnetic resonance imaging (MRI). METHODS This retrospective study included 115 surgery-proven HCCs with preoperative gadoxetic acid-enhanced MRI from August 2015 to September 2018. Image features were reviewed. Quantitative image analysis was performed using histogram analysis. HCC with progenitor phenotype was defined as positive for either cytokeratin 19 (CK19) or epithelial cell adhesion molecule (EpCAM) expression. Statistically significant variables for identifying HCCs with progenitor phenotype were determined at multivariate analyses. ROC analyses were used to determined cutoff values and the diagnostic performance of significant variables and combinations. Prediction nomogram was constructed based on multivariate analysis. RESULTS At multivariate regression analyses, AFP ≥ 155.25 ng/mL (p < 0.001), skewness on T2WI ≤ 1.10 (p = 0.024), uniformity on pre-T1WI ≤ 0.91 (p = 0.024), irregular tumor margin (p = 0.006), targetoid appearance (p = 0.001), and the absence of mosaic architecture (p = 0.014) were significant predictors of HCCs expressing progenitor cell markers. Combing any three of those significant variables, it provides a diagnostic accuracy of 0.86 (95% CI 0.78-0.92) with sensitivity of 0.97 (95% CI 0.86-1.00), and specificity of 0.74 (95% CI 0.63-0.83). The C-index of the regression coefficient-based nomogram was 0.94 (95% CI 0.91-0.98). CONCLUSIONS Noninvasive prediction of HCCs with progenitor phenotype can be achieved with high accuracy by integrated interpretation of biochemical and radiological information, representing a handy tool for precise patient management and the prediction of prognosis. KEY POINTS • Qualitative image features of irregular tumor margin, targetoid appearance, and the absence of mosaic architecture are significant predictors of hepatocellular carcinoma with progenitor phenotype. • Quantitative analyses using whole-lesion histogram analysis provides additional information for the prediction of hepatocellular carcinoma with progenitor phenotype. • Noninvasive prediction of hepatocellular carcinoma with progenitor phenotype can be achieved with high accuracy by integrated interpretation of clinical information and qualitative and quantitative imaging analyses.
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Affiliation(s)
- Jie Chen
- West China School of Medicine, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Zhenru Wu
- Laboratory of Pathology, West China Hospital, Sichuan University, B2 Building, No. 88, South Ke Yuan Road, Chengdu, 610041, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Hanyu Jiang
- West China School of Medicine, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Xijiao Liu
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Ting Duan
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Likun Cao
- West China School of Medicine, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Zheng Ye
- West China School of Medicine, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Zhen Zhang
- West China School of Medicine, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Ling Ma
- Application Advanced Team, GE Healthcare, Shanghai, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China.
| | - Yujun Shi
- Laboratory of Pathology, West China Hospital, Sichuan University, B2 Building, No. 88, South Ke Yuan Road, Chengdu, 610041, China.
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20
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Associations Between Apparent Diffusion Coefficient Values and the Prognostic Factors of Breast Cancer. J Comput Assist Tomogr 2019; 43:931-936. [PMID: 31738207 DOI: 10.1097/rct.0000000000000936] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Breast imaging can offer great information about breast cancer heterogeneity. The purpose of this study was to analyze the relationship between apparent diffusion coefficient (ADC) values and various prognostic factors and investigate whether ADC values are useful for breast cancer diagnosis, evaluation of treatment response, and determination of prognosis. METHODS A total of 111 cases of breast cancer were included in this study. Magnetic resonance findings were recorded according to the Breast Imaging Reporting and Data System magnetic resonance imaging lexicon. Diffusion-weighted imaging rim sign and minimum, maximum, and difference ADC values (ADCdiff) were also evaluated. RESULTS ADCdiff was related to all prognostic factors such as histological grade, Ki-67, tumor size, molecular subtype, axillary node metastasis, lymphvascular invasion, internal enhancement pattern, intratumoral high T2 signal, peritumoral edema, and diffusion-weighted imaging rim sign, whereas minimum and maximum ADC values showed variable associations. CONCLUSIONS Apparent diffusion coefficient values were shown to be correlated with many proven or possible prognostic factors of breast cancer. In particular, ADCdiff can reflect tumor heterogeneity and showed higher correlation.
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Surov A, Meyer HJ, Wienke A. Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions. BMC Cancer 2019; 19:955. [PMID: 31615463 PMCID: PMC6794799 DOI: 10.1186/s12885-019-6201-4] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 09/24/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions. METHODS MEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy of breast lesions up to December 2018. Overall, 123 items were identified. The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength. The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions. RESULTS The acquired 123 studies comprised 13,847 breast lesions. Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%). The mean ADC value of the malignant lesions was 1.03 × 10- 3 mm2/s and the mean value of the benign lesions was 1.5 × 10- 3 mm2/s. The calculated ADC values of benign lesions were over the value of 1.00 × 10- 3 mm2/s. This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area). CONCLUSION An ADC threshold of 1.00 × 10- 3 mm2/s can be recommended for distinguishing breast cancers from benign lesions.
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Affiliation(s)
- Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstr. 20, 04103, Leipzig, Germany. .,Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081, Ulm, Germany.
| | - Hans Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstr. 20, 04103, Leipzig, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Magdeburger Str. 8, 06097, Halle, Germany
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22
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Iima M, Honda M, Sigmund EE, Ohno Kishimoto A, Kataoka M, Togashi K. Diffusion MRI of the breast: Current status and future directions. J Magn Reson Imaging 2019; 52:70-90. [PMID: 31520518 DOI: 10.1002/jmri.26908] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 08/12/2019] [Indexed: 12/30/2022] Open
Abstract
Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.
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Affiliation(s)
- Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Department of Clinical Innovative Medicine, Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital, Kyoto, Japan
| | - Maya Honda
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Eric E Sigmund
- Department of Radiology, NYU Langone Health, New York, New York, USA.,Center for Advanced Imaging and Innovation (CAI2R), New York, New York, USA
| | - Ayami Ohno Kishimoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masako Kataoka
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kaori Togashi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
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23
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Diffusion-weighted MRI of estrogen receptor-positive, HER2-negative, node-negative breast cancer: association between intratumoral heterogeneity and recurrence risk. Eur Radiol 2019; 30:66-76. [PMID: 31385051 DOI: 10.1007/s00330-019-06383-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 07/03/2019] [Accepted: 07/19/2019] [Indexed: 01/01/2023]
Abstract
OBJECTIVES To investigate possible associations between quantitative apparent diffusion coefficient (ADC) metrics derived from whole-lesion histogram analysis and breast cancer recurrence risk in women with estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, node-negative breast cancer who underwent the Oncotype DX assay. METHODS This retrospective study was conducted on 105 women (median age, 48 years) with ER-positive, HER2-negative, node-negative breast cancer who underwent the Oncotype DX test and preoperative diffusion-weighted imaging (DWI). Histogram analysis of pixel-based ADC data of whole tumors was performed, and various ADC histogram parameters (mean, 5th, 25th, 50th, 75th, and 95th percentiles of ADCs) were extracted. The ADC difference value (defined as the difference between the 5th and 95th percentiles of ADCs) was calculated to assess intratumoral heterogeneity. Associations between quantitative ADC metrics and the recurrence risk, stratified using the Oncotype DX recurrence score (RS), were evaluated. RESULTS Whole-lesion histogram analysis showed that the ADC difference value was different between the low-risk recurrence (RS < 18) and the non-low-risk recurrence (RS ≥ 18; intermediate to high risk of recurrence) groups (0.600 × 10-3 mm2/s vs. 0.746 × 10-3 mm2/s, p < 0.001). Multivariate regression analysis demonstrated that a lower ADC difference value (< 0.559 × 10-3 mm2/s; odds ratio [OR] = 5.998; p = 0.007) and a small tumor size (≤ 2 cm; OR = 3.866; p = 0.012) were associated with a low risk of recurrence after adjusting for clinicopathological factors. CONCLUSIONS The ADC difference value derived from whole-lesion histogram analysis might serve as a quantitative DWI biomarker of the recurrence risk in women with ER-positive, HER2-negative, node-negative invasive breast cancer. KEY POINTS • A lower ADC difference value and a small tumor size were associated with a low risk of recurrence of breast cancer. • The ADC difference value could be a quantitative marker for intratumoral heterogeneity. • Whole-lesion histogram analysis of the ADC could be helpful for discriminating the low-risk from non-low-risk recurrence groups.
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24
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Hirata A, Hayano K, Ohira G, Imanishi S, Hanaoka T, Murakami K, Aoyagi T, Shuto K, Matsubara H. Volumetric histogram analysis of apparent diffusion coefficient for predicting pathological complete response and survival in esophageal cancer patients treated with chemoradiotherapy. Am J Surg 2019; 219:1024-1029. [PMID: 31387687 DOI: 10.1016/j.amjsurg.2019.07.040] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 07/23/2019] [Accepted: 07/27/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND The purpose of the study was to evaluate whether histogram analysis of apparent diffusion coefficient (ADC) can predict pathological complete response (pCR) and survival in patients with esophageal squamous cell carcinoma (ESCC) after chemoradiotherapy (CRT). METHODS We retrospectively identified 58 patients with ESCC who underwent surgery after CRT between 2007 and 2016. Associations of pretreatment histogram derived ADC parameters with pathological response and survival were analyzed. RESULTS Tumors achieved pCR (10 patients, 17.2%) showed significant lower ADC, higher kurtosis, and higher skewness than those of non-pCR (p = 0.005, 0.007, <0.001, respectively). Receiver operating characteristics analysis demonstrated skewness was the best predictor for pCR (AUC = 0.86), with a cut off value of 0.50 (accuracy, 86.2%). In Kaplan-Meier analysis, patients with higher skewness tumors (≥0.50) showed a significantly better recurrence free survival (p = 0.032, log-rank). CONCLUSIONS Histogram analysis of ADC can enable prediction of pCR and survival in ESCC patients treated with preoperative CRT. A SHORT SUMMARY ADC histogram analysis can be an imaging biomarker for esophageal cancer patients treated with CRT.
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Affiliation(s)
- Atsushi Hirata
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Japan
| | - Koichi Hayano
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Japan.
| | - Gaku Ohira
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Japan
| | - Shunsuke Imanishi
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Japan
| | - Toshiharu Hanaoka
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Japan
| | - Kentaro Murakami
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Japan
| | - Tomoyoshi Aoyagi
- Department of Surgery, Funabashi Municipal Medical Center, Japan
| | - Kiyohiko Shuto
- Department of Surgery, Teikyo University Chiba Medical Center, Japan
| | - Hisahiro Matsubara
- Department of Frontier Surgery, Chiba University Graduate School of Medicine, Japan
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25
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Xie T, Wang Z, Zhao Q, Bai Q, Zhou X, Gu Y, Peng W, Wang H. Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer. Front Oncol 2019; 9:505. [PMID: 31259153 PMCID: PMC6587031 DOI: 10.3389/fonc.2019.00505] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 05/28/2019] [Indexed: 12/15/2022] Open
Abstract
Objective: To investigate whether machine learning analysis of multiparametric MR radiomics can help classify immunohistochemical (IHC) subtypes of breast cancer. Study design: One hundred and thirty-four consecutive patients with pathologically-proven invasive ductal carcinoma were retrospectively analyzed. A total of 2,498 features were extracted from the DCE and DWI images, together with the new calculated images, including DCE images changing over six time points (DCEsequential) and DWI images changing over three b-values (DWIsequential). We proposed a novel two-stage feature selection method combining traditional statistics and machine learning-based methods. The accuracies of the 4-IHC classification and triple negative (TN) vs. non-TN cancers were assessed. Results: For the 4-IHC classification task, the best accuracy of 72.4% was achieved based on linear discriminant analysis (LDA) or subspace discrimination of assembled learning in conjunction with 20 selected features, and only small dependent emphasis of Kendall-tau-b for sequential features, based on the DWIsequential with the LDA model, yielding an accuracy of 53.7%. The linear support vector machine (SVM) and medium k-nearest neighbor using eight features yielded the highest accuracy of 91.0% for comparing TN to non-TN cancers, and the maximum variance for DWIsequential alone, together with a linear SVM model, achieved an accuracy of 83.6%. Conclusions: Whole-tumor radiomics on MR multiparametric images, DCE images changing over time points, and DWI images changing over different b-values provide a non-invasive analytical approach for breast cancer subtype classification and TN cancer identification.
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Affiliation(s)
- Tianwen Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Zhe Wang
- Human Phenome Institute, Fudan University, Shanghai, China.,Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
| | - Qiufeng Zhao
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Qianming Bai
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Xiaoyan Zhou
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - He Wang
- Human Phenome Institute, Fudan University, Shanghai, China.,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
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26
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Dong X, Chunrong Y, Hongjun H, Xuexi Z. Differentiating the lymph node metastasis of breast cancer through dynamic contrast-enhanced magnetic resonance imaging. BJR Open 2019; 1:20180023. [PMID: 33178917 PMCID: PMC7592437 DOI: 10.1259/bjro.20180023] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 02/26/2019] [Accepted: 02/27/2019] [Indexed: 01/30/2023] Open
Abstract
Objective: Lymph node metastasis is an important trait of breast cancer, and tumors with different lymph node statuses require various clinical treatments. This study was designed to evaluate the lymph node metastasis of breast cancer through pharmacokinetic and histogram analysis via dynamic contrast-enhanced (DCE) MRI. Methods and materials: A retrospective analysis was conducted to quantitatively evaluate the lymph node statuses of patients with breast cancer. A total of 75 patients, i.e. 34 patients with lymph node metastasis and 41 patients without lymph node metastasis, were involved in this research. Of the patients with lymph node metastases, 19 had sentinel lymph node metastasis, and 15 had axillary lymph node metastasis. MRI was conducted using a 3.0 T imaging device. Segmentation was carried out on the regions of interest (ROIs) in breast tumors under DCE-MRI, and pharmacokinetic and histogram parameters were calculated from the same ROIs. Mann–Whitney U test was performed, and receiver operating characteristic curves for the parameters of the two groups were constructed to determine their diagnostic values. Results: Pharmacokinetic parameters, including Ktrans, Kep, area under the curve of time–concentration, and time to peak, which were derived from the extended Tofts linear model for DCE-MRI, could highlight the tumor areas in the breast and reveal the increased perfusion. Conversely, the pharmacokinetic parameters showed no significant difference between the patients with and without lymph node metastases. By contrast, the parameters from the histogram analysis yielded promising results. The entropy of the ROIs exhibited the best diagnostic ability between patients with and without lymph node metastases (p < 0.01, area under the curve of receiver operating characteristic = 0.765, specificity = 0.706, sensitivity = 0.780). Conclusion: In comparison with the pharmacokinetic parameters, the histogram analysis of the MR images could reveal the differences between patients with and without lymph node metastases. The entropy from the histogram indicated that the diagnostic ability was highly sensitive and specific. Advances in knowledge: This research gave out a promising result on the differentiating lymph node metastases through histogram analysis on tumors in DCE-MR images. Histogram could reveal the tumors heterogenicity between patients with different lymph node status.
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Affiliation(s)
- Xu Dong
- WeiHai Central Hospital, Weihai City, ShanDong, China
| | - Yu Chunrong
- WeiHai Central Hospital, Weihai City, ShanDong, China
| | - Hou Hongjun
- WeiHai Central Hospital, Weihai City, ShanDong, China
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27
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Camps-Herrero J. Diffusion-weighted imaging of the breast: current status as an imaging biomarker and future role. BJR Open 2019; 1:20180049. [PMID: 33178933 PMCID: PMC7592470 DOI: 10.1259/bjro.20180049] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 02/07/2019] [Accepted: 02/12/2019] [Indexed: 12/13/2022] Open
Abstract
Diffusion-weighted imaging (DWI) of the breast is a MRI sequence that shows several advantages when compared to the dynamic contrast-enhanced sequence: it does not need intravenous contrast, it is relatively quick and easy to implement (artifacts notwithstanding). In this review, the current applications of DWI for lesion characterization and prognosis as well as for response evaluation are analyzed from the point of view of the necessary steps to become a useful surrogate of underlying biological processes (tissue architecture and cellularity): from the proof of concept, to the proof of mechanism, the proof of principle and finally the proof of effectiveness. Future applications of DWI in screening, DWI modeling and radiomics are also discussed.
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Affiliation(s)
- Julia Camps-Herrero
- Head of Radiology Department, Breast Unit. Hospital Universitario de la Ribera, Alzira, Spain
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28
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Computerized Image Analysis to Differentiate Benign and Malignant Breast Tumors on Magnetic Resonance Diffusion Weighted Image. J Comput Assist Tomogr 2019; 43:93-97. [DOI: 10.1097/rct.0000000000000793] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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29
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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: 57] [Impact Index Per Article: 9.5] [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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Kim JY, Kim JJ, Lee JW, Lee NK, Lee G, Kang T, Park H, Son YH, Grimm R. Risk stratification of ductal carcinoma in situ using whole-lesion histogram analysis of the apparent diffusion coefficient. Eur Radiol 2018; 29:485-493. [PMID: 30073498 DOI: 10.1007/s00330-018-5666-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 07/01/2018] [Accepted: 07/13/2018] [Indexed: 01/01/2023]
Abstract
OBJECTIVES To investigate the value of the whole-lesion histogram apparent diffusion coefficient (ADC) metrics for differentiating low-risk from non-low-risk ductal carcinoma in situ (DCIS). METHODS The authors identified 93 women with pure DCIS who had undergone preoperative MR imaging and diffusion-weighted imaging from 2013 to 2016. Histogram analysis of pixel-based ADC data of the whole tumour volume was performed by two radiologists using a software tool. The results were compared between low-risk and non-low-risk DCIS. Associations between quantitative ADC metrics and low-risk DCIS were evaluated by receiver operating characteristics (ROC) curve and logistic regression analyses. RESULTS In whole-lesion histogram analysis, mean ADC and 5th, 50th and 95th percentiles of ADC were significantly different between low-risk and non-low-risk DCIS (1.522, 1.207, 1.536 and 1.854 × 10-3 mm2/s versus 1.270, 0.917, 1.261 and 1.657 × 10-3 mm2/s, respectively; p = .004, p = .003, p = .004 and p = .024, respectively). ROC curve analysis for differentiating low-risk DCIS revealed that 5th percentile ADC yielded the largest area under the curve (0.786) among the metrics of whole-lesion histogram, and the optimal cut-off point was 1.078 × 10-3 mm2/s (sensitivity 80%, specificity 75.9%, p = .001). Multivariate regression analysis revealed that a high 5th percentile of ADC (> 1.078× 10-3 mm2/s; odds ratio [OR] = 10.494, p = .016), small tumour size (≤ 2 cm; OR = 12.692, p = .008) and low Ki-67 status (< 14%; OR = 10.879, p = .046) were significantly associated with low-risk DCIS. CONCLUSIONS Assessment with whole-lesion histogram analysis of the ADC could be helpful for identifying patients with low-risk DCIS. KEY POINTS • Whole-lesion histogram ADC metrics could be helpful for differentiating low-risk from non-low-risk DCIS. • A high 5th percentile ADC was a significant factor associated with low-risk DCIS. • Risk stratification of DCIS is important for their management.
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Affiliation(s)
- Jin You Kim
- Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute, 1-10, Ami-Dong, Seo-gu, Busan, 602-739, Republic of Korea. .,Medical Research Institute, Pusan National University School of Medicine, Busan, Republic of Korea.
| | - Jin Joo Kim
- Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute, 1-10, Ami-Dong, Seo-gu, Busan, 602-739, Republic of Korea
| | - Ji Won Lee
- Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute, 1-10, Ami-Dong, Seo-gu, Busan, 602-739, Republic of Korea
| | - Nam Kyung Lee
- Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute, 1-10, Ami-Dong, Seo-gu, Busan, 602-739, Republic of Korea
| | - Geewon Lee
- Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute, 1-10, Ami-Dong, Seo-gu, Busan, 602-739, Republic of Korea
| | - Taewoo Kang
- Busan Cancer Center, Pusan National University Hospital, Busan, Republic of Korea
| | - Heesung Park
- Busan Cancer Center, Pusan National University Hospital, Busan, Republic of Korea
| | | | - Robert Grimm
- Siemens Healthineers, MR Application Predevelopment, Erlangen, Germany
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31
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Nagasaka K, Satake H, Ishigaki S, Kawai H, Naganawa S. Histogram analysis of quantitative pharmacokinetic parameters on DCE-MRI: correlations with prognostic factors and molecular subtypes in breast cancer. Breast Cancer 2018; 26:113-124. [PMID: 30069785 DOI: 10.1007/s12282-018-0899-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 07/26/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND Breast cancer heterogeneity influences poor prognoses thorough therapy resistance. This study quantitatively evaluated intratumoral heterogeneity through a histogram analysis of dynamic contrast-enhanced MRI (DCE-MRI) pharmacokinetic parameters, and determined correlations with prognostic factors and molecular subtypes. METHODS We retrospectively investigated 101 invasive ductal breast cancers from 99 women who underwent preoperative DCE-MRI between July 2012 and November 2014. Pharmacokinetic parameters (Ktrans, kep, and ve) were obtained by the Tofts model. For each parameter, the mean, standard deviation, coefficient of variation, skewness, and kurtosis values of tumor were calculated, and prognostic factors and subtypes associations were assessed. RESULTS The mean of ve was lower in cancers with high Ki-67 than in cancers with low Ki-67 (P = 0.002). The coefficient of variation of ve was higher in cancers with estrogen receptor negativity than in cancers with estrogen receptor positivity (P < 0.001). The coefficient of variation of ve was also higher in cancers with high Ki-67 than in cancers with low Ki-67 (P < 0.001). The skewness of ve was higher in cancers with high nuclear grade than in cancers with low nuclear grade (P = 0.006). Triple-negative cancers showed higher ve coefficient of variation than did those with luminal A (P < 0.001) and B (P = 0.006). CONCLUSIONS Various ve parameters correlated with breast cancer prognostic factors and molecular subtypes.
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Affiliation(s)
- Ken Nagasaka
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan.
| | - Hiroko Satake
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
| | - Satoko Ishigaki
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
| | - Hisashi Kawai
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan
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Wu LF, Rao SX, Xu PJ, Yang L, Chen CZ, Liu H, Huang JF, Fu CX, Halim A, Zeng MS. Pre-TACE kurtosis of ADC total derived from histogram analysis for diffusion-weighted imaging is the best independent predictor of prognosis in hepatocellular carcinoma. Eur Radiol 2018; 29:213-223. [PMID: 29922932 DOI: 10.1007/s00330-018-5482-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 04/05/2018] [Accepted: 04/11/2018] [Indexed: 02/07/2023]
Abstract
PURPOSE To determine the feasibility of pre-TACE IVIM imaging based on histogram analysis for predicting prognosis in the treatment of unresectable hepatocellular carcinoma (HCC). MATERIALS AND METHODS Fifty-five patients prospectively underwent 1.5T MRI 1 week before TACE. Histogram metrics for IVIM parameters and ADCs maps between responders and non-responders with mRECIST assessment were compared. Kaplan-Meier, log-rank tests and Cox proportional hazard regression model were used to correlate variables with time to progression (TTP). RESULTS Mean (p = 0.022), median (p = 0.043), and 25th percentile (p < 0.001) of perfusion fraction (PF), mean (p < 0.001), median (p < 0.001), 25th percentile (p < 0.001) and 75th percentile (p = 0.001) of ADC(0,500), mean (p = 0.005), median (p = 0.008) and 25th percentile (p = 0.039) of ADCtotal were higher, while skewness and kurtosis of PF (p = 0.001, p = 0.005, respectively), kurtosis of ADC(0,500) and ADCtotal (p = 0.005, p = 0.001, respectively) were lower in responders compared to non-responders. Multivariable analysis demonstrated that mRECIST was associated with TTP independently, and kurtosis of ADCtotal had the best predictive performance for disease progression. CONCLUSION Pre-TACE kurtosis of ADCtotal is the best independent predictor for TTP. KEY POINTS • mRECIST was associated with TTP independently. • Lower kurtosis and higher mean for ADCs tend to have good response. • Pre-TACE kurtosis of ADC total is the best independent predictor for TTP.
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Affiliation(s)
- Li-Fang Wu
- Shanghai Institute of Medical Imaging, Department of Radiology, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Sheng-Xiang Rao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, No. 180, Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Peng-Ju Xu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, No. 180, Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Li Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, No. 180, Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Cai-Zhong Chen
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, No. 180, Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Hao Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, No. 180, Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Jian-Feng Huang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, No. 180, Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Cai-Xia Fu
- Siemens Healthcare, Siemens MR Center, Gaoxin C. Ave., 2nd, Hi-Tech Industrial Park, Shenzhen, 518057, China
| | - Alice Halim
- Fudan University, No. 130, Dongan Road, Xuhui District, Shanghai, 200032, China
| | - Meng-Su Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, No. 180, Fenglin Road, Xuhui District, Shanghai, 200032, China.
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