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Lai B, Yi Y, Yang X, Li X, Xu L, Yan Z, Yang L, Han R, Hu H, Duan X. Dynamic contrast-enhanced and diffusion-weighted MRI of cervical carcinoma: Correlations with Ki-67 proliferation status. Magn Reson Imaging 2024; 112:136-143. [PMID: 39029603 DOI: 10.1016/j.mri.2024.07.010] [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: 05/09/2024] [Revised: 06/15/2024] [Accepted: 07/16/2024] [Indexed: 07/21/2024]
Abstract
OBJECTIVES To investigate the association of quantitative parameter (apparent diffusion coefficient [ADC]) from diffusion-weighted imaging (DWI) and various quantitative and semiquantitative parameters from dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) with Ki-67 proliferation index (PI) in cervical carcinoma (CC). METHODS A total of 102 individuals with CC who received 3.0 T MRI examination (DWI and DCE MRI) between October 2016 and December 2022 were enrolled in our investigation. Two radiologists separately assessed the ADC parameter and various quantitative and semiquantitative parameters including (volume transfer constant [Ktrans], rate constant [kep], extravascular extracellular space volume fraction [ve], volume fraction of plasma [vp], time to peak [TTP], maximum concentration [MaxCon], maximal slope [MaxSlope] and area under curve [AUC]) for each tumor. Their association with Ki-67 PI was analyzed by Spearman association analysis. The discrepancy between low-proliferation and high-proliferation groups was subsequently analyzed. The receiver operating characteristic (ROC) curve analysis utilized to identify optimal cut-off points for significant parameters. RESULTS Both ADC (ρ = -0.457, p < 0.001) and Ktrans (ρ = -0.467, p < 0.001) indicated a strong negative association with Ki-67 PI. Ki-67 PI showed positive correlations with TTP, MaxCon, MaxSlope and AUC (ρ = 0.202, 0.231, 0.309, 0.235, respectively; all p values<0.05). Compared with the low-proliferation group, high-Ki-67 group presented a significantly lower ADC (0.869 ± 0.125 × 10-3 mm2/s vs. 1.149 ± 0.318 × 10-3 mm2/s; p < 0.001) and Ktrans (1.314 ± 1.162 min-1vs. 0.391 ± 0.390 min-1; p < 0.001), also significantly higher MaxCon values (0.756 ± 0.959 vs. 0.422 ± 0.341; p < 0.05) and AUC values (2.373 ± 3.012 vs. 1.273 ± 1.000; p < 0.05). The cut-offs of ADC, Ktrans, MaxCon and AUC for discrimating low- and high-Ki-67 groups were 0.920 × 10-3 mm2/s, 0.304 min-1, 0.209 and 1.918, respectively. CONCLUSIONS ADC, Ktrans, TTP, MaxCon, MaxSlope and AUC are associated with Ki-67 PI. ADC and Ktrans exhibited high performance to discriminate low and high Ki-67 status of CC.
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Affiliation(s)
- Bingjia Lai
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yongju Yi
- Information Technology Department, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, Guangdong, China
| | - Xiaojun Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China
| | - Xiumei Li
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China
| | - Longjiahui Xu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China
| | - Zhuoheng Yan
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China
| | - Lu Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China
| | - Riyu Han
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China
| | - Huijun Hu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China.
| | - Xiaohui Duan
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120, Guangdong, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
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Yang D, Ren Y, Wang C. Histogram analysis of intravoxel incoherent motion imaging: Correlation with molecular prognostic factors and combined subtypes of breast cancer. Magn Reson Imaging 2024; 111:210-216. [PMID: 38777242 DOI: 10.1016/j.mri.2024.05.010] [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: 03/20/2024] [Revised: 05/18/2024] [Accepted: 05/18/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To look for links between diffusion and IVIM parameters and different molecular subtypes and prognostic factors through histogram analysis. MATERIALS AND METHODS A total of 139 patients with breast cancer who had pre-operative MRI examinations were enrolled in this retrospective study. Histograms of the diffusion and IVIM parameters were analyzed for the whole tumor, and an association was investigated between the parameters and the different molecular prognostic factors and subtypes using the nonparametric test, Spearman's rank correlation, and receiver operating characteristic (ROC) curve. RESULTS The histogram metrics of the diffusion and IVIM parameters were significantly different for molecular prognostic factors such as human epidermal receptor factor-2 (HER2), progesterone receptor, estrogen receptor, and ki-67. All histogram metrics displayed a poor correlation with all groups (r = -0.28-0.29). There were significant differences in the histogram metrics for the Luminal B-HER2 (-) vs. HER2-positive (non-luminal) subtypes in the mean and 10th percentile D, with the area under the curves (AUCs) of 0.742 and 0.700, respectively, and for the Luminal A and HER2-positive (non-luminal) subtypes in the 90th percentile and entropy of D*, with AUCs of 0.769 and 0.727, respectively. CONCLUSION The histogram metrics of IVIM parameters exhibited links with breast cancer prognosis factors and combined subtypes.
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Affiliation(s)
- Dan Yang
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China.
| | - Yike Ren
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China
| | - Chunhong Wang
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China
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Zheng Y, Li L, Wang R, Jiang W, Ai K, Gan T, Wang P. Combination of IVIM with DCE-MRI for diagnostic and prognostic evaluation of breast cancer. Magn Reson Imaging 2024:S0730-725X(24)00177-2. [PMID: 38971263 DOI: 10.1016/j.mri.2024.07.003] [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: 01/10/2024] [Revised: 06/14/2024] [Accepted: 07/03/2024] [Indexed: 07/08/2024]
Abstract
PURPOSE To identify the most effective combination of DCE-MRI (Ktrans,Kep) and IVIM (D,f) and analyze the correlations of these parameters with prognostic indicators (ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size) to improve the diagnostic and prognostic efficiency in breast cancer. METHODS This is a prospective study. We performed T1WI, T2WI, IVIM, DCE-MRI at 3 T MRI examinations on benign and malignant breast lesions that met the inclusion criteria. We also collected pathological results of corresponding lesions, including ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size. The diagnostic efficacy of DCE-MRI, IVIM imaging, and their combination for benign and malignant breast lesions was assessed. Correlations between the DCE-MRI and IVIM parameters and prognostic indicators were assessed. RESULTS Overall,59 female patients with 62 lesions (22 benign lesions and 40 malignant lesions) were included in this study. The malignant group showed significantly lower D values (p < 0.05) and significantly higher Ktrans, Kep, and f values (p < 0.05). The AUC values of DCE, IVIM, DCE + IVIM were 0.828, 0.882, 0.901. Ktrans, Kep, D and f values were correlated with the pathological grade (p < 0.05); Ktrans was negatively correlated with ER expression (r = -0.519, p < 0.05); Kep was correlated with PR expression and the Ki-67 index (r = -0.489, 0.330, p < 0.05); the DCE and IVIM parameters showed no significant correlations with the HER2 and ALN (p > 0.05). Tumor diameter was correlated with the Kep, D and f values (r = 0.246, -0.278, 0.293; p < 0.05). CONCLUSION IVIM and DCE-MRI allowed differential diagnosis of benign and malignant breast lesions, and their combination showed significantly better diagnostic efficiency. DCE- and IVIM-derived parameters showed correlations with some prognostic factors for breast cancer.
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Affiliation(s)
- Yurong Zheng
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China.
| | - Li Li
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - Rui Wang
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - Weilong Jiang
- Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, Gansu 730000, China
| | - Kai Ai
- Philips Healthcare, Xi'an, China
| | - Tiejun Gan
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - Pengfei Wang
- Department of Magnetic Resonance, LanZhou University Second Hospital, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
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Kim JY, Partridge SC. Non-contrast Breast MR Imaging. Radiol Clin North Am 2024; 62:661-678. [PMID: 38777541 PMCID: PMC11116814 DOI: 10.1016/j.rcl.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Considering the high cost of dynamic contrast-enhanced MR imaging and various contraindications and health concerns related to administration of intravenous gadolinium-based contrast agents, there is emerging interest in non-contrast-enhanced breast MR imaging. Diffusion-weighted MR imaging (DWI) is a fast, unenhanced technique that has wide clinical applications in breast cancer detection, characterization, prognosis, and predicting treatment response. It also has the potential to serve as a non-contrast MR imaging screening method. Standardized protocols and interpretation strategies can help to enhance the clinical utility of breast DWI. A variety of other promising non-contrast MR imaging techniques are in development, but currently, DWI is closest to clinical integration, while others are still mostly used in the research setting.
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Affiliation(s)
- Jin You Kim
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | - Savannah C Partridge
- Department of Radiology, University of Washington, Seattle, WA, USA; Fred Hutchinson Cancer Center, Seattle, WA, USA.
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Tabnak P, HajiEsmailPoor Z, Baradaran B, Pashazadeh F, Aghebati Maleki L. MRI-Based Radiomics Methods for Predicting Ki-67 Expression in Breast Cancer: A Systematic Review and Meta-analysis. Acad Radiol 2024; 31:763-787. [PMID: 37925343 DOI: 10.1016/j.acra.2023.10.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/01/2023] [Accepted: 10/04/2023] [Indexed: 11/06/2023]
Abstract
RATIONALE AND OBJECTIVES The purpose of this systematic review and meta-analysis was to assess the quality and diagnostic accuracy of MRI-based radiomics for predicting Ki-67 expression in breast cancer. MATERIALS AND METHODS A systematic literature search was performed to find relevant studies published in different databases, including PubMed, Web of Science, and Embase up until March 10, 2023. All papers were independently evaluated for eligibility by two reviewers. Studies that matched research questions and provided sufficient data for quantitative synthesis were included in the systematic review and meta-analysis, respectively. The quality of the articles was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. The predictive value of MRI-based radiomics for Ki-67 antigen in patients with breast cancer was assessed using pooled sensitivity (SEN), specificity, and area under the curve (AUC). Meta-regression was performed to explore the cause of heterogeneity. Different covariates were used for subgroup analysis. RESULTS 31 studies were included in the systematic review; among them, 21 reported sufficient data for meta-analysis. 20 training cohorts and five validation cohorts were pooled separately. The pooled sensitivity, specificity, and AUC of MRI-based radiomics for predicting Ki-67 expression in training cohorts were 0.80 [95% CI, 0.73-0.86], 0.82 [95% CI, 0.78-0.86], and 0.88 [95%CI, 0.85-0.91], respectively. The corresponding values for validation cohorts were 0.81 [95% CI, 0.72-0.87], 0.73 [95% CI, 0.62-0.82], and 0.84 [95%CI, 0.80-0.87], respectively. Based on QUADAS-2, some risks of bias were detected for reference standard and flow and timing domains. However, the quality of the included article was acceptable. The mean RQS score of the included articles was close to 6, corresponding to 16.6% of the maximum possible score. Significant heterogeneity was observed in pooled sensitivity and specificity of training cohorts (I2 > 75%). We found that using deep learning radiomic methods, magnetic field strength (3 T vs. 1.5 T), scanner manufacturer, region of interest structure (2D vs. 3D), route of tissue sampling, Ki-67 cut-off, logistic regression for model construction, and LASSO for feature reduction as well as PyRadiomics software for feature extraction had a great impact on heterogeneity according to our joint model analysis. Diagnostic performance in studies that used deep learning-based radiomics and multiple MRI sequences (e.g., DWI+DCE) was slightly higher. In addition, radiomic features derived from DWI sequences performed better than contrast-enhanced sequences in terms of specificity and sensitivity. No publication bias was found based on Deeks' funnel plot. Sensitivity analysis showed that eliminating every study one by one does not impact overall results. CONCLUSION This meta-analysis showed that MRI-based radiomics has a good diagnostic accuracy in differentiating breast cancer patients with high Ki-67 expression from low-expressing groups. However, the sensitivity and specificity of these methods still do not surpass 90%, restricting them from being used as a supplement to current pathological assessments (e.g., biopsy or surgery) to predict Ki-67 expression accurately.
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Affiliation(s)
- Peyman Tabnak
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H.); Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.)
| | - Zanyar HajiEsmailPoor
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H.); Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.)
| | - Behzad Baradaran
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.)
| | - Fariba Pashazadeh
- Research Center for Evidence-Based Medicine, Iranian Evidence-Based Medicine (EBM) Centre: A Joanna Briggs Institute (JBI) Centre of Excellence, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (F.P.)
| | - Leili Aghebati Maleki
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.); Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran (P.T., Z.H., B.B., L.A.M.).
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Petrillo A, Fusco R, Barretta ML, Granata V, Mattace Raso M, Porto A, Sorgente E, Fanizzi A, Massafra R, Lafranceschina M, La Forgia D, Trombadori CML, Belli P, Trecate G, Tenconi C, De Santis MC, Greco L, Ferranti FR, De Soccio V, Vidiri A, Botta F, Dominelli V, Cassano E, Boldrini L. Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome. LA RADIOLOGIA MEDICA 2023; 128:1347-1371. [PMID: 37801198 DOI: 10.1007/s11547-023-01718-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/01/2023] [Indexed: 10/07/2023]
Abstract
OBJECTIVE The objective of the study was to evaluate the accuracy of radiomics features obtained by MR images to predict Breast Cancer Histological Outcome. METHODS A total of 217 patients with malignant lesions were analysed underwent MRI examinations. Considering histological findings as the ground truth, four different types of findings were used in both univariate and multivariate analyses: (1) G1 + G2 vs G3 classification; (2) presence of human epidermal growth factor receptor 2 (HER2 + vs HER2 -); (3) presence of the hormone receptor (HR + vs HR -); and (4) presence of luminal subtypes of breast cancer. RESULTS The best accuracy for discriminating HER2 + versus HER2 - breast cancers was obtained considering nine predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 88% on validation set). The best accuracy for discriminating HR + versus HR - breast cancers was obtained considering nine predictors by T2-weighted subtraction images and a decision tree (accuracy of 90% on validation set). The best accuracy for discriminating G1 + G2 versus G3 breast cancers was obtained considering 16 predictors by early phase T1-weighted subtraction images in a linear regression model with an accuracy of 75%. The best accuracy for discriminating luminal versus non-luminal breast cancers was obtained considering 27 predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 94% on validation set). CONCLUSIONS The combination of radiomics analysis and artificial intelligence techniques could be used to support physician decision-making in prediction of Breast Cancer Histological Outcome.
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Affiliation(s)
- Antonella Petrillo
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy.
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013, Naples, Italy
| | - Maria Luisa Barretta
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Mauro Mattace Raso
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Annamaria Porto
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Eugenio Sorgente
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Annarita Fanizzi
- Direzione Scientifica-IRCCS, Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- SSD Fisica Sanitaria-IRCCS Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | - Miria Lafranceschina
- Struttura Semplice Dipartimentale di Radiodiagnostica Senologica-IRCCS Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiodiagnostica Senologica-IRCCS Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | | | - Paolo Belli
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
| | - Giovanna Trecate
- Department of Radiodiagnostic and Magnetic Resonance, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133, Milan, Italy
| | - Chiara Tenconi
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133, Milan, Italy
| | - Maria Carmen De Santis
- De Santis Radiation Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133, Milan, Italy
| | - Laura Greco
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Romana Ferranti
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Valeria De Soccio
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Botta
- Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141, Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141, Milan, Italy
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
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Uncu UY, Aydin Aksu S. Correlation of Perfusion Metrics with Ki-67 Proliferation Index and Axillary Involvement as a Prognostic Marker in Breast Carcinoma Cases: A Dynamic Contrast-Enhanced Perfusion MRI Study. Diagnostics (Basel) 2023; 13:3260. [PMID: 37892081 PMCID: PMC10606869 DOI: 10.3390/diagnostics13203260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/09/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023] Open
Abstract
Our study aims to reveal clinically helpful prognostic markers using quantitative radiologic data from perfusion magnetic resonance imaging for patients with locally advanced carcinoma, using the Ki-67 index as a surrogate. Patients who received a breast cancer diagnosis and had undergone dynamic contrast-enhanced magnetic resonance imaging of the breast for pretreatment evaluation and follow-up were searched retrospectively. We evaluated the MRI studies for perfusion parameters and various categories and compared them to the Ki-67 index. Axillary involvement was categorized as low (N0-N1) or high (N2-N3) according to clinical stage. A total sum of 60 patients' data was included in this study. Perfusion parameters and Ki-67 showed a significant correlation with the transfer constant (Ktrans) (ρ = 0.554 p = 0.00), reverse transfer constant (Kep) (ρ = 0.454 p = 0.00), and initial area under the gadolinium curve (IAUGC) (ρ = 0.619 p = 0.00). The IAUGC was also significantly different between axillary stage groups (Z = 2.478 p = 0.013). Outside of our primary hypothesis, associations between axillary stage and contrast enhancement (x2 = 8.023 p = 0.046) and filling patterns (x2 = 8.751 p = 0.013) were detected. In conclusion, these parameters are potential prognostic markers in patients with moderate Ki-67 indices, such as those in our study group. The relationship between axillary status and perfusion parameters also has the potential to determine patients who would benefit from limited axillary dissection.
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Affiliation(s)
- Ulas Yalim Uncu
- Department of Radiology, Van Training and Research Hospital, University of Health Sciences, 65300 Van, Turkey
| | - Sibel Aydin Aksu
- Department of Radiology, Haydarpasa Numune Training and Research Hospital, University of Health Sciences, 34668 Istanbul, Turkey;
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Yuan L, Lin X, Zhao P, Ma H, Duan S, Sun S. Correlations between DKI and DWI with Ki-67 in gastric adenocarcinoma. Acta Radiol 2023; 64:1792-1798. [PMID: 36740857 DOI: 10.1177/02841851231153035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND Diffusion kurtosis imaging (DKI) has been applied for gastric adenocarcinoma. Correlations between its parameters and Ki-67 are unclear. PURPOSE To investigate the correlation between DKI and diffusion-weighted imaging (DWI) parameters with the Ki-67 index in gastric adenocarcinoma. MATERIAL AND METHODS A total of 54 patients with gastric adenocarcinoma were enrolled in the study and underwent DWI and DKI at 3.0-T MRI before surgery. Based on the settings of the regions of interest, the DWI and DKI parameters (including apparent diffusion coefficient [ADC], diffusion kurtosis [K], and diffusion coefficient [DK]) of each patient's gastric adenocarcinoma were measured and calculated. The participants were divided into two groups (low Ki-67 group and high Ki-67 groups). The intraclass correlation coefficient (ICC) and independent-sample t-test were used to compare differences in each parameter between two groups. Spearman's correlation coefficient was calculated to determine the correlation between Ki-67 and the parameters. Each parameter was compared using the area under the receiver operating characteristic curve. All parameters were included in the multivariate logistic regression analysis to explore the relationship between each parameter and high Ki-67 index. RESULTS ADC and DK were negatively relevant with Ki-67 and K was positively relevant with Ki-67 in gastric adenocarcinoma. ADC, DK, and K had diagnostic efficiency in differentiating the low Ki-67 group from the high Ki-67 group. A higher K value independently predicted a high Ki-67 status. CONCLUSION DWI and DKI reflected the proliferative characteristics of gastric adenocarcinoma. K was the strongest independent factor for predicting high Ki-67 status.
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Affiliation(s)
- Letian Yuan
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, PR China
| | - Xiangtao Lin
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, PR China
| | - Peng Zhao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, PR China
| | - Hui Ma
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, PR China
| | - Shuai Duan
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, PR China
| | - Shanshan Sun
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, PR China
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Liu Y, Wang S, Qu J, Tang R, Wang C, Xiao F, Pang P, Sun Z, Xu M, Li J. High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4. BMC Med Imaging 2023; 23:58. [PMID: 37076817 PMCID: PMC10116788 DOI: 10.1186/s12880-023-01015-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/06/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND BI-RADS 4 breast lesions are suspicious for malignancy with a range from 2 to 95%, indicating that numerous benign lesions are unnecessarily biopsied. Thus, we aimed to investigate whether high-temporal-resolution dynamic contrast-enhanced MRI (H_DCE-MRI) would be superior to conventional low-temporal-resolution DCE-MRI (L_DCE-MRI) in the diagnosis of BI-RADS 4 breast lesions. METHODS This single-center study was approved by the IRB. From April 2015 to June 2017, patients with breast lesions were prospectively included and randomly assigned to undergo either H_DCE-MRI, including 27 phases, or L_DCE-MRI, including 7 phases. Patients with BI-RADS 4 lesions were diagnosed by the senior radiologist in this study. Using a two-compartment extended Tofts model and a three-dimensional volume of interest, several pharmacokinetic parameters reflecting hemodynamics, including Ktrans, Kep, Ve, and Vp, were obtained from the intralesional, perilesional and background parenchymal enhancement areas, which were labeled the Lesion, Peri and BPE areas, respectively. Models were developed based on hemodynamic parameters, and the performance of these models in discriminating between benign and malignant lesions was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS A total of 140 patients were included in the study and underwent H_DCE-MRI (n = 62) or L_DCE-MRI (n = 78) scans; 56 of these 140 patients had BI-RADS 4 lesions. Some pharmacokinetic parameters from H_DCE-MRI (Lesion_Ktrans, Kep, and Vp; Peri_Ktrans, Kep, and Vp) and from L_DCE-MRI (Lesion_Kep, Peri_Vp, BPE_Ktrans and BPE_Vp) were significantly different between benign and malignant breast lesions (P < 0.01). ROC analysis showed that Lesion_Ktrans (AUC = 0.866), Lesion_Kep (AUC = 0.929), Lesion_Vp (AUC = 0.872), Peri_Ktrans (AUC = 0.733), Peri_Kep (AUC = 0.810), and Peri_Vp (AUC = 0.857) in the H_DCE-MRI group had good discrimination performance. Parameters from the BPE area showed no differentiating ability in the H_DCE-MRI group. Lesion_Kep (AUC = 0.767), Peri_Vp (AUC = 0.726), and BPE_Ktrans and BPE_Vp (AUC = 0.687 and 0.707) could differentiate between benign and malignant breast lesions in the L_DCE-MRI group. The models were compared with the senior radiologist's assessment for the identification of BI-RADS 4 breast lesions. The AUC, sensitivity and specificity of Lesion_Kep (0.963, 100.0%, and 88.9%, respectively) in the H_DCE-MRI group were significantly higher than those of the same parameter in the L_DCE-MRI group (0.663, 69.6% and 75.0%, respectively) for the assessment of BI-RADS 4 breast lesions. The DeLong test was conducted, and there was a significant difference only between Lesion_Kep in the H_DCE-MRI group and the senior radiologist (P = 0.04). CONCLUSIONS Pharmacokinetic parameters (Ktrans, Kep and Vp) from the intralesional and perilesional regions on high-temporal-resolution DCE-MRI, especially the intralesional Kep parameter, can improve the assessment of benign and malignant BI-RADS 4 breast lesions to avoid unnecessary biopsy.
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Affiliation(s)
- Yufeng Liu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Shiwei Wang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jingjing Qu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Rui Tang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Chundan Wang
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
- Department of Pathology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China
| | - Fengchun Xiao
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
- Department of Pathology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China
| | - Peipei Pang
- GE Healthcare, Precision Health Institution, Hangzhou, China
| | - Zhichao Sun
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China.
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
| | - Jiaying Li
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China.
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
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EL-Metwally D, Monier D, Hassan A, Helal AM. Preoperative prediction of Ki-67 status in invasive breast carcinoma using dynamic contrast-enhanced MRI, diffusion-weighted imaging and diffusion tensor imaging. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2023. [DOI: 10.1186/s43055-023-01007-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023] Open
Abstract
Abstract
Background
The Ki-67 is a beneficial marker of tumor aggressiveness. It is proliferation index that has been used to distinguish luminal B from luminal A breast cancers. By fast progress in quantitative radiology modalities, tumor biology and genetics can be assessed in a more accurate, predictive, and cost-effective method. The aim of this study was to assess the role of dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging and diffusion tensor imaging in prediction of Ki-67 status in patients with invasive breast carcinoma estimate cut off values between breast cancer with high Ki-67 status and those with low Ki-67 status.
Results
Cut off ADC (apparent diffusion co-efficient) value of 0.657 mm2/s had 96.4% sensitivity, 75% specificity and 93.8% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off maximum enhancement value of 1715 had 96.4% sensitivity, 75% specificity and 93.8% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off washout rate of 0.73 I/S had 60.7% sensitivity, 75% specificity and 62.5% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off time to peak value of 304 had 71.4% sensitivity, 75% specificity and 71.9% accuracy in differentiating cases with high Ki67 from those with low Ki67.
Conclusions
ADC, time to peak and maximum enhancement values had high sensitivity, specificity and accuracy in differentiating breast cancer with high Ki-67 status from those with low Ki-67 status.
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11
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Yao FF, Zhang Y. A review of quantitative diffusion-weighted MR imaging for breast cancer: Towards noninvasive biomarker. Clin Imaging 2023; 98:36-58. [PMID: 36996598 DOI: 10.1016/j.clinimag.2023.03.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/03/2023] [Accepted: 03/21/2023] [Indexed: 04/01/2023]
Abstract
Quantitative diffusion-weighted imaging (DWI) is an important adjunct to conventional breast MRI and shows promise as a noninvasive biomarker of breast cancer in multiple clinical scenarios, from the discrimination of benign and malignant lesions, prediction, and evaluation of treatment response to a prognostic assessment of breast cancer. Various quantitative parameters are derived from different DWI models based on special prior knowledge and assumptions, have different meanings, and are easy to confuse. In this review, we describe the quantitative parameters derived from conventional and advanced DWI models commonly used in breast cancer and summarize the promising clinical applications of these quantitative parameters. Although promising, it is still challenging for these quantitative parameters to become clinically useful noninvasive biomarkers in breast cancer, as multiple factors may result in variations in quantitative parameter measurements. Finally, we briefly describe some considerations regarding the factors that cause variations.
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Affiliation(s)
- Fei-Fei Yao
- Department of MRI in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, China.
| | - Yan Zhang
- Department of MRI in the First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
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Liu M, Bian J. Radiomics signatures based on contrast-enhanced CT for preoperative prediction of the Ki-67 proliferation state in gastrointestinal stromal tumors. Jpn J Radiol 2023:10.1007/s11604-023-01391-5. [PMID: 36652141 DOI: 10.1007/s11604-023-01391-5] [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: 10/16/2022] [Accepted: 01/07/2023] [Indexed: 01/19/2023]
Abstract
PURPOSE This study aimed to evaluate the Ki-67 proliferation state in patients with gastrointestinal stromal tumors (GISTs) using radiomics prediction signatures based on contrast-enhanced computed tomography (CE-CT). MATERIALS AND METHODS This single-center, retrospective study involved 103 patients (48 men and 55 women, mean age 61.1 ± 10.6 years) who had pathologically confirmed GISTs after curative resection, including 63 with low Ki-67 proliferation level (Ki-67 labeling index ≤ 6%) and 40 with high Ki-67 proliferation level (Ki-67 labeling index > 6%). Radiomics features of the delineated lesions were preoperatively extracted from three-phase CE-CT images, including the arterial, venous, and delayed phases. The most relevant features were selected to construct the radiomics signatures using a logistic regression algorithm. Significant demographic characteristics and semantic features on CT were selected to develop a nomogram along with the optimal radiomics feature. We calculated the sensitivity, specificity, accuracy, F1 score, and area under the receiver operating characteristic (ROC) curve to evaluate the predictive performance of radiomics signatures. RESULTS Ten quantitative radiomics features (two first-order and eight texture features) were selected to construct radiomics signatures. The radiomics signature based on the three-phase CE-CT images showed better predictive performance than that based on the single-phase CE-CT images, with an area under the curve (AUC) of 0.83 (95% CI 0.73-0.92) and F1 score of 82% in the training dataset and an AUC of 0.80 (95% CI 0.63-0.95) and F1 score of 75% in the testing dataset. The nomogram showed good calibration. CONCLUSION Radiomics signatures using CE-CT images are generalizable and could be used in clinical practice to determine the proliferation state of Ki-67 in GISTs.
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Affiliation(s)
- Meijun Liu
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, No.467 Zhongshan Road, Shahekou District, Dalian, 116027, Liaoning Province, China
| | - Jie Bian
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, No.467 Zhongshan Road, Shahekou District, Dalian, 116027, Liaoning Province, China.
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Wang W, Lv S, Xun J, Wang L, Zhao F, Wang J, Zhou Z, Chen Y, Sun Z, Zhu L. Comparison of diffusion kurtosis imaging and dynamic contrast enhanced MRI in prediction of prognostic factors and molecular subtypes in patients with breast cancer. Eur J Radiol 2022; 154:110392. [DOI: 10.1016/j.ejrad.2022.110392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 05/18/2022] [Accepted: 05/31/2022] [Indexed: 11/16/2022]
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Zhong M, Yang Z, Chen X, Huang R, Wang M, Fan W, Dai Z, Chen X. Readout-Segmented Echo-Planar Diffusion-Weighted MR Imaging Improves the Differentiation of Breast Cancer Receptor Statuses Compared With Conventional Diffusion-Weighted Imaging. J Magn Reson Imaging 2022; 56:691-699. [PMID: 35038210 PMCID: PMC9542110 DOI: 10.1002/jmri.28065] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Readout-segmented echo-planar diffusion-weighted imaging (RS-EPI) can improve image quality and signal-to-noise ratio, the resulting apparent diffusion coefficient (ADC) value acts as a more sensitive biomarker to characterize tumors. However, data regarding the differentiation of breast cancer (BC) receptor statuses using RS-EPI are limited. PURPOSE To determine whether RS-EPI improves the differentiation of receptor statuses compared with conventional single-shot (SS) EPI in breast MRI. STUDY TYPE Retrospective. POPULATION A total of 151 BC women with the mean age of 50.6 years. FIELD STRENGTH/SEQUENCE A 3 T/ RS-EPI and SS-EPI. ASSESSMENT The ADCs of the lesion and normal background tissue from the two sequences were collected by two radiologists with 15 years of experience working of breast MRI (M.H.Z. and X.F.C.), and a normalized ADC was calculated by dividing the mean ADC value of the lesion by the mean ADC value of the normal background tissue. STATISTICAL TESTS Agreement between the ADC measurements from the two sequences was assessed using the Pearson correlation coefficient and Bland-Altman plots. One-way analysis of variance, Kruskal-Wallis test, and median difference were used to compare the ADC measurements for all lesions and different receptor statuses. A P value less than 0.05 indicated a significant result. RESULTS The ADC measurements of all lesions and normal background tissues were significantly higher on RS-EPI than on SS-EPI (1.82 ± 0.33 vs. 1.55 ± 0.30 and 0.83 ± 0.11 vs. 0.79 ± 0.10). The normalized ADC was lower on RS-EPI than on SS-EPI (0.47 ± 0.11 vs. 0.53 ± 0.12, a median difference of -0.04 [95% CI: -0.256 to 0.111]). For both diffusion methods, only the ADC measurement of RS-EPI was higher for human epidermal growth factor receptor-2 (HER-2)-positive tumors than for HER-2-negative tumors (0.87 ± 0.10 vs. 0.81 ± 0.11), and this measurement was associated with HER-2 positive status (adjusted odds ratio [OR] = 654.4); however, similar results were not observed for the ADC measurement of SS-EPI (0.80 ± 0.10 vs. 0.78 ± 0.11 with P = 0.199 and adjusted OR = 0.21 with P = 0.464, respectively). DATA CONCLUSION RS-EPI can improve the distinction between HER-2-positive and HER-2-negative breast cancer, complementing the clinical application of diffusion imaging. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Minghao Zhong
- Department of Radiology, Meizhou People's HospitalMeizhou514031China
| | - Zhiqi Yang
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031 China
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, 514031 China
| | - Xiaofeng Chen
- Department of Radiology, Meizhou People's HospitalMeizhou514031China
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka PopulationMeizhou514031China
| | - Ruibin Huang
- Department of RadiologyFirst Affiliated Hospital of Shantou University Medical CollegeShantou515000China
| | - Mengzhu Wang
- MR Scientific Marketing, Siemens HealthineersGuangzhou510620China
| | - Weixiong Fan
- Department of Radiology, Meizhou People's HospitalMeizhou514031China
| | - Zhuozhi Dai
- Department of Radiology, Shantou Central Hospital, Shantou, Guangdong, 515041 China
| | - Xiangguang Chen
- Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, 514031 China
- Department of Radiology, Meizhou People's Hospital, Meizhou, 514031 China
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Fan M, Yuan C, Huang G, Xu M, Wang S, Gao X, Li L. A framework for deep multitask learning with multiparametric magnetic resonance imaging for the joint prediction of histological characteristics in breast cancer. IEEE J Biomed Health Inform 2022; 26:3884-3895. [PMID: 35635826 DOI: 10.1109/jbhi.2022.3179014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The clinical management and decision-making process related to breast cancer are based on multiple histological indicators. This study aims to jointly predict the Ki-67 expression level, luminal A subtype and histological grade molecular biomarkers using a new deep multitask learning method with multiparametric magnetic resonance imaging. A multitask learning network structure was proposed by introducing a common-task layer and task-specific layers to learn the high-level features that are common to all tasks and related to a specific task, respectively. A network pretrained with knowledge from the ImageNet dataset was used and fine-tuned with MRI data. Information from multiparametric MR images was fused using the strategy at the feature and decision levels. The area under the receiver operating characteristic curve (AUC) was used to measure model performance. For single-task learning using a single image series, the deep learning model generated AUCs of 0.752, 0.722, and 0.596 for the Ki-67, luminal A and histological grade prediction tasks, respectively. The performance was improved by freezing the first 5 convolutional layers, using 20% shared layers and fusing multiparametric series at the feature level, which achieved AUCs of 0.819, 0.799 and 0.747 for Ki-67, luminal A and histological grade prediction tasks, respectively. Our study showed advantages in jointly predicting correlated clinical biomarkers using a deep multitask learning framework with an appropriate number of fine-tuned convolutional layers by taking full advantage of common and complementary imaging features. Multiparametric image series-based multitask learning could be a promising approach for the multiple clinical indicator-based management of breast cancer.
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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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Wang W, Zhang X, Zhu L, Chen Y, Dou W, Zhao F, Zhou Z, Sun Z. Prediction of Prognostic Factors and Genotypes in Patients With Breast Cancer Using Multiple Mathematical Models of MR Diffusion Imaging. Front Oncol 2022; 12:825264. [PMID: 35174093 PMCID: PMC8841854 DOI: 10.3389/fonc.2022.825264] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/07/2022] [Indexed: 01/31/2023] Open
Abstract
Purpose To explore the clinical value of apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) based on diffusion-weighted MRI (DW-MRI) for predicting genotypes and prognostic factors of breast cancer. Materials and Methods A total of 227 patients with breast cancer confirmed by pathology were reviewed retrospectively. Diffusion-weighted imaging (DWI), IVIM, and DKI were performed in all patients. The corresponding ADC, true diffusion coefficient (D), perfusion-related diffusion coefficient (D*), perfusion fraction (f), mean diffusion rate (MD), and mean kurtosis value (MK) were measured. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve were used to analyze the diagnostic efficacy in predicting the Nottingham prognostic index (NPI), the expression of antigen Ki-67, and the molecular subtypes of breast cancer. The nomogram of the combined genotype-prediction model was established based on the multivariate logistic regression model results. Results D* and MK values were significantly higher in the high-grade Nottingham group (NPI ≥ 3.4) than the low-grade Nottingham group (NPI < 3.4) (p < 0.01). When D* ≥ 30.95 × 10−3 mm2/s and MK ≥ 0.69, the NPI tended to be high grade (with areas under the curve (AUCs) of 0.712 and 0.647, respectively). The combination of D* and MK demonstrated the highest AUC of 0.734 in grading NPI with sensitivity and accuracy of 71.7% and 77.1%, respectively. Additionally, higher D*, f, and MK and lower ADC and D values were observed in the high Ki-67 than low Ki-67 expression groups (p < 0.05). The AUC of the combined model (D + D* + f + MK) was 0.755, being significantly higher than that of single parameters (Z = 2.770~3.244, p = 0.001~0.006) in distinguishing high from low Ki-67 expression. D* and f values in the Luminal A subtype were significantly lower than in other subtypes (p < 0.05). Luminal B showed decreased D value compared with other subtypes (p < 0.05). The HER-2-positive subtype demonstrated increased ADC values compared with the Luminal B subtype (p < 0.05). Luminal A/B showed significantly lower D, D*, MD, and MK than the non-Luminal subtypes (p < 0.05). The combined model (D + D* + MD + MK) showed an AUC of 0.830 in diagnosing the Luminal and non-Luminal subtypes, which is significantly higher than that of a single parameter (Z = 3.273~4.440, p < 0.01). f ≥ 54.30% [odds ratio (OR) = 1.038, p < 0.001] and MK ≥ 0.68 (OR = 24.745, p = 0.012) were found to be significant predictors of triple-negative subtypes. The combination of f and MK values demonstrated superior diagnostic performance with AUC, sensitivity, specificity, and accuracy of 0.756, 67.5%, 77.5%, and 82.4%, respectively. Moreover, as shown in the calibration curve, strong agreements were observed between nomogram prediction probability and actual findings in the prediction of genotypes (p = 0.22, 0.74). Conclusion DWI, IVIM, and DKI, as MR diffusion imaging techniques with different mathematical models showed potential to identify the prognosis and genotype of breast cancer. In addition, the combination of these three models can improve the diagnostic efficiency and thus may contribute to opting for an appropriate therapeutic approach in clinic treatment.
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Affiliation(s)
- Weiwei Wang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Xindong Zhang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Laimin Zhu
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Yueqin Chen
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | | | - Fan Zhao
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Zhe Zhou
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Zhanguo Sun
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
- *Correspondence: Zhanguo Sun,
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Feng S, Yin J. Radiomics of dynamic contrast-enhanced magnetic resonance imaging parametric maps and apparent diffusion coefficient maps to predict Ki-67 status in breast cancer. Front Oncol 2022; 12:847880. [PMID: 36895526 PMCID: PMC9989944 DOI: 10.3389/fonc.2022.847880] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 10/27/2022] [Indexed: 11/26/2022] Open
Abstract
Purpose This study was aimed at evaluating whether a radiomics model based on the entire tumor region from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps and apparent diffusion coefficient (ADC) maps could indicate the Ki-67 status of patients with breast cancer. Materials and methods This retrospective study enrolled 205 women with breast cancer who underwent clinicopathological examination. Among them, 93 (45%) had a low Ki-67 amplification index (Ki-67 positivity< 14%), and 112 (55%) had a high Ki-67 amplification index (Ki-67 positivity ≥ 14%). Radiomics features were extracted from three DCE-MRI parametric maps and ADC maps calculated from two different b values of diffusion-weighted imaging sequences. The patients were randomly divided into a training set (70% of patients) and a validation set (30% of patients). After feature selection, we trained six support vector machine classifiers by combining different parameter maps and used 10-fold cross-validation to predict the expression level of Ki-67. The performance of six classifiers was evaluated with receiver operating characteristic (ROC) analysis, sensitivity, and specificity in both cohorts. Results Among the six classifiers constructed, a radiomics feature set combining three DCE-MRI parametric maps and ADC maps yielded an area under the ROC curve (AUC) of 0.839 (95% confidence interval [CI], 0.768-0.895) within the training set and 0.795 (95% CI, 0.674-0.887) within the independent validation set. Additionally, the AUC value, compared with that for a single parameter map, was moderately increased by combining features from the three parametric maps. Conclusions Radiomics features derived from the DCE-MRI parametric maps and ADC maps have the potential to serve as imaging biomarkers to determine Ki-67 status in patients with breast cancer.
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Affiliation(s)
- Shuqian Feng
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.,School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
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Huang Z, Li X, Wang Z, Meng N, Fu F, Han H, Li D, Bai Y, Wei W, Fang T, Feng P, Yuan J, Yang Y, Wang M. Application of Simultaneous 18 F-FDG PET With Monoexponential, Biexponential, and Stretched Exponential Model-Based Diffusion-Weighted MR Imaging in Assessing the Proliferation Status of Lung Adenocarcinoma. J Magn Reson Imaging 2021; 56:63-74. [PMID: 34888990 DOI: 10.1002/jmri.28010] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/18/2021] [Accepted: 11/18/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Ki-67 proliferation index (PI) is important for providing information on tumor behavior, treatment response, and prognosis. Integrated positron emission tomography/magnetic resonance (PET/MR) may have the potential to assess Ki-67 PI in patients with lung adenocarcinoma. PURPOSE To explore the value of simultaneous 18 F-fluorodeoxyglucose (18 F-FDG) PET/MR-derived parameters in assessing the proliferation status of lung adenocarcinoma and to determine the best combination of parameters. STUDY TYPE Prospective. POPULATION Seventy-eight patients with lung adenocarcinoma and with Ki-67 PI. FIELD STRENGTH/SEQUENCE 3.0 T, simultaneous PET/MRI including diffusion-weighted imaging (DWI) and 18 F-FDG PET. ASSESSMENT DWI-derived parameters, namely, apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), diffusion heterogeneity index (α), and distributed diffusion coefficient (DDC); and PET-derived parameters, namely, maximum standardized uptake value (SUVmax ), metabolic tumor volume (MTV), and total lesion glycolytic volume (TLG), were calculated and compared between the high (>25%) and low (≤25%) Ki-67 PI groups. The correlations between PET-derived parameters and DWI-derived parameters were analyzed. STATISTICAL TESTS Student's t-test, Mann-Whitney U test, chi-square test, and receiver operating characteristic (ROC) curves. A P-value <0.05 was considered statistically significant. RESULTS The SUVmax , MTV, TLG, ADC, D, and DDC values were significantly different between the high (N = 35) and low Ki-67 PI groups (N = 43). D, SUVmax , and MTV independently predicted the Ki-67 PI status. The combination of D, SUVmax , and MTV had the largest area under the ROC curve (AUC = 0.900), which was significantly larger than the AUC alone of DDC (AUC = 0.725), SUVmax (AUC = 0.815), MTV (AUC = 0.774), or TLG (AUC = 0.783). The perfusion fraction did not correlate with SUVmax , MTV, or TLG (r = -0.03, -0.11, and -0.04, respectively; P = 0.786, 0.348, and 0.733). DATA CONCLUSION The combination of D, SUVmax , and MTV may predict Ki-67 PI status. No correlation was observed between perfusion parameters and metabolic parameters. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Zhun Huang
- Department of Medical Imaging, Henan University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China.,Henan Key Laboratory for Medical Imaging of Neurological Diseases, Zhengzhou, China
| | - Xiaochen Li
- Henan Key Laboratory for Medical Imaging of Neurological Diseases, Zhengzhou, China.,Department of Medical imaging, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Zhixue Wang
- Department of Radiology, The First Affiliated Hospital of Henan University, Kaifeng, China
| | - Nan Meng
- Henan Key Laboratory for Medical Imaging of Neurological Diseases, Zhengzhou, China.,Department of Medical imaging, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Fangfang Fu
- Henan Key Laboratory for Medical Imaging of Neurological Diseases, Zhengzhou, China.,Department of Medical imaging, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Hui Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Dujuan Li
- Department of Medical imaging, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Yan Bai
- Henan Key Laboratory for Medical Imaging of Neurological Diseases, Zhengzhou, China.,Department of Medical imaging, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Wei Wei
- Henan Key Laboratory for Medical Imaging of Neurological Diseases, Zhengzhou, China.,Department of Medical imaging, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Ting Fang
- Henan Key Laboratory for Medical Imaging of Neurological Diseases, Zhengzhou, China.,Department of Medical imaging, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Pengyang Feng
- Department of Medical Imaging, Henan University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China.,Henan Key Laboratory for Medical Imaging of Neurological Diseases, Zhengzhou, China
| | - Jianmin Yuan
- Central Research Institute, UIH Group, Shanghai, China
| | - Yang Yang
- Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China.,Henan Key Laboratory for Medical Imaging of Neurological Diseases, Zhengzhou, China.,Department of Medical imaging, Zhengzhou University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
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Kang HS, Kim JY, Kim JJ, Kim S, Lee NK, Lee JW, Suh HB, Hwangbo L, Son Y, Grimm R. Diffusion Kurtosis MR Imaging of Invasive Breast Cancer: Correlations With Prognostic Factors and Molecular Subtypes. J Magn Reson Imaging 2021; 56:110-120. [PMID: 34792837 DOI: 10.1002/jmri.27999] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 11/05/2021] [Accepted: 11/09/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND The associations between diffusion kurtosis imaging (DKI)-derived parameters and clinical prognostic factors of breast cancer have not been fully evaluated; this knowledge may have implications for outcome prediction and treatment strategies. PURPOSE To determine associations between quantitative diffusion parameters derived from DKI and diffusion-weighted imaging (DWI) and the prognostic factors and molecular subtypes of breast cancer. STUDY TYPE Retrospective. POPULATION A total of 383 women (mean age, 53.8 years; range, 31-82 years) with breast cancer who underwent preoperative breast MRI including DKI and DWI. FIELD STRENGTH/SEQUENCE A 3.0 T; DKI using a spin-echo echo-planar imaging (EPI) sequence (b values: 200, 500, 1000, 1500, and 2000 sec/mm2 ), DWI using a readout-segmented EPI sequence (b values: 0 and 1000 sec/mm2 ) and dynamic contrast-enhanced breast MRI. ASSESSMENT Two radiologists (J.Y.K. and H.S.K. with 9 years and 1 year of experience in MRI, respectively) independently measured kurtosis, diffusivity, and apparent diffusion coefficient (ADC) values of breast cancer by manually placing a regions of interest within the lesion. Diffusion measures were compared according to nodal status, grade, and molecular subtypes. STATISTICAL TESTS Kruskal-Wallis test, Mann-Whitney U test with Bonferroni correction, receiver operating characteristic (ROC) analysis, and multivariate logistic regression analysis. (Statistical significance level of P < 0.05). RESULTS All diffusion measures showed significant differences according to axillary nodal status and histological grade. Kurtosis showed significant differences among molecular subtypes. The luminal subtype (median 1.163) showed a higher kurtosis value compared to the HER2-positive (median 0.962) or triple-negative subtypes (median 1.072). ROC analysis for differentiating HER2-positive from luminal subtypes revealed that kurtosis yielded the highest area under the curve of 0.781. In multivariate analyses, kurtosis remained a significant factor associated with differentiation between HER2-positive and luminal (odds ratio [OR] = 0.993), triple-negative and luminal (OR = 0.995), and HER2-positive and triple-negative subtypes (OR = 0.994). DATA CONCLUSION Quantitative diffusion parameters derived from DKI and DWI are associated with prognostic factors for breast cancer. Moreover, DKI-derived kurtosis can help distinguish between the molecular subtypes of breast cancer. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: 3.
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Affiliation(s)
- Han Sol Kang
- Department of Radiology, Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | - Jin You Kim
- Department of Radiology, Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | - Jin Joo Kim
- Department of Radiology, Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | - Suk Kim
- Department of Radiology, Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | - Nam Kyung Lee
- Department of Radiology, Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | - Ji Won Lee
- Department of Radiology, Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | - Hie Bum Suh
- Department of Radiology, Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | - Lee Hwangbo
- Department of Radiology, Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | - Yohan Son
- Siemens Healthineers Ltd. Seoul, Korea
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Fan M, Zhang Y, Fu Z, Xu M, Wang S, Xie S, Gao X, Wang Y, Li L. A deep matrix completion method for imputing missing histological data in breast cancer by integrating DCE-MRI radiomics. Med Phys 2021; 48:7685-7697. [PMID: 34724248 DOI: 10.1002/mp.15316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 10/13/2021] [Accepted: 10/14/2021] [Indexed: 02/01/2023] Open
Abstract
PURPOSE Clinical indicators of histological information are important for breast cancer treatment and operational decision making, but these histological data suffer from frequent missing values due to various experimental/clinical reasons. The limited amount of histological information from breast cancer samples impedes the accuracy of data imputation. The purpose of this study was to impute missing histological data, including Ki-67 expression level, luminal A subtype, and histological grade, by integrating tumor radiomics. METHODS To this end, a deep matrix completion (DMC) method was proposed for imputing missing histological data using nonmissing features composed of histological and tumor radiomics (termed radiohistological features). DMC finds a latent nonlinear association between radiohistological features across all samples and samples for all the features. Radiomic features of morphologic, statistical, and texture were extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) inside the tumor. Experiments on missing histological data imputation were performed with a variable number of features and missing data rates. The performance of the DMC method was compared with those of the nonnegative matrix factorization (NMF) and collaborative filtering (MCF)-based data imputation methods. The area under the curve (AUC) was used to assess the performance of missing histological data imputation. RESULTS By integrating radiomics from DCE-MRI, the DMC method showed significantly better performance in terms of AUC than that using only histological data. Additionally, DMC using 120 radiomic features showed an optimal prediction performance (AUC = 0.793), which was better than the NMF (AUC = 0.756) and MCF methods (AUC = 0.706; corrected p = 0.001). The DMC method consistently performed better than the NMF and MCF methods with a variable number of radiomic features and missing data rates. CONCLUSIONS DMC improves imputation performance by integrating tumor histological and radiomics data. This study transforms latent imaging-scale patterns for interactions with molecular-scale histological information and is promising in the tumor characterization and management of patients.
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Affiliation(s)
- Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - You Zhang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Zhenyu Fu
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Shiwei Wang
- Department of Radiology, First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Sangma Xie
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, USA
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
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Whole Volume Apparent Diffusion Coefficient (ADC) Histogram as a Quantitative Imaging Biomarker to Differentiate Breast Lesions: Correlation with the Ki-67 Proliferation Index. BIOMED RESEARCH INTERNATIONAL 2021; 2021:4970265. [PMID: 34258262 PMCID: PMC8249125 DOI: 10.1155/2021/4970265] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 06/09/2021] [Indexed: 11/18/2022]
Abstract
Objectives To evaluate the value of the whole volume apparent diffusion coefficient (ADC) histogram in distinguishing between benign and malignant breast lesions and differentiating different molecular subtypes of breast cancers and to assess the correlation between ADC histogram parameters and Ki-67 expression in breast cancers. Methods The institutional review board approved this retrospective study. Between September 2016 and February 2019, 189 patients with 84 benign lesions and 105 breast cancers underwent magnetic resonance imaging (MRI). Volumetric ADC histograms were created by placing regions of interest (ROIs) on the whole lesion. The relationships between the ADC parameters and Ki-67 were analysed using Spearman's correlation analysis. Results Of the 189 breast lesions included, there were significant differences in patient age (P < 0.001) and lesion size (P = 0.006) between the benign and malignant lesions. The results also demonstrated significant differences in all ADC histogram parameters between benign and malignant lesions (all P < 0.001). The median and mean ADC histogram parameters performed better than the other ADC histogram parameters (AUCs were 0.943 and 0.930, respectively). The receiver operating characteristic (ROC) analysis revealed that the 10th percentile ADC value and entropy could determine the human epidermal growth factor receptor 2 (HER-2) status (both P = 0.001) and estrogen receptor (ER)/progesterone receptor (PR) status (P = 0.020 and P = 0.041, respectively). Among all breast cancer lesions, 35 tumours in the low-proliferation group (Ki − 67 < 14%) and 70 tumours in the high-proliferation group (Ki − 67 ≥ 14) were analysed with ROC curves and correlation analyses. The ROC analysis revealed that entropy and skewness could determine the Ki-67 status (P = 0.007 and P < 0.001, respectively), and there were weak correlations between ADC entropy (r = 0.383) and skewness (r = 0.209) and the Ki-67 index. Conclusion The volumetric ADC histogram could serve as an imaging marker to determine breast lesion characteristics and may be a supplemental method in predicting tumour proliferation in breast cancer.
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Wu W, Jiang G, Xu Z, Wang R, Pan A, Gao M, Yu T, Huang L, Quan Q, Li J. Three-dimensional pulsed continuous arterial spin labeling and intravoxel incoherent motion imaging of nasopharyngeal carcinoma: correlations with Ki-67 proliferation status. Quant Imaging Med Surg 2021; 11:1394-1405. [PMID: 33816177 PMCID: PMC7930700 DOI: 10.21037/qims-20-349] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 11/05/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Recurrence and distant metastasis are still the main problems affecting the long-term prognosis of nasopharyngeal carcinoma (NPC) patients, and may be related to the Ki-67 proliferation status. We therefore explored the potential correlation between Ki-67 proliferation status in NPC with the parameters derived from two imaging techniques: three-dimensional pulsed continuous arterial spin labeling (3D pCASL) and intravoxel incoherent motion (IVIM). METHODS Thirty-six patients with pathologically confirmed NPC were included, and the Ki-67 labeling index (LI) was measured by immunohistochemistry. All patients underwent plain and contrast-enhanced magnetic resonance imaging (MRI), IVIM, and 3D pCASL examination. The mean, maximum, and minimum of blood flow (BF), minimum of apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) parameters were all measured, and Spearman's correlation analysis was performed to evaluate the relationships between these parameters and the Ki-67 LI. According to the Ki-67 values, the patients were divided into two groups: high (>50%) and low (≤50%). The rank-sum test (Mann-Whitney U test) was then used to compare the differences in quantitative parameters between the high and low Ki-67 groups. RESULTS Ki-67 LI was positively correlated with BFmean and BFmax (r=0.415 and 0.425). D*mean and D*min did have positive correlation with Ki-67, but this was not significant (P=0.082 and 0.072). BFmax was significantly different between the high and low Ki-67 groups (P=0.028). CONCLUSIONS 3D pCASL and IVIM are noninvasive functional MR perfusion imaging techniques that can evaluate perfusion information and perfusion parameters. Our study suggests that 3D pCASL is more effective than IVIM for assessing the proliferation status of NPC, which is beneficial for evaluating the prognosis of patients. Furthermore, BFmax is the best biomarker for distinguishing high from low Ki-67 levels.
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Affiliation(s)
- Wenxiu Wu
- Department of Radiology, The First People’s Hospital of Foshan, Foshan, China
| | - Guihua Jiang
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Zhifeng Xu
- Department of Radiology, The First People’s Hospital of Foshan, Foshan, China
| | - Ruoning Wang
- Minimally Invasive Center, Tumor hospital, Sun Yat-Sen University, Guangzhou, China
| | - Aizhen Pan
- Department of Radiology, The First People’s Hospital of Foshan, Foshan, China
| | - Mingyong Gao
- Department of Radiology, The First People’s Hospital of Foshan, Foshan, China
| | - Tian Yu
- Department of Radiology, The First People’s Hospital of Foshan, Foshan, China
| | - Linwen Huang
- Department of Radiology, The First People’s Hospital of Foshan, Foshan, China
| | - Qiang Quan
- Nasopharyngeal Radiotherapy Department 2, The First People’s Hospital of Foshan, Foshan, China
| | - Jin Li
- Pathology Department, The First People’s Hospital of Foshan, Foshan, China
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Predictive Ki-67 Proliferation Index of Cervical Squamous Cell Carcinoma Based on IVIM-DWI Combined with Texture Features. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:8873065. [PMID: 33531882 PMCID: PMC7826202 DOI: 10.1155/2021/8873065] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/10/2020] [Accepted: 01/04/2021] [Indexed: 11/25/2022]
Abstract
Purpose This study aims to determine whether IVIM-DWI combined with texture features based on preoperative IVIM-DWI could be used to predict the Ki-67 PI, which is a widely used cell proliferation biomarker in CSCC. Methods A total of 70 patients were included. Among these patients, 16 patients were divided into the Ki-67 PI <50% group and 54 patients were divided into the Ki-67 PI ≥50% group based on the retrospective surgical evaluation. All patients were examined using a 3.0T MRI unit with one standard protocol, including an IVIM-DWI sequence with 10 b values (0–1,500 sec/mm2). The maximum level of CSCC with a b value of 800 sec/mm2 was selected. The parameters (diffusion coefficient (D), microvascular volume fraction (f), and pseudodiffusion coefficient (D∗)) were calculated with the ADW 4.6 workstation, and the texture features based on IVIM-DWI were measured using GE AK quantitative texture analysis software. The texture features included the first order, GLCM, GLSZM, GLRLM, and wavelet transform features. The differences in IVIM-DWI parameters and texture features between the two groups were compared, and the ROC curve was performed for parameters with group differences, and in combination. Results The D value in the Ki-67 PI ≥50% group was lower than that in the Ki-67 PI <50% group (P < 0.05). A total of 1,050 texture features were obtained using AK software. Through univariate logistic regression, mPMR feature selection, and multivariate logistic regression, three texture features were obtained: wavelet_HHL_GLRLM_ LRHGLE, lbp_3D_k_ firstorder_IR, and wavelet_HLH_GLCM_IMC1. The AUC of the prediction model based on the three texture features was 0.816, and the combined D value and three texture features was 0.834. Conclusions Texture analysis on IVIM-DWI and its parameters was helpful for predicting Ki-67 PI and may provide a noninvasive method to investigate important imaging biomarkers for CSCC.
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Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization. Cancers (Basel) 2020; 12:cancers12123763. [PMID: 33327532 PMCID: PMC7765071 DOI: 10.3390/cancers12123763] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/05/2020] [Accepted: 12/09/2020] [Indexed: 12/21/2022] Open
Abstract
Simple Summary Confirming whether a breast lesion is benign or malignant usually involves an invasive tissue sample with an image-guided breast biopsy, which may cause substantial inconvenience to the patient. The purpose of this study was to investigate whether imaging biomarkers obtained from noninvasive dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast can help differentiate benign from malignant lesions and characterize breast cancers to the same extent as a biopsy. In a sample of 37 patients with suspicious findings on mammography or ultrasound, we found that the radiologists’ diagnostic accuracy was improved when subjective Breast Imaging-Reporting and Data System (BI-RADS) evaluation was augmented with the use of pharmacokinetic markers. This study serves as a starting point for future collaborative research with the potential of providing valuable noninvasive tools for improved breast cancer diagnosis. Abstract The purpose of this study was to investigate whether ultra-high-field dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast at 7T using quantitative pharmacokinetic (PK) analysis can differentiate between benign and malignant breast tumors for improved breast cancer diagnosis and to predict molecular subtypes, histologic grade, and proliferation rate in breast cancer. In this prospective study, 37 patients with 43 lesions suspicious on mammography or ultrasound underwent bilateral DCE-MRI of the breast at 7T. PK parameters (KTrans, kep, Ve) were evaluated with two region of interest (ROI) approaches (2D whole-tumor ROI or 2D 10 mm standardized ROI) manually drawn by two readers (senior reader, R1, and R2) independently. Histopathology served as the reference standard. PK parameters differentiated benign and malignant lesions (n = 16, 27, respectively) with good accuracy (AUCs = 0.655–0.762). The addition of quantitative PK analysis to subjective BI-RADS classification improved breast cancer detection from 88.4% to 97.7% for R1 and 86.04% to 97.67% for R2. Different ROI approaches did not influence diagnostic accuracy for both readers. Except for KTrans for whole-tumor ROI for R2, none of the PK parameters were valuable to predict molecular subtypes, histologic grade, or proliferation rate in breast cancer. In conclusion, PK-enhanced BI-RADS is promising for the noninvasive differentiation of benign and malignant breast tumors.
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Combination of DCE-MRI and DWI in Predicting the Treatment Effect of Concurrent Chemoradiotherapy in Esophageal Carcinoma. BIOMED RESEARCH INTERNATIONAL 2020; 2020:2576563. [PMID: 32626736 PMCID: PMC7315287 DOI: 10.1155/2020/2576563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 12/26/2019] [Accepted: 01/30/2020] [Indexed: 11/17/2022]
Abstract
Background Concurrent chemoradiotherapy (CCRT) is the main treatment for esophageal cancer, but the response to treatment varies from individual to individual. MR imaging methods, such as diffusion-weighted (DW) MRI and the use of dynamic contrast-enhanced (DCE) MRI, have the potential to provide additional biomarkers that could evaluate the effect of CCRT in patients with esophageal carcinoma. Materials and Methods Fifty-six patients with esophageal carcinoma, verified by histopathology, underwent MRI examination before and at midtreatment (4th week, radiotherapy 30-40 Gy) using the Siemens 3.0 T MR System. Parameter maps of apparent diffusion coefficient (ADC), and DCE maps of volume transfer constant (K rans), rate contrast (k ep), and extracellular fluid space (v e), were computed using a Siemens Company Multimodality Workplace (MMWP) model. Comparison of histogram parameters and their diagnostic performance was determined using the Mann-Whitney U test and receiver operating characteristic (ROC) analysis. Results 56 patient MRI scans were available for analysis at baseline and at the third week, respectively. Pretreatment K rans, pretreatment k ep, pretreatment ADC (P < 0.05), and during-treatment K rans (P < 0.05) and ΔK rans and ΔADC (P < 0.05) were significantly different after CCRT. Based on the binary logistic model, the ROC analysis demonstrated that the combined predictors demonstrated a high diagnostic performance with an AUC of 0.939. The sensitivity and specificity were 98.6% and 73.8%, respectively. Conclusion The combination of DCE and DWI can be used as an early biomarker in the prediction of the effect of CCRT three weeks after treatment in esophageal carcinoma.
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Kang SR, Kim HW, Kim HS. Evaluating the Relationship Between Dynamic Contrast-Enhanced MRI (DCE-MRI) Parameters and Pathological Characteristics in Breast Cancer. J Magn Reson Imaging 2020; 52:1360-1373. [PMID: 32524658 DOI: 10.1002/jmri.27241] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 05/13/2020] [Accepted: 05/15/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Dynamic contrast-enhanced MRI (DCE-MRI) is used to evaluate tumor microvasculature. However, studies demonstrating an association between perfusion parameters derived from DCE-MRI and histopathologic characteristics are limited to a small set of histopathologic factors, and the results are inconsistent. PURPOSE To evaluate the relationship between DCE-MRI perfusion parameters and common histopathologic tumor characteristics used to predict angiogenesis and determine prognosis in breast cancer. STUDY TYPE Retrospective. POPULATION In all, 105 breast cancer patients with invasive ductal carcinoma (122 lesions). FIELD STRENGTH/SEQUENCE 3.0T, turbo spin-echo (TSE) T1 -weighted, fat-suppressed T2 -weighted, TSE T2 -weighted, and dynamic unenhanced and contrast-enhanced 3D T1 high-resolution isotropic volume examination. ASSESSMENT One reviewer obtained perfusion parameters (Ktrans , kep , ve , and vp ) of each breast cancer from DCE MRI using the extended Tofts model with a fixed baseline T1 value and a population-based arterial input function. The relationship between DCE-MRI perfusion parameters and histopathologic tumor characteristics used to predict angiogenesis and determine prognosis was evaluated. STATISTICAL TESTS Student's t-test, Mann-Whitney U-test, analysis of variance (ANOVA), and Kruskal-Wallis test were used. RESULTS Triple-negative breast cancers exhibited higher Ktrans and kep than luminal cancers (P < 0.05). Estrogen receptor (ER)-negative tumors showed higher Ktrans than ER-positive tumors (P < 0.05). Progesterone receptor (PR)-negative tumors presented higher ve than PR-positive tumors (P < 0.05). Tumors with higher Ki-67 showed higher kep than tumors with lower Ki-67 (P < 0.05). P53-positive tumors exhibited higher Ktrans and kep than p53-negative tumors (P < 0.05). Higher histologic grade tumors (grade II/III) presented higher Ktrans , kep , vp (P < 0.05) than grade I tumors. Tumors with LVSI presented higher Ktrans and kep than tumors without LVSI (P < 0.05). DATA CONCLUSION Breast cancer presenting higher Ktrans and kep on DCE-MRI was associated with poor prognostic histopathologic factors. Therefore, pretreatment DCE-MRI perfusion parameters may be useful imaging biomarkers for the evaluation of tumor prognosis and angiogenesis. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Se Ri Kang
- Department of Radiology, Wonkwang University Hospital, Iksan, Republic of Korea
| | - Hye Won Kim
- Department of Radiology, Wonkwang University Hospital, Wonkwang University School of Medicine, Iksan, Republic of Korea
| | - Hun Soo Kim
- Department of Pathology, Wonkwang University Hospital, Wonkwang University School of Medicine, Iksan, Republic of Korea
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Zhou X, Gao F, Duan S, Zhang L, Liu Y, Zhou J, Bai G, Tao W. Radiomic features of Pk-DCE MRI parameters based on the extensive Tofts model in application of breast cancer. Phys Eng Sci Med 2020; 43:517-524. [PMID: 32524436 DOI: 10.1007/s13246-020-00852-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 02/11/2020] [Indexed: 01/03/2023]
Abstract
To explore radiomic features of pharmacokinetic dynamic contrast-enhanced (Pk-DCE) MRI on the extensive Tofts model to diagnose breast cancer and predict molecular phenotype. Breast lesions enrolled must undergo Pk-DCE MRI before treatment or puncture, and be identified as primary lesions by pathology. Ktrans, Kep, Ve and Vp were generated on the extensive Tofts model. Radiomic features (histogram, geometry and texture features) were extracted from parametric maps and selected by LASSO. The subjects were divided into training and validation cohort with a ratio of 4:1 to construct model in diagnosis of breast cancer. Feature analysis was made to predict the molecular phenotype. Area under curve (AUC), sensitivity, specificity and accuracy were used to evaluate radiomic features. DeLong's test was performed to compare AUC values. 228 breast lesions met the criteria were used to discrimination and 126 malignant lesions were used to study molecular phenotypes. The number of training cohort and validation cohort were 182 and 46, respectively. The AUC of Ktrans, Kep, Ve, and Vp was 0.95, 0.93, 0.89, and 0.96, and their accuracy was 85%, 89%, 89%, 94% respectively in diagnosis of breast lesions, while their AUC was 0.71 to 0.77, 0.61 to 0.68, and 0.67 to 0.74 to predict ER/PR, Her-2, and Ki-67. There was no significant difference among parameters (P > 0.05). Radiomic features based on Pk-DCE MRI have an advantage to diagnose breast cancer and less ability to predict molecular phenotypes, which are beneficial to guide clinical treatment of breast lesions in some extent.
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Affiliation(s)
- Xiaoyu Zhou
- Research Center of Internet Things (Sensory Mine), China University of Mining and Technology, Xuzhou, People's Republic of China.,Faculty of Mechanical Electronic and Information Engineering, Jiangsu Vocational College of Finance and Economics, Huai'an, People's Republic of China
| | - Feng Gao
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, People's Republic of China
| | - Shaofeng Duan
- GE Healthcare China, Shanghai, People's Republic of China
| | - Lianmei Zhang
- Department of Pathology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, People's Republic of China
| | - Yan Liu
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huanghe Road No. 1, Huai'an, 223300, Jiangsu Province, People's Republic of China
| | - Junyi Zhou
- Department of Medical Imaging, Jiangsu University, Zhenjiang, People's Republic of China
| | - Genji Bai
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huanghe Road No. 1, Huai'an, 223300, Jiangsu Province, People's Republic of China.
| | - Weijing Tao
- Department of Nuclear Medicine, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huanghe Road No.1, Huai'an, 223300, Jiangsu Province, People's Republic of China.
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Fan M, Yuan W, Zhao W, Xu M, Wang S, Gao X, Li L. Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics. IEEE J Biomed Health Inform 2019; 24:1632-1642. [PMID: 31794406 DOI: 10.1109/jbhi.2019.2956351] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Histologic grade and Ki-67 proliferation status are important clinical indictors for breast cancer prognosis and treatment. The purpose of this study is to improve prediction accuracy of these clinical indicators based on tumor radiomic analysis. METHODS We jointly predicted Ki-67 and tumor grade with a multitask learning framework by separately utilizing radiomics from tumor MRI series. Additionally, we showed how multitask learning models (MTLs) could be extended to combined radiomics from the MRI series for a better prediction based on the assumption that features from different sources of images share common patterns while providing complementary information. Tumor radiomic analysis was performed with morphological, statistical and textural features extracted on the DWI and dynamic contrast-enhanced MRI (DCE-MRI) series of the precontrast and subtraction images, respectively. RESULTS Joint prediction of Ki-67 status and tumor grade on MR images using the MTL achieved performance improvements over that of single-task-based predictive models. Similarly, for the prediction tasks of Ki-67 and tumor grade, the MTL for combined precontrast and apparent diffusion coefficient (ADC) images achieved AUCs of 0.811 and 0.816, which were significantly better than that of the single-task- based model with p values of 0.005 and 0.017, respectively. CONCLUSION Mapping MRI radiomics to two related clinical indicators improves prediction performance for both Ki-67 expression level and tumor grade. SIGNIFICANCE Joint prediction of indicators by multitask learning that combines correlations of MRI radiomics is important for optimal tumor therapy and treatment because clinical decisions are made by integrating multiple clinical indicators.
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Surov A, Chang YW, Li L, Martincich L, Partridge SC, Kim JY, Wienke A. Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis. BMC Cancer 2019; 19:1043. [PMID: 31690273 PMCID: PMC6833245 DOI: 10.1186/s12885-019-6298-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 10/27/2019] [Indexed: 12/14/2022] Open
Abstract
Background Radiological imaging plays a central role in the diagnosis of breast cancer (BC). Some studies suggest MRI techniques like diffusion weighted imaging (DWI) may provide further prognostic value by discriminating between tumors with different biologic characteristics including receptor status and molecular subtype. However, there is much contradictory reported data regarding such associations in the literature. The purpose of the present study was to provide evident data regarding relationships between quantitative apparent diffusion coefficient (ADC) values on DWI and pathologic prognostic factors in BC. Methods Data from 5 centers (661 female patients, mean age, 51.4 ± 10.5 years) were acquired. Invasive ductal carcinoma (IDC) was diagnosed in 625 patients (94.6%) and invasive lobular carcinoma in 36 cases (5.4%). Luminal A carcinomas were diagnosed in 177 patients (28.0%), luminal B carcinomas in 279 patients (44.1%), HER 2+ carcinomas in 66 cases (10.4%), and triple negative carcinomas in 111 patients (17.5%). The identified lesions were staged as T1 in 51.3%, T2 in 43.0%, T3 in 4.2%, and as T4 in 1.5% of the cases. N0 was found in 61.3%, N1 in 33.1%, N2 in 2.9%, and N3 in 2.7%. ADC values between different groups were compared using the Mann–Whitney U test and by the Kruskal-Wallis H test. The association between ADC and Ki 67 values was calculated by Spearman’s rank correlation coefficient. Results ADC values of different tumor subtypes overlapped significantly. Luminal B carcinomas had statistically significant lower ADC values compared with luminal A (p = 0.003) and HER 2+ (p = 0.007) lesions. No significant differences of ADC values were observed between luminal A, HER 2+ and triple negative tumors. There were no statistically significant differences of ADC values between different T or N stages of the tumors. Weak statistically significant correlation between ADC and Ki 67 was observed in luminal B carcinoma (r = − 0.130, p = 0.03). In luminal A, HER 2+ and triple negative tumors there were no significant correlations between ADC and Ki 67. Conclusion ADC was not able to discriminate molecular subtypes of BC, and cannot be used as a surrogate marker for disease stage or proliferation activity.
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Affiliation(s)
- Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Germany.
| | - Yun-Woo Chang
- Department of Radiology, Soonchunhyang University Hospital, 59 Daesakwan-ro, Yongsan-gu, Seoul, 140-743, Republic of Korea
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Laura Martincich
- Unit of Radiology, Institute for Cancer Research and Treatment (IRCC), Strada Provinciale 142, 10060 Candiolo, Turin, Italy
| | - Savannah C Partridge
- Department of Radiology, University of Washington, Seattle, Washington 825 Eastlake Ave. E, G2-600, Seattle, WA, 98109, USA
| | - 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, South Korea
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Magdeburger Str, 06097, Halle, Germany
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Luo N, Ji Y, Huang X, Liu Y, Liu L, Jin G, Zhao X, Zhu X, Su D. Changes in Apparent Diffusion Coefficient as Surrogate Marker for Changes in Ki-67 Index Due to Neoadjuvant Chemotherapy in Patients with Invasive Breast Cancer. Acad Radiol 2019; 26:1352-1357. [PMID: 30711409 DOI: 10.1016/j.acra.2019.01.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 01/20/2019] [Accepted: 01/20/2019] [Indexed: 11/18/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate possible correlation between changes in apparent diffusion coefficient (ADC) and Ki-67 index as a result of neoadjuvant chemotherapy (NAC) in patients with invasive breast cancer. METHODS AND MATERIALS Between February 2016 and October 2017, 87 patients with breast cancer underwent diffusion-weighted magnetic resonance imaging (b = 0 and 800 sec/mm2) before and after NAC. ADC and tumor diameter before and after NAC were compared to the Ki-67 index determined from biopsy or surgical specimens. RESULTS Ki-67 index did not correlate significantly with ADC before NAC (p = 0.862) or afterwards (p = 0.292), nor did it correlate with tumor diameter before (p = 0.545) or afterwards (p = 0.478). However, change in ADC as a result of NAC correlated inversely with change in Ki-67 index (r = -0.326, p = 0.002). The percentage change in Ki-67 index did not correlate with the percentage change in ADC (p = 0.404). Similarly, the change in Ki-67 index or percentage change in that index did not correlate with the change in tumor diameter (p = 0.075) or percentage change in tumor diameter (p = 0.233). CONCLUSION Comparison of pre- and post-NAC ADC can be used to estimate the change in Ki-67 index in patients with invasive breast cancer.
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Affiliation(s)
- Ningbin Luo
- Department of Radiology, Guangxi Medical University Affiliated Cancer Hospital, No. 71 Hedi Road, Nanning, Guangxi 530021, China
| | - Yinan Ji
- Department of Breast Surgery, Guangxi Medical University Affiliated Cancer Hospital, Nanning, Guangxi, China
| | - Xiangyang Huang
- Department of Radiology, Guangxi Medical University Affiliated Cancer Hospital, No. 71 Hedi Road, Nanning, Guangxi 530021, China
| | - Yu Liu
- Department of Radiology, Guangxi Medical University Affiliated Cancer Hospital, No. 71 Hedi Road, Nanning, Guangxi 530021, China
| | - Lidong Liu
- Department of Radiology, Guangxi Medical University Affiliated Cancer Hospital, No. 71 Hedi Road, Nanning, Guangxi 530021, China
| | - Guanqiao Jin
- Department of Radiology, Guangxi Medical University Affiliated Cancer Hospital, No. 71 Hedi Road, Nanning, Guangxi 530021, China
| | - Xin Zhao
- Department of Radiology, Guangxi Medical University Affiliated Cancer Hospital, No. 71 Hedi Road, Nanning, Guangxi 530021, China
| | - Xuna Zhu
- Department of Radiology, Guangxi Medical University Affiliated Cancer Hospital, No. 71 Hedi Road, Nanning, Guangxi 530021, China
| | - Danke Su
- Department of Radiology, Guangxi Medical University Affiliated Cancer Hospital, No. 71 Hedi Road, Nanning, Guangxi 530021, China.
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Whole-lesion histogram and texture analyses of breast lesions on inline quantitative DCE mapping with CAIPIRINHA-Dixon-TWIST-VIBE. Eur Radiol 2019; 30:57-65. [PMID: 31372782 DOI: 10.1007/s00330-019-06365-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 06/17/2019] [Accepted: 07/10/2019] [Indexed: 01/01/2023]
Abstract
PURPOSE To investigate the diagnostic capability of whole-lesion (WL) histogram and texture analysis of dynamic contrast-enhanced (DCE) MRI inline-generated quantitative parametric maps using CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) to differentiate malignant from benign breast lesions and breast cancer subtypes. MATERIALS AND METHODS From February 2018 to November 2018, DCE MRI using CDTV was performed on 211 patients. The inline-generated parametric maps included Ktrans, kep, Ve, and IAUGC60. Histogram and texture features were extracted from the above parametric maps respectively based on a WL analysis. Student's t tests, one-way ANOVAs, Mann-Whitney U tests, Jonckheere-Terpstra tests, and ROC curves were used for statistical analysis. RESULTS Compared with benign breast lesions, malignant breast lesions showed significantly higher Ktrans_median, 5th percentile, entropy, and diff-entropy, IAUGC60_median, 5th percentile, entropy, and diff-entropy, kep_mean, median, 5th percentile, entropy, and diff-entropy, and Ve_95th percentile, diff-variance, and contrast, and significantly lower kep_skewness and Ve_SD, entropy, diff-entropy, and skewness (all p ≤ 0.011). The combination of all the extracted parameters yielded an AUC of 0.85 (sensitivity 76%, specificity 86%). kep_contrast showed a significant difference among different subtypes of breast cancer (p = 0.006). kep_skewness showed a significant difference between lymph node-positive and lymph node-negative breast cancer (p = 0.007). The IAGC60_5th percentile had an AUC of 0.71 (sensitivity 50%, specificity 91%) for differentiating between high- and low-proliferation groups of breast cancer. CONCLUSIONS The WL histogram and texture analyses of CDTV-DCE-derived parameters may give additional information for further evaluation of breast cancer. KEY POINTS • Inline DCE mapping with CDTV is effective and time-saving. • WL histogram and texture-extracted features could distinguish breast cancer from benign lesions accurately. • kep_contrast, kep_skewness, and IAUGC60_5th percentile could predict breast cancer subtypes, lymph node metastasis, and proliferation abilities, respectively.
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Tao WJ, Zhang HX, Zhang LM, Gao F, Huang W, Liu Y, Zhu Y, Bai GJ. Combined application of pharamcokinetic DCE-MRI and IVIM-DWI could improve detection efficiency in early diagnosis of ductal carcinoma in situ. J Appl Clin Med Phys 2019; 20:142-150. [PMID: 31124276 PMCID: PMC6612698 DOI: 10.1002/acm2.12624] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 04/29/2019] [Accepted: 05/02/2019] [Indexed: 12/23/2022] Open
Abstract
PURPOSE Ductal carcinoma in situ (DCIS) is a precursor of invasive ductal breast carcinoma (IDC). This study aimed to use pharamcokinetic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the early diagnosis of DCIS. METHODS Forty-seven patients, including 25 with DCIS (age: 28-70 yr, mean age: 48.7 yr) and 22 with benign disease (age: 25-67 yr, mean age: 43.1 yr) confirmed by pathology, underwent pharamcokinetic DCE-MRI and IVIM-DWI in this study. The quantitative parameters Ktrans , Kep , Ve , Vp , and D, f, D* were obtained by processing of DCE-MRI and IVIM-DWI images with Omni-Kinetics and MITK-Diffusion softwares, respectively. Parameters were analyzed statistically using GraphPad Prism and MedCalc softwares. RESULTS All low-grade DCIS lesions demonstrated mass enhancement with clear boundaries, while most middle-grade and high-grade DCIS lesions showed non-mass-like enhancement (NMLE). DCIS lesions were significantly different from benign lesions in terms of Ktrans , Kep , and D (t = 5.959, P < 0.0001; t = 5.679, P < 0.0001; and t = 5.629, P < 0.0001, respectively). The AUC of Ktrans , Kep , D and the combined indicator of Ktrans , Kep, and D were 0.936, 0.902, 0.860, and 0.976, respectively. There was a significant difference in diagnostic efficacy only between D and the combined indicator (Z = 2.408, P = 0.016). CONCLUSION DCE-MRI and IVIM-DWI could make for the early diagnosis of DCIS, and reduce the misdiagnosis of DCIS and over-treatment of benign lesions.
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Affiliation(s)
- Wei-Jing Tao
- Department of Nuclear Medicine, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an City, Jiangsu Province, China
| | - Hui-Xin Zhang
- Department of Ultrasound, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an City, Jiangsu Province, China
| | - Lian-Mei Zhang
- Department of Pathology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China
| | - Feng Gao
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing City, Jiangsu Province, China
| | - Wei Huang
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an City, Jiangsu Province, China
| | - Yan Liu
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an City, Jiangsu Province, China
| | - Yan Zhu
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an City, Jiangsu Province, China
| | - Gen-Ji Bai
- Department of Radiology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an City, Jiangsu Province, China
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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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Liu Y, Huang Y, Han J, Wang J, Li F, Zhou J. Association Between Shear Wave Elastography of Virtual Touch Tissue Imaging Quantification Parameters and the Ki-67 Proliferation Status in Luminal-Type Breast Cancer. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2019; 38:73-80. [PMID: 29708280 DOI: 10.1002/jum.14663] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Revised: 03/16/2018] [Accepted: 03/20/2018] [Indexed: 06/08/2023]
Abstract
OBJECTIVES To evaluate the association between shear wave elastography parameters using virtual touch tissue imaging quantification (VTIQ) and the Ki-67 index in luminal-type breast cancer. METHODS Eighty-one patients with 82 lesions of pathologic confirmed luminal-type breast cancer underwent virtual touch tissue imaging quantification examination before surgery between December 2015 and June 2016. Patients were divided into 2 groups according to the Ki-67 index (≥14% versus < 14%), which is used to define luminal type B and luminal type A, respectively. The mean shear wave velocity (SWVmean ) and lesion-to-adjacent tissues ratio (SWV ratio) were calculated for each lesion. RESULTS The SWVmean , SWV ratio, histologic grade, axillary lymph node involvement, and lymphovascular invasion showed a significant positive association with a high Ki-67 index (all P < .05). Receiver operating characteristic curve analysis for the differential diagnosis between high (≥14%) and low (<14%) Ki-67 groups displayed that the optimal cutoff value for SWVmean and SWV ratio were 3.99 meters per second and 2.861, with sensitivity 94% and 72%, specificity 40.6% and 56.2%, and area under the receiver operating characteristic curve of 0.689 and 0.651, respectively. Univariate analysis showed that SWVmean (P = .005), SWV ratio (P = .029), histologic grade (P = .011), presence of axillary node involvement (P = .004), and lymphovascular invasion (P = .008) were significantly associated with high Ki-67 status. Multivariable analysis displayed that SWVmean (hazard ratio [HR], 1.459, 95% confidence interval [CI], 1.028-2.072; P = .035), histologic grade (HR, 4.105; 95% CI, 1.142-14.763; P = .031), and presence of axillary node involvement (HR, 3.75; 95% CI, 1.228-11.453; P = .020) maintained significance for predicting high Ki-67 status. CONCLUSIONS The SWVmean using the virtual touch tissue imaging quantification method showed significant correlation with the Ki-67 index, suggesting the potential to assess tumor proliferation status in luminal-type breast cancer with a noninvasive manner.
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Affiliation(s)
- Yubo Liu
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 6, Guangzhou, China
| | - Yini Huang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 6, Guangzhou, China
| | - Jing Han
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 6, Guangzhou, China
| | - Jianwei Wang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 6, Guangzhou, China
| | - Fei Li
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 6, Guangzhou, China
| | - Jianhua Zhou
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 6, Guangzhou, China
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Quantitative dynamic contrast-enhanced MR imaging for differentiating benign, borderline, and malignant ovarian tumors. Abdom Radiol (NY) 2018; 43:3132-3141. [PMID: 29556691 DOI: 10.1007/s00261-018-1569-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
PURPOSE This study aimed to investigate the diagnostic performance of quantitative DCE-MRI for characterizing ovarian tumors. METHODS We prospectively assessed the differences of quantitative DCE-MRI parameters (Ktrans, kep, and ve) among 15 benign, 28 borderline, and 66 malignant ovarian tumors; and between type I (n = 28) and type II (n = 29) of epithelial ovarian carcinomas (EOCs). DCE-MRI data were analyzed using whole solid tumor volume region of interest (ROI) method, and quantitative parameters were calculated based on a modified Tofts model. The non-parametric Kruskal-Wallis test, Mann-Whitney U test, Pearson's chi-square test, intraclass correlation coefficient (ICC), variance test, and receiver operating characteristic curves (ROC) were used for statistical analysis. RESULTS The largest Ktrans and kep values were observed in ovarian malignant tumors, followed by borderline and benign tumors (all P < 0.001). Kep was the better parameter for differentiating benign tumors from borderline and malignant tumors, with a sensitivity of 89.3% and 95.5%, a specificity of 86.7% and 100%, an accuracy of 88.4% and 96.3%, and an area under the curve (AUC) of 0.94 and 0.992, respectively, whereas Ktrans was better for differentiating borderline from malignant tumors with a sensitivity of 60.7%, a specificity of 78.8%, an accuracy of 73.4%, and an AUC of 0.743. In addition, a combination with kep could further improve the sensitivity to 78.9%. The median Ktrans and kep values were significantly higher in type II than in type I EOCs. CONCLUSION DCE-MRI with volume quantification is a technically feasible method, and can be used for the differentiation of ovarian tumors and for discriminating between type I and type II EOCs.
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Ravanelli M, Grammatica A, Tononcelli E, Morello R, Leali M, Battocchio S, Agazzi GM, Buglione di Monale E Bastia M, Maroldi R, Nicolai P, Farina D. Correlation between Human Papillomavirus Status and Quantitative MR Imaging Parameters including Diffusion-Weighted Imaging and Texture Features in Oropharyngeal Carcinoma. AJNR Am J Neuroradiol 2018; 39:1878-1883. [PMID: 30213805 DOI: 10.3174/ajnr.a5792] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Accepted: 05/27/2018] [Indexed: 12/21/2022]
Abstract
BACKGROUND AND PURPOSE The incidence of Oropharyngeal Squampus Cell Carcinoma (OPSCC) cases is increasing especially in the Western countries due to the spreading of human papilloma virus (HPV) infection. Radiological investigations, MRI in particular, are used in the daily clinical practice to stage OPSCC. The aim of this study was to investigate the association of quantitative MR imaging features including diffusion-weighted imaging and human papillomavirus status in oropharyngeal squamous cell carcinoma. MATERIALS AND METHODS We retrospectively analyzed 59 patients with untreated histologically proved T2-T4 oropharyngeal squamous cell carcinoma. Human papillomavirus status was determined by viral DNA detection on tissue samples. MR imaging protocol included T2-weighted, contrast-enhanced T1-weighted (volumetric interpolated brain examination), and DWI sequences. Parametric maps of apparent diffusion coefficient were obtained from DWI sequences. Texture analysis was performed on T2 and volumetric-interpolated brain examination sequences and on ADC maps. Differences in quantitative MR imaging features between tumors positive and negative for human papillomavirus and among subgroups of patients stratified by smoking status were tested using the nonparametric Mann-Whitney U test; the false discovery rate was controlled using the Benjamini-Hochberg correction; and a predictive model for human papillomavirus status was built using multivariable logistic regression. RESULTS Twenty-eight patients had human papillomavirus-positive oropharyngeal squamous cell carcinoma, while 31 patients had human papillomavirus-negative oropharyngeal squamous cell carcinoma. Tumors positive for human papillomavirus had a significantly lower mean ADC compared with those negative for it (median, 850.87 versus median, 1033.68; P < .001). Texture features had a lower discriminatory power for human papillomavirus status. Skewness on volumetric interpolated brain examination sequences was significantly higher in the subgroup of patients positive for human papillomavirus and smokers (P = .003). A predictive model based on smoking status and mean ADC yielded a sensitivity of 83.3% and specificity 92.6% in classifying human papillomavirus status. CONCLUSIONS ADC is significantly lower in oropharyngeal squamous cell carcinoma positive for human papillomavirus compared with oropharyngeal squamous cell carcinoma negative for it. ADC and smoking status allowed noninvasive prediction of human papillomavirus status with a good accuracy. These results should be validated and further investigated on larger prospective studies.
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Affiliation(s)
- M Ravanelli
- From the Departments of Radiology (M.R., E.T., M.L., G.M.A., R.M., D.F.)
| | - A Grammatica
- Otolaryngology-Head and Neck Surgery (A.G., R.M., P.N.)
| | - E Tononcelli
- From the Departments of Radiology (M.R., E.T., M.L., G.M.A., R.M., D.F.)
| | - R Morello
- From the Departments of Radiology (M.R., E.T., M.L., G.M.A., R.M., D.F.).,Otolaryngology-Head and Neck Surgery (A.G., R.M., P.N.)
| | - M Leali
- From the Departments of Radiology (M.R., E.T., M.L., G.M.A., R.M., D.F.)
| | | | - G M Agazzi
- From the Departments of Radiology (M.R., E.T., M.L., G.M.A., R.M., D.F.)
| | | | - R Maroldi
- From the Departments of Radiology (M.R., E.T., M.L., G.M.A., R.M., D.F.)
| | - P Nicolai
- Otolaryngology-Head and Neck Surgery (A.G., R.M., P.N.)
| | - D Farina
- From the Departments of Radiology (M.R., E.T., M.L., G.M.A., R.M., D.F.)
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Surov A, Clauser P, Chang YW, Li L, Martincich L, Partridge SC, Kim JY, Meyer HJ, Wienke A. Can diffusion-weighted imaging predict tumor grade and expression of Ki-67 in breast cancer? A multicenter analysis. Breast Cancer Res 2018; 20:58. [PMID: 29921323 PMCID: PMC6011203 DOI: 10.1186/s13058-018-0991-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 05/18/2018] [Indexed: 01/24/2023] Open
Abstract
Background Numerous studies have analyzed associations between apparent diffusion coefficient (ADC) and histopathological features such as Ki-67 proliferation index in breast cancer (BC), with mixed results. The purpose of this study was to perform a multicenter analysis to determine relationships between ADC and expression of Ki-67 and tumor grade in BC. Methods For this study, data from six centers were acquired. The sample comprises 870 patients (all female; mean age, 52.6 ± 10.8 years). In every case, breast magnetic resonance imaging with diffusion-weighted imaging was performed. The comparison of ADC values in groups was performed by Mann-Whitney U test where the p values are adjusted for multiple testing (Bonferroni correction). The association between ADC and Ki-67 values was calculated by Spearman’s rank correlation coefficient. Sensitivity, specificity, negative and positive predictive values, accuracy, and AUC were calculated for the diagnostic procedures. ADC thresholds were chosen to maximize the Youden index. Results Overall, data of 870 patients were acquired for this study. The mean ADC value of the tumors was 0.98 ± 0.22 × 10− 3 mm2 s− 1. ROC analysis showed that it is impossible to differentiate high/moderate grade tumors from grade 1 lesions using ADC values. Youden index identified a threshold ADC value of 1.03 with a sensitivity of 56.2% and specificity of 67.9%. The positive predictive value was 18.2%, and the negative predictive value was 92.4%. The level of the Ki-67 proliferation index was available for 845 patients. The mean value was 12.33 ± 21.77%. ADC correlated with weak statistical significant with expression of Ki-67 (p = − 0.202, p < 0.001). ROC analysis was performed to distinguish tumors with high proliferative potential from tumors with low expression of Ki-67 using ADC values. Youden index identified a threshold ADC value of 0.91 (sensitivity 64%, specificity 50%, positive predictive value 67.7%, negative predictive value 45.0%). Conclusions ADC cannot be used as a surrogate marker for proliferation activity and/or for tumor grade in breast cancer.
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Affiliation(s)
- Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstrasse 20, 04103, Leipzig, Germany.
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel, 18-20 1090, Vienna, Austria
| | - Yun-Woo Chang
- Department of Radiology, Soonchunhyang University Hospital, 59 Daesakwan-ro, Yongsan-gu, Seoul, 140-743, Republic of Korea
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Laura Martincich
- Unit of Radiology, Institute for Cancer Research and Treatment of Candiolo (IRCC), Strada Provinciale 142, 10060 Candiolo, Turin, Italy
| | - Savannah C Partridge
- Department of Radiology, University of Washington, 825 Eastlake Avenue E, G2-600, Seattle, WA, 98109, USA
| | - 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, Korea
| | - Hans Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Liebigstrasse 20, 04103, Leipzig, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Magdeburger Strasse, 06097, Halle, Germany
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Chen J, Chen C, Xia C, Huang Z, Zuo P, Stemmer A, Song B. Quantitative free-breathing dynamic contrast-enhanced MRI in hepatocellular carcinoma using gadoxetic acid: correlations with Ki67 proliferation status, histological grades, and microvascular density. Abdom Radiol (NY) 2018; 43:1393-1403. [PMID: 28939963 DOI: 10.1007/s00261-017-1320-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
PURPOSE To validate a free-breathing dynamic contrast-enhanced-MRI (DCE-MRI) in hepatocellular carcinoma (HCC) patients using gadoxetic acid, and to determine the relationship between DCE-MRI parameters and histological results. METHODS Thirty-four HCC patients were included in this prospective study. Free-breathing DCE-MRI data was acquired preoperatively on a 3.0 Tesla scanner. Perfusion parameters (K trans, K ep, V e and the semi-quantitative parameter of initial area under the gadolinium concentration-time curve, iAUC) were calculated and compared with tumor enhancement at contrast-enhanced CT. The relationship between DCE-MRI parameters and Ki67 indices, histological grades and microvascular density (MVD) was determined by correlation analysis. Differences of perfusion parameters between different histopathological groups were compared. Receiver operation characteristic (ROC) analysis of discriminating high-grades (grade III and IV) from low-grades (grade I and II) HCC was performed for perfusion parameters. RESULTS Significant relationship was found between DCE-MRI and CT results. The DCE-MRI derived K trans were significantly negatively correlated with Ki-67 indices (rho = - 0.408, P = 0.017) and the histological grades (rho = - 0.444, P = 0.009) of HCC, and K ep and V e were significantly related with tumor MVD (rho = - 0.405, P = 0.017 for K ep; and rho = 0.385, P = 0.024 for V e). K trans, K ep, and iAUC demonstrated moderate diagnostic performance (iAUC = 0.78, 0.77 and 0.80, respectively) for discriminating high-grades from low-grades HCC without significant differences. CONCLUSIONS The DCE-MRI derived parameters demonstrated weak but significant correlations with tumor proliferation status, histological grades or microvascular density, respectively. This free-breathing DCE-MRI is technically feasible and offers a potential avenue toward non-invasive evaluation of HCC malignancy.
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Affiliation(s)
- Jie Chen
- West China Medical School of Sichuan University, Chengdu, 610041, Sichuan, China
| | - Chenyang Chen
- Department of Radiology, West China Hospital of Sichuan University, Guoxuexiang No. 37, Chengdu, 610041, Sichuan province, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital of Sichuan University, Guoxuexiang No. 37, Chengdu, 610041, Sichuan province, China
| | - Zixing Huang
- Department of Radiology, West China Hospital of Sichuan University, Guoxuexiang No. 37, Chengdu, 610041, Sichuan province, China
| | - Panli Zuo
- MR Collaboration NE Asia, Siemens Healthcare, Beijing, 100000, China
| | | | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Guoxuexiang No. 37, Chengdu, 610041, Sichuan province, China.
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Li T, Yu T, Li L, Lu L, Zhuo Y, Lian J, Xiong Y, Kong D, Li K. Use of diffusion kurtosis imaging and quantitative dynamic contrast-enhanced MRI for the differentiation of breast tumors. J Magn Reson Imaging 2018; 48:1358-1366. [PMID: 29717790 DOI: 10.1002/jmri.26059] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 04/04/2018] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Breast MRI is a sensitive imaging technique to assess breast cancer but its effectiveness still remains to be improved. PURPOSE To evaluate the diagnostic performance of diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and quantitative dynamic contrast-enhanced (DCE)-MRI in differentiating malignant from benign breast lesions independently or jointly and to explore whether correlations exist among these parameters. STUDY TYPE Retrospective. POPULATION In all, 106 patients with breast lesions (47 malignant, 59 benign). SEQUENCE DKI sequence with seven b values and quantitative DCE sequence on 3.0T MRI. ASSESSMENT Diffusion parameters (mean diffusivity [MD], mean diffusivity [MK], and apparent diffusion coefficient [ADC]) from DKI and DWI and perfusion parameters from DCE (Ktrans , kep , ve , and vp ) were calculated by two experienced radiologists after postprocessing. Disagreement between the two observers was resolved by consensus. STATISTICAL TESTS The parameters in benign and malignant lesions were compared by Student's t-test. The diagnostic performances of DKI and quantitative DCE, either alone or in combination, were evaluated by receiver operating characteristic (ROC) analysis. The Spearman correlation test was used to evaluate correlations among the diffusion parameters and perfusion parameters. RESULTS MK, MD, ADC, Ktrans , and kep values were significantly different between breast cancer and benign lesions (P < 0.05). MK from DKI demonstrated the highest AUC of 0.849, which is significantly higher than ADC derived from conventional DWI (z = 3.345, P = 0.0008). The specificity of DCE-MRI-derived parameters was improved when combining diffusion parameters, such as ADC and MK. The highest diagnostic specificity (93.2%) was obtained when kep and ADC were combined. kep was correlated moderately positively with MK (r = 0.516) and moderately negatively with MD (r = -0.527). Ktrans was weakly positively correlated with MK with an r of 0.398 and weakly negatively correlated with MD with an r of -0.450. DATA CONCLUSION DKI is more valuable than conventional DWI in distinguishing between benign and malignant breast lesions. DKI exhibits promise as a quantitative technique to augment quantitative DCE-MRI. Diffusion parameters derived from DKI were statistically correlated with perfusion parameters from quantitative DCE-MRI. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1358-1366.
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Affiliation(s)
- Ting Li
- Department of Radiology, Shanghai General Hospital, Shanghai, 201620, P.R. China
| | - Tao Yu
- Department of Medical Imaging, Cancer Hospital of China Medical University, Shenyang, 110042, P.R. China
| | - Lyu Li
- Philips Healthcare, Shanghai, China
| | - Lunbo Lu
- Department of Radiology, Shanghai General Hospital, Shanghai, 201620, P.R. China
| | - Yaoyao Zhuo
- Department of Radiology, Shanghai General Hospital, Shanghai, 201620, P.R. China
| | - Jingge Lian
- Department of Radiology, Shanghai General Hospital, Shanghai, 201620, P.R. China
| | - Yun Xiong
- School of Computer Science and Technology, Fudan University, Shanghai Key Laboratory of Data Science, Shanghai, 201203, P.R. China
| | - Dexing Kong
- School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, P.R. China
| | - Kangan Li
- Department of Radiology, Shanghai General Hospital, Shanghai, 201620, P.R. China
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Incoronato M, Grimaldi AM, Cavaliere C, Inglese M, Mirabelli P, Monti S, Ferbo U, Nicolai E, Soricelli A, Catalano OA, Aiello M, Salvatore M. Relationship between functional imaging and immunohistochemical markers and prediction of breast cancer subtype: a PET/MRI study. Eur J Nucl Med Mol Imaging 2018; 45:1680-1693. [DOI: 10.1007/s00259-018-4010-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 04/05/2018] [Indexed: 02/06/2023]
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Xiao Z, Zhong Y, Tang Z, Qiang J, Qian W, Wang R, Wang J, Wu L, Tang W, Zhang Z. Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion MR imaging of sinonasal malignancies: correlations with Ki-67 proliferation status. Eur Radiol 2018; 28:2923-2933. [PMID: 29383521 DOI: 10.1007/s00330-017-5286-x] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Revised: 12/11/2017] [Accepted: 12/22/2017] [Indexed: 01/12/2023]
Abstract
OBJECTIVES To explore the correlations of parameters derived from standard diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) with the Ki-67 proliferation status. METHODS Seventy-five patients with histologically proven sinonasal malignancies who underwent standard DWI, DKI and IVIM were retrospectively reviewed. The mean, minimum, maximum and whole standard DWI [apparent diffusion coefficient (ADC)], DKI [diffusion kurtosis (K) and diffusion coefficient (Dk)] and IVIM [pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion fraction (f)] parameters were measured and correlated with the Ki-67 labelling index (LI). The Ki-67 LI was categorised as high (> 50%) or low (≤ 50%). RESULTS The K and f values were positively correlated with the Ki-67 LI (rho = 0.295~0.532), whereas the ADC, Dk and D values were negatively correlated with the Ki-67 LI (rho = -0.443~-0.277). The ADC, Dk and D values were lower, whereas the K value was higher in sinonasal malignancies with a high Ki-67 LI than in those in a low Ki-67 LI (all p < 0.05). A higher maximum K value (Kmax > 0.977) independently predicted a high Ki-67 status [odds ratio (OR) = 7.614; 95% confidence interval (CI) = 2.197-38.674; p = 0.017]. CONCLUSION ADC, Dk, K, D and f are correlated with Ki-67 LI. Kmax is the strongest independent factor for predicting Ki-67 status. KEY POINTS • DWI-derived parameters from different models are capable of providing different pathophysiological information. • DWI, DKI and IVIM parameters are associated with Ki-67 proliferation status. • K max derived from DKI is the strongest independent factor for the prediction of Ki-67 proliferation status.
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Affiliation(s)
- Zebin Xiao
- Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China
| | - Yufeng Zhong
- Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China.,Department of Radiology, Jinshan Hospital of Shanghai Medical School, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China
| | - Zuohua Tang
- Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China.
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital of Shanghai Medical School, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China.
| | - Wen Qian
- Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China
| | - Rong Wang
- Department of Radiology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, People's Republic of China
| | - Jie Wang
- Department of Radiotherapy, Eye & ENT Hospital of Shanghai Medical School, Fudan University, Shanghai, 200031, China
| | - Lingjie Wu
- Department of Otolaryngology, Eye & ENT Hospital of Shanghai Medical School, Fudan University, Shanghai, 200031, China
| | - Wenlin Tang
- Siemens Healthcare Ltd., Shanghai, 201318, People's Republic of China
| | - Zhongshuai Zhang
- Siemens Healthcare Ltd., Shanghai, 201318, People's Republic of China
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Aydin H, Guner B, Esen Bostanci I, Bulut ZM, Aribas BK, Dogan L, Gulcelik MA. Is there any relationship between adc values of diffusion-weighted imaging and the histopathological prognostic factors of invasive ductal carcinoma? Br J Radiol 2018; 91:20170705. [PMID: 29299933 DOI: 10.1259/bjr.20170705] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
OBJECTIVE MRI is being used increasingly as a modality that can provide important information about breast cancer. Diffusion-weighted imaging (DWI) is an imaging technique from which apparent diffusion coefficient (ADC) values can be calculated in addition to obtaining important structural information which cannot be obtained from other imaging studies. We did not find any significant relationships between ADC values and prognostic factors, but did provide some explanations for conflicting results in the literature. METHODS The ADC results of 61 females with invasive ductal carcinomas were evaluated. DWI was performed and ADC values were calculated from the area in which restriction of diffusion was the highest in ADC mapping. B value was 500 and region of interest (ROI) was designated between 49 and 100 mm2. Calculations were performed automatically by the device. Tissue samples were obtained for prognostic factor evaluation. The relationships between ADC and prognostic factors were investigated. Comparisons between groups were made with one-way ANOVA and Kruskal Wallis test. Pairwise comparisons were made with Dunn's test. Analyses of categorical variables were made with Chi-square test. RESULTS We found a weak negative correlation between ADC and Ki-67 values (r = -0.279; p = 0.029). When we compared ADC values in regard to tumour type, we found no significant differences for tumour grade, Ki-67 positivity, estrogen receptor positivity, progesterone receptor positivity, C-erb B2, lymphovascular invasion and ductal carcinoma in situ or lobular carcinoma in situ component. On a side note, we found that mean ADC values decreased as tumour grade increased; however, this was not statistically significant. CONCLUSION The literature contains studies that report conflicting results which may be caused by differences in B values, ROI area and magnetic field strength. Multicentre studies and systematic reviews of these findings may produce crucial data for the use of DWI in breast cancer. Advances in knowledge: To determine if any significant relationship exists between DWI findings and prognostic factors of breast cancer.
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Affiliation(s)
- Hale Aydin
- 1 Department of Radiology, Dr AY Ankara Oncology Research and Training Hospital , Ankara , Turkey
| | - Bahar Guner
- 1 Department of Radiology, Dr AY Ankara Oncology Research and Training Hospital , Ankara , Turkey
| | - Isil Esen Bostanci
- 1 Department of Radiology, Dr AY Ankara Oncology Research and Training Hospital , Ankara , Turkey
| | - Zarife Melda Bulut
- 2 Department of Pathology, Dr AY Ankara Oncology Research and Training Hospital , Ankara , Turkey
| | - Bilgin Kadri Aribas
- 1 Department of Radiology, Dr AY Ankara Oncology Research and Training Hospital , Ankara , Turkey
| | - Lutfi Dogan
- 3 Department of General Surgery, Dr AY Ankara Oncology Research and Training Hospital , Ankara , Turkey
| | - Mehmet Ali Gulcelik
- 3 Department of General Surgery, Dr AY Ankara Oncology Research and Training Hospital , Ankara , Turkey.,Department of General Surgery, Gulhane Research and Training Hospital, Ankara , Turkey
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Fan M, Cheng H, Zhang P, Gao X, Zhang J, Shao G, Li L. DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers. J Magn Reson Imaging 2017; 48:237-247. [PMID: 29219225 DOI: 10.1002/jmri.25921] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 11/22/2017] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Breast tumor heterogeneity is related to risk factors that lead to worse prognosis, yet such heterogeneity has not been well studied. PURPOSE To predict the Ki-67 status of estrogen receptor (ER)-positive breast cancer patients via analysis of tumor heterogeneity with subgroup identification based on patterns of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). STUDY TYPE Retrospective study. POPULATION Seventy-seven breast cancer patients with ER-positive breast cancer were investigated, of whom 51 had low Ki-67 expression. FIELD STRENGTH/SEQUENCE T1 -weighted 3.0T DCE-MR images. ASSESSMENT Each tumor was partitioned into multiple subregions using three methods based on patterns of dynamic enhancement: 1) time to peak (TTP), 2) peak enhancement rate (PER), and 3) kinetic pattern clustering (KPC). In each tumor subregion, 18 texture features were computed. STATISTICAL TESTING Univariate and multivariate logistic regression analyses were performed using a leave-one-out-based cross-validation (LOOCV) method. The partitioning results were compared with the same feature extraction methods across the whole tumor. RESULTS In the univariate analysis, the best-performing feature was the texture statistic of sum variance in the tumor subregion with early TTP for differentiating between patients with high and low Ki-67 expression (area under the receiver operating characteristic curves, AUC = 0.748). Multivariate analysis showed that features from the tumor subregion associated with early TTP yielded the highest performance (AUC = 0.807) among the subregions for predicting the Ki-67 status. Among all regions, the tumor area with high PER at a precontrast MR image achieved the highest performance (AUC = 0.722), while the subregion that exhibited the highest overall enhancement rate based on KPC had an AUC of 0.731. These three models based on intratumoral texture analysis significantly (P < 0.01) outperformed the model using features from the whole tumor (AUC = 0.59). DATA CONCLUSION Texture analysis of intratumoral heterogeneity has the potential to serve as a valuable clinical marker to enhance the prediction of breast cancer prognosis. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017.
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Affiliation(s)
- Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Hu Cheng
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Peng Zhang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Xin Gao
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal, Saudi Arabia
| | - Juan Zhang
- Zhejiang Cancer Hospital, Zhejiang, Hangzhou, China
| | | | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
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Kawashima H, Miyati T, Ohno N, Ohno M, Inokuchi M, Ikeda H, Gabata T. Differentiation Between Luminal-A and Luminal-B Breast Cancer Using Intravoxel Incoherent Motion and Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Acad Radiol 2017; 24:1575-1581. [PMID: 28778511 DOI: 10.1016/j.acra.2017.06.016] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Revised: 06/17/2017] [Accepted: 06/19/2017] [Indexed: 01/15/2023]
Abstract
RATIONALE AND OBJECTIVES The study aimed to investigate whether intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) can differentiate luminal-B from luminal-A breast cancer MATERIALS AND METHODS: Biexponential analyses of IVIM and DCE MRI were performed using a 3.0-T MRI scanner, involving 134 patients with 137 pathologically confirmed luminal-type invasive breast cancers. Luminal-type breast cancer was categorized as luminal-B breast cancer (LBBC, Ki-67 ≧ 14%) or luminal-A breast cancer (LABC, Ki-67 < 14%). Quantitative parameters from IVIM (pure diffusion coefficient [D], perfusion-related diffusion coefficient [D*], and fraction [f]) and DCE MRI (initial percentage of enhancement and signal enhancement ratio [SER]) were calculated. The apparent diffusion coefficient (ADC) was also calculated using monoexponential fitting. We correlated these data with the Ki-67 status. RESULTS The D and ADC values of LBBC were significantly lower than those of LABC (P = 0.028, P = 0.037). The SER of LBBC was significantly higher than that of LABC (P = 0.004). A univariate analysis showed that a significantly lower D (<0.847 x 10-3 mm2/s), lower ADC (<0.960 × 10-3 mm2/s), and higher SER (>1.071) values were associated with LBBC (all P values <0.01), compared to LABC. In a multivariate analysis, a higher SER (>1.071; odds ratio: 3.0099, 95% confidence interval: 1.4246-6.3593; P = 0.003) value and a lower D (<0.847 × 10-3 mm2/s; odds ratio: 2.6878, 95% confidence interval: 1.0445-6.9162; P = 0.040) value were significantly associated with LBBC, compared to LABC. CONCLUSION The SER derived from DCE MRI and the D derived from IVIM are associated independently with the Ki-67 status in patients with luminal-type breast cancer.
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Affiliation(s)
- Hiroko Kawashima
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa 920-0942, Japan; Department of Breast Oncology, Kanazawa University Hospital, Kanazawa, Japan.
| | - Tosiaki Miyati
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa 920-0942, Japan
| | - Naoki Ohno
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa 920-0942, Japan
| | - Masako Ohno
- Radiology Division, Kanazawa University Hospital, Kanazawa, Japan
| | - Masafumi Inokuchi
- Department of Breast Oncology, Kanazawa University Hospital, Kanazawa, Japan
| | - Hiroko Ikeda
- Division of Pathology, Kanazawa University Hospital, Kanazawa, Japan
| | - Toshifumi Gabata
- Department of Radiology, Kanazawa University Hospital, Kanazawa, Japan
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Amornsiripanitch N, Nguyen VT, Rahbar H, Hippe DS, Gadi VK, Rendi MH, Partridge SC. Diffusion-weighted MRI characteristics associated with prognostic pathological factors and recurrence risk in invasive ER+/HER2- breast cancers. J Magn Reson Imaging 2017; 48:226-236. [PMID: 29178616 DOI: 10.1002/jmri.25909] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 11/14/2017] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Hormone receptor-positive breast cancer is the most common subtype; better tools to identify which patients in this group would derive clear benefit from chemotherapy are needed. PURPOSE To evaluate the prognostic potential of diffusion-weighted MRI (DWI) by investigating associations with pathologic biomarkers and a genomic assay for 10-year recurrence risk. STUDY TYPE Retrospective. SUBJECTS In all, 107 consecutive patients (from 2/2010 to 1/2013) with estrogen receptor (ER)-positive/HER2neu-negative invasive breast cancer who had the 21-gene recurrence score (RS) test (Oncotype DX, Genomic Health). FIELD STRENGTH/SEQUENCE Each subject underwent presurgical 3T breast MRI, which included DWI (b = 0, 800 s/mm2 ). ASSESSMENT Apparent diffusion coefficient (ADC) and contrast-to-noise ratio (CNR) were measured for each lesion by a fifth year radiology resident. Pathological markers (Nottingham histologic grade, Ki-67, RS) were determined from pathology reports. Medical records were reviewed to assess recurrence-free survival. STATISTICAL TESTS RS was stratified into low (<18), moderate (18-30), and high (>30)-risk groups. Associations of DWI characteristics with pathologic biomarkers were evaluated by binary or ordinal logistic regression, as appropriate, with adjustment for multiple comparisons. Post-hoc comparisons between specific groups were also performed. RESULTS ADCmean (odds ratio [OR] = 0.61 per 1 standard deviation [SD] increase, adj. P = 0.044) and CNR (OR = 1.76 per 1-SD increase, adj. P = 0.026) were significantly associated with increasing tumor grade. DWI CNR was also significantly associated with a high (Ki-67 ≥14%) proliferation rate (OR = 2.55 per 1-SD increase, adj. P = 0.026). While there were no statistically significant linear associations in ADC (adj. P = 0.80-0.85) and CNR (adj. P = 0.56) across all three RS groups by ordinal logistic regression, post-hoc analyses suggested that high RS lesions exhibited lower ADCmean (P = 0.037) and ADCmax (P = 0.004) values and higher CNR (P = 0.008) compared to lesions with a low or moderate RS. DATA CONCLUSION DWI characteristics correlated with tumor grade, proliferation index, and RS, and may potentially help to identify those with highest recurrence risk and most potential benefit from chemotherapy. LEVEL OF EVIDENCE 3 Technical Efficacy Stage 3 J. Magn. Reson. Imaging 2017.
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Affiliation(s)
| | - Vicky T Nguyen
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Habib Rahbar
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Daniel S Hippe
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Vijayakrishna K Gadi
- Department of Medicine/Oncology, University of Washington, Seattle, Washington, USA
| | - Mara H Rendi
- Department of Pathology, University of Washington, Seattle, Washington, USA
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Abstract
Diffusion-weighted imaging (DWI) holds promise to address some of the shortcomings of routine clinical breast magnetic resonance imaging (MRI) and to expand the capabilities of imaging in breast cancer management. DWI reflects tissue microstructure, and provides unique information to aid in characterization of breast lesions. Potential benefits under investigation include improving diagnostic accuracy and guiding treatment decisions. As a result, DWI is increasingly being incorporated into breast MRI protocols and multicenter trials are underway to validate single-institution findings and to establish clinical guidelines. Advancements in DWI acquisition and modeling approaches are helping to improve image quality and extract additional biologic information from breast DWI scans, which may extend diagnostic and prognostic value.
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Affiliation(s)
- Savannah C Partridge
- *Department of Radiology, Breast Imaging Section, Seattle Cancer Care Alliance, University of Washington, Seattle, WA †University of Massachusetts Memorial Medical Center, Worcester, MA
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Heterogeneity of Diffusion-Weighted Imaging in Tumours and the Surrounding Stroma for Prediction of Ki-67 Proliferation Status in Breast Cancer. Sci Rep 2017; 7:2875. [PMID: 28588280 PMCID: PMC5460128 DOI: 10.1038/s41598-017-03122-z] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 04/24/2017] [Indexed: 12/23/2022] Open
Abstract
Breast tissue heterogeneity is related to risk factors that lead to more aggressive tumour growth and worse prognosis, yet such heterogeneity has not been well characterized. The aim of this study is to reveal the heterogeneous signal patterns of the apparent diffusion coefficient (ADC) of a tumour and its surrounding stromal tissue and to predict the Ki-67 proliferation status in oestrogen receptor (ER)-positive breast cancer patients. A dataset of 82 patients who underwent diffusion-weighted imaging (DWI) examination was collected. The ADC map was segmented into regions comprising the tumour and the surrounding stromal shells. To reflect correlations between each region in terms of its mean ADC value, a functional graph was constructed consisting of nodes as regions and edges as interactions between two nodes. Analysis of the graph revealed a higher average degree in samples over-expressing Ki-67 than in samples with low Ki-67 expression. In the low-Ki-67 group, most of the identified edges represented correlations between adjacent regions, whereas additional edges representing correlations between non-adjacent regions were found in the high-Ki-67 group. The ADC signal in various breast stromal regions surrounding the tumour showed a discriminative pattern and would be valuable for estimating the Ki-67 proliferation status by DWI.
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