Intratumor heterogeneity of DCE-MRI reveals Ki-67 proliferation status in breast cancer

Abstract Background: To provide evident data regarding relationships between quantitative dynamic contrast enhanced magnetic resonance imaging (DCE MRI) and prognostic factors in breast cancer (BC).Methods: Data from 4 centers (200 female patients, mean age, 51.2 ± 11.5 years) were acquired. The following data were collected: histopathological diagnosis, tumor grade, stage, hormone receptor status, KI 67, and DCE MRI values including Ktrans (volume transfer constant), Ve (volume of the extravascular extracellular leakage space (EES) and Kep (diffusion of contrast medium from the EES back to the plasma). DCE MRI values between different groups were compared using the Mann–Whitney U test and by the Kruskal-Wallis H test. The association between DCE MRI and Ki 67 values was calculated by Spearman’s rank correlation coefficient. Results: DCE MRI values of different tumor subtypes overlapped significantly. There were no statistically significant differences of DCE MRI values between different tumor grades. All DCE MRI parameters correlated with KI 67: Ktrans, r = 0.44, p=0.0001; Ve, r = 0.34, p=0.0001; Kep, r = 0.28, p=0.002. ROC analysis identified a Ktrans threshold of 0.3 min-1 for discrimination of tumors with low KI 67 expression (<25%) and high KI 67 expression (≥25%): sensitivity, 75.5%, specificity, 73.0%, accuracy, 74.0%, AUC, 0.78. DCE MRI values overlapped between tumors with different T and N stages.Conclusion: Ktrans, Kep, and Ve cannot be used as reliable a surrogate marker for hormone receptor status, tumor stage and grade in BC. Ktrans may discriminate lesions with high and lower proliferation activity.

Download Full-text

Expression of PCNA, Ki-67 and COX-2 in breast cancer based on DCE-MRI image information

Journal of Infection and Public Health ◽

10.1016/j.jiph.2019.06.024 ◽

2020 ◽

Vol 13 (12) ◽

pp. 2032-2037 ◽

Cited By ~ 2

Author(s):

Xiaoming Qiu ◽

Hong Wang ◽

Zhen Wang ◽

Yufei Fu ◽

Jianjun Yin

Keyword(s):

Breast Cancer ◽

Ki 67 ◽

Image Information ◽

Dce Mri ◽

Cox 2 ◽

Mri Image

Download Full-text

Intra-tumoral distribution of Ki-67 and Cyclin D1 in ER+ mammary carcinoma: quantitative evaluation

African Health Sciences ◽

10.4314/ahs.v21i1.7 ◽

2021 ◽

Vol 21 (1) ◽

pp. 41-6

Author(s):

Mohammedi Latifa ◽

Djillali Doula Fatima ◽

Mesli Farida ◽

Senhadji Rachid

Keyword(s):

Breast Cancer ◽

Cyclin D1 ◽

Poor Response ◽

Labeling Index ◽

Intratumor Heterogeneity ◽

Cellular Mechanisms ◽

Ki 67 ◽

Heterogeneous Distribution ◽

Er Positive

Background: In spite of the strong evidence demonstrating the role of overexpression of Ki-67 and Cyclin D1 markers in breast carcinomas, clinical and pathological data remain to be discussed. This can be explained partly by intratumor het- erogeneity. Objectives: To define the prevalence and clinical significance of Ki-67 and Cyclin D1 overexpression in primary breast tumors ER positive, while highlighting the existence of intratumor heterogeneity in this type of cancer. Materials and methods: 51 ER positive breast cancer tumors were used to evaluate the intratumoral distribution of Ki-67 and Cyclin D1 expression. Image acquisition and visualization of the markers were performed by optical microscopy and stereology sampling method. Results: The mean Ki-67 labeling index was distributed heterogeneously in the same tumor, from 20.67±6.87 to 45.10±10.65. The coefficient of variation (COV) revealed dispersion values between 13.4% and 42.9%. Associated with positive ER status, all the tumors presented a Cyclin D1 expression with a COV varying between 19% and 28.5% and a mean labeling index fluctuating between 19.40±4.42 and 41.64±10.08 within the same patient showing important intratumor heterogeneous distribution. Conclusion: In this study, we have adopted a strictly quantitative approach to evaluate and demonstrate intratumor hetero- geneity. This establishes one of the main factors for poor response to cancer therapy. To achieve this, intratumor heteroge- neity should be usually definable and quantifiable but this domain awaits future progress and methods need to move towards a better understanding of molecular and cellular mechanisms that initiate and maintain this tumor heterogeneity. Keywords: Breast cancer; Cyclin D1; ER+; Intra-tumoral heterogeneity; Ki-67.

Download Full-text

Analysis of DCE-MRI features in tumor and the surrounding stroma for prediction of Ki-67 proliferation status in breast cancer

Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications ◽

10.1117/12.2293047 ◽

2018 ◽

Author(s):

Lihua Li ◽

Hui Li ◽

Ming Fan ◽

Yuanzhe Li ◽

Hu Cheng ◽

...

Keyword(s):

Breast Cancer ◽

Ki 67 ◽

Dce Mri ◽

Mri Features

Download Full-text

A deep matrix factorization framework for identifying underlying tissue-specific patterns of DCE-MRI: applications for molecular subtype classification in breast cancer

Physics in Medicine and Biology ◽

10.1088/1361-6560/ac3a25 ◽

2021 ◽

Author(s):

Ming Fan ◽

Wei Yuan ◽

Weifen Liu ◽

Xin Gao ◽

Maosheng Xu ◽

...

Keyword(s):

Breast Cancer ◽

Matrix Factorization ◽

Molecular Subtypes ◽

Molecular Subtype ◽

Intratumor Heterogeneity ◽

Luminal A ◽

Tissue Specific ◽

Dce Mri ◽

Dynamic Patterns ◽

Better Than

Abstract Objective Breast cancer is heterogeneous in that different angiogenesis and blood flow characteristics could be present within a tumor. The pixel kinetics of DCE-MRI can assume several distinct signal patterns related to specific tissue characteristics. Identification of the latent, tissue-specific dynamic patterns of intratumor heterogeneity can shed light on the biological mechanisms underlying the heterogeneity of tumors. Approach To mine this information, we propose a deep matrix factorization-based dynamic decomposition (DMFDE) model specifically designed according to DCE-MRI characteristics. The time-series imaging data were decomposed into tissue-specific dynamic patterns and their corresponding proportion maps. The image pixel matrix and the reference matrix of population-level kinetics obtained by clustering the dynamic signals were used as the inputs. Two multilayer neural network branches were designed to collaboratively project the input matrix into a latent dynamic pattern and a dynamic proportion matrix, which was justified using simulated data. Clinical implications of DMFDE were assessed by radiomics analysis of proportion maps obtained from the tumor/parenchyma region for classifying the luminal A subtype. Main results The decomposition performance of DMFDE was evaluated by the root mean square error (RMSE) and was shown to be better than that of the conventional convex analysis of mixtures (CAM) method. The predictive model with K=3, 4, and 5 dynamic proportion maps generated AUC values of 0.780, 0.786 and 0.790, respectively, in distinguishing between luminal A and nonluminal A tumors, which are better than the CAM method (AUC=0.726). The combination of statistical features from images with different proportion maps has the highest prediction value (AUC= 0.813), which is significantly higher than that based on CAM. Conclusion This proposed method identified the latent dynamic patterns associated with different molecular subtypes, and radiomics analysis based on the pixel compositions of the uncovered dynamic patterns was able to determine molecular subtypes of breast cancer.

Download Full-text