Histogram analysis of quantitative pharmacokinetic parameters on DCE-MRI: correlations with prognostic factors and molecular subtypes in breast cancer

Breast Cancer ◽  
2018 ◽  
Vol 26 (1) ◽  
pp. 113-124 ◽  
Author(s):  
Ken Nagasaka ◽  
Hiroko Satake ◽  
Satoko Ishigaki ◽  
Hisashi Kawai ◽  
Shinji Naganawa
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Molecular Subtypes ◽  
Histogram Analysis ◽  
Pharmacokinetic Parameters ◽  
Dce Mri

scholarly journals Differentiating the lymph node metastasis of breast cancer through dynamic contrast-enhanced magnetic resonance imaging

BJR|Open ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 20180023
Author(s):  
Xu Dong ◽  
Yu Chunrong ◽  
Hou Hongjun ◽  
Zhang Xuexi
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Node Metastases ◽  
Area Under The Curve ◽  
Histogram Analysis ◽  
Pharmacokinetic Parameters ◽  
Dce Mri ◽  
Node Metastasis ◽  
Node Metastases

Objective: Lymph node metastasis is an important trait of breast cancer, and tumors with different lymph node statuses require various clinical treatments. This study was designed to evaluate the lymph node metastasis of breast cancer through pharmacokinetic and histogram analysis via dynamic contrast-enhanced (DCE) MRI. Methods and materials: A retrospective analysis was conducted to quantitatively evaluate the lymph node statuses of patients with breast cancer. A total of 75 patients, i.e. 34 patients with lymph node metastasis and 41 patients without lymph node metastasis, were involved in this research. Of the patients with lymph node metastases, 19 had sentinel lymph node metastasis, and 15 had axillary lymph node metastasis. MRI was conducted using a 3.0 T imaging device. Segmentation was carried out on the regions of interest (ROIs) in breast tumors under DCE-MRI, and pharmacokinetic and histogram parameters were calculated from the same ROIs. Mann–Whitney U test was performed, and receiver operating characteristic curves for the parameters of the two groups were constructed to determine their diagnostic values. Results: Pharmacokinetic parameters, including Ktrans, Kep, area under the curve of time–concentration, and time to peak, which were derived from the extended Tofts linear model for DCE-MRI, could highlight the tumor areas in the breast and reveal the increased perfusion. Conversely, the pharmacokinetic parameters showed no significant difference between the patients with and without lymph node metastases. By contrast, the parameters from the histogram analysis yielded promising results. The entropy of the ROIs exhibited the best diagnostic ability between patients with and without lymph node metastases (p < 0.01, area under the curve of receiver operating characteristic = 0.765, specificity = 0.706, sensitivity = 0.780). Conclusion: In comparison with the pharmacokinetic parameters, the histogram analysis of the MR images could reveal the differences between patients with and without lymph node metastases. The entropy from the histogram indicated that the diagnostic ability was highly sensitive and specific. Advances in knowledge: This research gave out a promising result on the differentiating lymph node metastases through histogram analysis on tumors in DCE-MR images. Histogram could reveal the tumors heterogenicity between patients with different lymph node status.

scholarly journals Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors

2015 ◽  
Vol 26 (8) ◽  
pp. 2547-2558 ◽  
Author(s):  
Gene Young Cho ◽  
Linda Moy ◽  
Sungheon G. Kim ◽  
Steven H. Baete ◽  
Melanie Moccaldi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Histogram Analysis ◽  
Intravoxel Incoherent Motion ◽  
Histological Subtype

Prediction of breast cancer molecular subtypes using DCE-MRI based on CNNs combined with ensemble learning

2021 ◽  
Author(s):  
Rong Sun ◽  
Zi-jun Meng ◽  
Xuewen Hou ◽  
Yang Chen ◽  
Yi-feng Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ensemble Learning ◽  
Molecular Subtypes ◽  
Dce Mri

scholarly journals Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Meijie Liu ◽  
Ning Mao ◽  
Heng Ma ◽  
Jianjun Dong ◽  
Kun Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Regression Method ◽  
Pharmacokinetic Parameters ◽  
Combined Model ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Preoperative Prediction ◽  
Contrast Enhanced

Abstract Background To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer. Methods A total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models. Results Seven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer. Conclusions The model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.

Survival profile in breast cancer molecular subtypes without systemic and locoregional treatment.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 11599-11599
Author(s):  
Sherry X. Yang ◽  
Eric Polley
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Molecular Subtypes ◽  
Multivariable Analysis ◽  
Cox Proportional Hazards Model ◽  
Locoregional Therapy ◽  
Rank Test ◽  
Luminal A ◽  
Luminal B

11599 Background: It is unclear whether survival varies among breast cancer molecular subtypes without systemic and locoregional therapy. This study aims to evaluate the survival profile by molecular subtypes after surgery. Methods: In total, we evaluated 301 women with invasive breast cancer with stage I, II or III disease. Patients were classified into four major breast cancer subtypes by immunohistochemistry/FISH classifiers: luminal-A (ER+ and/or PR+/HER2-), luminal-B (ER+ and/or PR+/HER2+), HER2-enriched (HER2+/ER-/PR-) or basal-like (ER-/PR-/HER2-; triple-negative). Overall survival (OS) was analyzed by Kaplan-Meier analysis, and log-rank test for differences. Association between clinical outcome and subtype adjusting for breast cancer prognostic factors was assessed by multivariable Cox proportional hazards model. Results: All patients did not receive systemic chemotherapy and hormone therapy as well as radiation therapy. Luminal A was the most common subtype (N = 224), followed by basal-like (N = 43), luminal B (N = 21) and HER2-enriched (N = 13). Median follow-up for OS was 197 months (range: 1 – 273 months). Age at diagnosis was statistically different among the subtypes, with basal-like and luminal B having high proportions less than 50 years (P = 0.047). Patients with basal-like and HER2-enriched had more high grade tumors (P < 0.001). Notably, there was no difference in OS among the four subtypes (log-rank P = 0.983). In multivariable analysis, the adjusted hazard ratio (HR) was 1.1 for luminal A vs. luminal B (P = 0.781), 0.62 in luminal A vs. HER2-enriched (P = 0.273), or 0.67 in luminal A vs. basal-like (P = 0.158). In contrast, the adjusted HR were 2.2 in age less than 50 years (P = 0.0017), and 1.1 for number of positive nodes (P = 0.00074). Conclusions: OS, through long-term clinical follow-up, is not significantly different among molecular subtypes if not controlling for other prognostic factors in patients who only received surgery. Age and number of positive nodes are independent prognostic factors in patients with no systemic and locoregional treatments.

scholarly journals Quantitative Three-Dimensional Assessment of the Pharmacokinetic Parameters of Intra- and Peri-tumoural Tissues on Breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging

2021 ◽  
Author(s):  
A. Niukkanen ◽  
H. Okuma ◽  
M. Sudah ◽  
P. Auvinen ◽  
A. Mannermaa ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Three Dimensional ◽  
Pharmacokinetic Parameters ◽  
High Grade ◽  
Breast Cancers ◽  
Resonance Imaging ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

AbstractWe aimed to assess the feasibility of three-dimensional (3D) segmentation and to investigate whether semi-quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters are associated with traditional prognostic factors for breast cancer. In addition, we evaluated whether both intra-tumoural and peri-tumoural DCE parameters can differentiate the breast cancers that are more aggressive from those that are less aggressive. Consecutive patients with newly diagnosed invasive breast cancer and structural breast MRI (3.0 T) were included after informed consent. Fifty-six patients (mean age, 57 years) with mass lesions of > 7 mm in diameter were included. A semi-automatic image post-processing algorithm was developed to measure 3D pharmacokinetic information from the DCE-MRI images. The kinetic parameters were extracted from time-signal curves, and the absolute tissue contrast agent concentrations were calculated with a reference tissue model. Markedly, higher intra-tumoural and peri-tumoural tissue concentrations of contrast agent were found in high-grade tumours (n = 44) compared to low-grade tumours (n = 12) at every time point (P = 0.006–0.040), providing positive predictive values of 90.6–92.6% in the classification of high-grade tumours. The intra-tumoural and peri-tumoural signal enhancement ratios correlated with tumour grade, size, and Ki67 activity. The intra-observer reproducibility was excellent. We developed a model to measure the 3D intensity data of breast cancers. Low- and high-grade tumours differed in their intra-tumoural and peri-tumoural enhancement characteristics. We anticipate that pharmacokinetic parameters will be increasingly used as imaging biomarkers to model and predict tumour behavior, prognoses, and responses to treatment.

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