scholarly journals Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Mingming Ma ◽  
Liangyu Gan ◽  
Yuan Jiang ◽  
Naishan Qin ◽  
Changxin Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Cross Validation ◽  
Triple Negative ◽  
Characteristic Curve ◽  
Three Dimensional ◽  
Training Dataset ◽  
Breast Cancer Patients ◽  
Dce Mri ◽  
Model Based

Purpose. To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could be used to differentiate triple-negative breast cancer (TNBC) and nontriple-negative breast cancer (non-TNBC). Materials and Methods. This retrospective study included DCE-MRI images of 81 breast cancer patients (44 TNBC and 37 non-TNBC) from August 2018 to October 2019. The MR scans were achieved at a 1.5 T MR scanner. For each patient, the largest tumor mass was selected to analyze. Three-dimensional (3D) images of the regions of interest (ROIs) were automatically segmented on the third DCE phase by a deep learning segmentation model; then, the ROIs were checked and revised by 2 radiologists. DCE-MRI radiomics features were extracted from the 3D tumor volume. The patients were randomly divided into training ( N = 57 ) and test ( N = 24 ) cohorts. The machine learning classifier was built in the training dataset, and 5-fold cross-validation was performed on the training cohort to train and validate. The data of the test cohort were used to investigate the predictive power of the radiomics model in predicting TNBC and non-TNBC. The performance of the model was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results. The radiomics model based on 15 features got the best performance. The AUC achieved 0.741 for the cross-validation, and 0.867 for the independent testing cohort. Conclusion. The radiomics model based on automatic image segmentation of DCE-MRI can be used to distinguish TNBC and non-TNBC.

scholarly journals A gene signature predicts response to neoadjuvant chemotherapy in triple-negative breast cancer patients

2019 ◽  
Vol 39 (5) ◽  
Author(s):  
Tianzhi Zheng ◽  
Zhiyuan Pang ◽  
Zhao Zhao
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Neoadjuvant Chemotherapy ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Characteristic Curve ◽  
Complete Response ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Breast Cancer Patients

Abstract Triple-negative breast cancer (TNBC) accounts for approximately 15% of all breast cancer cases. TNBC is highly aggressive and associated with poor prognosis. The present study aimed to compare gene expression between TNBC patients with pathological complete response (pCR) and those with not complete response (nCR) to neoadjuvant chemotherapy. Microarray data of 16 TNBC patients received neoadjuvant chemotherapy were identified from the Gene Expression Omnibus database and 10 patients of them had pCR. We found that 250 coding genes and 155 long noncoding RNAs (lncRNAs) were statistically differentially expressed between patients with pCR and nCR. Receiver operator characteristic curve and area under the curve (AUC) were calculated to assess predictive value of differentially expressed genes. A gene signature of three coding genes and two lncRNA was developed: 2.318*TCF3 + 7.349*CREB1 + 0.891*CEP44 + 0.091*NR_023392.1 + 1.424*NR_048561.1 − 106.682. The gene signature was further validated and had an AUC = 0.829. In summary, we profiled gene expression in pCR patients and developed a gene signature, which was effective to predict pCR among TNBC patients received neoadjuvant chemotherapy.

scholarly journals Radiomic Analysis of the Heterogeneous Tumor and Surrounding Parenchyma Based on DCE-MRI Decomposition to Predict HER2 Expression in Breast Cancer

2021 ◽  
Author(s):  
Peng Zhang ◽  
Juan Yan ◽  
Zhongqi Liu ◽  
Xiangsheng Li ◽  
Qianxiang Zhou
Keyword(s):  
Breast Cancer ◽  
Contrast Enhancement ◽  
Cross Validation ◽  
Test Group ◽  
Her2 Expression ◽  
Breast Cancer Patients ◽  
Enhancement Pattern ◽  
Dce Mri ◽  
Validation Test ◽  
Heterogeneous Tumor

Abstract Background Human epidermal growth factor receptor-2 (HER2) correlates with cancer heterogeneity, and the identification of HER2 expression is invasive immunohistochemistry in the clinic. To determine whether noninvasive predictors of HER2 expression are implied in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).Methods 189/47 breast cancer patients collected from The Cancer Imaging Archive (TCIA) were used as a cross-validation/test group. A convex analysis of mixtures (CAM) was conducted to decompose heterogeneous tissues inside and outside the tumor. Their DCE-MRI images were decomposed into relatively homogeneous subregions with different contrast enhancement patterns. The predictor of HER2 expression was composed of radiomic features acquired from intratumoural or peritumoural subregions. The area under the curve (AUC) of receiver operating characteristic (ROC) was used to assess the predictive power.Results The predictor formed in the undecomposed tumor was used as a baseline for comparison (AUC=0.691±0.072/0.625±0.056 in cross-validation/test group). The intratumoural subregion with a contrast enhancement pattern corresponding to the plateau of signal intensity formed a more robust predictor (AUC=0.816±0.059/0.785±0.067, P=0.0128/0.0389). Peritumoral parenchyma of <20 mm from the tumor margin was also researched (AUC=0.589±0.083/0.524±0.064). The peritumoural subregion with a contrast enhancement pattern corresponding to steady enhancement also formed a helpful predictor compared to the undecomposed parenchyma (AUC=0.702±0.068/0.681±0.042, P=0.0128/0.0389). The best predictor was formed when two predictors from subregions were fused together (AUC=0.851±0.057/0.812±0.045, P=0.0011/0.0397).Conclusions A subregion rather than a heterogeneous tumor itself provided a more accurate predictor of HER2 expression. Radiomic predictors from intratumoural and peritumoural subregions were complementary to each other.

scholarly journals Feasibility of quantitative and volumetric enhancement measurement to assess tumor response in patients with breast cancer after early neoadjuvant chemotherapy

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199101
Author(s):  
Jie Ding ◽  
Hongyan Xiao ◽  
Weiwei Deng ◽  
Fengjiao Liu ◽  
Rongrong Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Predictive Value ◽  
Tumor Response ◽  
Characteristic Curve ◽  
Lesion Volume ◽  
Breast Cancer Patients ◽  
Dce Mri ◽  
Significant Difference

Objective To evaluate the feasibility of quantitative enhancing lesion volume (ELV) for evaluating the responsiveness of breast cancer patients to early neoadjuvant chemotherapy (NAC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods Seventy-five women with breast cancer underwent DCE-MRI before and after NAC. Lesions were assessed by ELV, response evaluation criteria in solid tumors 1.1 (RECIST 1.1), and total lesion volume (TLV). The diagnostic and pathological predictive performances of the methods were compared and color maps were compared with pathological results. Results ELV identified 29%, 67%, and 4% of cases with partial response, stable disease, and progressive disease, respectively. There was no significant difference in evaluation performances among the methods. The sensitivity, specificity, positive predictive value, negative predictive value (NPV), and accuracy of ELV for predicting pathologic response were 72%, 92%, 81.8%, 86.8%, and 85.3%, respectively, with the highest sensitivity, NPV, and accuracy of the three methods. The area under the receiver operating characteristic curve was also highest for ELV. Pre- and post-NAC color maps reflecting tumor activity were consistent with pathological necrosis. Conclusions ELV may help evaluate the responsiveness of breast cancer patients to NAC, and may provide a good tumor-response indicator through the ability to indicate tumor viability.

A novel three-long noncoding RNA risk score system for the prognostic prediction of triple-negative breast cancer

2021 ◽  
Vol 15 (1) ◽  
pp. 43-55
Author(s):  
Chao Yuan ◽  
Hongjun Yuan ◽  
Li Chen ◽  
Miaomiao Sheng ◽  
Wenru Tang
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Triple Negative Breast Cancer ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Triple Negative ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Gene Expression Omnibus ◽  
Prognostic Prediction

Background: Triple-negative breast cancer (TNBC) is characterized by fast tumor increase, rapid recurrence and natural metastasis. We aimed to identify a genetic signature for predicting the prognosis of TNBC. Materials & methods: We conducted a weighted correlation network analysis of datasets from the Gene Expression Omnibus. Multivariate Cox regression was used to construct a risk score model. Results: The multi-factor risk scoring model was meaningfully associated with the prognosis of patients with TBNC. The predictive power of the model was demonstrated by the time-dependent receiver operating characteristic curve and Kaplan–Meier curve, and verified using a validation set. Conclusion: We established a long noncoding RNA-based model for the prognostic prediction of TNBC.

scholarly journals DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

2021 ◽  
Vol 28 ◽  
pp. 107327482098851
Author(s):  
Zeng-Hong Wu ◽  
Yun Tang ◽  
Yan Zhou
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Methylation Status ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Consensus Clustering ◽  
Breast Cancer Patients ◽  
Cancer Subtypes ◽  
Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

scholarly journals PITX2 DNA-Methylation: Predictive versus Prognostic Value for Anthracycline-Based Chemotherapy in Triple-Negative Breast Cancer Patients

Breast Care ◽  
2020 ◽  
pp. 1-9
Author(s):  
Rudolf Napieralski ◽  
Gabriele Schricker ◽  
Gert Auer ◽  
Michaela Aubele ◽  
Jonathan Perkins ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Cancer Patients ◽  
Triple Negative Breast Cancer ◽  
Predictive Value ◽  
Untreated Patient ◽  
Triple Negative ◽  
Statistical Significance ◽  
Breast Cancer Patients ◽  
The Impact

<b><i>Background:</i></b> PITX2 DNA methylation has been shown to predict outcomes in high-risk breast cancer patients after anthracycline-based chemotherapy. To determine its prognostic versus predictive value, the impact of PITX2 DNA methylation on outcomes was studied in an untreated cohort vs. an anthracycline-treated triple-negative breast cancer (TNBC) cohort. <b><i>Material and Methods:</i></b> The percent DNA methylation ratio (PMR) of paired-like homeodomain transcription factor 2 (PITX2) was determined by a validated methylation-specific real-time PCR test. Patient samples of routinely collected archived formalin-fixed paraffin-embedded (FFPE) tissue and clinical data from 144 TNBC patients of 2 independent cohorts (i.e., 66 untreated patients and 78 patients treated with anthracycline-based chemotherapy) were analyzed. <b><i>Results:</i></b> The risk of 5- and 10-year overall survival (OS) increased continuously with rising PITX2 DNA methylation in the anthracycline-treated population, but it increased only slightly during 10-year follow-up time in the untreated patient population. PITX2 DNA methylation with a PMR cutoff of 2 did not show significance for poor vs. good outcomes (OS) in the untreated patient cohort (HR = 1.55; <i>p</i> = 0.259). In contrast, the PITX2 PMR cutoff of 2 identified patients with poor (PMR &#x3e;2) vs. good (PMR ≤2) outcomes (OS) with statistical significance in the anthracycline-treated cohort (HR = 3.96; <i>p</i> = 0.011). The results in the subgroup of patients who did receive anthracyclines only (no taxanes) confirmed this finding (HR = 5.71; <i>p</i> = 0.014). <b><i>Conclusion:</i></b> In this hypothesis-generating study PITX2 DNA methylation demonstrated predominantly predictive value in anthracycline treatment in TNBC patients. The risk of poor outcome (OS) correlates with increasing PITX2 DNA methylation.

scholarly journals Expression of Phosphorylated BRD4 Is Markedly Associated with the Activation Status of the PP2A Pathway and Shows a Strong Prognostic Value in Triple Negative Breast Cancer Patients

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1246
Author(s):  
Marta Sanz-Álvarez ◽  
Ion Cristóbal ◽  
Melani Luque ◽  
Andrea Santos ◽  
Sandra Zazo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Molecular Subtype ◽  
Molecular Target ◽  
Prognostic Impact ◽  
Breast Cancer Patients ◽  
Bet Inhibitors ◽  
Brd4 Inhibition ◽  
Bet Protein

The bromodomain-containing protein 4 (BRD4), a member of the bromodomain and extra-terminal domain (BET) protein family, has emerged in the last years as a promising molecular target in many tumors including breast cancer. The triple negative breast cancer (TNBC) represents the molecular subtype with the worst prognosis and a current therapeutic challenge, and TNBC cells have been reported to show a preferential sensitivity to BET inhibitors. Interestingly, BRD4 phosphorylation (pBRD4) was found as an alteration that confers resistance to BET inhibition and PP2A proposed as the phosphatase responsible to regulate pBRD4 levels. However, the potential clinical significance of pBRD4, as well as its potential correlation with the PP2A pathway in TNBC, remains to be investigated. Here, we evaluated the expression levels of pBRD4 in a series of 132 TNBC patients. We found high pBRD4 levels in 34.1% of cases (45/132), and this alteration was found to be associated with the development of patient recurrences (p = 0.007). Interestingly, BRD4 hyperphosphorylation predicted significantly shorter overall (p < 0.001) and event-free survival (p < 0.001). Moreover, multivariate analyses were performed to confirm its independent prognostic impact in our cohort. In conclusion, our findings show that BRD4 hyperphosphorylation is an alteration associated with PP2A inhibition that defines a subgroup of TNBC patients with unfavorable prognosis, suggesting the potential clinical and therapeutic usefulness of the PP2A/BRD4 axis as a novel molecular target to overcome resistance to treatments based on BRD4 inhibition.

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