A Generative Model Based Approach for Zero-Shot Breast Cancer Segmentation Explaining Pixels’ Contribution to the Model’s Prediction

2021 ◽  
pp. 401-425
Author(s):  
Preeti Mukherjee ◽  
Mainak Pal ◽  
Lidia Ghosh ◽  
Amit Konar
Keyword(s):  
Breast Cancer ◽  
Generative Model ◽  
Model Based

Preclinical Molecular Imaging of the Translocator Protein (TSPO) in a Metastases Model Based on Breast Cancer Xenografts Propagated in the Murine Brain

2012 ◽  
Vol 12 (4) ◽  
pp. 458-466
Author(s):  
Shelby K. Wyatt ◽  
H. Charles Manning ◽  
Mingfeng Bai ◽  
Moneeb Ehtesham ◽  
Khubaib Y. Mapara ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Molecular Imaging ◽  
Translocator Protein ◽  
Model Based ◽  
Murine Brain ◽  
Breast Cancer Xenografts

Generative Model based Data Augmentation for Special Person Classification

2020 ◽  
Author(s):  
Zijie Guo ◽  
Rong Zhi ◽  
Wuqaing Zhang ◽  
Baofeng Wang ◽  
Zhijie Fang ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Generative Model ◽  
Model Based ◽  
Special Person

scholarly journals Prognostic Value of A Risk Score Model Constructed By m6A Methylation Regulators In Breast Cancer

2021 ◽  
Author(s):  
Xiaowei Qiu ◽  
Qiaoli Zhang ◽  
Jingnan Xu ◽  
Xin Jiang ◽  
Xuewei Qi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Prognostic Value ◽  
Validation Cohort ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Rna Methylation ◽  
Model Based ◽  
Prognosis Model ◽  
M6a Rna Methylation

Abstract Background: N6-methyladenosine (m6A) methylation modification can affect the tumorigenesis, progression, and metastasis of breast cancer (BC). Up to now, a prognostic model based on m6A methylation regulators for BC is still lacking. This study aimed to construct an accurate prediction prognosis model by m6A methylation regulators for BC patients.Methods: After processing of The Cancer Genome Atlas (TCGA) datasets, the differential expression and correlation analysis of m6A RNA methylation regulators were applied. Next, tumor samples were clustered into different groups and clinicopathologic features in different clusters were explored. By univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analysis, m6A regulators with prognostic value were identified to develop a prediction model. Furthermore, we constructed and validated a predictive nomogram to predict the prognosis of BC patients.Results: 19 m6A related genes were extracted and 908 BC patients enrolled from TCGA dataset. After univariate Cox and LASSO analysis, 3 m6A RNA methylation regulators (YTHDF3, ZC3H13 and HNRNPC) were selected to establish the prognosis model based on median risk score (RS) in training and validation cohort. With the increasing of RS, the expression levels of YTHDF3 and ZC3H13 were individually elevated, while the HNRNPC expressed decreasingly. By survival analysis and Receiver Operating Characteristic (ROC) curve, we found that the overall survival (OS) of high-risk group was significantly shorter than that of the low-risk group based on Kaplan-Meier (KM) analysis in each cohort. Univariate and multivariate analysis identified the RS, age, and pathological stage are independent prognostic factors. A nomogram was constructed to predict 1- and 3-year OS and the calibration plots validate the performance. The C-index of nomogram reached 0.757 (95% CI:0.7-0.814) in training cohort and 0.749 (95% CI:0.648-0.85) in validation cohort, respectively.Conclusions: We successfully constructed a predictive prognosis model by m6A RNA methylation regulators. These results indicated that the m6A RNA methylation regulators are potential therapeutic targets of BC patients.

scholarly journals RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

2022 ◽  
Vol 12 ◽  
Author(s):  
Lan-Xin Mu ◽  
You-Cheng Shao ◽  
Lei Wei ◽  
Fang-Fang Chen ◽  
Jing-Wei Zhang
Keyword(s):  
Breast Cancer ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Impact ◽  
Immune Microenvironment ◽  
Model Based ◽  
Tumor Immune Microenvironment

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p < 0.001) and m6aRiskscore (p < 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

A simulation model-based clinical decision tool to guide personalized treatment based on individual characteristics: Does 21-gene recurrence score assay testing change decisions?

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12507-e12507
Author(s):  
Jinani Jayasekera ◽  
Joseph A. Sparano ◽  
Young Chandler ◽  
Claudine Isaacs ◽  
Allison W. Kurian ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Simulation Model ◽  
Early Stage ◽  
Clinical Decision ◽  
Distant Recurrence ◽  
Decision Tool ◽  
Decision Tools ◽  
Model Based ◽  
Life Years ◽  
Size Grade

e12507 Background: There is a need for web-based decision tools that integrate clinicopathologic features and genomic information to guide breast cancer therapy for women with node-negative, hormone receptor positive, HER2 negative (“early-stage”) breast cancer. We developed a novel simulation model-based clinical decision tool that provides prognostic estimates of treatment outcomes based on age, tumor size, grade, and comorbidities with and without 21-gene recurrence scores (RS). Methods: We adapted an extant breast cancer simulation model developed within the NCI-funded Cancer Intervention and Surveillance Modeling Network (CISNET) to derive estimates for the 10-year risks of distant recurrence, breast cancer-specific mortality, other cause mortality and life-years gained with endocrine vs. chemo-endocrine therapy for individual women based on their age, tumor size, grade, and comorbidity-level with and without RS test results. The model used an empiric Bayesian analytical approach to combine information from clinical trials, registry and claims data to provide individual estimates. External validation of the model was performed by comparing model-based breast cancer mortality rates and observed rates in the Surveillance Epidemiology and End Results (SEER) registry. Results: Several exemplar profiles were selected to illustrate the clinical utility of the decision tool. For example, the absolute chemotherapy benefit for 10-year distant recurrence risk and life-years gained, without RS testing, and the outcomes if a woman got tested and had a RS 16-20 are provided below for a 40-44-year-old woman and a 65–69-year-old woman diagnosed with a small (≤2cm), intermediate grade tumor and mild comorbidities. Conclusions: Simulation modeling is useful for creating clinical decision tools to support shared decision making for early-stage breast cancer treatment.[Table: see text]

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