Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images

Wang, Xiaoxiao; Zou, Chong; Zhang, Yi; Li, Xiuqing; Wang, Chenxi; Fei, Ke; Chen, Jie; Wang, Wei; Wang, Dian; Xu, Xinyu; Xie, Ling; Zhang, Yifen

doi:10.3389/fgene.2021.661109

Cited by 41 publications

(29 citation statements)

References 30 publications

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“…An alternative strategy for multi-feature assessment could include application of deep learning and artificial intelligence to histology specimens in order to identify tumors displaying HRD, as has been used to determine tissue of origin of cancers of unknown primary [152,153]. These techniques show promise for identifying germline BRCA mutant breast cancer based on histological features [154]. Another challenge in pancreatic cancer is difficulty obtaining sufficient tissue for testing, especially longitudinally.…”

Section: Current Limitations and Next-generation Strategies In Testing For Hrdmentioning

confidence: 99%

Determinants of Homologous Recombination Deficiency in Pancreatic Cancer

Wattenberg

Reiss

2021

Cancers

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Pancreatic cancer is a treatment-resistant malignancy associated with high mortality. However, defective homologous recombination (HR), a DNA repair mechanism required for high-fidelity repair of double-strand DNA breaks, is a therapeutic vulnerability. Consistent with this, a subset of patients with pancreatic cancer show unique tumor responsiveness to HR-dependent DNA damage triggered by certain treatments (platinum chemotherapy and PARP inhibitors). While pathogenic mutations in HR genes are a major driver of this sensitivity, another layer of diverse tumor intrinsic and extrinsic factors regulate the HR deficiency (HRD) phenotype. Defining the mechanisms that drive HRD may guide the development of novel strategies and therapeutics to induce treatment sensitivity in non-HRD tumors. Here, we discuss the complexity underlying HRD in pancreatic cancer and highlight implications for identifying and treating this distinct subset of patients.

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Section: Current Limitations and Next-generation Strategies In Testing For Hrdmentioning

confidence: 99%

Determinants of Homologous Recombination Deficiency in Pancreatic Cancer

Wattenberg

Reiss

2021

Cancers

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“…Wang et al. ( 151 ) obtained similar outcomes employed a ResNet network to anticipate breast cancer’s BRCA mutation status. Farahmand et al.…”

Section: Some Case Studies and Applicationsmentioning

confidence: 90%

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

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The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

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“…The most important key point of the study was the risk assessment of two designed different artificial intelligence methods were based on cancer-associate genes and gene variants. Two recent study aimed to predict breast cancer using histopathology and radiology images for BRCA-mutation carriers using deep learning [35] and machine learning [36], respectively. This current study mainly focused on gene variant-based risk assessment in cancer.…”

Section: Discussionmentioning

confidence: 99%

“…In the literature, many studies have used artificial intelligence models and created risk assessment or early prediction software [27][28][29][30][31][32][33][34][35][36][37]. To the best of our knowledge, this is the first study to assess breast cancer risk using BRCA1 and BRCA2 genetic variants using the MATLAB for both fuzzy logic and neural network.…”

Section: Introductionmentioning

confidence: 99%

BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

Senturk

Tuncel

Dogan

et al. 2021

Genes

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Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients’ data were used to train the system using Mamdani’s Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network’s overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations’ risk assessment in breast cancers as well as a unique tool for personalized medicine software.

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Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images

Cited by 41 publications

References 30 publications

Determinants of Homologous Recombination Deficiency in Pancreatic Cancer

Determinants of Homologous Recombination Deficiency in Pancreatic Cancer

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

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