Factors associated with de novo metastatic disease in invasive breast cancer: comparison of artificial neural network and logistic regression models

Qiu, Chunyan; Jiang, Lingong; Cao, Yangsen; Hu, Can; Yu, Yiyi; Zhang, Huojun

doi:10.21037/tcr.2019.01.01

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…These in silico methods were developed using diverse features. Some methods incorporated clinicopathological-based features [20] , [21] , [22] , other methods incorporated image-based features [23] , [24] , [25] or text-based features [26] , while the more recent methods incorporate omics-based data as features. This review summarizes the in silico metastasis-related prediction methods that incorporate omics data as features.…”

Section: Metastasismentioning

confidence: 99%

Machine learning and deep learning methods that use omics data for metastasis prediction

Albaradei

Thafar

Alsaedi

et al. 2021

Computational and Structural Biotechnology Journal

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Knowing metastasis is the primary cause of cancer-related deaths, incentivized research directed towards unraveling the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications, and is now being used to predict the onset of metastasis to improve diagnostics and disease therapies. In this regard, predicting metastasis onset has also been explored using artificial intelligence approaches that are machine learning, and more recently, deep learning-based. This review summarizes the different machine learning and deep learning-based metastasis prediction methods developed to date. We also detail the different types of molecular data used to build the models and the critical signatures derived from the different methods. We further highlight the challenges associated with using machine learning and deep learning methods, and provide suggestions to improve the predictive performance of such methods.

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Section: Metastasismentioning

confidence: 99%

Machine learning and deep learning methods that use omics data for metastasis prediction

Albaradei

Thafar

Alsaedi

et al. 2021

Computational and Structural Biotechnology Journal

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“…It made our research topic more consistent. With the additional information available in the clinical practice, some prediction models can predict the possibility of metastasis based on the patient's gender, age and history to assist diagnosis [32] , [33] , [34] , [35] , [36] .…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis

et al. 2021

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Background Early diagnosis of tumor metastasis is crucial for clinical treatment. Artificial intelligence (AI) has shown great promise in the field of medicine. We therefore aimed to evaluate the diagnostic accuracy of AI algorithms in detecting tumor metastasis using medical radiology imaging. Methods We searched PubMed and Web of Science for studies published from January 1, 1997, to January 30, 2020. Studies evaluating an AI model for the diagnosis of tumor metastasis from medical images were included. We excluded studies that used histopathology images or medical wave-form data and those focused on the region segmentation of interest. Studies providing enough information to construct contingency tables were included in a meta-analysis. Findings We identified 2620 studies, of which 69 were included. Among them, 34 studies were included in a meta-analysis with a pooled sensitivity of 82% (95% CI 79–84%), specificity of 84% (82–87%) and AUC of 0·90 (0·87–0·92). Analysis for different AI algorithms showed a pooled sensitivity of 87% (83–90%) for machine learning and 86% (82–89%) for deep learning, and a pooled specificity of 89% (82–93%) for machine learning, and 87% (82–91%) for deep learning. Interpretation AI algorithms may be used for the diagnosis of tumor metastasis using medical radiology imaging with equivalent or even better performance to health-care professionals, in terms of sensitivity and specificity. At the same time, rigorous reporting standards with external validation and comparison to health-care professionals are urgently needed for AI application in the medical field. Funding College students' innovative entrepreneurial training plan program .

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“…Chunyan Qiu et al constructed artificial neural network (ANN) and logistic regression (LR) models in 2019 and compared them to find out the important factors related to the occurrence of new metastasis of invasive breast cancer. Finally, it is found that the artificial neural network model is superior to the traditional LR model in the recognition of breast cancer neonatal metastasis [17].…”

Section: B Development Of Deep Learning Algorithmsmentioning

confidence: 99%

Intelligent Imaging Technology in Diagnosis of Colorectal Cancer Using Deep Learning

Yang

Liang

et al. 2019

IEEE Access

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In order to explore the application of deep learning based intelligent imaging technology in the diagnosis of colorectal cancer, Tangdu Hospital patients are selected as the research object in this study. By scanning the cancer sites, then distinguishing and extracting the features of the tumors, the collected data are input into the designed in-depth learning intelligent assistant diagnosis system for comparison. The results show that in the analysis of image prediction accuracy, the best prediction accuracy of T1-weighted image method is matrix GLCM (gray level co-occurrence matrix) algorithm, the best prediction accuracy of adding T1-weighted image method is matrix MGLSZM (multi-gray area size matrix) algorithm, and the best prediction accuracy of T2-weighted image method is ALL combination of all texture features, and the best prediction accuracy of three imaging sequences is not more than 0.8. In the AUC analysis of the area under the curve of different texture features, it is found that T2-weighted imaging method has obvious advantages in differentiating colorectal cancer from other methods. Therefore, through this study, it is found that in the use of deep learning intelligent assistant diagnosis system for the diagnosis of colorectal cancer, it can provide useful information for the clinical diagnosis of colorectal cancer to a certain extent. Although there are some deficiencies in the research process, it still provides experimental basis for the diagnosis and treatment of colorectal cancer in later clinical stage.INDEX TERMS Deep learning, colorectal cancer, weighted images, accuracy, texture features.

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Factors associated with de novo metastatic disease in invasive breast cancer: comparison of artificial neural network and logistic regression models

Cited by 7 publications

References 25 publications

Machine learning and deep learning methods that use omics data for metastasis prediction

Machine learning and deep learning methods that use omics data for metastasis prediction

Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis

Intelligent Imaging Technology in Diagnosis of Colorectal Cancer Using Deep Learning

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