A Multimodal Deep Neural Network for Human Breast Cancer Prognosis Prediction by Integrating Multi-Dimensional Data

Sun, Dongdong; Wang, Minghui; Li, Ao

doi:10.1109/tcbb.2018.2806438

Cited by 246 publications

(158 citation statements)

References 43 publications

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“…In a study to predict breast cancer prognosis, Sun et al used a method named minimum redundancy maximum relevance (mRMR) [65] to reduce the dimensionality of gene expression data and copy number alternation (CNA) data by extracting 400 and 200 genes, respectively, from these datasets [66]. Next, 3 NN models were built using features selected from gene expression data, CNA data or clinical data, respectively.…”

Section: Feature Extraction From Gene Expression Data To Build Fully mentioning

confidence: 99%

“…MsigDB: Molecular Signatures Database; 17 AUC: area under the curve of ROC; 18 SGD: stochastic gradient descent;19 METABRIC: Molecular Taxonomy of Breast Cancer International Consortium; 20 mPS: molecular prognostic score. a Links to source codes if available from publications: Sun et al, 2018[66]: https://github.com/USTC-HIlab/MDNNMD.Huang et al, 2019 [67]: https://github.com/huangzhii/SALMON/.Hao et al, 2018 [62]: https://github.com/DataX-JieHao/PASNet. Shimizu and Nakayama, 2019,[69]: https://hideyukishimizu.github.io/mPS_breast.…”

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The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

281

161

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Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.

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Section: Feature Extraction From Gene Expression Data To Build Fully mentioning

confidence: 99%

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confidence: 99%

The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

281

161

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“…Thus, survival rate analysis has become essential for clinicians to select the best treatment methods based on the patient's clinical data [14,15]; and survival predictor models have been developed in oncology to investigate the relationship between information obtained at the time of diagnosis and the overall patient's survival [16]. This has been further facilitated by the recent access to large datasets of digital images, e.g.…”

Section: Introductionmentioning

confidence: 99%

DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images

Shirazi

Fornaciari

Bagherian

et al. 2020

Med Biol Eng Comput

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Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and its clinical outcomes. Still, there are no highly accurate automated methods to correlate histolopathological images with brain cancer patients' survival, which can help in scheduling patients therapeutic treatment and allocate time for preclinical studies to guide personalized treatments. We now propose a new classifier, namely, DeepSurvNet powered by deep convolutional neural networks, to accurately classify in 4 classes brain cancer patients' survival rate based on histopathological images (class I, 0-6 months; class II, 6-12 months; class III, 12-24 months; and class IV, >24 months survival after diagnosis). After training and testing of DeepSurvNet model on a public brain cancer dataset, The Cancer Genome Atlas, we have generalized it using independent testing on unseen samples. Using DeepSurvNet, we obtained precisions of 0.99 and 0.8 in the testing phases on the mentioned datasets, respectively, which shows DeepSurvNet is a reliable classifier for brain cancer patients' survival rate classification based on histopathological images. Finally, analysis of the frequency of mutations revealed differences in terms of frequency and type of genes associated to each class, supporting the idea of a different genetic fingerprint associated to patient survival. We conclude that DeepSurvNet constitutes a new artificial intelligence tool to assess the survival rate in brain cancer.

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“…Although computers can process multi-dimensional data such as changes of variables over time, few models have used these inputs to predict future clinical events. 9,10 Vitamin K antagonists (VKAs) continue to be prescribed for the prevention of stroke in patients with AF, despite the more recent introduction of non-VKA oral anticoagulants (NOACs). 11,12 VKAs are the only recommended choice of OAC for AF patients with hemodynamically overt mitral stenosis and mechanical heart valve.…”

Section: Introductionmentioning

confidence: 99%

New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on vitamin K antagonists: GARFIELD-AF

Goto

Pieper

Bassand

et al. 2019

European Heart Journal - Cardiovascular Pharmacotherapy

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Aims Most clinical risk stratification models are based on measurement at a single time-point rather than serial measurements. Artificial intelligence (AI) is able to predict one-dimensional outcomes from multi-dimensional datasets. Using data from Global Anticoagulant Registry in the Field (GARFIELD)-AF registry, a new AI model was developed for predicting clinical outcomes in atrial fibrillation (AF) patients up to 1 year based on sequential measures of prothrombin time international normalized ratio (PT-INR) within 30 days of enrolment. Methods and results Patients with newly diagnosed AF who were treated with vitamin K antagonists (VKAs) and had at least three measurements of PT-INR taken over the first 30 days after prescription were analysed. The AI model was constructed with multilayer neural network including long short-term memory and one-dimensional convolution layers. The neural network was trained using PT-INR measurements within days 0–30 after starting treatment and clinical outcomes over days 31–365 in a derivation cohort (cohorts 1–3; n = 3185). Accuracy of the AI model at predicting major bleed, stroke/systemic embolism (SE), and death was assessed in a validation cohort (cohorts 4–5; n = 1523). The model’s c-statistic for predicting major bleed, stroke/SE, and all-cause death was 0.75, 0.70, and 0.61, respectively. Conclusions Using serial PT-INR values collected within 1 month after starting VKA, the new AI model performed better than time in therapeutic range at predicting clinical outcomes occurring up to 12 months thereafter. Serial PT-INR values contain important information that can be analysed by computer to help predict adverse clinical outcomes.

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A Multimodal Deep Neural Network for Human Breast Cancer Prognosis Prediction by Integrating Multi-Dimensional Data

Cited by 246 publications

References 43 publications

The Application of Deep Learning in Cancer Prognosis Prediction

The Application of Deep Learning in Cancer Prognosis Prediction

DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images

New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on vitamin K antagonists: GARFIELD-AF

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