scholarly journals The Application of Deep Learning in Cancer Prognosis Prediction

Cancers ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 603 ◽  
Author(s):  
Wan Zhu ◽  
Longxiang Xie ◽  
Jianye Han ◽  
Xiangqian Guo
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Driving Forces ◽  
Digital Pathology ◽  
Cancer Recurrence ◽  
Clinical Information ◽  
Cancer Prognosis ◽  
Generic Model ◽  
Prognosis Prediction ◽  
Transcriptomics Data

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.

scholarly journals Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

2021 ◽  
Vol 2021 ◽  
pp. 1-28
Author(s):  
Ahsan Bin Tufail ◽  
Yong-Kui Ma ◽  
Mohammed K. A. Kaabar ◽  
Francisco Martínez ◽  
A. R. Junejo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Diagnosis ◽  
Computer Aided Design ◽  
Feature Recognition ◽  
Cancer Prognosis ◽  
Generic Model ◽  
Prognosis Prediction ◽  
Cad System ◽  
Diagnosis And Prognosis ◽  
Cancer Diagnosis And Prognosis

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.

scholarly journals Development of Biologically Interpretable Multimodal Deep Learning Model for Cancer Prognosis Prediction

2021 ◽  
Author(s):  
Zarif L Azher ◽  
Louis J Vaickus ◽  
Lucas A Salas ◽  
Brock Christensen ◽  
Joshua Levy
Keyword(s):  
Deep Learning ◽  
Clinical Features ◽  
Preliminary Analysis ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Biomedical Data ◽  
Cancer Subtypes ◽  
Prognosis Prediction ◽  
Modeling Approach

Robust cancer prognostication can enable more effective patient care and management, which may potentially improve health outcomes. Deep learning has proven to be a powerful tool to extract meaningful information from cancer patient data. In recent years it has displayed promise in quantifying prognostication by predicting patient risk. However, most current deep learning-based cancer prognosis prediction methods use only a single data source and miss out on learning from potentially rich relationships across modalities. Existing multimodal approaches are challenging to interpret in a biological or medical context, limiting real-world clinical integration as a trustworthy prognostic decision aid. Here, we developed a multimodal modeling approach that can integrate information from the central modalities of gene expression, DNA methylation, and histopathological imaging with clinical information for cancer prognosis prediction. Our multimodal modeling approach combines pathway and gene-based sparsely coded layers with patch-based graph convolutional networks to facilitate biological interpretation of the model results. We present a preliminary analysis that compares the potential applicability of combining all modalities to uni- or bi-modal approaches. Leveraging data from four cancer subtypes from the Cancer Genome Atlas, results demonstrate the encouraging performance of our multimodal approach (C-index=0.660 without clinical features; C-index=0.665 with clinical features) across four cancer subtypes versus unimodal approaches and existing state-of-the-art approaches. This work brings insight to the development of interpretable multimodal methods of applying AI to biomedical data and can potentially serve as a foundation for clinical implementations of such software. We plan to follow up this preliminary analysis with an in-depth exploration of factors to improve multimodal modeling approaches on an in-house dataset.

scholarly journals Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features

Genes ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 240 ◽  
Author(s):  
Gangcai Xie ◽  
Chengliang Dong ◽  
Yinfei Kong ◽  
Jiang Zhong ◽  
Mingyao Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prior Knowledge ◽  
Clinical Features ◽  
Large Scale ◽  
Real Data ◽  
Group Lasso ◽  
Cancer Prognosis ◽  
Survival Prediction ◽  
Omics Data ◽  
Patients With Cancer

Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of patients censored at specific time points. Multi-omics data, including genome-wide gene expression, methylation, protein expression, copy number alteration, and somatic mutation data, are becoming increasingly common in cancer studies. To harness the rich information in multi-omics data, we developed GDP (Group lass regularized Deep learning for cancer Prognosis), a computational tool for survival prediction using both clinical and multi-omics data. GDP integrated a deep learning framework and Cox proportional hazard model (CPH) together, and applied group lasso regularization to incorporate gene-level group prior knowledge into the model training process. We evaluated its performance in both simulated and real data from The Cancer Genome Atlas (TCGA) project. In simulated data, our results supported the importance of group prior information in the regularization of the model. Compared to the standard lasso regularization, we showed that group lasso achieved higher prediction accuracy when the group prior knowledge was provided. We also found that GDP performed better than CPH for complex survival data. Furthermore, analysis on real data demonstrated that GDP performed favorably against other methods in several cancers with large-scale omics data sets, such as glioblastoma multiforme, kidney renal clear cell carcinoma, and bladder urothelial carcinoma. In summary, we demonstrated that GDP is a powerful tool for prognosis of patients with cancer, especially when large-scale molecular features are available.

scholarly journals PAI-WSIT: a Comprehensive Curated Resource for Cancerous Pathology With Deep Learning

2021 ◽  
Author(s):  
Changjiang Zhou ◽  
Xiaobing Feng ◽  
Yi Jin ◽  
Harvest F. Gu ◽  
Youcai Zhao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Data Collection ◽  
Function Analysis ◽  
Digital Pathology ◽  
Clinical Information ◽  
Improve Patient Care ◽  
The Cancer Genome Atlas ◽  
Precision Oncology ◽  
Region Detection

Abstract BackgroundThe possibility of digitizing whole-slide images (WSI) of tissue has led to the advent of artificial intelligence (AI) in digital pathology. Advances in precision oncology have resulted in an increasing demand for predictive assays that enable mining of subvisual morphometric phenotypes and might improve patient care ultimately. Hence, a pathologist-annotated and artificial intelligence-empowered platform for integration and analysis of WSI data and molecular detection data in tumors was established, called PAI-WSIT (http://www.paiwsit.com).MethodsThe standardized data collection process was used for data collection in PAI-WSIT, while a multifunctional annotation tool was developed and a user-friendly search engine and web interface were integrated for the database access. Furthermore, deep learning frameworks were applied in two tasks to detect malignant regions and classify phenotypic subtypes in colorectal cancers (CRCs), respectively.ResultsPAI-WSIT recorded 8633 WSIs of 1772 tumor cases, of which CRC from four regional hospitals in China and The Cancer Genome Atlas (TCGA) were the main ones, as well as cancers in breast, lung, prostate, bladder, and kidneys from two Chinese hospitals. A total of 1298 WSIs with high-quality annotations were evaluated by a panel of 8 pathologists. Gene detection reports of 582 tumor cases were collected. Clinical information of all tumor cases was documented. Besides, we reached overall accuracy of 0.933 in WSI classification for malignant region detection of CRC, and aera under the curves (AUC) of 0.719 on colorectal subtype dataset.ConclusionsCollectively, the annotation function, data integration and AI function analysis of PAI-WSIT provide support for AI-assisted tumor diagnosis, all of which have provided a comprehensive curation of carcinomas pathology.

Integrating multi-omics data through deep learning for accurate cancer prognosis prediction

2021 ◽  
pp. 104481
Author(s):  
Hua Chai ◽  
Xiang Zhou ◽  
Zhongyue Zhang ◽  
Jiahua Rao ◽  
Huiying Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Prognosis ◽  
Omics Data ◽  
Prognosis Prediction

FADS1 is a Prognostic Biomarker in Bladder Cancer: A Study Based on TCGA Data

2020 ◽  
Vol 23 ◽  
Author(s):  
Ying Lu ◽  
Jing Shao ◽  
Xu Shu ◽  
Yaofei Jiang ◽  
Jianfang Rong ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Histological Grade ◽  
Clinical Stage ◽  
Fatty Acid Desaturase ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Normal Tissues ◽  
Potential Biomarker ◽  
T Classification

Aim and Objective: Fatty acid desaturase 1 (FADS1) has been reported to be a potential biomarker in various cancers. However, no study has explored the relationship between FADS1 expression and bladder cancer. Our study aimed to investigate the role of FADS1 in bladder cancer prognosis via The Cancer Genome Atlas (TCGA). Materials and Methods: RNA-Seq expression of 414 tumor tissues and 19 paired normal tissues, as well as corresponding clinical data, were downloaded from TCGA database. Two cancer cases were excluded due to a lack of clinical information. The association between FADS1 and the clinicopathological features of bladder cancer was analyzed. This study was conducted in October of 2019 in China. Results: The high expression of FADS1 in bladder cancer was significantly related to histological grade (OR = 0.155 for low vs. high), clinical stage (OR=2.074 for III or IV vs. I or II), T classification (OR=2.326 for T3 or T4 vs. T1 or T2), lymphatic metastasis (OR=1.923 for N1 or N2 or N3 vs. N0) and distant metastasis (OR=4.883 for yes vs. no) (all p-values <0.05). Bladder cancer with high FADS1 levels was related to a worse prognosis than bladder cancer with low FADS1 levels (p= 1.626*10-5 ), according to median expression value 3.622. FADS1 was an independent factor of overall survival in bladder cancer, with a hazard ratio of 1.048 (95%CI: 1.020–1.077, p = 0.001). Conclusions: Increased FADS1 expression in bladder cancer is associated with advanced clinical pathological features and may be a potential biomarker for poor prognosis.

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