Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning

Gut ◽  
2020 ◽  
pp. gutjnl-2020-320930 ◽  
Author(s):  
Jie-Yi Shi ◽  
Xiaodong Wang ◽  
Guang-Yu Ding ◽  
Zhou Dong ◽  
Jing Han ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Genetic Alterations ◽  
The Cancer Genome Atlas ◽  
Clinical Staging ◽  
Patient Treatment ◽  
Prognostic Indicators ◽  
Omics Data ◽  
Learning Framework ◽  
Staging Systems

ObjectiveTumour pathology contains rich information, including tissue structure and cell morphology, that reflects disease progression and patient survival. However, phenotypic information is subtle and complex, making the discovery of prognostic indicators from pathological images challenging.DesignAn interpretable, weakly supervised deep learning framework incorporating prior knowledge was proposed to analyse hepatocellular carcinoma (HCC) and explore new prognostic phenotypes on pathological whole-slide images (WSIs) from the Zhongshan cohort of 1125 HCC patients (2451 WSIs) and TCGA cohort of 320 HCC patients (320 WSIs). A ‘tumour risk score (TRS)’ was established to evaluate patient outcomes, and then risk activation mapping (RAM) was applied to visualise the pathological phenotypes of TRS. The multi-omics data of The Cancer Genome Atlas(TCGA) HCC were used to assess the potential pathogenesis underlying TRS.ResultsSurvival analysis revealed that TRS was an independent prognosticator in both the Zhongshan cohort (p<0.0001) and TCGA cohort (p=0.0003). The predictive ability of TRS was superior to and independent of clinical staging systems, and TRS could evenly stratify patients into up to five groups with significantly different prognoses. Notably, sinusoidal capillarisation, prominent nucleoli and karyotheca, the nucleus/cytoplasm ratio and infiltrating inflammatory cells were identified as the main underlying features of TRS. The multi-omics data of TCGA HCC hint at the relevance of TRS to tumour immune infiltration and genetic alterations such as the FAT3 and RYR2 mutations.ConclusionOur deep learning framework is an effective and labour-saving method for decoding pathological images, providing a valuable means for HCC risk stratification and precise patient treatment.

scholarly journals Performance Comparison of Deep Learning Autoencoders for Cancer Subtype Detection Using Multi-Omics Data

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2013
Author(s):  
Edian F. Franco ◽  
Pratip Rana ◽  
Aline Cruz ◽  
Víctor V. Calderón ◽  
Vasco Azevedo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Fusion ◽  
Similarity Measures ◽  
Research Problem ◽  
Optimal Number ◽  
Performance Comparison ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Omics Data ◽  
Cancer Subtype

A heterogeneous disease such as cancer is activated through multiple pathways and different perturbations. Depending upon the activated pathway(s), the survival of the patients varies significantly and shows different efficacy to various drugs. Therefore, cancer subtype detection using genomics level data is a significant research problem. Subtype detection is often a complex problem, and in most cases, needs multi-omics data fusion to achieve accurate subtyping. Different data fusion and subtyping approaches have been proposed over the years, such as kernel-based fusion, matrix factorization, and deep learning autoencoders. In this paper, we compared the performance of different deep learning autoencoders for cancer subtype detection. We performed cancer subtype detection on four different cancer types from The Cancer Genome Atlas (TCGA) datasets using four autoencoder implementations. We also predicted the optimal number of subtypes in a cancer type using the silhouette score and found that the detected subtypes exhibit significant differences in survival profiles. Furthermore, we compared the effect of feature selection and similarity measures for subtype detection. For further evaluation, we used the Glioblastoma multiforme (GBM) dataset and identified the differentially expressed genes in each of the subtypes. The results obtained are consistent with other genomic studies and can be corroborated with the involved pathways and biological functions. Thus, it shows that the results from the autoencoders, obtained through the interaction of different datatypes of cancer, can be used for the prediction and characterization of patient subgroups and survival profiles.

scholarly journals Identification of Multi-omics Biomarkers and Construction of the Novel Prognostic Model for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Xiaokai Yan ◽  
Chiying Xiao ◽  
Kunyan Yue ◽  
Min Chen ◽  
Hang Zhou
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Prognostic Model ◽  
Prognostic Models ◽  
Prognostic Indicators ◽  
Omics Data ◽  
Training Set ◽  
Test Set ◽  
Model Based ◽  
Cox Analysis

Abstract Background: Change in the genome plays a crucial role in cancerogenesis and many biomarkers can be used as effective prognostic indicators in diverse tumors. Currently, although many studies have constructed some predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance of which is unsatisfactory. To fill this shortcoming, we hope to construct a novel and accurate prognostic model with multi-omics data to guide prognostic assessments of HCC. Methods: The TCGA training set was used to identify crucial biomarkers and construct single-omic prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. Then the performances of single-omic models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), and receiver operating characteristic (ROC) curve, in the TCGA test set and external cohorts. Besides, a comprehensive model based on multi-omics data was constructed via multiple Cox analysis, and the performance of which was evaluated in the TCGA training set and TCGA test set. Results: We identified 16 key mRNAs, 20 key lncRNAs, 5 key miRNAs, 5 key CNV genes, and 7 key SNPs which were significantly associated with the prognosis of HCC, and constructed 5 single-omic models which showed relatively good performance in prognostic prediction with c-index ranged from 0.63 to 0.75 in the TCGA training set and test set. Besides, we validated the mRNA model and the SNP model in two independent external datasets respectively, and good discriminating abilities were observed through survival analysis (P < 0.05). Moreover, the multi-omics model based on mRNA, lncRNA, miRNA, CNV, and SNP information presented a quite strong predictive ability with c-index over 0.80 and all AUC values at 1,3,5-years more than 0.84.Conclusion: In this study, we identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC, and constructed five single-omic models and an integrated multi-omics model that may provide effective and reliable guides for prognosis assessment and treatment decision-making.

scholarly journals Identification of Multi-omics Biomarkers and Construction of the Novel Prognostic Model for Hepatocellular Carcinoma

2022 ◽  
Author(s):  
Xiaokai Yan ◽  
Chiying Xiao ◽  
Kunyan Yue ◽  
Min Chen ◽  
Hang Zhou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Prognostic Model ◽  
Prognostic Models ◽  
Prognostic Indicators ◽  
Omics Data ◽  
Training Set ◽  
Test Set ◽  
Model Based ◽  
Cox Analysis

Abstract Background: Change in the genome plays a crucial role in cancerogenesis and many biomarkers can be used as effective prognostic indicators in diverse tumors. Currently, although many studies have constructed some predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance of which is unsatisfactory. To fill this shortcoming, we hope to construct a novel and accurate prognostic model with multi-omics data to guide prognostic assessments of HCC. Methods: The TCGA training set was used to identify crucial biomarkers and construct single-omic prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. Then the performances of single-omic models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), and receiver operating characteristic (ROC) curve, in the TCGA test set and external cohorts. Besides, a comprehensive model based on multi-omics data was constructed via multiple Cox analysis, and the performance of which was evaluated in the TCGA training set and TCGA test set. Results: We identified 16 key mRNAs, 20 key lncRNAs, 5 key miRNAs, 5 key CNV genes, and 7 key SNPs which were significantly associated with the prognosis of HCC, and constructed 5 single-omic models which showed relatively good performance in prognostic prediction with c-index ranged from 0.63 to 0.75 in the TCGA training set and test set. Besides, we validated the mRNA model and the SNP model in two independent external datasets respectively, and good discriminating abilities were observed through survival analysis (P < 0.05). Moreover, the multi-omics model based on mRNA, lncRNA, miRNA, CNV, and SNP information presented a quite strong predictive ability with c-index over 0.80 and all AUC values at 1,3,5-years more than 0.84.Conclusion: In this study, we identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC, and constructed five single-omic models and an integrated multi-omics model that may provide effective and reliable guides for prognosis assessment and treatment decision-making.

scholarly journals Clinical Staging and Flowcytomteric CD38 and Zap 70 Prognostic Indicators in Sudanese Patients with Chronic Lymphocytic Leukemia

2020 ◽  
Author(s):  
Enaam Abdelrhman Abdelgader ◽  
Nada Hassan Eltayeb ◽  
Tasniem Ahmed Eltahir ◽  
Osama Ali Altayeb ◽  
Eman Abbass Fadul ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Stage Iv ◽  
Cross Sectional Study ◽  
Lymphocytic Leukemia ◽  
Clinical Staging ◽  
Prognostic Indicators ◽  
P Value ◽  
Cross Sectional ◽  
Cd38 Expression ◽  
Staging Systems

Background: The clinical course of chronic lymphocytic leukemia is highly variable. The determination of ZAP70 and CD38 is increasingly utilized as prognostic factor for chronic lymphocytic leukemia. The aim of conducting this study was to investigate the frequency of CD38 and ZAP70 expression among Sudanese Chronic lymphocytic leukemia (CLL) patients and to relate them to the Binet and Rai clinical staging systems. Method: A total of 93 patients (mean age; 62.29 ± 11.68, sd) were enrolled in this cross-sectional study. CD38 and ZAP70 expression levels were measured with four color flowcytometry using the cut-off values of 20% for ZAP70 and 30% for CD38 expression. Staging was assessed by using clinical examination and CBC for all patients. Data were analyzed using the Statistical Package for Social science for Windows (SPSS), version 22. Results: There were 93 CLL patients and the median age of the group was 63 years (36–95 years). About 71% of the patients presented with lymphadenopathy, 53.8% with splenomegaly, 73.1% with anemia, and 45.2% with thrombocytopenia. There was higher frequency of Binet stage C and Rai stage IV (62 [66.6%] patients and 34 [36.5%] patients, respectively). In addition, CD38 and ZAP70 showed higher frequency among Binet and Rai advance stages. ZAP70 and CD38 positivity were detected in 21 patients (22.6%) and 31 patients (33.3%), respectively. There was no statistically significant association between ZAP70 and CD38 and clinical staging systems (P-value > 0.05). Conclusion: No significant association was observed between Flowcytometric (CD38 and Zap70) Prognostic Indicators and clinical staging systems. Keywords: chronic lymphocytic Leukemia, Flowcytometry, ZAP70, CD38, clinical staging systems

Clinical staging systems for hepatocellular carcinoma: Comparison with estrogen receptor classification

2003 ◽  
Vol 38 ◽  
pp. 106-107
Author(s):  
E. Villa ◽  
A. Colantoni ◽  
C. Camma ◽  
A. Grottola ◽  
I. Ferretti ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Estrogen Receptor ◽  
Clinical Staging ◽  
Staging Systems

scholarly journals Evaluation of Eight Different Clinical Staging Systems Associated with Overall Survival of Chinese Patients with Hepatocellular Carcinoma

2015 ◽  
Vol 128 (3) ◽  
pp. 316-321 ◽  
Author(s):  
Jian-Jun Zhao ◽  
Tao Yan ◽  
Hong Zhao ◽  
Jian-Guo Zhou ◽  
Zhen Huang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Chinese Patients ◽  
Clinical Staging ◽  
Staging Systems

Estrogen Receptor Classification for Hepatocellular Carcinoma: Comparison With Clinical Staging Systems

2003 ◽  
Vol 21 (3) ◽  
pp. 441-446 ◽  
Author(s):  
Erica Villa ◽  
Alessandra Colantoni ◽  
Calogero Cammà ◽  
Antonella Grottola ◽  
Paola Buttafoco ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Estrogen Receptor ◽  
Multivariate Analysis ◽  
Classification System ◽  
Scoring System ◽  
Scoring Systems ◽  
Classification Systems ◽  
Clinical Staging ◽  
Therapeutic Decisions ◽  
Staging Systems

Purpose: Several scoring systems to evaluate patients with hepatocellular carcinoma (HCC) exist. A good scoring system should provide information on prognosis and guide therapeutic decisions. The presence of variant liver estrogen receptor (ER) transcripts in the tumor has been shown to be the strongest negative predictor of survival in HCC. The aim of this study was to compare the predictive value of the commonly applied clinical scoring systems for survival of patients with HCC with that of the evaluation of ER in patients with HCC (molecular scoring system). Materials and Methods: HCC was staged according to the Okuda classification, Barcelona Clinic Liver Cancer classification, Italian classification system (CLIP), French classification, and ER status in 96 patients. Analysis of survival was performed according to the Kaplan-Maier test and was made for each classification system and ER. A comparison between classifications was made by univariate and multivariate analysis. Results: Among the clinical classification systems, only the CLIP was able to identify patient populations with good, intermediate, and poor prognosis. On multivariate analysis, ER classification was shown to be the best predictive classification for survival of patients with HCC (P <.0001). This difference was the result of a better allocation of patients with ominous prognosis (variant ER) having nevertheless good clinical score. Conclusion: The evaluation of the presence of wild-type or variant ER transcripts in the tumor is the best predictor of survival in patients with HCC. Its accuracy in discriminating patients with good or unfavorable prognosis is significantly greater than that of the commonly used scoring systems for the staging of HCC.

scholarly journals OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3047
Author(s):  
Xiaoyu Zhang ◽  
Yuting Xing ◽  
Kai Sun ◽  
Yike Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
Personalised Medicine ◽  
High Dimensional ◽  
Survival Prediction ◽  
Omics Data ◽  
Learning Framework ◽  
Deep Embedding ◽  
Task Strategy

High-dimensional omics data contain intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data, due to the large number of molecular features and small number of available samples, which is also called “the curse of dimensionality” in machine learning. To tackle this problem and pave the way for machine learning-aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed supports multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy compared to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various applications of high-dimensional omics data and has great potential to facilitate more accurate and personalised clinical decision making.

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