scholarly journals Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Tao Li ◽  
Peizhen Xie ◽  
Jie Liu ◽  
Mingliang Chen ◽  
Shuang Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Diseases ◽  
Characteristic Curve ◽  
Diagnostic System ◽  
The Cancer Genome Atlas ◽  
Smart Healthcare ◽  
Hospital Systems ◽  
Cancer Genome Atlas ◽  
Degree Of Malignancy ◽  
Malignant Lesions

In traditional hospital systems, diagnosis and localization of melanoma are the critical challenges for pathological analysis, treatment instructions, and prognosis evaluation particularly in skin diseases. In literature, various studies have been reported to address these issues; however, a prominent smart diagnosis system is needed to be developed for the smart healthcare system. In this study, a deep learning-enabled diagnostic system is proposed and implemented that it has the capacity to automatically detect malignant melanoma in whole slide images (WSIs). In this system, the convolutional neural network (CNN), sophisticated statistical method, and image processing algorithms were integrated and implemented to locate benign and malignant lesions which are extremely useful in the diagnoses process of melanoma disease. To verify the exceptional performance of the proposed scheme, it is implemented in a multicenter database, which has 701 WSIs (641 WSIs from Central South University Xiangya Hospital (CSUXH) and 60 WSIs from the Cancer Genome Atlas (TCGA)). Experimental results have verified that the proposed system has achieved an area under the receiver operating characteristic curve (AUROC) of 0.971. Furthermore, the lesion area on the WSIs is represented by its degree of malignancy. These results show that the proposed system has the capacity to fully automate the diagnosis and localization problem of the melanoma in the smart healthcare systems.

scholarly journals Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3811
Author(s):  
Hyun-Jong Jang ◽  
In-Hye Song ◽  
Sung-Hak Lee
Keyword(s):  
Deep Learning ◽  
Relative Proportion ◽  
The Cancer Genome Atlas ◽  
Cancer Tissue ◽  
Mucinous Tumor ◽  
Receiver Operating Characteristic Curves ◽  
Tumor Tissues ◽  
Cancer Genome Atlas ◽  
Stomach Adenocarcinoma ◽  
Tumor Types

Histomorphologic types of gastric cancer (GC) have significant prognostic values that should be considered during treatment planning. Because the thorough quantitative review of a tissue slide is a laborious task for pathologists, deep learning (DL) can be a useful tool to support pathologic workflow. In the present study, a fully automated approach was applied to distinguish differentiated/undifferentiated and non-mucinous/mucinous tumor types in GC tissue whole-slide images from The Cancer Genome Atlas (TCGA) stomach adenocarcinoma dataset (TCGA-STAD). By classifying small patches of tissue images into differentiated/undifferentiated and non-mucinous/mucinous tumor tissues, the relative proportion of GC tissue subtypes can be easily quantified. Furthermore, the distribution of different tissue subtypes can be clearly visualized. The patch-level areas under the curves for the receiver operating characteristic curves for the differentiated/undifferentiated and non-mucinous/mucinous classifiers were 0.932 and 0.979, respectively. We also validated the classifiers on our own GC datasets and confirmed that the generalizability of the classifiers is excellent. The results indicate that the DL-based tissue classifier could be a useful tool for the quantitative analysis of cancer tissue slides. By combining DL-based classifiers for various molecular and morphologic variations in tissue slides, the heterogeneity of tumor tissues can be unveiled more efficiently.

scholarly journals A Deep Learning Approach for Rapid Mutational Screening in Melanoma

2019 ◽  
Author(s):  
Randie H. Kim ◽  
Sofia Nomikou ◽  
Nicolas Coudray ◽  
George Jour ◽  
Zarmeena Dawood ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structural Characteristics ◽  
Tumor Biology ◽  
Model Performance ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Clinical Settings ◽  
Mutational Screening ◽  
Cancer Genome Atlas ◽  
Whole Slide Images

AbstractImage-based analysis as a rapid method for mutation detection can be advantageous in research or clinical settings when tumor tissue is limited or unavailable for direct testing. Here, we applied a deep convolutional neural network (CNN) to whole slide images of melanomas from 256 patients and developed a fully automated model that first selects for tumor-rich areas (Area Under the Curve AUC=0.96) then predicts for the presence of mutated BRAF in our test set (AUC=0.72) Model performance was cross-validated on melanoma images from The Cancer Genome Atlas (AUC=0.75). We confirm that the mutated BRAF genotype is linked to phenotypic alterations at the level of the nucleus through saliency mapping and pathomics analysis, which reveal that cells with mutated BRAF exhibit larger and rounder nuclei. Not only do these findings provide additional insights on how BRAF mutations affects tumor structural characteristics, deep learning-based analysis of histopathology images have the potential to be integrated into higher order models for understanding tumor biology, developing biomarkers, and predicting clinical outcomes.

scholarly journals Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features

2020 ◽  
Author(s):  
James M. Dolezal ◽  
Anna Trzcinska ◽  
Chih-Yi Liao ◽  
Sara Kochanny ◽  
Elizabeth Blair ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Gene Expression Signature ◽  
Thyroid Neoplasms ◽  
The Cancer Genome Atlas ◽  
Tumor Subtype ◽  
Ras Gene ◽  
Cancer Genome Atlas ◽  
Subtype A ◽  
Nuclear Features

AbstractNoninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) are follicular-patterned thyroid neoplasms defined by nuclear atypia and indolent behavior. They harbor RAS mutations, rather than BRAFV600E mutations as is observed in papillary thyroid carcinomas with extensive follicular growth. Reliably identifying NIFTPs aids in safe therapy de-escalation, but has proven to be challenging due to interobserver variability and morphologic heterogeneity. The genomic scoring system BRS (BRAF-RAS score) was developed to quantify the extent to which a tumor’s expression profile resembles a BRAFV600E or RAS-mutant neoplasm. We proposed that deep learning prediction of BRS could differentiate NIFTP from other follicular-patterned neoplasms. A deep learning model was trained on slides from a dataset of 115 thyroid neoplasms to predict tumor subtype (NIFTP, PTC-EFG, or classic PTC), and was used to generate predictions for 497 thyroid neoplasms within The Cancer Genome Atlas (TCGA). Within follicular-patterned neoplasms, tumors with positive BRS (RAS-like) were 8.5 times as likely to carry an NIFTP prediction than tumors with negative BRS (89.7% vs 10.5%, P < 0.0001). To test the hypothesis that BRS may serve as a surrogate for biological processes that determine tumor subtype, a separate model was trained on TCGA slides to predict BRS as a linear outcome. This model performed well in cross-validation on the training set (R2 = 0.67, dichotomized AUC = 0.94). In our internal cohort, NIFTPs were near universally predicted to have RAS-like BRS; as a sole discriminator of NIFTP status, predicted BRS performed with an AUC of 0.99 globally and 0.97 when restricted to follicular-patterned neoplasms. BRAFV600E-mutant PTC-EFG had BRAFV600E-like predicted BRS (mean −0.49), nonmutant PTC-EFG had more intermediate predicted BRS (mean −0.17), and NIFTP had RAS-like BRS (mean 0.35; P < 0.0001). In summary, histologic features associated with the BRAF-RAS gene expression spectrum are detectable by deep learning and can aid in distinguishing indolent NIFTP from PTCs.

scholarly journals An lncRNA Model for Predicting the Prognosis of Hepatocellular Carcinoma Patients and ceRNA Mechanism

2021 ◽  
Vol 8 ◽  
Author(s):  
Hao Zhang ◽  
Renzheng Liu ◽  
Lin Sun ◽  
Xiao Hu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Rate ◽  
Liver Cancer ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
The Difference ◽  
Experimental Group

Liver cancer is a highly malignant tumor. Notably, recent studies have found that long non-coding RNAs (lncRNAs) play a prominent role in the prognosis of patients with liver cancer. Herein, we attempted to construct an lncRNA model to accurately predict the survival rate in liver cancer. Based on The Cancer Genome Atlas (TCGA) database, we first identified 1066 lncRNAs with differential expression. The patient data obtained from TCGA were divided into the experimental group and the verification group. According to the difference in lncRNAs, we used single-factor and multi-factor Cox regression to select the genes needed to build the model in the experimental group, which were verified in the verification group. The results showed that the model could accurately predict the survival rate of patients in the high and low risk groups. The reliability of the model was also confirmed by the area under the receiver operating characteristic curve. Our model is significantly correlated with different clinicopathological features. Finally, we built a ceRNA network based on lncRNAs, which was used to display miRNAs and mRNAs related to lncRNAs. In summary, we constructed an lncRNA model to predict the survival rate of patients with hepatocellular carcinoma.

scholarly journals Co-expression analysis and ceRNA network reveal eight novel potential lncRNA biomarkers in hepatocellular carcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8101
Author(s):  
Ren-chao Zou ◽  
Zhi-tian Shi ◽  
Shu-feng Xiao ◽  
Yang Ke ◽  
Hao-ran Tang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Primary Liver Cancer ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Transcriptome Data ◽  
Cerna Network ◽  
Cancer Genome Atlas ◽  
Degree Of Malignancy

Background Hepatocellular carcinoma (HCC) is the most common primary liver cancer in the world, with a high degree of malignancy and recurrence. The influence of the ceRNA network in tumor on the biological function of liver cancer is very important, It has been reported that many lncRNA play a key role in liver cancer development. In our study, integrated data analysis revealed potential eight novel lncRNA biomarkers in hepatocellular carcinoma. Methods Transcriptome data and clinical data were downloaded from the The Cancer Genome Atlas (TCGA) data portal. Weighted gene co-expression network analysis was performed to identify the expression pattern of genes in liver cancer. Then, the ceRNA network was constructed using transcriptome data. Results The integrated analysis of miRNA and RNAseq in the database show eight novel lncRNAs that may be involved in important biological pathways, including TNM and disease development in liver cancer. We performed function enrichment analysis of mRNAs affected by these lncRNAs. Conclusions By identifying the ceRNA network and the lncRNAs that affect liver cancer, we showed that eight novel lncRNAs play an important role in the development and progress of liver cancer.

scholarly journals Plasma-Derived miRNA-222 as a Candidate Marker for Papillary Thyroid Cancer

2020 ◽  
Vol 21 (17) ◽  
pp. 6445
Author(s):  
Aistė Kondrotienė ◽  
Albertas Daukša ◽  
Daina Pamedytytė ◽  
Mintautė Kazokaitė ◽  
Aurelija Žvirblienė ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Papillary Thyroid Cancer ◽  
Characteristic Curve ◽  
Nodular Goiter ◽  
Diagnostic Value ◽  
The Cancer Genome Atlas ◽  
Papillary Thyroid ◽  
Potential Marker ◽  
Cancer Genome Atlas ◽  
Plasma Mirna

We analyzed five miRNA molecules (miR-221; miR-222; miR-146b; miR-21; miR-181b) in the plasma of patients with papillary thyroid cancer (PTC), nodular goiter (NG) and healthy controls (HC) and evaluated their diagnostic value for differentiation of PTC from NG and HC. Preoperative PTC plasma miRNA expression (n = 49) was compared with plasma miRNA in the HC group (n = 57) and patients with NG (n = 23). It was demonstrated that miR-221; miR-222; miR-146b; miR-21 and miR-181b were overexpressed in preoperative PTC plasma samples compared to HC (p < 0.0001; p < 0.0001; p < 0.0001; p < 0.0001; p < 0.002; respectively). The upregulation in tumor tissue of these miRNAs was consistent with The Cancer Genome Atlas Thyroid Carcinoma dataset. A significant decrease in miR-21; miR-221; miR-146b and miR-181b expression was observed in the plasma of PTC patients after total thyroidectomy (p = 0.004; p = 0.001; p = 0.03; p = 0.036; respectively). The levels of miR-222 were significantly higher in the preoperative PTC compared to the NG group (p = 0.004). ROC curve (receiver operating characteristic curve) analysis revealed miR-222 as a potential marker in distinguishing PTC from NG (AUC 0.711; p = 0.004). In conclusion; circulating miR-222 profiles might be useful in discriminating PTC from NG.

scholarly journals Molecular and Clinical Characterization of UBE2S in Glioma as a Biomarker for Poor Prognosis and Resistance to Chemo-Radiotherapy

2021 ◽  
Vol 11 ◽  
Author(s):  
Li Hu ◽  
Xingbo Cheng ◽  
Zev Binder ◽  
Zhibin Han ◽  
Yibo Yin ◽  
...  
Keyword(s):  
Poor Prognosis ◽  
Growth Factor Receptor ◽  
Idh1 Mutation ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Akt Phosphorylation ◽  
Cancer Genome Atlas ◽  
Degree Of Malignancy ◽  
Genome Atlas

Glioblastoma is the most common and lethal brain cancer globally. Clinically, this cancer has heterogenous molecular and clinical characteristics. Studies have shown that UBE2S is highly expressed in many cancers. But its expression profile in glioma, and the correlation with clinical outcomes is unknown. RNA sequencing data of glioma samples was downloaded from the Chinese Glioma Genome Atlas and The Cancer Genome Atlas. A total of 114 cases of glioma tissue samples (WHO grades II-IV) were used to conduct protein expression assays. The molecular and biological characteristics of UBE2S, and its prognostic value were analyzed. The results showed that high UBE2S expression was associated with a higher grade of glioma and PTEN mutations. In addition, UBE2S affected the degree of malignancy of glioma and the development of chemo-radiotherapy resistance. It was also found to be an independent predictor of worse survival of LGG patients. Furthermore, we identified five UBE2S ubiquitination sites and found that UBE2S was associated with Akt phosphorylation in malignant glioblastoma. The results also revealed that UBE2S expression was negatively correlated with 1p19q loss and IDH1 mutation; positively correlated with epidermal growth factor receptor amplification and PTEN mutation. This study demonstrates that UBE2S expression strongly correlates with glioma malignancy and resistance to chemo-radiotherapy. It is also a crucial biomarker of poor prognosis.

scholarly journals Classifying subtypes and predicting survival of renal cell carcinoma using histopathology image-based deep learning

2021 ◽  
Author(s):  
Fei Wang ◽  
Yuanzhe Geng ◽  
Ting Wang ◽  
Ke Zhao ◽  
Bin Xu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Deep Learning ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Good Prognosis ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Clear Cell Rcc ◽  
Validation Set

Abstract Classifying histopathological subtypes and predicting survival of renal cell carcinoma (RCC) patients are critical steps towards treatment. In this work, we first proposed a deep learning method involving patch-based segmentation, intelligent feature extraction and heatmap visualization for classifying RCC into clear cell RCC, papillary RCC, chromophobe RCC, and adjacent benign tissue. This algorithm was trained and validated using 2,374,446 patches, 6,340 whole-slide images, 2,399 patients from The Cancer Genome Atlas and 6 independent centers. The classifiers provided areas under the curves of 0.979 to 0.996 in the internal phase, and 0.914 to 0.995 in the 6-center external phase. Furthermore, a modified deep learning approach comprising automated detection of regions of interest, patch-level learning, and morphological features-based risk scoring was developed for predicting survival of clear cell RCC patients. The prognostication model provided a hazard ratio for poor versus good prognosis of 2.63 [95% confidence interval (CI) 1.53–4.50, P = 4.35e-4] in the testing set, and 2.57 [95% CI 1.43–4.64, P = 1.68e-3] in an independent validation set using multivariable analyses. In conclusion, the developed histopathology image-based deep learning frameworks have the clinical potential to assist pathologists in systematically evaluating histological information of RCC patients.

scholarly journals Identification of 12 cancer types through genome deep learning

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yingshuai Sun ◽  
Sitao Zhu ◽  
Kailong Ma ◽  
Weiqing Liu ◽  
Yao Yue ◽  
...  
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Specific Model ◽  
The Cancer Genome Atlas ◽  
Genomic Information ◽  
Inter Observer Variability ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Cancer Tissues ◽  
The Relationship

AbstractCancer is a major cause of death worldwide, and an early diagnosis is required for a favorable prognosis. Histological examination is the gold standard for cancer identification; however, large amount of inter-observer variability exists in histological diagnosis. Numerous studies have shown cancer genesis is accompanied by an accumulation of harmful mutations, potentiating the identification of cancer based on genomic information. We have proposed a method, GDL (genome deep learning), to study the relationship between genomic variations and traits based on deep neural networks. We analyzed 6,083 samples’ WES (Whole Exon Sequencing) mutations files from 12 cancer types obtained from the TCGA (The Cancer Genome Atlas) and 1,991 healthy samples’ WES data from the 1000 Genomes project. We constructed 12 specific models to distinguish between certain type of cancer and healthy tissues, a total-specific model that can identify healthy and cancer tissues, and a mixture model to distinguish between all 12 types of cancer based on GDL. We demonstrate that the accuracy of specific, mixture and total specific model are 97.47%, 70.08% and 94.70% for cancer identification. We developed an efficient method for the identification of cancer based on genomic information that offers a new direction for disease diagnosis.

scholarly journals Implementation and Application of an Intelligent Pterygium Diagnosis System Based on Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Wei Xu ◽  
Ling Jin ◽  
Peng-Zhi Zhu ◽  
Kai He ◽  
Wei-Hua Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
Anterior Segment ◽  
Diagnostic System ◽  
Diagnostic Model ◽  
Diagnosis System ◽  
Kappa Value ◽  
Group 2 ◽  
Sensitivity Specificity ◽  
Group 1

Objective: This study aims to implement and investigate the application of a special intelligent diagnostic system based on deep learning in the diagnosis of pterygium using anterior segment photographs.Methods: A total of 1,220 anterior segment photographs of normal eyes and pterygium patients were collected for training (using 750 images) and testing (using 470 images) to develop an intelligent pterygium diagnostic model. The images were classified into three categories by the experts and the intelligent pterygium diagnosis system: (i) the normal group, (ii) the observation group of pterygium, and (iii) the operation group of pterygium. The intelligent diagnostic results were compared with those of the expert diagnosis. Indicators including accuracy, sensitivity, specificity, kappa value, the area under the receiver operating characteristic curve (AUC), as well as 95% confidence interval (CI) and F1-score were evaluated.Results: The accuracy rate of the intelligent diagnosis system on the 470 testing photographs was 94.68%; the diagnostic consistency was high; the kappa values of the three groups were all above 85%. Additionally, the AUC values approached 100% in group 1 and 95% in the other two groups. The best results generated from the proposed system for sensitivity, specificity, and F1-scores were 100, 99.64, and 99.74% in group 1; 90.06, 97.32, and 92.49% in group 2; and 92.73, 95.56, and 89.47% in group 3, respectively.Conclusion: The intelligent pterygium diagnosis system based on deep learning can not only judge the presence of pterygium but also classify the severity of pterygium. This study is expected to provide a new screening tool for pterygium and benefit patients from areas lacking medical resources.

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