Computational pathology,a field at the intersection of computer science and pathology,leverages digital technology to enhance diagnostic accuracy and efficiency.With the digitization of pathology and the development o...Computational pathology,a field at the intersection of computer science and pathology,leverages digital technology to enhance diagnostic accuracy and efficiency.With the digitization of pathology and the development of artificial intelligence,computational pathology has made significant strides in the automatic analysis of pathology images,including pathological structure segmentation,tumor classification,and prognosis analysis.Driven by large-scale datasets and advanced methods,computational pathology is moving toward building foundation models to reach more general applications.Generative methods provide a new perspective on addressing challenges in computational pathology.However,challenges in data security and model reliability,reproducibility,and clinical application remain.This review outlines the evolution of computational pathology from pathology slide digitization to pathology image analysis,consolidates the development of foundation and generative models in computational pathology,and discusses the key challenges that persist.Finally,we introduce some rising techniques for precision pathology.展开更多
BACKGROUND Identifying genetic mutations in cancer patients have been increasingly important because distinctive mutational patterns can be very informative to determine the optimal therapeutic strategy. Recent studie...BACKGROUND Identifying genetic mutations in cancer patients have been increasingly important because distinctive mutational patterns can be very informative to determine the optimal therapeutic strategy. Recent studies have shown that deep learning-based molecular cancer subtyping can be performed directly from the standard hematoxylin and eosin(H&E) sections in diverse tumors including colorectal cancers(CRCs). Since H&E-stained tissue slides are ubiquitously available, mutation prediction with the pathology images from cancers can be a time-and cost-effective complementary method for personalized treatment.AIM To predict the frequently occurring actionable mutations from the H&E-stained CRC whole-slide images(WSIs) with deep learning-based classifiers.METHODS A total of 629 CRC patients from The Cancer Genome Atlas(TCGA-COAD and TCGA-READ) and 142 CRC patients from Seoul St. Mary Hospital(SMH) were included. Based on the mutation frequency in TCGA and SMH datasets, we chose APC, KRAS, PIK3CA, SMAD4, and TP53 genes for the study. The classifiers were trained with 360 × 360 pixel patches of tissue images. The receiver operating characteristic(ROC) curves and area under the curves(AUCs) for all the classifiers were presented.RESULTS The AUCs for ROC curves ranged from 0.693 to 0.809 for the TCGA frozen WSIs and from 0.645 to 0.783 for the TCGA formalin-fixed paraffin-embedded WSIs.The prediction performance can be enhanced with the expansion of datasets. When the classifiers were trained with both TCGA and SMH data, the prediction performance was improved.CONCLUSION APC, KRAS, PIK3CA, SMAD4, and TP53 mutations can be predicted from H&E pathology images using deep learning-based classifiers, demonstrating the potential for deep learning-based mutation prediction in the CRC tissue slides.展开更多
BACKGROUND Phosphoglycerate kinase 1(PGK1)has been identified as a possible biomarker for breast cancer(BC)and may play a role in the development and advancement of triple-negative BC(TNBC).AIM To explore the PGK1 and...BACKGROUND Phosphoglycerate kinase 1(PGK1)has been identified as a possible biomarker for breast cancer(BC)and may play a role in the development and advancement of triple-negative BC(TNBC).AIM To explore the PGK1 and BC research status and PGK1 expression and mecha-nism differences among TNBC,non-TNBC,and normal breast tissue.METHODS PGK1 and BC related literature was downloaded from Web of Science Core Co-llection Core Collection.Publication counts,key-word frequency,cooperation networks,and theme trends were analyzed.Normal breast,TNBC,and non-TNBC mRNA data were gathered,and differentially expressed genes obtained.Area under the summary receiver operating characteristic curves,sensitivity and specificity of PGK1 expression were determined.Kaplan Meier revealed PGK1’s prognostic implication.PGK1 co-expressed genes were explored,and Gene Onto-logy,Kyoto Encyclopedia of Genes and Genomes,and Disease Ontology applied.Protein-protein interaction networks were constructed.Hub genes identified.RESULTS PGK1 and BC related publications have surged since 2020,with China leading the way.The most frequent keyword was“Expression”.Collaborative networks were found among co-citations,countries,institutions,and authors.PGK1 expression and BC progression were research hotspots,and PGK1 expression and BC survival were research frontiers.In 16 TNBC vs non-cancerous breast and 15 TNBC vs non-TNBC datasets,PGK1 mRNA levels were higher in 1159 TNBC than 1205 non-cancerous breast cases[standardized mean differences(SMD):0.85,95%confidence interval(95%CI):0.54-1.16,I²=86%,P<0.001].PGK1 expression was higher in 1520 TNBC than 7072 non-TNBC cases(SMD:0.25,95%CI:0.03-0.47,I²=91%,P=0.02).Recurrence free survival was lower in PGK1-high-expression than PGK1-low-expression group(hazard ratio:1.282,P=0.023).PGK1 co-expressed genes were concentrated in ATP metabolic process,HIF-1 signaling,and glycolysis/gluconeogenesis pathways.CONCLUSION PGK1 expression is a research hotspot and frontier direction in the BC field.PGK1 may play a strong role in promoting cancer in TNBC by mediating metabolism and HIF-1 signaling pathways.展开更多
Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression.Distinguishing stroma from epithelial tissues is critically important for spatial c...Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression.Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment.Here,we propose BrcaSeg,an image analysis pipeline based on a convolutional neural network(CNN)model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin(H&E)stained histopathological images.The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas(TCGA)Program.BrcaSeg achieves a classification accuracy of 91.02%,which outperforms other state-of-the-art methods.Using this model,we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data.We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios.Gene Ontology(GO)enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes,whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues.Taken all together,our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors.BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.展开更多
基金supported in part by the Shenzhen Natural Science Fund(the Stable Support Plan Program 20220810144949003)the Key Technology Development Program of Shenzhen(JSGG20210713091811036)+2 种基金the Key-Area Research and Development Program of Guangdong Province(2021B0101420005)the Shenzhen Key Laboratory Foundation(ZDSYS20200811143757022)the Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems(2024B1212010004).
文摘Computational pathology,a field at the intersection of computer science and pathology,leverages digital technology to enhance diagnostic accuracy and efficiency.With the digitization of pathology and the development of artificial intelligence,computational pathology has made significant strides in the automatic analysis of pathology images,including pathological structure segmentation,tumor classification,and prognosis analysis.Driven by large-scale datasets and advanced methods,computational pathology is moving toward building foundation models to reach more general applications.Generative methods provide a new perspective on addressing challenges in computational pathology.However,challenges in data security and model reliability,reproducibility,and clinical application remain.This review outlines the evolution of computational pathology from pathology slide digitization to pathology image analysis,consolidates the development of foundation and generative models in computational pathology,and discusses the key challenges that persist.Finally,we introduce some rising techniques for precision pathology.
基金Supported by Research Fund of Seoul St. Mary’s Hospital made in the program year of 2018。
文摘BACKGROUND Identifying genetic mutations in cancer patients have been increasingly important because distinctive mutational patterns can be very informative to determine the optimal therapeutic strategy. Recent studies have shown that deep learning-based molecular cancer subtyping can be performed directly from the standard hematoxylin and eosin(H&E) sections in diverse tumors including colorectal cancers(CRCs). Since H&E-stained tissue slides are ubiquitously available, mutation prediction with the pathology images from cancers can be a time-and cost-effective complementary method for personalized treatment.AIM To predict the frequently occurring actionable mutations from the H&E-stained CRC whole-slide images(WSIs) with deep learning-based classifiers.METHODS A total of 629 CRC patients from The Cancer Genome Atlas(TCGA-COAD and TCGA-READ) and 142 CRC patients from Seoul St. Mary Hospital(SMH) were included. Based on the mutation frequency in TCGA and SMH datasets, we chose APC, KRAS, PIK3CA, SMAD4, and TP53 genes for the study. The classifiers were trained with 360 × 360 pixel patches of tissue images. The receiver operating characteristic(ROC) curves and area under the curves(AUCs) for all the classifiers were presented.RESULTS The AUCs for ROC curves ranged from 0.693 to 0.809 for the TCGA frozen WSIs and from 0.645 to 0.783 for the TCGA formalin-fixed paraffin-embedded WSIs.The prediction performance can be enhanced with the expansion of datasets. When the classifiers were trained with both TCGA and SMH data, the prediction performance was improved.CONCLUSION APC, KRAS, PIK3CA, SMAD4, and TP53 mutations can be predicted from H&E pathology images using deep learning-based classifiers, demonstrating the potential for deep learning-based mutation prediction in the CRC tissue slides.
基金Supported by the Guangxi Zhuang Autonomous Region Health Commission Scientific Research Project,No.Z-A20220530.
文摘BACKGROUND Phosphoglycerate kinase 1(PGK1)has been identified as a possible biomarker for breast cancer(BC)and may play a role in the development and advancement of triple-negative BC(TNBC).AIM To explore the PGK1 and BC research status and PGK1 expression and mecha-nism differences among TNBC,non-TNBC,and normal breast tissue.METHODS PGK1 and BC related literature was downloaded from Web of Science Core Co-llection Core Collection.Publication counts,key-word frequency,cooperation networks,and theme trends were analyzed.Normal breast,TNBC,and non-TNBC mRNA data were gathered,and differentially expressed genes obtained.Area under the summary receiver operating characteristic curves,sensitivity and specificity of PGK1 expression were determined.Kaplan Meier revealed PGK1’s prognostic implication.PGK1 co-expressed genes were explored,and Gene Onto-logy,Kyoto Encyclopedia of Genes and Genomes,and Disease Ontology applied.Protein-protein interaction networks were constructed.Hub genes identified.RESULTS PGK1 and BC related publications have surged since 2020,with China leading the way.The most frequent keyword was“Expression”.Collaborative networks were found among co-citations,countries,institutions,and authors.PGK1 expression and BC progression were research hotspots,and PGK1 expression and BC survival were research frontiers.In 16 TNBC vs non-cancerous breast and 15 TNBC vs non-TNBC datasets,PGK1 mRNA levels were higher in 1159 TNBC than 1205 non-cancerous breast cases[standardized mean differences(SMD):0.85,95%confidence interval(95%CI):0.54-1.16,I²=86%,P<0.001].PGK1 expression was higher in 1520 TNBC than 7072 non-TNBC cases(SMD:0.25,95%CI:0.03-0.47,I²=91%,P=0.02).Recurrence free survival was lower in PGK1-high-expression than PGK1-low-expression group(hazard ratio:1.282,P=0.023).PGK1 co-expressed genes were concentrated in ATP metabolic process,HIF-1 signaling,and glycolysis/gluconeogenesis pathways.CONCLUSION PGK1 expression is a research hotspot and frontier direction in the BC field.PGK1 may play a strong role in promoting cancer in TNBC by mediating metabolism and HIF-1 signaling pathways.
基金supported by Indiana University Precision Health Initiative to KH and JZthe NSFC-Guangdong Joint Fund of China (Grant No. U1501256) to QFShenzhen Peacock Plan (Grant No. KQTD2016053112051497) to XZ and ND.
文摘Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression.Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment.Here,we propose BrcaSeg,an image analysis pipeline based on a convolutional neural network(CNN)model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin(H&E)stained histopathological images.The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas(TCGA)Program.BrcaSeg achieves a classification accuracy of 91.02%,which outperforms other state-of-the-art methods.Using this model,we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data.We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios.Gene Ontology(GO)enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes,whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues.Taken all together,our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors.BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.
