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Detecting circulating tumor material and digital pathology imaging during pancreatic cancer progression

World Journal of Gastrointestinal Oncology 2017年第6期9卷 235-250页

作者： Radim Moravec Rao Divi Mukesh Verma Surveillance Research Program Division of Cancer Control and Population SciencesNational Cancer InstituteNational Institutes of Health Methods and Technologies Branch Division of Cancer Control and Population SciencesNational Cancer InstituteNational Institutes of Health

Pancreatic cancer(PC) is a leading cause of cancerrelated death worldwide. Clinical symptoms typically present late when treatment options are limited and survival expectancy is very short. Metastatic mutations are heterogeneous and can accumulate up to twenty years before PC diagnosis. Given such genetic diversity, detecting and managing the complex states of disease progression may be limited to imaging modalities and markers present in circulation. Recent developments in digital pathology imaging show potential for early PC detection, making a differential diagnosis, and predicting treatment sensitivity leading to long-term survival in advanced stage patients. Despite large research efforts, the only serum marker currently approved for clinical use is CA 19-9. Utility of CA 19-9 has been shown to improve when it is used in combination with PC-specific markers. Efforts are being made to develop early-screening assays that can detect tumor-derived material, present in circulation, before metastasis takes a significant course. Detection of markers that identify circulating tumor cells and tumor-derived extracellular vesicles(EVs) in biofluid samples offers a promising non-invasive method for this purpose. Circulating tumor cells exhibit varying expression of epithelial and mesenchymal markers depending on the state of tumor differentiation. This offers a possibility for monitoring disease progression using minimally invasive procedures. EVs also offer the benefit of detecting molecular cargo of tumor origin and add the potential to detect circulating vesicle markers from tumors that lack invasive properties. This review integrates recent genetic insights of PC progression with developments in digitalpathology and early detection of tumor-derived circulating material.

关键词： Circulating tumor cells digital pathology Early detection Exosomes Pancreatic cancer

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Biobanking in the digital pathology era

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Oncology Research 2021年第4期29卷 229-233页

作者： GIUSEPPINA BONIZZI LORENZO ZATTONI NICOLA FUSCO Biobank for Translational and digital Medicine Unit Division of PathologyIEOEuropean Institute of Oncology IRCCSUniversity of MilanMilan20141Italy

digital pathology is becoming more and more important to achieve the goal of precision *** in whole-slide imaging,software integration,and the accessibility of storage solutions have changed the pathologists’clinical practice,not only in terms of laboratory workﬂow but also for diagnosis and biomarkers *** parallel with the pathology setting advancement,translational medicine is approaching the unprecedented opportunities unrevealed by artiﬁcial intelligence(AI).Indeed,the increased usage of biobanks’datasets in research provided new challenges for AI applications,such as advanced algorithms,and computer-aided *** this scenario,machine learning-based approaches are being propose in order to improve biobanks from biospecimens collection repositories to computational *** date,evidence on how to implement digital biobanks in translational medicine is still *** viewpoint article summarizes the currently available literature that supports the biobanks’role in the digital pathology era,and to provide possible practical applications of digital biobanks.

关键词： Biobank pathology digital pathology Biomarkers Translational research

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Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology

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World Journal of Gastroenterology 2021年第21期27卷 2818-2833页

作者： Hiroshi Yoshida Tomoharu Kiyuna Department of Diagnostic pathology National Cancer Center HospitalTokyo 104-0045Japan digital Healthcare Business Development Office NEC CorporationTokyo 108-8556Japan

Tremendous advances in artificial intelligence(AI)in medical image analysis have been achieved in recent *** integration of AI is expected to cause a revolution in various areas of medicine,including gastrointestinal(GI)***,deep learning algorithms have shown promising benefits in areas of diagnostic histopathology,such as tumor identification,classification,prognosis prediction,and biomarker/genetic alteration *** AI cannot substitute pathologists,carefully constructed AI applications may increase workforce productivity and diagnostic accuracy in pathology *** of these promising advances,unlike the areas of radiology or cardiology imaging,no histopathology-based AI application has been approved by a regulatory authority or for public ***,implying that there are still some obstacles to be overcome before AI applications can be safely and effectively implemented in real-life pathology *** challenges have been identified at different stages of the development process,such as needs identification,data curation,model development,validation,regulation,modification of daily workflow,and cost-effectiveness *** aim of this review is to present challenges in the process of AI development,validation,and regulation that should be overcome for its implementation in real-life GI pathology practice.

关键词： Artificial intelligence Deep learning digital image analysis digital pathology Clinical implementation Gastrointestinal cancer

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An integrative approach of digital image analysis and transcriptome profiling to explore potential predictive biomarkers for TGFβblockade therapy

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Acta Pharmaceutica Sinica B 2022年第9期12卷 3594-3601页

作者： Robert Pomponio Qi Tang Anthony Mei Anne Caron Bema Coulibaly Joachim Theilhaber Maximilian Rogers-Grazado Michele Sanicola-Nadel Souad Naimi Reza Olfati-Saber Cecile Combeau Jack Pollard Tun Tun Lin Rui Wang Sanofi Translational Medicine Early Development Cambridge MA 02142USA Sanofi Artificial Intelligence and Deep Analytics Data and Data Sciences CambridgeMA 02142USA Sanofi Translational Science Vitry sur Seine 94403France Sanofi Precision Oncology Cambridge MA 02142USA Sanofi Oncology Early Development Cambridge MA 02142USA

Increasing evidence suggests that the presence and spatial localization and distribution pattern of tumor infiltrating lymphocytes(TILs)is associate with response to *** studies have identified TGFβactivity and signaling as a determinant of T cell exclusion in the tumor microenvironment and poor response to PD-1/PD-L1 *** we coupled the artificial intelligence(AI)-powered digital image analysis and gene expression profiling as an integrative approach to quantify distribution of TILs and characterize the associated TGFβpathway *** of T cell spatial distribution in the solid tumor biopsies revealed substantial differences in the distribution *** digital image analysis approach achieves 74%concordance with the pathologist assessment for tumor-immune *** transcriptomic profiling suggests that the TIL score was negatively correlated with TGFβpathway activation,together with elevated TGFβsignaling activity observed in excluded and desert tumor *** present results demonstrate that the automated digital pathology algorithm for quantitative analysis of CD8 immunohistochemistry image can successfully assign the tumor into one of three infiltration phenotypes:immune desert,immune excluded or immune *** association between“cold”tumor-immune phenotypes and TGFβsignature further demonstrates their potential as predictive biomarkers to identify appropriate patients that may benefit from TGFβblockade.

关键词： digital pathology Artificial intelligence TGFβ Predictive biomarker Tumor topography T cell infiltration Transcriptomic profiling Machine learning

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Epithelial Layer Estimation Using Curvatures and Textural Features for Dysplastic Tissue Detection

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Computers, Materials & Continua 2021年第4期67卷 761-777页

作者： Afzan Adam Abdul Hadi Abd Rahman Nor Samsiah Sani Zaid Abdi Alkareem Alyessari Nur Jumaadzan Zaleha Mamat Basela Hasan Faculty of Information Science and Technology Center for Artificial Intelligence TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43300Malaysia Faculty of Science and Technology School of Mathematical SciencesUniversiti Kebangsaan MalaysiaBangiSelangor43300Malaysia Department of Information Technology and Computing Faculty of Computer StudiesArab Open UniversityAmmanJordan

Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling *** morphological pattern of an epithelial layer is greatly ***,the shape deformation model can normalise the distortions,but it needs a 2D *** theory,on the other hand,is not yet tested on digital pathology ***,this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial *** boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight *** epithelial layer’s depth is estimated from the tissue edges and the connected nucleolus ***,the textural and spatial features along the estimated layer are used for dysplastic tissue *** proposed method achieved better performance compared to the whole tissue regions in terms of detecting dysplastic *** result shows a leap of kappa points from fair to a substantial agreement with the expert’s ground truth *** improved results demonstrate that curvatures have been effective in reducing the boundary effects on the epithelial layer of ***,quantifying and classifying the morphological patterns for dysplasia can be *** textural and spatial features on the detected epithelial layer can capture the changes in tissue.

关键词： digital pathology grading dysplasia tissue boundary effect

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Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning

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World Journal of Gastroenterology 2020年第40期26卷 6207-6223页

作者： Hyun-Jong Jang Ahwon Lee J Kang In Hye Song Sung Hak Lee Department of Physiology Department of Biomedicine and Health SciencesCatholic Neuroscience InstituteThe Catholic University of KoreaSeoul 06591South Korea Department of Hospital pathology Seoul St.Mary’s HospitalCollege of MedicineThe Catholic University of KoreaSeoul 06591South Korea

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 *** To predict the frequently occurring actionable mutations from the H&E-stained CRC whole-slide images(WSIs) with deep learning-based *** 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 *** 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 *** 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 *** 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.

关键词： Colorectal cancer Mutation Deep learning Computational pathology Computer-aided diagnosis digital pathology

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Liver Fibrosis Helps to Distinguish Autoimmune Hepatitis from DILI with Autoimmune Features: A Review of Twenty Cases

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Journal of Clinical and Translational Hepatology 2019年第1期7卷 21-26页

作者： Christopher A.Febres-Aldana Sarah Alghamdi Kritika Krishnamurthy Robert J.Poppiti Department of pathology and Laboratory Medicine Mount Sinai Medical CenterMiami BeachFLUSA Herbert Wertheim College of Medicine Florida International UniversityMiamiFLUSA

Background and Aims:Drug-induced liver injury with autoimmune features(AI-DILI)mimics the clinical presentation,and laboratory and pathologic features of idiopathic autoimmune hepatitis(AIH).We aimed to identify histopathologic hallmarks to differentiate these ***:All liver biopsies archived for the past 10 years were reviewed retrospectively to identify cases of recently detected liver injury associated with predominantly lymphoplasmacytic interphase hepatitis,positive markers for liver autoimmunity,and negative tests for viral *** cases were divided into AIH(n=12)or AI-DILI(n=8)*** qualitative evaluation of necroinflammatory changes and liver fibrosis were performed according to the Scheuer scoring *** densities were determined using ImageJ(V1.51t,National Institutes of Health,Bethesda,MD,USA).Fibrosis was assessed on Masson trichrome-stained slides,and collagen deposition was estimated following a protocol of color ***:Necroinflammatory changes as well as densities(portal and lobular)of neutrophils and eosinophils,intracellular cholestasis,and regenerative changes did not differ between the two groups(P≥0.05).Neutrophil densities but not eosinophils showed a positive correlation with the severity of hepatocellular damage(r=0.6and 0.58,*** aminotransferase,P<0.05).Ceroidladen macrophages but not histiocytic aggregates appeared to be more common in AI-DILI(P<0.05).AIH patients presented more often with evidence of chronic damage,including higher scores of fibrosis and collagen deposition,in comparison to AI-DILI(P<0.05).Conclusions:Although there is no histologic feature pathognomonic for AI-DILI or AIH,advanced stages of liver fibrosis can be used to support the diagnosis of AIH in some *** diagnosis of AI-DILI requires follow-up and demonstration of complete remission after drug withdrawal with no need for immunosuppression.

关键词： DILI Autoimmune hepatitis Liver fibrosis digital pathology

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Integrating pathomics with radiomics and genomics for cancer prognosis:A brief review

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Chinese Journal of Cancer Research 2021年第5期33卷 563-573页

作者： Cheng Lu Rakesh Shiradkar Zaiyi Liu Biomedical Engineering Department Case Western Reserve UniversityCleveland 44106OHUSA Department of Radiology Guangzhou First People’s HospitalSchool of MedicineSouth China University of TechnologyGuangzhou 510080China

In the last decade,the focus of computational pathology research community has shifted from replicating the pathological examination for diagnosis done by pathologists to unlocking and discovering"sub-visual"prognostic image cues from the histopathological *** we are getting more knowledge and experience in digital pathology,the emerging goal is to integrate other-omics or modalities that will contribute for building a better prognostic *** this paper,we provide a brief review of representative works that focus on integrating pathomics with radiomics and genomics for cancer *** includes:correlation of pathomics and genomics;fusion of pathomics and genomics;fusion of pathomics and *** also present challenges,potential opportunities,and avenues for future work.

关键词： Radiomics pathomics genomics prognosis digital pathology

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Prognostic value of a modified Immunoscore in patients with stage Ⅰ-Ⅲ resectable colon cancer

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Chinese Journal of Cancer Research 2021年第3期33卷 379-390页

作者： Ke Zhao Xiaomei Wu Zhenhui Li Yingyi Wang Zeyan Xu Yajun Li Lin Wu Su Yao Yanqi Huang Changhong Liang Zaiyi Liu Department of Radiology Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China School of Medicine South China University of TechnologyGuangzhou 510006China Department of Radiology the Sixth Affiliated Hospital of Sun Yat-sen UniversityGuangzhou 510655China Department of Radiology the Third Affiliated Hospital of Kunming Medical UniversityYunnan Cancer HospitalYunnan Cancer CenterKunming 650118China Department of Radiology Zhuhai People's HospitalZhuhai Hospital Affiliated with Jinan UniversityZhuhai 519000China Department of pathology the Third Affiliated Hospital of Kunming Medical UniversityYunnan Cancer HospitalYunnan Cancer CenterKunming 650118China Department of pathology Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China the Second School of Clinical Medicine Southern Medical UniversityGuangzhou 510080China

Objective: The Immunoscore method has proved fruitful for predicting prognosis in patients with colon ***, there is still room for improvement in this scoring method to achieve further advances in its clinical translation. This study aimed to develop and validate a modified Immunoscore(IS-mod) system for predicting overall survival(OS) in patients with stage Ⅰ-Ⅲ colon ***: The IS-mod was proposed by counting CD3+ and CD8+ immune cells in regions of the tumor core and its invasive margin by drawing two lines of interest. A discovery cohort(N=212) and validation cohort(N=103)from two centers were used to evaluate the prognostic value of the ***: In the discovery cohort, 5-year survival rates were 88.6% in the high IS-mod group and 60.7% in the low IS-mod group. Multivariate analysis confirmed that the IS-mod was an independent prognostic factor for OS[adjusted hazard ratio(HR)=0.36, 95% confidence interval(95% CI): 0.20-0.63]. With less annotation and computation cost, the IS-mod achieved performance comparable to that of the Immunoscore-like(IS-like) system(C-index, 0.676 vs. 0.661, P=0.231). The 2-category IS-mod using 47.5% as the threshold had a better prognostic value than that using a fixed threshold of 25%(C-index, 0.653 vs. 0.573, P=0.004). Similar results were confirmed in the validation ***: Our method simplifies the annotation and accelerates the calculation of Immunoscore method,thus making it easier for clinical implementation. The IS-mod achieved comparable prognostic performance when compared to the IS-like system in both cohorts. Besides, we further found that even with a small reference set(N≥120), the IS-mod still demonstrated a stable prognostic value. This finding may inspire other institutions to develop a local reference set of an IS-mod system for more accurate risk stratification of colon cancer.

关键词： Immunoscore colon cancer whole-slide image overall survival digital pathology

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Prognostic and predictive role of immune microenvironment in colorectal cancer

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World Journal of Gastrointestinal Oncology 2024年第3期16卷 643-652页

作者： Olesya Kuznetsova Mikhail Fedyanin Larisa Zavalishina Larisa Moskvina Olga Kuznetsova Alexandra Lebedeva Alexey Tryakin Galina Kireeva Gleb Borshchev Sergei Tjulandin Ekaterina Ignatova Department of Chemotherapy Federal State Budgetary Institution(N.N.Blokhin National Medical Research Center of Oncology)of the Ministry of Health of the Russian FederationMoscow 115478Russia Department of pathology Russian Medical Academy of Continuous Professional EducationMoscow 123242Russia OncoAtlas LCC Moscow 115478Russia Federal State Budgetary Institution“National Medical and Surgical Center named after N.I.Pirogov”of the Ministry of Health of the Russian Federation Moscow 105203Russia STOONCO:Science and Technology in Oncology Moscow 115478Russia

Colorectal cancer(CRC)represents a molecularly heterogeneous disease and one of the most frequent causes of cancer-related death *** traditional classification of CRC is based on pathomorphological and molecular character-istics of tumor cells(mucinous,ring-cell carcinomas,etc.),analysis of mechanisms of carcinogenesis involved(chromosomal instability,microsatellite instability,CpG island methylator phenotype)and mutational statuses of commonly altered genes(KRAS,NRAS,BRAF,APC,etc.),as well as expression signatures(CMS 1-4).It is also suggested that the tumor microenvironment is a key player in tumor progression and metastasis in *** to the latest data,the immune microenvironment can also be predictive of the response to immune checkpoint *** this review,we highlight how the immune environment influences CRC prognosis and sensitivity to systemic therapy.

关键词： Immunoscore Immune microenvironment Colorectal cancer Gastrointestinal cancers Predictive biomarkers digital pathology

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