Automated histological classification of whole-slide images of gastric biopsy specimens

Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm

The aim of this study is to establish a deep learning (DL) model to predict the pathological type of gastric adenocarcinoma cancer based on whole-slide images(WSIs). We downloaded 356 histopathological images of gastric adenocarcinoma (STAD) patients from The Cancer Genome Atlas database and randomly divided them into the training set, validation set and test set (8:1:1). Additionally, 80 H&E-stained WSIs of STAD were collected for external validation. The CLAM tool was used to cut the WSIs and further construct the model by DL algorithm, achieving an accuracy of over 90% in identifying and predicting histopathological subtypes. External validation results demonstrated the model had a certain generalization ability. Moreover, DL features were extracted from the model to further investigate the differences in immune infiltration and patient prognosis between the two subtypes. The DL model can accurately predict the pathological classification of STAD patients, and provide certain reference value for clinical diagnosis. The nomogram combining DL-signature, gene-signature and clinical features can be used as a prognostic classifier for clinical decision-making and treatment.

Applications of artificial intelligence in digital pathology for gastric cancer

Gastric cancer is one of the most common cancers and is one of the leading causes of cancer-related deaths in worldwide. Early diagnosis and treatment are essential for a positive outcome. The integration of artificial intelligence in the pathology field is increasingly widespread, including histopathological images analysis. In recent years, the application of digital pathology technology emerged as a potential solution to enhance the understanding and management of gastric cancer. Through sophisticated image analysis algorithms, artificial intelligence technologies facilitate the accuracy and sensitivity of gastric cancer diagnosis and treatment and personalized therapeutic strategies. This review aims to evaluate the current landscape and future potential of artificial intelligence in transforming gastric cancer pathology, so as to provide ideas for future research.

Evaluation of the value of combined detection of tumor markers CA724, carcinoembryonic antigen, CA242, and CA19-9 in gastric cancer

BACKGROUND

Gastric cancer is a global health concern that poses a significant threat to human well-being.

AIM

To detecting serum changes in carcinoembryonic antigen (CEA), carbohydrate antigens (CA) 724, CA242, and CA19-9 expression among patients with gastric cancer.

METHODS

Eighty patients diagnosed with gastric cancer between January 2020 and January 2023 were included in the observation group, while 80 patients with benign gastric diseases were included in the control group. Both groups were tested for tumor markers (CA724, CEA, CA242, and CA19-9]. Tumor marker indicators (CA724, CEA, CA242, and CA19-9) were compared between the two groups, assessing positive rates of tumor markers across various stages in the observation group. Additionally, single and combined detection of various tumor markers were examined.

RESULTS

The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value observed for the combined detection of CA724, CEA, CA242, and CA19-9 were higher than those of CA724, CEA, CA242, and CA19-9 individually. Therefore, the combined detection of CA724, CEA, CA242, and CA19-9 has a high diagnostic accuracy and could reduce the occurrence of missed or misdiagnosed cases, facilitating the early diagnosis and treatment of patients.

CONCLUSION

CA724, CEA, CA242, and CA19-9 serum levels in gastric cancer patients significantly surpassed those in non-gastric cancer patients (P < 0.05). Their combined detection can improve the diagnostic accuracy for gastric cancer, warranting clinical promotion.

Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies

This review aims to present a comprehensive overview of the current landscape of artificial intelligence (AI) applications in the analysis of tubular gastrointestinal biopsies. These publications cover a spectrum of conditions, ranging from inflammatory ailments to malignancies. Moving beyond the conventional diagnosis based on hematoxylin and eosin-stained whole-slide images, the review explores additional implications of AI, including its involvement in interpreting immunohistochemical results, molecular subtyping, and the identification of cellular spatial biomarkers. Furthermore, the review examines how AI can contribute to enhancing the quality and control of diagnostic processes, introducing new workflow options, and addressing the limitations and caveats associated with current AI platforms in this context.

Curcumin inhibits the growth and invasion of gastric cancer by regulating long noncoding RNA AC022424.2

BACKGROUND

Gastric cancer, characterized by a multifactorial etiology and high heterogeneity, continues to confound researchers in terms of its pathogenesis. Curcumin, a natural anticancer agent, exhibits therapeutic promise in gastric cancer. Its effects include promoting cell apoptosis, curtailing tumor angiogenesis, and enhancing sensitivity to radiation and chemotherapy. Long noncoding RNAs (lncRNAs) have garnered significant attention as biomarkers for early screening, diagnosis, treatment, and drug response because of their remarkable specificity and sensitivity. Recent investigations have revealed an association between aberrant lncRNA expression and early diagnosis, clinical staging, metastasis, drug sensitivity, and prognosis in gastric cancer. A profound understanding of the intricate mechanisms through which lncRNAs influence gastric cancer development can provide novel insights for precision treatment and tailored management of patients with gastric cancer. This study aimed to unravel the potential of curcumin in suppressing the malignant behavior of gastric cancer cells by upregulating specific lncRNAs and modulating gastric cancer onset and progression.

AIM

To identify lncRNAs associated with curcumin treatment and investigate the role of lncRNA AC022424.2 in the effects of curcumin on gastric cancer cell apoptosis, proliferation, and invasion. Furthermore, these findings were validated in clinical samples.

METHODS

The study employed CCK-8 assays to assess the impact of curcumin on gastric cancer cell proliferation, flow cytometry to investigate its effects on apoptosis, and scratch and Transwell assays to evaluate its influence on the migration and invasion of BGC-823 and MGC-803 cells. Western blotting was used to gauge changes in the protein expression levels of CDK6, CDK4, Bax, Bcl-2, caspase-3, P65, and the PI3K/Akt/mTOR pathway in gastric cancer cell lines after curcumin treatment. Differential expression of lncRNAs before and after curcumin treatment was assessed using lncRNA sequencing and validated using quantitative reverse transcription polymerase chain reaction (qRT-PCR) in BGC-823 and MGC-803 cells. AC022424.2-1 knockdown BGC-823 and MGC-803 cells were generated to scrutinize the impact of lncRNA AC022424.2 on apoptosis, proliferation, migration, and invasion of gastric cancer cells. Western blotting was performed to ascertain changes in the expression of proteins implicated in the PI3K/Akt/mTOR and NF-κB signaling pathways. RT-PCR was employed to measure lncRNA AC022424.2 expression in clinical gastric cancer tissues and to correlate its expression with clinical pathological characteristics.

RESULTS

Curcumin induced apoptosis and hindered proliferation, migration, and invasion of gastric cancer cells in a dose- and time-dependent manner. LncRNA AC022424.2 was upregulated after curcumin treatment, and its knockdown enhanced cancer cell aggressiveness. LncRNA AC022424.2 may have affected cancer cells via the PI3K/Akt/mTOR and NF-κB signaling pathways. LncRNA AC022424.2 downregulation was correlated with lymph node metastasis, making it a potential diagnostic and prognostic marker.

CONCLUSION

Curcumin has potential anticancer effects on gastric cancer cells by regulating lncRNA AC022424.2. This lncRNA plays a significant role in cancer cell behavior and may have clinical implications in diagnosis and prognosis evaluation. The results of this study enhance our understanding of gastric cancer development and precision treatment.

Artificial Intelligence-Enabled Gastric Cancer Interpretations: Are We There yet?

The integration of digital pathology and artificial intelligence (AI) is revolutionizing pathology by providing pathologists with new tools to improve workflow, enhance diagnostic accuracy, and undertake novel discovery. The capability of AI to recognize patterns and features in digital images beyond human perception is particularly valuable, thereby providing additional information for prognostic and predictive purposes. AI-based tools diagnose gastric carcinoma in digital images, detect gastric carcinoma metastases in lymph nodes, automate Ki-67 scoring in gastric neuroendocrine tumors, and quantify tumor-infiltrating lymphocytes. This article provides an overview of all of these applications of AI pertaining to gastric cancer.

Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer

Diagnosing primary liver cancers, particularly hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC), is a challenging and labor-intensive process, even for experts, and secondary liver cancers further complicate the diagnosis. Artificial intelligence (AI) offers promising solutions to these diagnostic challenges by facilitating the histopathological classification of tumors using digital whole slide images (WSIs). This study aimed to develop a deep learning model for distinguishing HCC, CC, and metastatic colorectal cancer (mCRC) using histopathological images and to discuss its clinical implications. The WSIs from HCC, CC, and mCRC were used to train the classifiers. For normal/tumor classification, the areas under the curve (AUCs) were 0.989, 0.988, and 0.991 for HCC, CC, and mCRC, respectively. Using proper tumor tissues, the HCC/other cancer type classifier was trained to effectively distinguish HCC from CC and mCRC, with a concatenated AUC of 0.998. Subsequently, the CC/mCRC classifier differentiated CC from mCRC with a concatenated AUC of 0.995. However, testing on an external dataset revealed that the HCC/other cancer type classifier underperformed with an AUC of 0.745. After combining the original training datasets with external datasets and retraining, the classification drastically improved, all achieving AUCs of 1.000. Although these results are promising and offer crucial insights into liver cancer, further research is required for model refinement and validation.

Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment

Gastric endoscopic screening is an effective way to decide appropriate gastric cancer treatment at an early stage, reducing gastric cancer-associated mortality rate. Although artificial intelligence has brought a great promise to assist pathologist to screen digitalized endoscopic biopsies, existing artificial intelligence systems are limited to be utilized in planning gastric cancer treatment. We propose a practical artificial intelligence-based decision support system that enables five subclassifications of gastric cancer pathology, which can be directly matched to general gastric cancer treatment guidance. The proposed framework is designed to efficiently differentiate multi-classes of gastric cancer through multiscale self-attention mechanism using 2-stage hybrid vision transformer networks, by mimicking the way how human pathologists understand histology. The proposed system demonstrates its reliable diagnostic performance by achieving class-average sensitivity of above 0.85 for multicentric cohort tests. Moreover, the proposed system demonstrates its great generalization capability on gastrointestinal track organ cancer by achieving the best class-average sensitivity among contemporary networks. Furthermore, in the observational study, artificial intelligence-assisted pathologists show significantly improved diagnostic sensitivity within saved screening time compared to human pathologists. Our results demonstrate that the proposed artificial intelligence system has a great potential for providing presumptive pathologic opinion and supporting decision of appropriate gastric cancer treatment in practical clinical settings.

Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology

The European Society of Toxicologic Pathology (ESTP) initiated a survey through its Pathology 2.0 workstream in partnership with sister professional societies in Europe and North America to generate a snapshot of artificial intelligence (AI) usage in the field of toxicologic pathology. In addition to demographic information, some general questions explored AI relative to (1) the current status of adoption across organizations; (2) technical and methodological aspects; (3) perceived business value and finally; and (4) roadblocks and perspectives. AI has become increasingly established in toxicologic pathology with most pathologists being supportive of its development despite some areas of uncertainty. A salient feature consisted of the variability of AI awareness and adoption among the responders, as the spectrum extended from pathologists having developed familiarity and technical skills in AI, to colleagues who had no interest in AI as a tool in toxicologic pathology. Despite a general enthusiasm for these techniques, the overall understanding and trust in AI algorithms as well as their added value in toxicologic pathology were generally low, suggesting room for the need for increased awareness and education. This survey will serve as a basis to evaluate the evolution of AI penetration and acceptance in this domain.

A state-of-the-art survey of artificial neural networks for Whole-slide Image analysis: From popular Convolutional Neural Networks to potential visual transformers

In recent years, with the advancement of computer-aided diagnosis (CAD) technology and whole slide image (WSI), histopathological WSI has gradually played a crucial aspect in the diagnosis and analysis of diseases. To increase the objectivity and accuracy of pathologists' work, artificial neural network (ANN) methods have been generally needed in the segmentation, classification, and detection of histopathological WSI. However, the existing review papers only focus on equipment hardware, development status and trends, and do not summarize the art neural network used for full-slide image analysis in detail. In this paper, WSI analysis methods based on ANN are reviewed. Firstly, the development status of WSI and ANN methods is introduced. Secondly, we summarize the common ANN methods. Next, we discuss publicly available WSI datasets and evaluation metrics. These ANN architectures for WSI processing are divided into classical neural networks and deep neural networks (DNNs) and then analyzed. Finally, the application prospect of the analytical method in this field is discussed. The important potential method is Visual Transformers.

Recent advances in endoscopic management of gastric neoplasms

The development and clinical application of new diagnostic endoscopic technologies such as endoscopic ultrasonography with biopsy, magnification endoscopy, and narrow-band imaging, more recently supplemented by artificial intelligence, have enabled wider recognition and detection of various gastric neoplasms including early gastric cancer (EGC) and subepithelial tumors, such as gastrointestinal stromal tumors and neuroendocrine tumors. Over the last decade, the evolution of novel advanced therapeutic endoscopic techniques, such as endoscopic mucosal resection, endoscopic submucosal dissection, endoscopic full-thickness resection, and submucosal tunneling endoscopic resection, along with the advent of a broad array of endoscopic accessories, has provided a promising and yet less invasive strategy for treating gastric neoplasms with the advantage of a reduced need for gastric surgery. Thus, the management algorithms of various gastric tumors in a defined subset of the patient population at low risk of lymph node metastasis and amenable to endoscopic resection, may require revision considering upcoming data given the high success rate of en bloc resection by experienced endoscopists. Moreover, endoscopic surveillance protocols for precancerous gastric lesions will continue to be refined by systematic reviews and meta-analyses of further research. However, the lack of familiarity with subtle endoscopic changes associated with EGC, as well as longer procedural time, evolving resection techniques and tools, a steep learning curve of such high-risk procedures, and lack of coding are issues that do not appeal to many gastroenterologists in the field. This review summarizes recent advances in the endoscopic management of gastric neoplasms, with special emphasis on diagnostic and therapeutic methods and their future prospects.

A semi-supervised learning approach with consistency regularization for tumor histopathological images analysis

Introduction

Manual inspection of histopathological images is important in clinical cancer diagnosis. Pathologists implement pathological diagnosis and prognostic evaluation through the microscopic examination of histopathological slices. This entire process is time-consuming, laborious, and challenging for pathologists. The modern use of whole-slide imaging, which scans histopathology slides to digital slices, and analysis using computer-aided diagnosis is an essential problem.

Methods

To solve the problem of difficult labeling of histopathological data, and improve the flexibility of histopathological analysis in clinical applications, we herein propose a semi-supervised learning algorithm coupled with consistency regularization strategy, called"Semi- supervised Histopathology Analysis Network"(Semi-His-Net), for automated normal-versus-tumor and subtype classifications. Specifically, when inputted disturbing versions of the same image, the model should predict similar outputs. Based on this, the model itself can assign artificial labels to unlabeled data for subsequent model training, thereby effectively reducing the labeled data required for training.

Results

Our Semi-His-Net is able to classify patches from breast cancer histopathological images into normal tissue and three other different tumor subtypes, achieving an accuracy was 90%. The average AUC of cross-classification between tumors reached 0.893.

Discussion

To overcome the limitations of visual inspection by pathologists for histopathology images, such as long time and low repeatability, we have developed a deep learning-based framework (Semi-His-Net) for automatic classification subdivision of the subtypes contained in the whole pathological images. This learning-based framework has great potential to improve the efficiency and repeatability of histopathological image diagnosis.

Anonymization of whole slide images in histopathology for research and education

Objective

The exchange of health-related data is subject to regional laws and regulations, such as the General Data Protection Regulation (GDPR) in the EU or the Health Insurance Portability and Accountability Act (HIPAA) in the United States, resulting in non-trivial challenges for researchers and educators when working with these data. In pathology, the digitization of diagnostic tissue samples inevitably generates identifying data that can consist of sensitive but also acquisition-related information stored in vendor-specific file formats. Distribution and off-clinical use of these Whole Slide Images (WSIs) are usually done in these formats, as an industry-wide standardization such as DICOM is yet only tentatively adopted and slide scanner vendors currently do not provide anonymization functionality.

Methods

We developed a guideline for the proper handling of histopathological image data particularly for research and education with regard to the GDPR. In this context, we evaluated existing anonymization methods and examined proprietary format specifications to identify all sensitive information for the most common WSI formats. This work results in a software library that enables GDPR-compliant anonymization of WSIs while preserving the native formats.

Results

Based on the analysis of proprietary formats, all occurrences of sensitive information were identified for file formats frequently used in clinical routine, and finally, an open-source programming library with an executable CLI tool and wrappers for different programming languages was developed.

Conclusions

Our analysis showed that there is no straightforward software solution to anonymize WSIs in a GDPR-compliant way while maintaining the data format. We closed this gap with our extensible open-source library that works instantaneously and offline.

Artificial intelligence applications in pathological diagnosis of gastric cancer

Globally, gastric cancer is the third leading cause of death from tumors. Prevention and individualized treatment are considered to be the best options for reducing the mortality rate of gastric cancer. Artificial intelligence (AI) technology has been widely used in the field of gastric cancer, including diagnosis, prognosis, and image analysis. Eligible papers were identified from PubMed and IEEE up to April 13, 2022. Through the comparison of these articles, the application status of AI technology in the diagnosis of gastric cancer was summarized, including application types, application scenarios, advantages and limitations. This review presents the current state and role of AI in the diagnosis of gastric cancer based on four aspects: 1) accurate sampling from early diagnosis (endoscopy), 2) digital pathological diagnosis, 3) molecules and genes, and 4) clinical big data analysis and prognosis prediction. AI plays a very important role in facilitating the diagnosis of gastric cancer; however, it also has shortcomings such as interpretability. The purpose of this review is to provide assistance to researchers working in this domain.

Two-step artificial intelligence system for endoscopic gastric biopsy improves the diagnostic accuracy of pathologists

Background

Endoscopic biopsy is the pivotal procedure for the diagnosis of gastric cancer. In this study, we applied whole-slide images (WSIs) of endoscopic gastric biopsy specimens to develop an endoscopic gastric biopsy assistant system (EGBAS).

Methods

The EGBAS was trained using 2373 WSIs expertly annotated and internally validated on 245 WSIs. A large-scale, multicenter test dataset of 2003 WSIs was used to externally evaluate EGBAS. Eight pathologists were compared with the EGBAS using a man-machine comparison test dataset. The fully manual performance of the pathologists was also compared with semi-manual performance using EGBAS assistance.

Results

The average area under the curve of the EGBAS was 0·979 (0·958-0·990). For the diagnosis of all four categories, the overall accuracy of EGBAS was 86·95%, which was significantly higher than pathologists (P< 0·05). The EGBAS achieved a higher κ score (0·880, very good κ) than junior and senior pathologists (0·641 ± 0·088 and 0·729 ± 0·056). With EGBAS assistance, the overall accuracy (four-tier classification) of the pathologists increased from 66·49 ± 7·73% to 73·83 ± 5·73% (P< 0·05). The length of time for pathologists to manually complete the dataset was 461·44 ± 117·96 minutes; this time was reduced to 305·71 ± 82·43 minutes with EGBAS assistance (P = 0·00).

Conclusions

The EGBAS is a promising system for improving the diagnosis ability and reducing the workload of pathologists.

Multi-modality artificial intelligence in digital pathology

In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin-eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors' work and discusses the opportunities and challenges of AI.

Endocytoscopic Observation of Esophageal Lesions: Our Own Experience and a Review of the Literature

This review outlines the process of the development of the endocytoscope (EC) with reference to previously reported studies including our own. The EC is an ultra-high-magnification endoscope capable of imaging at the cellular level. The esophagus is the most suitable site for EC observation because it is amenable to vital staining. The diagnosis of esophageal lesions using EC is based on nuclear density and nuclear abnormality, allowing biopsy histology to be omitted. The observation of nuclear abnormality requires a magnification of ×600 or higher using digital technology. Several staining methods have been proposed, but single staining with toluidine blue or methylene blue is most suitable because the contrast at the border of a cancerous area can be easily identified. A three-tier classification of esophageal lesions visualized by EC is proposed: Type 1 (non-cancerous), Type 2 (endocytoscopic borderline), and Type 3 (cancerous). Since characteristic EC images reflecting pathology can be obtained from non-cancerous esophageal lesions, a modified form of classification with four additional characteristic non-cancerous EC features has also been proposed. Recently, deep-learning AI for analysis of esophageal EC images has revealed that its diagnostic accuracy is comparable to that of expert pathologists.

Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer.

Automated recognition of glomerular lesions in the kidneys of mice by using deep learning

Background

In recent years, digital pathology has been rapidly developing and applied throughout the world. Especially in clinical settings, it has been utilized in a variety of situations, including automated cancer diagnosis. Conversely, in non-clinical research, it has not yet been utilized as much as in clinical settings. We have been performing automated recognition of various pathological animal tissues and quantitative analysis of pathological findings, including liver and lung. In this study, we attempted to construct an artificial intelligence (AI)-based trained model that can automatedly recognize glomerular lesions in mouse kidneys that are characterized by complex structures.

Materials and methods

By using hematoxylin and eosin (HE)-stained whole slide images (WSI) from Col4a3 KO mice as variation data, normal glomeruli and glomerular lesions were annotated, and deep learning (DL) was performed with the use of the neural network classifier DenseNet system in HALO AI. The trained model was refined by correcting the annotation of misrecognized tissue area and reperforming DL. The accuracy of the trained model was confirmed by comparing the AI-obtained results with the pathological grades evaluated by pathologists. The generality of the trained model was also confirmed by analyzing the WSI of adriamycin (ADR)-induced nephropathy mice, which is a different disease model.

Results

Glomerular lesions (including mesangial proliferation, crescent formation, and sclerosis) observed in Col4a3 KO mice and ADR mice were detected by our trained model. The number of glomerular lesions detected by our trained model were also highly correlated with that of counted by pathologists.

Conclusion

In this study, we constructed a trained model allowing us to automatedly recognize glomerular lesions in the mouse kidney with the use of the HALO AI system. The findings and insights of this study will facilitate the development of digital pathology in non-clinical research and improve the probability of success in drug discovery research.

Deep learning for automatic diagnosis of gastric dysplasia using whole-slide histopathology images in endoscopic specimens

BACKGROUND

Distinguishing gastric epithelial regeneration change from dysplasia and histopathological diagnosis of dysplasia is subject to interobserver disagreement in endoscopic specimens. In this study, we developed a method to distinguish gastric epithelial regeneration change from dysplasia and further subclassify dysplasia. Meanwhile, optimized the cross-hospital diagnosis using domain adaption (DA).

METHODS

897 whole slide images (WSIs) of endoscopic specimens from two hospitals were divided into training, internal validation, and external validation cohorts. We developed a deep learning (DL) with DA (DLDA) model to classify gastric dysplasia and epithelial regeneration change into three categories: negative for dysplasia (NFD), low-grade dysplasia (LGD), and high-grade dysplasia (HGD)/intramucosal invasion neoplasia (IMN). The diagnosis based on the DLDA model was compared to 12 pathologists using 100 gastric biopsy cases.

RESULTS

In the internal validation cohort, the diagnostic performance measured by the macro-averaged area under the receiver operating characteristic curve (AUC) was 0.97. In the independent external validation cohort, our DLDA models increased macro-averaged AUC from 0.67 to 0.82. In terms of the NFD and HGD cases, our model's diagnostic sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were significantly higher than junior and senior pathologists. Our model's diagnostic sensitivity, NPV, was higher than specialist pathologists.

CONCLUSIONS

We demonstrated that our DLDA model could distinguish gastric epithelial regeneration change from dysplasia and further subclassify dysplasia in endoscopic specimens. Meanwhile, achieved significant improvement of diagnosis cross-hospital.

Sampling Based Tumor Recognition in Whole-Slide Histology Image With Deep Learning Approaches

Histopathological identification of tumor tissue is one of the routine pathological diagnoses for pathologists. Recently, computational pathology has been successfully interpreted by a variety of deep learning-based applications. Nevertheless, the high-efficient and spatial-correlated processing of individual patches have always attracted attention in whole-slide image (WSI) analysis. In this paper, we propose a high-throughput system to detect tumor regions in colorectal cancer histology slides precisely. We train a deep convolutional neural network (CNN) model and design a Monte Carlo (MC) adaptive sampling method to estimate the most representative patches in a WSI. Two conditional random field (CRF) models are designed, namely the correction CRF and the prediction CRF are integrated for spatial dependencies of patches. We use three datasets of colorectal cancer from The Cancer Genome Atlas (TCGA) to evaluate the performance of the system. The overall diagnostic time can be reduced from 56.7 percent to 71.7 percent on the slides of a varying tumor distribution, with an increase in classification accuracy.

Artificial Intelligence Assisted Topographic Mapping System for Endoscopic Submucosal Dissection Specimens

Background

Endoscopic submucosal dissection (ESD), a minimally invasive surgery used to treat early gastrointestinal malignancies, has been widely embraced around the world. The gross reconstruction of ESD specimens can facilitate a more precise pathological diagnosis and allow endoscopists to explore lesions thoroughly. The traditional method of mapping is time-consuming and inaccurate. We aim to design a topographic mapping system via artificial intelligence to perform the job automatically.

Methods

The topographic mapping system was built using computer vision techniques. We enrolled 23 ESD cases at the Peking Union Medical College Hospital from September to November 2019. The reconstruction maps were created for each case using both the traditional approach and the system.

Results

Using the system, the time saved per case ranges from 34 to 3,336 s. Two approaches revealed no significant variations in the shape, size, or tumor area.

Conclusion

We developed an AI-assisted system that would help pathologists complete the ESD topographic mapping process rapidly and accurately.

Identifying pathological slices of gastric cancer via deep learning

BACKGROUND

The accuracy of histopathology diagnosis largely depends on the pathologist's experience. It usually takes over 10 years to cultivate a senior pathologist, and small numbers of them lead to a high workload for those available. Meanwhile, inconsistent diagnostic results may arise among different pathologists, especially in complex cases, because diagnosis based on morphology is subjective. Computerized analysis based on deep learning has shown potential benefits as a diagnostic strategy.

METHODS

This research aims to automatically determine the location of gastric cancer (GC) in the images of GC slices through artificial intelligence. We use image data from a regional teaching hospital in Taiwan for training. We collect images of patients diagnosed with GC from January 1, 2019 to December 31, 2020. In this study, scanned images are used to dissect 13,600 images from 50 different patients with GC sections whose GC sections are stained with hematoxylin and eosin (H&E stained) through a whole slide scanner, the scanned images from 50 different GC slice patients are divided into 80% training combinations, 2200 images of 40 patients are trained. The remaining 20%, totaling 10 people, are validated from a test set of 550 images.

RESULTS

The validation results show that 91% of the correct rates are interpreted as GC images through deep learning. The sensitivity, specificity, PPV, and NPV were 84.9%, 94%, 87.7%, and 92.5%, respectively. After creating a 3D model through the grayscale value, the position of the GC is completely marked by the 3D model. The purpose of this research is to use artificial intelligence (AI) to determine the location of the GC in the image of GC slice.

CONCLUSION

In patients undergoing pancreatectomy for pancreatic cancer, intraoperative infusion of lidocaine did not improve overall or disease-free survival. Reduced formation of circulating NETs was absent in pancreatic tumour tissue.

CONCLUSION

For AI to assist pathologists in daily practice, to help a pathologist making a definite diagnosis is not the prime purpose at present time. The benefits could come from cancer screening and double-check quality control in a heavy workload which could distract the attention of pathologist during the time constraint examination process. We propose a two-steps method to identify cancerous areas in endoscopic gastric biopsy slices via deep learning. Then a 3D model is used to further mark all the positions of GC in the picture, and the model overcomes the problem that deep learning can't catch all GC.

Artificial Intelligence in Endoscopy

Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.

AI and Clinical Decision Making: The Limitations and Risks of Computational Reductionism in Bowel Cancer Screening

Advances in artificial intelligence in healthcare are frequently promoted as ‘solutions’ to improve the accuracy, safety, and quality of clinical decisions, treatments, and care. Despite some diagnostic success, however, AI systems rely on forms of reductive reasoning and computational determinism that embed problematic assumptions about clinical decision-making and clinical practice. Clinician autonomy, experience, and judgement are reduced to inputs and outputs framed as binary or multi-class classification problems benchmarked against a clinician’s capacity to identify or predict disease states. This paper examines this reductive reasoning in AI systems for colorectal cancer (CRC) to highlight their limitations and risks: (1) in AI systems themselves due to inherent biases in (a) retrospective training datasets and (b) embedded assumptions in underlying AI architectures and algorithms; (2) in the problematic and limited evaluations being conducted on AI systems prior to system integration in clinical practice; and (3) in marginalising socio-technical factors in the context-dependent interactions between clinicians, their patients, and the broader health system. The paper argues that to optimise benefits from AI systems and to avoid negative unintended consequences for clinical decision-making and patient care, there is a need for more nuanced and balanced approaches to AI system deployment and evaluation in CRC.

Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning

Signet ring cell carcinoma (SRCC) is a malignant tumor of the digestive system. This tumor has long been considered to be poorly differentiated and highly invasive because it has a higher rate of metastasis than well-differentiated adenocarcinoma. But some studies in recent years have shown that the prognosis of some SRCC is more favorable than other poorly differentiated adenocarcinomas, which suggests that SRCC has different degrees of biological behavior. Therefore, we need to find a histological stratification that can predict the biological behavior of SRCC. Some studies indicate that the morphological status of cells can be linked to the invasiveness potential of cells, however, the traditional histopathological examination can not objectively define and evaluate them. Recent improvements in biomedical image analysis using deep learning (DL) based neural networks could be exploited to identify and analyze SRCC. In this study, we used DL to identify each cancer cell of SRCC in whole slide images (WSIs) and quantify their morphological characteristics and atypia. Our results show that the biological behavior of SRCC can be predicted by quantifying the morphology of cancer cells by DL. This technique could be used to predict the biological behavior and may change the stratified treatment of SRCC.

The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers

Gastrointestinal cancers account for 22.5% of cancer related deaths worldwide and represent circa 20% of all cancers. In the last decades, we have witnessed a shift from histology-based to molecular-based classifications using genomic, epigenomic, and transcriptomic data. The molecular based classification revealed new prognostic markers and may aid the therapy selection. Because of the high-costs to perform a molecular classification, in recent years immunohistochemistry-based surrogate classification were developed which permit the stratification of patients, and in parallel multiple groups developed hematoxylin and eosin whole slide image analysis for sub-classifying these entities. Hence, we are witnessing a return to an image-based classification with the purpose to infer hidden information from routine histology images that would permit to detect the patients that respond to specific therapies and would be able to predict their outcome. In this review paper, we will discuss the current histological, molecular, and immunohistochemical classifications of the most common gastrointestinal cancers, gastric adenocarcinoma, and colorectal adenocarcinoma, and will present key aspects for developing a new artificial intelligence aided image-based classification of these malignancies.

Artificial intelligence in pathological evaluation of gastrointestinal cancers

The integration of artificial intelligence (AI) has shown promising benefits in many fields of diagnostic histopathology, including for gastrointestinal cancers (GCs), such as tumor identification, classification, and prognosis prediction. In parallel, recent evidence suggests that AI may help reduce the workload in gastrointestinal pathology by automatically detecting tumor tissues and evaluating prognostic parameters. In addition, AI seems to be an attractive tool for biomarker/genetic alteration prediction in GC, as it can contain a massive amount of information from visual data that is complex and partially understandable by pathologists. From this point of view, it is suggested that advances in AI could lead to revolutionary changes in many fields of pathology. Unfortunately, these findings do not exclude the possibility that there are still many hurdles to overcome before AI applications can be safely and effectively applied in actual pathology practice. These include a broad spectrum of challenges from needs identification to cost-effectiveness. Therefore, unlike other disciplines of medicine, no histopathology-based AI application, including in GC, has ever been approved either by a regulatory authority or approved for public reimbursement. The purpose of this review is to present data related to the applications of AI in pathology practice in GC and present the challenges that need to be overcome for their implementation.

Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular Carcinoma

Background

An accurate pathological diagnosis of hepatocellular carcinoma (HCC), one of the malignant tumors with the highest mortality rate, is time-consuming and heavily reliant on the experience of a pathologist. In this report, we proposed a deep learning model that required minimal noise reduction or manual annotation by an experienced pathologist for HCC diagnosis and classification.

Methods

We collected a whole-slide image of hematoxylin and eosin-stained pathological slides from 592 HCC patients at the First Affiliated Hospital, College of Medicine, Zhejiang University between 2015 and 2020. We propose a noise-specific deep learning model. The model was trained initially with 137 cases cropped into multiple-scaled datasets. Patch screening and dynamic label smoothing strategies are adopted to handle the histopathological liver image with noise annotation from the perspective of input and output. The model was then tested in an independent cohort of 455 cases with comparable tumor types and differentiations.

Results

Exhaustive experiments demonstrated that our two-step method achieved 87.81% pixel-level accuracy and 98.77% slide-level accuracy in the test dataset. Furthermore, the generalization performance of our model was also verified using The Cancer Genome Atlas dataset, which contains 157 HCC pathological slides, and achieved an accuracy of 87.90%.

Conclusions

The noise-specific histopathological classification model of HCC based on deep learning is effective for the dataset with noisy annotation, and it significantly improved the pixel-level accuracy of the regular convolutional neural network (CNN) model. Moreover, the model also has an advantage in detecting well-differentiated HCC and microvascular invasion.

Early gastrointestinal cancer: The application of artificial intelligence

Early gastrointestinal (GI) cancer has been the core of clinical endoscopic work. Its early detection and treatment are tightly associated with patients’ prognoses. As a novel technology, artificial intelligence has been improved and applied in the field of endoscopy. Studies on detection, diagnosis, risk, and prognosis evaluation of diseases in the GI tract have been in development, including precancerous lesions, adenoma, early GI cancers, and advanced GI cancers. In this review, research on esophagus, stomach, and colon was concluded, and associated with the process from precancerous lesions to early GI cancer, such as from Barrett’s esophagus to early esophageal cancer, from dysplasia to early gastric cancer, and from adenoma to early colonic cancer. A status quo of research on early GI cancers and artificial intelligence was provided.

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

Tremendous advances in artificial intelligence (AI) in medical image analysis have been achieved in recent years. The integration of AI is expected to cause a revolution in various areas of medicine, including gastrointestinal (GI) pathology. Currently, deep learning algorithms have shown promising benefits in areas of diagnostic histopathology, such as tumor identification, classification, prognosis prediction, and biomarker/genetic alteration prediction. While AI cannot substitute pathologists, carefully constructed AI applications may increase workforce productivity and diagnostic accuracy in pathology practice. Regardless 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 reimbursement. Thus, implying that there are still some obstacles to be overcome before AI applications can be safely and effectively implemented in real-life pathology practice. The 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 balance. The 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.

Artificial intelligence in gastroenterology and hepatology: Status and challenges

Originally proposed by John McCarthy in 1955, artificial intelligence (AI) has achieved a breakthrough and revolutionized the processing methods of clinical medicine with the increasing workloads of medical records and digital images. Doctors are paying attention to AI technologies for various diseases in the fields of gastroenterology and hepatology. This review will illustrate AI technology procedures for medical image analysis, including data processing, model establishment, and model validation. Furthermore, we will summarize AI applications in endoscopy, radiology, and pathology, such as detecting and evaluating lesions, facilitating treatment, and predicting treatment response and prognosis with excellent model performance. The current challenges for AI in clinical application include potential inherent bias in retrospective studies that requires larger samples for validation, ethics and legal concerns, and the incomprehensibility of the output results. Therefore, doctors and researchers should cooperate to address the current challenges and carry out further investigations to develop more accurate AI tools for improved clinical applications.

Clinicopathological characteristics of Epstein-Barr virus and microsatellite instability subtypes of early gastric neoplasms classified by the Japanese and the World Health Organization criteria

Gastric cancer is a heterogenous disease with different phenotypes, genotypes, and clinical outcomes, including sensitivity to treatments and prognoses. Recent medical advances have enabled the classification of this heterogenous disease into several groups and the consequent analysis of their clinicopathological characteristics. Gastric cancer associated with Epstein–Barr virus (EBV) and microsatellite‐unstable tumors are considered to be the two major subtypes as they are clearly defined by well‐established methodologies, such as in situ hybridization and polymerase chain reaction‐based analyses, respectively. However, discrepancies in the histological diagnosis of gastric neoplasms remain problematic, and international harmonization should be performed to improve our understanding of gastric carcinogenesis. We re‐evaluated Japanese cases of early gastric cancer according to the current World Health Organization (WHO) criteria and classified them into genomic subtypes based on microsatellite instability (MSI) and EBV positivity to determine the initial genetic events in gastric carcinogenesis. A total of 113 Japanese early gastric cancers (including low‐ and high‐grade dysplasias) treated with endoscopic resection over 5 years were archived in our hospital. A histological re‐evaluation according to the WHO criteria revealed 54 adenocarcinomas, which were divided into 6 EBV‐positive (11.1%), 7 MSI‐high (MSI‐H, 13.0%), and 41 microsatellite stable cases (75.9%). MSI‐H adenocarcinoma was confirmed by an immunohistochemistry assay of mismatch repair proteins. Programmed death‐ligand 1 immunostaining with two antibodies (E1L3N and SP263) was positive in tumor cells of one MSI‐H adenocarcinoma case (1/7, 14.3%). The proportion of stained cells was higher with clone SP263 than with E1L3N. Histologically, EBV‐positive carcinomas were poorly differentiated (83.8%), and MSI‐H cancers were frequent in well to moderately differentiated adenocarcinoma (85.7%), indicating that the EBV‐positive subtype presented with high‐grade morphology even when an early lesion. Our study indicates that the WHO criteria are useful for subdividing Japanese early gastric cancers, and this subdivision may be useful for comparative analysis of precursor lesions and early carcinoma.

Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review

BACKGROUND

The role of Artificial intelligence (AI) which is defined as the ability of computers to perform tasks that normally require human intelligence is constantly expanding. Medicine was slow to embrace AI. However, the role of AI in medicine is rapidly expanding and promises to revolutionize patient care in the coming years. In addition, it has the ability to democratize high level medical care and make it accessible to all parts of the world.

MAIN TEXT

Among specialties of medicine, some like radiology were relatively quick to adopt AI whereas others especially pathology (and surgical pathology in particular) are only just beginning to utilize AI. AI promises to play a major role in accurate diagnosis, prognosis and treatment of cancers. In this paper, the general principles of AI are defined first followed by a detailed discussion of its current role in medicine. In the second half of this comprehensive review, the current and future role of AI in surgical pathology is discussed in detail including an account of the practical difficulties involved and the fear of pathologists of being replaced by computer algorithms. A number of recent studies which demonstrate the usefulness of AI in the practice of surgical pathology are highlighted.

CONCLUSION

AI has the potential to transform the practice of surgical pathology by ensuring rapid and accurate results and enabling pathologists to focus on higher level diagnostic and consultative tasks such as integrating molecular, morphologic and clinical information to make accurate diagnosis in difficult cases, determine prognosis objectively and in this way contribute to personalized care.

Deep learning-based image-analysis algorithm for classification and quantification of multiple histopathological lesions in rat liver

Artificial intelligence (AI)-based image analysis is increasingly being used for preclinical safety-assessment studies in the pharmaceutical industry. In this paper, we present an AI-based solution for preclinical toxicology studies. We trained a set of algorithms to learn and quantify multiple typical histopathological findings in whole slide images (WSIs) of the livers of young Sprague Dawley rats by using a U-Net-based deep learning network. The trained algorithms were validated using 255 liver WSIs to detect, classify, and quantify seven types of histopathological findings (including vacuolation, bile duct hyperplasia, and single-cell necrosis) in the liver. The algorithms showed consistently good performance in detecting abnormal areas. Approximately 75% of all specimens could be classified as true positive or true negative. In general, findings with clear boundaries with the surrounding normal structures, such as vacuolation and single-cell necrosis, were accurately detected with high statistical scores. The results of quantitative analyses and classification of the diagnosis based on the threshold values between “no findings” and “abnormal findings” correlated well with diagnoses made by professional pathologists. However, the scores for findings ambiguous boundaries, such as hepatocellular hypertrophy, were poor. These results suggest that deep learning-based algorithms can detect, classify, and quantify multiple findings simultaneously on rat liver WSIs. Thus, it can be a useful supportive tool for a histopathological evaluation, especially for primary screening in rat toxicity studies.

Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives

Artificial intelligence (AI) using machine or deep learning algorithms is attracting increasing attention because of its more accurate image recognition ability and prediction performance than human-aid analyses. The application of AI models to gastrointestinal (GI) clinical oncology has been investigated for the past decade. AI has the capacity to automatically detect and diagnose GI tumors with similar diagnostic accuracy to expert clinicians. AI may also predict malignant potential, such as tumor histology, metastasis, patient survival, resistance to cancer treatments and the molecular biology of tumors, through image analyses of radiological or pathological imaging data using complex deep learning models beyond human cognition. The introduction of AI-assisted diagnostic systems into clinical settings is expected in the near future. However, limitations associated with the evaluation of GI tumors by AI models have yet to be resolved. Recent studies on AI-assisted diagnostic models of gastric and colorectal cancers in the endoscopic, pathological, and radiological fields were herein reviewed. The limitations and future perspectives for the application of AI systems in clinical settings have also been discussed. With the establishment of a multidisciplinary team containing AI experts in each medical institution and prospective studies, AI-assisted medical systems will become a promising tool for GI cancer.

A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies

PURPOSE

Gastric cancer remains the leading cause of cancer-related deaths in Northeast Asia. Population-based endoscopic screenings in the region have yielded successful results in early detection of gastric tumors. Endoscopic screening rates are continuously increasing, and there is a need for an automatic computerized diagnostic system to reduce the diagnostic burden. In this study, we developed an algorithm to classify gastric epithelial tumors automatically and assessed its performance in a large series of gastric biopsies and its benefits as an assistance tool.

EXPERIMENTAL DESIGN

Using 2,434 whole-slide images, we developed an algorithm based on convolutional neural networks to classify a gastric biopsy image into one of three categories: negative for dysplasia (NFD), tubular adenoma, or carcinoma. The performance of the algorithm was evaluated by using 7,440 biopsy specimens collected prospectively. The impact of algorithm-assisted diagnosis was assessed by six pathologists using 150 gastric biopsy cases.

RESULTS

Diagnostic performance evaluated by the AUROC curve in the prospective study was 0.9790 for two-tier classification: negative (NFD) versus positive (all cases except NFD). When limited to epithelial tumors, the sensitivity and specificity were 1.000 and 0.9749. Algorithm-assisted digital image viewer (DV) resulted in 47% reduction in review time per image compared with DV only and 58% decrease to microscopy.

CONCLUSIONS

Our algorithm has demonstrated high accuracy in classifying epithelial tumors and its benefits as an assistance tool, which can serve as a potential screening aid system in diagnosing gastric biopsy specimens.

Artificial intelligence-based detection of pharyngeal cancer using convolutional neural networks

OBJECTIVES

The prognosis for pharyngeal cancer is relatively poor. It is usually diagnosed in an advanced stage. Although the recent development of narrow-band imaging (NBI) and increased awareness among endoscopists have enabled detection of superficial pharyngeal cancer, these techniques are still not prevalent worldwide. Nevertheless, artificial intelligence (AI)-based deep learning has led to significant advancements in various medical fields. Here, we demonstrate the diagnostic ability of AI-based detection of pharyngeal cancer from endoscopic images in esophagogastroduodenoscopy.

METHODS

We retrospectively collected 5403 training images of pharyngeal cancer from 202 superficial cancers and 45 advanced cancers from the Cancer Institute Hospital, Tokyo, Japan. Using these images, we developed an AI-based diagnostic system with convolutional neural networks. We prepared 1912 validation images from 35 patients with 40 pharyngeal cancers and 40 patients without pharyngeal cancer to evaluate our system.

RESULTS

Our AI-based diagnostic system correctly detected all pharyngeal cancer lesions (40/40) in the patients with cancer, including three small lesions smaller than 10 mm. For each image, the AI-based system correctly detected pharyngeal cancers in images obtained via NBI with a sensitivity of 85.6%, much higher sensitivity than that for images obtained via white light imaging (70.1%). The novel diagnostic system took only 28 s to analyze 1912 validation images.

CONCLUSIONS

The novel AI-based diagnostic system detected pharyngeal cancer with high sensitivity. It could facilitate early detection, thereby leading to better prognosis and quality of life for patients with pharyngeal cancers in the near future.

Artificial intelligence in gastric cancer: Application and future perspectives

Gastric cancer is the fourth leading cause of cancer-related mortality across the globe, with a 5-year survival rate of less than 40%. In recent years, several applications of artificial intelligence (AI) have emerged in the gastric cancer field based on its efficient computational power and learning capacities, such as image-based diagnosis and prognosis prediction. AI-assisted diagnosis includes pathology, endoscopy, and computerized tomography, while researchers in the prognosis circle focus on recurrence, metastasis, and survival prediction. In this review, a comprehensive literature search was performed on articles published up to April 2020 from the databases of PubMed, Embase, Web of Science, and the Cochrane Library. Thereby the current status of AI-applications was systematically summarized in gastric cancer. Moreover, future directions that target this field were also analyzed to overcome the risk of overfitting AI models and enhance their accuracy as well as the applicability in clinical practice.

Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis

Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients. So far, computer-aided diagnosis has not been widely applied in pathological field yet as currently well-addressed tasks are only the tip of the iceberg. Whole slide image (WSI) classification is a quite challenging problem. First, the scarcity of annotations heavily impedes the pace of developing effective approaches. Pixelwise delineated annotations on WSIs are time consuming and tedious, which poses difficulties in building a large-scale training dataset. In addition, a variety of heterogeneous patterns of tumor existing in high magnification field are actually the major obstacle. Furthermore, a gigapixel scale WSI cannot be directly analyzed due to the immeasurable computational cost. How to design the weakly supervised learning methods to maximize the use of available WSI-level labels that can be readily obtained in clinical practice is quite appealing. To overcome these challenges, we present a weakly supervised approach in this article for fast and effective classification on the whole slide lung cancer images. Our method first takes advantage of a patch-based fully convolutional network (FCN) to retrieve discriminative blocks and provides representative deep features with high efficiency. Then, different context-aware block selection and feature aggregation strategies are explored to generate globally holistic WSI descriptor which is ultimately fed into a random forest (RF) classifier for the image-level prediction. To the best of our knowledge, this is the first study to exploit the potential of image-level labels along with some coarse annotations for weakly supervised learning. A large-scale lung cancer WSI dataset is constructed in this article for evaluation, which validates the effectiveness and feasibility of the proposed method. Extensive experiments demonstrate the superior performance of our method that surpasses the state-of-the-art approaches by a significant margin with an accuracy of 97.3%. In addition, our method also achieves the best performance on the public lung cancer WSIs dataset from The Cancer Genome Atlas (TCGA). We highlight that a small number of coarse annotations can contribute to further accuracy improvement. We believe that weakly supervised learning methods have great potential to assist pathologists in histology image diagnosis in the near future.

Gastric cancer

Gastric cancer is the fifth most common cancer and the third most common cause of cancer death globally. Risk factors for the condition include Helicobacter pylori infection, age, high salt intake, and diets low in fruit and vegetables. Gastric cancer is diagnosed histologically after endoscopic biopsy and staged using CT, endoscopic ultrasound, PET, and laparoscopy. It is a molecularly and phenotypically highly heterogeneous disease. The main treatment for early gastric cancer is endoscopic resection. Non-early operable gastric cancer is treated with surgery, which should include D2 lymphadenectomy (including lymph node stations in the perigastric mesentery and along the celiac arterial branches). Perioperative or adjuvant chemotherapy improves survival in patients with stage 1B or higher cancers. Advanced gastric cancer is treated with sequential lines of chemotherapy, starting with a platinum and fluoropyrimidine doublet in the first line; median survival is less than 1 year. Targeted therapies licensed to treat gastric cancer include trastuzumab (HER2-positive patients first line), ramucirumab (anti-angiogenic second line), and nivolumab or pembrolizumab (anti-PD-1 third line).

Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning

The early detection and accurate histopathological diagnosis of gastric cancer increase the chances of successful treatment. The worldwide shortage of pathologists offers a unique opportunity for the use of artificial intelligence assistance systems to alleviate the workload and increase diagnostic accuracy. Here, we report a clinically applicable system developed at the Chinese PLA General Hospital, China, using a deep convolutional neural network trained with 2,123 pixel-level annotated H&E-stained whole slide images. The model achieves a sensitivity near 100% and an average specificity of 80.6% on a real-world test dataset with 3,212 whole slide images digitalized by three scanners. We show that the system could aid pathologists in improving diagnostic accuracy and preventing misdiagnoses. Moreover, we demonstrate that our system performs robustly with 1,582 whole slide images from two other medical centres. Our study suggests the feasibility and benefits of using histopathological artificial intelligence assistance systems in routine practice scenarios.