Hypothesis-free deep survival learning applied to the tumour microenvironment in gastric cancer

Deep learning based digital pathology for predicting treatment response to first-line PD-1 blockade in advanced gastric cancer

BACKGROUND

Advanced unresectable gastric cancer (GC) patients were previously treated with chemotherapy alone as the first-line therapy. However, with the Food and Drug Administration's (FDA) 2022 approval of programmed cell death protein 1 (PD-1) inhibitor combined with chemotherapy as the first-li ne treatment for advanced unresectable GC, patients have significantly benefited. However, the significant costs and potential adverse effects necessitate precise patient selection. In recent years, the advent of deep learning (DL) has revolutionized the medical field, particularly in predicting tumor treatment responses. Our study utilizes DL to analyze pathological images, aiming to predict first-line PD-1 combined chemotherapy response for advanced-stage GC.

METHODS

In this multicenter retrospective analysis, Hematoxylin and Eosin (H&E)-stained slides were collected from advanced GC patients across four medical centers. Treatment response was evaluated according to iRECIST 1.1 criteria after a comprehensive first-line PD-1 immunotherapy combined with chemotherapy. Three DL models were employed in an ensemble approach to create the immune checkpoint inhibitors Response Score (ICIsRS) as a novel histopathological biomarker derived from Whole Slide Images (WSIs).

RESULTS

Analyzing 148,181 patches from 313 WSIs of 264 advanced GC patients, the ensemble model exhibited superior predictive accuracy, leading to the creation of ICIsNet. The model demonstrated robust performance across four testing datasets, achieving AUC values of 0.92, 0.95, 0.96, and 1 respectively. The boxplot, constructed from the ICIsRS, reveals statistically significant disparities between the well response and poor response (all p-values < = 0.001).

CONCLUSION

ICIsRS, a DL-derived biomarker from WSIs, effectively predicts advanced GC patients' responses to PD-1 combined chemotherapy, offering a novel approach for personalized treatment planning and allowing for more individualized and potentially effective treatment strategies based on a patient's unique response situations.

Closing the loop - the role of pathologists in digital and computational pathology research

An increasing number of manuscripts related to digital and computational pathology are being submitted to The Journal of Pathology: Clinical Research as part of the continuous evolution from digital imaging and algorithm-based digital pathology to computational pathology and artificial intelligence. However, despite these technological advances, tissue analysis still relies heavily on pathologists' annotations. There are three crucial elements to the pathologist's role during annotation tasks: granularity, time constraints, and responsibility for the interpretation of computational results. Granularity involves detailed annotations, including case level, regional, and cellular features; and integration of attributions from different sources. Time constraints due to pathologist shortages have led to the development of techniques to expedite annotation tasks from cell-level attributions up to so-called unsupervised learning. The impact of pathologists may seem diminished, but their role is crucial in providing ground truth and connecting pathological knowledge generation with computational advancements. Measures to display results back to pathologists and reflections about correctly applied diagnostic criteria are mandatory to maintain fidelity during human-machine interactions. Collaboration and iterative processes, such as human-in-the-loop machine learning are key for continuous improvement, ensuring the pathologist's involvement in evaluating computational results and closing the loop for clinical applicability. The journal is interested particularly in the clinical diagnostic application of computational pathology and invites submissions that address the issues raised in this editorial.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq to unravel the molecular mechanisms underlying the immune microenvironment in the development of intestinal-type gastric cancer

Intestinal-type gastric cancer (IGC) is the most frequent type of gastric cancer in high-incidence populations. The early stages of IGC growth successively include nonatrophic gastritis (NAG), chronic atrophic gastritis (CAG) and intestinal metaplasia (IM). However, the mechanisms of IGC development through these stages remain unclear. For this study, single-cell RNA-seq data related to IGC were downloaded from the GEO database, and immune cells of the tumor microenvironment (TME) were annotated using R software. Changes in the proportion of immune cells and altered cell-to-cell interactions were explored at different disease stages using R software, with a focus on plasma cells. Additionally, IGC samples from the TCGA database were used for immune cell infiltration analysis, and a Cox proportional risk regression model was constructed to identify possible prognostic genes. The results indicated that for precancerous lesions, interactions between immune cells were mainly dominated by chemokines to stimulate the infiltration and activation of immune cells. In tumors, intercellular movement of upregulated molecules and amplified signals were associated with the tumor necrosis factor family and immunosuppression to escape immune surveillance and promote tumor growth. Regarding prognostic analysis, IGLC3, IGLV1-44, IGKV1-16, IGHV3-21, IGLV1-51, and IGLV3-19 were found to be novel biomarkers for IGC. Our analysis of the IGC single-cell atlas together with bulk transcriptome data contributes to understanding TME heterogeneity at the molecular level during IGC development and provides insights for elucidating the mechanism of IGC and discovering novel targets for precise therapy.

Digital histopathological images of biopsy predict response to neoadjuvant chemotherapy for locally advanced gastric cancer

BACKGROUND

Neoadjuvant chemotherapy (NAC) has been recognized as an effective therapeutic option for locally advanced gastric cancer as it is expected to reduce tumor size, increase the resection rate, and improve overall survival. However, for patients who are not responsive to NAC, the best operation timing may be missed together with suffering from side effects. Therefore, it is paramount to differentiate potential respondents from non-respondents. Histopathological images contain rich and complex data that can be exploited to study cancers. We assessed the ability of a novel deep learning (DL)-based biomarker to predict pathological responses from images of hematoxylin and eosin (H&E)-stained tissue.

METHODS

In this multicentre observational study, H&E-stained biopsy sections of patients with gastric cancer were collected from four hospitals. All patients underwent NAC followed by gastrectomy. The Becker tumor regression grading (TRG) system was used to evaluate the pathologic chemotherapy response. Based on H&E-stained slides of biopsies, DL methods (Inception-V3, Xception, EfficientNet-B5, and ensemble CRSNet models) were employed to predict the pathological response by scoring the tumor tissue to obtain a histopathological biomarker, the chemotherapy response score (CRS). The predictive performance of the CRSNet was evaluated.

RESULTS

69,564 patches from 230 whole-slide images of 213 patients with gastric cancer were obtained in this study. Based on the F1 score and area under the curve (AUC), an optimal model was finally chosen, named the CRSNet model. Using the ensemble CRSNet model, the response score derived from H&E staining images reached an AUC of 0.936 in the internal test cohort and 0.923 in the external validation cohort for predicting pathological response. The CRS of major responders was significantly higher than that of minor responders in both internal and external test cohorts (both p < 0.001).

CONCLUSION

In this study, the proposed DL-based biomarker (CRSNet model) derived from histopathological images of the biopsy showed potential as a clinical aid for predicting the response to NAC in patients with locally advanced GC. Therefore, the CRSNet model provides a novel tool for the individualized management of locally advanced gastric cancer.

Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep learning

According to the World Health Organization, the proportion of the world's population over 60 years will approximately double by 2050. This progressive increase in the elderly population will lead to a dramatic growth of age-related diseases, resulting in tremendous pressure on the sustainability of healthcare systems globally. In this context, finding more efficient ways to address cancers, a set of diseases whose incidence is correlated with age, is of utmost importance. Prevention of cancers to decrease morbidity relies on the identification of precursor lesions before the onset of the disease, or at least diagnosis at an early stage. In this article, after briefly discussing some of the most prominent endoscopic approaches for gastric cancer diagnostics, we review relevant progress in three emerging technologies that have significant potential to play pivotal roles in next-generation endoscopy systems: biomimetic vision (with special focus on compound eye cameras), non-linear optical microscopies, and Deep Learning. Such systems are urgently needed to enhance the three major steps required for the successful diagnostics of gastrointestinal cancers: detection, characterization, and confirmation of suspicious lesions. In the final part, we discuss challenges that lie en route to translating these technologies to next-generation endoscopes that could enhance gastrointestinal imaging, and depict a possible configuration of a system capable of (i) biomimetic endoscopic vision enabling easier detection of lesions, (ii) label-free in vivo tissue characterization, and (iii) intelligently automated gastrointestinal cancer diagnostic.

Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology

BACKGROUND

The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding even the most complex heterogeneity in tumor tissue and mapping the spatial cartography of the tumor immunity allows the best and optimized selection of immune modulating agents to (re-)activate the patient's immune system and direct it against the individual cancer in the most effective way.

RECENT FINDINGS

Primary cancer and metastases maintain a high degree of plasticity to escape any immune surveillance and continue to evolve depending on many intrinsic and extrinsic factors In the field of immune-oncology (IO) immune modulating agents are recognized as practice changing therapeutic modalities. Recent studies have shown that an optimal and lasting efficacy of IO therapeutics depends on the understanding of the spatial communication network and functional context of immune and cancer cells within the tumor microenvironment. Artificial intelligence (AI) provides an insight into the immune-cancer-network through the visualization of very complex tumor and immune interactions in cancer tissue specimens and allows the computer-assisted development and clinical validation of such digital biomarker.

CONCLUSIONS

The successful implementation of AI-supported digital biomarker solutions guides the clinical selection of effective immune therapeutics based on the retrieval and visualization of spatial and contextual information from cancer tissue images and standardized data. As such, computational pathology (CP) turns into "precision pathology" delivering individual therapy response prediction. Precision Pathology does not only include digital and computational solutions but also high levels of standardized processes in the routine histopathology workflow and the use of mathematical tools to support clinical and diagnostic decisions as the basic principle of a "precision oncology".

Artificial intelligence assists precision medicine in cancer treatment

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.

Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology

Recent research has provided a wealth of data supporting the application of artificial intelligence (AI)-based applications in routine pathology practice. Indeed, it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors, increasing reliability, and improving workflow. In addition, the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior, course, and treatment in many types of tumors has also been noted. Regarding gastrointestinal system (GIS) cancers, the contribution of AI methods to pathological diagnosis has been investigated in many studies. On the other hand, studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few. For this purpose, the potential of AI models has been studied over a broad spectrum, from tumor subtyping to the identification of new digital biomarkers. The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated. Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers, a wide range of challenges still need to be solved, from laboratory infrastructure to improving the robustness of algorithms, before incorporating AI applications into real-life GIS pathology practice. This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology.

Artificial intelligence in gastric cancer: applications and challenges

Gastric cancer (GC) is one of the most common malignant tumors with high mortality. Accurate diagnosis and treatment decisions for GC rely heavily on human experts' careful judgments on medical images. However, the improvement of the accuracy is hindered by imaging conditions, limited experience, objective criteria, and inter-observer discrepancies. Recently, the developments of machine learning, especially deep-learning algorithms, have been facilitating computers to extract more information from data automatically. Researchers are exploring the far-reaching applications of artificial intelligence (AI) in various clinical practices, including GC. Herein, we aim to provide a broad framework to summarize current research on AI in GC. In the screening of GC, AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological confirmation. In the diagnosis of GC, AI can support tumor-node-metastasis (TNM) staging and subtype classification. For treatment decisions, AI can help with surgical margin determination and prognosis prediction. Meanwhile, current approaches are challenged by data scarcity and poor interpretability. To tackle these problems, more regulated data, unified processing procedures, and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.

Alotaibi S, Marzouk R, S. Salama A, Rizwanullah M, Zamani AS, Atta Abdelmageed A, I. Eldesouki M. Manta Ray Foraging Optimization Transfer Learning-Based Gastric Cancer Diagnosis and Classification on Endoscopic Images

Gastric cancer (GC) diagnoses using endoscopic images have gained significant attention in the healthcare sector. The recent advancements of computer vision (CV) and deep learning (DL) technologies pave the way for the design of automated GC diagnosis models. Therefore, this study develops a new Manta Ray Foraging Optimization Transfer Learning technique that is based on Gastric Cancer Diagnosis and Classification (MRFOTL-GCDC) using endoscopic images. For enhancing the quality of the endoscopic images, the presented MRFOTL-GCDC technique executes the Wiener filter (WF) to perform a noise removal process. In the presented MRFOTL-GCDC technique, MRFO with SqueezeNet model is used to derive the feature vectors. Since the trial-and-error hyperparameter tuning is a tedious process, the MRFO algorithm-based hyperparameter tuning results in enhanced classification results. Finally, the Elman Neural Network (ENN) model is utilized for the GC classification. To depict the enhanced performance of the presented MRFOTL-GCDC technique, a widespread simulation analysis is executed. The comparison study reported the improvement of the MRFOTL-GCDC technique for endoscopic image classification purposes with an improved accuracy of 99.25%.

Comprehensive Analysis of Stromal and Serum Markers in Gastric Cancer

A comprehensive analysis of the cell phenotype of the inflammatory infiltrate of the tumor stroma represents a promising area of molecular oncology. The study of not only soluble forms of various immunoregulatory molecules, but also their membrane-bound forms is also considered highly relevant. We performed a comprehensive analysis of tissue and circulating forms of the PD-1 and PD-L1 proteins, as well as macrophage and B-cell markers in the tumor stroma of gastric cancer, to assess their clinical and prognostic significance. The tumor and blood plasma samples from 63 gastric cancer patients were studied using ELISA and immunohistochemistry. Malignant gastric tumors were shown to be strongly infiltrated by B-cells, and their number was comparable to that of macrophages. For PU.1 expression, an association with tumor size was observed; i.e., larger tumors were characterized by fewer PU.1+ infiltrating cells (p = 0.005). No clinical significance was found for CD20 and CD163, but their numbers were higher at earlier stages of the disease and in the absence of metastases. It was also demonstrated that the PD-L1 content in tumor cells was not associated with the clinical and morphological characteristics of GC. At the same time, PD-L1 expression in tumor stromal cells was associated with the presence of distant metastases. The analysis of the prognostic significance of all the markers studied demonstrated that CD163 was statistically significantly associated with a poor prognosis for the disease (p = 0.019). In addition, PD-L1 expression in tumor cells tended to indicate a favorable prognosis (p = 0.122). The results obtained in this work indicate that the study of soluble and tissue markers of tumor stroma is promising in prognosticating the course of GC. The search for combinations of markers seems to be highly promising, with their comprehensive analysis capable of helping personalize advanced antitumor therapy.

Integrative Histology-Genomic Analysis Predicts Hepatocellular Carcinoma Prognosis Using Deep Learning

Cancer prognosis analysis is of essential interest in clinical practice. In order to explore the prognostic power of computational histopathology and genomics, this paper constructs a multi-modality prognostic model for survival prediction. We collected 346 patients diagnosed with hepatocellular carcinoma (HCC) from The Cancer Genome Atlas (TCGA), each patient has 1-3 whole slide images (WSIs) and an mRNA expression file. WSIs were processed by a multi-instance deep learning model to obtain the patient-level survival risk scores; mRNA expression data were processed by weighted gene co-expression network analysis (WGCNA), and the top hub genes of each module were extracted as risk factors. Information from two modalities was integrated by Cox proportional hazard model to predict patient outcomes. The overall survival predictions of the multi-modality model (Concordance index (C-index): 0.746, 95% confidence interval (CI): ±0.077) outperformed these based on histopathology risk score or hub genes, respectively. Furthermore, in the prediction of 1-year and 3-year survival, the area under curve of the model achieved 0.816 and 0.810. In conclusion, this paper provides an effective workflow for multi-modality prognosis of HCC, the integration of histopathology and genomic information has the potential to assist clinical prognosis management.

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.

Single-Cell Atlas of Lineage States, Tumor Microenvironment, and Subtype-Specific Expression Programs in Gastric Cancer

Gastric cancer heterogeneity represents a barrier to disease management. We generated a comprehensive single-cell atlas of gastric cancer (>200,000 cells) comprising 48 samples from 31 patients across clinical stages and histologic subtypes. We identified 34 distinct cell-lineage states including novel rare cell populations. Many lineage states exhibited distinct cancer-associated expression profiles, individually contributing to a combined tumor-wide molecular collage. We observed increased plasma cell proportions in diffuse-type tumors associated with epithelial-resident KLF2 and stage-wise accrual of cancer-associated fibroblast subpopulations marked by high INHBA and FAP coexpression. Single-cell comparisons between patient-derived organoids (PDO) and primary tumors highlighted inter- and intralineage similarities and differences, demarcating molecular boundaries of PDOs as experimental models. We complemented these findings by spatial transcriptomics, orthogonal validation in independent bulk RNA-sequencing cohorts, and functional demonstration using in vitro and in vivo models. Our results provide a high-resolution molecular resource of intra- and interpatient lineage states across distinct gastric cancer subtypes.

SIGNIFICANCE

We profiled gastric malignancies at single-cell resolution and identified increased plasma cell proportions as a novel feature of diffuse-type tumors. We also uncovered distinct cancer-associated fibroblast subtypes with INHBA-FAP-high cell populations as predictors of poor clinical prognosis. Our findings highlight potential origins of deregulated cell states in the gastric tumor ecosystem. This article is highlighted in the In This Issue feature, p. 587.

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.