Artificial intelligence technologies empowering identification of novel diagnostic molecular markers in gastric cancer

Strategies for the comprehensive treatment of gastric cancer ovarian metastasis

The incidence of gastric cancer is currently increasing, with gastrectomy and D2 Lymphadenectomy being the primary treatments for resectable gastric cancer. For advanced or unresectable metastatic gastric cancer, systemic treatment options include chemotherapy, targeted therapy, or immunotherapy. Krukenberg tumours are types of metastatic tumours in the ovaries that exclusively occur in female patients and that originate from the stomach. Previously deemed an incurable stage of advanced disease, the prognosis of patients with ovarian metastases from gastric cancer depends on multiple factors, including tumour biology and treatment response. With the development of comprehensive cancer treatment options, the combination of surgery and chemotherapy has gradually become the primary treatment for patients with ovarian metastasis from gastric cancer. Individualised comprehensive treatment regimens are crucial for improving patient outcomes. This review summarises the comprehensive treatment strategies for ovarian metastasis from gastric cancer.

Artificial intelligence in cytopathological applications for cancer: a review of accuracy and analytic validity

BACKGROUND

Cytopathological examination serves as a tool for diagnosing solid tumors and hematologic malignancies. Artificial intelligence (AI)-assisted methods have been widely discussed in the literature for increasing sensitivity, specificity and accuracy in the diagnosis of cytopathological clinical samples. Many of these tools are also used in clinical practice. There is a growing body of literature describing the role of AI in clinical settings, particularly in improving diagnostic accuracy and providing predictive and prognostic insights.

METHODS

A comprehensive search for this systematic review was conducted using databases Google, PUBMED (n = 450) and Google Scholar (n = 1067) with the keywords "Artificial Intelligence" AND "cytopathological" and "fine needle aspiration" AND "Deep Learning" AND "Machine Learning" AND "Hematologic Disorders" AND "Lung Cancer" AND "Pap Smear" and "cervical cancer screening" AND "Thyroid Cancer" AND "Breast Cancer" and "Sensitivity" and "Specificity". The search focused on literature reviews and systematic reviews published in English language between 2020 and 2024. PRISMA guidelines were adhered to with studies included and excluded as depicted in a flowchart. 417 results were screened with 34 studies were chosen for this review.

RESULTS

In the screening of patients with cervical cancer, bone marrow and peripheral blood smears and benign and malignant lesions in the lung, AI-assisted methods, particularly machine learning and deep learning (a subset of machine learning) methods, were applied to cytopathological data. These methods yielded greater diagnostic accuracy, specificity and sensitivity and decreased interobserver variability. Data sets were collected for both training and validation. Human machine combined performance was also found to be comparable to standalone performance in comparison with medical performance as well.

CONCLUSIONS

The use of AI in the analysis of cytopathological samples in research and clinical settings is increasing, and the involvement of pathologists in AI workflows is becoming increasingly important.

Comparative investigation of neoadjuvant immunotherapy versus adjuvant immunotherapy in perioperative patients with cancer: a global-scale, cross-sectional, and large-sample informatics study

BACKGROUND

Neoadjuvant and adjuvant immunotherapies for cancer have evolved through a series of remarkable and critical research advances; however, addressing their similarities and differences is imperative in clinical practice. Therefore, this study aimed to examine their similarities and differences from the perspective of informatics analysis.

METHODS

This cross-sectional study retrospectively analyzed extensive relevant studies published between 2014 and 2023 using stringent search criteria, excluding nonpeer-reviewed and non-English documents. The main outcome variables are publication volume, citation volume, connection strength, occurrence frequency, relevance percentage, and development percentage. Furthermore, an integrated comparative analysis was conducted using unsupervised hierarchical clustering, spatiotemporal analysis, regression statistics, and Walktrap algorithm analysis.

RESULTS

This analysis included 1373 relevant studies. Advancements in neoadjuvant and adjuvant immunotherapies have been promising over the last decade, with an annual growth rate of 25.18 vs. 6.52% and global collaboration (International Co-authorships) of 19.93 vs. 19.84%. Respectively, five dominant research clusters were identified through unsupervised hierarchical clustering based on machine learning, among which Cluster 4 (Balance of neoadjuvant immunotherapy efficacy and safety) and Cluster 2 (Adjuvant immunotherapy clinical trials) [Average Publication Year (APY): 2021.70±0.70 vs. 2017.54±4.59] are emerging research populations. Burst and regression curve analyses uncovered domain pivotal research signatures, including microsatellite instability (R 2 =0.7500, P =0.0025) and biomarkers (R 2 =0.6505, P =0.0086) in neoadjuvant scenarios, and the tumor microenvironment (R 2 =0.5571, P =0.0209) in adjuvant scenarios. The Walktrap algorithm further revealed that 'neoadjuvant immunotherapy, nonsmall cell lung cancer (NSCLC), immune checkpoint inhibitors, melanoma' and 'adjuvant immunotherapy, melanoma, hepatocellular carcinoma, dendritic cells' (Relevance Percentage: 100 vs. 100%, Development Percentage: 37.5 vs. 17.1%) are extremely relevant to this field but remain underdeveloped, highlighting the need for further investigation.

CONCLUSION

This study identified pivotal research signatures and provided substantial predictions for neoadjuvant and adjuvant cancer immunotherapies. In addition, comprehensive quantitative comparisons revealed a notable shift in focus within this field, with neoadjuvant immunotherapy taking precedence over adjuvant immunotherapy after 2020; such a qualitative finding facilitate proper decision-making for subsequent research and mitigate the wastage of healthcare resources.

Comparative investigation of neoadjuvant immunotherapy versus adjuvant immunotherapy in perioperative patients with cancer: a global-scale, cross-sectional, and large-sample informatics study

Background:

Neoadjuvant and adjuvant immunotherapies for cancer have evolved through a series of remarkable and critical research advances; however, addressing their similarities and differences is imperative in clinical practice. Therefore, this study aimed to examine their similarities and differences from the perspective of informatics analysis.

Methods:

This cross-sectional study retrospectively analyzed extensive relevant studies published between 2014 and 2023 using stringent search criteria, excluding nonpeer-reviewed and non-English documents. The main outcome variables are publication volume, citation volume, connection strength, occurrence frequency, relevance percentage, and development percentage. Furthermore, an integrated comparative analysis was conducted using unsupervised hierarchical clustering, spatiotemporal analysis, regression statistics, and Walktrap algorithm analysis.

Results:

This analysis included 1373 relevant studies. Advancements in neoadjuvant and adjuvant immunotherapies have been promising over the last decade, with an annual growth rate of 25.18 vs. 6.52% and global collaboration (International Co-authorships) of 19.93 vs. 19.84%. Respectively, five dominant research clusters were identified through unsupervised hierarchical clustering based on machine learning, among which Cluster 4 (Balance of neoadjuvant immunotherapy efficacy and safety) and Cluster 2 (Adjuvant immunotherapy clinical trials) [Average Publication Year (APY): 2021.70±0.70 vs. 2017.54±4.59] are emerging research populations. Burst and regression curve analyses uncovered domain pivotal research signatures, including microsatellite instability (R2=0.7500, P=0.0025) and biomarkers (R2=0.6505, P=0.0086) in neoadjuvant scenarios, and the tumor microenvironment (R2=0.5571, P=0.0209) in adjuvant scenarios. The Walktrap algorithm further revealed that ‘neoadjuvant immunotherapy, nonsmall cell lung cancer (NSCLC), immune checkpoint inhibitors, melanoma’ and ‘adjuvant immunotherapy, melanoma, hepatocellular carcinoma, dendritic cells’ (Relevance Percentage: 100 vs. 100%, Development Percentage: 37.5 vs. 17.1%) are extremely relevant to this field but remain underdeveloped, highlighting the need for further investigation.

Conclusion:

This study identified pivotal research signatures and provided substantial predictions for neoadjuvant and adjuvant cancer immunotherapies. In addition, comprehensive quantitative comparisons revealed a notable shift in focus within this field, with neoadjuvant immunotherapy taking precedence over adjuvant immunotherapy after 2020; such a qualitative finding facilitate proper decision-making for subsequent research and mitigate the wastage of healthcare resources.

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.