The effects of vascularization on tumor development: A systematic review and meta-analysis of pre-clinical studies

PI-YOLO: dynamic sparse attention and lightweight convolutional based YOLO for vessel detection in pathological images

Vessel density within tumor tissues strongly correlates with tumor proliferation and serves as a critical marker for tumor grading. Recognition of vessel density by pathologists is subject to a strong inter-rater bias, thus limiting its prognostic value. There are many challenges in the task of object detection in pathological images, including complex image backgrounds, dense distribution of small targets, and insignificant differences between the features of the target to be detected and the image background. To address these problems and thus help physicians quantify blood vessels in pathology images, we propose Pathological Images-YOLO (PI-YOLO), an enhanced detection network based on YOLOv7. PI-YOLO incorporates the BiFormer attention mechanism, enhancing global feature extraction and accelerating processing for regions with subtle differences. Additionally, it introduces the CARAFE upsampling module, which optimizes feature utilization and information retention for small targets. Furthermore, the GSConv module improves the ELAN module, reducing model parameters and enhancing inference speed while preserving detection accuracy. Experimental results show that our proposed PI-YOLO network has higher detection accuracy compared to Faster-RCNN, SSD, RetinaNet, YOLOv5 network, and the latest YOLOv7 network, with a mAP value of 87.48%, which is 2.83% higher than the original model. We also validated the performance of this network on the ICPR 2012 mitotic dataset with an F1 value of 0.8678, outperforming other methods, demonstrating the advantages of our network in the task of target detection in complex pathology images.

Outlook and opportunities for engineered environments of breast cancer dormancy

Dormant, disseminated breast cancer cells resist treatment and may relapse into malignant metastases after decades of quiescence. Identifying how and why these dormant breast cancer cells are triggered into outgrowth is a key unsolved step in treating latent, metastatic breast cancer. However, our understanding of breast cancer dormancy in vivo is limited by technical challenges and ethical concerns with triggering the activation of dormant breast cancer. In vitro models avoid many of these challenges by simulating breast cancer dormancy and activation in well-controlled, bench-top conditions, creating opportunities for fundamental insights into breast cancer biology that complement what can be achieved through animal and clinical studies. In this review, we address clinical and preclinical approaches to treating breast cancer dormancy, how precisely controlled artificial environments reveal key interactions that regulate breast cancer dormancy, and how future generations of biomaterials could answer further questions about breast cancer dormancy.

Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology

The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine the prognosis and aggressiveness of the tumor. Traditional manual evaluation methods are time consuming and subject to inter-observer variability. Blood vessel detection is a perfect task for artificial intelligence, which is capable of rapid analyzing thousands of tissue structures in whole slide images. The development of computer vision solutions requires the segmentation of tissue regions, the extraction of features and the training of machine learning models. In this review, we focus on the methodologies employed by researchers to identify blood vessels and vascular invasion across a range of tumor localizations, including breast, lung, colon, brain, renal, pancreatic, gastric and oral cavity cancers. Contemporary models herald a new era of computational pathology in morphological diagnostics.

Realistic aspects behind the application of the rat model of chemically-induced mammary cancer: Practical guidelines to obtain the best results

Cancer is one of the most important public health problems worldwide. Despite the great contribution of in-vitro studies for biomedical research, animals are essential to study diseases' biopathology and diagnosis, and searching for new preventive and therapeutic strategies. Breast cancer is currently the most common cancer globally, accounting for 12.5% of all new annual cancer cases worldwide. Although the rat model of mammary cancer chemically-induced is widely used to study this disease, there is a lack of standardization in procedures for cancer induction, sample collection, and analysis. Therefore, it is important to provide a practical guide for researchers aiming to work with this model to make the analysis of results more uniform. Thus, in this review, we provide the researchers with a detailed step-by-step guide to implement a rat model of mammary cancer, based on our wide experience in this field, to obtain the best results, maximum throughput of each experiment, and easy comparison among researches.

Nestin-Expressing Cells in the Lung: The Bad and the Good Parts

Nestin is a member of the intermediate filament family, which is expressed in a variety of stem or progenitor cells as well as in several types of malignancies. Nestin might be involved in tissue homeostasis or repair, but its expression has also been associated with processes that lead to a poor prognosis in various types of cancer. In this article, we review the literature related to the effect of nestin expression in the lung. According to most of the reports in the literature, nestin expression in lung cancer leads to an aggressive phenotype and resistance to chemotherapy as well as radiation treatments due to the upregulation of phenomena such as cell proliferation, angiogenesis, and metastasis. Furthermore, nestin may be involved in the pathogenesis of some non-cancer-related lung diseases. On the other hand, evidence also indicates that nestin-positive cells may have a role in lung homeostasis and be capable of generating various types of lung tissues. More research is necessary to establish the true value of nestin expression as a prognostic factor and therapeutic target in lung cancer in addition to its usefulness in therapeutic approaches for pulmonary diseases.

The Challenging Melanoma Landscape: From Early Drug Discovery to Clinical Approval

Melanoma is recognized as the most dangerous type of skin cancer, with high mortality and resistance to currently used treatments. To overcome the limitations of the available therapeutic options, the discovery and development of new, more effective, and safer therapies is required. In this review, the different research steps involved in the process of antimelanoma drug evaluation and selection are explored, including information regarding in silico, in vitro, and in vivo experiments, as well as clinical trial phases. Details are given about the most used cell lines and assays to perform both two- and three-dimensional in vitro screening of drug candidates towards melanoma. For in vivo studies, murine models are, undoubtedly, the most widely used for assessing the therapeutic potential of new compounds and to study the underlying mechanisms of action. Here, the main melanoma murine models are described as well as other animal species. A section is dedicated to ongoing clinical studies, demonstrating the wide interest and successful efforts devoted to melanoma therapy, in particular at advanced stages of the disease, and a final section includes some considerations regarding approval for marketing by regulatory agencies. Overall, considerable commitment is being directed to the continuous development of optimized experimental models, important for the understanding of melanoma biology and for the evaluation and validation of novel therapeutic strategies.

Metronomic Anti-Cancer Therapy: A Multimodal Therapy Governed by the Tumor Microenvironment

Simple Summary

Metronomic chemotherapy with different mechanisms of action against cancer cells and their microenvironment represents an exceptional holistic cancer treatment. Each type of tumor has its own characteristics, including each individual tumor in each patient. Understanding the complexity of the dynamic interactions that take place between tumor and stromal cells and the microenvironment in tumor progression and metastases, as well as the response of the host and the tumor itself to anticancer therapy, will allow therapeutic actions with long-lasting effects to be implemented using metronomic regimens. This study aims to highlight the complexity of cellular interactions in the tumor microenvironment and summarize some of the preclinical and clinical results that explain the multimodality of metronomic therapy, which, together with its low toxicity, supports an inhibitory effect on the primary tumor and metastases. We also highlight the possible use of nano-therapeutic agents as good partners for metronomic chemotherapy.

Abstract

The concept of cancer as a systemic disease, and the therapeutic implications of this, has gained special relevance. This concept encompasses the interactions between tumor and stromal cells and their microenvironment in the complex setting of primary tumors and metastases. These factors determine cellular co-evolution in time and space, contribute to tumor progression, and could counteract therapeutic effects. Additionally, cancer therapies can induce cellular and molecular responses in the tumor and host that allow them to escape therapy and promote tumor progression. In this study, we describe the vascular network, tumor-infiltrated immune cells, and cancer-associated fibroblasts as sources of heterogeneity and plasticity in the tumor microenvironment, and their influence on cancer progression. We also discuss tumor and host responses to the chemotherapy regimen, at the maximum tolerated dose, mainly targeting cancer cells, and a multimodal metronomic chemotherapy approach targeting both cancer cells and their microenvironment. In a combination therapy context, metronomic chemotherapy exhibits antimetastatic efficacy with low toxicity but is not exempt from resistance mechanisms. As such, a better understanding of the interactions between the components of the tumor microenvironment could improve the selection of drug combinations and schedules, as well as the use of nano-therapeutic agents against certain malignancies.