Clinical Characteristics and Local Histopathological Modulators of Endometriosis and Its Progression

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(3), P. 1789 - 1789

Published: Feb. 1, 2024

Endometriosis (E) and adenomyosis (A) are associated with a wide spectrum of symptoms may present various histopathological transformations, such as the presence hyperplasia, atypia, malignant transformation occurring under influence local inflammatory, vascular hormonal factors by alteration tumor suppressor proteins inhibition cell apoptosis, an increased degree lesion proliferation. Material methods: This retrospective study included 243 patients from whom tissue E/A or normal control uterine was harvested stained histochemical classical immunohistochemical staining. We assessed symptomatology patients, structure ectopic epithelium neovascularization, hormone receptors, inflammatory cells oncoproteins involved in development. Atypical areas were analyzed using multiple immunolabeling techniques. Results: The cytokeratin (CK) CK7+/CK20− expression profile E foci differentiated them digestive metastases. neovascularization marker cluster differentiation (CD) 34+ increased, especially A foci. T:CD3+ lymphocytes, B:CD20+ CD68+ macrophages tryptase+ mast abundant, cases transformation, being markers proinflammatory microenvironment. In addition, we found significantly division index (Ki67+), inactivation genes p53, B-cell lymphoma 2 (BCL-2) Phosphatase tensin homolog (PTEN) E/A-transformed malignancy. Conclusions: Proinflammatory/vascular/hormonal changes trigger progression onset cellular atypia exacerbating symptoms, pain vaginal bleeding. These triggers represent future therapeutic targets.

Language: Английский

---

A systematic analysis of deep learning in genomics and histopathology for precision oncology

BMC Medical Genomics, Journal Year: 2024, Volume and Issue: 17(1)

Published: Feb. 5, 2024

Abstract Background Digitized histopathological tissue slides and genomics profiling data are available for many patients with solid tumors. In the last 5 years, Deep Learning (DL) has been broadly used to extract clinically actionable information biological knowledge from pathology genomic in cancer. addition, a number of recent studies have introduced multimodal DL models designed simultaneously process both images as inputs. By comparing patterns one modality those another, capable achieving higher performance compared their unimodal counterparts. However, application these methodologies across various tumor entities clinical scenarios lacks consistency. Methods Here, we present systematic survey academic literature 2010 November 2023, aiming quantify pathology, genomics, combined use types. After filtering 3048 publications, our search identified 534 relevant articles which then were evaluated by basic (diagnosis, grading, subtyping) advanced (mutation, drug response survival prediction) types, publication year addressed cancer tissue. Results Our analysis reveals predominant genomics. there is notable surge incorporation within domains. Furthermore, while applied primarily targets identification histology-specific individual tissues, more commonly pan-cancer context. Multimodal DL, on contrary, remains niche topic, evidenced limited focusing prognosis predictions. Conclusion summary, quantitative indicates that not only well-established role histopathology but also being successfully integrated into applications. considerable potential harnessing further tasks, such predicting response. Nevertheless, this review underlines need research bridge existing gaps fields.

Language: Английский

---

Advancing cancer diagnosis and treatment: integrating image analysis and AI algorithms for enhanced clinical practice

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(4)

Published: Jan. 25, 2025

Language: Английский

---

Advances in the understanding and therapeutic manipulation of cancer immune responsiveness: a Society for Immunotherapy of Cancer (SITC) review

Journal for ImmunoTherapy of Cancer, Journal Year: 2025, Volume and Issue: 13(1), P. e008876 - e008876

Published: Jan. 1, 2025

Cancer immunotherapy-including immune checkpoint inhibition (ICI) and adoptive cell therapy (ACT)-has become a standard, potentially curative treatment for subset of advanced solid liquid tumors. However, most patients with cancer do not benefit from the rapidly evolving improvements in understanding principal mechanisms determining responsiveness (CIR); including patient-specific genetically determined acquired factors, as well intrinsic biology. Though CIR is multifactorial, fundamental concepts are emerging that should be considered design novel therapeutic strategies related clinical studies. Recent advancements approaches to address limitations current treatments discussed here, specific focus on ICI ACT.

Language: Английский

---

Adversarial attacks and adversarial robustness in computational pathology

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Sept. 29, 2022

Abstract Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks. Hence, it is essential quantify mitigate this risk before widespread clinical use. Here, we show that convolutional neural networks (CNNs) highly susceptible white- black-box attacks clinically relevant weakly-supervised classification tasks. Adversarially robust training dual batch normalization (DBN) possible mitigation strategies but require precise knowledge of the type attack used inference. We demonstrate vision transformers (ViTs) perform equally well compared CNNs at baseline, orders magnitude more At a mechanistic level, associated with latent representation categories ViTs CNNs. Our results line previous theoretical studies provide empirical evidence learners computational pathology. This implies large-scale rollout models should rely on rather than CNN-based classifiers inherent protection against perturbation input data, especially

Language: Английский

---

A survey on artificial intelligence in histopathology image analysis

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2022, Volume and Issue: 12(6)

Published: July 27, 2022

Abstract The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed use computer systems analysis. Extensive research Artificial Intelligence (AI) with a huge progress been conducted resulting efficient, effective, robust algorithms for several applications including cancer diagnosis, prognosis, treatment. These offer highly accurate predictions but lack transparency, understandability, actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand mechanism behind decisions made by AI methods increase user trust also broaden clinical setting. From survey over 150 papers, we explore different that have applied contributed analysis workflow. We first address histopathological process. present an overview various learning‐based, XAI, actionable relevant deep learning imaging. evaluation XAI need ensure their reliability on field. This article is categorized under: Application Areas > Health Care

Language: Английский

---

Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer

Clinical Cancer Research, Journal Year: 2022, Volume and Issue: 29(2), P. 316 - 323

Published: Sept. 9, 2022

Immunotherapy by immune checkpoint inhibitors has become a standard treatment strategy for many types of solid tumors. However, the majority patients with cancer will not respond, and predicting response to this therapy is still challenge. Artificial intelligence (AI) methods can extract meaningful information from complex data, such as image data. In clinical routine, radiology or histopathology images are ubiquitously available. AI been used predict immunotherapy images, either directly indirectly via surrogate markers. While none these currently in academic commercial developments pointing toward potential adoption near future. Here, we summarize state art AI-based biomarkers based on images. We point out limitations, caveats, pitfalls, including biases, generalizability, explainability, which relevant researchers health care providers alike, outline key use cases new class predictive biomarkers.

Language: Английский

---

Towards label-efficient automatic diagnosis and analysis: a comprehensive survey of advanced deep learning-based weakly-supervised, semi-supervised and self-supervised techniques in histopathological image analysis

Physics in Medicine and Biology, Journal Year: 2022, Volume and Issue: 67(20), P. 20TR01 - 20TR01

Published: Sept. 9, 2022

Histopathological images contain abundant phenotypic information and pathological patterns, which are the gold standards for disease diagnosis essential prediction of patient prognosis treatment outcome. In recent years, computer-automated analysis techniques histopathological have been urgently required in clinical practice, deep learning methods represented by convolutional neural networks gradually become mainstream field digital pathology. However, obtaining large numbers fine-grained annotated data this is a very expensive difficult task, hinders further development traditional supervised algorithms based on data. More studies started to liberate from paradigm, most representative ones weakly paradigm weak annotation, semi-supervised limited self-supervised image representation learning. These new led wave automatic targeted at annotation efficiency. With survey over 130 papers, we present comprehensive systematic review latest learning, computational pathology both technical methodological perspectives. Finally, key challenges future trends these techniques.

Language: Английский

---

Artificial intelligence applications in prostate cancer

Prostate Cancer and Prostatic Diseases, Journal Year: 2023, Volume and Issue: 27(1), P. 37 - 45

Published: June 9, 2023

Language: Английский

---

Overcoming the challenges to implementation of artificial intelligence in pathology

JNCI Journal of the National Cancer Institute, Journal Year: 2023, Volume and Issue: 115(6), P. 608 - 612

Published: March 17, 2023

Pathologists worldwide are facing remarkable challenges with increasing workloads and lack of time to provide consistently high-quality patient care. The application artificial intelligence (AI) digital whole-slide images has the potential democratizing access expert pathology affordable biomarkers by supporting pathologists in provision timely accurate diagnosis as well oncologists directly extracting prognostic predictive from tissue slides. long-awaited adoption AI pathology, however, not materialized, transformation is happening at a much slower pace than that observed other fields (eg, radiology). Here, we critical summary developments computational last 10 years, outline key hurdles ways overcome them, perspective for AI-supported precision oncology future.

Language: Английский

---