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

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SwinCup: Cascaded swin transformer for histopathological structures segmentation in colorectal cancer

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 216, P. 119452 - 119452

Published: Dec. 30, 2022

Transformer models have recently become the dominant architecture in many computer vision tasks, including image classification, object detection, and segmentation. The main reason behind their success is ability to incorporate global context information into learning process. By utilising self-attention, recent advancements design enable consider long-range dependencies. In this paper, we propose a novel transformer, named Swin with Cascaded UPsampling (SwinCup) model for segmentation of histopathology images. We use hierarchical shifted windows as an encoder extract features. multi-scale feature extraction transformer enables attend different areas at scales. A cascaded up-sampling decoder used improve its aggregation. Experiments on GLAS CRAG colorectal cancer datasets were validate model, achieving average 0.90 (F1 score) surpassing state-of-the-art by (23%).

Language: Английский

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A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms

Journal of Imaging, Journal Year: 2023, Volume and Issue: 9(9), P. 173 - 173

Published: Aug. 27, 2023

Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing thyroid-related abnormalities. The aim of this research is improve the diagnosis accuracy thyroid abnormality detection models that can be utilized alleviate undue pressure on healthcare professionals. In research, we proposed deep learning, metaheuristics, and a MCDM algorithms-based framework detect abnormalities from ultrasound histopathological images. method uses three recently learning techniques (DeiT, Swin Transformer, Mixer-MLP) extract features image datasets. feature extraction are based Image Transformer MLP models. There large number redundant overfit classifiers reduce generalization capabilities classifiers. order avoid overfitting problem, six transformation (PCA, TSVD, FastICA, ISOMAP, LLE, UMP) analyzed dimensionality data. five different (LR, NB, SVC, KNN, RF) evaluated using 5-fold stratified cross-validation technique transformed dataset. Both datasets exhibit class imbalances hence, used evaluate performance. MEREC-TOPSIS for ranking at analysis stages. first stage, best classification chosen, whereas, second reduction wrapper selection mode. Two best-ranked further selected weighted average ensemble meta-heuristics FOX-optimization algorithm. PCA+FOX optimization-based + random forest model achieved highest TOPSIS score performed exceptionally well with an 99.13%, F2-score 98.82%, AUC-ROC 99.13% Similarly, 90.65%, 92.01%, 95.48% This study exploits combination novelty algorithms cancer capabilities. outperforms current state-of-the-art methods significantly medical professionals by reducing excessive burden fraternity.

Language: Английский

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Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis

Diagnostics, Journal Year: 2024, Volume and Issue: 14(4), P. 422 - 422

Published: Feb. 14, 2024

Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, advent deep learning techniques has showcased notable potential elevating precision efficiency data analysis. The proposed work introduces novel approach that harnesses power Transfer Learning to capitalize on knowledge gleaned from pre-trained models, adapting it nuanced landscape breast histopathology. Our model, Learning-based concatenated exhibits substantial performance enhancements compared traditional methodologies. Leveraging well-established pretrained models such as VGG-16, MobileNetV2, ResNet50, DenseNet121—each Convolutional Neural Network architecture designed for classification tasks—this study meticulously tunes hyperparameters optimize model performance. implementation is systematically benchmarked against individual classifiers data. Remarkably, our achieves an impressive training accuracy 98%. outcomes experiments underscore efficacy this four-level advancing By synergizing strengths transfer learning, holds augment diagnostic capabilities pathologists, thereby contributing more informed personalized planning individuals diagnosed with cancer. This research heralds promising stride toward leveraging cutting-edge technology refine understanding management cancer, marking advancement intersection artificial intelligence healthcare.

Language: Английский

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Histopathology image classification: highlighting the gap between manual analysis and AI automation

Frontiers in Oncology, Journal Year: 2024, Volume and Issue: 13

Published: Jan. 17, 2024

The field of histopathological image analysis has evolved significantly with the advent digital pathology, leading to development automated models capable classifying tissues and structures within diverse pathological images. Artificial intelligence algorithms, such as convolutional neural networks, have shown remarkable capabilities in pathology tasks, including tumor identification, metastasis detection, patient prognosis assessment. However, traditional manual methods generally low accuracy diagnosing colorectal cancer using This study investigates use AI classification analytics images histogram oriented gradients method. develops an AI-based architecture for images, aiming achieve high performance less complexity through specific parameters layers. In this study, we investigate complicated state classification, explicitly focusing on categorizing nine distinct tissue types. Our research used open-source multi-centered datasets that included records 100.000 non-overlapping from 86 patients training 7180 50 testing. compares two approaches, artificial intelligence-based algorithms machine learning models, automate classification. comprises primary tasks: binary distinguishing between normal tissues, multi-classification, encompassing types, adipose, background, debris, stroma, lymphocytes, mucus, smooth muscle, colon mucosa, tumor. findings show systems can 0.91 0.97 multi-class classifications. comparison, directed gradient features Random Forest classifier achieved rates 0.75 0.44 classifications, respectively. are generalizable, allowing them be integrated into histopathology diagnostics procedures improve diagnostic efficiency. CNN model outperforms existing techniques, demonstrating its potential precision effectiveness analysis. emphasizes importance maintaining data consistency applying normalization during preparation stage It particularly highlights assess

Language: Английский

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The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review

Diagnostics, Journal Year: 2024, Volume and Issue: 14(4), P. 388 - 388

Published: Feb. 10, 2024

Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that workflow combines DP and artificial intelligence (AI) applied histopathology potential value supporting diagnosis, treatment evaluation, prognosis prediction diseases. Here, we provide systematic review use this field hepatology. Based on PRISMA 2020 criteria, search PubMed, SCOPUS, Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted specifications objectives each study, AI tools used, results obtained. From 266 initial records identified, 25 eligible selected, mainly conducted human tissues. Most performed using whole-slide imaging systems for acquisition different machine learning deep methods image pre-processing, segmentation, feature extractions, classification. Of note, most selected demonstrated good performance as classifiers histological images compared pathologist annotations. Promising date bode well not-too-distant inclusion these techniques clinical practice.

Language: Английский

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Improving Colorectal Cancer Diagnosis Using MIRNet and InceptionV3 on Histopathological Images

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 321 - 334

Published: Jan. 1, 2025

Language: Английский

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Advanced brain tumor segmentation using DeepLabV3Plus with Xception encoder on a multi-class MR image dataset

Multimedia Tools and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 21, 2025

Language: Английский

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Artificial intelligence in cancer pathology: Applications, challenges, and future directions

CytoJournal, Journal Year: 2025, Volume and Issue: 22, P. 45 - 45

Published: April 19, 2025

The application of artificial intelligence (AI) in cancer pathology has shown significant potential to enhance diagnostic accuracy, streamline workflows, and support precision oncology. This review examines the current applications AI across various types, including breast, lung, prostate, colorectal cancer, where aids tissue classification, mutation detection, prognostic predictions. key technologies driving these advancements include machine learning, deep computer vision, which enable automated analysis histopathological images multi-modal data integration. Despite promising developments, challenges persist, ensuring privacy, improving model interpretability, meeting regulatory standards. Furthermore, this explores future directions AI-driven pathology, real-time diagnostics, explainable AI, global accessibility, emphasizing importance collaboration between pathologists. Addressing leveraging AI’s full could lead a more efficient, equitable, personalized approach care.

Language: Английский

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PSO-SLIC algorithm: A novel automated method for the generation of high-homogeneity slope units using DEM data

Geomorphology, Journal Year: 2024, Volume and Issue: 463, P. 109367 - 109367

Published: July 31, 2024

Language: Английский

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