AI Medical Compendium Journal:
Pathology, research and practice

Showing 1 to 10 of 25 articles

Integrating multi-omics data with artificial intelligence to decipher the role of tumor-infiltrating lymphocytes in tumor immunotherapy.

Pathology, research and practice
Tumor-infiltrating lymphocytes (TILs) are capable of recognizing tumor antigens, impacting tumor prognosis, predicting the efficacy of neoadjuvant therapies, contributing to the development of new cell-based immunotherapies, studying the tumor immune...

Exploring multi-instance learning in whole slide imaging: Current and future perspectives.

Pathology, research and practice
Whole slide images (WSI), due to their gigabyte-scale size and ultra-high resolution, play a significant role in diagnostic pathology. However, the enormous data size makes it difficult to directly input these images into image processing units (GPU)...

Clinical advantages in providing artificial intelligence-assisted prostate cancer diagnosis: A pilot study.

Pathology, research and practice
Prostate cancer is a prevalent male malignancy, with increasing incidence rates placing significant diagnostic burdens on pathology services worldwide. Artificial intelligence (AI) is emerging as a promising aid in enhancing diagnostic efficiency and...

Automated annotation of virtual dual stains to generate convolutional neural network for detecting cancer metastases in H&E-stained lymph nodes.

Pathology, research and practice
CONTEXT: Staging cancer patients is crucial and requires analyzing all removed lymph nodes microscopically for metastasis. For this pivotal task, convolutional neural networks (CNN) can reduce workload and improve diagnostic accuracy.

PESI-MS combined with AI to build a prediction model for lymph node metastasis of papillary thyroid cancer.

Pathology, research and practice
OBJECTIVE: Construct a prediction model for lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) using Probe Electrospray Ionization Mass Spectrometry (PESI - MS) combined with artificial intelligence (AI), to assist in the preoperative p...

Self-HER2Net: A generative self-supervised framework for HER2 classification in IHC histopathology of breast cancer.

Pathology, research and practice
Breast cancer is a significant global health concern, where precise identification of proteins like Human Epidermal Growth Factor Receptor 2 (HER2) in cancer cells via Immunohistochemistry (IHC) is pivotal for treatment decisions. HER2 overexpression...

Role of artificial intelligence -based machine learning model in predicting HER2/neu gene status in breast cancer.

Pathology, research and practice
Our study investigated the predictive efficacy of AI-based Machine Learning (ML) model for determining HER2 status in a population of 3424 breast cancer patients. Multivariate logistic regression analysis identified several independent variables that...

Multiomics evaluation and machine learning optimize molecular classification, prediction of prognosis and immunotherapy response for ovarian cancer.

Pathology, research and practice
BACKGROUND: Ovarian cancer (OC), owing to its substantial heterogeneity and high invasiveness, has historically been devoid of precise, individualized treatment options. This study aimed to establish integrated consensus subtypes of OC using differen...

Automated assessment of skin histological tissue structures by artificial intelligence in cutaneous melanoma.

Pathology, research and practice
BACKGROUND: Prognostic histopathological features such as mitosis in melanoma are excluded from the staging systems due to inter-observer variability and time constraints. While digital pathology offers artificial intelligence-driven solutions, exist...

Artificial intelligence challenge of discriminating cutaneous arteritis and polyarteritis nodosa based on hematoxylin-and-eosin images of skin biopsy specimens.

Pathology, research and practice
Diseases that develop necrotizing vasculitis of cutaneous muscular arteries include cutaneous arteritis (CA) and polyarteritis nodosa (PAN). It is difficult to distinguish them based on skin biopsy findings alone. This study demonstrated that artific...