AIMC Topic: Neoplasm Grading

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Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology.

Archives of pathology & laboratory medicine
CONTEXT: Automated prostate cancer detection using machine learning technology has led to speculation that pathologists will soon be replaced by algorithms. This review covers the development of machine learning algorithms and their reported effectiv...

Deep CNNs for glioma grading on conventional MRIs: Performance analysis, challenges, and future directions.

Mathematical biosciences and engineering : MBE
The increasing global incidence of glioma tumors has raised significant healthcare concerns due to their high mortality rates. Traditionally, tumor diagnosis relies on visual analysis of medical imaging and invasive biopsies for precise grading. As a...

Significance of the cribriform morphology area ratio for biochemical recurrence in Gleason score 4 + 4 prostate cancer patients following robot-assisted radical prostatectomy.

Cancer medicine
BACKGROUND: In prostate cancer, histological cribriform patterns are categorized as Gleason pattern 4, and recent studies have indicated that their size and percentage are associated with the risk of biochemical recurrence (BCR). However, these studi...

Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier.

Technology in cancer research & treatment
INTRODUCTION: This study aimed to devise a diagnostic algorithm, termed the Refined Radiomics and Deep Learning Features-Guided CatBoost Classifier (RRDLC-Classifier), and evaluate its efficacy in predicting pathological high-grade patterns in patien...

Machine Learning and Radiomics in Gliomas.

Advances in experimental medicine and biology
The integration of machine learning (ML) and radiomics is emerging as a pivotal advancement in glioma research, offering novel insights into the diagnosis, prognosis, and treatment of these complex tumors. Radiomics involves the extraction of a multi...

An Artificial Intelligence-assisted Diagnostic System Improves Upper Urine Tract Cytology Diagnosis.

In vivo (Athens, Greece)
BACKGROUND/AIM: To evaluate efficacy of the AIxURO system, a deep learning-based artificial intelligence (AI) tool, in enhancing the accuracy and reliability of urine cytology for diagnosing upper urinary tract cancers.

Automated cutaneous squamous cell carcinoma grading using deep learning with transfer learning.

Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie
INTRODUCTION: Histological grading of cutaneous squamous cell carcinoma (cSCC) is crucial for prognosis and treatment decisions, but manual grading is subjective and time-consuming.

Design and Development of Hypertuned Deep learning Frameworks for Detection and Severity Grading of Brain Tumor using Medical Brain MR images.

Current medical imaging
BACKGROUND: Brain tumor is a grave illness causing worldwide fatalities. The current detection methods for brain tumors are manual, invasive, and rely on histopathological analysis. Determining the type of brain tumor after its detection relies on bi...

Development and validation of a clinical prediction model for glioma grade using machine learning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive.

[Stage at diagnosis of prostate cancer in an institutional hospital. Review and comparison of national and international data].

Revista medica de Chile
INTRODUCTION: Prostate cancer (PCa) is a disease with a high prevalence and incidence worldwide. Screening has pursued the early diagnosis of this disease to provide early treatment. We sought to characterize patients from a local hospital with respe...