Several recent studies have indicated that cells at the invasive tumour margins often are different from cells within other parts of various human cancers. In this work, we have studied all squamous cell carcinomas of the floor of the mouth registered in Norway during the years 1963-1972 (N = 96). Borderline cases and cases given no treatment were excluded. Of the remaining 79 cases, biopsy specimens acceptable for histological grading were obtained from 61 patients. Only the most invasive margins of the tumours were histologically graded independently by two pathologists according to a multifactorial grading system. The results confirmed our previous findings that grading of invasive tumour margins is an independent prognostic factor in Cox's multivariate survival analysis (P less than 0.01). Inter-observer agreement was calculated by kappa statistics, and good agreement was obtained (kappa = 0.63). Neither agreement nor prognostic value was improved after calibration of the pathologists. Conventional Borders' grading of the whole biopsy had no prognostic value (P less than 0.38). We conclude that invasive cell grading may be of value for treatment planning of oral cancers, and that further studies of the deep, invasive parts of oral and other cancers are needed in order to obtain a better understanding of tumour cell invasion and metastasis.

Download full-text PDF

Source
http://dx.doi.org/10.1002/path.1711660409DOI Listing

Publication Analysis

Top Keywords

deep invasive
8
invasive margins
8
squamous cell
8
cell carcinomas
8
invasive tumour
8
tumour margins
8
oral cancers
8
invasive
6
grading
5
malignancy grading
4

Similar Publications

Introduction: The risk of mortality associated with cardiac arrhythmias is considerable, and their diagnosis presents significant challenges, often resulting in misdiagnosis. This situation highlights the necessity for an automated, efficient, and real-time detection method aimed at enhancing diagnostic accuracy and improving patient outcomes.

Methods: The present study is centered on the development of a portable deep learning model for the detection of arrhythmias via electrocardiogram (ECG) signals, referred to as CardioAttentionNet (CANet).

View Article and Find Full Text PDF

Meibomian gland alterations in allergic conjunctivitis: insights from a novel quantitative analysis algorithm.

Front Cell Dev Biol

January 2025

Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.

Purpose: To investigate the changes in meibomian gland (MG) structure in allergic conjunctivitis (AC) patients using an intelligent quantitative analysis algorithm and to explore the relationship between these changes and clinical parameters.

Methods: A total of 252 eyes from patients with AC and 200 eyes from normal controls were examined. Infrared meibography was performed using the non-contact mode of the Keratograph 5M.

View Article and Find Full Text PDF

Dysregulated mitophagy is essential for mitochondrial quality control within human cancers. However, identifying hub genes regulating mitophagy and developing mitophagy-based treatments to combat drug resistance remains challenging. Herein, BayeDEM (Bayesian-optimized Deep learning for identifying Essential genes of Mitophagy) was proposed for such a task.

View Article and Find Full Text PDF

Next generation bioelectronic medicine: making the case for non-invasive closed-loop autonomic neuromodulation.

Bioelectron Med

January 2025

SecondWave Systems Incorporated, Head Quarters, Minneapolis-Saint Paul, MN, 55104, USA.

The field of bioelectronic medicine has advanced rapidly from rudimentary electrical therapies to cutting-edge closed-loop systems that integrate real-time physiological monitoring with adaptive neuromodulation. Early innovations, such as cardiac pacemakers and deep brain stimulation, paved the way for these sophisticated technologies. This review traces the historical and technological progression of bioelectronic medicine, culminating in the emerging potential of closed-loop devices for multiple disorders of the brain and body.

View Article and Find Full Text PDF

Deep learning in surgical process Modeling: A systematic review of workflow recognition.

J Biomed Inform

January 2025

Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China; National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology), China. Electronic address:

Objective: The application of artificial intelligence (AI) in health care has led to a surge of interest in surgical process modeling (SPM). The objective of this study is to investigate the role of deep learning in recognizing surgical workflows and extracting reliable patterns from datasets used in minimally invasive surgery, thereby advancing the development of context-aware intelligent systems in endoscopic surgeries.

Methods: We conducted a comprehensive search of articles related to SPM from 2018 to April 2024 in the PubMed, Web of Science, Google Scholar, and IEEE Xplore databases.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!