Background/aims: The aim of this research is to investigate the international and intra-observer differences in the macroscopic classification of early colorectal cancer between Japan and China.
Methodology: Color pictures of 9 cases of early colorectal cancer were distributed to 6 Japanese and 5 Chinese endoscopists. After reviewing the pictures, the doctors made their classificatory diagnoses independently and indicated their findings on which the diagnoses were based.
Results: There was some consistency in the classification of distinctly elevated lesions among all the Japanese and Chinese endoscopists. However, some elevated lesions classified as type II in Japan might be diagnosed as type I by Chinese endoscopists. For superficial lesions consisting of elevation plus central depression, IIa+IIc or IIc+IIa, were classified according to the ratio of elevation and depression. Although international difference is not significant, inter-observer differences still exist in classifying these lesions. In addition, the differences in laterally spreading tumor were mainly due to terminology.
Conclusions: Japanese and Chinese doctors share a lot of similarities in the classification of flat elevated lesions; however, both international and inter-observer differences still exist in the macroscopic classification for early CRC.
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J Cytol
November 2024
Department of Pathology, Mardin Training and Research Hospital, Mardin, Turkey.
Background: The Bethesda System for Reporting Thyroid Cytology (TBSRTC) recommended for the interpretation of needle aspiration cytology of the thyroid, is the most widely used worldwide. Studies have shown that the disagreement between observers, especially in the Bethesda III and IV diagnostic categories, is not insignificant at 10%-40%. In the TBSRTC 2023 version, some definitions were removed and simplified, and molecular pathology was proposed as a complement to cytopathology.
View Article and Find Full Text PDFRadiography (Lond)
December 2024
Newcastle Upon Tyne Hospitals NHS Foundation Trust, Northern Centre for Cancer Care, Newcastle Upon Tyne, United Kingdom; Newcastle University, Translational and Clinical Research Institute, Newcastle Upon Tyne, United Kingdom.
Purpose/objective: MR-only radiotherapy planning exploits the benefits of MRI soft-tissue delineation, whilst negating the registration inaccuracies caused by MRI CT fusion. Fiducial markers have conventionally been used in prostate radiotherapy to reduce on-treatment image matching variability. However, this is an invasive procedure for the patient, and presents technical difficulties in an MR-only pathway as fiducial markers are difficult to visualise on MRI.
View Article and Find Full Text PDFArch Orthop Trauma Surg
December 2024
Sitaram Bhartia Institute of Science and Research, New Delhi, India.
Purpose: Achieving precise postoperative alignment is critical for the long-term success of total knee arthroplasty (TKA). Long-leg standing radiograph (LLR) at 6 weeks post-op is the gold standard for assessing alignment, but its reliance on weight-bearing and positioning makes it less practical in the early postoperative period. Supine computed tomography scanogram (CTS) offers a potential alternative.
View Article and Find Full Text PDFBr J Radiol
December 2024
Rheumatology Unit, Department of Internal Medicine, Faculty of Medicine, Universiti Teknologi MARA, 47000, Sungai Buloh, Malaysia.
Objectives: This study explores the correlation between volunteer demographics with enthesis stiffness and intra and inter -observer agreements using shear wave elastography (SWE).
Methods: 98 healthy volunteers were recruited. SWE was performed on quadriceps, suprapatellar, infrapatellar, and Achilles entheses.
PLoS One
December 2024
Digital Environment Research Institute (DERI), Queen Mary University of London, London, United Kingdom.
Deep learning techniques are increasingly being used to classify medical imaging data with high accuracy. Despite this, due to often limited training data, these models can lack sufficient generalizability to predict unseen test data, produced in different domains, with comparable performance. This study focuses on thyroid histopathology image classification and investigates whether a Generative Adversarial Network [GAN], trained with just 156 patient samples, can produce high quality synthetic images to sufficiently augment training data and improve overall model generalizability.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!