For a long time the lung has been regarded as inaccessible to ultrasound. However, recent clinical studies have shown that this organ can be examined by this technique, which appears, in some situations, to be superior to thoracic radiography. The examination does not require special equipment and is possible using a combination of simple qualitative signs: lung sliding, the presence of B lines and the demonstration of the lung point. The lung sliding corresponds to the artefact produced by the movement of the two pleural layers, one against the other. The B lines indicate the presence of an interstitial syndrome. The presence of lung sliding and/or B lines has a negative predictive value of 100% and formally excludes a pneumothorax in the area where the probe has been applied. The presence of the lung point is pathognomonic of pneumothorax but the sensitivity is no more than 60%. Ultrasound is therefore a rapid and simple means of excluding a pneumothorax (lung sliding or B lines) and of confirming a pneumothorax when the lung point is visible. The question that remains is whether ultrasound can totally replace radiography in the management of this disorder.
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http://dx.doi.org/10.1016/j.rmr.2015.05.014 | DOI Listing |
NPJ Digit Med
December 2024
School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China.
Due to the large size and lack of fine-grained annotation, Whole Slide Images (WSIs) analysis is commonly approached as a Multiple Instance Learning (MIL) problem. However, previous studies only learn from training data, posing a stark contrast to how human clinicians teach each other and reason about histopathologic entities and factors. Here, we present a novel knowledge concept-based MIL framework, named ConcepPath, to fill this gap.
View Article and Find Full Text PDFJAMA Oncol
December 2024
Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Importance: Only a small fraction of patients with advanced non-small cell lung cancer (NSCLC) respond to immune checkpoint inhibitor (ICI) treatment. For optimal personalized NSCLC care, it is imperative to identify patients who are most likely to benefit from immunotherapy.
Objective: To develop a supervised deep learning-based ICI response prediction method; evaluate its performance alongside other known predictive biomarkers; and assess its association with clinical outcomes in patients with advanced NSCLC.
J Pathol Inform
December 2024
Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, Lisbon 1049-001, Portugal.
Whole slide images (WSI), obtained by high-resolution digital scanning of microscope slides at multiple scales, are the cornerstone of modern Digital Pathology. However, they represent a particular challenge to artificial intelligence (AI)-based/AI-mediated analysis because pathology labeling is typically done at slide-level, instead of tile-level. It is not just that medical diagnostics is recorded at the specimen level, the detection of oncogene mutation is also experimentally obtained, and recorded by initiatives like The Cancer Genome Atlas (TCGA), at the slide level.
View Article and Find Full Text PDFNPJ Precis Oncol
December 2024
College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
Spread through air spaces (STAS) is a distinct invasion pattern in lung cancer, crucial for prognosis assessment and guiding surgical decisions. Histopathology is the gold standard for STAS detection, yet traditional methods are subjective, time-consuming, and prone to misdiagnosis, limiting large-scale applications. We present VERN, an image analysis model utilizing a feature-interactive Siamese graph encoder to predict STAS from lung cancer histopathological images.
View Article and Find Full Text PDFNPJ Precis Oncol
December 2024
Department of Pathology and Laboratory Medicine, SUNY Upstate Medical University, Syracuse, NY, USA.
Immune checkpoint inhibitors (ICI) have become integral to treatment of non-small cell lung cancer (NSCLC). However, reliable biomarkers predictive of immunotherapy efficacy are limited. Here, we introduce HistoTME, a novel weakly supervised deep learning approach to infer the tumor microenvironment (TME) composition directly from histopathology images of NSCLC patients.
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