Publications by authors named "R L Attanoos"

Article Synopsis
  • Globally, while people are living longer, many experience a decline in health due to age-related diseases, highlighting the need for better classification systems to address these issues.
  • A consensus meeting with 150 experts established criteria for identifying ageing-related pathologies, requiring a 70% agreement for approval among participants.
  • The agreed criteria focus on conditions that progress with age, contribute to functional decline, and are backed by human studies, setting a foundation for future classification and staging efforts.
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Occupational lung/thoracic diseases are a major global public health issue. They comprise a diverse spectrum of health conditions with complex pathology, most of which arise following chronic heavy workplace exposures to various mineral dusts, metal fumes, or following inhaled organic particulate reactions. Many occupational lung diseases could become irreversible; thus accurate diagnosis is mandatory to minimize dust exposure and consequently reduce damage to the respiratory system.

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The histopathologic distinction of lung adenocarcinoma (LADC) subtypes is subject to high interobserver variability, which can compromise the optimal assessment of patient prognosis. Therefore, this study developed convolutional neural networks capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the recently established LADC tumor grades. Consensus LADC histopathologic images were obtained from 17 expert pulmonary pathologists and one pathologist in training.

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Context.—: The accurate identification of different lung adenocarcinoma histologic subtypes is important for determining prognosis but can be challenging because of overlaps in the diagnostic features, leading to considerable interobserver variability.

Objective.

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