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P1.04.18 Diagnosis of Sub-Cm Lung Nodules With Deep Learning Segmentation and Radiomics-Based Machine Learning Classifiers

Journal article reporting diagnosis of sub-centimeter lung nodules using deep learning segmentation and radiomics-based machine learning classifiers. Publisher listed as Elsevier BV with publication date 2025-10-01.

Original caption: Cardiac CT in 64-year-old woman with chronic cough and cardiac complaints showed a nodule in the left lower lobe. Dedicated chest CT confirmed persistence of the nodule and solitary nature. Axial CT-images in lung window setting a show a complex nodule with spiculation, pleural tags, irregular air bronchogram with bronchial interruption sign and ground glass component. Maximum intensity projection (MIP) images b better demonstrate convergence of the vessels towards the lung nod
Illustrative image: Original caption: Cardiac CT in 64-year-old woman with chronic cough and cardiac complaints showed a nodule in the left lower lobe. Dedicated chest CT confirmed persistence of the nodule and solitary nature. Axial CT-images in lung window setting a show a complex nodule with spiculation, pleural tags, irregular air bronchogram with bronchial interruption sign and ground glass component. Maximum intensity projection (MIP) images b better demonstrate convergence of the vessels towards the lung nod — Article authors: Annemie Snoeckx, Pieter Reyntiens, Damien Desbuquoit, Maarten J. Spinhoven, Paul E. Van Schil, Jan P. van Meerbeeck, Paul M. Parizel/Wikimedia Commons, CC BY 4.0

Categories: science-and-space, public-health, technology

Generated scores

Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.

Confidence
4/10
Geographic reach
1/10
Global importance
3/10
Impact magnitude
3/10
Positivity
6/10
Urgency
2/10

Why it matters

Describes machine-learning methods for imaging diagnosis of small lung nodules.

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