Machine Learning Computational Model to predict Lung Cancer Using Electronic Medical Records - Vienna 2024

Extending from lung cancer screening to diagnostics and further

Chair(s): Georgia Hardavella, Jo?o Pedro Fernandes Cruz Oliveira Pereira

Session type: Oral presentation

Machine Learning Computational Model to predict Lung Cancer Using Electronic Medical Records

Dekel Shlomi

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Session content

Blood biomarker-aided cancer discrimination of screen-detected small lung nodule: The Cancer Signature Ensemble

Seung Hun Jang

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Poster

Abstract

Slides

Characteristics of lung nodules detected by low-dose CT scan: results from the French lung cancer screening study DEP KP80

Nicolas Storme

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Poster

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Two years experience with the Czech population pilot programme for lung cancer early detection

Martina Koziar Vasakova

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Poster

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Forceps and cryobiopsy of peripheral pulmonary lesions with 1.1 or 1.7 mm cryoprobes: An open-label, randomised trial

Thomas Gaisl

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Poster

Abstract

Slides

Diagnostic yield of ssRAB combined with CBCT imaging in comparison with CBCT-based navigation bronchoscopy using a passive catheter-based approach: preliminary results

Aniek Bruinen

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Poster

Abstract

Slides

Machine Learning Computational Model to predict Lung Cancer Using Electronic Medical Records

Dekel Shlomi

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Poster

Abstract

Slides

Two-year survival and disease recurrence after endosonography with or without confirmatory mediastinoscopy for resectable lung cancer (MEDIASTrial)

Jelle Bousema

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Poster

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Slides

The importance of incidental lung cancer nodules diagnosed in a population that does not meet the USPSTF criteria

Li Yang

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Poster

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Lung Cancer Screening in People With COPD: The Pamplona-IELCAP Experience

Maria Navamuel Andueza

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Poster

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Late Breaking Abstract - Identification and Significance of Single Nucleotide Variants in Large Cell Neuroendocrine Carcinoma of the Lung

Ilona Tietzova

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Poster

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Discussion and Q&A

CDN Connection Issue

The ERS Congress 2024, Vienna

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