Development and Validation Of The Large Nodule Radiomics Predictive Vector (LN-RPV)

Published: 18 October 2022| Version 1 | DOI: 10.17632/rz72hs5dvg.1
Contributors:
Benjamin Hunter, Mitchell Chen, Prashanthi Ratnakumar, Esubalew Alemu, Andrew Logan, Kristofer Linton-Reid, Daniel Tong, Nishanthi Senthivel, Amyn Bhamani, Susannah Bloch, Samuel Kemp, Laura Boddy, Sejal Jain, Shafick Gareeboo, Bhavin Rawal, Simon Doran, Neal Navani, Arjun Nair, Catey Bunce, Stan Kaye, Matthew Blackledge, Eric Aboagye, Anand Devaraj, Richard Lee

Description

Large lung nodules are not currently stratified well by existing clinical guidelines. Here we provide the data needed to generate the 'large nodule radiomics predictive vector' as described in 'A Radiomics-Based Decision Support Tool Improves Lung Cancer Diagnosis In Combination With The Herder Score in Large Lung Nodules', published in EBioMedicine. This model is able to classify large lung nodules according to cancer risk. The radiomics features were extracted from manual nodule segmentations using TexLab 2.0. The 'Outcome' column refers to the cancer status of the nodule (0: benign, 1: malignant). Access to the source images or clinicodemographic data will be considered on request to Dr. Richard Lee (richard.lee@rmh.nhs.uk).

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Institutions

  • Imperial College London

Categories

Oncology, Lung, Early Diagnosis, Radiomics

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