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dc.contributor.authorHunter, B
dc.contributor.authorChen, M
dc.contributor.authorRatnakumar, P
dc.contributor.authorAlemu, E
dc.contributor.authorLogan, A
dc.contributor.authorLinton-Reid, K
dc.contributor.authorTong, D
dc.contributor.authorSenthivel, N
dc.contributor.authorBhamani, A
dc.contributor.authorBloch, S
dc.contributor.authorKemp, SV
dc.contributor.authorBoddy, L
dc.contributor.authorJain, S
dc.contributor.authorGareeboo, S
dc.contributor.authorRawal, B
dc.contributor.authorDoran, S
dc.contributor.authorNavani, N
dc.contributor.authorNair, A
dc.contributor.authorBunce, C
dc.contributor.authorKaye, S
dc.contributor.authorBlackledge, M
dc.contributor.authorAboagye, EO
dc.contributor.authorDevaraj, A
dc.contributor.authorLee, RW
dc.identifierARTN 104344
dc.identifier.citationEBioMedicine, 2022, 86 pp. 104344 -en_US
dc.description.abstractBACKGROUND: Large lung nodules (≥15 mm) have the highest risk of malignancy, and may exhibit important differences in phenotypic or clinical characteristics to their smaller counterparts. Existing risk models do not stratify large nodules well. We aimed to develop and validate an integrated segmentation and classification pipeline, incorporating deep-learning and traditional radiomics, to classify large lung nodules according to cancer risk. METHODS: 502 patients from five U.K. centres were recruited to the large-nodule arm of the retrospective LIBRA study between July 2020 and April 2022. 838 CT scans were used for model development, split into training and test sets (70% and 30% respectively). An nnUNet model was trained to automate lung nodule segmentation. A radiomics signature was developed to classify nodules according to malignancy risk. Performance of the radiomics model, termed the large-nodule radiomics predictive vector (LN-RPV), was compared to three radiologists and the Brock and Herder scores. FINDINGS: 499 patients had technically evaluable scans (mean age 69 ± 11, 257 men, 242 women). In the test set of 252 scans, the nnUNet achieved a DICE score of 0.86, and the LN-RPV achieved an AUC of 0.83 (95% CI 0.77-0.88) for malignancy classification. Performance was higher than the median radiologist (AUC 0.75 [95% CI 0.70-0.81], DeLong p = 0.03). LN-RPV was robust to auto-segmentation (ICC 0.94). For baseline solid nodules in the test set (117 patients), LN-RPV had an AUC of 0.87 (95% CI 0.80-0.93) compared to 0.67 (95% CI 0.55-0.76, DeLong p = 0.002) for the Brock score and 0.83 (95% CI 0.75-0.90, DeLong p = 0.4) for the Herder score. In the international external test set (n = 151), LN-RPV maintained an AUC of 0.75 (95% CI 0.63-0.85). 18 out of 22 (82%) malignant nodules in the Herder 10-70% category in the test set were identified as high risk by the decision-support tool, and may have been referred for earlier intervention. INTERPRETATION: The model accurately segments and classifies large lung nodules, and may improve upon existing clinical models. FUNDING: This project represents independent research funded by: 1) Royal Marsden Partners Cancer Alliance, 2) the Royal Marsden Cancer Charity, 3) the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, 4) the National Institute for Health Research (NIHR) Biomedical Research Centre at Imperial College London, 5) Cancer Research UK (C309/A31316).
dc.format.extent104344 -
dc.subjectDeep learning
dc.subjectEarly diagnosis
dc.subjectLung cancer
dc.subjectLung nodules
dc.subjectMachine learning
dc.subjectRetrospective Studies
dc.subjectLung Neoplasms
dc.subjectTomography, X-Ray Computed
dc.subjectPrecancerous Conditions
dc.titleA radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules.en_US
dc.typeJournal Article
rioxxterms.typeJournal Article/Reviewen_US
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Magnetic Resonance
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Computational Imaging
icr.researchteamMagnetic Resonanceen_US
icr.researchteamComputational Imagingen_US
dc.contributor.icrauthorDoran, Simon
dc.contributor.icrauthorBlackledge, Matthew
icr.provenanceDeposited by Mr Arek Surman on 2023-01-13. Deposit type is initial. No. of files: 1. Files: A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung no.pdf

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