A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules.

BACKGROUND

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).

Informations de copyright

Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests Dr Navani is supported by a Medical Research Council Clinical Academic Research Partnership (MR/T02481X/1). This work was partly undertaken at the University College London Hospitals/University College London that received a proportion of funding from the Department of Health’s National Institute for Health Research (NIHR) Biomedical Research Centre’s funding scheme (NN). Dr Navani reports honoraria for educational talks or advisory boards from Amgen, Astra Zeneca, Boehringer Ingelheim, Bristol Myers Squibb, Guardant Health, Janssen, Lilly, Merck Sharp & Dohme, Olympus, OncLive, PeerVoice, Pfizer, and Takeda. Dr Nair receives research grants from the Department of Health’s NIHR Biomedical Research Centre and GRAIL. He has received consulting fees from Aidence BV, Faculty Science Limited and MSD. He has received a travel bursary from Takeda. He participates on advisory boards for Aidence BV and Faculty Science Limited. He has leadership roles within the British Society of Thoracic Imaging, the British Lung Foundation and the NHS England Targeted Lung Health Checks Programme. Dr Lee is funded by the Royal Marsden NIHR BRC, and Royal Marsden Cancer charity. RL's institution receives compensation for time spent in a secondment role for the lung health check program and as a National Specialty Lead for the National Institute of Health and Care Research. He has received research funding from CRUK, Innovate UK (co-funded by GE Healthcare, Roche and Optellum), SBRI (co-applicant in grants with QURE.AI), RM Partners Cancer Alliance and NIHR (co-applicant in grants with Optellum). He has received honoraria from CRUK. Professor Devaraj is employed by and has stocks in Brainomix. He receives consulting fees from Roche and Boehringer Ingelheim. The other authors report no potential conflict of interest.