A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules.
A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules.
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DOI：
10.1016/j.ebiom.2022.104344
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发表时间：
2022-12
期刊：
影响因子：
11.1
通讯作者：
Lee, Richard W.
中科院分区：
文献类型：
--
作者：
Hunter, Benjamin;Chen, Mitchell;Ratnakumar, Prashanthi;Alemu, Esubalew;Logan, Andrew;Linton-Reid, Kristofer;Tong, Daniel;Senthivel, Nishanthi;Bhamani, Amyn;Bloch, Susannah;Kemp, Samuel, V;Boddy, Laura;Jain, Sejal;Gareeboo, Shafick;Rawal, Bhavin;Doran, Simon;Navani, Neal;Nair, Arjun;Bunce, Catey;Kaye, Stan;Blackledge, Matthew;Aboagye, Eric O.;Devaraj, Anand;Lee, Richard W.
大的肺结节（≥15 mm）具有最高的恶性风险，并且可能在表型或临床特征方面与较小的对应物存在重要差异。现有的风险模型不能很好地对大结核进行分层。我们的目标是开发和验证一个集成的分割和分类管道，结合深度学习和传统放射组学，根据癌症风险对大肺结节进行分类。502名患者来自5个英国在2020年7月至2022年4月期间，招募了10个中心参加回顾性LIBRA研究的大结节研究。838个CT扫描用于模型开发，分为训练集和测试集（分别为70%和30%）。训练nnUNet模型以自动化肺结节分割。放射组学特征被开发用于根据恶性风险对结节进行分类。将放射组学模型的性能（称为大结节放射组学预测向量（LN-RPV））与三名放射科医生和Brock和Herder评分进行比较。499例患者接受了技术上可评价的扫描（平均年龄69 ± 11岁，257例男性，242例女性）。在252次扫描的测试集中，nnUNet的DICE评分为0.86，LN-RPV的恶性分类AUC为0.83（95% CI 0.77-0.88）。性能高于中位放射科医生（AUC 0.75 [95% CI 0.70-0.81]，DeLong p = 0.03）。LN-RPV对自动分割具有鲁棒性（ICC 0.94）。对于测试集（117例患者）的基线实性结节，LN-RPV的AUC为0.87（95% CI 0.80-0.93），而Brock评分为0.67（95% CI 0.55-0.76，DeLong p = 0.002），Herder评分为0.83（95% CI 0.75-0.90，DeLong p = 0.4）。在国际外部试验集中（n = 151），LN-RPV的AUC保持在0.75（95% CI 0.63-0.85）。在测试集中，Herder 10-70%类别中的22个恶性结节中有18个（82%）被决策支持工具确定为高风险，并且可能已被转诊进行早期干预。该模型准确地分割和分类大的肺结节，并可以改善现有的临床模型。该项目代表了由以下机构资助的独立研究：1），2），3）皇家马斯登（NIHR）生物医学研究中心，4）（NIHR）生物医学研究中心，5）（C309/A31316）。
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. 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. 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. The model accurately segments and classifies large lung nodules, and may improve upon existing clinical models. This project represents independent research funded by: 1) , 2) the , 3) the (NIHR) Biomedical Research Centre at the Royal Marsden and , 4) the (NIHR) Biomedical Research Centre at , 5) (C309/A31316).
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DOI：
10.1183/16000617.0025-2017
发表时间：
2017-12-31
期刊：
European respiratory review : an official journal of the European Respiratory Society
影响因子：
--
作者：
通讯作者：
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影响因子：
5.2
作者：
Hunter B;Hindocha S;Lee RW
通讯作者：
Lee RW
影响因子：
9.8
作者：
Bakr S;Gevaert O;Echegaray S;Ayers K;Zhou M;Shafiq M;Zheng H;Benson JA;Zhang W;Leung ANC;Kadoch M;Hoang CD;Shrager J;Quon A;Rubin DL;Plevritis SK;Napel S
通讯作者：
Napel S
DOI：
10.1164/rccm.201903-0505oc
发表时间：
2020-07-15
影响因子：
24.7
作者：
Massion, Pierre P.;Antic, Sanja;Gleeson, Fergus
通讯作者：
Gleeson, Fergus
DOI：
10.1016/j.jtho.2016.07.002
发表时间：
2016-12
期刊：
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer
影响因子：
--
作者：
Hawkins S;Wang H;Liu Y;Garcia A;Stringfield O;Krewer H;Li Q;Cherezov D;Gatenby RA;Balagurunathan Y;Goldgof D;Schabath MB;Hall L;Gillies RJ
通讯作者：
Gillies RJ