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Wang Y, Zhou C, Ying L, Chan HP, Lee E, Chughtai A, Hadjiiski LM, Kazerooni EA. Enhancing Early Lung Cancer Diagnosis: Predicting Lung Nodule Progression in Follow-Up Low-Dose CT Scan with Deep Generative Model. Cancers (Basel) 2024; 16:2229. [PMID: 38927934 PMCID: PMC11201561 DOI: 10.3390/cancers16122229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 05/31/2024] [Accepted: 06/01/2024] [Indexed: 06/28/2024] Open
Abstract
Early diagnosis of lung cancer can significantly improve patient outcomes. We developed a Growth Predictive model based on the Wasserstein Generative Adversarial Network framework (GP-WGAN) to predict the nodule growth patterns in the follow-up LDCT scans. The GP-WGAN was trained with a training set (N = 776) containing 1121 pairs of nodule images with about 1-year intervals and deployed to an independent test set of 450 nodules on baseline LDCT scans to predict nodule images (GP-nodules) in their 1-year follow-up scans. The 450 GP-nodules were finally classified as malignant or benign by a lung cancer risk prediction (LCRP) model, achieving a test AUC of 0.827 ± 0.028, which was comparable to the AUC of 0.862 ± 0.028 achieved by the same LCRP model classifying real follow-up nodule images (p = 0.071). The net reclassification index yielded consistent outcomes (NRI = 0.04; p = 0.62). Other baseline methods, including Lung-RADS and the Brock model, achieved significantly lower performance (p < 0.05). The results demonstrated that the GP-nodules predicted by our GP-WGAN model achieved comparable performance with the nodules in the real follow-up scans for lung cancer diagnosis, indicating the potential to detect lung cancer earlier when coupled with accelerated clinical management versus the current approach of waiting until the next screening exam.
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Affiliation(s)
- Yifan Wang
- Department of Radiology, The University of Michigan Medical School, Ann Arbor, MI 48109-0904, USA; (Y.W.); (H.-P.C.); (E.L.); (A.C.); (L.M.H.); (E.A.K.)
- Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, MI 48109-2122, USA;
| | - Chuan Zhou
- Department of Radiology, The University of Michigan Medical School, Ann Arbor, MI 48109-0904, USA; (Y.W.); (H.-P.C.); (E.L.); (A.C.); (L.M.H.); (E.A.K.)
| | - Lei Ying
- Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, MI 48109-2122, USA;
| | - Heang-Ping Chan
- Department of Radiology, The University of Michigan Medical School, Ann Arbor, MI 48109-0904, USA; (Y.W.); (H.-P.C.); (E.L.); (A.C.); (L.M.H.); (E.A.K.)
| | - Elizabeth Lee
- Department of Radiology, The University of Michigan Medical School, Ann Arbor, MI 48109-0904, USA; (Y.W.); (H.-P.C.); (E.L.); (A.C.); (L.M.H.); (E.A.K.)
| | - Aamer Chughtai
- Department of Radiology, The University of Michigan Medical School, Ann Arbor, MI 48109-0904, USA; (Y.W.); (H.-P.C.); (E.L.); (A.C.); (L.M.H.); (E.A.K.)
- Diagnostic Radiology, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Lubomir M. Hadjiiski
- Department of Radiology, The University of Michigan Medical School, Ann Arbor, MI 48109-0904, USA; (Y.W.); (H.-P.C.); (E.L.); (A.C.); (L.M.H.); (E.A.K.)
| | - Ella A. Kazerooni
- Department of Radiology, The University of Michigan Medical School, Ann Arbor, MI 48109-0904, USA; (Y.W.); (H.-P.C.); (E.L.); (A.C.); (L.M.H.); (E.A.K.)
- Department of Internal Medicine, The University of Michigan Medical School, Ann Arbor, MI 48109-0904, USA
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