Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy
| Autor: | Stefano Trebeschi, Zuhir Bodalal, Thierry N. Boellaard, Teresa M. Tareco Bucho, Silvia G. Drago, Ieva Kurilova, Adriana M. Calin-Vainak, Andrea Delli Pizzi, Mirte Muller, Karlijn Hummelink, Koen J. Hartemink, Thi Dan Linh Nguyen-Kim, Egbert F. Smit, Hugo J. W. L. Aerts, Regina G. H. Beets-Tan |
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| Přispěvatelé: | School Office GROW, RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, Beeldvorming, MUMC+: DA BV Research (9), RS: Carim - B06 Imaging, Faculteit FHML Centraal, University of Zurich, Beets-Tan, Regina G H |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Cancer Research
medicine.medical_specialty medicine.medical_treatment Atelectasis 610 Medicine & health lcsh:RC254-282 030218 nuclear medicine & medical imaging 03 medical and health sciences PREOPERATIVE PREDICTION 0302 clinical medicine medicine 1306 Cancer Research Lung cancer Original Research Lung non small cell lung cancer business.industry 10042 Clinic for Diagnostic and Interventional Radiology Area under the curve LYMPH-NODE METASTASIS Mediastinum Immunotherapy lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens medicine.disease artificial intelligence MODEL treatment monitoring medicine.anatomical_structure Oncology 030220 oncology & carcinogenesis Cohort 2730 Oncology Radiology immunotherapy Nivolumab business checkpoint inhibitors |
| Zdroj: | Frontiers in Oncology, 11:609054. Frontiers Media S.A. Trebeschi, S, Bodalal, Z, Boellaard, T N, Tareco Bucho, T M, Drago, S G, Kurilova, I, Calin-Vainak, A M, Delli Pizzi, A, Muller, M, Hummelink, K, Hartemink, K J, Nguyen-Kim, T D L, Smit, E F, Aerts, H J W L & Beets-Tan, R G H 2021, ' Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy ', Frontiers in Oncology, vol. 11, 609054 . https://doi.org/10.3389/fonc.2021.609054 Frontiers in Oncology Frontiers in Oncology, 11. FRONTIERS MEDIA SA Frontiers in Oncology, Vol 11 (2021) |
| ISSN: | 2234-943X |
| Popis: | BackgroundCheckpoint inhibitors provided sustained clinical benefit to metastatic lung cancer patients. Nonetheless, prognostic markers in metastatic settings are still under research. Imaging offers distinctive advantages, providing whole-body information non-invasively, while routinely available in most clinics. We hypothesized that more prognostic information can be extracted by employing artificial intelligence (AI) for treatment monitoring, superior to 2D tumor growth criteria.MethodsA cohort of 152 stage-IV non-small-cell lung cancer patients (NSCLC) (73 discovery, 79 test, 903CTs), who received nivolumab were retrospectively collected. We trained a neural network to identify morphological changes on chest CT acquired during patients’ follow-ups. A classifier was employed to link imaging features learned by the network with overall survival.ResultsOur results showed significant performance in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.69 (p < 0.01), up to AUC 0.75 (p < 0.01) in the first 3 to 5 months of treatment, and 0.67 AUC (p = 0.01) for durable clinical benefit (6 months progression-free survival). We found the AI-derived survival score to be independent of clinical, radiological, PDL1, and histopathological factors. Visual analysis of AI-generated prognostic heatmaps revealed relative prognostic importance of morphological nodal changes in the mediastinum, supraclavicular, and hilar regions, lung and bone metastases, as well as pleural effusions, atelectasis, and consolidations.ConclusionsOur results demonstrate that deep learning can quantify tumor- and non–tumor-related morphological changes important for prognostication on serial imaging. Further investigation should focus on the implementation of this technique beyond thoracic imaging. |
| Databáze: | OpenAIRE |
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