1
|
Lucia F, Louis T, Cousin F, Bourbonne V, Visvikis D, Mievis C, Jansen N, Duysinx B, Le Pennec R, Nebbache M, Rehn M, Hamya M, Geier M, Salaun PY, Schick U, Hatt M, Coucke P, Hustinx R, Lovinfosse P. Multicentric development and evaluation of [ 18F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy. Eur J Nucl Med Mol Imaging 2024; 51:1097-1108. [PMID: 37987783 DOI: 10.1007/s00259-023-06510-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 11/03/2023] [Indexed: 11/22/2023]
Abstract
PURPOSE To develop machine learning models to predict regional and/or distant recurrence in patients with early-stage non-small cell lung cancer (ES-NSCLC) after stereotactic body radiation therapy (SBRT) using [18F]FDG PET/CT and CT radiomics combined with clinical and dosimetric parameters. METHODS We retrospectively collected 464 patients (60% for training and 40% for testing) from University Hospital of Liège and 63 patients from University Hospital of Brest (external testing set) with ES-NSCLC treated with SBRT between 2010 and 2020 and who had undergone pretreatment [18F]FDG PET/CT and planning CT. Radiomic features were extracted using the PyRadiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Clinical, radiomic, and combined models were trained and tested using a neural network approach to predict regional and/or distant recurrence. RESULTS In the training (n = 273) and testing sets (n = 191 and n = 63), the clinical model achieved moderate performances to predict regional and/or distant recurrence with C-statistics from 0.53 to 0.59 (95% CI, 0.41, 0.67). The radiomic (original_firstorder_Entropy, original_gldm_LowGrayLevelEmphasis and original_glcm_DifferenceAverage) model achieved higher predictive ability in the training set and kept the same performance in the testing sets, with C-statistics from 0.70 to 0.78 (95% CI, 0.63, 0.88) while the combined model performs moderately well with C-statistics from 0.50 to 0.62 (95% CI, 0.37, 0.69). CONCLUSION Radiomic features extracted from pre-SBRT analog and digital [18F]FDG PET/CT outperform clinical parameters in the prediction of regional and/or distant recurrence and to discuss an adjuvant systemic treatment in ES-NSCLC. Prospective validation of our models should now be carried out.
Collapse
Affiliation(s)
- François Lucia
- Radiation Oncology Department, University Hospital, Brest, France.
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium.
- Service de Radiothérapie, CHRU Morvan, 2 Avenue Foch, 29609 Cedex, Brest, France.
| | - Thomas Louis
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - François Cousin
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - Vincent Bourbonne
- Radiation Oncology Department, University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - Carole Mievis
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Nicolas Jansen
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | | | - Romain Le Pennec
- Nuclear Medicine Department, University Hospital, Brest, France
- GETBO, INSERM, UMR 1304, University of Brest, UBO, Brest, France
| | - Malik Nebbache
- Radiation Oncology Department, University Hospital, Brest, France
| | - Martin Rehn
- Radiation Oncology Department, University Hospital, Brest, France
| | - Mohamed Hamya
- Radiation Oncology Department, University Hospital, Brest, France
| | - Margaux Geier
- Medical Oncology Department, University Hospital, Brest, France
| | - Pierre-Yves Salaun
- Nuclear Medicine Department, University Hospital, Brest, France
- GETBO, INSERM, UMR 1304, University of Brest, UBO, Brest, France
| | - Ulrike Schick
- Radiation Oncology Department, University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Philippe Coucke
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| |
Collapse
|
2
|
Ko RB, Abelson JA, Fleischmann D, Louie JD, Hwang GL, Sze DY, Schüler E, Kielar KN, Maxim PG, Le QT, Hara WH, Diehn M, Kothary N, Loo BW. Pulmonary interstitial lymphography: A prospective trial with potential impact on stereotactic ablative radiotherapy planning for early-stage lung cancer. Radiother Oncol 2024; 191:110079. [PMID: 38163486 DOI: 10.1016/j.radonc.2023.110079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 12/29/2023] [Indexed: 01/03/2024]
Abstract
This prospective feasibility trial investigated pulmonary interstitial lymphography to identify thoracic primary nodal drainage (PND). A post-hoc analysis of nodal recurrences was compared with PND for patients with early-stage lung cancer; larger studies are needed to establish correlation. Exploratory PND-inclusive stereotactic ablative radiotherapy plans were assessed for dosimetric feasibility.
Collapse
Affiliation(s)
- Ryan B Ko
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA; Oakland University William Beaumont School of Medicine, Auburn Hills, MI, USA.
| | - Jonathan A Abelson
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA; Coastal Radiation Oncology, San Luis Obispo, CA, USA.
| | - Dominik Fleischmann
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - John D Louie
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Gloria L Hwang
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Daniel Y Sze
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Emil Schüler
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Kayla N Kielar
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA; Varian Medical Systems, Stanford, CA, USA
| | - Peter G Maxim
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiation Oncology, University of California, Irvine, CA, USA
| | - Quynh-Thu Le
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Wendy H Hara
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nishita Kothary
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
| | - Billy W Loo
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
| |
Collapse
|