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Han C, Qiu J, Bai L, Liu T, Chen J, Wang H, Dang J. Pneumonitis Risk After Chemoradiotherapy With and Without Immunotherapy in Patients With Locally Advanced Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis. Int J Radiat Oncol Biol Phys 2024; 119:1179-1207. [PMID: 38360117 DOI: 10.1016/j.ijrobp.2024.01.217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 12/28/2023] [Accepted: 01/28/2024] [Indexed: 02/17/2024]
Abstract
PURPOSE Chemoradiotherapy (CRT) combined with immune checkpoint inhibitors (ICIs) is the standard of care for patients with unresectable and locally advanced non-small cell lung cancer. This study aimed to determine whether the addition of ICIs to CRT is associated with an increased risk of pneumonitis. METHODS AND MATERIALS The PubMed, Embase, Cochrane Library, and Web of Science databases were searched for eligible studies published between January 1, 2015, and July 31, 2023. The outcome of interest was the incidence rate of pneumonitis. A random-effects model was used for statistical analysis. RESULTS A total of 185 studies with 24,527 patients were included. The pooled rate of grade ≥2 pneumonitis for CRT plus ICIs was significantly higher than that for CRT alone (29.6%; 95% CI, 25.7%-33.6% vs 20.2%; 95% CI, 17.7%-22.8%; P < .0001) but not that of grade ≥3 (5.7%; 95% CI, 4.8%-6.6% vs 5.6%; 95% CI, 4.7%-6.5%; P = .64) or grade 5 (0.1%; 95% CI, 0.0%-0.2% vs 0.3%; 95% CI, 0.1%-0.4%; P = .68). The results from the subgroup analyses of prospective studies, retrospective studies, Asian and non-Asian studies, concurrent CRT (cCRT), and durvalumab consolidation were comparable to the overall results. However, CRT or cCRT plus PD-1 inhibitors not only significantly increased the incidence of grade ≥2 but also that of grade ≥3 pneumonitis compared to CRT alone or cCRT plus PD-L1 inhibitors. CONCLUSIONS Compared with CRT alone, durvalumab consolidation after CRT appears to be associated with a higher incidence of moderate pneumonitis and CRT plus PD-1 inhibitors with an increased risk of severe pneumonitis. Nevertheless, these findings are based on observational studies and need to be validated in future large head-to-head studies.
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Affiliation(s)
- Chong Han
- Department of Radiation Oncology, First Hospital of China Medical University, Shenyang, China
| | - Jingping Qiu
- Department of Radiation Oncology, First Hospital of China Medical University, Shenyang, China
| | - Lu Bai
- Department of Radiation Oncology, First Hospital of China Medical University, Shenyang, China
| | - Tingting Liu
- Department of Radiation Oncology, Anshan Cancer Hospital, Anshan, China
| | - Jun Chen
- Department of Radiation Oncology, Shenyang Tenth People's Hospital, Shenyang, China
| | - He Wang
- Department of Radiation Oncology, First Hospital of China Medical University, Shenyang, China
| | - Jun Dang
- Department of Radiation Oncology, First Hospital of China Medical University, Shenyang, China.
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2
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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.
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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
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Bourbonne V, Morjani M, Pradier O, Hatt M, Jaouen V, Querellou S, Visvikis D, Lucia F, Schick U. PET/CT-Based Radiogenomics Supports KEAP1/NFE2L2 Pathway Targeting for Non-Small Cell Lung Cancer Treated with Curative Radiotherapy. J Nucl Med 2024:jnumed.123.266749. [PMID: 38360055 DOI: 10.2967/jnumed.123.266749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/02/2024] [Indexed: 02/17/2024] Open
Abstract
In lung cancer patients, radiotherapy is associated with a increased risk of local relapse (LR) when compared with surgery but with a preferable toxicity profile. The KEAP1/NFE2L2 mutational status (MutKEAP1/NFE2L2) is significantly correlated with LR in patients treated with radiotherapy but is rarely available. Prediction of MutKEAP1/NFE2L2 with noninvasive modalities could help to further personalize each therapeutic strategy. Methods: Based on a public cohort of 770 patients, model RNA (M-RNA) was first developed using continuous gene expression levels to predict MutKEAP1/NFE2L2, resulting in a binary output. The model PET/CT (M-PET/CT) was then built to predict M-RNA binary output using PET/CT-extracted radiomics features. M-PET/CT was validated on an external cohort of 151 patients treated with curative volumetric modulated arc radiotherapy. Each model was built, internally validated, and evaluated on a separate cohort using a multilayer perceptron network approach. Results: The M-RNA resulted in a C statistic of 0.82 in the testing cohort. With a training cohort of 101 patients, the retained M-PET/CT resulted in an area under the curve of 0.90 (P < 0.001). With a probability threshold of 20% applied to the testing cohort, M-PET/CT achieved a C statistic of 0.7. The same radiomics model was validated on the volumetric modulated arc radiotherapy cohort as patients were significantly stratified on the basis of their risk of LR with a hazard ratio of 2.61 (P = 0.02). Conclusion: Our approach enables the prediction of MutKEAP1/NFE2L2 using PET/CT-extracted radiomics features and efficiently classifies patients at risk of LR in an external cohort treated with radiotherapy.
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Affiliation(s)
- Vincent Bourbonne
- Department of Radiation Oncology, University Hospital, Brest, France;
- LaTIM UMR 1101 INSERM, University Brest, Brest, France
| | - Moncef Morjani
- Department of Radiation Oncology, University Hospital, Brest, France
| | - Olivier Pradier
- Department of Radiation Oncology, University Hospital, Brest, France
- LaTIM UMR 1101 INSERM, University Brest, Brest, France
| | - Mathieu Hatt
- LaTIM UMR 1101 INSERM, University Brest, Brest, France
| | - Vincent Jaouen
- LaTIM UMR 1101 INSERM, University Brest, Brest, France
- Institut Mines-Télécom Atlantique, Brest, France
| | - Solène Querellou
- Nuclear Medicine Department, University Hospital, Brest, France; and
- Groupe d'Étude de la Thrombose Occidentale GETBO (INSERM UMR 1304), Université de Bretagne Occidentale, Brest, France
| | | | - François Lucia
- Department of Radiation Oncology, University Hospital, Brest, France
- LaTIM UMR 1101 INSERM, University Brest, Brest, France
| | - Ulrike Schick
- Department of Radiation Oncology, University Hospital, Brest, France
- LaTIM UMR 1101 INSERM, University Brest, Brest, France
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McWilliam A, Palma G, Abravan A, Acosta O, Appelt A, Aznar M, Monti S, Onjukka E, Panettieri V, Placidi L, Rancati T, Vasquez Osorio E, Witte M, Cella L. Voxel-based analysis: Roadmap for clinical translation. Radiother Oncol 2023; 188:109868. [PMID: 37683811 DOI: 10.1016/j.radonc.2023.109868] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 07/11/2023] [Accepted: 08/20/2023] [Indexed: 09/10/2023]
Abstract
Voxel-based analysis (VBA) allows the full, 3-dimensional, dose distribution to be considered in radiotherapy outcome analysis. This provides new insights into anatomical variability of pathophysiology and radiosensitivity by removing the need for a priori definition of organs assumed to drive the dose response associated with patient outcomes. This approach may offer powerful biological insights demonstrating the heterogeneity of the radiobiology across tissues and potential associations of the radiotherapy dose with further factors. As this methodological approach becomes established, consideration needs to be given to translating VBA results to clinical implementation for patient benefit. Here, we present a comprehensive roadmap for VBA clinical translation. Technical validation needs to demonstrate robustness to methodology, where clinical validation must show generalisability to external datasets and link to a plausible pathophysiological hypothesis. Finally, clinical utility requires demonstration of potential benefit for patients in order for successful translation to be feasible. For each step on the roadmap, key considerations are discussed and recommendations provided for best practice.
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Affiliation(s)
- Alan McWilliam
- The Division of Cancer Sciences, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK.
| | - Giuseppe Palma
- Institute of Nanotechnology, National Research Council, Lecce, Italy.
| | - Azadeh Abravan
- The Division of Cancer Sciences, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK
| | - Oscar Acosta
- University Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000, Rennes, France
| | - Ane Appelt
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Marianne Aznar
- The Division of Cancer Sciences, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK
| | - Serena Monti
- Institute of Biostructures and Bioimaging, National Research Council, Naples, Italy
| | - Eva Onjukka
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Sweden
| | - Vanessa Panettieri
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; Central Clinical School, Monash University, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Australia
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Tiziana Rancati
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Eliana Vasquez Osorio
- The Division of Cancer Sciences, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK
| | - Marnix Witte
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Laura Cella
- Institute of Biostructures and Bioimaging, National Research Council, Naples, Italy
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Liu T, Li S, Ding S, Qiu J, Ren C, Chen J, Wang H, Wang X, Li G, He Z, Dang J. Comparison of post-chemoradiotherapy pneumonitis between Asian and non-Asian patients with locally advanced non-small cell lung cancer: a systematic review and meta-analysis. EClinicalMedicine 2023; 64:102246. [PMID: 37781162 PMCID: PMC10539643 DOI: 10.1016/j.eclinm.2023.102246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 08/28/2023] [Accepted: 09/13/2023] [Indexed: 10/03/2023] Open
Abstract
Background Pneumonitis is a common complication for patients with locally advanced non-small cell lung cancer undergoing definitive chemoradiotherapy (CRT). It remains unclear whether there is ethnic difference in the incidence of post-CRT pneumonitis. Methods PubMed, Embase, Cochrane Library, and Web of Science were searched for eligible studies from January 1, 2000 to April 30, 2023. The outcomes of interest were incidence rates of pneumonitis. The random-effect model was used for statistical analysis. This meta-analysis was registered with PROSPERO (CRD42023416490). Findings A total of 248 studies involving 28,267 patients were included. Among studies of CRT without immunotherapy, the pooled rates of pneumonitis for Asian patients were significantly higher than that for non-Asian patients (all grade: 66.8%, 95% CI: 59.2%-73.9% vs. 28.1%, 95% CI: 20.4%-36.4%; P < 0.0001; grade ≥2: 25.1%, 95% CI: 22.9%-27.3% vs. 14.9%, 95% CI: 12.0%-18.0%; P < 0.0001; grade ≥3: 6.5%, 95% CI: 5.6%-7.3% vs. 4.6%, 95% CI: 3.4%-5.9%; P = 0.015; grade 5: 0.6%, 95% CI: 0.3%-0.9% vs. 0.1%, 95% CI: 0.0%-0.2%; P < 0.0001). Regarding studies of CRT plus immunotherapy, Asian patients had higher rates of all-grade (74.8%, 95% CI: 63.7%-84.5% vs. 34.3%, 95% CI: 28.7%-40.2%; P < 0.0001) and grade ≥2 (34.0%, 95% CI: 30.7%-37.3% vs. 24.6%, 95% CI: 19.9%-29.3%; P = 0.001) pneumonitis than non-Asian patients, but with no significant differences in the rates of grade ≥3 and grade 5 pneumonitis. Results from subgroup analyses were generally similar to that from the all studies. In addition, the pooled median/mean of lung volume receiving ≥20 Gy and mean lung dose were relatively low in Asian studies compared to that in non-Asian studies. Interpretation Asian patients are likely to have a higher incidence of pneumonitis than non-Asian patients, which appears to be due to the poor tolerance of lung to radiation. Nevertheless, these findings are based on observational studies and with significant heterogeneity, and need to be validated in future large prospective studies focusing on the subject. Funding None.
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Affiliation(s)
- Tingting Liu
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
- Department of Radiation Oncology, Anshan Cancer Hospital, Anshan, China
| | - Sihan Li
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Silu Ding
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Jingping Qiu
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Chengbo Ren
- Department of Radiation Oncology, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, China
| | - Jun Chen
- Department of Radiation Oncology, Shenyang Tenth People's Hospital, Shenyang, China
| | - He Wang
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Xiaoling Wang
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Guang Li
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Zheng He
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Jun Dang
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
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Sosa-Marrero C, Acosta O, Pasquier D, Thariat J, Delpon G, Fiorino C, Rancatti T, Malard O, Foray N, de Crevoisier R. Voxel-wise analysis: A powerful tool to predict radio-induced toxicity and potentially perform personalised planning in radiotherapy. Cancer Radiother 2023; 27:638-642. [PMID: 37517974 DOI: 10.1016/j.canrad.2023.06.024] [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: 06/22/2023] [Accepted: 06/27/2023] [Indexed: 08/01/2023]
Abstract
Dose - volume histograms have been historically used to study the relationship between the planned radiation dose and healthy tissue damage. However, this approach considers neither spatial information nor heterogenous radiosensitivity within organs at risk, depending on the tissue. Recently, voxel-wise analyses have emerged in the literature as powerful tools to fully exploit three-dimensional information from the planned dose distribution. They allow to identify anatomical subregions of one or several organs in which the irradiation dose is associated with a given toxicity. These methods rely on an accurate anatomical alignment, usually obtained by means of a non-rigid registration. Once the different anatomies are spatially normalised, correlations between the three-dimensional dose and a given toxicity can be explored voxel-wise. Parametric or non-parametric statistical tests can be performed on every voxel to identify the voxels in which the dose is significantly different between patients presenting or not toxicity. Several anatomical subregions associated with genitourinary, gastrointestinal, cardiac, pulmonary or haematological toxicity have already been identified in the literature for prostate, head and neck or thorax irradiation. Voxel-wise analysis appears therefore first particularly interesting to increase toxicity prediction capability by identifying specific subregions in the organs at risk whose irradiation is highly predictive of specific toxicity. The second interest is potentially to decrease the radio-induced toxicity by limiting the dose in the predictive subregions, while not decreasing the dose in the target volume. Limitations of the approach have been pointed out.
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Affiliation(s)
- C Sosa-Marrero
- Université de Rennes, CLCC Eugène-Marquis, Inserm, LTSI - UMR 1099, 35000 Rennes, France
| | - O Acosta
- Université de Rennes, CLCC Eugène-Marquis, Inserm, LTSI - UMR 1099, 35000 Rennes, France
| | - D Pasquier
- Radiotherapy Department, centre Oscar-Lambret, 59000 Lille, France; Université de Lille, CNRS, école centrale de Lille, Cristal UMR 9189, Lille, France
| | - J Thariat
- Department of Radiation Oncology, centre François-Baclesse, 14000 Caen, France
| | - G Delpon
- Medical physics department, institut de cancérologie de l'Ouest, IMT Atlantique, Nantes université, CNRS/IN2P3, Subatech, Nantes, France
| | - C Fiorino
- Medical Physics, San Raffaele Scientific Institute, Via Olgettina 690, 20132 Milan, Italy
| | - T Rancatti
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - O Malard
- Service de chirurgie oto-rhinolaryngologique (ORL) et chirurgie cervicofaciale, Hôtel-Dieu, CHU de Nantes, Nantes, France
| | - N Foray
- Centre Léon-Bérard, Inserm U1296 "Radiation: Defense/Health/Environment", 69008 Lyon, France
| | - R de Crevoisier
- Université de Rennes, CLCC Eugène-Marquis, Inserm, LTSI - UMR 1099, 35000 Rennes, France; Département de radiothérapie, centre Eugène-Marquis, 35000 Rennes, France.
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7
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Lucia F, Bourbonne V, Pleyers C, Dupré PF, Miranda O, Visvikis D, Pradier O, Abgral R, Mervoyer A, Classe JM, Rousseau C, Vos W, Hermesse J, Gennigens C, De Cuypere M, Kridelka F, Schick U, Hatt M, Hustinx R, Lovinfosse P. Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer. Eur J Nucl Med Mol Imaging 2023; 50:2514-2528. [PMID: 36892667 DOI: 10.1007/s00259-023-06180-w] [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: 11/04/2022] [Accepted: 02/27/2023] [Indexed: 03/10/2023]
Abstract
PURPOSE To develop machine learning models to predict para-aortic lymph node (PALN) involvement in patients with locally advanced cervical cancer (LACC) before chemoradiotherapy (CRT) using 18F-FDG PET/CT and MRI radiomics combined with clinical parameters. METHODS We retrospectively collected 178 patients (60% for training and 40% for testing) in 2 centers and 61 patients corresponding to 2 further external testing cohorts with LACC between 2010 to 2022 and who had undergone pretreatment analog or digital 18F-FDG PET/CT, pelvic MRI and surgical PALN staging. Only primary tumor volumes were delineated. Radiomics features were extracted using the Radiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Different prediction models were trained using a neural network approach with either clinical, radiomics or combined models. They were then evaluated on the testing and external validation sets and compared. RESULTS In the training set (n = 102), the clinical model achieved a good prediction of the risk of PALN involvement with a C-statistic of 0.80 (95% CI 0.71, 0.87). However, it performed in the testing (n = 76) and external testing sets (n = 30 and n = 31) with C-statistics of only 0.57 to 0.67 (95% CI 0.36, 0.83). The ComBat-radiomic (GLDZM_HISDE_PET_FBN64 and Shape_maxDiameter2D3_PET_FBW0.25) and ComBat-combined (FIGO 2018 and same radiomics features) models achieved very high predictive ability in the training set and both models kept the same performance in the testing sets, with C-statistics from 0.88 to 0.96 (95% CI 0.76, 1.00) and 0.85 to 0.92 (95% CI 0.75, 0.99), respectively. CONCLUSIONS Radiomic features extracted from pre-CRT analog and digital 18F-FDG PET/CT outperform clinical parameters in the decision to perform a para-aortic node staging or an extended field irradiation to PALN. Prospective validation of our models should now be carried out.
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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.
| | - Vincent Bourbonne
- Radiation Oncology Department, University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Clémence Pleyers
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | | | - Omar Miranda
- Radiation Oncology Department, University Hospital, Brest, France
| | | | - Olivier Pradier
- Radiation Oncology Department, University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Ronan Abgral
- Nuclear Medicine Department, University Hospital, Brest, France
- EA GETBO 3878, IFR 148, University of Brest, UBO, Brest, France
| | - Augustin Mervoyer
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest Centre René Gauducheau, Saint Herblain, France
| | - Jean-Marc Classe
- Department of Surgical Oncology, Institut de Cancérologie de l'Ouest Centre René Gauducheau, Saint Herblain, France
| | - Caroline Rousseau
- Université de Nantes, CNRS, Inserm, CRCINA, F-44000, Nantes, France
- ICO René Gauducheau, F-44800, Saint-Herblain, France
| | - Wim Vos
- Radiomics SA, Liège, Belgium
| | - Johanne Hermesse
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Christine Gennigens
- Department of Medical Oncology, University Hospital of Liège, Liège, Belgium
| | | | - Frédéric Kridelka
- Department of Gynecology, University Hospital of Liège, Liège, Belgium
| | - 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
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
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8
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Combination of Radiomics Features and Functional Radiosensitivity Enhances Prediction of Acute Pulmonary Toxicity in a Prospective Validation Cohort of Patients with a Locally Advanced Lung Cancer Treated with VMAT-Radiotherapy. J Pers Med 2022; 12:jpm12111926. [DOI: 10.3390/jpm12111926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 11/01/2022] [Accepted: 11/14/2022] [Indexed: 11/22/2022] Open
Abstract
Introduction: The standard of care for people with locally advanced lung cancer (LALC) who cannot be operated on is (chemo)-radiation. Despite the application of dose constraints, acute pulmonary toxicity (APT) still often occurs. Prediction of APT is of paramount importance for the development of innovative therapeutic combinations. The two models were previously individually created. With success, the Rad-model incorporated six radiomics functions. After additional validation in prospective cohorts, a Pmap-model was created by identifying a specific region of the right posterior lung and incorporating several clinical and dosimetric parameters. To create and test a novel model to forecast the risk of APT in two cohorts receiving volumetric arctherapy radiotherapy (VMAT), we aimed to include all the variables in this study. Methods: In the training cohort, we retrospectively included all patients treated by VMAT for LALC at one institution between 2015 and 2018. APT was assessed according to the CTCAE v4.0 scale. Usual clinical and dosimetric features, as well as the mean dose to the pre-defined Pmap zone (DMeanPmap), were processed using a neural network approach and subsequently validated on an observational prospective cohort. The model was evaluated using the area under the curve (AUC) and balanced accuracy (Bacc). Results: 165 and 42 patients were enrolled in the training and test cohorts, with APT rates of 22.4 and 19.1%, respectively. The AUCs for the Rad and Pmap models in the validation cohort were 0.83 and 0.81, respectively, whereas the AUC for the combined model (Comb-model) was 0.90. The Bacc for the Rad, Pmap, and Comb models in the validation cohort were respectively 78.7, 82.4, and 89.7%. Conclusion: The accuracy of prediction models were increased by combining radiomics, DMeanPmap, and common clinical and dosimetric features. The use of this model may improve the evaluation of APT risk and provide access to novel therapeutic alternatives, such as dose escalation or creative therapy combinations.
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VMAT-Based Planning Allows Sparing of a Spatial Dose Pattern Associated with Radiation Pneumonitis in Patients Treated with Radiotherapy for a Locally Advanced Lung Cancer. Cancers (Basel) 2022; 14:cancers14153702. [PMID: 35954366 PMCID: PMC9367460 DOI: 10.3390/cancers14153702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/24/2022] [Accepted: 07/27/2022] [Indexed: 12/07/2022] Open
Abstract
Introduction: In patients treated with radiotherapy for locally advanced lung cancer, respect for dose constraints to organs at risk (OAR) insufficiently protects patients from acute pulmonary toxicity (APT), such toxicities being associated with a potential impact on the treatment’s completion and the patient’s quality of life. Dosimetric planning does not take into account regional lung functionality. An APT prediction model combining usual dosimetry features with the mean dose (DMeanPmap) received by a voxel-based volume (Pmap) localized in the posterior right lung has been previously developed. A DMeanPmap of ≥30.3 Gy or a predicted APT probability (ProbAPT) of ≥8% were associated with a higher risk of APT. In the present study, the authors aim to demonstrate the possibility of decreasing the DMeanPmap via a volumetric arctherapy (VMAT)-based adapted planning and evaluate the impact on the risk of APT. Methods: Among the 207 patients included in the initial study, only patients who presented with APT of ≥grade 2 and with a probability of APT ≥ 8% based on the prediction model were included. Dosimetry planning was optimized with a new constraint (DMeanPmap < 30.3 Gy) added to the usual constraints. The initial and optimized treatment plans were compared using the t-test for the independent variables and the non-parametric Mann−Whitney U test otherwise, regarding both doses to the OARs and PTV (Planning Target Volume) coverage. Conformity and heterogeneity indexes were also compared. The risk of APT was recalculated using the new dosimetric features and the APT prediction model. Results: Dosimetric optimization was considered successful for 27 out of the 44 included patients (61.4%), meaning the dosimetric constraint on the Pmap region was achieved without compromising the PTV coverage (p = 0.61). The optimization significantly decreased the median DMeanPmap from 28.8 Gy (CI95% 24.2−33.4) to 22.1 Gy (CI95% 18.3−26.0). When recomputing the risk of APT using the new dosimetric features, the optimization significantly reduced the risk of APT (p < 0.0001) by reclassifying 43.2% (19/44) of the patients. Conclusion: Our approach appears to be both easily implementable on a daily basis and efficient at reducing the risk of APT. Regional radiosensitivity should be considered in usual lung dose constraints, opening the possibility of new treatment strategies, such as dose escalation or innovative treatment associations.
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