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Adachi T, Nakamura M, Matsuo Y, Karasawa K, Kokubo M, Sakamoto T, Hiraoka M, Mizowaki T. Prospective external validation of radiomics-based predictive model of distant metastasis after dynamic tumor tracking stereotactic body radiation therapy in patients with non-small-cell lung cancer: A multi-institutional analysis. J Appl Clin Med Phys 2024:e14475. [PMID: 39178139 DOI: 10.1002/acm2.14475] [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: 02/22/2024] [Accepted: 07/03/2024] [Indexed: 08/25/2024] Open
Abstract
BACKGROUND AND PURPOSE This study aims to externally validate a predictive model for distant metastasis (DM) with computed tomography (CT)-based radiomics features in prospectively enrolled non-small-cell lung cancer patients undergoing dynamic tumor-tracking stereotactic body radiation therapy (DTT-SBRT). MATERIALS AND METHODS The study collected retrospective data from 567 patients across 11 institutions as the training dataset and prospectively enrolled 42 patients from four institutions as the external test dataset. Four clinical features were collected, and 944 CT-based radiomic features were extracted from gross tumor volumes. After standardization and feature selection, DM predictive models were developed using fine and gray regression (FG) and random survival forest (RSF), incorporating clinical and radiomic features, and their combinations within the training dataset. Then, the model was applied to the test dataset, dividing patients into high- and low-risk groups based on medians of risk scores. Model performance was assessed using the concordance index (C-index), and the statistical significance between groups was evaluated using Gray's test. RESULTS In the training dataset, 122 of 567 patients (21.5%) developed DM, compared to 9 of 42 patients (21.4%) in the test dataset. In the test dataset, the C-indices of the clinical, radiomics, and hybrid models with FG were 0.559, 0.544, and 0.560, respectively, whereas those with RSF were 0.576, 0.604, and 0.627, respectively. The hybrid model with RSF, which exhibited the best predictive performance of all models, identified 7 of 23 patients (30.4%) as high risk and 2 of 19 patients (10.5%) as low risk for DM incidence in the test dataset (p = 0.116). CONCLUSION Although predictive models for DM lack significance when applied to prospectively enrolled cases undergoing DTT-lung SBRT, the model with RSF exhibits a consistent capacity to effectively classify patients at a high risk of developing DM.
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Affiliation(s)
- Takanori Adachi
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mitsuhiro Nakamura
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yukinori Matsuo
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Katsuyuki Karasawa
- Division of Radiation Oncology, Department, of Radiology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan
| | - Masaki Kokubo
- Department of Radiation Oncology, Kobe City Medical Center General Hospital, Hyogo, Japan
| | - Takashi Sakamoto
- Department of Radiation Oncology, Kyoto Katsura Hospital, Kyoto, Japan
| | - Masahiro Hiraoka
- Department of Radiation Oncology, Japanese Red Cross Society Wakayama Medical Center, Wakayama, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Wang Q, Wu T, Zhao R, Li Y, Chen X, Shen S, Zhang X. Development and Validation of a Prediction Model for Thyroid Dysfunction in Patients During Immunotherapy. Endocr Pract 2024:S1530-891X(24)00604-9. [PMID: 39004306 DOI: 10.1016/j.eprac.2024.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/24/2024] [Accepted: 07/08/2024] [Indexed: 07/16/2024]
Abstract
OBJECTIVE This study was designed to develop and validate a predictive model for assessing the risk of thyroid toxicity following treatment with immune checkpoint inhibitors. METHODS A retrospective analysis was conducted on a cohort of 586 patients diagnosed with malignant tumors who received programmed cell death 1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. Logistic regression analyses were performed on the training set to identify risk factors of thyroid dysfunction, and a nomogram was developed based on these findings. Internal validation was performed using K-fold cross-validation on the validation set. The performance of the nomogram was assessed in terms of discrimination and calibration. Additionally, decision curve analysis was utilized to demonstrate the decision efficiency of the model. RESULTS Our clinical prediction model consisted of 4 independent predictors of thyroid immune-related adverse events, namely baseline thyrotropin (TSH, OR = 1.427, 95%CI:1.163-1.876), baseline thyroglobulin antibody (TgAb, OR = 1.105, 95%CI:1.035-1.180), baseline thyroid peroxidase antibody (TPOAb, OR = 1.172, 95%CI:1.110-1.237), and baseline platelet count (platelet, OR = 1.004, 95%CI:1.000-1.007). The developed nomogram achieved excellent discrimination with an area under the curve of 0.863 (95%CI: 0.817-0.909) and 0.885 (95%CI: 0.827-0.944) in the training and internal validation cohorts respectively. Calibration curves exhibited a good fit, and the decision curve indicated favorable clinical benefits. CONCLUSION The proposed nomogram serves as an effective and intuitive tool for predicting the risk of thyroid immune-related adverse events, facilitating clinicians making individualized decisions based on patient-specific information.
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Affiliation(s)
- Qian Wang
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China
| | - Tingting Wu
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China
| | - Ru Zhao
- Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China; Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Yuanqin Li
- Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China; Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Xuetao Chen
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China
| | - Shanmei Shen
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China.
| | - Xiaowen Zhang
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China.
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Ni J, Chen H, Yu L, Guo T, Zhou Y, Jiang S, Ye R, Yang X, Chu L, Chu X, Li H, Liu W, Gu Y, Yuan Z, Gong J, Zhu Z. Predicting Regional Recurrence and Prognosis in Stereotactic Body Radiation Therapy-Treated Clinical Stage I Non-small Cell Lung Cancer Using a Radiomics Model Constructed With Surgical Data. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00743-0. [PMID: 38936632 DOI: 10.1016/j.ijrobp.2024.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/13/2024] [Accepted: 06/15/2024] [Indexed: 06/29/2024]
Abstract
PURPOSE Risk stratification of regional recurrence (RR) is clinically important in the design of adjuvant treatment and surveillance strategies in patients with clinical stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). This study aimed to develop a radiomics model predicting occult lymph node metastasis (OLNM) using surgical data and apply it to the prediction of RR in SBRT-treated early-stage NSCLC patients. METHODS AND MATERIALS Patients with clinical stage I NSCLC who underwent curative surgery with systematic lymph node dissection from January 2013 to December 2018 (the training cohort) and from January 2019 to December 2020 (the validation cohort) were included. A preoperative computed tomography-based radiomics model, a clinical feature model, and a fusion model predicting OLNM were constructed. The performance of the 3 models was quantified and compared in the training and validation cohorts. Subsequently, the radiomics model was used to predict RR in a cohort of consecutive SBRT-treated early-stage NSCLC patients from 2 academic medical centers. RESULTS A total of 769 patients were included. Eight computed tomography features were identified in the radiomics model, achieving areas under the curves of 0.85 (95% CI, 0.81-0.89) and 0.83 (95% CI, 0.80-0.88) in the training and validation cohorts, respectively. Nevertheless, adding clinical features did not improve the performance of the radiomics model. With a median follow-up of 40.0 (95% CI, 35.2-44.8) months, 32 of the 213 patients in the SBRT cohort developed RR and those in the high-risk group based on the radiomics model had a higher cumulative incidence of RR (P < .001) and shorter regional recurrence-free survival (P = .02), progression-free survival (P = .004) and overall survival (P = .006) than those in the low-risk group. CONCLUSIONS The radiomics model based on pathologically confirmed data effectively identified patients with OLNM, which may be useful in the risk stratification among SBRT-treated patients with clinical stage I NSCLC.
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Affiliation(s)
- Jianjiao Ni
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Hongru Chen
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Lu Yu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Tiantian Guo
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Yue Zhou
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Shanshan Jiang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Ruiting Ye
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Xi Yang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Li Chu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Xiao Chu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Haiming Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhiyong Yuan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
| | - Jing Gong
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
| | - Zhengfei Zhu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Clinical Research Center for Radiation Oncology, Shanghai, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai, China.
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Louis T, Lucia F, Cousin F, Mievis C, Jansen N, Duysinx B, Le Pennec R, Visvikis D, Nebbache M, Rehn M, Hamya M, Geier M, Salaun PY, Schick U, Hatt M, Coucke P, Lovinfosse P, Hustinx R. Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence. Sci Rep 2024; 14:9028. [PMID: 38641673 PMCID: PMC11031577 DOI: 10.1038/s41598-024-58551-4] [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: 12/05/2023] [Accepted: 04/01/2024] [Indexed: 04/21/2024] Open
Abstract
The primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography of early-stage non-small cell lung cancer patients, which remain unaffected by variations in segmentation quality and in computed tomography image acquisition protocol. The robustness of these features to segmentation variations was assessed by analyzing the correlation of feature values extracted from lesion volumes delineated by two annotators. The robustness to variations in acquisition protocol was evaluated by examining the correlation of features extracted from high-dose and low-dose computed tomography scans, both of which were acquired for each patient as part of the stereotactic body radiotherapy planning process. Among 106 radiomic features considered, 21 were identified as robust. An analysis including univariate and multivariate assessments was subsequently conducted to estimate the predictive performance of these robust features on the outcome of early-stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. The univariate predictive analysis revealed that robust features demonstrated superior predictive potential compared to non-robust features. The multivariate analysis indicated that linear regression models built with robust features displayed greater generalization capabilities by outperforming other models in predicting the outcomes of an external validation dataset.
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Affiliation(s)
- Thomas Louis
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium.
| | - François Lucia
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium.
- Radiation Oncology Department, University Hospital of Brest, Brest, France.
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France.
| | - François Cousin
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - Carole Mievis
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Nicolas Jansen
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Bernard Duysinx
- Division of Pulmonology, University Hospital of Liège, Liège, Belgium
| | - Romain Le Pennec
- Nuclear Medicine Department, University Hospital of Brest, Brest, France
- GETBO INSERM UMR 1304, University of Brest, UBO, Brest, France
| | | | - Malik Nebbache
- Radiation Oncology Department, University Hospital of Brest, Brest, France
| | - Martin Rehn
- Radiation Oncology Department, University Hospital of Brest, Brest, France
| | - Mohamed Hamya
- Radiation Oncology Department, University Hospital of Brest, Brest, France
| | - Margaux Geier
- Medical Oncology Department, University Hospital of Brest, Brest, France
| | - Pierre-Yves Salaun
- Nuclear Medicine Department, University Hospital of Brest, Brest, France
- GETBO INSERM UMR 1304, University of Brest, UBO, Brest, France
| | - Ulrike Schick
- Radiation Oncology Department, University Hospital of Brest, Brest, France
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - Philippe Coucke
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
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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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Yu L, Zhang Z, Yi H, Wang J, Li J, Wang X, Bai H, Ge H, Zheng X, Ni J, Qi H, Guan Y, Xu W, Zhu Z, Xing L, Dekker A, Wee L, Traverso A, Ye Z, Yuan Z. A PET/CT radiomics model for predicting distant metastasis in early-stage non-small cell lung cancer patients treated with stereotactic body radiotherapy: a multicentric study. Radiat Oncol 2024; 19:10. [PMID: 38254106 PMCID: PMC10802016 DOI: 10.1186/s13014-024-02402-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
OBJECTIVES Stereotactic body radiotherapy (SBRT) is a treatment option for patients with early-stage non-small cell lung cancer (NSCLC) who are unfit for surgery. Some patients may experience distant metastasis. This study aimed to develop and validate a radiomics model for predicting distant metastasis in patients with early-stage NSCLC treated with SBRT. METHODS Patients at five institutions were enrolled in this study. Radiomics features were extracted based on the PET/CT images. After feature selection in the training set (from Tianjin), CT-based and PET-based radiomics signatures were built. Models based on CT and PET signatures were built and validated using external datasets (from Zhejiang, Zhengzhou, Shandong, and Shanghai). An integrated model that included CT and PET radiomic signatures was developed. The performance of the proposed model was evaluated in terms of its discrimination, calibration, and clinical utility. Multivariate logistic regression was used to calculate the probability of distant metastases. The cutoff value was obtained using the receiver operator characteristic curve (ROC), and the patients were divided into high- and low-risk groups. Kaplan-Meier analysis was used to evaluate the distant metastasis-free survival (DMFS) of different risk groups. RESULTS In total, 228 patients were enrolled. The median follow-up time was 31.4 (2.0-111.4) months. The model based on CT radiomics signatures had an area under the curve (AUC) of 0.819 in the training set (n = 139) and 0.786 in the external dataset (n = 89). The PET radiomics model had an AUC of 0.763 for the training set and 0.804 for the external dataset. The model combining CT and PET radiomics had an AUC of 0.835 for the training set and 0.819 for the external dataset. The combined model showed a moderate calibration and a positive net benefit. When the probability of distant metastasis was greater than 0.19, the patient was considered to be at high risk. The DMFS of patients with high- and low-risk was significantly stratified (P < 0.001). CONCLUSIONS The proposed PET/CT radiomics model can be used to predict distant metastasis in patients with early-stage NSCLC treated with SBRT and provide a reference for clinical decision-making. In this study, the model was established by combining CT and PET radiomics signatures in a moderate-quantity training cohort of early-stage NSCLC patients treated with SBRT and was successfully validated in independent cohorts. Physicians could use this easy-to-use model to assess the risk of distant metastasis after SBRT. Identifying subgroups of patients with different risk factors for distant metastasis is useful for guiding personalized treatment approaches.
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Affiliation(s)
- Lu Yu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Zhen Zhang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - HeQing Yi
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Jin Wang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Junyi Li
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Xiaofeng Wang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Hui Bai
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Hong Ge
- The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoli Zheng
- The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Jianjiao Ni
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Haoran Qi
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Science, Jinan, Shandong, China
| | - Yong Guan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Wengui Xu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Zhengfei Zhu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Ligang Xing
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Science, Jinan, Shandong, China
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
| | - Zhiyong Yuan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
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Ramella S, Soda P. Stage I Stereotactic Body Radiation Therapy Outcome's Prediction Models: Perfection Is the Enemy of Utility. J Thorac Oncol 2023; 18:262-264. [PMID: 36842809 DOI: 10.1016/j.jtho.2023.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 01/08/2023] [Indexed: 02/28/2023]
Affiliation(s)
- Sara Ramella
- Operative Research Unit of Radiation Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Roma, Italy; Research Unit of Radiation Oncology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Roma, Italy.
| | - Paolo Soda
- Research Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico, Roma, Italy; Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå University, Umeå, Sweden
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Yu F, Wu W, Zhang L, Li S, Yao X, Wang J, Ni Y, Meng Q, Yang R, Wang F, Shi L. Cervical lymph node metastasis prediction of postoperative papillary thyroid carcinoma before 131I therapy based on clinical and ultrasound characteristics. Front Endocrinol (Lausanne) 2023; 14:1122517. [PMID: 36875475 PMCID: PMC9982841 DOI: 10.3389/fendo.2023.1122517] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 02/07/2023] [Indexed: 02/19/2023] Open
Abstract
BACKGROUND The status of lymph nodes is crucial to determine the dose of radioiodine-131(131I) for postoperative papillary thyroid carcinoma (PTC). We aimed to develop a nomogram for predicting residual and recurrent cervical lymph node metastasis (CLNM) in postoperative PTC before 131I therapy. METHOD Data from 612 postoperative PTC patients who underwent 131I therapy from May 2019 to December 2020 were retrospectively analyzed. Clinical and ultrasound features were collected. Univariate and multivariate logistic regression analyses were performed to determine the risk factors of CLNM. Receiver operating characteristic (ROC) analysis was used to weigh the discrimination of prediction models. To generate nomograms, models with high area under the curves (AUC) were selected. Bootstrap internal validation, calibration curves and decision curves were used to assess the prediction model's discrimination, calibration, and clinical usefulness. RESULTS A total of 18.79% (115/612) of postoperative PTC patients had CLNM. Univariate logistic regression analysis found serum thyroglobulin (Tg), serum thyroglobulin antibodies (TgAb), overall ultrasound diagnosis and seven ultrasound features (aspect transverse ratio, cystic change, microcalcification, mass hyperecho, echogenicity, lymphatic hilum structure and vascularity) were significantly associated with CLNM. Multivariate analysis revealed higher Tg, higher TgAb, positive overall ultrasound and ultrasound features such as aspect transverse ratio ≥ 2, microcalcification, heterogeneous echogenicity, absence of lymphatic hilum structure and abundant vascularity were independent risk factors for CLNM. ROC analysis showed the use of Tg and TgAb combined with ultrasound (AUC = 0.903 for "Tg+TgAb+Overall ultrasound" model, AUC = 0.921 for "Tg+TgAb+Seven ultrasound features" model) was superior to any single variant. Nomograms constructed for the above two models were validated internally and the C-index were 0.899 and 0.914, respectively. Calibration curves showed satisfied discrimination and calibration of the two nomograms. DCA also proved that the two nomograms were clinically useful. CONCLUSION Through the two accurate and easy-to-use nomograms, the possibility of CLNM can be objectively quantified before 131I therapy. Clinicians can use the nomograms to evaluate the status of lymph nodes in postoperative PTC patients and consider a higher dose of 131I for those with high scores.
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Affiliation(s)
- Fei Yu
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Wenyu Wu
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Liuting Zhang
- Department of Functional Examination, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Shaohua Li
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiaochen Yao
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jun Wang
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yudan Ni
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Qingle Meng
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Rui Yang
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Feng Wang
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- *Correspondence: Liang Shi, ; Feng Wang,
| | - Liang Shi
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- *Correspondence: Liang Shi, ; Feng Wang,
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