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Fatania K, Frood R, Mistry H, Short SC, O’Connor J, Scarsbrook AF, Currie S. Tumour Size and Overall Survival in a Cohort of Patients with Unifocal Glioblastoma: A Uni- and Multivariable Prognostic Modelling and Resampling Study. Cancers (Basel) 2024; 16:1301. [PMID: 38610979 PMCID: PMC11011077 DOI: 10.3390/cancers16071301] [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: 02/15/2024] [Revised: 03/15/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024] Open
Abstract
Published models inconsistently associate glioblastoma size with overall survival (OS). This study aimed to investigate the prognostic effect of tumour size in a large cohort of patients diagnosed with GBM and interrogate how sample size and non-linear transformations may impact on the likelihood of finding a prognostic effect. In total, 279 patients with a IDH-wildtype unifocal WHO grade 4 GBM between 2014 and 2020 from a retrospective cohort were included. Uni-/multivariable association between core volume, whole volume (CV and WV), and diameter with OS was assessed with (1) Cox proportional hazard models +/- log transformation and (2) resampling with 1,000,000 repetitions and varying sample size to identify the percentage of models, which showed a significant effect of tumour size. Models adjusted for operation type and a diameter model adjusted for all clinical variables remained significant (p = 0.03). Multivariable resampling increased the significant effects (p < 0.05) of all size variables as sample size increased. Log transformation also had a large effect on the chances of a prognostic effect of WV. For models adjusted for operation type, 19.5% of WV vs. 26.3% log-WV (n = 50) and 69.9% WV and 89.9% log-WV (n = 279) were significant. In this large well-curated cohort, multivariable modelling and resampling suggest tumour volume is prognostic at larger sample sizes and with log transformation for WV.
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Affiliation(s)
- Kavi Fatania
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds General Infirmary, Leeds LS1 3EX, UK (A.F.S.); (S.C.)
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK;
| | - Russell Frood
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds General Infirmary, Leeds LS1 3EX, UK (A.F.S.); (S.C.)
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK;
| | - Hitesh Mistry
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (H.M.)
| | - Susan C. Short
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK;
- Department of Oncology, Leeds Teaching Hospitals NHS Trust, St James’s University Hospital, Leeds LS9 7TF, UK
| | - James O’Connor
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (H.M.)
- Department of Radiology, The Christie Hospital, Manchester M20 4BX, UK
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London SM2 5NG, UK
| | - Andrew F. Scarsbrook
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds General Infirmary, Leeds LS1 3EX, UK (A.F.S.); (S.C.)
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK;
| | - Stuart Currie
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds General Infirmary, Leeds LS1 3EX, UK (A.F.S.); (S.C.)
- Leeds Institute of Medical Research, University of Leeds, Leeds LS2 9TJ, UK;
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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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Hunter B, Argyros C, Inglese M, Linton-Reid K, Pulzato I, Nicholson AG, Kemp SV, L Shah P, Molyneaux PL, McNamara C, Burn T, Guilhem E, Mestas Nuñez M, Hine J, Choraria A, Ratnakumar P, Bloch S, Jordan S, Padley S, Ridge CA, Robinson G, Robbie H, Barnett J, Silva M, Desai S, Lee RW, Aboagye EO, Devaraj A. Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis. Br J Cancer 2023; 129:1949-1955. [PMID: 37932513 PMCID: PMC10703918 DOI: 10.1038/s41416-023-02480-y] [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: 01/18/2023] [Revised: 09/21/2023] [Accepted: 10/23/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Methods to improve stratification of small (≤15 mm) lung nodules are needed. We aimed to develop a radiomics model to assist lung cancer diagnosis. METHODS Patients were retrospectively identified using health records from January 2007 to December 2018. The external test set was obtained from the national LIBRA study and a prospective Lung Cancer Screening programme. Radiomics features were extracted from multi-region CT segmentations using TexLab2.0. LASSO regression generated the 5-feature small nodule radiomics-predictive-vector (SN-RPV). K-means clustering was used to split patients into risk groups according to SN-RPV. Model performance was compared to 6 thoracic radiologists. SN-RPV and radiologist risk groups were combined to generate "Safety-Net" and "Early Diagnosis" decision-support tools. RESULTS In total, 810 patients with 990 nodules were included. The AUC for malignancy prediction was 0.85 (95% CI: 0.82-0.87), 0.78 (95% CI: 0.70-0.85) and 0.78 (95% CI: 0.59-0.92) for the training, test and external test datasets, respectively. The test set accuracy was 73% (95% CI: 65-81%) and resulted in 66.67% improvements in potentially missed [8/12] or delayed [6/9] cancers, compared to the radiologist with performance closest to the mean of six readers. CONCLUSIONS SN-RPV may provide net-benefit in terms of earlier cancer diagnosis.
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Affiliation(s)
- Benjamin Hunter
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Christos Argyros
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Marianna Inglese
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
- Department of Biomedicine and Prevention, University of Rome, Tor Vergata, Italy
| | - Kristofer Linton-Reid
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Ilaria Pulzato
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Andrew G Nicholson
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Histopathology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
| | - Samuel V Kemp
- Nottingham University Hospitals NHS Trust, Department of Respiratory Medicine, Nottingham, UK
| | - Pallav L Shah
- Imperial College London, National Heart and Lung Institute, London, UK
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Respiratory Medicine, London, UK
| | - Philip L Molyneaux
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Respiratory Medicine, London, UK
| | - Cillian McNamara
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Toby Burn
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Emily Guilhem
- King's College Hospital, Department of Radiology, London, UK
| | | | - Julia Hine
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Anika Choraria
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
| | - Prashanthi Ratnakumar
- Imperial College London, National Heart and Lung Institute, London, UK
- St Mary's Hospital, Imperial College Healthcare Trust, Department of Respiratory Medicine, London, UK
| | - Susannah Bloch
- Imperial College London, National Heart and Lung Institute, London, UK
- St Mary's Hospital, Imperial College Healthcare Trust, Department of Respiratory Medicine, London, UK
| | - Simon Jordan
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Thoracic Surgery, London, UK
| | - Simon Padley
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
| | - Carole A Ridge
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
| | - Graham Robinson
- The Royal United Hospital, Bath, Department of Radiology, Bath, UK
| | - Hasti Robbie
- King's College Hospital, Department of Radiology, London, UK
| | - Joseph Barnett
- Department of Radiology, Royal Free Hospital, London, UK
| | - Mario Silva
- Section of "Scienze Radiologiche", Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Sujal Desai
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK
- Imperial College London, National Heart and Lung Institute, London, UK
- Imperial College London, Margaret Turner-Warwick Centre for Fibrosing Lung Disease, London, UK
| | - Richard W Lee
- Imperial College London, National Heart and Lung Institute, London, UK
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
- Early Diagnosis and Detection, Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
| | - Eric O Aboagye
- Imperial College London, Faculty of Medicine, Department of Surgery & Cancer, London, UK
| | - Anand Devaraj
- The Royal Brompton and Harefield Hospitals, Guy's and St Thomas's NHS Foundation Trust, Department of Radiology, London, UK.
- Imperial College London, National Heart and Lung Institute, London, UK.
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van der Kroft G, Wee L, Rensen SS, Brecheisen R, van Dijk DPJ, Eickhoff R, Roeth AA, Ulmer FT, Dekker A, Neumann UP, Olde Damink SWM. Identifying radiomics signatures in body composition imaging for the prediction of outcome following pancreatic cancer resection. Front Oncol 2023; 13:1062937. [PMID: 37637046 PMCID: PMC10449585 DOI: 10.3389/fonc.2023.1062937] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 06/26/2023] [Indexed: 08/29/2023] Open
Abstract
Background Computerized radiological image analysis (radiomics) enables the investigation of image-derived phenotypes by extracting large numbers of quantitative features. We hypothesized that radiomics features may contain prognostic information that enhances conventional body composition analysis. We aimed to investigate whether body composition-associated radiomics features hold additional value over conventional body composition analysis and clinical patient characteristics used to predict survival of pancreatic ductal adenocarcinoma (PDAC) patients. Methods Computed tomography images of 304 patients undergoing elective pancreatic cancer resection were analysed. 2D radiomics features were extracted from skeletal muscle and subcutaneous and visceral adipose tissue (SAT and VAT) compartments from a single slice at the third lumbar vertebra. The study population was randomly split (80:20) into training and holdout subsets. Feature ranking with Least Absolute Shrinkage Selection Operator (LASSO) followed by multivariable stepwise Cox regression in 1000 bootstrapped re-samples of the training data was performed and tested on the holdout data. The fitted regression predictors were used as "scores" for a clinical (C-Score), body composition (B-Score), and radiomics (R-Score) model. To stratify patients into the highest 25% and lowest 25% risk of mortality compared to the middle 50%, the Harrell Concordance Index was used. Results Based on LASSO and stepwise cox regression for overall survival, ASA ≥3 and age were the most important clinical variables and constituted the C-score, and VAT-index (VATI) was the most important body composition variable and constituted the B-score. Three radiomics features (SATI_original_shape2D_Perimeter, VATI_original_glszm_SmallAreaEmphasis, and VATI_original_firstorder_Maximum) emerged as the most frequent set of features and yielded an R-Score. Of the mean concordance indices of C-, B-, and R-scores, R-score performed best (0.61, 95% CI 0.56-0.65, p<0.001), followed by the C-score (0.59, 95% CI 0.55-0.63, p<0.001) and B-score (0.55, 95% CI 0.50-0.60, p=0.03). Kaplan-Meier projection revealed that C-, B, and R-scores showed a clear split in the survival curves in the training set, although none remained significant in the holdout set. Conclusion It is feasible to implement a data-driven radiomics approach to body composition imaging. Radiomics features provided improved predictive performance compared to conventional body composition variables for the prediction of overall survival of PDAC patients undergoing primary resection.
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Affiliation(s)
- Gregory van der Kroft
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Sander S. Rensen
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| | - Ralph Brecheisen
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
| | - David P. J. van Dijk
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| | - Roman Eickhoff
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
| | - Anjali A. Roeth
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
| | - Florian T. Ulmer
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Ulf P. Neumann
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
| | - Steven W. M. Olde Damink
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
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5
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Hunter B, Bunce C, Blackledge M, Aboagye E, Lee R. Response to A. Eleuteri regarding "A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules". EBioMedicine 2023; 94:104687. [PMID: 37392598 PMCID: PMC10338198 DOI: 10.1016/j.ebiom.2023.104687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 06/15/2023] [Indexed: 07/03/2023] Open
Affiliation(s)
- Benjamin Hunter
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK; Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Catey Bunce
- Clinical Trials Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, SM2 5PT, UK
| | - Matthew Blackledge
- Computational Imaging Group, The Institute of Cancer Research, Cotswold Road, Sutton, SM2 5NG, UK
| | - Eric Aboagye
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Richard Lee
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK; Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK; National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse Street, London, SW3 6LY, UK.
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6
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Zeng H, Tohidinezhad F, De Ruysscher DKM, Willems YCP, Degens JHRJ, van Kampen-van den Boogaart VEM, Pitz C, Cortiula F, Brandts L, Hendriks LEL, Traverso A. The Association of Gross Tumor Volume and Its Radiomics Features with Brain Metastases Development in Patients with Radically Treated Stage III Non-Small Cell Lung Cancer. Cancers (Basel) 2023; 15:cancers15113010. [PMID: 37296973 DOI: 10.3390/cancers15113010] [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: 05/10/2023] [Revised: 05/22/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
PURPOSE To identify clinical risk factors, including gross tumor volume (GTV) and radiomics features, for developing brain metastases (BM) in patients with radically treated stage III non-small cell lung cancer (NSCLC). METHODS Clinical data and planning CT scans for thoracic radiotherapy were retrieved from patients with radically treated stage III NSCLC. Radiomics features were extracted from the GTV, primary lung tumor (GTVp), and involved lymph nodes (GTVn), separately. Competing risk analysis was used to develop models (clinical, radiomics, and combined model). LASSO regression was performed to select radiomics features and train models. Area under the receiver operating characteristic curves (AUC-ROC) and calibration were performed to assess the models' performance. RESULTS Three-hundred-ten patients were eligible and 52 (16.8%) developed BM. Three clinical variables (age, NSCLC subtype, and GTVn) and five radiomics features from each radiomics model were significantly associated with BM. Radiomic features measuring tumor heterogeneity were the most relevant. The AUCs and calibration curves of the models showed that the GTVn radiomics model had the best performance (AUC: 0.74; 95% CI: 0.71-0.86; sensitivity: 84%; specificity: 61%; positive predictive value [PPV]: 29%; negative predictive value [NPV]: 95%; accuracy: 65%). CONCLUSION Age, NSCLC subtype, and GTVn were significant risk factors for BM. GTVn radiomics features provided higher predictive value than GTVp and GTV for BM development. GTVp and GTVn should be separated in clinical and research practice.
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Affiliation(s)
- Haiyan Zeng
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Fariba Tohidinezhad
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Dirk K M De Ruysscher
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Yves C P Willems
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
| | - Juliette H R J Degens
- Department of Respiratory Medicine, Zuyderland Medical Center, 6419 PC Heerlen, The Netherlands
| | | | - Cordula Pitz
- Department of Pulmonary Diseases, Laurentius Hospital, 6043 CV Roermond, The Netherlands
| | - Francesco Cortiula
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
- Department of Medical Oncology, University Hospital of Udine, 33100 Udine, Italy
| | - Lloyd Brandts
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands
| | - Lizza E L Hendriks
- Department of Pulmonary Diseases, Maastricht, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, 6202 AZ Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
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7
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Tohidinezhad F, Bontempi D, Zhang Z, Dingemans AM, Aerts J, Bootsma G, Vansteenkiste J, Hashemi S, Smit E, Gietema H, Aerts HJ, Dekker A, Hendriks LEL, Traverso A, De Ruysscher D. Computed tomography-based radiomics for the differential diagnosis of pneumonitis in stage IV non-small cell lung cancer patients treated with immune checkpoint inhibitors. Eur J Cancer 2023; 183:142-151. [PMID: 36857819 DOI: 10.1016/j.ejca.2023.01.027] [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: 12/15/2022] [Revised: 01/29/2023] [Accepted: 01/29/2023] [Indexed: 02/11/2023]
Abstract
INTRODUCTION Immunotherapy-induced pneumonitis (IIP) is a serious side-effect which requires accurate diagnosis and management with high-dose corticosteroids. The differential diagnosis between IIP and other types of pneumonitis (OTP) remains challenging due to similar radiological patterns. This study was aimed to develop a prediction model to differentiate IIP from OTP in patients with stage IV non-small cell lung cancer (NSCLC) who developed pneumonitis during immunotherapy. METHODS Consecutive patients with metastatic NSCLC treated with immunotherapy in six centres in the Netherlands and Belgium from 2017 to 2020 were reviewed and cause-specific pneumonitis events were identified. Seven regions of interest (segmented lungs and spheroidal/cubical regions surrounding the inflammation) were examined to extract the most predictive radiomic features from the chest computed tomography images obtained at pneumonitis manifestation. Models were internally tested regarding discrimination, calibration and decisional benefit. To evaluate the clinical application of the models, predicted labels were compared with the separate clinical and radiological judgements. RESULTS A total of 556 patients were reviewed; 31 patients (5.6%) developed IIP and 41 patients developed OTP (7.4%). The line of immunotherapy was the only predictive factor in the clinical model (2nd versus 1st odds ratio = 0.08, 95% confidence interval:0.01-0.77). The best radiomic model was achieved using a 75-mm spheroidal region of interest which showed an optimism-corrected area under the receiver operating characteristic curve of 0.83 (95% confidence interval:0.77-0.95) with negative and positive predictive values of 80% and 79%, respectively. Good calibration and net benefits were achieved for the radiomic model across the entire range of probabilities. A correct diagnosis was provided by the radiomic model in 10 out of 12 cases with non-conclusive radiological judgements. CONCLUSION Radiomic biomarkers applied to computed tomography imaging may support clinicians making the differential diagnosis of pneumonitis in patients with NSCLC receiving immunotherapy, especially when the radiologic assessment is non-conclusive.
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Affiliation(s)
- Fariba Tohidinezhad
- Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Dennis Bontempi
- Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Zhen Zhang
- Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Anne-Marie Dingemans
- Department of Pulmonary Diseases, School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Joachim Aerts
- Department of Pulmonary Medicine, School of Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Gerben Bootsma
- Department of Pulmonary Diseases, Zuyderland Hospital, Heerlen, the Netherlands
| | - Johan Vansteenkiste
- Department of Respiratory Oncology, University Hospital KU Leuven, Leuven, Belgium
| | - Sayed Hashemi
- Department of Pulmonary Medicine, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands
| | - Egbert Smit
- Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Hester Gietema
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Hugo Jwl Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands; Departments of Radiation Oncology and Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Andre Dekker
- Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Lizza E L Hendriks
- Department of Pulmonary Diseases, School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands
| | - Dirk De Ruysscher
- Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands.
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Zhang Z, Wang Z, Yan M, Yu J, Dekker A, Zhao L, Wee L. Radiomics and Dosiomics Signature From Whole Lung Predicts Radiation Pneumonitis: A Model Development Study With Prospective External Validation and Decision-curve Analysis. Int J Radiat Oncol Biol Phys 2023; 115:746-758. [PMID: 36031028 DOI: 10.1016/j.ijrobp.2022.08.047] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 08/10/2022] [Accepted: 08/20/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE Radiation pneumonitis (RP) is one of the common side effects of radiation therapy in the thoracic region. Radiomics and dosiomics quantify information implicit within medical images and radiation therapy dose distributions. In this study we demonstrate the prognostic potential of radiomics, dosiomics, and clinical features for RP prediction. METHODS AND MATERIALS Radiomics, dosiomics, dose-volume histogram (DVH) metrics, and clinical parameters were obtained on 314 retrospectively collected and 35 prospectively enrolled patients diagnosed with lung cancer between 2013 to 2019. A radiomics risk score (R score) and dosiomics risk score (D score), as well as a DVH-score, were calculated based on logistic regression after feature selection. Six models were built using different combinations of R score, D score, DVH score, and clinical parameters to evaluate their added prognostic power. Overoptimism was evaluated by bootstrap resampling from the training set, and the prospectively collected cohort was used as the external test set. Model calibration and decision-curve characteristics of the best-performing models were evaluated. For ease of further evaluation, nomograms were constructed for selected models. RESULTS A model built by integrating all of the R score, D score, and clinical parameters had the best discriminative ability with areas under the curve of 0.793 (95% confidence interval [CI], 0.735-0.851), 0.774 (95% CI, 0.762-0.786), and 0.855 (95% CI, 0.719-0.990) in the training, bootstrapping, and external test sets, respectively. The calibration curve image showed good agreement between the predicted and actual values, with a slope of 1.21 and intercept of -0.04. The decision curve image showed a positive net benefit for the final model based on the nomogram. CONCLUSIONS Radiomic and dosiomic features have the potential to assist with the prediction of RP, and the combination of radiomics, dosiomics, and clinical parameters led to the best prognostic model in the present study.
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Affiliation(s)
- Zhen Zhang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Zhixiang Wang
- Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Meng Yan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Jiaqi Yu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Andre Dekker
- Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Lujun Zhao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
| | - Leonard Wee
- Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands
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Liang X, He W, Huang C, Feng Z, Guan X, Liu Y, Sun Z, Li Z. Preoperative prediction of invasive behavior of pancreatic solid pseudopapillary neoplasm by MRI-based multiparametric radiomics models. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3782-3791. [PMID: 35976419 DOI: 10.1007/s00261-022-03639-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/25/2022] [Accepted: 07/27/2022] [Indexed: 01/18/2023]
Abstract
OBJECTIVE A log-combined model was developed to predict the invasive behavior of pancreatic solid pseudopapillary neoplasm (pSPN) based on clinical and radiomic features extracted from multiparametric magnetic resonance imaging (MRI). MATERIALS AND METHODS A total of 111 patients with pathologically confirmed pSPN who underwent preoperative plain and contrast-enhanced MRI were included, and divided into an invasive group (n = 34) and non-invasive group (n = 77). Clinical features and laboratory data related to pSPN invasive behavior were analyzed. Regions of interest were delineated based on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI) to extract radiomic features. Correlation analysis was performed for these features, followed by L1_based feature selection (C = 0.15). A logistic regression algorithm was used to construct models based on each of the four sequences and a log-combined model was used to integrate the sequences. A receiver operating characteristic (ROC) curve was plotted to evaluate the model performance, and the Brier score was used to assess the overall accuracy of the model predictions. RESULTS The area under the ROC curve was 0.68, 0.73, 0.71, and 0.49 for Log-T1WI, Log-T2WI, Log-DWI, and Log-CE models, respectively, and 0.81 for the log-combined model. The accuracy, precision, sensitivity, and specificity of the log-combined model were 0.77, 0.88, 0.75, and 0.78, respectively. The best performance was obtained with the log-combined model with a Brier score of 0.18. Tumor location was identified as a significant clinical feature in comparison between the two groups (p < 0.05), and invasive pSPN was more frequent in the tail of the pancreas. CONCLUSION The log-combined model based on multiparametric MRI and clinical features can be used as a non-invasive diagnostic tool for preoperative prediction of pSPN invasive behavior and to facilitate the development of individualized treatment strategies and monitoring management plans.
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Affiliation(s)
- Xiuqun Liang
- Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region & Research Center of Radiology, Guangxi Academy of Medical Sciences, Nanning, 530021, Guangxi, China
| | - Wenguang He
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310007, Zhejiang, China
| | - Chencui Huang
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co, Ltd, Beijing, 100080, China
| | - Zhan Feng
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310007, Zhejiang, China
| | - Xiaohui Guan
- Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region & Research Center of Radiology, Guangxi Academy of Medical Sciences, Nanning, 530021, Guangxi, China
| | - Ying Liu
- Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region & Research Center of Radiology, Guangxi Academy of Medical Sciences, Nanning, 530021, Guangxi, China
| | - Zeyong Sun
- Department of Radionuclide, The People's Hospital of Guangxi Zhuang Autonomous Region & Research Center of Radionuclide, Guangxi Academy of Medical Sciences, Nanning, 530021, Guangxi, China
| | - Zhi Li
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310007, Zhejiang, China.
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A multicenter study on the preoperative prediction of gastric cancer microsatellite instability status based on computed tomography radiomics. Abdom Radiol (NY) 2022; 47:2036-2045. [PMID: 35391567 DOI: 10.1007/s00261-022-03507-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/21/2022] [Accepted: 03/22/2022] [Indexed: 12/16/2022]
Abstract
PURPOSE To construct and validate a radiomics feature model based on computed tomography (CT) images and clinical characteristics to predict the microsatellite instability (MSI) status of gastric cancer patients before surgery. METHODS We retrospectively collected the upper abdominal or the entire abdominal-enhanced CT scans of 189 gastric cancer patients before surgery. The patients underwent postoperative gastric cancer MSI status testing, and the dates of their radiologic images and clinicopathological data were from January 2015 to August 2021. These 189 patients were divided into a training set (n = 90) and an external validation set (n = 99). The patients were divided by MSI status into the MSI-high (H) arm (30 and 33 patients in the training set and external validation set, respectively) and MSI-low/stable (L/S) arm (60 and 66 patients in the training set and external validation set, respectively). In the training set, the clinical characteristics and tumor radiologic characteristics of the patients were extracted, and the tenfold cross-validation method was used for internal validation of the training set. The external validation set was used to assess its generalized performance. A receiver-operating characteristic (ROC) curve was plotted to assess the model performance, and the area under the curve (AUC) was calculated. RESULTS The AUC of the radiomics model in the training set and external validation set was 0.8228 [95% confidence interval (CI) 0.7355-0.9101] and 0.7603 [95% CI 0.6625-0.8581], respectively, showing that the constructed radiomics model exhibited satisfactory generalization capabilities. The accuracy, sensitivity, and specificity of the training dataset were 0.72, 0.63, and 0.77, respectively. The accuracy, sensitivity, and specificity of the external validation dataset were 0.67, 0.79, and 0.60, respectively. Statistical analysis was carried out on the clinical data, and there was statistical significance for the tumor site and age (p < 0.05). MSI-H gastric cancer was mostly seen in the gastric antrum and older patients. CONCLUSIONS Radiomics markers based on CT images and clinical characteristics have the potential to be a non-invasive auxiliary diagnostic tool for preoperative assessment of gastric cancer MSI status, and they can aid in clinical decision-making and improve patient outcomes.
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