1
|
Cheng SH, Lee SY, Lee HH. Harnessing the Power of Radiotherapy for Lung Cancer: A Narrative Review of the Evolving Role of Magnetic Resonance Imaging Guidance. Cancers (Basel) 2024; 16:2710. [PMID: 39123438 PMCID: PMC11311467 DOI: 10.3390/cancers16152710] [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: 06/27/2024] [Revised: 07/22/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024] Open
Abstract
Compared with computed tomography (CT), magnetic resonance imaging (MRI) traditionally plays a very limited role in lung cancer management, although there is plenty of room for improvement in the current CT-based workflow, for example, in structures such as the brachial plexus and chest wall invasion, which are difficult to visualize with CT alone. Furthermore, in the treatment of high-risk tumors such as ultracentral lung cancer, treatment-associated toxicity currently still outweighs its benefits. The advent of MR-Linac, an MRI-guided radiotherapy (RT) that combines MRI with a linear accelerator, could potentially address these limitations. Compared with CT-based technologies, MR-Linac could offer superior soft tissue visualization, daily adaptive capability, real-time target tracking, and an early assessment of treatment response. Clinically, it could be especially advantageous in the treatment of central/ultracentral lung cancer, early-stage lung cancer, and locally advanced lung cancer. Increasing demands for stereotactic body radiotherapy (SBRT) for lung cancer have led to MR-Linac adoption in some cancer centers. In this review, a broad overview of the latest research on imaging-guided radiotherapy (IGRT) with MR-Linac for lung cancer management is provided, and development pertaining to artificial intelligence is also highlighted. New avenues of research are also discussed.
Collapse
Affiliation(s)
- Sarah Hsin Cheng
- Department of Clinical Education and Training, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan;
| | - Shao-Yun Lee
- Department of Medical Education, Taichung Veterans General Hospital, Taichung 407, Taiwan;
| | - Hsin-Hua Lee
- Department of Radiation Oncology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Ph.D. Program in Environmental and Occupational Medicine, Kaohsiung Medical University and National Health Research Institutes, Kaohsiung 807, Taiwan
- Department of Radiation Oncology, Faculty of Medicine, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Center for Cancer Research, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| |
Collapse
|
2
|
Habibi MA, Rashidi F, Habibzadeh A, Mehrtabar E, Arshadi MR, Mirjani MS. Prediction of the treatment response and local failure of patients with brain metastasis treated with stereotactic radiosurgery using machine learning: A systematic review and meta-analysis. Neurosurg Rev 2024; 47:199. [PMID: 38684566 DOI: 10.1007/s10143-024-02391-3] [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: 01/20/2024] [Revised: 04/01/2024] [Accepted: 04/07/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND Stereotactic radiosurgery (SRS) effectively treats brain metastases. It can provide local control, symptom relief, and improved survival rates, but it poses challenges in selecting optimal candidates, determining dose and fractionation, monitoring for toxicity, and integrating with other modalities. Practical tools to predict patient outcomes are also needed. Machine learning (ML) is currently used to predict treatment outcomes. We aim to investigate the accuracy of ML in predicting treatment response and local failure of brain metastasis treated with SRS. METHODS PubMed, Scopus, Web of Science (WoS), and Embase were searched until April 16th, which was repeated on October 17th, 2023 to find possible relevant papers. The study preparation adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. The statistical analysis was performed by the MIDAS package of STATA v.17. RESULTS A total of 17 articles were reviewed, of which seven and eleven were related to the clinical use of ML in predicting local failure and treatment response. The ML algorithms showed sensitivity and specificity of 0.89 (95% CI: 0.84-0.93) and 0.87 (95% CI: 0.81-0.92) for predicting treatment response. The positive likelihood ratio was 7.1 (95% CI: 4.5-11.1), the negative likelihood ratio was 0.13 (95% CI: 0.08-0.19), and the diagnostic odds ratio was 56 (95% CI: 25-125). Moreover, the pooled estimates for sensitivity and specificity of ML algorithms for predicting local failure were 0.93 (95% CI: 0.76-0.98) and 0.80 (95% CI: 0.53-0.94). The positive likelihood ratio was 4.7 (95% CI: 1.6-14.0), the negative likelihood ratio was 0.09 (95% CI: 0.02-0.39), and the diagnostic odds ratio was 53 (95% CI: 5-606). CONCLUSION ML holds promise in predicting treatment response and local failure in brain metastasis patients receiving SRS. However, further studies and improvements in the treatment process can refine the models and effectively integrate them into clinical practice.
Collapse
Affiliation(s)
- Mohammad Amin Habibi
- Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
| | - Farhang Rashidi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Adriana Habibzadeh
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
| | - Ehsan Mehrtabar
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Arshadi
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Sina Mirjani
- Student Research Committee, Faculty of Medicine, Qom University of Medical Science, Qom, Iran
| |
Collapse
|
3
|
Gong J, Wang T, Wang Z, Chu X, Hu T, Li M, Peng W, Feng F, Tong T, Gu Y. Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model. Cancer Imaging 2024; 24:1. [PMID: 38167564 PMCID: PMC10759676 DOI: 10.1186/s40644-023-00623-1] [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: 08/01/2023] [Accepted: 10/16/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and computed tomography (CT) radiomics-based ensemble learning model. METHODS This retrospective study included 602 stage IIIA-IVB NSCLC patients, 309 BM patients and 293 non-BM patients, from two centers. Patients were divided into a training cohort (N = 376), an internal validation cohort (N = 161) and an external validation cohort (N = 65). Lung tumors were first segmented by using a three-dimensional (3D) deep residual U-Net network. Then, a total of 1106 radiomics features were computed by using pretreatment lung CT images to decode the imaging phenotypes of primary lung cancer. To reduce the dimensionality of the radiomics features, recursive feature elimination configured with the least absolute shrinkage and selection operator (LASSO) regularization method was applied to select the optimal image features after removing the low-variance features. An ensemble learning algorithm of the extreme gradient boosting (XGBoost) classifier was used to train and build a prediction model by fusing radiomics features and clinical features. Finally, Kaplan‒Meier (KM) survival analysis was used to evaluate the prognostic value of the prediction score generated by the radiomics-clinical model. RESULTS The fused model achieved area under the receiver operating characteristic curve values of 0.91 ± 0.01, 0.89 ± 0.02 and 0.85 ± 0.05 on the training and two validation cohorts, respectively. Through KM survival analysis, the risk score generated by our model achieved a significant prognostic value for BM-free survival (BMFS) and overall survival (OS) in the two cohorts (P < 0.05). CONCLUSIONS Our results demonstrated that (1) the fusion of radiomics and clinical features can improve the prediction performance in predicting BM risk, (2) the radiomics model generates higher performance than the clinical model, and (3) the radiomics-clinical fusion model has prognostic value in predicting the BMFS and OS of NSCLC patients.
Collapse
Affiliation(s)
- Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Ting Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Zezhou Wang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Department of Cancer Prevention, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Shanghai Municipal Hospital Oncological Specialist Alliance, Shanghai, 200032, China
| | - Xiao Chu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Tingdan Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Menglei Li
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Feng Feng
- Department of Medical Imaging, Nantong Tumor Hospital, Nantong University, Nantong, 226361, China.
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| |
Collapse
|
4
|
DeVries DA, Tang T, Albweady A, Leung A, Laba J, Johnson C, Lagerwaard F, Zindler J, Hajdok G, Ward AD. Predicting stereotactic radiosurgery outcomes with multi-observer qualitative appearance labelling versus MRI radiomics. Sci Rep 2023; 13:20977. [PMID: 38017055 PMCID: PMC10684869 DOI: 10.1038/s41598-023-47702-8] [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: 05/10/2023] [Accepted: 11/17/2023] [Indexed: 11/30/2023] Open
Abstract
Qualitative observer-based and quantitative radiomics-based analyses of T1w contrast-enhanced magnetic resonance imaging (T1w-CE MRI) have both been shown to predict the outcomes of brain metastasis (BM) stereotactic radiosurgery (SRS). Comparison of these methods and interpretation of radiomics-based machine learning (ML) models remains limited. To address this need, we collected a dataset of n = 123 BMs from 99 patients including 12 clinical features, 107 pre-treatment T1w-CE MRI radiomic features, and BM post-SRS progression scores. A previously published outcome model using SRS dose prescription and five-way BM qualitative appearance scoring was evaluated. We found high qualitative scoring interobserver variability across five observers that negatively impacted the model's risk stratification. Radiomics-based ML models trained to replicate the qualitative scoring did so with high accuracy (bootstrap-corrected AUC = 0.84-0.94), but risk stratification using these replicated qualitative scores remained poor. Radiomics-based ML models trained to directly predict post-SRS progression offered enhanced risk stratification (Kaplan-Meier rank-sum p = 0.0003) compared to using qualitative appearance. The qualitative appearance scoring enabled interpretation of the progression radiomics-based ML model, with necrotic BMs and a subset of heterogeneous BMs predicted as being at high-risk of post-SRS progression, in agreement with current radiobiological understanding. Our study's results show that while radiomics-based SRS outcome models out-perform qualitative appearance analysis, qualitative appearance still provides critical insight into ML model operation.
Collapse
Affiliation(s)
- David A DeVries
- Department of Medical Biophysics, Western University, London, N6A 3K7, Canada.
- Gerald C. Baines Centre, London Health Sciences Centre, London, N6A 5W9, Canada.
| | - Terence Tang
- Department of Radiation Oncology, London Health Sciences Centre, London, N6A 5W9, Canada
| | - Ali Albweady
- Department of Radiology, Unaizah College of Medicine and Medical Sciences, Qassim University, 56219, Buraidah, Saudi Arabia
| | - Andrew Leung
- Department of Medical Imaging, Western University, London, N6A 3K7, Canada
| | - Joanna Laba
- Department of Radiation Oncology, London Health Sciences Centre, London, N6A 5W9, Canada
- Department of Oncology, Western University, London, N6A 3K7, Canada
| | - Carol Johnson
- Gerald C. Baines Centre, London Health Sciences Centre, London, N6A 5W9, Canada
| | - Frank Lagerwaard
- Department of Radiation Oncology, Amsterdam University Medical Centre, Amsterdam, 1081, The Netherlands
| | - Jaap Zindler
- Department of Radiation Oncology, Haaglanden Medical Centre, Den Hague, 2512VA, The Netherlands
- Holland Proton Centre, Delft, 2629JA, The Netherlands
| | - George Hajdok
- Department of Medical Biophysics, Western University, London, N6A 3K7, Canada
| | - Aaron D Ward
- Department of Medical Biophysics, Western University, London, N6A 3K7, Canada
- Gerald C. Baines Centre, London Health Sciences Centre, London, N6A 5W9, Canada
- Department of Oncology, Western University, London, N6A 3K7, Canada
| |
Collapse
|
5
|
Diehl CD, Giordano FA, Grosu AL, Ille S, Kahl KH, Onken J, Rieken S, Sarria GR, Shiban E, Wagner A, Beck J, Brehmer S, Ganslandt O, Hamed M, Meyer B, Münter M, Raabe A, Rohde V, Schaller K, Schilling D, Schneider M, Sperk E, Thomé C, Vajkoczy P, Vatter H, Combs SE. Opportunities and Alternatives of Modern Radiation Oncology and Surgery for the Management of Resectable Brain Metastases. Cancers (Basel) 2023; 15:3670. [PMID: 37509330 PMCID: PMC10377800 DOI: 10.3390/cancers15143670] [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: 04/18/2023] [Revised: 07/10/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
Postsurgical radiotherapy (RT) has been early proven to prevent local tumor recurrence, initially performed with whole brain RT (WBRT). Subsequent to disadvantageous cognitive sequalae for the patient and the broad distribution of modern linear accelerators, focal irradiation of the tumor has omitted WBRT in most cases. In many studies, the effectiveness of local RT of the resection cavity, either as single-fraction stereotactic radiosurgery (SRS) or hypo-fractionated stereotactic RT (hFSRT), has been demonstrated to be effective and safe. However, whereas prospective high-level incidence is still lacking on which dose and fractionation scheme is the best choice for the patient, further ablative techniques have come into play. Neoadjuvant SRS (N-SRS) prior to resection combines straightforward target delineation with an accelerated post-surgical phase, allowing an earlier start of systemic treatment or rehabilitation as indicated. In addition, low-energy intraoperative RT (IORT) on the surgical bed has been introduced as another alternative to external beam RT, offering sterilization of the cavity surface with steep dose gradients towards the healthy brain. This consensus paper summarizes current local treatment strategies for resectable brain metastases regarding available data and patient-centered decision-making.
Collapse
Affiliation(s)
- Christian D Diehl
- Department of Radiation Oncology, Technical University of Munich (TUM), Klinikum rechts der Isar, 81675 München, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 80336 München, Germany
| | - Frank A Giordano
- Department of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
| | - Anca-L Grosu
- Department of Radiation Oncology, University Medical Center, Medical Faculty, 79106 Freiburg, Germany
| | - Sebastian Ille
- Department of Neurosurgery, Faculty of Medicine, Technical University of Munich, 81675 München, Germany
| | - Klaus-Henning Kahl
- Department of Radiation Oncology, University Medical Center Augsburg, 86156 Augsburg, Germany
| | - Julia Onken
- Department of Neurosurgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
- Berlin Institute of Health, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Stefan Rieken
- Department of Radiotherapy and Radiation Oncology, University Medical Center Göttingen, 37075 Göttingen, Germany
- Comprehensive Cancer Center Niedersachsen (CCC-N), 37075 Göttingen, Germany
| | - Gustavo R Sarria
- Department of Radiation Oncology, University Hospital Bonn, University of Bonn, 53127 Bonn, Germany
| | - Ehab Shiban
- Department of Neurosurgery, University Medical Center Augsburg, 86156 Augsburg, Germany
| | - Arthur Wagner
- Department of Neurosurgery, Faculty of Medicine, Technical University of Munich, 81675 München, Germany
| | - Jürgen Beck
- Department of Neurosurgery, University Hospital Freiburg, 79106 Freiburg, Germany
| | - Stefanie Brehmer
- Department of Neurosurgery, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
| | - Oliver Ganslandt
- Neurosurgical Clinic, Klinikum Stuttgart, 70174 Stuttgart, Germany
| | - Motaz Hamed
- Department of Neurosurgery, University Hospital Bonn, 53127 Bonn, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, Faculty of Medicine, Technical University of Munich, 81675 München, Germany
| | - Marc Münter
- Department of Radiation Oncology, Klinikum Stuttgart Katharinenhospital, 70174 Stuttgart, Germany
| | - Andreas Raabe
- Department of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Veit Rohde
- Department of Neurosurgery, Universitätsmedizin Göttingen, 37075 Göttingen, Germany
| | - Karl Schaller
- Department of Neurosurgery, University of Geneva Medical Center & Faculty of Medicine, 1211 Geneva, Switzerland
| | - Daniela Schilling
- Department of Radiation Oncology, Technical University of Munich (TUM), Klinikum rechts der Isar, 81675 München, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Matthias Schneider
- Department of Neurosurgery, University Hospital Bonn, 53127 Bonn, Germany
| | - Elena Sperk
- Mannheim Cancer Center, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
| | - Claudius Thomé
- Department of Neurosurgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Peter Vajkoczy
- Department of Neurosurgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Hartmut Vatter
- Department of Neurosurgery, University Hospital Bonn, 53127 Bonn, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Technical University of Munich (TUM), Klinikum rechts der Isar, 81675 München, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, 80336 München, Germany
| |
Collapse
|
6
|
DeVries DA, Tang T, Alqaidy G, Albweady A, Leung A, Laba J, Lagerwaard F, Zindler J, Hajdok G, Ward AD. Dual-center validation of using magnetic resonance imaging radiomics to predict stereotactic radiosurgery outcomes. Neurooncol Adv 2023; 5:vdad064. [PMID: 37358938 PMCID: PMC10289521 DOI: 10.1093/noajnl/vdad064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023] Open
Abstract
Background MRI radiomic features and machine learning have been used to predict brain metastasis (BM) stereotactic radiosurgery (SRS) outcomes. Previous studies used only single-center datasets, representing a significant barrier to clinical translation and further research. This study, therefore, presents the first dual-center validation of these techniques. Methods SRS datasets were acquired from 2 centers (n = 123 BMs and n = 117 BMs). Each dataset contained 8 clinical features, 107 pretreatment T1w contrast-enhanced MRI radiomic features, and post-SRS BM progression endpoints determined from follow-up MRI. Random decision forest models were used with clinical and/or radiomic features to predict progression. 250 bootstrap repetitions were used for single-center experiments. Results Training a model with one center's dataset and testing it with the other center's dataset required using a set of features important for outcome prediction at both centers, and achieved area under the receiver operating characteristic curve (AUC) values up to 0.70. A model training methodology developed using the first center's dataset was locked and externally validated with the second center's dataset, achieving a bootstrap-corrected AUC of 0.80. Lastly, models trained on pooled data from both centers offered balanced accuracy across centers with an overall bootstrap-corrected AUC of 0.78. Conclusions Using the presented validated methodology, radiomic models trained at a single center can be used externally, though they must utilize features important across all centers. These models' accuracies are inferior to those of models trained using each individual center's data. Pooling data across centers shows accurate and balanced performance, though further validation is required.
Collapse
Affiliation(s)
- David A DeVries
- Department of Medical Biophysics, Western University, London, ON, Canada
- Gerald C. Baines Centre, London Health Sciences Centre, London, ON, Canada
| | - Terence Tang
- Department of Radiation Oncology, London Regional Cancer Program, London, ON, Canada
| | - Ghada Alqaidy
- Radiodiagnostic and Medical Imaging Department, King Fahad Armed Forces Hospital, Jeddah, Saudi Arabia
| | - Ali Albweady
- Department of Radiology, Unaizah College of Medicine and Medical Sciences, Qassim University, Unaizah, Saudi Arabia
| | - Andrew Leung
- Department of Medical Imaging, Western University, London, ON, Canada
| | - Joanna Laba
- Department of Radiation Oncology, London Regional Cancer Program, London, ON, Canada
- Department of Oncology, Western University, London, ON, Canada
| | - Frank Lagerwaard
- Department of Radiation Oncology, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Jaap Zindler
- Department of Radiation Oncology, Haaglanden Medical Centre, Den Haag, The Netherlands
- Holland Proton Therapy Centre, Delft, The Netherlands
| | - George Hajdok
- Department of Medical Biophysics, Western University, London, ON, Canada
| | - Aaron D Ward
- Department of Medical Biophysics, Western University, London, ON, Canada
- Gerald C. Baines Centre, London Health Sciences Centre, London, ON, Canada
- Department of Oncology, Western University, London, ON, Canada
| |
Collapse
|
7
|
Performance sensitivity analysis of brain metastasis stereotactic radiosurgery outcome prediction using MRI radiomics. Sci Rep 2022; 12:20975. [PMID: 36471160 PMCID: PMC9722896 DOI: 10.1038/s41598-022-25389-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 11/29/2022] [Indexed: 12/09/2022] Open
Abstract
Recent studies have used T1w contrast-enhanced (T1w-CE) magnetic resonance imaging (MRI) radiomic features and machine learning to predict post-stereotactic radiosurgery (SRS) brain metastasis (BM) progression, but have not examined the effects of combining clinical and radiomic features, BM primary cancer, BM volume effects, and using multiple scanner models. To investigate these effects, a dataset of n = 123 BMs from 99 SRS patients with 12 clinical features, 107 pre-treatment T1w-CE radiomic features, and BM progression determined by follow-up MRI was used with a random decision forest model and 250 bootstrapped repetitions. Repeat experiments assessed the relative accuracy across primary cancer sites, BM volume groups, and scanner model pairings. Correction for accuracy imbalances across volume groups was investigated by removing volume-correlated features. We found that using clinical and radiomic features together produced the most accurate model with a bootstrap-corrected area under the receiver operating characteristic curve of 0.77. Accuracy also varied by primary cancer site, BM volume, and scanner model pairings. The effect of BM volume was eliminated by removing features at a volume-correlation coefficient threshold of 0.25. These results show that feature type, primary cancer, volume, and scanner model are all critical factors in the accuracy of radiomics-based prognostic models for BM SRS that must be characterised and controlled for before clinical translation.
Collapse
|
8
|
Li G, Wu X, Ma X. Artificial intelligence in radiotherapy. Semin Cancer Biol 2022; 86:160-171. [PMID: 35998809 DOI: 10.1016/j.semcancer.2022.08.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 08/18/2022] [Indexed: 11/19/2022]
Abstract
Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.
Collapse
Affiliation(s)
- Guangqi Li
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xin Wu
- Head & Neck Oncology ward, Division of Radiotherapy Oncology, Cancer Center, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xuelei Ma
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
| |
Collapse
|
9
|
Machine learning in neuro-oncology: toward novel development fields. J Neurooncol 2022; 159:333-346. [PMID: 35761160 DOI: 10.1007/s11060-022-04068-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/11/2022] [Indexed: 10/17/2022]
Abstract
PURPOSE Artificial Intelligence (AI) involves several and different techniques able to elaborate a large amount of data responding to a specific planned outcome. There are several possible applications of this technology in neuro-oncology. METHODS We reviewed, according to PRISMA guidelines, available studies adopting AI in different fields of neuro-oncology including neuro-radiology, pathology, surgery, radiation therapy, and systemic treatments. RESULTS Neuro-radiology presented the major number of studies assessing AI. However, this technology is being successfully tested also in other operative settings including surgery and radiation therapy. In this context, AI shows to significantly reduce resources and costs maintaining an elevated qualitative standard. Pathological diagnosis and development of novel systemic treatments are other two fields in which AI showed promising preliminary data. CONCLUSION It is likely that AI will be quickly included in some aspects of daily clinical practice. Possible applications of these techniques are impressive and cover all aspects of neuro-oncology.
Collapse
|
10
|
Nowakowski A, Lahijanian Z, Panet-Raymond V, Siegel PM, Petrecca K, Maleki F, Dankner M. Radiomics as an emerging tool in the management of brain metastases. Neurooncol Adv 2022; 4:vdac141. [PMID: 36284932 PMCID: PMC9583687 DOI: 10.1093/noajnl/vdac141] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Brain metastases (BM) are associated with significant morbidity and mortality in patients with advanced cancer. Despite significant advances in surgical, radiation, and systemic therapy in recent years, the median overall survival of patients with BM is less than 1 year. The acquisition of medical images, such as computed tomography (CT) and magnetic resonance imaging (MRI), is critical for the diagnosis and stratification of patients to appropriate treatments. Radiomic analyses have the potential to improve the standard of care for patients with BM by applying artificial intelligence (AI) with already acquired medical images to predict clinical outcomes and direct the personalized care of BM patients. Herein, we outline the existing literature applying radiomics for the clinical management of BM. This includes predicting patient response to radiotherapy and identifying radiation necrosis, performing virtual biopsies to predict tumor mutation status, and determining the cancer of origin in brain tumors identified via imaging. With further development, radiomics has the potential to aid in BM patient stratification while circumventing the need for invasive tissue sampling, particularly for patients not eligible for surgical resection.
Collapse
Affiliation(s)
- Alexander Nowakowski
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Québec, Canada
| | - Zubin Lahijanian
- McGill University Health Centre, Department of Diagnostic Radiology, McGill University, Montreal, Québec, Canada
| | - Valerie Panet-Raymond
- McGill University Health Centre, Department of Diagnostic Radiology, McGill University, Montreal, Québec, Canada
| | - Peter M Siegel
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Québec, Canada
| | - Kevin Petrecca
- Montreal Neurological Institute-Hospital, McGill University, Montreal, Québec, Canada
| | - Farhad Maleki
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
| | - Matthew Dankner
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Québec, Canada
| |
Collapse
|