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Noble DJ, Ramaesh R, Brothwell M, Elumalai T, Barrett T, Stillie A, Paterson C, Ajithkumar T. The Evolving Role of Novel Imaging Techniques for Radiotherapy Planning. Clin Oncol (R Coll Radiol) 2024; 36:514-526. [PMID: 38937188 DOI: 10.1016/j.clon.2024.05.018] [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: 03/24/2024] [Revised: 05/20/2024] [Accepted: 05/30/2024] [Indexed: 06/29/2024]
Abstract
The ability to visualise cancer with imaging has been crucial to the evolution of modern radiotherapy (RT) planning and delivery. And as evolving RT technologies deliver increasingly precise treatment, the importance of accurate identification and delineation of disease assumes ever greater significance. However, innovation in imaging technology has matched that seen with RT delivery platforms, and novel imaging techniques are a focus of much research activity. How these imaging modalities may alter and improve the diagnosis and staging of cancer is an important question, but already well served by the literature. What is less clear is how novel imaging techniques may influence and improve practical and technical aspects of RT planning and delivery. In this review, current gold standard approaches to integration of imaging, and potential future applications of bleeding-edge imaging technology into RT planning pathways are explored.
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Affiliation(s)
- D J Noble
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK; Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
| | - R Ramaesh
- Department of Radiology, Western General Hospital, Edinburgh, UK
| | - M Brothwell
- Department of Clinical Oncology, University College London Hospitals, London, UK
| | - T Elumalai
- Department of Oncology, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, UK
| | - T Barrett
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, UK
| | - A Stillie
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - C Paterson
- Beatson West of Scotland Cancer Centre, Great Western Road, Glasgow G12 0YN, UK
| | - T Ajithkumar
- Department of Oncology, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, UK
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Paganetti H, Simone CB, Bosch WR, Haas-Kogan D, Kirsch DG, Li H, Liang X, Liu W, Mahajan A, Story MD, Taylor PA, Willers H, Xiao Y, Buchsbaum JC. NRG Oncology White Paper on the Relative Biological Effectiveness in Proton Therapy. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)02974-2. [PMID: 39059509 DOI: 10.1016/j.ijrobp.2024.07.2152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 06/17/2024] [Accepted: 07/06/2024] [Indexed: 07/28/2024]
Abstract
This position paper, led by the NRG Oncology Particle Therapy Work Group, focuses on the concept of relative biologic effect (RBE) in clinical proton therapy (PT), with the goal of providing recommendations for the next-generation clinical trials with PT on the best practice of investigating and using RBE, which could deviate from the current standard proton RBE value of 1.1 relative to photons. In part 1, current clinical utilization and practice are reviewed, giving the context and history of RBE. Evidence for variation in RBE is presented along with the concept of linear energy transfer (LET). The intertwined nature of tumor radiobiology, normal tissue constraints, and treatment planning with LET and RBE considerations is then reviewed. Part 2 summarizes current and past clinical data and then suggests the next steps to explore and employ tools for improved dynamic models for RBE. In part 3, approaches and methods for the next generation of prospective clinical trials are explored, with the goal of optimizing RBE to be both more reflective of clinical reality and also deployable in trials to allow clinical validation and interpatient comparisons. These concepts provide the foundation for personalized biologic treatments reviewed in part 4. Finally, we conclude with a summary including short- and long-term scientific focus points for clinical PT. The practicalities and capacity to use RBE in treatment planning are reviewed and considered with more biological data in hand. The intermediate step of LET optimization is summarized and proposed as a potential bridge to the ultimate goal of case-specific RBE planning that can be achieved as a hypothesis-generating tool in near-term proton trials.
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Affiliation(s)
- Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts; Department of Radiation Oncology, Harvard Medical School, Boston, Massachusetts
| | - Charles B Simone
- New York Proton Center, New York, New York; Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Walter R Bosch
- Department of Radiation Oncology, Washington University, St. Louis, Missouri
| | - Daphne Haas-Kogan
- Department of Radiation Oncology, Harvard Medical School, Boston, Massachusetts; Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Boston, Massachusetts
| | - David G Kirsch
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Heng Li
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic Florida, Jacksonville, Florida
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona
| | - Anita Mahajan
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
| | - Michael D Story
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
| | | | - Henning Willers
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts; Department of Radiation Oncology, Harvard Medical School, Boston, Massachusetts
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jeffrey C Buchsbaum
- National Cancer Institute, National Institutes of Health, Bethesda, Maryland.
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Abbaspour S, Barahman M, Abdollahi H, Arabalibeik H, Hajainfar G, Babaei M, Iraji H, Barzegartahamtan M, Ay MR, Mahdavi SR. Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study. Biomed Phys Eng Express 2023; 10:015017. [PMID: 37995359 DOI: 10.1088/2057-1976/ad0f3e] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 11/23/2023] [Indexed: 11/25/2023]
Abstract
Purpose.This study aims to predict radiotherapy-induced rectal and bladder toxicity using computed tomography (CT) and magnetic resonance imaging (MRI) radiomics features in combination with clinical and dosimetric features in rectal cancer patients.Methods.A total of sixty-three patients with locally advanced rectal cancer who underwent three-dimensional conformal radiation therapy (3D-CRT) were included in this study. Radiomics features were extracted from the rectum and bladder walls in pretreatment CT and MR-T2W-weighted images. Feature selection was performed using various methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-square (Chi2), Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and SelectPercentile. Predictive modeling was carried out using machine learning algorithms, such as K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Gradient Boosting (XGB), and Linear Discriminant Analysis (LDA). The impact of the Laplacian of Gaussian (LoG) filter was investigated with sigma values ranging from 0.5 to 2. Model performance was evaluated in terms of the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, and specificity.Results.A total of 479 radiomics features were extracted, and 59 features were selected. The pre-MRI T2W model exhibited the highest predictive performance with an AUC: 91.0/96.57%, accuracy: 90.38/96.92%, precision: 90.0/97.14%, sensitivity: 93.33/96.50%, and specificity: 88.09/97.14%. These results were achieved with both original image and LoG filter (sigma = 0.5-1.5) based on LDA/DT-RF classifiers for proctitis and cystitis, respectively. Furthermore, for the CT data, AUC: 90.71/96.0%, accuracy: 90.0/96.92%, precision: 88.14/97.14%, sensitivity: 93.0/96.0%, and specificity: 88.09/97.14% were acquired. The highest values were achieved using XGB/DT-XGB classifiers for proctitis and cystitis with LoG filter (sigma = 2)/LoG filter (sigma = 0.5-2), respectively. MRMR/RFE-Chi2 feature selection methods demonstrated the best performance for proctitis and cystitis in the pre-MRI T2W model. MRMR/MRMR-Lasso yielded the highest model performance for CT.Conclusion.Radiomics features extracted from pretreatment CT and MR images can effectively predict radiation-induced proctitis and cystitis. The study found that LDA, DT, RF, and XGB classifiers, combined with MRMR, RFE, Chi2, and Lasso feature selection algorithms, along with the LoG filter, offer strong predictive performance. With the inclusion of a larger training dataset, these models can be valuable tools for personalized radiotherapy decision-making.
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Affiliation(s)
- Samira Abbaspour
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maedeh Barahman
- Department of Radiation Oncology, Firoozgar Hospital, Firoozgar Clinical Research Development Center (FCRDC), Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Hossein Arabalibeik
- Research Center for Science and Technology in Medicine (RCSTM), Tehran University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajainfar
- Rajaie Cardiovascular Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mohammadreza Babaei
- Department of Interventional Radiology, Firouzgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Hamed Iraji
- Department of Interventional Radiology, Firouzgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Barzegartahamtan
- Clinical Research Development Unit, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Ay
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Seied Rabi Mahdavi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
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