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Parrella G, Annunziata S, Morelli L, Molinelli S, Magro G, Ciocca M, Riva G, Ciccone LP, Iannalfi A, Paganelli C, Orlandi E, Baroni G. A dosiomics approach to treatment outcome modeling in carbon ion radiotherapy for skull base chordomas. Phys Med 2024; 124:103421. [PMID: 38968695 DOI: 10.1016/j.ejmp.2024.103421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 04/23/2024] [Accepted: 06/29/2024] [Indexed: 07/07/2024] Open
Abstract
PURPOSE To investigate the role of dosiomics features extracted from physical dose (DPHYS), RBE-weighted dose (DRBE) and dose-averaged Linear Energy Transfer (LETd), to predict the risk of local recurrence (LR) in skull base chordoma (SBC) treated with Carbon Ion Radiotherapy (CIRT). Thus, define and evaluate dosiomics-driven tumor control probability (TCP) models. MATERIALS AND METHODS 54 SBC patients were retrospectively selected for this study. A regularized Cox proportional hazard model (r-Cox) and Survival Support Vector Machine (s-SVM) were tuned within a repeated Cross Validation (CV) and patients were stratified in low/high risk of LR. Models' performance was evaluated through Harrell's concordance statistic (C-index), and survival was represented through Kaplan-Meier (KM) curves. A multivariable logistic regression was fit to the selected feature sets to generate a dosiomics-driven TCP model for each map. These were compared to a reference model built with clinical parameters in terms of f-score and accuracy. RESULTS The LETd maps reached a test C-index of 0.750 and 0.786 with r-Cox and s-SVM, and significantly separated KM curves. DPHYS maps and clinical parameters showed promising CV outcomes with C-index above 0.8, despite a poorer performance on the test set and patients stratification. The LETd-based TCP showed a significatively higher f-score (0.67[0.52-0.70], median[IQR]) compared to the clinical model (0.4[0.32-0.63], p < 0.025), while DPHYS achieved a significatively higher accuracy (DPHYS: 0.73[0.65-0.79], Clinical: 0.6 [0.52-0.72]). CONCLUSION This analysis supports the role of LETd as relevant source of prognostic factors for LR in SBC treated with CIRT. This is reflected in the TCP modeling, where LETd and DPHYS showed an improved performance with respect to clinical models.
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Affiliation(s)
- Giovanni Parrella
- Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, Italy.
| | - Simone Annunziata
- Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, Italy
| | - Letizia Morelli
- Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, Italy
| | - Silvia Molinelli
- Centro Nazionale di Adroterapia Oncologica, Medical Physics Unit, Pavia, Italy
| | - Giuseppe Magro
- Centro Nazionale di Adroterapia Oncologica, Medical Physics Unit, Pavia, Italy
| | - Mario Ciocca
- Centro Nazionale di Adroterapia Oncologica, Medical Physics Unit, Pavia, Italy
| | - Giulia Riva
- Centro Nazionale di Adroterapia Oncologica, Radiotherapy Unit, Pavia, Italy
| | - Lucia Pia Ciccone
- Centro Nazionale di Adroterapia Oncologica, Radiotherapy Unit, Pavia, Italy
| | - Alberto Iannalfi
- Centro Nazionale di Adroterapia Oncologica, Radiotherapy Unit, Pavia, Italy
| | - Chiara Paganelli
- Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, Italy
| | - Ester Orlandi
- Centro Nazionale di Adroterapia Oncologica, Radiation Oncology Clinical Unit, Pavia, Italy; University of Pavia, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, Pavia, Italy
| | - Guido Baroni
- Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, Italy
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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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Park YI, Cho MS, Chang JS, Kim JS, Kim YB, Lee IJ, Hong CS, Choi SH. Normal tissue complication probability models of hypothyroidism after radiotherapy for breast cancer. Clin Transl Radiat Oncol 2024; 45:100734. [PMID: 38317677 PMCID: PMC10839258 DOI: 10.1016/j.ctro.2024.100734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/19/2024] [Accepted: 01/21/2024] [Indexed: 02/07/2024] Open
Abstract
Purpose We aimed to develop Lyman-Kutcher-Burman (LKB) and multivariable normal tissue complication probability (NTCP) models to predict the risk of radiation-induced hypothyroidism (RIHT) in breast cancer patients. Materials and methods A total of 1,063 breast cancer patients who underwent whole breast irradiation between 2009 and 2016 were analyzed. Individual dose-volume histograms were used to generate LKB and multivariable logistic regression models. LKB model was fit using the thyroid radiation dose-volume parameters. A multivariable model was constructed to identify potential dosimetric and clinical parameters associated with RIHT. Internal validation was conducted using bootstrapping techniques, and model performance was evaluated using the area under the curve (AUC) and Hosmer-Lemeshow (HL) goodness-of-fit test. Results RIHT developed in 4 % of patients with a median follow-up of 77.7 months. LKB and multivariable NTCP models exhibited significant agreement between the predicted and observed results (HL P values > 0.05). The multivariable NTCP model outperformed the LKB model in predicting RIHT (AUC 0.62 vs. 0.54). In the multivariable model, systemic therapy, age, and percentage of thyroid volume receiving ≥ 10 Gy (V10) were significant prognostic factors for RIHT. The cumulative incidence of RIHT was significantly higher in patients who exceeded the cut-off values for all three risk predictors (systemic therapy, age ≥ 40 years, and thyroid V10 ≥ 26 %, P < 0.005). Conclusions Systemic therapy, age, and V10 of the thyroid were identified as strong risk factors for the development of RIHT. Our NTCP models provide valuable insights to clinicians for predicting and preventing hypothyroidism by identifying high-risk patients.
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Affiliation(s)
- Ye-In Park
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Min-Seok Cho
- Department of Radiation Oncology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Gyeonggi do, South Korea
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Korea
- BC Cancer - Vancouver Centre, Vancouver, British Columbia, Canada
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Yong Bae Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Ik Jae Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Chae-Seon Hong
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Seo Hee Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Korea
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Ubeira-Gabellini MG, Mori M, Palazzo G, Cicchetti A, Mangili P, Pavarini M, Rancati T, Fodor A, Del Vecchio A, Di Muzio NG, Fiorino C. Comparing Performances of Predictive Models of Toxicity after Radiotherapy for Breast Cancer Using Different Machine Learning Approaches. Cancers (Basel) 2024; 16:934. [PMID: 38473296 DOI: 10.3390/cancers16050934] [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: 01/17/2024] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024] Open
Abstract
PURPOSE Different ML models were compared to predict toxicity in RT on a large cohort (n = 1314). METHODS The endpoint was RTOG G2/G3 acute toxicity, resulting in 204/1314 patients with the event. The dataset, including 25 clinical, anatomical, and dosimetric features, was split into 984 for training and 330 for internal tests. The dataset was standardized; features with a high p-value at univariate LR and with Spearman ρ>0.8 were excluded; synthesized data of the minority were generated to compensate for class imbalance. Twelve ML methods were considered. Model optimization and sequential backward selection were run to choose the best models with a parsimonious feature number. Finally, feature importance was derived for every model. RESULTS The model's performance was compared on a training-test dataset over different metrics: the best performance model was LightGBM. Logistic regression with three variables (LR3) selected via bootstrapping showed performances similar to the best-performing models. The AUC of test data is slightly above 0.65 for the best models (highest value: 0.662 with LightGBM). CONCLUSIONS No model performed the best for all metrics: more complex ML models had better performances; however, models with just three features showed performances comparable to the best models using many (n = 13-19) features.
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Affiliation(s)
| | - Martina Mori
- Medical Physics, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Gabriele Palazzo
- Medical Physics, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Alessandro Cicchetti
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Paola Mangili
- Medical Physics, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Maddalena Pavarini
- Medical Physics, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Tiziana Rancati
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Andrei Fodor
- Radiotherapy, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | | | - Nadia Gisella Di Muzio
- Radiotherapy, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
- Department of Radiotherapy, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Claudio Fiorino
- Medical Physics, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
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Connor M, Salans M, Karunamuni R, Unnikrishnan S, Huynh-Le MP, Tibbs M, Qian A, Reyes A, Stasenko A, McDonald C, Moiseenko V, El-Naqa I, Hattangadi-Gluth JA. Fine Motor Skill Decline After Brain Radiation Therapy-A Multivariate Normal Tissue Complication Probability Study of a Prospective Trial. Int J Radiat Oncol Biol Phys 2023; 117:581-593. [PMID: 37150258 PMCID: PMC10911396 DOI: 10.1016/j.ijrobp.2023.04.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 03/20/2023] [Accepted: 04/29/2023] [Indexed: 05/09/2023]
Abstract
PURPOSE Brain radiation therapy can impair fine motor skills (FMS). Fine motor skills are essential for activities of daily living, enabling hand-eye coordination for manipulative movements. We developed normal tissue complication probability (NTCP) models for the decline in FMS after fractionated brain radiation therapy (RT). METHODS AND MATERIALS On a prospective trial, 44 patients with primary brain tumors received fractioned RT; underwent high-resolution volumetric magnetic resonance imaging, diffusion tensor imaging, and comprehensive FMS assessments (Delis-Kaplan Executive Function System Trail Making Test Motor Speed [DKEFS-MS]; and Grooved Pegboard dominant/nondominant hands) at baseline and 6 months postRT. Regions of interest subserving motor function (including cortex, superficial white matter, thalamus, basal ganglia, cerebellum, and white matter tracts) were autosegmented using validated methods and manually verified. Dosimetric and clinical variables were included in multivariate NTCP models using automated bootstrapped logistic regression, least absolute shrinkage and selection operator logistic regression, and random forests with nested cross-validation. RESULTS Half of the patients showed a decline on grooved pegboard test of nondominant hands, 17 of 42 (40.4%) on grooved pegboard test of -dominant hands, and 11 of 44 (25%) on DKEFS-MS. Automated bootstrapped logistic regression selected a 1-term model including maximum dose to dominant postcentral white matter. The least absolute shrinkage and selection operator logistic regression selected this term and steroid use. The top 5 variables in the random forest were all dosimetric: maximum dose to dominant thalamus, mean dose to dominant caudate, mean and maximum dose to the dominant corticospinal tract, and maximum dose to dominant postcentral white matter. This technique performed best with an area under the curve of 0.69 (95% CI, 0.68-0.70) on nested cross-validation. CONCLUSIONS We present the first NTCP models for FMS impairment after brain RT. Dose to several supratentorial motor-associated regions of interest correlated with a decline in dominant-hand fine motor dexterity in patients with primary brain tumors in multivariate models, outperforming clinical variables. These data can guide prospective fine motor-sparing strategies for brain RT.
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Affiliation(s)
- Michael Connor
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California
| | - Mia Salans
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California
| | - Roshan Karunamuni
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California
| | - Soumya Unnikrishnan
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California
| | | | - Michelle Tibbs
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California
| | - Alexander Qian
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California
| | - Anny Reyes
- Department of Psychiatry, University of California San Diego, San Diego, California
| | - Alena Stasenko
- Department of Psychiatry, University of California San Diego, San Diego, California
| | - Carrie McDonald
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
| | - Vitali Moiseenko
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California
| | - Issam El-Naqa
- Department of Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Jona A Hattangadi-Gluth
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California.
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Gueiderikh A, Sarrade T, Kirova Y, De La Lande B, De Vathaire F, Auzac G, Martin AL, Everhard S, Meillan N, Bourgier C, Benyoucef A, Lacornerie T, Pasquier D, Racadot S, Moignier A, Paris F, André F, Deutsch E, Duchemann B, Allodji RS, Rivera S. Radiation-induced lung injury after breast cancer treatment: incidence in the CANTO-RT cohort and associated clinical and dosimetric risk factors. Front Oncol 2023; 13:1199043. [PMID: 37456251 PMCID: PMC10342531 DOI: 10.3389/fonc.2023.1199043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 05/26/2023] [Indexed: 07/18/2023] Open
Abstract
Purpose Radiation-induced lung injury (RILI) is strongly associated with various clinical conditions and dosimetric parameters. Former studies have led to reducing radiotherapy (RT) doses to the lung and have favored the discontinuation of tamoxifen during RT. However, the monocentric design and variability of dosimetric parameters chosen have limited further improvement. The aim of our study was to assess the incidence of RILI in current practice and to determine clinical and dosimetric risk factors associated with RILI occurrence. Material and methods Data from 3 out of the 10 top recruiting centers in CANTO-RT, a subset of the CANTO prospective longitudinal cohort (NCT01993498), were retrospectively analyzed for RILI occurrence. This cohort, which recruited invasive cT0-3 cN0-3 M0 breast cancer patients from 2012 to 2018, prospectively recorded the occurrence of adverse events by questionnaires and medical visits at the end of, and up to 60 months after treatment. RILI adverse events were defined in all patients by the association of clinical symptoms and compatible medical imaging. Results RILI was found in 38/1565 (2.4%) patients. Grade II RILI represented 15/38 events (39%) and grade III or IV 2/38 events (6%). There were no grade V events. The most frequently used technique for treatment was 3D conformational RT (96%). In univariable analyses, we confirmed the association of RILI occurrence with pulmonary medical history, absence of cardiovascular disease medical history, high pT and pN, chemotherapy use, nodal RT. All dosimetric parameters were highly correlated and had close predictive value. In the multivariable analysis adjusted for chemotherapy use and nodal involvement, pulmonary medical history (OR=3.05, p<0.01) and high V30 Gy (OR=1.06, p=0.04) remained statistically significant risk factors for RILI occurrence. V30 Gy >15% was significantly associated with RILI occurrence in a multivariable analysis (OR=3.07, p=0.03). Conclusion Our study confirms the pulmonary safety of breast 3D RT in CANTO-RT. Further analyses with modern radiation therapy techniques such as IMRT are needed. Our results argue in favor of a dose constraint to the ipsilateral lung using V30 Gy not exceeding 15%, especially in patients presenting pulmonary medical history. Pulmonary disease records should be taken into account for RT planning.
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Affiliation(s)
- Anna Gueiderikh
- Gustave Roussy, Radiation Therapy Department, Villejuif, France
| | | | - Youlia Kirova
- Department of Radiation Oncology, Institut Curie, Paris, France
- University Versailles, St. Quentin, France
| | | | - Florent De Vathaire
- Gustave Roussy, Comprehensive Cancer Research Center, Villejuif, France
- Centre for Research in Epidemiology and Population Health, U1018 Institut National de la Santé et de la Recherche Médicale (INSERM), Villejuif, France
- Paris-Saclay University, Unité Mixte de Recherche (UMR) 1018, Villejuif, France
| | - Guillaume Auzac
- Gustave Roussy, Radiation Therapy Department, Villejuif, France
| | | | | | - Nicolas Meillan
- Centre for Research in Epidemiology and Population Health, U1018 Institut National de la Santé et de la Recherche Médicale (INSERM), Villejuif, France
- Paris-Saclay University, Gustave Roussy, Institut National de la Santé et de la Recherche Médicale (INSERM) 1030, Villejuif, France
- Radiation Therapy Department, CH Victor Dupouy, Argenteuil, France
| | - Celine Bourgier
- Montpellier University, Montpellier, France
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Institut National de la Santé et de la Recherche Médicale (INSERM) U1194, Montpellier, France
- Fédération Universitaire d’Oncologie Radiothérapie d’Occitanie Méditerranée, Institut régional du Cancer Montpellier (ICM), Montpellier, France
| | - Ahmed Benyoucef
- Radiation Therapy Department, Henri Becquerel Center, Rouen, France
| | - Thomas Lacornerie
- Centre Oscar Lambret, Academic Department of Radiation Oncology, 3 rue Combemale, Lille, France
| | - David Pasquier
- Centre Oscar Lambret, Academic Department of Radiation Oncology, 3 rue Combemale, Lille, France
- Univ. Lille, &, Centre National de la Recherche Scientifique (CNRS), Centrale Lille, Unité Mixte de Recherche (UMR) 9189 – Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL), Lille, France
| | | | - Alexandra Moignier
- Radiotherapy Department, Institut de Cancérologie de l’Ouest, Nantes, France
| | - François Paris
- Radiotherapy Department, Institut de Cancérologie de l’Ouest, Nantes, France
- Nantes Université, Nantes - Angers Cancer and Immunology Research Center (CRCI2NA), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Nantes, France
| | - Fabrice André
- Gustave Roussy, Medical Oncology Department, Villejuif, France
- Paris-Saclay University, Gustave Roussy, Institut National de la Santé et de la Recherche Médicale (INSERM) U981, Villejuif, France
| | - Eric Deutsch
- Gustave Roussy, Radiation Therapy Department, Villejuif, France
- Paris-Saclay University, Gustave Roussy, Institut National de la Santé et de la Recherche Médicale (INSERM) 1030, Villejuif, France
| | | | - Rodrigue Setcheou Allodji
- Gustave Roussy, Comprehensive Cancer Research Center, Villejuif, France
- Centre for Research in Epidemiology and Population Health, U1018 Institut National de la Santé et de la Recherche Médicale (INSERM), Villejuif, France
- Paris-Saclay University, Unité Mixte de Recherche (UMR) 1018, Villejuif, France
| | - Sofia Rivera
- Gustave Roussy, Radiation Therapy Department, Villejuif, France
- Paris-Saclay University, Gustave Roussy, Institut National de la Santé et de la Recherche Médicale (INSERM) 1030, Villejuif, France
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Dennstädt F, Medová M, Putora PM, Glatzer M. Parameters of the Lyman Model for Calculation of Normal-Tissue Complication Probability: A Systematic Literature Review. Int J Radiat Oncol Biol Phys 2023; 115:696-706. [PMID: 36029911 DOI: 10.1016/j.ijrobp.2022.08.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/10/2022] [Accepted: 08/13/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE The Lyman model is one of the most used radiobiological models for calculation of normal-tissue complication probability (NTCP). Since its introduction in 1985, many authors have published parameter values for the model based on clinical data of different radiotherapeutic situations. This study attempted to collect the entirety of radiobiological parameter sets published to date and provide an overview of the data basis for different variations of the model. Furthermore, it sought to compare the parameter values and calculated NTCPs for selected endpoints with sufficient data available. METHODS AND MATERIALS A systematic literature analysis was performed, searching for publications that provided parameters for the different variations of the Lyman model in the Medline database using PubMed. Parameter sets were grouped into 13 toxicity-related endpoint groups. For 3 selected endpoint groups (≤25% reduction of saliva 12 months after irradiation of the parotid, symptomatic pneumonitis after irradiation of the lung, and bleeding of grade 2 or less after irradiation of the rectum), parameter values were compared and differences in calculated NTCP values were analyzed. RESULTS A total of 509 parameter sets from 130 publications were identified. Considerable heterogeneities were detected regarding the number of parameters available for different radio-oncological situations. Furthermore, for the 3 selected endpoints, large differences in published parameter values were found. These translated into great variations of calculated NTCPs, with maximum ranges of 35.2% to 93.4% for the saliva endpoint, of 39.4% to 90.4% for the pneumonitis endpoint, and of 5.4% to 99.3% for the rectal bleeding endpoint. CONCLUSIONS The detected heterogeneity of the data as well as the large variations of published radiobiological parameters underline the necessity for careful interpretation when using such parameters for NTCP calculations. Appropriate selection of parameters and validation of values are essential when using the Lyman model.
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Affiliation(s)
- Fabio Dennstädt
- Department of Radiation Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland.
| | - Michaela Medová
- Department of Radiation Oncology, University of Bern, Bern, Switzerland; Department for BioMedical Research, Inselspital Bern, Bern, Switzerland
| | - Paul Martin Putora
- Department of Radiation Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland; Department of Radiation Oncology, University of Bern, Bern, Switzerland
| | - Markus Glatzer
- Department of Radiation Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland
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8
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Cai G, Li C, Li J, Yang J, Li C, Sun L, Li J, Yu J, Meng X. Cardiac Substructures Dosimetric Predictors for Cardiac Toxicity After Definitive Radiotherapy in Esophageal Cancer. Int J Radiat Oncol Biol Phys 2023; 115:366-381. [PMID: 35973623 DOI: 10.1016/j.ijrobp.2022.08.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 07/19/2022] [Accepted: 08/05/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE To investigate the predictive value of the cardiac substructures (CSs) dosimetric parameters for cardiac toxicity after definitive radiation therapy in locally advanced esophageal cancer. METHODS AND MATERIALS Between August 2010 and January 2016, 716 patients with stage 2-3 esophageal cancer receiving definitive radiation therapy at 2 institutions were divided into training (n = 432) and external validation (n = 284) cohorts. Dose-volume histogram parameters for the whole heart (WH) and CSs were extracted. Competing risks and Cox regressions analyses were performed. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) and the Brier score. RESULTS With a median follow-up of 93 months, 68 patients (15.7%) developed grade ≥3 cardiac events (G3+ CEs), with a median of 13.5 months to the first event. Multivariable analysis showed left ventricle, left anterior descending (LAD), and mean left circumflex (LCX) variables were significantly associated with G3+ CEs. The AUCs and Brier scores demonstrated favorable predictive accuracies of the models integrating these CS variables when predicting G3+ CEs in the training and validation cohorts. However, compared with the WH variables, the CS variables did not significantly improve the prediction of G3+ CEs. Nevertheless, when G3+ acute coronary syndrome and/or congestive heart failure (ACS/CHF) CE was the outcome of interest, models based on the LAD or LCX variables were superior to the WH variable models in training and validation cohorts. CONCLUSIONS Models based on CS variables showed favorable predictive accuracy for G3+ CEs. The LAD and LCX variables significantly improved the prediction of G3+ ACS/CHF events compared with the WH variables. Radiation doses to CSs, such as LCX and LAD, should be monitored to help reduce the occurrence of significant CEs in patients with esophageal cancer undergoing definitive radiation therapy.
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Affiliation(s)
- Guoxin Cai
- Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Chuanbao Li
- Department of Emergency, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Department of Cardiovascular Medicine, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Jisheng Li
- Department of Medical Oncology, Cancer Center, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Jianmin Yang
- Department of Cardiovascular Medicine, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Chengming Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Liangchao Sun
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Jianing Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Jinming Yu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Xue Meng
- Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
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9
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Sminia P, Guipaud O, Viktorsson K, Ahire V, Baatout S, Boterberg T, Cizkova J, Dostál M, Fernandez-Palomo C, Filipova A, François A, Geiger M, Hunter A, Jassim H, Edin NFJ, Jordan K, Koniarová I, Selvaraj VK, Meade AD, Milliat F, Montoro A, Politis C, Savu D, Sémont A, Tichy A, Válek V, Vogin G. Clinical Radiobiology for Radiation Oncology. RADIOBIOLOGY TEXTBOOK 2023:237-309. [DOI: 10.1007/978-3-031-18810-7_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
AbstractThis chapter is focused on radiobiological aspects at the molecular, cellular, and tissue level which are relevant for the clinical use of ionizing radiation (IR) in cancer therapy. For radiation oncology, it is critical to find a balance, i.e., the therapeutic window, between the probability of tumor control and the probability of side effects caused by radiation injury to the healthy tissues and organs. An overview is given about modern precision radiotherapy (RT) techniques, which allow optimal sparing of healthy tissues. Biological factors determining the width of the therapeutic window are explained. The role of the six typical radiobiological phenomena determining the response of both malignant and normal tissues in the clinic, the 6R’s, which are Reoxygenation, Redistribution, Repopulation, Repair, Radiosensitivity, and Reactivation of the immune system, is discussed. Information is provided on tumor characteristics, for example, tumor type, growth kinetics, hypoxia, aberrant molecular signaling pathways, cancer stem cells and their impact on the response to RT. The role of the tumor microenvironment and microbiota is described and the effects of radiation on the immune system including the abscopal effect phenomenon are outlined. A summary is given on tumor diagnosis, response prediction via biomarkers, genetics, and radiomics, and ways to selectively enhance the RT response in tumors. Furthermore, we describe acute and late normal tissue reactions following exposure to radiation: cellular aspects, tissue kinetics, latency periods, permanent or transient injury, and histopathology. Details are also given on the differential effect on tumor and late responding healthy tissues following fractionated and low dose rate irradiation as well as the effect of whole-body exposure.
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10
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Fu G, Xie Y, Pan J, Qiu Y, He H, Li Z, Li J, Feng Y, Lv X. Longitudinal study of irradiation-induced brain functional network alterations in patients with nasopharyngeal carcinoma. Radiother Oncol 2022; 173:277-284. [PMID: 35718009 DOI: 10.1016/j.radonc.2022.06.008] [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: 09/12/2021] [Revised: 06/04/2022] [Accepted: 06/12/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND To investigate radiotherapy (RT)-related brain network changes in patients with nasopharyngeal carcinoma (NPC) over time and develop least absolute shrinkage and selection operator (LASSO)-based multivariable normal tissue complication probability (NTCP) models to predict RT-related brain network changes. METHODS 36 NPC patients were followed up at four timepoints: baseline, within 3 months (acute), 6 months (subacute), and 12 months (delayed) post-RT. 15 comparable healthy controls (HCs) were finally included and followed up in parallel. Functional neuroimaging data, dose-volume parameters of bilateral temporal lobes and Montreal Cognitive Assessment (MoCA) were acquired. Graph theoretical analysis and mixed-design analysis of variance were performed to investigate how the brain global and nodal changes were affected by RT. Multivariate logistic regression NTCP models were developed. LASSO with nested cross-validation strategy was used to select features. The relationships between network changes and MoCA changes were also examined. RESULTS Significant changes were detected in nodal efficiency (NE) in NPC patients but not in HCs over time. Altered NE was distributed in the bilateral frontal, temporal lobes and the right insula, which showed a "decrease-increase/recovery" pattern over time. Among all models, the model for predicting NE changes of STG.R showed a relatively good performance (area under the receiver operating curve: 0.68), and D20cc and V20 to right temporal lobe outperformed in this model. CONCLUSION Our findings indicate that RT-induced brain injury begin at the acute period and follow a recovery over time. Furthermore, our study presents prediction models for brain dysfunction based on the dosimetric and clinical parameters.
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Affiliation(s)
- Gui Fu
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Yuanyao Xie
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Jie Pan
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Yingwei Qiu
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Haoqiang He
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Zhipeng Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Jing Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China; Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
| | - Xiaofei Lv
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.
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11
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Chen M, Wang Z, Jiang S, Sun J, Wang L, Sahoo N, Brandon Gunn G, Frank SJ, Xu C, Chen J, Nguyen QN, Chang JY, Liao Z, Ronald Zhu X, Zhang X. Predictive performance of different NTCP techniques for radiation-induced esophagitis in NSCLC patients receiving proton radiotherapy. Sci Rep 2022; 12:9178. [PMID: 35655073 PMCID: PMC9163134 DOI: 10.1038/s41598-022-12898-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 05/18/2022] [Indexed: 11/24/2022] Open
Abstract
This study aimed to compare the predictive performance of different modeling methods in developing normal tissue complication probability (NTCP) models for predicting radiation-induced esophagitis (RE) in non–small cell lung cancer (NSCLC) patients receiving proton radiotherapy. The dataset was composed of 328 NSCLC patients receiving passive-scattering proton therapy and 41.6% of the patients experienced ≥ grade 2 RE. Five modeling methods were used to build NTCP models: standard Lyman–Kutcher–Burman (sLKB), generalized LKB (gLKB), multivariable logistic regression using two variable selection procedures-stepwise forward selection (Stepwise-MLR), and least absolute shrinkage and selection operator (LASSO-MLR), and support vector machines (SVM). Predictive performance was internally validated by a bootstrap approach for each modeling method. The overall performance, discriminative ability, and calibration were assessed using the Negelkerke R2, area under the receiver operator curve (AUC), and Hosmer–Lemeshow test, respectively. The LASSO-MLR model showed the best discriminative ability with an AUC value of 0.799 (95% confidence interval (CI): 0.763–0.854), and the best overall performance with a Negelkerke R2 value of 0.332 (95% CI: 0.266–0.486). Both of the optimism-corrected Negelkerke R2 values of the SVM and sLKB models were 0.301. The optimism-corrected AUC of the gLKB model (0.796) was higher than that of the SVM model (0.784). The sLKB model had the smallest optimism in the model variation and discriminative ability. In the context of classification and probability estimation for predicting the NTCP for radiation-induced esophagitis, the MLR model developed with LASSO provided the best predictive results. The simplest LKB modeling had similar or even better predictive performance than the most complex SVM modeling, and it was least likely to overfit the training data. The advanced machine learning approach might have limited applicability in clinical settings with a relatively small amount of data.
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Affiliation(s)
- Mei Chen
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.,Department of Radiation Physics, Unit 1150, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX, 77030, USA
| | - Zeming Wang
- Department of Radiation Physics, Unit 1150, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX, 77030, USA
| | - Shengpeng Jiang
- Department of Radiation Physics, Unit 1150, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX, 77030, USA.,Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 30060, China
| | - Jian Sun
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 30060, China.,Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Li Wang
- Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Narayan Sahoo
- Department of Radiation Physics, Unit 1150, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX, 77030, USA
| | - G Brandon Gunn
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Steven J Frank
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Cheng Xu
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jiayi Chen
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Quynh-Nhu Nguyen
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Joe Y Chang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - X Ronald Zhu
- Department of Radiation Physics, Unit 1150, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX, 77030, USA
| | - Xiaodong Zhang
- Department of Radiation Physics, Unit 1150, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX, 77030, USA.
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12
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Köthe A, Feuvret L, Weber DC, Safai S, Lomax AJ, Fattori G. Assessment of Radiation-Induced Optic Neuropathy in a Multi-Institutional Cohort of Chordoma and Chondrosarcoma Patients Treated with Proton Therapy. Cancers (Basel) 2021; 13:cancers13215327. [PMID: 34771490 PMCID: PMC8582447 DOI: 10.3390/cancers13215327] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 10/19/2021] [Accepted: 10/21/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Proton therapy is an effective therapeutic option for the treatment of skull-base tumors that require high radiation doses to be controlled. On rare occasions, patients suffer from radiation-induced optic neuropathy (RION) to the detriment of their post-treatment quality-of-life. We have collected multi-institutional data of 289 skull-base patients having received high doses to the optic apparatus from proton therapy or proton–photon mixed treatments and have observed a RION incidence rate (all grades) of 4.2% (12). We have furthermore confirmed older age and hypertension as risk factors for the onset of this side effect, with tumor involvement or its proximity to the optic apparatus and repeated surgical procedures showing moderate association. Our findings were consolidated into a NTCP model that can support pre-treatment patient segmentation into risk groups and the planning of necessary treatment countermeasures. However, further data and validation are necessary to confirm validity of the model. Abstract Radiation-induced optic neuropathy (RION) is a rare side effect following radiation therapy involving the optic structures whose onset is, due to the low amount of available data, challenging to predict. We have analyzed a multi-institutional cohort including 289 skull-base cancer patients treated with proton therapy who all received >45 GyRBE to the optic apparatus. An overall incidence rate of 4.2% (12) was observed, with chordoma patients being at higher risk (5.8%) than chondrosarcoma patients (3.2%). Older age and arterial hypertension, tumor involvement, and repeated surgeries (>3) were found to be associated with RION. Based on bootstrapping and cross-validation, a NTCP model based on age and hypertension was determined to be the most robust, showing good classification ability (AUC-ROC 0.77) and calibration on our dataset. We suggest the application of this model with a threshold of 6% to segment patients into low and high-risk groups before treatment planning. However, further data and external validation are warranted before clinical application.
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Affiliation(s)
- Andreas Köthe
- Center for Proton Therapy, Paul Scherrer Institute, 5232 Villigen, Switzerland; (D.C.W.); (S.S.); (A.J.L.); (G.F.)
- Department of Physics, ETH Zürich, 8093 Zürich, Switzerland
- Correspondence:
| | - Loïc Feuvret
- Center for Proton Therapy, Institut Curie, 91400 Orsay, France;
- Department of Radiation Oncology, AP-HP, Hôpitaux Universitaires La Salpêtrière Charles Foix, Sorbonne Université, 75013 Paris, France
| | - Damien Charles Weber
- Center for Proton Therapy, Paul Scherrer Institute, 5232 Villigen, Switzerland; (D.C.W.); (S.S.); (A.J.L.); (G.F.)
- Department of Radiation Oncology, University Hospital Zürich, 8091 Zürich, Switzerland
- Department of Radiation Oncology, University Hospital Bern, 3010 Bern, Switzerland
| | - Sairos Safai
- Center for Proton Therapy, Paul Scherrer Institute, 5232 Villigen, Switzerland; (D.C.W.); (S.S.); (A.J.L.); (G.F.)
| | - Antony John Lomax
- Center for Proton Therapy, Paul Scherrer Institute, 5232 Villigen, Switzerland; (D.C.W.); (S.S.); (A.J.L.); (G.F.)
- Department of Physics, ETH Zürich, 8093 Zürich, Switzerland
| | - Giovanni Fattori
- Center for Proton Therapy, Paul Scherrer Institute, 5232 Villigen, Switzerland; (D.C.W.); (S.S.); (A.J.L.); (G.F.)
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13
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Köthe A, van Luijk P, Safai S, Kountouri M, Lomax AJ, Weber DC, Fattori G. Combining Clinical and Dosimetric Features in a PBS Proton Therapy Cohort to Develop a NTCP Model for Radiation-Induced Optic Neuropathy. Int J Radiat Oncol Biol Phys 2021; 110:587-595. [DOI: 10.1016/j.ijrobp.2020.12.052] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 10/30/2020] [Accepted: 12/31/2020] [Indexed: 01/17/2023]
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14
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Prayongrat A, Srimaneekarn N, Sriswasdi S, Ito YM, Katoh N, Tamura M, Dekura Y, Toramatsu C, Khorprasert C, Amornwichet N, Alisanant P, Hirata Y, Hayter A, Shirato H, Shimizu S, Kobashi K. Assessment of the confidence interval in the multivariable normal tissue complication probability model for predicting radiation-induced liver disease in primary liver cancer. JOURNAL OF RADIATION RESEARCH 2021; 62:483-493. [PMID: 33899102 PMCID: PMC8127660 DOI: 10.1093/jrr/rrab011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 01/04/2021] [Accepted: 01/26/2021] [Indexed: 06/12/2023]
Abstract
We developed a confidence interval-(CI) assessing model in multivariable normal tissue complication probability (NTCP) modeling for predicting radiation-induced liver disease (RILD) in primary liver cancer patients using clinical and dosimetric data. Both the mean NTCP and difference in the mean NTCP (ΔNTCP) between two treatment plans of different radiotherapy modalities were further evaluated and their CIs were assessed. Clinical data were retrospectively reviewed in 322 patients with hepatocellular carcinoma (n = 215) and intrahepatic cholangiocarcinoma (n = 107) treated with photon therapy. Dose-volume histograms of normal liver were reduced to mean liver dose (MLD) based on the fraction size-adjusted equivalent uniform dose. The most predictive variables were used to build the model based on multivariable logistic regression analysis with bootstrapping. Internal validation was performed using the cross-validation leave-one-out method. Both the mean NTCP and the mean ΔNTCP with 95% CIs were calculated from computationally generated multivariate random sets of NTCP model parameters using variance-covariance matrix information. RILD occurred in 108/322 patients (33.5%). The NTCP model with three clinical and one dosimetric parameter (tumor type, Child-Pugh class, hepatitis infection status and MLD) was most predictive, with an area under the receiver operative characteristics curve (AUC) of 0.79 (95% CI 0.74-0.84). In eight clinical subgroups based on the three clinical parameters, both the mean NTCP and the mean ΔNTCP with 95% CIs were able to be estimated computationally. The multivariable NTCP model with the assessment of 95% CIs has potential to improve the reliability of the NTCP model-based approach to select the appropriate radiotherapy modality for each patient.
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Affiliation(s)
- Anussara Prayongrat
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | | | - Sira Sriswasdi
- Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Computational Molecular Biology Group, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Yoichi M Ito
- Biostatistics Division, Clinical Research and Medical Innovation Center, Hokkaido University Hospital, Sapporo, Japan
| | - Norio Katoh
- Department of Radiation Oncology, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Masaya Tamura
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, Japan
| | - Yasuhiro Dekura
- Department of Radiation Oncology, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Chie Toramatsu
- Department of Radiation Oncology, Tokyo Women’s Medical University, Tokyo, Japan
| | - Chonlakiet Khorprasert
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Napapat Amornwichet
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Petch Alisanant
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Yuichi Hirata
- Central Institute of Isotope Science, Hokkaido University, Sapporo, Japan
| | - Anthony Hayter
- Department of Business Information and Analytics, University of Denver, CO, USA
| | - Hiroki Shirato
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
- Department of Proton Beam Therapy, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Shinichi Shimizu
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, Japan
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
- Department of Radiation Medical Science and Engineering, Faculty of Medicine, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Keiji Kobashi
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, Japan
- Department of Radiation Medical Science and Engineering, Faculty of Medicine, Hokkaido University Graduate School of Medicine, Sapporo, Japan
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Coates JTT, Pirovano G, El Naqa I. Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges. J Med Imaging (Bellingham) 2021; 8:031902. [PMID: 33768134 PMCID: PMC7985651 DOI: 10.1117/1.jmi.8.3.031902] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/12/2021] [Indexed: 12/14/2022] Open
Abstract
The power of predictive modeling for radiotherapy outcomes has historically been limited by an inability to adequately capture patient-specific variabilities; however, next-generation platforms together with imaging technologies and powerful bioinformatic tools have facilitated strategies and provided optimism. Integrating clinical, biological, imaging, and treatment-specific data for more accurate prediction of tumor control probabilities or risk of radiation-induced side effects are high-dimensional problems whose solutions could have widespread benefits to a diverse patient population-we discuss technical approaches toward this objective. Increasing interest in the above is specifically reflected by the emergence of two nascent fields, which are distinct but complementary: radiogenomics, which broadly seeks to integrate biological risk factors together with treatment and diagnostic information to generate individualized patient risk profiles, and radiomics, which further leverages large-scale imaging correlates and extracted features for the same purpose. We review classical analytical and data-driven approaches for outcomes prediction that serve as antecedents to both radiomic and radiogenomic strategies. Discussion then focuses on uses of conventional and deep machine learning in radiomics. We further consider promising strategies for the harmonization of high-dimensional, heterogeneous multiomics datasets (panomics) and techniques for nonparametric validation of best-fit models. Strategies to overcome common pitfalls that are unique to data-intensive radiomics are also discussed.
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Affiliation(s)
- James T. T. Coates
- Massachusetts General Hospital & Harvard Medical School, Center for Cancer Research, Boston, Massachusetts, United States
| | - Giacomo Pirovano
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, New York, United States
| | - Issam El Naqa
- Moffitt Cancer Center and Research Institute, Department of Machine Learning, Tampa, Florida, United States
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16
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Zhang Y, Feng W, Gao LT, Cai XW, Liu Q, Zhu ZF, Fu XL, Yu W. Long-term follow-up of a phase I/II trial of radiation dose escalation by simultaneous integrated boost for locally advanced esophageal squamous cell carcinoma. Radiother Oncol 2021; 159:190-196. [PMID: 33812913 DOI: 10.1016/j.radonc.2021.03.031] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 03/02/2021] [Accepted: 03/26/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND To observe the long-term survival and late adverse events in a phase Ⅰ/Ⅱ trial (NCT01843049) of dose escalation for thoracic esophageal squamous cell carcinoma (ESCC) with simultaneous integrated boost (SIB) technique. METHODS Patients with ESCC were treated with escalating radiation dose of four predefined levels. Dose of 62.5-64 Gy/25-32 fractions was delivered to the gross tumor volume (GTV), with (Level 3&4) or without (Level 1&2) a SIB up to 70 Gy for pre-treatment 50% SUVmax area of GTV. Patients also received 2 cycles of chemotherapy of cisplatin and fluorouracil concurrently and 2 more cycles after radiotherapy. RESULTS Median follow-up duration was 17.2 (2.5-83.4) months for all 44 patients and 47.2 (3.9-83.4) months for 25 survivors. The 3-year overall survival and progression-free survival rates were 57.6% and 41.0%, respectively. One, one, four and twelve severe (grade≥3) esophageal late adverse events (SEAE) occurred in patients of Level 1/2/3/4 (n = 5/10/16/13), with median occurrence time of 6.5 months. In univariable and multivariable competing risk models, maximal dose of the esophagus (Dmax) was found to have significant impact on the incidence of SEAE, and the cutoff distinguishing patients who developed SEAE or not was 77 Gy. CONCLUSION Boosting the gross tumor to 63 Gy while delivering 50.4 Gy to subclinical diseases in 28 fractions in locally advanced ESCC is well tolerated with promising long-term survival. Intenser dose regimen should be considered with caution before further toxicity assessment. Esophageal Dmax was significantly associated with severe late esophageal injury, while more findings of dose-volume predictors need larger-sample investigation.
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Affiliation(s)
- Ying Zhang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Wen Feng
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Lan-Ting Gao
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Xu-Wei Cai
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China; Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qi Liu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zheng-Fei Zhu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiao-Long Fu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China; Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Wen Yu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China; Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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17
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Humbert-Vidan L, Patel V, Oksuz I, King AP, Guerrero Urbano T. Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer. Br J Radiol 2021; 94:20200026. [PMID: 33684314 PMCID: PMC8010531 DOI: 10.1259/bjr.20200026] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 12/16/2020] [Accepted: 01/25/2021] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVES Mandible osteoradionecrosis (ORN) is one of the most severe toxicities in patients with head and neck cancer (HNC) undergoing radiotherapy (RT). The existing literature focuses on the correlation of mandible ORN and clinical and dosimetric factors. This study proposes the use of machine learning (ML) methods as prediction models for mandible ORN incidence. METHODS A total of 96 patients (ORN incidence ratio of 1:1) treated between 2011 and 2015 were selected from the local HNC toxicity database. Demographic, clinical and dosimetric data (based on the mandible dose-volume histogram) were considered as model variables. Prediction accuracy (measured using a stratified fivefold nested cross-validation), sensitivity, specificity, precision and negative predictive value were used to evaluate the prediction performance of a multivariate logistic regression (LR) model, a support vector machine (SVM) model, a random forest (RF) model, an adaptive boosting (AdaBoost) model and an artificial neural network (ANN) model. The different models were compared based on their prediction accuracy and using the McNemar's hypothesis test. RESULTS The ANN model (77% accuracy), closely followed by the SVM (76%), AdaBoost (75%) and LR (75%) models, showed the highest overall prediction accuracy. The RF model (71%) showed the lowest prediction accuracy. However, based on the McNemar's test applied to all model pair combinations, no statistically significant difference between the models was found. CONCLUSION Based on our results, we encourage the use of ML-based prediction models for ORN incidence as has already been done for other HNC toxicity end points. ADVANCES IN KNOWLEDGE This research opens a new path towards personalised RT for HNC using ML to predict mandible ORN incidence.
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Affiliation(s)
| | - Vinod Patel
- Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | | | - Andrew Peter King
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
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18
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Teng F, Fan W, Luo Y, Xu S, Gong H, Ge R, Zhang X, Wang X, Ma L. A Risk Prediction Model by LASSO for Radiation-Induced Xerostomia in Patients With Nasopharyngeal Carcinoma Treated With Comprehensive Salivary Gland-Sparing Helical Tomotherapy Technique. Front Oncol 2021; 11:633556. [PMID: 33718219 PMCID: PMC7953987 DOI: 10.3389/fonc.2021.633556] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 02/03/2021] [Indexed: 11/13/2022] Open
Abstract
Objective This study aimed to develop a least absolute shrinkage and selection operator (LASSO)-based multivariable normal tissue complication probability (NTCP) model to predict radiation-induced xerostomia in patients with nasopharyngeal carcinoma (NPC) treated with comprehensive salivary gland–sparing helical tomotherapy technique. Methods and Materials LASSO with the extended bootstrapping technique was used to build multivariable NTCP models to predict factors of patient-reported xerostomia relieved by 50% and 80% compared with the level at the end of radiation therapy within 1 year and 2 years, R50-1year and R80-2years, in 203 patients with NPC. The model assessment was based on 10-fold cross-validation and the area under the receiver operating characteristic curve (AUC). Results The prediction model by LASSO with 10-fold cross-validation showed that radiation-induced xerostomia recovery could be predicted by prognostic factors of R50-1year (age, gender, T stage, UICC/AJCC stage, parotid Dmean, oral cavity Dmean, and treatment options) and R80-2years (age, gender, T stage, UICC/AJCC stage, oral cavity Dmean, N stage, and treatment options). These prediction models also demonstrated a good performance by the AUC. Conclusion The prediction models of R50-1year and R80-2years by LASSO with 10-fold cross-validation were recommended to validate the NTCP model before comprehensive salivary gland–sparing radiation therapy in patients with NPC.
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Affiliation(s)
- Feng Teng
- Department of Radiation Oncology, China-Japan Friendship Hospital, Beijing, China.,Department of Radiation Oncology, Medical School of the Chinese People's Liberation Army (PLA), Beijing, China
| | - Wenjun Fan
- Affiliated Foshan Maternity and Child Healthcare Hospital, Southern Medical University, Foshan, China.,Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.,Department of Radiation Oncology, Armed Police Corps Hospital of Henan Province, Zhengzhou, China
| | - Yanrong Luo
- Department of Radiation Oncology, Medical School of the Chinese People's Liberation Army (PLA), Beijing, China.,Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shouping Xu
- Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Hanshun Gong
- Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ruigang Ge
- Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xinxin Zhang
- Departmant of Otorhinolaryngology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiaoning Wang
- School of Data Science and Media Intelligence, Communication University of China, Beijing, China.,State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China
| | - Lin Ma
- Department of Radiation Oncology, Medical School of the Chinese People's Liberation Army (PLA), Beijing, China.,Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China
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19
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Paganetti H, Beltran C, Both S, Dong L, Flanz J, Furutani K, Grassberger C, Grosshans DR, Knopf AC, Langendijk JA, Nystrom H, Parodi K, Raaymakers BW, Richter C, Sawakuchi GO, Schippers M, Shaitelman SF, Teo BKK, Unkelbach J, Wohlfahrt P, Lomax T. Roadmap: proton therapy physics and biology. Phys Med Biol 2021; 66. [DOI: 10.1088/1361-6560/abcd16] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 11/23/2020] [Indexed: 12/12/2022]
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20
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Bondar G, Silacheva I, Bao TM, Deshmukh S, Kulkarni NS, Nakade T, Grogan T, Elashoff D, Deng MC. Initial independent validation of a genomic heart failure survival prediction algorithm. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2021. [DOI: 10.1080/23808993.2021.1882847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Galyna Bondar
- Department of Medicine, Division of Cardiology, David Geffen School of Medicine, UCLA Medical Center, Los Angeles, California, United States
- LeukoLifeDx, Inc.,Rumson, New Jersey, United States
| | - Irina Silacheva
- Department of Medicine, Division of Cardiology, David Geffen School of Medicine, UCLA Medical Center, Los Angeles, California, United States
| | - Tra-Mi Bao
- Department of Medicine, Division of Cardiology, David Geffen School of Medicine, UCLA Medical Center, Los Angeles, California, United States
- LeukoLifeDx, Inc.,Rumson, New Jersey, United States
| | - Sumeet Deshmukh
- Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, UK
| | - Neha S. Kulkarni
- Academic Unit of Reproductive and Developmental Medicine, University of Sheffield, Sheffield, UK
| | - Taisuke Nakade
- Department of Medicine, Division of Cardiology, David Geffen School of Medicine, UCLA Medical Center, Los Angeles, California, United States
| | - Tristan Grogan
- Department of Medicine, Division of Cardiology, David Geffen School of Medicine, UCLA Medical Center, Los Angeles, California, United States
| | - David Elashoff
- Department of Medicine, Division of Cardiology, David Geffen School of Medicine, UCLA Medical Center, Los Angeles, California, United States
| | - Mario C. Deng
- Department of Medicine, Division of Cardiology, David Geffen School of Medicine, UCLA Medical Center, Los Angeles, California, United States
- LeukoLifeDx, Inc.,Rumson, New Jersey, United States
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21
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Yakar M, Etiz D, Metintas M, Ak G, Celik O. Prediction of Radiation Pneumonitis With Machine Learning in Stage III Lung Cancer: A Pilot Study. Technol Cancer Res Treat 2021; 20:15330338211016373. [PMID: 33969761 PMCID: PMC8129486 DOI: 10.1177/15330338211016373] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Background: Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy (RT). As risk factors in the development of RP, patient and tumor characteristics, dosimetric parameters, and treatment features are intertwined, and it is not always possible to associate RP with a single parameter. This study aimed to determine the algorithm that most accurately predicted RP development with machine learning. Methods: Of the 197 cases diagnosed with stage III lung cancer and underwent RT and chemotherapy between 2014 and 2020, 193 were evaluated. The CTCAE 5.0 grading system was used for the RP evaluation. Synthetic minority oversampling technique was used to create a balanced data set. Logistic regression, artificial neural networks, eXtreme Gradient Boosting (XGB), Support Vector Machines, Random Forest, Gaussian Naive Bayes and Light Gradient Boosting Machine algorithms were used. After the correlation analysis, a permutation-based method was utilized for as a variable selection. Results: RP was seen in 51 of the 193 cases. Parameters affecting RP were determined as, total(t)V5, ipsilateral lung Dmax, contralateral lung Dmax, total lung Dmax, gross tumor volume, number of chemotherapy cycles before RT, tumor size, lymph node localization and asbestos exposure. LGBM was found to be the algorithm that best predicted RP at 85% accuracy (confidence interval: 0.73-0.96), 97% sensitivity, and 50% specificity. Conclusion: When the clinical and dosimetric parameters were evaluated together, the LGBM algorithm had the highest accuracy in predicting RP. However, in order to use this algorithm in clinical practice, it is necessary to increase data diversity and the number of patients by sharing data between centers.
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Affiliation(s)
- Melek Yakar
- Department of Radiation Oncology, Medical Faculty of Osmangazi University, Eskişehir, Turkey.,Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey
| | - Durmus Etiz
- Department of Radiation Oncology, Medical Faculty of Osmangazi University, Eskişehir, Turkey.,Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey
| | - Muzaffer Metintas
- Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey.,Department of Chest Diseases, Medical Faculty of Osmangazi University, Eskişehir, Turkey
| | - Guntulu Ak
- Department of Chest Diseases, Medical Faculty of Osmangazi University, Eskişehir, Turkey
| | - Ozer Celik
- Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey.,Department of Mathematics-Computer, Eskisehir Osmangazi University, Eskişehir, Turkey
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22
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Leech M, Osman S, Jain S, Marignol L. Mini review: Personalization of the radiation therapy management of prostate cancer using MRI-based radiomics. Cancer Lett 2020; 498:210-216. [PMID: 33160001 DOI: 10.1016/j.canlet.2020.10.033] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/14/2020] [Accepted: 10/21/2020] [Indexed: 12/21/2022]
Abstract
Decisions on how to treat prostate cancer with radiation therapy are guideline-based but as such guidelines have been developed for populations of patients, this invariably leads to overly aggressive treatment in some patients and insufficient treatment in others. Heterogeneity within prostate tumors and in metastatic sites, even within the same patient, is believed to be a major cause of treatment failure. Radiomics biomarkers, more commonly referred to as radiomics 'features", provide readily available, cost-effective, non-invasive tools for screening, detecting tumors and serial monitoring of patients, including assessments of response to therapy and identification of therapeutic complications. Radiomics offers the potential to analyse whole tumors in 3D, as well as sub-regions or 'habitats' within tumors. Combining quantitative information from imaging with pathology, demographic details and other biomarkers will pave the way for personalised treatment selection and monitoring in prostate cancer. The aim of this review is to consider if MRI-based radiomics can bridge the gap between population-based management and personalised management of prostate cancer.
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Affiliation(s)
- Michelle Leech
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, School of Medicine, Trinity St. James's Cancer Institute, Trinity College, Dublin, Ireland.
| | - Sarah Osman
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Lisburn Road, Belfast, BT9 7AE, United Kingdom
| | - Suneil Jain
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Lisburn Road, Belfast, BT9 7AE, United Kingdom
| | - Laure Marignol
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, School of Medicine, Trinity St. James's Cancer Institute, Trinity College, Dublin, Ireland
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23
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Meijers A, Seller OC, Free J, Bondesson D, Seller Oria C, Rabe M, Parodi K, Landry G, Langendijk JA, Both S, Kurz C, Knopf AC. Assessment of range uncertainty in lung-like tissue using a porcine lung phantom and proton radiography. ACTA ACUST UNITED AC 2020; 65:155014. [DOI: 10.1088/1361-6560/ab91db] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Clinical implication in the use of the AAA algorithm versus the AXB in nasopharyngeal carcinomas by comparison of TCP and NTCP values. Radiat Oncol 2020; 15:150. [PMID: 32532351 PMCID: PMC7291676 DOI: 10.1186/s13014-020-01591-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 06/03/2020] [Indexed: 12/25/2022] Open
Abstract
Purpose Retrospective analysis of volumetric modulated arc therapy treatment plans to investigate qualitative, possible, clinical consequences of the use of AAA versus AXB in nasopharyngeal cancer (NPC) cases. Methods The dose distribution of 26 treatment plans, produced using RapidArc technique and AAA algorithm, were recalculated using AXB and the same number of monitor units provided by AAA and clinically delivered to each patient. The potential clinical effect of dosimetric differences in the planning target volume (PTV) and in organs at risk (OAR) were evaluated by comparing TCP and NTCP values. The Wilcoxon Signed Rank test was used for statistical comparison of all results obtained from the use of the two algorithms. Results The poorer coverage of the PTV, with higher prescribed dose, was reflected in the TCP, which was significantly lower when AXB was used, the median value was 81.55% (range: 74.90, 88.60%) and 84.10% (range: 77.70, 89.90%) for AAA (p < 0.001). OAR mean dose was lower in the AXB recalculated plan than the AAA plan and the difference was statistically significant for all the structures. The NTCP for developing mandible necrosis showed the largest median percentage difference between AAA and AXB (56.6%), the NTCP of risk for larynx edema of Grade ≥ 2 followed with 12.2%. Conclusions Differences in dose distribution of NPC treatment plans recalculated with AXB are of clinical significance in those situations where the PTV and OAR involve air or bone, media in which AXB has been shown to more accurately represent the true dose distribution. The availability of AXB algorithm could improve patient dose estimation, increasing the data consistency of clinical trials.
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25
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Panettieri V, Rancati T, Onjukka E, Ebert MA, Joseph DJ, Denham JW, Steigler A, Millar JL. External Validation of a Predictive Model of Urethral Strictures for Prostate Patients Treated With HDR Brachytherapy Boost. Front Oncol 2020; 10:910. [PMID: 32596153 PMCID: PMC7300245 DOI: 10.3389/fonc.2020.00910] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 05/11/2020] [Indexed: 12/12/2022] Open
Abstract
Purpose: For prostate cancer treatment, comparable or superior biochemical control was reported when using External-Beam-Radiotherapy (EBRT) with High-Dose-Rate-Brachytherapy (HDRB)-boost, compared to dose-escalation with EBRT alone. The conformal doses produced by HDRB could allow further beneficial prostate dose-escalation, but increase in dose is limited by normal tissue toxicity. Previous works showed correlation between urethral dose and incidence of urinary toxicity, but there is a lack of established guidelines on the dose constraints to this organ. This work aimed at fitting a Normal-Tissue-Complication-Probability model to urethral stricture data collected at one institution and validating it with an external cohort, looking at neo-adjuvant androgen deprivation as dose-modifying factor. Materials and Methods: Clinical and dosimetric data of 258 patients, with a toxicity rate of 12.8%, treated at a single institution with a variety of prescription doses, were collected to fit the Lyman–Kutcher–Burman (LKB) model using the maximum likelihood method. Due to the different fractionations, doses were converted into 2 Gy-equivalent doses (α/β = 5 Gy), and urethral stricture was used as an end-point. For validation, an external cohort of 187 patients treated as part of the TROG (Trans Tasman Radiation Oncology Group) 03.04 RADAR trial with a toxicity rate of 8.7%, was used. The goodness of fit was assessed using calibration plots. The effect of neo-adjuvant androgen deprivation (AD) was analyzed separating patients who had received it prior to treatment from those who did not receive it. Results: The obtained LKB parameters were TD50 = 116.7 Gy and m = 0.23; n was fixed to 0.3, based on numerical optimization of the likelihood. The calibration plot showed a good agreement between the observed toxicity and the probability predicted by the model, confirmed by bootstrapping. For the external validation, the calibration plot showed that the observed toxicity obtained with the RADAR patients was well-represented by the fitted LKB model parameters. When patients were stratified by the use of AD TD50 decreased when AD was not present. Conclusions: Lyman–Kutcher–Burman model parameters were fitted to the risk of urethral stricture and externally validated with an independent cohort, to provide guidance on urethral tolerance doses for patients treated with a HDRB boost. For patients that did not receive AD, model fitting provided a lower TD50 suggesting a protective effect on urethra toxicity.
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Affiliation(s)
- Vanessa Panettieri
- Alfred Health Radiation Oncology, Alfred Hospital, Melbourne, VIC, Australia.,Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia
| | - Tiziana Rancati
- Prostate Cancer Program, Scientific Directorate, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Eva Onjukka
- Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Martin A Ebert
- Radiation Oncology, Sir Charles Gairdner Hospital, Perth, WA, Australia.,School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia.,5D Clinics, Claremont, WA, Australia
| | - David J Joseph
- 5D Clinics, Claremont, WA, Australia.,GenesisCare, Subiaco, WA, Australia.,School of Surgery, University of Western Australia, WA, Australia
| | - James W Denham
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Allison Steigler
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Jeremy L Millar
- Alfred Health Radiation Oncology, Alfred Hospital, Melbourne, VIC, Australia.,Central Clinical School, Monash University, Melbourne, VIC, Australia
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26
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Isaksson LJ, Pepa M, Zaffaroni M, Marvaso G, Alterio D, Volpe S, Corrao G, Augugliaro M, Starzyńska A, Leonardi MC, Orecchia R, Jereczek-Fossa BA. Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy. Front Oncol 2020; 10:790. [PMID: 32582539 PMCID: PMC7289968 DOI: 10.3389/fonc.2020.00790] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 04/22/2020] [Indexed: 12/20/2022] Open
Abstract
In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability. Therefore, an overview of contemporary research is needed in order to familiarize practitioners with common methods and strategies. Here, we present a review of ML-based models for predicting and classifying RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the ML methods used, and the main results achieved. Our work overviews published research in multiple cancer sites, including brain, breast, esophagus, gynecological, head and neck, liver, lung, and prostate cancers. The aim is to define the current state of the art and main achievements within the field for both researchers and clinicians.
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Affiliation(s)
- Lars J Isaksson
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Matteo Pepa
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Mattia Zaffaroni
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giulia Marvaso
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Daniela Alterio
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Stefania Volpe
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giulia Corrao
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Matteo Augugliaro
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Anna Starzyńska
- Department of Oral Surgery, Medical University of Gdańsk, Gdańsk, Poland
| | - Maria C Leonardi
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Roberto Orecchia
- Scientific Directorate, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Barbara A Jereczek-Fossa
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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27
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Luo Y, Chen S, Valdes G. Machine learning for radiation outcome modeling and prediction. Med Phys 2020; 47:e178-e184. [DOI: 10.1002/mp.13570] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 03/26/2019] [Accepted: 04/09/2019] [Indexed: 12/18/2022] Open
Affiliation(s)
- Yi Luo
- Department of Radiation Oncology University of Michigan Ann Arbor MI 48103USA
| | - Shifeng Chen
- Department of Radiation Oncology University of Maryland School of Medicine Baltimore MD 21201USA
| | - Gilmer Valdes
- Department of Radiation Oncology University of California San Francisco CA 94158USA
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28
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Frezza A, Joachim-Paquet C, Chauvin M, Després P. Validation of irtGPUMCD, a GPU-based Monte Carlo internal dosimetry framework for radionuclide therapy. Phys Med 2020; 73:95-104. [PMID: 32334403 DOI: 10.1016/j.ejmp.2020.04.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 04/07/2020] [Accepted: 04/12/2020] [Indexed: 11/28/2022] Open
Abstract
PURPOSE Monte Carlo (MC) simulations are highly desirable for dose treatment planning and evaluation in radiation oncology. This is true also in emerging nuclear medicine applications such as internal radiotherapy with radionuclides. The purpose of this study is the validation of irtGPUMCD, a GPU-based MC code for dose calculations in internal radiotherapy. METHODS The female and male phantoms of the International Commission on Radiological Protection (ICRP 110) were used as benchmarking geometries for this study focused on 177Lu and including 99mTc and 131I. Dose calculations were also conducted for a real patient. For phantoms, twelve anatomical structures were considered as target/source organs. The S-values were evaluated with irtGPUMCD simulations (108 photons), with gamma branching ratios of ICRP 107 publication. The 177Lu electrons S-values were calculated for source organs only, based on local deposition of dose in irtGPUMCD. The S-value relative difference between irtGPUMCD and IDAC-DOSE were evaluated for all targets/sources considered. A DVHs comparison with GATE was conducted. An exponential track length estimator was introduced in irtGPUMCD to increase computational efficiency. RESULTS The relative S-value differences between irtGPUMCD and IDAC-DOSE were <5% while this comparison with GATE was <1%. The DVHs dosimetric indices comparison between GATE and irtGPUMCD for the patient led to an excellent agreement (<2%). The time required for the simulation of 108 photons was 1.5 min for the female phantom, and one minute for the real patient (<1% uncertainty). These results are promising and let envision the use of irtGPUMCD for internal dosimetry in clinical applications.
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Affiliation(s)
- Andrea Frezza
- Department of Physics, Engineering Physics and Optics and Cancer Research Center, Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Charles Joachim-Paquet
- Department of Physics, Engineering Physics and Optics and Cancer Research Center, Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Maxime Chauvin
- CRCT, UMR 1037, Inserm, Université Toulouse III Paul Sabatier, Toulouse, France
| | - Philippe Després
- Department of Physics, Engineering Physics and Optics and Cancer Research Center, Université Laval, Quebec City, QC G1V 0A6, Canada; Department of Radiation Oncology and Research Center of CHU de Québec - Université Laval, Quebec City, QC G1R 2J6, Canada.
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29
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Traverso A, Kazmierski M, Zhovannik I, Welch M, Wee L, Jaffray D, Dekker A, Hope A. Machine learning helps identifying volume-confounding effects in radiomics. Phys Med 2020; 71:24-30. [PMID: 32088562 DOI: 10.1016/j.ejmp.2020.02.010] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 02/12/2020] [Accepted: 02/13/2020] [Indexed: 01/06/2023] Open
Abstract
PURPOSE Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding effects in radiomics features. Methods 841 radiomics features were extracted from two retrospective publicly available datasets of lung and head neck cancers using open source software. Unsupervised hierarchical clustering and principal component analysis (PCA) identified relations between radiomics and clinical outcomes (overall survival). Bootstrapping techniques with logistic regression verified features' prognostic power and robustness. Results Over 80% of the features had large pairwise correlations. Nearly 30% of the features presented strong correlations with tumor volume. Using volume-independent features for clustering and PCA did not allow risk stratification of patients. Clinical predictors outperformed radiomics features in bootstrapping and logistic regression. Conclusions The adoption of safeguards in radiomics is imperative to improve the quality of radiomics studies. We proposed machine learning (ML) - based methods for robust radiomics signatures development.
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Affiliation(s)
- Alberto Traverso
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands; Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.
| | - Michal Kazmierski
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ivan Zhovannik
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands; Department of Radiation Oncology, Radboudumc, Nijmegen, The Netherlands
| | - Mattea Welch
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands; Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - David Jaffray
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Andrew Hope
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada
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El Naqa I, Haider MA, Giger ML, Ten Haken RK. Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century. Br J Radiol 2020; 93:20190855. [PMID: 31965813 PMCID: PMC7055429 DOI: 10.1259/bjr.20190855] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 01/12/2020] [Accepted: 01/13/2020] [Indexed: 12/15/2022] Open
Abstract
Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI's inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI's chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.
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Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Masoom A Haider
- Department of Medical Imaging and Lunenfeld-Tanenbaum Research Institute, University of Toronto, Toronto, ON, Canada
| | | | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
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Annede P, Cosset JM, Van Limbergen E, Deutsch E, Haie-Meder C, Chargari C. Radiobiology: Foundation and New Insights in Modeling Brachytherapy Effects. Semin Radiat Oncol 2020; 30:4-15. [DOI: 10.1016/j.semradonc.2019.08.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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32
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Belciug S. Radiotherapist at work. Artif Intell Cancer 2020. [DOI: 10.1016/b978-0-12-820201-2.00006-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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33
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Palma G, Monti S, Conson M, Pacelli R, Cella L. Normal tissue complication probability (NTCP) models for modern radiation therapy. Semin Oncol 2019; 46:210-218. [PMID: 31506196 DOI: 10.1053/j.seminoncol.2019.07.006] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 07/31/2019] [Indexed: 02/07/2023]
Abstract
Mathematical models of normal tissue complication probability (NTCP) able to robustly predict radiation-induced morbidities (RIM) play an essential role in the identification of a personalized optimal plan, and represent the key to maximizing the benefits of technological advances in radiation therapy (RT). Most modern RT techniques pose, however, new challenges in estimating the risk of RIM. The aim of this report is to schematically review NTCP models in the framework of advanced radiation therapy techniques. Issues relevant to hypofractionated stereotactic body RT and ion beam therapy are critically reviewed. Reirradiation scenarios for new or recurrent malignances and NTCP are also illustrated. A new phenomenological approach to predict RIM is suggested.
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Affiliation(s)
- Giuseppe Palma
- National Research Council, Institute of Biostructures and Bioimaging, Napoli, Italy
| | - Serena Monti
- National Research Council, Institute of Biostructures and Bioimaging, Napoli, Italy
| | - Manuel Conson
- Department of Advanced Biomedical Sciences, Federico II University School of Medicine, Naples, Italy
| | - Roberto Pacelli
- Department of Advanced Biomedical Sciences, Federico II University School of Medicine, Naples, Italy
| | - Laura Cella
- National Research Council, Institute of Biostructures and Bioimaging, Napoli, Italy.
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Huang CC, Chao PJ, Guo SS, Wang CJ, Luo HL, Su YL, Lee TF, Fang FM. Developing a multivariable normal tissue complication probability model to predict late rectal bleeding following intensity-modulated radiation therapy. J Cancer 2019; 10:2588-2593. [PMID: 31258765 PMCID: PMC6584341 DOI: 10.7150/jca.29606] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 04/27/2019] [Indexed: 12/01/2022] Open
Abstract
Purpose: To develop a multivariable normal tissue complication probability (NTCP) model to predict moderate to severe late rectal bleeding following intensity-modulated radiation therapy (IMRT). Methods and materials: Sixty-eight patients with localized prostate cancer treated by IMRT from 2008 to 2011 were enrolled. The median follow-up time was 56 months. According to the criteria of D'Amico risk classifications, there were 9, 20 and 39 patients in low, intermediate and high-risk groups, respectively. Forty-two patients were combined with androgen deprivation therapy. Fifteen patients had suffered from grade 2 or more (grade 2+) late rectal bleeding. The numbers of predictors for a multivariable logistic regression NTCP model were determined by the least absolute shrinkage and selection operator (LASSO). Results: The most important predictors for late rectal bleeding ranked by LASSO were platelet count, risk group and the relative volume of rectum receiving at least 65 Gy (V65). The NTCP model of grade 2+ rectal bleeding was as follows: S = -17.49 + Platelets (1000/μL) * (-0.025) + Risk group * Corresponding coefficient (low-risk group = 0; intermediate-risk group = 19.07; high-risk group = 20.41) + V65 * 0.045. Conclusions: A LASSO-based multivariable NTCP model comprising three important predictors (platelet count, risk group and V65) was established to predict the incidence of grade 2+ late rectal bleeding after IMRT.
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Affiliation(s)
- Chun-Chieh Huang
- Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan.,Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Pei-Ju Chao
- Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan.,Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Shih-Sian Guo
- Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan.,Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Chong-Jong Wang
- Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Hao-Lun Luo
- Department of Urology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Yu-Li Su
- Division of Hematology-Oncology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Tsair-Fwu Lee
- Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan.,Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.,Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Fu-Min Fang
- Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
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Hamming-Vrieze O, Depauw N, Craft DL, Chan AW, Rasch CRN, Verheij M, Sonke JJ, Kooy HM. Impact of setup and range uncertainties on TCP and NTCP following VMAT or IMPT of oropharyngeal cancer patients. Phys Med Biol 2019; 64:095001. [PMID: 30921775 DOI: 10.1088/1361-6560/ab1459] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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36
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Cui S, Luo Y, Tseng HH, Ten Haken RK, El Naqa I. Combining handcrafted features with latent variables in machine learning for prediction of radiation-induced lung damage. Med Phys 2019; 46:2497-2511. [PMID: 30891794 DOI: 10.1002/mp.13497] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 02/18/2019] [Accepted: 03/08/2019] [Indexed: 12/23/2022] Open
Abstract
PURPOSE There has been burgeoning interest in applying machine learning methods for predicting radiotherapy outcomes. However, the imbalanced ratio of a large number of variables to a limited sample size in radiation oncology constitutes a major challenge. Therefore, dimensionality reduction methods can be a key to success. The study investigates and contrasts the application of traditional machine learning methods and deep learning approaches for outcome modeling in radiotherapy. In particular, new joint architectures based on variational autoencoder (VAE) for dimensionality reduction are presented and their application is demonstrated for the prediction of lung radiation pneumonitis (RP) from a large-scale heterogeneous dataset. METHODS A large-scale heterogeneous dataset containing a pool of 230 variables including clinical factors (e.g., dose, KPS, stage) and biomarkers (e.g., single nucleotide polymorphisms (SNPs), cytokines, and micro-RNAs) in a population of 106 nonsmall cell lung cancer (NSCLC) patients who received radiotherapy was used for modeling RP. Twenty-two patients had grade 2 or higher RP. Four methods were investigated, including feature selection (case A) and feature extraction (case B) with traditional machine learning methods, a VAE-MLP joint architecture (case C) with deep learning and lastly, the combination of feature selection and joint architecture (case D). For feature selection, Random forest (RF), Support Vector Machine (SVM), and multilayer perceptron (MLP) were implemented to select relevant features. Specifically, each method was run for multiple times to rank features within several cross-validated (CV) resampled sets. A collection of ranking lists were then aggregated by top 5% and Kemeny graph methods to identify the final ranking for prediction. A synthetic minority oversampling technique was applied to correct for class imbalance during this process. For deep learning, a VAE-MLP joint architecture where a VAE aimed for dimensionality reduction and an MLP aimed for classification was developed. In this architecture, reconstruction loss and prediction loss were combined into a single loss function to realize simultaneous training and weights were assigned to different classes to mitigate class imbalance. To evaluate the prediction performance and conduct comparisons, the area under receiver operating characteristic curves (AUCs) were performed for nested CVs for both handcrafted feature selections and the deep learning approach. The significance of differences in AUCs was assessed using the DeLong test of U-statistics. RESULTS An MLP-based method using weight pruning (WP) feature selection yielded the best performance among the different hand-crafted feature selection methods (case A), reaching an AUC of 0.804 (95% CI: 0.761-0.823) with 29 top features. A VAE-MLP joint architecture (case C) achieved a comparable but slightly lower AUC of 0.781 (95% CI: 0.737-0.808) with the size of latent dimension being 2. The combination of handcrafted features (case A) and latent representation (case D) achieved a significant AUC improvement of 0.831 (95% CI: 0.805-0.863) with 22 features (P-value = 0.000642 compared with handcrafted features only (Case A) and P-value = 0.000453 compared to VAE alone (Case C)) with an MLP classifier. CONCLUSION The potential for combination of traditional machine learning methods and deep learning VAE techniques has been demonstrated for dealing with limited datasets in modeling radiotherapy toxicities. Specifically, latent variables from a VAE-MLP joint architecture are able to complement handcrafted features for the prediction of RP and improve prediction over either method alone.
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Affiliation(s)
- Sunan Cui
- Applied Physics Program, University of Michigan, Ann Arbor, MI, USA
| | - Yi Luo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Huan-Hsin Tseng
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
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Kearney V, Chan JW, Valdes G, Solberg TD, Yom SS. The application of artificial intelligence in the IMRT planning process for head and neck cancer. Oral Oncol 2018; 87:111-116. [DOI: 10.1016/j.oraloncology.2018.10.026] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 10/18/2018] [Accepted: 10/20/2018] [Indexed: 12/28/2022]
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38
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Tomasik B, Chałubińska-Fendler J, Chowdhury D, Fendler W. Potential of serum microRNAs as biomarkers of radiation injury and tools for individualization of radiotherapy. Transl Res 2018; 201:71-83. [PMID: 30021695 DOI: 10.1016/j.trsl.2018.06.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 05/31/2018] [Accepted: 06/04/2018] [Indexed: 12/30/2022]
Abstract
Due to tremendous technological advances, radiation oncologists are now capable of personalized treatment plans and deliver the dose in a highly precise manner. However, a crucial challenge is how to escalate radiation doses to cancer cells while reducing damage to surrounding healthy tissues. This determines the probability of achieving therapeutic success whilst safeguarding patients from complications. The current dose constraints rely on observational data. Therefore, incidental toxicity observed in a minority of patients limits the admissible dose thresholds for the whole population, theoretically narrowing down the curative potential of radiotherapy. Future tools for measurements of individual's radiosensitivity before and during treatment would allow proper treatment personalization. Variation in tissue tolerance is at least partially genetically-determined and recent progress in the field of molecular biology raises the possibility that novel assays will allow to predict the response to ionizing radiation. Recently, microRNAs have garnered interest as stable biomarkers of tumor radiation response and normal-tissue toxicity. Preclinical studies in mice and nonhuman primates have shown that serum circulating microRNAs can be used to accurately distinguish pre- and postirradiation states and predict the biological impact of high-dose irradiation. First reports from human studies are also encouraging, however biology-driven precision radiation oncology, which tailors treatment to individual patient's needs, still remains to be translated into clinical studies. In this review, we summarize current knowledge about the potential of serum microRNAs as biodosimeters and biomarkers for radiation injury to lung and hematopoietic cells.
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Affiliation(s)
- Bartłomiej Tomasik
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland; Postgraduate School of Molecular Medicine, Warsaw Medical University, Warsaw, Poland
| | | | - Dipanjan Chowdhury
- Department of Radiation Oncology, Harvard Medical School, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
| | - Wojciech Fendler
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland; Department of Radiation Oncology, Harvard Medical School, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
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Su TS, Luo R, Liang P, Cheng T, Zhou Y, Huang Y. A prospective cohort study of hepatic toxicity after stereotactic body radiation therapy for hepatocellular carcinoma. Radiother Oncol 2018; 129:136-142. [DOI: 10.1016/j.radonc.2018.02.031] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 02/18/2018] [Accepted: 02/26/2018] [Indexed: 12/22/2022]
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40
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El Naqa I, Ruan D, Valdes G, Dekker A, McNutt T, Ge Y, Wu QJ, Oh JH, Thor M, Smith W, Rao A, Fuller C, Xiao Y, Manion F, Schipper M, Mayo C, Moran JM, Ten Haken R. Machine learning and modeling: Data, validation, communication challenges. Med Phys 2018; 45:e834-e840. [PMID: 30144098 DOI: 10.1002/mp.12811] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 12/28/2017] [Accepted: 01/22/2018] [Indexed: 11/06/2022] Open
Abstract
With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatment response modeling, treatment planning, contouring, organ segmentation, image-guidance, motion tracking, quality assurance, and more. Despite this interest, practical clinical implementation of machine learning as part of the day-to-day clinical operations is still lagging. The aim of this white paper is to further promote progress in this new field of machine learning in radiation oncology by highlighting its untapped advantages and potentials for clinical advancement, while also presenting current challenges and open questions for future research. The targeted audience of this paper includes newcomers as well as practitioners in the field of medical physics/radiation oncology. The paper also provides general recommendations to avoid common pitfalls when applying these powerful data analytic tools to medical physics and radiation oncology problems and suggests some guidelines for transparent and informative reporting of machine learning results.
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Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Dan Ruan
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA
| | - Gilmer Valdes
- Department of Radiation Oncology, University of California Los San Francisco, San Francisco, CA, USA
| | - Andre Dekker
- GROW-School for Oncology and Developmental Biology, Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Todd McNutt
- Department of Radiation Oncology, John Hopkins University, Baltimore, MD, USA
| | - Yaorong Ge
- Department of Software and Information Systems, University of North Carolina, Charlotte, NC, USA
| | - Q Jackie Wu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Maria Thor
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Wade Smith
- Department of Radiation Oncology, University of Washington, Seattle, WA, USA
| | - Arvind Rao
- Department of Radiation Oncology, MD Anderson, Houston, TX, USA.,Department of Bioinformatics and Computational Biology, MD Anderson, Houston, TX, USA
| | - Clifton Fuller
- Department of Radiation Oncology, MD Anderson, Houston, TX, USA
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Frank Manion
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Matthew Schipper
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Charles Mayo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Jean M Moran
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Randall Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
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Annede P, Mailleux H, Sfumato P, Ferré M, Autret A, Varela Cagetti L, Macagno A, Fau P, Chargari C, Tallet A, Resbeut M, Zemmour C, Gonzague L, Boher JM. Multivariate normal tissue complication probability modeling of vaginal late toxicity after brachytherapy for cervical cancer. Brachytherapy 2018; 17:922-928. [PMID: 30061056 DOI: 10.1016/j.brachy.2018.07.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 05/22/2018] [Accepted: 07/03/2018] [Indexed: 11/18/2022]
Abstract
PURPOSE To explore the best variables combination for a predictive model of vaginal toxicity in cervical cancer patients undergoing brachytherapy (BT). METHODS AND MATERIALS Clinical and 3-dimensional dosimetric parameters were retrospectively extracted from an institutional database of consecutive patients undergoing intracavitary BT after external beam radiation therapy from 2006 to 2013 for a cervical cancer. A least absolute shrinkage and selection operator selection procedure in Cox's proportional hazards regression model was performed to select a set of relevant predictors for a multivariate normal tissue complication probability model of Grade ≥2 vaginal late toxicity. Outcomes reliability was internally assessed by bootstrap resampling method. RESULTS One hundred sixty-nine women were included in the present study with a median followup time of 3.8 years (interquartile range [IQR], 1.9-5.6 years). The 2 years and 5 years cumulative incidence rates of Grade ≥2 late vaginal toxicity were 19.9% and 27.5%, respectively. Among 31 metrics and six clinical factors extracted, the optimal model included two dosimetric variables: V70Gy and D5% (the percentage volume that received a dose greater or equal to 70 Gy and the minimum dose given to the hottest 5% volume, respectively). Area under the ROC curve at 2 and 5 years of followup were 0.85 and 0.91, respectively. Regarding internal validation, median area under the ROC curve of bootstrap predictions was 0.83 (IQR, 0.78-0.88) and 0.89 (IQR, 0.85-0.93) at 2 and 5 years of followup, respectively. CONCLUSIONS A multivariate normal tissue complication probability model for severe vaginal toxicity based on two dosimetric variables (V70Gy and D5%) provides reliable discrimination capability in a cohort of cervical cancer treated with external beam radiation therapy and BT.
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Affiliation(s)
- Pierre Annede
- Unit of Biostatistics and Methodology, Paoli Calmettes Institute, Comprehensive Cancer Centre, Marseille, France; Department of Radiotherapy, Paoli Calmettes Institute, Marseille, France.
| | - Hugues Mailleux
- Department of Radiotherapy, Paoli Calmettes Institute, Marseille, France
| | - Patrick Sfumato
- Unit of Biostatistics and Methodology, Paoli Calmettes Institute, Comprehensive Cancer Centre, Marseille, France
| | - Marjorie Ferré
- Department of Radiotherapy, Paoli Calmettes Institute, Marseille, France
| | - Aurélie Autret
- Unit of Biostatistics and Methodology, Paoli Calmettes Institute, Comprehensive Cancer Centre, Marseille, France
| | | | - Alban Macagno
- Department of Radiotherapy, Paoli Calmettes Institute, Marseille, France
| | - Pierre Fau
- Department of Radiotherapy, Paoli Calmettes Institute, Marseille, France
| | - Cyrus Chargari
- Radiotherapy Department, Gustave Roussy, Villejuif, France; Effets biologiques des rayonnements, Institut de Recherche Biomédicale des Armées, Bretigny-sur-Orge, France
| | - Agnès Tallet
- Department of Radiotherapy, Paoli Calmettes Institute, Marseille, France
| | - Michel Resbeut
- Department of Radiotherapy, Paoli Calmettes Institute, Marseille, France
| | - Christophe Zemmour
- Unit of Biostatistics and Methodology, Paoli Calmettes Institute, Comprehensive Cancer Centre, Marseille, France
| | - Laurence Gonzague
- Department of Radiotherapy, Paoli Calmettes Institute, Marseille, France
| | - Jean-Marie Boher
- Unit of Biostatistics and Methodology, Paoli Calmettes Institute, Comprehensive Cancer Centre, Marseille, France
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Chen J, Chen H, Zhong Z, Wang Z, Hrycushko B, Zhou L, Jiang S, Albuquerque K, Gu X, Zhen X. Investigating rectal toxicity associated dosimetric features with deformable accumulated rectal surface dose maps for cervical cancer radiotherapy. Radiat Oncol 2018; 13:125. [PMID: 29980214 PMCID: PMC6035458 DOI: 10.1186/s13014-018-1068-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Accepted: 06/18/2018] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Better knowledge of the dose-toxicity relationship is essential for safe dose escalation to improve local control in cervical cancer radiotherapy. The conventional dose-toxicity model is based on the dose volume histogram, which is the parameter lacking spatial dose information. To overcome this limit, we explore a comprehensive rectal dose-toxicity model based on both dose volume histogram and dose map features for accurate radiation toxicity prediction. METHODS Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively studied, including 12 with Grade ≥ 2 rectum toxicity and 30 patients with Grade 0-1 toxicity (non-toxicity patients). The cumulative equivalent 2-Gy rectal surface dose was deformably summed using the deformation vector fields obtained through a recent developed local topology preserved non-rigid point matching algorithm. The cumulative three-dimensional (3D) dose was flattened and mapped to a two-dimensional (2D) plane to obtain the rectum surface dose map (RSDM). The dose volume parameters (DVPs) were calculated from the 3D rectum surface, while the texture features and the dose geometric parameters (DGPs) were extracted from the 2D RSDM. Representative features further computed from DVPs, textures and DGPs by principle component analysis (PCA) and statistical analysis were respectively fed into a support vector machine equipped with a sequential feature selection procedure. The predictive powers of the representative features were compared with the GEC-ESTRO dosimetric parameters D0.1/1/2cm3. RESULTS Satisfactory predictive accuracy of sensitivity 74.75 and 84.75%, specificity 72.67 and 79.87%, and area under the receiver operating characteristic curve (AUC) 0.82 and 0.91 were respectively achieved by the PCA features and statistical significant features, which were superior to the D0.1/1/2cm3 (AUC 0.71). The relative area in dose levels of 64Gy, 67Gy, 68Gy, 87Gy, 88Gy and 89Gy, perimeters in dose levels of 89Gy, as well as two texture features were ranked as the important factors that were closely correlated with rectal toxicity. CONCLUSIONS Our extensive experimental results have demonstrated the feasibility of the proposed scheme. A future large patient cohort study is still needed for model validation.
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Affiliation(s)
- Jiawei Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Haibin Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Zichun Zhong
- Department of Computer Science, Wayne State University, Detroit, MI, 48202, USA
| | - Zhuoyu Wang
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 805 Sherbrooke Street West, Montreal, Quebec, H3A 0G4, Canada
| | - Brian Hrycushko
- Department of Radiation Oncology, The University of Texas, Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Linghong Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Steve Jiang
- Department of Radiation Oncology, The University of Texas, Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Kevin Albuquerque
- Department of Radiation Oncology, The University of Texas, Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Xuejun Gu
- Department of Radiation Oncology, The University of Texas, Southwestern Medical Center, Dallas, TX, 75390, USA.
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
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Christophides D, Appelt AL, Gusnanto A, Lilley J, Sebag-Montefiore D. Method for Automatic Selection of Parameters in Normal Tissue Complication Probability Modeling. Int J Radiat Oncol Biol Phys 2018; 101:704-712. [PMID: 29681482 DOI: 10.1016/j.ijrobp.2018.02.152] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 01/08/2018] [Accepted: 02/26/2018] [Indexed: 11/28/2022]
Abstract
PURPOSE To present a fully automatic method to generate multiparameter normal tissue complication probability (NTCP) models and compare its results with those of a published model, using the same patient cohort. METHODS AND MATERIALS Data were analyzed from 345 rectal cancer patients treated with external radiation therapy to predict the risk of patients developing grade 1 or ≥2 cystitis. In total, 23 clinical factors were included in the analysis as candidate predictors of cystitis. Principal component analysis was used to decompose the bladder dose-volume histogram into 8 principal components, explaining more than 95% of the variance. The data set of clinical factors and principal components was divided into training (70%) and test (30%) data sets, with the training data set used by the algorithm to compute an NTCP model. The first step of the algorithm was to obtain a bootstrap sample, followed by multicollinearity reduction using the variance inflation factor and genetic algorithm optimization to determine an ordinal logistic regression model that minimizes the Bayesian information criterion. The process was repeated 100 times, and the model with the minimum Bayesian information criterion was recorded on each iteration. The most frequent model was selected as the final "automatically generated model" (AGM). The published model and AGM were fitted on the training data sets, and the risk of cystitis was calculated. RESULTS The 2 models had no significant differences in predictive performance, both for the training and test data sets (P value > .05) and found similar clinical and dosimetric factors as predictors. Both models exhibited good explanatory performance on the training data set (P values > .44), which was reduced on the test data sets (P values < .05). CONCLUSIONS The predictive value of the AGM is equivalent to that of the expert-derived published model. It demonstrates potential in saving time, tackling problems with a large number of parameters, and standardizing variable selection in NTCP modeling.
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Affiliation(s)
- Damianos Christophides
- Leeds Cancer Centre, St. James's University Hospital, Leeds, United Kingdom; Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom.
| | - Ane L Appelt
- Leeds Cancer Centre, St. James's University Hospital, Leeds, United Kingdom; Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom; Danish Colorectal Cancer Center South, Vejle Hospital, Vejle, Denmark
| | - Arief Gusnanto
- Department of Statistics, University of Leeds, Leeds, United Kingdom
| | - John Lilley
- Leeds Cancer Centre, St. James's University Hospital, Leeds, United Kingdom
| | - David Sebag-Montefiore
- Leeds Cancer Centre, St. James's University Hospital, Leeds, United Kingdom; Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom
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Anacleto A, Dias J. Data Analysis in Radiotherapy Treatments. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2018. [DOI: 10.4018/ijehmc.2018070103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Radiotherapy is one of the main cancer treatments available today, together with chemotherapy and surgery. Radiotherapy treatments have to be planned for each patient in an individualized manner. The knowledge acquired from one single treatment can be used to improve the treatment planning and outcome of several other patients. In the last years, attention has been drawn to the added value of using data analysis for radiotherapy treatment planning, prediction of treatment outcomes, survival analysis and quality assurance. In this article, existing literature is reviewed.
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Affiliation(s)
- Ana Anacleto
- Faculty of Economics, University of Coimbra, Coimbra, Portugal
| | - Joana Dias
- Inesc-Coimbra, CeBER, Faculty of Economics, University of Coimbra, Coimbra, Portugal
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45
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Thompson RF, Valdes G, Fuller CD, Carpenter CM, Morin O, Aneja S, Lindsay WD, Aerts HJWL, Agrimson B, Deville C, Rosenthal SA, Yu JB, Thomas CR. Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? Radiother Oncol 2018; 129:421-426. [PMID: 29907338 DOI: 10.1016/j.radonc.2018.05.030] [Citation(s) in RCA: 131] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 05/29/2018] [Accepted: 05/30/2018] [Indexed: 12/16/2022]
Abstract
Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.
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Affiliation(s)
- Reid F Thompson
- Oregon Health & Science University, Portland, USA; VA Portland Health Care System, Portland, USA.
| | - Gilmer Valdes
- University of California San Francisco, San Francisco, USA
| | | | | | - Olivier Morin
- University of California San Francisco, San Francisco, USA
| | | | | | - Hugo J W L Aerts
- Brigham and Women's Hospital, Boston, USA; Dana Farber Cancer Institute, Boston, USA
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46
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Luo R, Wu VWC, He B, Gao X, Xu Z, Wang D, Yang Z, Li M, Lin Z. Development of a normal tissue complication probability (NTCP) model for radiation-induced hypothyroidism in nasopharyngeal carcinoma patients. BMC Cancer 2018; 18:575. [PMID: 29776390 PMCID: PMC5960211 DOI: 10.1186/s12885-018-4348-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 04/08/2018] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The objectives of this study were to build a normal tissue complication probability (NTCP) model of radiation-induced hypothyroidism (RHT) for nasopharyngeal carcinoma (NPC) patients and to compare it with other four published NTCP models to evaluate its efficacy. METHODS Medical notes of 174 NPC patients after radiotherapy were reviewed. Biochemical hypothyroidism was defined as an elevated level of serum thyroid-stimulating hormone (TSH) value with a normal or decreased level of serum free thyroxine (fT4) after radiotherapy. Logistic regression with leave-one-out cross-validation was performed to establish the NTCP model. Model performance was evaluated and compared by the area under the receiver operating characteristic curve (AUC) in our NPC cohort. RESULTS With a median follow-up of 24 months, 39 (22.4%) patients developed biochemical hypothyroidism. Gender, chemotherapy, the percentage thyroid volume receiving more than 50 Gy (V50), and the maximum dose of the pituitary (Pmax) were identified as the most predictive factors for RHT. A NTCP model based on these four parameters were developed. The model comparison was made in our NPC cohort and our NTCP model performed better in RHT prediction than the other four models. CONCLUSIONS This study developed a four-variable NTCP model for biochemical hypothyroidism in NPC patients post-radiotherapy. Our NTCP model for RHT presents a high prediction capability. TRIAL REGISTRATION This is a retrospective study without registration.
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Affiliation(s)
- Ren Luo
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Department of Radiation Oncology, Medical Center - University of Freiburg, Freiburg, Germany
| | - Vincent W C Wu
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China
| | - Binghui He
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Xiaoying Gao
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Zhenxi Xu
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Dandan Wang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Zhining Yang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Mei Li
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China.
| | - Zhixiong Lin
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China.
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Gago-Arias A, Sánchez-Nieto B, Espinoza I, Karger CP, Pardo-Montero J. Impact of different biologically-adapted radiotherapy strategies on tumor control evaluated with a tumor response model. PLoS One 2018; 13:e0196310. [PMID: 29698534 PMCID: PMC5919644 DOI: 10.1371/journal.pone.0196310] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 04/10/2018] [Indexed: 11/26/2022] Open
Abstract
Motivated by the capabilities of modern radiotherapy techniques and by the recent developments of functional imaging techniques, dose painting by numbers (DPBN) was proposed to treat tumors with heterogeneous biological characteristics. This work studies different DPBN optimization techniques for virtual head and neck tumors assessing tumor response in terms of cell survival and tumor control probability with a previously published tumor response model (TRM). Uniform doses of 2 Gy are redistributed according to the microscopic oxygen distribution and the density distribution of tumor cells in four virtual tumors with different biological characteristics. In addition, two different optimization objective functions are investigated, which: i) minimize tumor cell survival (OFsurv) or; ii) maximize the homogeneity of the density of surviving tumor cells (OFstd). Several adaptive schemes, ranging from single to daily dose optimization, are studied and the treatment response is compared to that of the uniform dose. The results show that the benefit of DPBN treatments depends on the tumor reoxygenation capability, which strongly differed among the set of virtual tumors investigated. The difference between daily (fraction by fraction) and three weekly optimizations (at the beginning of weeks 1, 3 and 4) was found to be small, and higher benefit was observed for the treatments optimized using OFsurv. This in silico study corroborates the hypothesis that DPBN may be beneficial for treatments of tumors which show reoxygenation during treatment, and that a few optimizations may be sufficient to achieve this therapeutic benefit.
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Affiliation(s)
- Araceli Gago-Arias
- Instituto de Física, Pontificia Universidad Católica de Chile, Santiago, Chile
- * E-mail:
| | | | - Ignacio Espinoza
- Instituto de Física, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Christian P. Karger
- National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
| | - Juan Pardo-Montero
- Grupo de Imaxe Molecular, Instituto de Investigación Sanitaria (IDIS), Santiago de Compostela, Spain
- Servizo de Radiofísica e Protección Radiolóxica, Complexo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Spain
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Feng M, Valdes G, Dixit N, Solberg TD. Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs. Front Oncol 2018; 8:110. [PMID: 29719815 PMCID: PMC5913324 DOI: 10.3389/fonc.2018.00110] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 03/29/2018] [Indexed: 12/14/2022] Open
Abstract
Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner.
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Affiliation(s)
- Mary Feng
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States
| | - Gilmer Valdes
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States
| | - Nayha Dixit
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States
| | - Timothy D Solberg
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States
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Constanzo J, Wei L, Tseng HH, El Naqa I. Radiomics in precision medicine for lung cancer. Transl Lung Cancer Res 2017; 6:635-647. [PMID: 29218267 PMCID: PMC5709132 DOI: 10.21037/tlcr.2017.09.07] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 09/14/2017] [Indexed: 12/11/2022]
Abstract
With the improvement of external radiotherapy delivery accuracy, such as intensity-modulated and stereotactic body radiation therapy, radiation oncology has recently entered in the era of precision medicine. Despite these precise irradiation modalities, lung cancers remain one of the most aggressive human cancers worldwide, possibly because of diverse genotypic alterations that drive and maintain lung tumorigenesis. It has been long recognized that imaging could aid in the diagnosis, tumor delineation, and monitoring of lung cancer. Moreover, accumulating evidence suggests that imaging information could be further used to tailor treatment type and intensity, as well as predict treatment outcomes in radiotherapy. However, these imaging tasks have been carried out either qualitatively or using simplistic metrics that doesn't take advantage of the full scale of imaging knowledge. Radiomics, which is a recent field of research that aims to provide a more quantitative representation of imaging information relating tumor phenotypes to clinical and genotypic endpoints by embedding extracted image features into predictive mathematical models. These predictive models can be a key component in the clinician decision making and treatment personalization. This review provides an overview of the radiomics application and its methodology for radiation oncology studies in lung cancer.
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Affiliation(s)
- Julie Constanzo
- Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg F-67000, France
| | - Lise Wei
- Department of Radiation Oncology, Physics Division, University of Michigan, Ann Arbor, MI 48103, USA
| | - Huan-Hsin Tseng
- Department of Radiation Oncology, Physics Division, University of Michigan, Ann Arbor, MI 48103, USA
| | - Issam El Naqa
- Department of Radiation Oncology, Physics Division, University of Michigan, Ann Arbor, MI 48103, USA
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50
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El Naqa I, Kerns SL, Coates J, Luo Y, Speers C, West CML, Rosenstein BS, Ten Haken RK. Radiogenomics and radiotherapy response modeling. Phys Med Biol 2017; 62:R179-R206. [PMID: 28657906 PMCID: PMC5557376 DOI: 10.1088/1361-6560/aa7c55] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.
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Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America
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