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Mansour IR, Miksys N, Beaulieu L, Vigneault É, Thomson RM. Haralick texture feature analysis for Monte Carlo dose distributions of permanent implant prostate brachytherapy. Brachytherapy 2024:S1538-4721(24)00393-3. [PMID: 39532616 DOI: 10.1016/j.brachy.2024.08.256] [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: 05/05/2024] [Revised: 07/10/2024] [Accepted: 08/26/2024] [Indexed: 11/16/2024]
Abstract
PURPOSE Demonstrate quantitative characterization of 3D patient-specific absorbed dose distributions using Haralick texture analysis, and interpret measures in terms of underlying physics and radiation dosimetry. METHODS Retrospective analysis is performed for 137 patients who underwent permanent implant prostate brachytherapy using two simulation conditions: "TG186" (realistic tissues including 0-3.8% intraprostatic calcifications; interseed attenuation) and "TG43" (water-model; no interseed attenuation). Five Haralick features (homogeneity, contrast, correlation, local homogeneity, entropy) are calculated using the original Haralick formalism, and a modified approach designed to reduce grey-level quantization sensitivity. Trends in textural features are compared to clinical dosimetric measures (D90; minimum absorbed dose to the hottest 90% of a volume) and changes in patient target volume % intraprostatic calcifications by volume (%IC). RESULTS Both original and modified measures quantify the spatial differences in absorbed dose distributions. Strong correlations between differences in textural measures calculated under TG43 and TG186 conditions and %IC are observed for all measures. For example, differences between measures of contrast and correlation increase and decrease respectively as patients with higher levels of %IC are evaluated, reflecting the large differences across adjacent voxels (higher absorbed dose in voxels with calcification) when calculated under TG186 conditions. Conversely, the D90 metric is relatively weakly correlated with textural measures, as it generally does not characterize the spatial distribution of absorbed dose. CONCLUSION Patient-specific 3D dose distributions may be quantified using Haralick analysis, and trends may be interpreted in terms of fundamental physics. Promising future directions include investigations of novel treatment modalities and clinical outcomes.
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Affiliation(s)
- Iymad R Mansour
- Carleton Laboratory for Radiotherapy Physics, Physics Department, Carleton University, Ottawa, Ontario, Canada
| | | | - Luc Beaulieu
- Service de Physique Médicale et de Radioprotection, Centre Intégré de Cancérologie, CHU de Québec- Université Laval et Centre de recherche du CHU de Québec, Québec, Québec, Canada; Département de Physique, de Génie Physique et d'Optique et Centre de Recherche sur le Cancer, Université Laval, Québec, Québec, Canada
| | - Éric Vigneault
- Centre de recherche sur le cancer, Département de Radio-Oncologie et Centre de recherche du CHU de Québec, UniversitéLaval, Québec City, Québec, Canada
| | - Rowan M Thomson
- Carleton Laboratory for Radiotherapy Physics, Physics Department, Carleton University, Ottawa, Ontario, Canada.
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2
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Zhang X, Zheng W, Huang S, Li H, Bi Z, Yang X. Xerostomia prediction in patients with nasopharyngeal carcinoma during radiotherapy using segmental dose distribution in dosiomics and radiomics models. Oral Oncol 2024; 158:107000. [PMID: 39226775 DOI: 10.1016/j.oraloncology.2024.107000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Revised: 07/31/2024] [Accepted: 08/14/2024] [Indexed: 09/05/2024]
Abstract
OBJECTIVES This study aimed to integrate radiomics and dosiomics features to develop a predictive model for xerostomia (XM) in nasopharyngeal carcinoma after radiotherapy. It explores the influence of distinct feature extraction methods and dose ranges on the performance. MATERIALS AND METHODS Data from 363 patients with nasopharyngeal carcinoma were retrospectively analyzed. We pioneered a dose-segmentation strategy, where the overall dose distribution (OD) was divided into four segmental dose distributions (SDs) at intervals of 15 Gy. Features were extracted using manual definition and deep learning, applying OD or SD and integrating radiomics and dosiomics, yielding corresponding feature scores (manually defined radiomics, MDR; manually defined dosiomics, MDD; deep learning-based radiomics, DLR; deep learning-based dosiomics, DLD). Subsequently, 18 models were developed by combining features and model types (random forest and support vector machine). RESULTS AND CONCLUSION Under OD, O(DLR_DLD) demonstrated exceptional performance, with an optimal area under the curve (AUC) of 0.81 and an average AUC of 0.71. Within SD, S(DLR_DLD) surpassed the other models, achieving an optimal AUC of 0.90 and an average AUC of 0.85. Therefore, the integration of dosiomics into radiomics can augment predictive efficacy. The dose-segmentation strategy can facilitate the extraction of more profound information. This indicates that ScoreDLR and ScoreMDR were negatively associated with XM, whereas ScoreDLD, derived from SD exceeding 15 Gy, displayed a positive association with XM. For feature extraction, deep learning was superior to manual definition.
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Affiliation(s)
- Xushi Zhang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, Guangdong Province, China; School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511400, Guangdong Province, China.
| | - Wanjia Zheng
- Department of Radiation Oncology, Southern Theater Air Force Hospital of the People's Liberation Army, Guangzhou 510050, Guangdong Province, China.
| | - Sijuan Huang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, Guangdong Province, China.
| | - Haojiang Li
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, Guangdong Province, China.
| | - Zhisheng Bi
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511400, Guangdong Province, China; Department of Emergency Medicine, the Second Affiliated Hospital, Guangzhou Medical University, Guangzhou 510260, Guangdong Province, China.
| | - Xin Yang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou 510060, Guangdong Province, China.
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3
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Humbert-Vidan L, Patel V, King AP, Urbano TG. Comparison of deep-learning multimodality data fusion strategies in mandibular osteoradionecrosis NTCP modelling using clinical variables and radiation dose distribution volumes. Phys Med Biol 2024; 69:20NT01. [PMID: 39357529 DOI: 10.1088/1361-6560/ad8290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 10/01/2024] [Indexed: 10/04/2024]
Abstract
Objective.Normal tissue complication probability (NTCP) modelling is rapidly embracing deep learning (DL) methods, acknowledging the importance of spatial dose information. Finding effective ways to combine information from radiation dose distribution maps (dosiomics) and clinical data involves technical challenges and requires domain knowledge. We propose different multi-modality data fusion strategies to facilitate future DL-based NTCP studies.Approach.Early, joint and late DL multi-modality fusion strategies were compared using clinical and mandibular radiation dose distribution volumes. These were contrasted with single-modality models: a random forest trained on non-image data (clinical, demographic and dose-volume metrics) and a 3D DenseNet-40 trained on image data (mandibular dose distribution maps). The study involved a matched cohort of 92 osteoradionecrosis cases and 92 controls from a single institution.Main results.The late fusion model exhibited superior discrimination and calibration performance, while the join fusion achieved a more balanced distribution of the predicted probabilities. Discrimination performance did not significantly differ between strategies. Late fusion, though less technically complex, lacks crucial inter-modality interactions for NTCP modelling. In contrast, joint fusion, despite its complexity, resulted in a single network training process which included intra- and inter-modality interactions in its model parameter optimisation.Significance.This study is a pioneering effort in comparing different strategies for including image data into DL-based NTCP models in combination with lower dimensional data such as clinical variables. The discrimination performance of such multi-modality NTCP models and the choice of fusion strategy will depend on the distribution and quality of both types of data. Multiple data fusion strategies should be compared and reported in multi-modality NTCP modelling using DL.
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Affiliation(s)
- Laia Humbert-Vidan
- Department of Medical Physics, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
- School of Cancer and Pharmaceutical Sciences, Comprehensive Cancer Centre, King's College London, London, United Kingdom
| | - Vinod Patel
- Department of Oral Surgery, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Andrew P King
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Teresa Guerrero Urbano
- School of Cancer and Pharmaceutical Sciences, Comprehensive Cancer Centre, King's College London, London, United Kingdom
- Department of Clinical Oncology, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
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4
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Jaikuna T, Wilson F, Azria D, Chang-Claude J, De Santis MC, Gutiérrez-Enríquez S, van Herk M, Hoskin P, Kotzki L, Lambrecht M, Lingard Z, Seibold P, Seoane A, Sperk E, Paul Symonds R, Talbot CJ, Rancati T, Rattay T, Reyes V, Rosenstein BS, de Ruysscher D, Vega A, Veldeman L, Webb A, West CML, Aznar MC, Vasquez Osorio E. Optimising inter-patient image registration for image-based data mining in breast radiotherapy. Phys Imaging Radiat Oncol 2024; 32:100635. [PMID: 39310222 PMCID: PMC11413750 DOI: 10.1016/j.phro.2024.100635] [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: 07/20/2024] [Revised: 08/19/2024] [Accepted: 08/20/2024] [Indexed: 09/25/2024] Open
Abstract
Background and purpose Image-based data mining (IBDM) requires spatial normalisation to reference anatomy, which is challenging in breast radiotherapy due to variations in the treatment position, breast shape and volume. We aim to optimise spatial normalisation for breast IBDM. Materials and methods Data from 996 patients treated with radiotherapy for early-stage breast cancer, recruited in the REQUITE study, were included. Patients were treated supine (n = 811), with either bilateral or ipsilateral arm(s) raised (551/260, respectively) or in prone position (n = 185). Four deformable image registration (DIR) configurations for extrathoracic spatial normalisation were tested. We selected the best-performing DIR configuration and further investigated two pathways: i) registering prone/supine cohorts independently and ii) registering all patients to a supine reference. The impact of arm positioning in the supine cohort was quantified. DIR accuracy was estimated using Normalised Cross Correlation (NCC), Dice Similarity Coefficient (DSC), mean Distance to Agreement (MDA), 95 % Hausdorff Distance (95 %HD), and inter-patient landmark registration uncertainty (ILRU). Results DIR using B-spline and normalised mutual information (NMI) performed the best across all evaluation metrics. Supine-supine registrations yielded highest accuracy (0.98 ± 0.01, 0.91 ± 0.04, 0.23 ± 0.19 cm, 1.17 ± 1.18 cm, 0.51 ± 0.26 cm for NCC, DSC, MDA, 95 %HD, and ILRU), followed by prone-prone and supine-prone registrations. Arm positioning had no significant impact on registration performance. For the best DIR strategy, uncertainty of 0.44 and 0.81 cm in the breast and shoulder regions was found. Conclusions B-spline algorithm using NMI and registered supine and prone cohorts independently provides the most optimal spatial normalisation strategy for breast IBDM.
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Affiliation(s)
- Tanwiwat Jaikuna
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Fiona Wilson
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
| | - David Azria
- University Federation of Radiation Oncology of Mediterranean Occitanie, Montpellier Cancer Institute ICM, Université Montpellier, INSERM 1194 IRCM, Montpellier, France
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Germany
| | | | - Sara Gutiérrez-Enríquez
- Hereditary Cancer Genetics Group, Vall d’Hebron Institute of Oncology (VHIO), Vall d’Hebron Hospital Campus, Barcelona, Spain
| | - Marcel van Herk
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
| | - Peter Hoskin
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
| | - Lea Kotzki
- University Federation of Radiation Oncology of Mediterranean Occitanie, Gard Cancer Institute ICG, CHU Caremeau, Nimes, France
| | | | - Zoe Lingard
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
| | - Petra Seibold
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alejandro Seoane
- Medical Physics Department, Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Elena Sperk
- Department of Radiation Oncology, Mannheim Cancer Center, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - R Paul Symonds
- Leicester Cancer Research Centre, University of Leicester, United Kingdom
| | | | - Tiziana Rancati
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Tim Rattay
- Leicester Cancer Research Centre, University of Leicester, United Kingdom
| | - Victoria Reyes
- Radiation Oncology Department, Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Barry S. Rosenstein
- Department of Radiation Oncology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, USA
| | - Dirk de Ruysscher
- Maastricht University Medical Center, Department of Radiation Oncology (Maastro Clinic), GROW School for Oncology and Developmental Biology, Maastricht, the Netherlands
| | - Ana Vega
- Fundación Pública Galega de Medicina Xenómica, Grupo de Medicina Xenómica (USC), Santiago de Compostela, Spain
- Instituto de Investigación Sanitaria de, Santiago de Compostela, Spain
- Biomedical Network on Rare Diseases (CIBERER), Spain
| | - Liv Veldeman
- Ghent University Hospital, Department of Radiation Oncology, Ghent, Belgium
| | - Adam Webb
- Department of Genetics and Genome Biology, University of Leicester, United Kingdom
| | - Catharine ML West
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
| | - Marianne C Aznar
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
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5
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Deasy JO. Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making. Semin Radiat Oncol 2024; 34:379-394. [PMID: 39271273 DOI: 10.1016/j.semradonc.2024.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
Radiotherapy aims to achieve a high tumor control probability while minimizing damage to normal tissues. Personalizing radiotherapy treatments for individual patients, therefore, depends on integrating physical treatment planning with predictive models of tumor control and normal tissue complications. Predictive models could be improved using a wide range of rich data sources, including tumor and normal tissue genomics, radiomics, and dosiomics. Deep learning will drive improvements in classifying normal tissue tolerance, predicting intra-treatment tumor changes, tracking accumulated dose distributions, and quantifying the tumor response to radiotherapy based on imaging. Mechanistic patient-specific computer simulations ('digital twins') could also be used to guide adaptive radiotherapy. Overall, we are entering an era where improved modeling methods will allow the use of newly available data sources to better guide radiotherapy treatments.
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Affiliation(s)
- Joseph O Deasy
- Department of Medical Physics, Attending Physicist, Chief, Service for Predictive Informatics, Chair, Memorial Sloan Kettering Cancer Center, New York, NY..
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6
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Hong CS, Park YI, Cho MS, Son J, Kim C, Han MC, Kim H, Lee H, Kim DW, Choi SH, Kim JS. Dose-toxicity surface histogram-based prediction of radiation dermatitis severity and shape. Phys Med Biol 2024; 69:115041. [PMID: 38759672 DOI: 10.1088/1361-6560/ad4d4e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 05/17/2024] [Indexed: 05/19/2024]
Abstract
Objective.This study aimed to develop a new approach to predict radiation dermatitis (RD) by using the skin dose distribution in the actual area of RD occurrence to determine the predictive dose by grade.Approach.Twenty-three patients with head and neck cancer treated with volumetric modulated arc therapy were prospectively and retrospectively enrolled. A framework was developed to segment the RD occurrence area in skin photography by matching the skin surface image obtained using a 3D camera with the skin dose distribution. RD predictive doses were generated using the dose-toxicity surface histogram (DTH) calculated from the skin dose distribution within the segmented RD regions classified by severity. We then evaluated whether the developed DTH-based framework could visually predict RD grades and their occurrence areas and shapes according to severity.Main results.The developed framework successfully generated the DTH for three different RD severities: faint erythema (grade 1), dry desquamation (grade 2), and moist desquamation (grade 3); 48 DTHs were obtained from 23 patients: 23, 22, and 3 DTHs for grades 1, 2, and 3, respectively. The RD predictive doses determined using DTHs were 28.9 Gy, 38.1 Gy, and 54.3 Gy for grades 1, 2, and 3, respectively. The estimated RD occurrence area visualized by the DTH-based RD predictive dose showed acceptable agreement for all grades compared with the actual RD region in the patient. The predicted RD grade was accurate, except in two patients.Significance. The developed DTH-based framework can classify and determine RD predictive doses according to severity and visually predict the occurrence area and shape of different RD severities. The proposed approach can be used to predict the severity and shape of potential RD in patients and thus aid physicians in decision making.
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Affiliation(s)
- Chae-Seon Hong
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ye-In Park
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Min-Seok Cho
- Department of Radiation Oncology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Gyeonggi do, Republic of Korea
| | - Junyoung Son
- Department of Radiation Oncology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Gyeonggi do, Republic of Korea
| | - Changhwan Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Min Cheol Han
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hojin Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ho Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dong Wook Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seo Hee Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
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7
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Corbeau A, Heemsbergen WD, Kuipers SC, Godart J, Creutzberg CL, Nout RA, de Boer SM. Predictive Factors for Toxicity After Primary Chemoradiation for Locally Advanced Cervical Cancer: A Systematic Review. Int J Radiat Oncol Biol Phys 2024; 119:127-142. [PMID: 37979708 DOI: 10.1016/j.ijrobp.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 11/01/2023] [Accepted: 11/04/2023] [Indexed: 11/20/2023]
Abstract
PURPOSE Women with locally advanced cervical cancer (LACC) undergoing primary platinum-based chemoradiotherapy and brachytherapy often experience toxicities. Normal-tissue complication probability (NTCP) models quantify toxicity risk and aid in optimizing radiation therapy to minimize side effects. However, it is unclear which predictors to include in an NTCP model. The aim of this systematic review was to provide an overview of the identified predictors contributing to gastrointestinal (GI), genitourinary (GU), and vaginal toxicities and insufficiency fractures for LACC. METHODS AND MATERIALS A systematic search was performed and articles evaluating the relationship between predictors and toxicities in women with LACC treated with primary chemoradiation were included. The Quality In Prognosis Studies tool was used to assess risk of bias, with high-risk studies being excluded from further analysis. Relationships between dose-volume parameters, patient and treatment characteristics, and toxicity endpoints were analyzed. RESULTS Seventy-three studies were identified. Twenty-six had a low or moderate risk of bias and were therefore included. Brachytherapy-related dose-volume parameters of the GI tract, including rectum and bowel equivalent dose in 2 Gy fractions (EQD2) D2 cm3, were frequently related to toxicities, unlike GU dose-volume parameters. Furthermore, (recto)vaginal point doses predicted toxicities. Few studies evaluated external beam radiation therapy dose-volume parameters and identified rectum EQD2 V30 Gy, V40 Gy, and V55 Gy, bowel and bladder EQD2 V40 Gy as toxicity predictors. Also, total reference air kerma and vaginal reference length were associated with toxicities. Relationships between patient characteristics and GI toxicity were inconsistent. The extent of vaginal involvement at diagnosis, baseline symptoms, and obesity predicted GU or vaginal toxicities. Only 1 study evaluated insufficiency fractures and demonstrated lower pretreatment bone densities to be associated. CONCLUSIONS This review detected multiple candidate predictors of toxicity. Larger studies should consider insufficiency fractures, assess dose levels from external beam radiation therapy, and quantify the relationship between the predictors and treatment-related toxicities in women with LACC to further facilitate NTCP model development for clinical use.
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Affiliation(s)
- Anouk Corbeau
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, The Netherlands.
| | - Wilma D Heemsbergen
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sander C Kuipers
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands; Department of Medical Physics & Informatics, HollandPTC, Delft, The Netherlands
| | - Jeremy Godart
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands; Department of Medical Physics & Informatics, HollandPTC, Delft, The Netherlands
| | - Carien L Creutzberg
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - Remi A Nout
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Stephanie M de Boer
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, The Netherlands
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8
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McWilliam A, Palma G, Abravan A, Acosta O, Appelt A, Aznar M, Monti S, Onjukka E, Panettieri V, Placidi L, Rancati T, Vasquez Osorio E, Witte M, Cella L. Voxel-based analysis: Roadmap for clinical translation. Radiother Oncol 2023; 188:109868. [PMID: 37683811 DOI: 10.1016/j.radonc.2023.109868] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 07/11/2023] [Accepted: 08/20/2023] [Indexed: 09/10/2023]
Abstract
Voxel-based analysis (VBA) allows the full, 3-dimensional, dose distribution to be considered in radiotherapy outcome analysis. This provides new insights into anatomical variability of pathophysiology and radiosensitivity by removing the need for a priori definition of organs assumed to drive the dose response associated with patient outcomes. This approach may offer powerful biological insights demonstrating the heterogeneity of the radiobiology across tissues and potential associations of the radiotherapy dose with further factors. As this methodological approach becomes established, consideration needs to be given to translating VBA results to clinical implementation for patient benefit. Here, we present a comprehensive roadmap for VBA clinical translation. Technical validation needs to demonstrate robustness to methodology, where clinical validation must show generalisability to external datasets and link to a plausible pathophysiological hypothesis. Finally, clinical utility requires demonstration of potential benefit for patients in order for successful translation to be feasible. For each step on the roadmap, key considerations are discussed and recommendations provided for best practice.
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Affiliation(s)
- Alan McWilliam
- The Division of Cancer Sciences, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK.
| | - Giuseppe Palma
- Institute of Nanotechnology, National Research Council, Lecce, Italy.
| | - Azadeh Abravan
- The Division of Cancer Sciences, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK
| | - Oscar Acosta
- University Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000, Rennes, France
| | - Ane Appelt
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Marianne Aznar
- The Division of Cancer Sciences, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK
| | - Serena Monti
- Institute of Biostructures and Bioimaging, National Research Council, Naples, Italy
| | - Eva Onjukka
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Sweden
| | - Vanessa Panettieri
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; Central Clinical School, Monash University, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3010, Australia
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Tiziana Rancati
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Eliana Vasquez Osorio
- The Division of Cancer Sciences, The University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK
| | - Marnix Witte
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Laura Cella
- Institute of Biostructures and Bioimaging, National Research Council, Naples, Italy
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9
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Alborghetti L, Castriconi R, Sosa Marrero C, Tudda A, Ubeira-Gabellini MG, Broggi S, Pascau J, Cubero L, Cozzarini C, De Crevoisier R, Rancati T, Acosta O, Fiorino C. Selective sparing of bladder and rectum sub-regions in radiotherapy of prostate cancer combining knowledge-based automatic planning and multicriteria optimization. Phys Imaging Radiat Oncol 2023; 28:100488. [PMID: 37694264 PMCID: PMC10482897 DOI: 10.1016/j.phro.2023.100488] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/12/2023] Open
Abstract
Background and Purpose The association between dose to selected bladder and rectum symptom-related sub-regions (SRS) and late toxicity after prostate cancer radiotherapy has been evidenced by voxel-wise analyses. The aim of the current study was to explore the feasibility of combining knowledge-based (KB) and multi-criteria optimization (MCO) to spare SRSs without compromising planning target volume (PTV) dose delivery, including pelvic-node irradiation. Materials and Methods Forty-five previously treated patients (74.2 Gy/28fr) were selected and SRSs (in the bladder, associated with late dysuria/hematuria/retention; in the rectum, associated with bleeding) were generated using deformable registration. A KB model was used to obtain clinically suitable plans (KB-plan). KB-plans were further optimized using MCO, aiming to reduce dose to the SRSs while safeguarding target dose coverage, homogeneity and avoiding worsening dose volume histograms of the whole bladder, rectum and other organs at risk. The resulting MCO-generated plans were examined to identify the best-compromise plan (KB + MCO-plan). Results The mean SRS dose decreased in almost all patients for each SRS. D1% also decreased in the large majority, less frequently for dysuria/bleeding SRS. Mean differences were statistically significant (p < 0.05) and ranged between 1.3 and 2.2 Gy with maximum reduction of mean dose up to 3-5 Gy for the four SRSs. The better sparing of SRSs was obtained without compromising PTVs coverage. Conclusions Selectively sparing SRSs without compromising PTV coverage is feasible and has the potential to reduce toxicities in prostate cancer radiotherapy. Further investigation to better quantify the expected risk reduction of late toxicities is warranted.
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Affiliation(s)
- Lisa Alborghetti
- IRCCS San Raffaele Scientific Institute, Medical Physics, Milano, Italy
| | | | - Carlos Sosa Marrero
- CLCC Eugène Marquis, INSERM, LTSI—UMR1099, F-35000, Univ Rennes, Rennes, France
| | - Alessia Tudda
- IRCCS San Raffaele Scientific Institute, Medical Physics, Milano, Italy
| | | | - Sara Broggi
- IRCCS San Raffaele Scientific Institute, Medical Physics, Milano, Italy
| | - Javier Pascau
- Universidad Carlos III de Madrid, Bioengineering Department, Madrid, Spain
| | - Lucia Cubero
- Universidad Carlos III de Madrid, Bioengineering Department, Madrid, Spain
| | - Cesare Cozzarini
- IRCCS San Raffaele Scientific Institute, Radiotherapy, Milano, Italy
| | | | - Tiziana Rancati
- Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Progetto Prostata, Milano, Italy
| | - Oscar Acosta
- CLCC Eugène Marquis, INSERM, LTSI—UMR1099, F-35000, Univ Rennes, Rennes, France
| | - Claudio Fiorino
- IRCCS San Raffaele Scientific Institute, Medical Physics, Milano, Italy
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Sosa-Marrero C, Acosta O, Pasquier D, Thariat J, Delpon G, Fiorino C, Rancatti T, Malard O, Foray N, de Crevoisier R. Voxel-wise analysis: A powerful tool to predict radio-induced toxicity and potentially perform personalised planning in radiotherapy. Cancer Radiother 2023; 27:638-642. [PMID: 37517974 DOI: 10.1016/j.canrad.2023.06.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 06/27/2023] [Indexed: 08/01/2023]
Abstract
Dose - volume histograms have been historically used to study the relationship between the planned radiation dose and healthy tissue damage. However, this approach considers neither spatial information nor heterogenous radiosensitivity within organs at risk, depending on the tissue. Recently, voxel-wise analyses have emerged in the literature as powerful tools to fully exploit three-dimensional information from the planned dose distribution. They allow to identify anatomical subregions of one or several organs in which the irradiation dose is associated with a given toxicity. These methods rely on an accurate anatomical alignment, usually obtained by means of a non-rigid registration. Once the different anatomies are spatially normalised, correlations between the three-dimensional dose and a given toxicity can be explored voxel-wise. Parametric or non-parametric statistical tests can be performed on every voxel to identify the voxels in which the dose is significantly different between patients presenting or not toxicity. Several anatomical subregions associated with genitourinary, gastrointestinal, cardiac, pulmonary or haematological toxicity have already been identified in the literature for prostate, head and neck or thorax irradiation. Voxel-wise analysis appears therefore first particularly interesting to increase toxicity prediction capability by identifying specific subregions in the organs at risk whose irradiation is highly predictive of specific toxicity. The second interest is potentially to decrease the radio-induced toxicity by limiting the dose in the predictive subregions, while not decreasing the dose in the target volume. Limitations of the approach have been pointed out.
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Affiliation(s)
- C Sosa-Marrero
- Université de Rennes, CLCC Eugène-Marquis, Inserm, LTSI - UMR 1099, 35000 Rennes, France
| | - O Acosta
- Université de Rennes, CLCC Eugène-Marquis, Inserm, LTSI - UMR 1099, 35000 Rennes, France
| | - D Pasquier
- Radiotherapy Department, centre Oscar-Lambret, 59000 Lille, France; Université de Lille, CNRS, école centrale de Lille, Cristal UMR 9189, Lille, France
| | - J Thariat
- Department of Radiation Oncology, centre François-Baclesse, 14000 Caen, France
| | - G Delpon
- Medical physics department, institut de cancérologie de l'Ouest, IMT Atlantique, Nantes université, CNRS/IN2P3, Subatech, Nantes, France
| | - C Fiorino
- Medical Physics, San Raffaele Scientific Institute, Via Olgettina 690, 20132 Milan, Italy
| | - T Rancatti
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - O Malard
- Service de chirurgie oto-rhinolaryngologique (ORL) et chirurgie cervicofaciale, Hôtel-Dieu, CHU de Nantes, Nantes, France
| | - N Foray
- Centre Léon-Bérard, Inserm U1296 "Radiation: Defense/Health/Environment", 69008 Lyon, France
| | - R de Crevoisier
- Université de Rennes, CLCC Eugène-Marquis, Inserm, LTSI - UMR 1099, 35000 Rennes, France; Département de radiothérapie, centre Eugène-Marquis, 35000 Rennes, France.
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11
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Mansour IR, Thomson RM. Haralick texture feature analysis for characterization of specific energy and absorbed dose distributions across cellular to patient length scales. Phys Med Biol 2023; 68. [PMID: 36731130 DOI: 10.1088/1361-6560/acb885] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 02/02/2023] [Indexed: 02/04/2023]
Abstract
Objective.To investigate an approach for quantitative characterization of the spatial distribution of dosimetric data by introducing Haralick texture feature analysis in this context.Approach.Monte Carlo simulations are used to generate 3D arrays of dosimetric data for 2 scenarios: (1) cell-scale microdosimetry: specific energy (energy imparted per unit mass) in cell-scale targets irradiated by photon spectra (125I,192Ir, 6 MV); (2) tumour-scale dosimetry: absorbed dose in voxels for idealized models of125I permanent implant prostate brachytherapy, considering 'TG186' (realistic tissues including 0% to 5% intraprostatic calcifications; interseed attenuation) and 'TG43' (water model, no interseed attenuation) conditions. Five prominent Haralick features (homogeneity, contrast, correlation, local homogeneity, entropy) are computed and trends are interpreted using fundamental radiation physics.Main results.In the cell-scale scenario, the Haralick measures quantify differences in 3D specific energy distributions due to source spectra. For example, contrast and entropy are highest for125I reflecting the large variations in specific energy in adjacent voxels (photoelectric interactions; relatively short range of electrons), while 6 MV has the highest homogeneity with smaller variations in specific energy between voxels (Compton scattering dominates; longer range of electrons). For the tumour-scale scenario, the Haralick measures quantify differences due to TG186/TG43 simulation conditions and the presence of calcifications. For example, as calcifications increase from 0% to 5%, contrast increases while correlation decreases, reflecting the large differences in absorbed dose in adjacent voxels (higher absorbed dose in voxels with calcification due to photoelectric interactions).Significance.Haralick texture analysis provides a quantitative method for the characterization of 3D dosimetric distributions across cellular to tumour length scales, with promising future applications including analyses of multiscale tissue models, patient-specific data, and comparison of treatment approaches.
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Affiliation(s)
- Iymad R Mansour
- Carleton Laboratory for Radiotherapy Physics, Physics Department, Carleton University, 1125 Colonel By Dr, Ottawa, K1S 5B6, Ontario, Canada
| | - Rowan M Thomson
- Carleton Laboratory for Radiotherapy Physics, Physics Department, Carleton University, 1125 Colonel By Dr, Ottawa, K1S 5B6, Ontario, Canada
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12
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Morelli L, Parrella G, Molinelli S, Magro G, Annunziata S, Mairani A, Chalaszczyk A, Fiore MR, Ciocca M, Paganelli C, Orlandi E, Baroni G. A Dosiomics Analysis Based on Linear Energy Transfer and Biological Dose Maps to Predict Local Recurrence in Sacral Chordomas after Carbon-Ion Radiotherapy. Cancers (Basel) 2022; 15:cancers15010033. [PMID: 36612029 PMCID: PMC9817801 DOI: 10.3390/cancers15010033] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/09/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Carbon Ion Radiotherapy (CIRT) is one of the most promising therapeutic options to reduce Local Recurrence (LR) in Sacral Chordomas (SC). The aim of this work is to compare the performances of survival models fed with dosiomics features and conventional DVH metrics extracted from relative biological effectiveness (RBE)-weighted dose (DRBE) and dose-averaged Linear Energy Transfer (LETd) maps, towards the identification of possible prognostic factors for LR in SC patients treated with CIRT. This retrospective study included 50 patients affected by SC with a focus on patients that presented a relapse in a high-dose region. Survival models were built to predict both LR and High-Dose Local Recurrencies (HD-LR). The models were evaluated through Harrell Concordance Index (C-index) and patients were stratified into high/low-risk groups. Local Recurrence-free Kaplan-Meier curves were estimated and evaluated through log-rank tests. The model with highest performance (median(interquartile-range) C-index of 0.86 (0.22)) was built on features extracted from LETd maps, with DRBE models showing promising but weaker results (C-index of 0.83 (0.21), 0.80 (0.21)). Although the study should be extended to a wider patient population, LETd maps show potential as a prognostic factor for SC HD-LR in CIRT, and dosiomics appears to be the most promising approach against more conventional methods (e.g., DVH-based).
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Affiliation(s)
- Letizia Morelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
- Correspondence: (L.M.); (G.P.); Tel.: +39-02-2399-9022 (G.P.)
| | - Giovanni Parrella
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
- Correspondence: (L.M.); (G.P.); Tel.: +39-02-2399-9022 (G.P.)
| | - Silvia Molinelli
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Giuseppe Magro
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Simone Annunziata
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Andrea Mairani
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
- Heidelberg Ion Beam Therapy Center (HIT), Im Neuenheimer Feld 450, 69120 Heidelberg, Germany
| | - Agnieszka Chalaszczyk
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Maria Rosaria Fiore
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Mario Ciocca
- Medical Physics Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Ester Orlandi
- Radiotherapy Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Campeggi, 53, 27100 Pavia, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
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Patrick HM, Kildea J. Technical note: rtdsm-An open-source software for radiotherapy dose-surface map generation and analysis. Med Phys 2022; 49:7327-7335. [PMID: 35912447 DOI: 10.1002/mp.15900] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 06/07/2022] [Accepted: 07/23/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Dose-outcome studies in radiation oncology have historically excluded spatial information due to dose-volume histograms being the most dominant source of dosimetric information. In recent years, dose-surface maps (DSMs) have become increasingly popular for characterization of spatial dose distributions and identification of radiosensitive subregions for hollow organs. However, methodological variations and lack of open-source, publicly offered code-sharing between research groups have limited reproducibility and wider adoption. PURPOSE This paper presents rtdsm, an open-source software for DSM calculation with the intent to improve the reproducibility of and the access to DSM-based research in medical physics and radiation oncology. METHODS A literature review was conducted to identify essential functionalities and prevailing calculation approaches to guide development. The described software has been designed to calculate DSMs from DICOM data with a high degree of user customizability and to facilitate DSM feature analysis. Core functionalities include DSM calculation, equivalent dose conversions, common DSM feature extraction, and simple DSM accumulation. RESULTS A number of use cases were used to qualitatively and quantitatively demonstrate the use and usefulness of rtdsm. Specifically, two DSM slicing methods, planar and noncoplanar, were implemented and tested, and the effects of method choice on output DSMs were demonstrated. An example comparison of DSMs from two different treatments was used to highlight the use cases of various built-in analysis functions for equivalent dose conversion and DSM feature extraction. CONCLUSIONS We developed and implemented rtdsm as a standalone software that provides all essential functionalities required to perform a DSM-based study. It has been made freely accessible under an open-source license on Github to encourage collaboration and community use.
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Affiliation(s)
- Haley M Patrick
- Medical Physics Unit, McGill University, Montreal, Quebec, Canada.,Cancer Research Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - John Kildea
- Medical Physics Unit, McGill University, Montreal, Quebec, Canada.,Cancer Research Program, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
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Yusufaly TI. Extending the relative seriality formalism for interpretable deep learning of normal tissue complication probability models. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1088/2632-2153/ac6932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Abstract
We formally demonstrate that the relative seriality (RS) model of normal tissue complication probability (NTCP) can be recast as a simple neural network with one convolutional and one pooling layer. This approach enables us to systematically construct deep relative seriality networks (DRSNs), a new class of mechanistic generalizations of the RS model with radiobiologically interpretable parameters amenable to deep learning. To demonstrate the utility of this formulation, we analyze a simplified example of xerostomia due to irradiation of the parotid gland during alpha radiopharmaceutical therapy. Using a combination of analytical calculations and numerical simulations, we show for both the RS and DRSN cases that the ability of the neural network to generalize without overfitting is tied to ‘stiff’ and ‘sloppy’ directions in the parameter space of the mechanistic model. These results serve as proof-of-concept for radiobiologically interpretable deep learning of NTCP, while simultaneously yielding insight into how such techniques can robustly generalize beyond the training set despite uncertainty in individual parameters.
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15
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Chao M, El Naqa I, Bakst RL, Lo YC, Peñagarícano JA. Cluster model incorporating heterogeneous dose distribution of partial parotid irradiation for radiotherapy induced xerostomia prediction with machine learning methods. Acta Oncol 2022; 61:842-848. [PMID: 35527717 DOI: 10.1080/0284186x.2022.2073187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
PURPOSE A cluster model incorporating heterogeneous dose distribution within the parotid gland was developed and validated retrospectively for radiotherapy (RT) induced xerostomia prediction with machine learning (ML) techniques. METHODS Sixty clusters were obtained at 1 Gy step size with threshold doses ranging from 1 to 60 Gy, for each of the enrolled 155 patients with HNC from three institutions. Feature clusters were selected with the neighborhood component analysis (NCA) and subsequently fed into four supervised ML models for xerostomia prediction comparison: support vector machines (SVM), k-nearest neighbor (kNN), naïve Bayes (NB), and random forest (RF). The predictive performance of each model was evaluated using cross validation resampling with the area-under-the-curves (AUC) of the receiver-operating-characteristic (ROC). The xerostomia predicting capacity using testing data was assessed with accuracy, sensitivity, and specificity for these models and three cluster connectivity choices. Mean dose based logistic regression served as the benchmark for evaluation. RESULTS Feature clusters identified by NCA fell in three threshold dose ranges: 5-15Gy, 25-35Gy, and 45-50Gy. Mean dose predictive power was 15% lower than that of the cluster model using the logistic regression classifier. Model validation demonstrated that kNN model outperformed slightly other three models but no substantial difference was observed. Applying the fine-tuned models to testing data yielded that the mean accuracy from SVM, kNN and NB models were between 0.68 and 0.7 while that of RF was ∼0.6. SVM model yielded the best sensitivity (0.76) and kNN model delivered consistent sensitivity and specificity. This is consistent with cross validation. Clusters calculated with three connectivity choices exhibited minimally different predictions. CONCLUSION Compared to mean dose, the proposed cluster model has shown its improvement as the xerostomia predictor. When combining with ML techniques, it could provide a clinically useful tool for xerostomia prediction and facilitate decision making during radiotherapy planning for patients with HNC.
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Affiliation(s)
- Ming Chao
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY, USA
| | - Issam El Naqa
- Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Richard L. Bakst
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY, USA
| | - Yeh-Chi Lo
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY, USA
| | - José A. Peñagarícano
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
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16
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Blake SW. Can dose convolution modelling explain bath and shower effects in rat spinal cord? Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac5c8e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 03/10/2022] [Indexed: 12/24/2022]
Abstract
Abstract
Objective. ‘Bath and shower’ effects were first seen in proton irradiations of rat spinal cord, where a low dose ‘bath’ reduced the smaller field ‘shower’ dose needed for limb paralysis giving the appearance of sensitisation of the cord or disproportionate response. This was difficult to reconcile with existing tissue complication models. The purpose of this investigation is to explore a different approach using a dose convolution algorithm to model the 50% isoeffect endpoint. Approach. Bath and shower dose distributions were convolved with Gaussian functions with widths specified by the σ parameter. The hypothesis was that the maximum value from the convolved distributions was constant for isoeffect across the modelled scenarios. A simpler field length dependent relative biological effectiveness (FLRBE) approach was also used for a subset of the data which gave results independent of σ. Main results. The maximum values from the convolved distributions were constant within ±17% across the bath and shower experiments for σ = 3.5 mm, whereas the maximum dose varied by a factor of four. The FLRBE results were also within ±14% confirming the validity of the dose convolution approach. Significance. A simple approach using dose convolution modelling of the 50% isotoxicity gave compelling consistency with the full range of bath and shower results, while the FLRBE approach confirmed the results for the symmetric field data. Convolution modelling and the effect of time interval were consistent with a signalling factor diffusion mechanism such as the ‘bystander effect’. The results suggest biological effectiveness is reduced for very small field sizes, requiring a higher isoeffect dose. By implication, the bath dose does not sensitise the cord to the shower dose; when biological effectiveness is accounted for, a small increase in the bath dose requires a significantly larger reduction in shower dose for isoeffect.
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Radiation-Induced Esophagitis in Non-Small-Cell Lung Cancer Patients: Voxel-Based Analysis and NTCP Modeling. Cancers (Basel) 2022; 14:cancers14071833. [PMID: 35406605 PMCID: PMC8997452 DOI: 10.3390/cancers14071833] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 03/31/2022] [Accepted: 03/31/2022] [Indexed: 12/19/2022] Open
Abstract
Simple Summary Radiation-induced esophagitis (RE) is a common dose-limiting complication associated with concurrent chemoradiation therapy for Non-Small-Cell Lung Cancer (NSCLC), and a wide range of esophageal dosimetric parameters have been described as predictive of RE. In this study, we characterize the risk of RE for NSCLC patients enrolled in a prospective trial comparing intensity-modulated RT versus passive scattering proton therapy for locally advanced NSCLC. Dose patterns associated with RE were analyzed by applying voxel-based analysis approaches, and predictive models for RE were finally investigated. Two predictive models for acute RE with good cross-validated predictive performances and discrimination capability were developed (thoracic esophageal model: ROC-AUC = 0.73; whole esophagus model: ROC-AUC = 0.70). Abstract The aim of our study is to characterize the risk of radiation-induced esophagitis (RE) in a cohort of Non-Small-Cell Lung Cancer (NSCLC) patients treated with concurrent chemotherapy and photon/proton therapy. For each patient, the RE was graded according to the CTCAE v.3. The esophageal dose-volume histograms (DVHs) were extracted. Voxel-based analyses (VBAs) were performed to assess the spatial patterns of the dose differences between patients with and without RE of grade ≥ 2. Two hierarchical NTCP models were developed by multivariable stepwise logistic regression based on non-dosimetric factors and on the DVH metrics for the whole esophagus and its anatomical subsites identified by the VBA. In the 173 analyzed patients, 76 (44%) developed RE of grade ≥ 2 at a median follow-up time of 31 days. The VBA identified regions of significant association between dose and RE in a region encompassing the thoracic esophagus. We developed two NTCP models, including the RT modality and a dosimetric factor: V55Gy for the model related to the whole esophagus, and the mean dose for the model designed on the thoracic esophagus. The cross-validated performance showed good predictions for both models (ROC-AUC of 0.70 and 0.73, respectively). The only slight improvement provided by the analysis of the thoracic esophageal subsites might be due to the relevant sparing of cervical and lower thoracic esophagus in the analyzed cohort. Further studies on larger cohorts and a more heterogeneous set of dose distributions are needed to validate these preliminary findings and shed further light on the spatial patterns of RE development.
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Ebert MA, Marcello M, Kennedy A, Haworth A, Holloway LC, Greer P, Dowling JA, Jameson MG, Roach D, Joseph DJ, Gulliford SL, Sydes MR, Hall E, Dearnaley DP. In Regard to Shortall et al. Int J Radiat Oncol Biol Phys 2022; 112:831-833. [PMID: 35101196 DOI: 10.1016/j.ijrobp.2021.10.140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 10/15/2021] [Indexed: 12/13/2022]
Affiliation(s)
- Martin A Ebert
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, Australia; Department of Physics, University of Western Australia, Crawley, Australia; 5D Clinics, Claremont, Australia
| | - Marco Marcello
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, Australia
| | - Angel Kennedy
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, Australia
| | - Annette Haworth
- School of Physics, University of Sydney, Camperdown, Australia
| | - Lois C Holloway
- School of Physics, University of Sydney, Camperdown, Australia; Department of Medical Physics, Liverpool Cancer Centre, Liverpool, Australia; South Western Sydney Clinical School, University of New South Wales, Liverpool, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Peter Greer
- School of Mathematical and Physical Sciences, University of Newcastle, Callaghan, Australia; Department of Radiation Oncology, Calvary Mater Newcastle, Waratah, Australia
| | - Jason A Dowling
- School of Physics, University of Sydney, Camperdown, Australia; South Western Sydney Clinical School, University of New South Wales, Liverpool, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia; School of Mathematical and Physical Sciences, University of Newcastle, Callaghan, Australia; CSIRO, Herston, Australia
| | - Michael G Jameson
- GenesisCare, Alexandria, Australia; St Vincent's Clinical School, University of New South Wales, Sydney, Australia
| | - Dale Roach
- South Western Sydney Clinical School, University of New South Wales, Liverpool, Australia
| | - David J Joseph
- 5D Clinics, Claremont, Australia; GenesisCare WA, Wembley, Australia; School of Surgery, University of Western Australia, Crawley, Australia
| | - Sarah L Gulliford
- Radiotherapy Department, University College London Hospitals NHS Foundation Trust, London, United Kingdom; Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Matthew R Sydes
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, United Kingdom
| | - Emma Hall
- Clinical Trials and Statistics Unit, The Institute of Cancer Research, Sutton, United Kingdom
| | - David P Dearnaley
- Academic UroOncology Unit, The Institute of Cancer Research and the Royal Marsden NHS Trust, London, United Kingdom
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Appelt AL, Elhaminia B, Gooya A, Gilbert A, Nix M. Deep Learning for Radiotherapy Outcome Prediction Using Dose Data - A Review. Clin Oncol (R Coll Radiol) 2022; 34:e87-e96. [PMID: 34924256 DOI: 10.1016/j.clon.2021.12.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 11/26/2021] [Accepted: 12/01/2021] [Indexed: 12/23/2022]
Abstract
Artificial intelligence, and in particular deep learning using convolutional neural networks, has been used extensively for image classification and segmentation, including on medical images for diagnosis and prognosis prediction. Use in radiotherapy prognostic modelling is still limited, however, especially as applied to toxicity and tumour response prediction from radiation dose distributions. We review and summarise studies that applied deep learning to radiotherapy dose data, in particular studies that utilised full three-dimensional dose distributions. Ten papers have reported on deep learning models for outcome prediction utilising spatial dose information, whereas four studies used reduced dimensionality (dose volume histogram) information for prediction. Many of these studies suffer from the same issues that plagued early normal tissue complication probability modelling, including small, single-institutional patient cohorts, lack of external validation, poor data and model reporting, use of late toxicity data without taking time-to-event into account, and nearly exclusive focus on clinician-reported complications. They demonstrate, however, how radiation dose, imaging and clinical data may be technically integrated in convolutional neural networks-based models; and some studies explore how deep learning may help better understand spatial variation in radiosensitivity. In general, there are a number of issues specific to the intersection of radiotherapy outcome modelling and deep learning, for example translation of model developments into treatment plan optimisation, which will require further combined effort from the radiation oncology and artificial intelligence communities.
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Affiliation(s)
- A L Appelt
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
| | - B Elhaminia
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Department of Electronic and Electrical Engineering, University of Leeds, Leeds, UK
| | - A Gooya
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Department of Electronic and Electrical Engineering, University of Leeds, Leeds, UK
| | - A Gilbert
- Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - M Nix
- Department of Medical Physics and Engineering, Leeds Cancer Centre, St James's University Hospital, Leeds, UK
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