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Fu Q, Chen X, Liu Y, Zhang J, Xu Y, Yang X, Huang M, Men K, Dai J. Improvement of accumulated dose distribution in combined cervical cancer radiotherapy with deep learning-based dose prediction. Front Oncol 2024; 14:1407016. [PMID: 39040460 PMCID: PMC11260613 DOI: 10.3389/fonc.2024.1407016] [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: 03/26/2024] [Accepted: 06/17/2024] [Indexed: 07/24/2024] Open
Abstract
Purpose Difficulties remain in dose optimization and evaluation of cervical cancer radiotherapy that combines external beam radiotherapy (EBRT) and brachytherapy (BT). This study estimates and improves the accumulated dose distribution of EBRT and BT with deep learning-based dose prediction. Materials and methods A total of 30 patients treated with combined cervical cancer radiotherapy were enrolled in this study. The dose distributions of EBRT and BT plans were accumulated using commercial deformable image registration. A ResNet-101-based deep learning model was trained to predict pixel-wise dose distributions. To test the role of the predicted accumulated dose in clinic, each EBRT plan was designed using conventional method and then redesigned referencing the predicted accumulated dose distribution. Bladder and rectum dosimetric parameters and normal tissue complication probability (NTCP) values were calculated and compared between the conventional and redesigned accumulated doses. Results The redesigned accumulated doses showed a decrease in mean values of V50, V60, and D2cc for the bladder (-3.02%, -1.71%, and -1.19 Gy, respectively) and rectum (-4.82%, -1.97%, and -4.13 Gy, respectively). The mean NTCP values for the bladder and rectum were also decreased by 0.02‰ and 0.98%, respectively. All values had statistically significant differences (p < 0.01), except for the bladder D2cc (p = 0.112). Conclusion This study realized accumulated dose prediction for combined cervical cancer radiotherapy without knowing the BT dose. The predicted dose served as a reference for EBRT treatment planning, leading to a superior accumulated dose distribution and lower NTCP values.
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Affiliation(s)
- Qi Fu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
| | - Xinyuan Chen
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
| | - Yuxiang Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
- School of Physics and Technology, Wuhan University, Wuhan, China
| | - Jingbo Zhang
- Department of Radiotherapy Technology, The Cancer and Tuberculosis Hospital, Jiamusi, China
| | - Yingjie Xu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
| | - Xi Yang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
| | - Manni Huang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
| | - Kuo Men
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medial Sciences and Peking Union Medical College, Beijing, China
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Abbaspour S, Barahman M, Abdollahi H, Arabalibeik H, Hajainfar G, Babaei M, Iraji H, Barzegartahamtan M, Ay MR, Mahdavi SR. Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study. Biomed Phys Eng Express 2023; 10:015017. [PMID: 37995359 DOI: 10.1088/2057-1976/ad0f3e] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 11/23/2023] [Indexed: 11/25/2023]
Abstract
Purpose.This study aims to predict radiotherapy-induced rectal and bladder toxicity using computed tomography (CT) and magnetic resonance imaging (MRI) radiomics features in combination with clinical and dosimetric features in rectal cancer patients.Methods.A total of sixty-three patients with locally advanced rectal cancer who underwent three-dimensional conformal radiation therapy (3D-CRT) were included in this study. Radiomics features were extracted from the rectum and bladder walls in pretreatment CT and MR-T2W-weighted images. Feature selection was performed using various methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-square (Chi2), Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and SelectPercentile. Predictive modeling was carried out using machine learning algorithms, such as K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Gradient Boosting (XGB), and Linear Discriminant Analysis (LDA). The impact of the Laplacian of Gaussian (LoG) filter was investigated with sigma values ranging from 0.5 to 2. Model performance was evaluated in terms of the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, and specificity.Results.A total of 479 radiomics features were extracted, and 59 features were selected. The pre-MRI T2W model exhibited the highest predictive performance with an AUC: 91.0/96.57%, accuracy: 90.38/96.92%, precision: 90.0/97.14%, sensitivity: 93.33/96.50%, and specificity: 88.09/97.14%. These results were achieved with both original image and LoG filter (sigma = 0.5-1.5) based on LDA/DT-RF classifiers for proctitis and cystitis, respectively. Furthermore, for the CT data, AUC: 90.71/96.0%, accuracy: 90.0/96.92%, precision: 88.14/97.14%, sensitivity: 93.0/96.0%, and specificity: 88.09/97.14% were acquired. The highest values were achieved using XGB/DT-XGB classifiers for proctitis and cystitis with LoG filter (sigma = 2)/LoG filter (sigma = 0.5-2), respectively. MRMR/RFE-Chi2 feature selection methods demonstrated the best performance for proctitis and cystitis in the pre-MRI T2W model. MRMR/MRMR-Lasso yielded the highest model performance for CT.Conclusion.Radiomics features extracted from pretreatment CT and MR images can effectively predict radiation-induced proctitis and cystitis. The study found that LDA, DT, RF, and XGB classifiers, combined with MRMR, RFE, Chi2, and Lasso feature selection algorithms, along with the LoG filter, offer strong predictive performance. With the inclusion of a larger training dataset, these models can be valuable tools for personalized radiotherapy decision-making.
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Affiliation(s)
- Samira Abbaspour
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maedeh Barahman
- Department of Radiation Oncology, Firoozgar Hospital, Firoozgar Clinical Research Development Center (FCRDC), Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Hossein Arabalibeik
- Research Center for Science and Technology in Medicine (RCSTM), Tehran University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajainfar
- Rajaie Cardiovascular Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mohammadreza Babaei
- Department of Interventional Radiology, Firouzgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Hamed Iraji
- Department of Interventional Radiology, Firouzgar Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Barzegartahamtan
- Clinical Research Development Unit, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Ay
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Seied Rabi Mahdavi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
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Cheon W, Han M, Jeong S, Oh ES, Lee SU, Lee SB, Shin D, Lim YK, Jeong JH, Kim H, Kim JY. Feature Importance Analysis of a Deep Learning Model for Predicting Late Bladder Toxicity Occurrence in Uterine Cervical Cancer Patients. Cancers (Basel) 2023; 15:3463. [PMID: 37444573 DOI: 10.3390/cancers15133463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/28/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023] Open
Abstract
(1) In this study, we developed a deep learning (DL) model that can be used to predict late bladder toxicity. (2) We collected data obtained from 281 uterine cervical cancer patients who underwent definitive radiation therapy. The DL model was trained using 16 features, including patient, tumor, treatment, and dose parameters, and its performance was compared with that of a multivariable logistic regression model using the following metrics: accuracy, prediction, recall, F1-score, and area under the receiver operating characteristic curve (AUROC). In addition, permutation feature importance was calculated to interpret the DL model for each feature, and the lightweight DL model was designed to focus on the top five important features. (3) The DL model outperformed the multivariable logistic regression model on our dataset. It achieved an F1-score of 0.76 and an AUROC of 0.81, while the corresponding values for the multivariable logistic regression were 0.14 and 0.43, respectively. The DL model identified the doses for the most exposed 2 cc volume of the bladder (BD2cc) as the most important feature, followed by BD5cc and the ICRU bladder point. In the case of the lightweight DL model, the F-score and AUROC were 0.90 and 0.91, respectively. (4) The DL models exhibited superior performance in predicting late bladder toxicity compared with the statistical method. Through the interpretation of the model, it further emphasized its potential for improving patient outcomes and minimizing treatment-related complications with a high level of reliability.
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Affiliation(s)
- Wonjoong Cheon
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Mira Han
- Biostatistics Collaboration Team, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Seonghoon Jeong
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Eun Sang Oh
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Sung Uk Lee
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Se Byeong Lee
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Dongho Shin
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Young Kyung Lim
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Jong Hwi Jeong
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Haksoo Kim
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
| | - Joo Young Kim
- Proton Therapy Center, National Cancer Center, Goyang-si 10408, Republic of Korea
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Zhao JZ, Ni R, Chow R, Rink A, Weersink R, Croke J, Raman S. Artificial intelligence applications in brachytherapy: A literature review. Brachytherapy 2023; 22:429-445. [PMID: 37248158 DOI: 10.1016/j.brachy.2023.04.003] [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: 02/02/2023] [Revised: 04/02/2023] [Accepted: 04/07/2023] [Indexed: 05/31/2023]
Abstract
PURPOSE Artificial intelligence (AI) has the potential to simplify and optimize various steps of the brachytherapy workflow, and this literature review aims to provide an overview of the work done in this field. METHODS AND MATERIALS We conducted a literature search in June 2022 on PubMed, Embase, and Cochrane for papers that proposed AI applications in brachytherapy. RESULTS A total of 80 papers satisfied inclusion/exclusion criteria. These papers were categorized as follows: segmentation (24), registration and image processing (6), preplanning (13), dose prediction and treatment planning (11), applicator/catheter/needle reconstruction (16), and quality assurance (10). AI techniques ranged from classical models such as support vector machines and decision tree-based learning to newer techniques such as U-Net and deep reinforcement learning, and were applied to facilitate small steps of a process (e.g., optimizing applicator selection) or even automate the entire step of the workflow (e.g., end-to-end preplanning). Many of these algorithms demonstrated human-level performance and offer significant improvements in speed. CONCLUSIONS AI has potential to augment, automate, and/or accelerate many steps of the brachytherapy workflow. We recommend that future studies adhere to standard reporting guidelines. We also stress the importance of using larger sample sizes and reporting results using clinically interpretable measures.
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Affiliation(s)
- Jonathan Zl Zhao
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Ruiyan Ni
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Ronald Chow
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Alexandra Rink
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Robert Weersink
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Jennifer Croke
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Srinivas Raman
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada.
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Jha AK, Mithun S, Sherkhane UB, Jaiswar V, Osong B, Purandare N, Kannan S, Prabhash K, Gupta S, Vanneste B, Rangarajan V, Dekker A, Wee L. Systematic review and meta-analysis of prediction models used in cervical cancer. Artif Intell Med 2023; 139:102549. [PMID: 37100501 DOI: 10.1016/j.artmed.2023.102549] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 11/18/2022] [Accepted: 04/04/2023] [Indexed: 04/28/2023]
Abstract
BACKGROUND Cervical cancer is one of the most common cancers in women with an incidence of around 6.5 % of all the cancer in women worldwide. Early detection and adequate treatment according to staging improve the patient's life expectancy. Outcome prediction models might aid treatment decisions, but a systematic review on prediction models for cervical cancer patients is not available. DESIGN We performed a systematic review for prediction models in cervical cancer following PRISMA guidelines. Key features that were used for model training and validation, the endpoints were extracted from the article and data were analyzed. Selected articles were grouped based on prediction endpoints i.e. Group1: Overall survival, Group2: progression-free survival; Group3: recurrence or distant metastasis; Group4: treatment response; Group5: toxicity or quality of life. We developed a scoring system to evaluate the manuscript. As per our criteria, studies were divided into four groups based on scores obtained in our scoring system, the Most significant study (Score > 60 %); Significant study (60 % > Score > 50 %); Moderately Significant study (50 % > Score > 40 %); least significant study (score < 40 %). A meta-analysis was performed for all the groups separately. RESULTS The first line of search selected 1358 articles and finally 39 articles were selected as eligible for inclusion in the review. As per our assessment criteria, 16, 13 and 10 studies were found to be the most significant, significant and moderately significant respectively. The intra-group pooled correlation coefficient for Group1, Group2, Group3, Group4, and Group5 were 0.76 [0.72, 0.79], 0.80 [0.73, 0.86], 0.87 [0.83, 0.90], 0.85 [0.77, 0.90], 0.88 [0.85, 0.90] respectively. All the models were found to be good (prediction accuracy [c-index/AUC/R2] >0.7) in endpoint prediction. CONCLUSIONS Prediction models of cervical cancer toxicity, local or distant recurrence and survival prediction show promising results with reasonable prediction accuracy [c-index/AUC/R2 > 0.7]. These models should also be validated on external data and evaluated in prospective clinical studies.
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Affiliation(s)
- Ashish Kumar Jha
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands; Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai, Maharashtra, India; Homi Bhabha National Institute, Mumbai, Maharashtra, India.
| | - Sneha Mithun
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands; Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai, Maharashtra, India; Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Umeshkumar B Sherkhane
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands; Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Vinay Jaiswar
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Biche Osong
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Nilendu Purandare
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai, Maharashtra, India; Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Sadhana Kannan
- Homi Bhabha National Institute, Mumbai, Maharashtra, India; Advance Centre for Treatment, Research, Education in Cancer, Mumbai, Maharashtra, India
| | - Kumar Prabhash
- Department of Medical Oncology, Tata Memorial Hospital, Mumbai, Maharashtra, India; Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Sudeep Gupta
- Department of Medical Oncology, Tata Memorial Hospital, Mumbai, Maharashtra, India; Homi Bhabha National Institute, Mumbai, Maharashtra, India; Advance Centre for Treatment, Research, Education in Cancer, Mumbai, Maharashtra, India
| | - Ben Vanneste
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai, Maharashtra, India; Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands
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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: 0] [Impact Index Per Article: 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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Niraula D, Cui S, Pakela J, Wei L, Luo Y, Ten Haken RK, El Naqa I. Current status and future developments in predicting outcomes in radiation oncology. Br J Radiol 2022; 95:20220239. [PMID: 35867841 PMCID: PMC9793488 DOI: 10.1259/bjr.20220239] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Advancements in data-driven technologies and the inclusion of information-rich multiomics features have significantly improved the performance of outcomes modeling in radiation oncology. For this current trend to be sustainable, challenges related to robust data modeling such as small sample size, low size to feature ratio, noisy data, as well as issues related to algorithmic modeling such as complexity, uncertainty, and interpretability, need to be mitigated if not resolved. Emerging computational technologies and new paradigms such as federated learning, human-in-the-loop, quantum computing, and novel interpretability methods show great potential in overcoming these challenges and bridging the gap towards precision outcome modeling in radiotherapy. Examples of these promising technologies will be presented and their potential role in improving outcome modeling will be discussed.
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Affiliation(s)
- Dipesh Niraula
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | - Sunan Cui
- Department of Radiation Oncology, Stanford Medicine, Stanford University, Stanford, USA
| | - Julia Pakela
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
| | - Yi Luo
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | | | - Issam El Naqa
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
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8
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Artificial Intelligence for Outcome Modeling in Radiotherapy. Semin Radiat Oncol 2022; 32:351-364. [DOI: 10.1016/j.semradonc.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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9
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Sun C, Lu Q, Zhang X, Zhang Y, Jia S, Wang J, Zhu H, He W, Zhang Z. Comparison between core needle biopsy and excisional biopsy for breast neoplasm. Medicine (Baltimore) 2021; 100:e26970. [PMID: 34449464 PMCID: PMC8389946 DOI: 10.1097/md.0000000000026970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/01/2021] [Indexed: 01/04/2023] Open
Abstract
This study aimed to explore clinical significance of core needle biopsy (CNB) in pathological diagnosis of breast neoplasm.Seventy one breast neoplasm samples were obtained from Tongzhou Maternal and Child Health Hospital of Beijing between the years of 2006 and 2014. Forty five specimens were obtained via CNB and cases offering 26 of them received neoadjuvant chemotherapy. Pathology, histology, and immunohistochemistry results were compared between CNB specimens and excisional biopsy.Upward and downward tendencies could be observed in CNB specimens and excisional biopsy, respectively, in all items. Tumor proportion of CNB tissues was (33 + 2)/45 = 77.78%, when ductal carcinoma in situ detected by both CNB and excisional biopsy was 31/45 = 68.89%, with a consistency of (31 + 3)/45 = 75.56%. Tumor thrombus detected by both CNB and excisional biopsy was 2/45 = 4.44%. Among cases receiving neoadjuvant chemotherapy, CNB and excisional biopsy, in mitotic figure, cytological scoring and histological grading, showed a total change rate of >50% (50%-75%), while changes in duct and cellular heteromorphism were not distinct. Cases showing changes were up to 73.08%, with 8/26 = 30.77% for rise and 11/26 = 42.31% for descent.CNB could be used for preoperative diagnosis of breast neoplasm, and help to determine proper treatment regimen, thus elevating the rate of breast conserving. However, this method still has several limitations, particularly in immunohistochemical tests of human epidermal receptor protein-2. Neoadjuvant chemotherapy may influence the accuracy of CNB diagnosis.
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Affiliation(s)
- Chunjie Sun
- Department of Pathology, Tongzhou Maternal and Child Health Hospital of Beijing, Beijing, China
| | - Qun Lu
- Tongzhou Maternal and Child Health Hospital of Beijing, Beijing, China
- Reproductive Medicine Center, Peking University People's Hospital, Beijing, China
| | - Xinrong Zhang
- B-mode Ultrasonography, Tongzhou Maternal and Child Health Hospital of Beijing, Beijing, China
| | - Yuehong Zhang
- Department of Family Planning, Tongzhou Maternal and Child Health Hospital of Beijing, Beijing, China
| | - Shuai Jia
- Department of Pathology, Tongzhou Maternal and Child Health Hospital of Beijing, Beijing, China
| | - Jing Wang
- Department of Galactophore, Tongzhou Maternal and Child Health Hospital of Beijing, Beijing, China
| | - Hailun Zhu
- Department of Pathology, Tongzhou Maternal and Child Health Hospital of Beijing, Beijing, China
| | - Wen He
- Department of Pathology, Tongzhou Maternal and Child Health Hospital of Beijing, Beijing, China
| | - Zhongqiu Zhang
- Department of Pathology, Tongzhou Maternal and Child Health Hospital of Beijing, Beijing, China
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Akcay M, Etiz D, Celik O, Ozen A. Evaluation of acute hematological toxicity by machine learning in gynecologic cancers using postoperative radiotherapy. Indian J Cancer 2021; 59:178-186. [PMID: 34380848 DOI: 10.4103/ijc.ijc_666_19] [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: 11/04/2022]
Abstract
Background The aim of the study is to investigate the factors affecting acute hematologic toxicity (HT) in the adjuvant radiotherapy (RT) of gynecologic cancers by machine learning. Methods Between January 2015 and September 2018, 121 patients with endometrium and cervical cancer who underwent adjuvant RT with volumetric-modulated arc therapy (VMAT) were evaluated. The relationship between patient and treatment characteristics and acute HT was investigated using machine learning techniques, namely Logistic Regression, XGBoost, Artificial Neural Network, Random Forest, Naive Bayes, Support Vector Machine (SVM), and Gaussian Naive Bayes (GaussianNB) algorithms. Results No HT was observed in 11 cases (9.1%) and at least one grade of HT was observed in 110 cases. There were 55 (45.5%) cases with ≤grade 2 HT (mild HT) and 66 (54.5%) cases with grade ≥3 HT (severe HT). None of the patients developed grade 5 HT. Of 24 variables that could affect acute HT, nine were determined as important variables. According to the results, the best machine learning technique for acute HT estimation was SVM (accuracy 70%, area under curve (AUC): 0.65, sensitivity 71.4%, specificity 66.6%). Parameters affecting hematologic toxicity were evaluated also by classical statistical methods and there was a statistically significant relationship between age, RT, and bone marrow (BM) maximum dose. Conclusion It is important to predict the patients who will develop acute HT in order to minimize the side effects of treatment. If these cases can be identified in advance, toxicity rates can be reduced by taking necessary precautions. These cases can be predicted with machine learning algorithms.
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Affiliation(s)
- Melek Akcay
- Department of Radiation Oncology, Medical Faculty of Osmangazi University, Eskişehir, 26480, Turkey
| | - Durmus Etiz
- Department of Radiation Oncology, Medical Faculty of Osmangazi University, Eskişehir, 26480, Turkey
| | - Ozer Celik
- Department of Mathematics - Computer Eskisehir Osmangazi University, Eskişehir, 26480, Turkey
| | - Alaattin Ozen
- Department of Radiation Oncology, Medical Faculty of Osmangazi University, Eskişehir, 26480, Turkey
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11
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Ebert MA, Gulliford S, Acosta O, de Crevoisier R, McNutt T, Heemsbergen WD, Witte M, Palma G, Rancati T, Fiorino C. Spatial descriptions of radiotherapy dose: normal tissue complication models and statistical associations. Phys Med Biol 2021; 66:12TR01. [PMID: 34049304 DOI: 10.1088/1361-6560/ac0681] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 05/28/2021] [Indexed: 12/20/2022]
Abstract
For decades, dose-volume information for segmented anatomy has provided the essential data for correlating radiotherapy dosimetry with treatment-induced complications. Dose-volume information has formed the basis for modelling those associations via normal tissue complication probability (NTCP) models and for driving treatment planning. Limitations to this approach have been identified. Many studies have emerged demonstrating that the incorporation of information describing the spatial nature of the dose distribution, and potentially its correlation with anatomy, can provide more robust associations with toxicity and seed more general NTCP models. Such approaches are culminating in the application of computationally intensive processes such as machine learning and the application of neural networks. The opportunities these approaches have for individualising treatment, predicting toxicity and expanding the solution space for radiation therapy are substantial and have clearly widespread and disruptive potential. Impediments to reaching that potential include issues associated with data collection, model generalisation and validation. This review examines the role of spatial models of complication and summarises relevant published studies. Sources of data for these studies, appropriate statistical methodology frameworks for processing spatial dose information and extracting relevant features are described. Spatial complication modelling is consolidated as a pathway to guiding future developments towards effective, complication-free radiotherapy treatment.
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Affiliation(s)
- Martin A Ebert
- School of Physics, Mathematics and Computing, University of Western Australia, Crawley, Western Australia, Australia
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
- 5D Clinics, Claremont, Western Australia, Australia
| | - Sarah Gulliford
- Department of Radiotherapy Physics, University College Hospitals London, United Kingdom
- Department of Medical Physics and Bioengineering, University College London, United Kingdom
| | - Oscar Acosta
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI-UMR 1099, F-35000 Rennes, France
| | | | - Todd McNutt
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | | | - Marnix Witte
- The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Giuseppe Palma
- Institute of Biostructures and Bioimaging, National Research Council, Napoli, Italy
| | - Tiziana Rancati
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
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12
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Luo W. Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning. Front Artif Intell 2021; 4:627369. [PMID: 34164615 PMCID: PMC8215338 DOI: 10.3389/frai.2021.627369] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 05/25/2021] [Indexed: 01/22/2023] Open
Abstract
Cervical cancer is a very common and severe disease in women worldwide. Accurate prediction of its clinical outcomes will help adjust or optimize the treatment of cervical cancer and benefit the patients. Statistical models, various types of medical images, and machine learning have been used for outcome prediction and obtained promising results. Compared to conventional statistical models, machine learning has demonstrated advantages in dealing with the complexity in large-scale data and discovering prognostic factors. It has great potential in clinical application and improving cervical cancer management. However, the limitations of prediction studies and prediction models including simplification, insufficient data, overfitting and lack of interpretability, indicate that more work is needed to make clinical outcome prediction more accurate, more reliable, and more practical for clinical use.
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Affiliation(s)
- Wei Luo
- Department of Radiation Medicine, University of Kentucky, Lexington, KY, United States
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13
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Radiomics Analysis of 3D Dose Distributions to Predict Toxicity of Radiotherapy for Cervical Cancer. J Pers Med 2021; 11:jpm11050398. [PMID: 34064918 PMCID: PMC8151048 DOI: 10.3390/jpm11050398] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 05/08/2021] [Accepted: 05/10/2021] [Indexed: 12/20/2022] Open
Abstract
Standard treatment for locally advanced cervical cancer (LACC) is chemoradiotherapy followed by brachytherapy. Despite radiation therapy advances, the toxicity rate remains significant. In this study, we compared the prediction of toxicity events after radiotherapy for locally advanced cervical cancer (LACC), based on either dose-volume histogram (DVH) parameters or the use of a radiomics approach applied to dose maps at the voxel level. Toxicity scores using the Common Terminology Criteria for Adverse Events (CTCAE v4), spatial dose distributions, and usual clinical predictors for the toxicity of 102 patients treated with chemoradiotherapy followed by brachytherapy for LACC were used in this study. In addition to usual DVH parameters, 91 radiomic features were extracted from rectum, bladder and vaginal 3D dose distributions, after discretization into a fixed bin width of 1 Gy. They were evaluated for predictive modelling of rectal, genitourinary (GU) and vaginal toxicities (grade ≥ 2). Logistic Normal Tissue Complication Probability (NTCP) models were derived using clinical parameters only or combinations of clinical, DVH and radiomics. For rectal acute/late toxicities, the area under the curve (AUC) using clinical parameters was 0.53/0.65, which increased to 0.66/0.63, and 0.76/0.87, with the addition of DVH or radiomics parameters, respectively. For GU acute/late toxicities, the AUC increased from 0.55/0.56 (clinical only) to 0.84/0.90 (+DVH) and 0.83/0.96 (clinical + DVH + radiomics). For vaginal acute/late toxicities, the AUC increased from 0.51/0.57 (clinical only) to 0.58/0.72 (+DVH) and 0.82/0.89 (clinical + DVH + radiomics). The predictive performance of NTCP models based on radiomics features was higher than the commonly used clinical and DVH parameters. Dosimetric radiomics analysis is a promising tool for NTCP modelling in radiotherapy.
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14
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Yakar M, Etiz D. Artificial intelligence in rectal cancer. Artif Intell Gastroenterol 2021; 2:10-26. [DOI: 10.35712/aig.v2.i2.10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 03/03/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
Accurate and rapid diagnosis is essential for correct treatment in rectal cancer. Determining the optimal treatment plan for a patient with rectal cancer is a complex process, and the oncological results and toxicity are not the same in every patient with the same treatment at the same stage. In recent years, the increasing interest in artificial intelligence in all fields of science has also led to the development of innovative tools in oncology. Artificial intelligence studies have increased in many steps from diagnosis to follow-up in rectal cancer. It is thought that artificial intelligence will provide convenience in many ways from personalized treatment to reducing the workload of the physician. Prediction algorithms can be standardized by sharing data between centers, diversifying data, and creating big data.
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Affiliation(s)
- Melek Yakar
- Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Eskisehir 26040, Turkey
- Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir 26040, Turkey
| | - Durmus Etiz
- Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Eskisehir 26040, Turkey
- Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir 26040, Turkey
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15
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Wang X, Zhang B, He Q, Kong Y, Dai Z, Meng H, Huang F, Zhang S, Zhu Y, Tan X, Zhen X. Rectum Protection by Rectal Gel Injection in Cervical Cancer Brachytherapy: A Dosimetric Study via Deformable Surface Dose Accumulation and Machine-Learning-Based Discriminative Modeling. Front Oncol 2021; 11:657208. [PMID: 33937068 PMCID: PMC8085420 DOI: 10.3389/fonc.2021.657208] [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: 01/22/2021] [Accepted: 03/29/2021] [Indexed: 11/13/2022] Open
Abstract
PURPOSE This retrospective study aimed to evaluate the dosimetric effects of a rectal insertion of Kushen Ningjiao on rectal protection using deformable dose accumulation and machine learning-based discriminative modelling. MATERIALS AND METHODS Sixty-two patients with cervical cancer enrolled in a clinical trial, who received a Kushen Ningjiao injection of 20 g into their rectum for rectal protection via high-dose rate brachytherapy (HDR-BT, 6 Gy/f), were studied. The cumulative equivalent 2-Gy fractional rectal surface dose was deformably summed using an in-house-developed topography-preserved point-matching deformable image registration method. The cumulative three-dimensional (3D) dose was flattened and mapped to a two-dimensional (2D) plane to obtain the rectal surface dose map (RSDM). For analysis, the rectal dose (RD) was further subdivided as follows: whole, anterior, and posterior 3D-RD and 2D-RSDM. The dose-volume parameters (DVPs) were extracted from the 3D-RD, while the dose geometric parameters (DGPs) and textures were extracted from the 2D-RSDM. These features were fed into 192 classification models (built with 8 classifiers and 24 feature selection methods) for discriminating the dose distributions between pre-Kushen Ningjiao and pro-Kushen Ningjiao. RESULTS The rectal insertion of Kushen Ningjiao dialated the rectum in the ambilateral direction, with the rectal column increased from pre-KN 15 cm3 to post-KN 18 cm3 (P < 0.001). The characteristics of DGPs accounted for the largest portions of the top-ranked features. The top-ranked dosimetric features extracted from the posterior rectum were more reliable indicators of the dosimetric effects/changes introduced by the rectal insertion of Kushen Ningjiao. A significant dosimetric impact was found on the dose-volume parameters D1.0cc-D2.5cc extracted on the posterior rectal wall. CONCLUSIONS The rectal insertion of Kushen Ningjiao incurs significant dosimetric changes on the posterior rectal wall. Whether this effect is eventually translated into clinical gains requires further long-term follow-up and more clinical data for confirmation.
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Affiliation(s)
- Xuetao Wang
- Radiation Oncology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Bailin Zhang
- Radiation Oncology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qiang He
- Radiation Oncology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yilin Kong
- Radiation Oncology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhenhui Dai
- Radiation Oncology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Haoyu Meng
- Radiation Oncology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Fangjun Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Shengfeng Zhang
- Radiation Oncology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yuanhu Zhu
- Radiation Oncology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiang Tan
- Radiation Oncology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
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16
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Glaser SM, Mohindra P, Mahantshetty U, Beriwal S. Complications of intracavitary brachytherapy for gynecologic cancers and their management: A comprehensive review. Brachytherapy 2021; 20:984-994. [PMID: 33478905 DOI: 10.1016/j.brachy.2020.11.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 11/21/2020] [Accepted: 11/23/2020] [Indexed: 10/22/2022]
Abstract
Intracavitary gynecologic brachytherapy in the form of tandem-based brachytherapy and vaginal cylinder-based brachytherapy represents a fundamental component of the treatment of women with cervical or uterine cancer due to the ability to deliver a therapeutic dose of radiation with sharp dose falloff. This results in highly effective treatment in terms of oncologic outcomes with an overall favorable toxicity profile. Still, complications and side effects of brachytherapy do exist. While advances in brachytherapy techniques have led to a significant decrease in the rates of toxicity, a thorough understanding of the potential complications is crucial to ensuring optimal outcomes for women with gynecologic cancer undergoing brachytherapy. Use of equivalent dose at 2 Gy per fraction (EQD2) models has allowed incorporation of external beam radiotherapy dose to the brachytherapy dose leading to development of consolidated dose constraints for organs-at-risk in the modern era. This manuscript offers a comprehensive review of potential complications associated with intracavitary brachytherapy for gynecologic cancer including predictive factors, mitigation tactics, and management strategies.
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Affiliation(s)
- Scott M Glaser
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA.
| | - Pranshu Mohindra
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD
| | | | - Sushil Beriwal
- Department of Radiation Oncology, UPMC Hillman Cancer Center, Pittsburgh, PA
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17
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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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18
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Reijtenbagh DMW, Godart J, Mens JWM, Heijkoop ST, Heemsbergen WD, Hoogeman MS. Patient-reported acute GI symptoms in locally advanced cervical cancer patients correlate with rectal dose. Radiother Oncol 2020; 148:38-43. [PMID: 32311599 DOI: 10.1016/j.radonc.2020.03.035] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 03/27/2020] [Accepted: 03/29/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND PURPOSE To investigate relationships between patient-reported acute gastro-intestinal symptoms in a locally advanced cervical cancer (LACC) prospective cohort and clinical and dosimetric parameters, while also taking spatial dose into account. MATERIAL AND METHODS A total of 103 patients was included, receiving radiotherapy based on a plan-library-based plan-of-the-day protocol, combined either with concurrent chemotherapy or with neo-adjuvant chemotherapy and concomitant hyperthermia. Toxicity endpoints were extracted from questionnaires sent out weekly during treatment and regularly in the acute phase after treatment. Endpoints were defined for symptoms concerning obstipation, diarrhea, fecal leakage, bowel cramps and rectal bleeding. Dose surface maps were constructed for the rectum. Clinical parameters and dosimetric parameters of the bowel bag and rectum were collected for all patients. RESULTS The use of concomitant chemotherapy and an increase in Planning Target Volume (PTV) resulted in a significant increase in reported diarrhea. The dose-volume parameters V5Gy-V25Gy of the rectum were found to be significant, unlike dose-volume parameters of the bowel bag. Additionally, a significantly higher dose to the inferior part of the rectum was found for patients reporting diarrhea. No significance was reached for fecal leakage and bowel cramps. CONCLUSION The significance of results for patients reporting diarrhea symptoms found for PTV volume indicates a potential benefit for a plan-of-the-day protocol. Additionally, the results suggest that a reduction of inferior rectum dose could decrease patient-reported diarrhea symptoms, while the administration of concomitant chemotherapy appears to lead to radiosensitizing effects that increase these symptoms.
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Affiliation(s)
| | - Jeremy Godart
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
| | - Jan-Willem M Mens
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
| | - Sabrina T Heijkoop
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
| | - Wilma D Heemsbergen
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
| | - Mischa S Hoogeman
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands; Department of Medical Physics & Informatics, HollandPTC, Delft, The Netherlands.
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19
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Swamidas J, Kirisits C, De Brabandere M, Hellebust TP, Siebert FA, Tanderup K. Image registration, contour propagation and dose accumulation of external beam and brachytherapy in gynecological radiotherapy. Radiother Oncol 2020; 143:1-11. [DOI: 10.1016/j.radonc.2019.08.023] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 08/23/2019] [Accepted: 08/28/2019] [Indexed: 02/07/2023]
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20
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Shortall J, Vasquez Osorio E, Chuter R, McWilliam A, Choudhury A, Kirkby K, Mackay R, van Herk M. Assessing localized dosimetric effects due to unplanned gas cavities during pelvic MR-guided radiotherapy using Monte Carlo simulations. Med Phys 2019; 46:5807-5815. [PMID: 31600837 DOI: 10.1002/mp.13857] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 10/01/2019] [Accepted: 10/01/2019] [Indexed: 01/28/2023] Open
Abstract
PURPOSE It has been proposed that beam modulation and opposing beam configurations can cancel effects of the Electron Return Effect (ERE) during MR-guided radiotherapy (MRgRT). However, this may not always be the case for unplanned gas cavities outside of the target in the pelvic region. We evaluate dosimetric effects, including effects in the rectal wall, due to unplanned spherical air cavities during MRgRT. METHODS Nine virtual cuboid water phantoms containing spherical air cavities (0.5-7.5 cm diameter) and a reference phantom without air were created. Monte Carlo dose calculations of 7 MV photons under the influence of a 1.5 T transverse magnetic field were produced using Monaco 5.19.02 Treatment Planning System (TPS) (Elekta AB, Stockholm, Sweden). Cavities in the path of a single and multiple beam plans were considered. Dose distributions of phantoms with and without air cavities were compared (ΔD% ) using a spherical coordinate system originating in the center of the cavity. Effects in the rectal wall were quantified by comparing dose volume histogram (DVH) parameters for solid and gaseous filling from simulated rectal wall structures. RESULTS Max(ΔD% ) of ~70% and 20% were observed around large cavities in the path of a single and multiple beam plans, respectively. Approximately 45 cm3 of phantom surrounding the largest cavity in a single beam received dose changes of >10%. Dmean in the rectal wall was unchanged when comparing gaseous and solid filling in the path of a single beam; however, D1cc and Dmax increased by up to ~45% and ~63%, respectively. CONCLUSIONS Unplanned gas cavities in the path of a single beam during pelvic MRgRT with a 1.5 T transverse magnetic field cause dose changes which may impact toxicity in the rectal wall, depending on local dose and fractionation. Effects are reduced but not eliminated with a five-beam plan.
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Affiliation(s)
- Jane Shortall
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Robert Chuter
- Department of Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Alan McWilliam
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Department of Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Ananya Choudhury
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Department of Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Karen Kirkby
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Department of Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Ranald Mackay
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Department of Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Marcel van Herk
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Department of Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
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21
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何 强, 王 学, 李 欣, 甄 鑫. [Prediction of rectal toxicity of radiotherapy for prostate cancer based on multi-modality feature and multi-classifiers]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2019; 39:972-979. [PMID: 31511219 PMCID: PMC6765590 DOI: 10.12122/j.issn.1673-4254.2019.08.15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Indexed: 01/23/2023]
Abstract
OBJECTIVE To evaluate rectal toxicity of radiotherapy for prostate cancer using a novel predictive model based on multi-modality and multi-classifier fusion. METHODS We retrospectively collected the clinical data from 44 prostate cancer patients receiving external beam radiation (EBRT), including the treatment data, clinical parameters, planning CT data and the treatment plans. The clinical parameter features and dosimetric features were extracted as two different modality features, and a subset of features was selected to train the 5 base classifiers (SVM, Decision Tree, K-nearest-neighbor, Random forests and XGBoost). To establish the multi-modality and multi-classifier fusion model, a multi-criteria decision-making based weight assignment algorithm was used to assign weights for each base classifier under the same modality. A repeat 5-fold cross-validation and the 4 indexes including the area under ROC curve (AUC), accuracy, sensitivity and specificity were used to evaluate the proposed model. In addition, the proposed model was compared quantitatively with different feature selection methods, different weight allocation algorithms, the model based on single mode single classifier, and two integrated models using other fusion methods. RESULTS Repeated (5 times) 5-fold cross validation of the proposed model showed an accuracy of 0.78 for distinguishing toxicity from non-toxicity with an AUC of 0.83, a specificity of 0.79 and a sensitivity of 0.76. CONCLUSIONS Compared with the models based on a single mode or a single classifier and other fusion models, the proposed model can more accurately predict rectal toxicity of radiotherapy for prostate cancer.
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Affiliation(s)
- 强 何
- 南方医科大学 生物医学工程学院,广东 广州 510515School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - 学涛 王
- 广州中医药大学第二附属医院,广东 广州 510120Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - 欣 李
- 南方医科大学 生物医学工程学院,广东 广州 510515School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - 鑫 甄
- 南方医科大学 生物医学工程学院,广东 广州 510515School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
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22
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Rigaud B, Klopp A, Vedam S, Venkatesan A, Taku N, Simon A, Haigron P, de Crevoisier R, Brock KK, Cazoulat G. Deformable image registration for dose mapping between external beam radiotherapy and brachytherapy images of cervical cancer. Phys Med Biol 2019; 64:115023. [PMID: 30913542 DOI: 10.1088/1361-6560/ab1378] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
For locally advanced cervical cancer (LACC), anatomy correspondence with and without BT applicator needs to be quantified to merge the delivered doses of external beam radiation therapy (EBRT) and brachytherapy (BT). This study proposed and evaluated different deformable image registration (DIR) methods for this application. Twenty patients who underwent EBRT and BT for LACC were retrospectively analyzed. Each patient had a pre-BT CT at EBRT boost (without applicator) and a CT and MRI at BT (with applicator). The evaluated DIR methods were the diffeomorphic Demons, commercial intensity and hybrid methods, and three different biomechanical models. The biomechanical models considered different boundary conditions (BCs). The impact of the BT devices insertion on the anatomy was quantified. DIR method performances were quantified using geometric criteria between the original and deformed contours. The BT dose was deformed toward the pre-CT BT by each DIR method. The impact of boundary conditions to drive the biomechanical model was evaluated based on the deformation vector field and dose differences. The GEC-ESTRO guideline dose indices were reported. Large organ displacements, deformations, and volume variations were observed between the pre-BT and BT anatomies. Rigid registration and intensity-based DIR resulted in poor geometric accuracy with mean Dice similarity coefficient (DSC) inferior to 0.57, 0.63, 0.42, 0.32, and 0.43 for the rectum, bladder, vagina, cervix and uterus, respectively. Biomechanical models provided a mean DSC of 0.96 for all the organs. By considering the cervix-uterus as one single structure, biomechanical models provided a mean DSC of 0.88 and 0.94 for the cervix and uterus, respectively. The deformed doses were represented for each DIR method. Caution should be used when performing DIR for this application as standard techniques may have unacceptable results. The biomechanical model with the cervix-uterus as one structure provided the most realistic deformations to propagate the BT dose toward the EBRT boost anatomy.
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Affiliation(s)
- B Rigaud
- Univ Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, F-35000 Rennes, France. Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America. Author to whom any correspondence should be addressed
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