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Ruiz Sarrias O, Gónzalez Deza C, Rodríguez Rodríguez J, Arrizibita Iriarte O, Vizcay Atienza A, Zumárraga Lizundia T, Sayar Beristain O, Aldaz Pastor A. Predicting Severe Haematological Toxicity in Gastrointestinal Cancer Patients Undergoing 5-FU-Based Chemotherapy: A Bayesian Network Approach. Cancers (Basel) 2023; 15:4206. [PMID: 37686482 PMCID: PMC10486471 DOI: 10.3390/cancers15174206] [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: 06/29/2023] [Revised: 07/28/2023] [Accepted: 08/16/2023] [Indexed: 09/10/2023] Open
Abstract
PURPOSE Severe toxicity is reported in about 30% of gastrointestinal cancer patients receiving 5-Fluorouracil (5-FU)-based chemotherapy. To date, limited tools exist to identify at risk patients in this setting. The objective of this study was to address this need by designing a predictive model using a Bayesian network, a probabilistic graphical model offering robust, explainable predictions. METHODS We utilized a dataset of 267 gastrointestinal cancer patients, conducting preprocessing, and splitting it into TRAIN and TEST sets (80%:20% ratio). The RandomForest algorithm assessed variable importance based on MeanDecreaseGini coefficient. The bnlearn R library helped design a Bayesian network model using a 10-fold cross-validation on the TRAIN set and the aic-cg method for network structure optimization. The model's performance was gauged based on accuracy, sensitivity, and specificity, using cross-validation on the TRAIN set and independent validation on the TEST set. RESULTS The model demonstrated satisfactory performance with an average accuracy of 0.85 (±0.05) and 0.80 on TRAIN and TEST datasets, respectively. The sensitivity and specificity were 0.82 (±0.14) and 0.87 (±0.07) for the TRAIN dataset, and 0.71 and 0.83 for the TEST dataset, respectively. A user-friendly tool was developed for clinical implementation. CONCLUSIONS Despite several limitations, our Bayesian network model demonstrated a high level of accuracy in predicting the risk of developing severe haematological toxicity in gastrointestinal cancer patients receiving 5-FU-based chemotherapy. Future research should aim at model validation in larger cohorts of patients and different clinical settings.
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Affiliation(s)
- Oskitz Ruiz Sarrias
- Department of Mathematics and Statistic, NNBi, 31191 Esquiroz, Navarra, Spain; (O.R.S.)
| | - Cristina Gónzalez Deza
- Department of Medical Oncology, Clínica Universidad De Navarra, 31008 Pamplona, Navarra, Spain; (C.G.D.); (J.R.R.); (T.Z.L.)
| | - Javier Rodríguez Rodríguez
- Department of Medical Oncology, Clínica Universidad De Navarra, 31008 Pamplona, Navarra, Spain; (C.G.D.); (J.R.R.); (T.Z.L.)
| | | | - Angel Vizcay Atienza
- Department of Medical Oncology, Clínica Universidad De Navarra, 31008 Pamplona, Navarra, Spain; (C.G.D.); (J.R.R.); (T.Z.L.)
| | - Teresa Zumárraga Lizundia
- Department of Medical Oncology, Clínica Universidad De Navarra, 31008 Pamplona, Navarra, Spain; (C.G.D.); (J.R.R.); (T.Z.L.)
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Ladbury C, Amini A, Govindarajan A, Mambetsariev I, Raz DJ, Massarelli E, Williams T, Rodin A, Salgia R. Integration of artificial intelligence in lung cancer: Rise of the machine. Cell Rep Med 2023; 4:100933. [PMID: 36738739 PMCID: PMC9975283 DOI: 10.1016/j.xcrm.2023.100933] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/14/2022] [Accepted: 01/17/2023] [Indexed: 02/05/2023]
Abstract
The goal of oncology is to provide the longest possible survival outcomes with the therapeutics that are currently available without sacrificing patients' quality of life. In lung cancer, several data points over a patient's diagnostic and treatment course are relevant to optimizing outcomes in the form of precision medicine, and artificial intelligence (AI) provides the opportunity to use available data from molecular information to radiomics, in combination with patient and tumor characteristics, to help clinicians provide individualized care. In doing so, AI can help create models to identify cancer early in diagnosis and deliver tailored therapy on the basis of available information, both at the time of diagnosis and in real time as they are undergoing treatment. The purpose of this review is to summarize the current literature in AI specific to lung cancer and how it applies to the multidisciplinary team taking care of these complex patients.
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Affiliation(s)
- Colton Ladbury
- Department of Radiation Oncology, City of Hope National Medical Center, 1500 E Duarte Road, Duarte, CA 91010, USA
| | - Arya Amini
- Department of Radiation Oncology, City of Hope National Medical Center, 1500 E Duarte Road, Duarte, CA 91010, USA.
| | - Ameish Govindarajan
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Isa Mambetsariev
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Dan J Raz
- Department of Surgery, City of Hope National Medical Center, Duarte, CA, USA
| | - Erminia Massarelli
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Terence Williams
- Department of Radiation Oncology, City of Hope National Medical Center, 1500 E Duarte Road, Duarte, CA 91010, USA
| | - Andrei Rodin
- Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Ravi Salgia
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
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3
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Kalendralis P, Luk SMH, Canters R, Eyssen D, Vaniqui A, Wolfs C, Murrer L, van Elmpt W, Kalet AM, Dekker A, van Soest J, Fijten R, Zegers CML, Bermejo I. Automatic quality assurance of radiotherapy treatment plans using Bayesian networks: A multi-institutional study. Front Oncol 2023; 13:1099994. [PMID: 36925935 PMCID: PMC10012863 DOI: 10.3389/fonc.2023.1099994] [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: 11/16/2022] [Accepted: 02/13/2023] [Indexed: 03/04/2023] Open
Abstract
Purpose Artificial intelligence applications in radiation oncology have been the focus of study in the last decade. The introduction of automated and intelligent solutions for routine clinical tasks, such as treatment planning and quality assurance, has the potential to increase safety and efficiency of radiotherapy. In this work, we present a multi-institutional study across three different institutions internationally on a Bayesian network (BN)-based initial plan review assistive tool that alerts radiotherapy professionals for potential erroneous or suboptimal treatment plans. Methods Clinical data were collected from the oncology information systems in three institutes in Europe (Maastro clinic - 8753 patients treated between 2012 and 2020) and the United States of America (University of Vermont Medical Center [UVMMC] - 2733 patients, University of Washington [UW] - 6180 patients, treated between 2018 and 2021). We trained the BN model to detect potential errors in radiotherapy treatment plans using different combinations of institutional data and performed single-site and cross-site validation with simulated plans with embedded errors. The simulated errors consisted of three different categories: i) patient setup, ii) treatment planning and iii) prescription. We also compared the strategy of using only diagnostic parameters or all variables as evidence for the BN. We evaluated the model performance utilizing the area under the receiver-operating characteristic curve (AUC). Results The best network performance was observed when the BN model is trained and validated using the dataset in the same center. In particular, the testing and validation using UVMMC data has achieved an AUC of 0.92 with all parameters used as evidence. In cross-validation studies, we observed that the BN model performed better when it was trained and validated in institutes with similar technology and treatment protocols (for instance, when testing on UVMMC data, the model trained on UW data achieved an AUC of 0.84, compared with an AUC of 0.64 for the model trained on Maastro data). Also, combining training data from larger clinics (UW and Maastro clinic) and using it on smaller clinics (UVMMC) leads to satisfactory performance with an AUC of 0.85. Lastly, we found that in general the BN model performed better when all variables are considered as evidence. Conclusion We have developed and validated a Bayesian network model to assist initial treatment plan review using multi-institutional data with different technology and clinical practices. The model has shown good performance even when trained on data from clinics with divergent profiles, suggesting that the model is able to adapt to different data distributions.
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Affiliation(s)
- Petros Kalendralis
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical center+, Maastricht, Netherlands
| | - Samuel M H Luk
- Department of Radiation Oncology, University of Vermont Medical Center, Burlington, VT, United States
| | - Richard Canters
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical center+, Maastricht, Netherlands
| | - Denis Eyssen
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical center+, Maastricht, Netherlands
| | - Ana Vaniqui
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical center+, Maastricht, Netherlands
| | - Cecile Wolfs
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical center+, Maastricht, Netherlands
| | - Lars Murrer
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical center+, Maastricht, Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical center+, Maastricht, Netherlands
| | - Alan M Kalet
- Department of Radiation Oncology, University of Washington Medical Center, Seattle, WA, United States
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical center+, Maastricht, Netherlands.,Brightlands Institute for Smart digital Society (BISS), Faculty of Science and Engineering, Maastricht University, Heerlen, Netherlands
| | - Johan van Soest
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical center+, Maastricht, Netherlands.,Brightlands Institute for Smart digital Society (BISS), Faculty of Science and Engineering, Maastricht University, Heerlen, Netherlands
| | - Rianne Fijten
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical center+, Maastricht, Netherlands
| | - Catharina M L Zegers
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical center+, Maastricht, Netherlands
| | - Inigo Bermejo
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical center+, Maastricht, Netherlands
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Ladbury C, Zarinshenas R, Semwal H, Tam A, Vaidehi N, Rodin AS, Liu A, Glaser S, Salgia R, Amini A. Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review. Transl Cancer Res 2022; 11:3853-3868. [PMID: 36388027 PMCID: PMC9641128 DOI: 10.21037/tcr-22-1626] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/07/2022] [Indexed: 11/25/2022]
Abstract
Background and Objective Machine learning (ML) models are increasingly being utilized in oncology research for use in the clinic. However, while more complicated models may provide improvements in predictive or prognostic power, a hurdle to their adoption are limits of model interpretability, wherein the inner workings can be perceived as a "black box". Explainable artificial intelligence (XAI) frameworks including Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are novel, model-agnostic approaches that aim to provide insight into the inner workings of the "black box" by producing quantitative visualizations of how model predictions are calculated. In doing so, XAI can transform complicated ML models into easily understandable charts and interpretable sets of rules, which can give providers with an intuitive understanding of the knowledge generated, thus facilitating the deployment of such models in routine clinical workflows. Methods We performed a comprehensive, non-systematic review of the latest literature to define use cases of model-agnostic XAI frameworks in oncologic research. The examined database was PubMed/MEDLINE. The last search was run on May 1, 2022. Key Content and Findings In this review, we identified several fields in oncology research where ML models and XAI were utilized to improve interpretability, including prognostication, diagnosis, radiomics, pathology, treatment selection, radiation treatment workflows, and epidemiology. Within these fields, XAI facilitates determination of feature importance in the overall model, visualization of relationships and/or interactions, evaluation of how individual predictions are produced, feature selection, identification of prognostic and/or predictive thresholds, and overall confidence in the models, among other benefits. These examples provide a basis for future work to expand on, which can facilitate adoption in the clinic when the complexity of such modeling would otherwise be prohibitive. Conclusions Model-agnostic XAI frameworks offer an intuitive and effective means of describing oncology ML models, with applications including prognostication and determination of optimal treatment regimens. Using such frameworks presents an opportunity to improve understanding of ML models, which is a critical step to their adoption in the clinic.
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Affiliation(s)
- Colton Ladbury
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Reza Zarinshenas
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Hemal Semwal
- Departments of Bioengineering and Integrated Biology and Physiology, University of California Los Angeles, Los Angeles, CA, USA
| | - Andrew Tam
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Nagarajan Vaidehi
- Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Andrei S Rodin
- Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - An Liu
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Scott Glaser
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Ravi Salgia
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Arya Amini
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, USA
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Chen Y, Zhu Y, Zhong K, Yang Z, Li Y, Shu X, Wang D, Deng P, Bai X, Gu J, Lu K, Zhang J, Zhao L, Zhu T, Wei K, Yi B. Optimization of anesthetic decision-making in ERAS using Bayesian network. Front Med (Lausanne) 2022; 9:1005901. [PMID: 36186765 PMCID: PMC9519180 DOI: 10.3389/fmed.2022.1005901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
Enhanced recovery after surgery (ERAS) can accelerate patient recovery. However, little research has been done on optimizing the ERAS-related measures and how the measures interact with each other. The Bayesian network (BN) is a graphical model that describes the dependencies between variables and is also a model for uncertainty reasoning. In this study, we aimed to develop a method for optimizing anesthetic decisions in ERAS and then investigate the relationship between anesthetic decisions and outcomes. First, assuming that the indicators used were independent, the effects of combinations of single indicators were analyzed based on BN. Additionally, the impact indicators for outcomes were selected with statistical tests. Then, based on the previously selected indicators, the Bayesian network was constructed using the proposed structure learning method based on Strongly Connected Components (SCC) Local Structure determination by Hill Climbing Twice (LSHCT) and adjusted according to the expert’s knowledge. Finally, the relationship is analyzed. The proposed method is validated by the real clinical data of patients with benign gynecological tumors from 3 hospitals in China. Postoperative length of stay (LOS) and total cost (TC) were chosen as the outcomes. Experimental results show that the ERAS protocol has some pivotal indicators influencing LOS and TC. Identifying the relationship between these indicators can help anesthesiologists optimize the ERAS protocol and make individualized decisions.
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Affiliation(s)
- Yuwen Chen
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences (CAS), Chongqing, China
| | - Yiziting Zhu
- Department of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Kunhua Zhong
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences (CAS), Chongqing, China
| | - Zhiyong Yang
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Yujie Li
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Xin Shu
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Dandan Wang
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Peng Deng
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Xuehong Bai
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Jianteng Gu
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Kaizhi Lu
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Ju Zhang
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences (CAS), Chongqing, China
| | - Lei Zhao
- Department of Anesthesiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Tao Zhu
- Department of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China
| | - Ke Wei
- Department of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Ke Wei,
| | - Bin Yi
- Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China
- Bin Yi,
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Kondylakis H, Ciarrocchi E, Cerda-Alberich L, Chouvarda I, Fromont LA, Garcia-Aznar JM, Kalokyri V, Kosvyra A, Walker D, Yang G, Neri E. Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks. Eur Radiol Exp 2022; 6:29. [PMID: 35773546 PMCID: PMC9247122 DOI: 10.1186/s41747-022-00281-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 04/20/2022] [Indexed: 11/10/2022] Open
Abstract
A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the Horizon 2020 "AI for Health Imaging" projects, which are all dedicated to the creation of imaging biobanks.
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Affiliation(s)
| | - Esther Ciarrocchi
- grid.5395.a0000 0004 1757 3729Department of Translational Research, University of Pisa, Pisa, Italy
| | | | - Ioanna Chouvarda
- grid.4793.90000000109457005Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Lauren A. Fromont
- grid.11478.3b0000 0004 1766 3695Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | | | - Varvara Kalokyri
- grid.5395.a0000 0004 1757 3729Department of Translational Research, University of Pisa, Pisa, Italy
| | - Alexandra Kosvyra
- grid.4793.90000000109457005Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dawn Walker
- grid.11835.3e0000 0004 1936 9262Department of Computer Science and Insigneo Institute of in silico Medicine, University of Sheffield, Sheffield, UK
| | - Guang Yang
- grid.7445.20000 0001 2113 8111National Heart and Lung Institute, Imperial College London, London, UK
| | - Emanuele Neri
- grid.5395.a0000 0004 1757 3729Department of Translational Research, University of Pisa, Pisa, Italy
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Improving the Quality of Care in Radiation Oncology using Artificial Intelligence. Clin Oncol (R Coll Radiol) 2021; 34:89-98. [PMID: 34887152 DOI: 10.1016/j.clon.2021.11.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 10/20/2021] [Accepted: 11/12/2021] [Indexed: 12/13/2022]
Abstract
Radiation therapy is a complex process involving multiple professionals and steps from simulation to treatment planning to delivery, and these procedures are prone to error. Additionally, the imaging and treatment delivery equipment in radiotherapy is highly complex and interconnected and represents another risk point in the quality of care. Numerous quality assurance tasks are carried out to ensure quality and to detect and prevent potential errors in the process of care. Recent developments in artificial intelligence provide potential tools to the radiation oncology community to improve the efficiency and performance of quality assurance efforts. Targets for artificial intelligence enhancement include the quality assurance of treatment plans, target and tissue structure delineation used in the plans, delivery of the plans and the radiotherapy delivery equipment itself. Here we review recent developments of artificial intelligence applications that aim to improve quality assurance processes in radiation therapy and discuss some of the challenges and limitations that require further development work to realise the potential of artificial intelligence for quality assurance.
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8
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Leclerc V, Ducher M, Ceraulo A, Bertrand Y, Bleyzac N. A Clinical Decision Support Tool to Find the Best Initial Intravenous Cyclosporine Regimen in Pediatric Hematopoietic Stem Cell Transplantation. J Clin Pharmacol 2021; 61:1485-1492. [PMID: 34105165 DOI: 10.1002/jcph.1924] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 06/04/2021] [Indexed: 12/20/2022]
Abstract
To optimize cyclosporine A (CsA) dosing regimen in pediatric patients undergoing hematopoietic stem cell transplantation (HSCT), we aimed to provide clinicians with a validated decision support tool for determining the most suitable first dose of intravenous CsA. We used a 10-year monocentric data set of pediatric patients undergoing HSCT. Discretization of all variables was performed according to literature or thanks to algorithms using Shannon entropy (from information theory) or equal width intervals. The first 8 years were used to build the Bayesian network model. This model underwent a 10-fold cross-validation, and then a prospective validation with data of the last 2 years. There were 3.3% and 4.1% of missing values in the training and the validation data set, respectively. After prospective validation, the Tree-Augmented Naïve Bayesian network shows interesting prediction performances with an average area under the receiver operating characteristic curve of 0.804, 32.8% of misclassified patients, a true-positive rate of 0.672, and a false-positive rate of 0.285. This validated model allows good predictions to propose an optimized and personalized initial CsA dose for pediatric patients undergoing HSCT. The clinical impact of its use should be further evaluated.
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Affiliation(s)
- Vincent Leclerc
- Targeted Therapies in Oncology, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France.,Pharmacy Department, Hôpital Pierre Garraud, Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France
| | - Michel Ducher
- Targeted Therapies in Oncology, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France.,Pharmacy Department, Hôpital Pierre Garraud, Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France
| | - Antony Ceraulo
- Institute of Pediatric Hematology and Oncology (IHOPe), Hematology Unit, Hospices Civils de Lyon and Claude Bernard University, Lyon, France
| | - Yves Bertrand
- Institute of Pediatric Hematology and Oncology (IHOPe), Hematology Unit, Hospices Civils de Lyon and Claude Bernard University, Lyon, France
| | - Nathalie Bleyzac
- Targeted Therapies in Oncology, Faculté de Médecine et de Maïeutique Lyon-Sud Charles Mérieux, Université Claude Bernard Lyon 1, Oullins, France
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9
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Phillips MH, Serra LM, Dekker A, Ghosh P, Luk SMH, Kalet A, Mayo C. Ontologies in radiation oncology. Phys Med 2020; 72:103-113. [PMID: 32247963 DOI: 10.1016/j.ejmp.2020.03.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 03/17/2020] [Accepted: 03/19/2020] [Indexed: 01/27/2023] Open
Abstract
Ontologies are a formal, computer-compatible method for representing scientific knowledge about a given domain. They provide a standardized vocabulary, taxonomy and set of relations between concepts. When formatted in a standard way, they can be read and reasoned upon by computers as well as by humans. At the 2019 International Conference on the Use of Computers in Radiation Therapy, there was a session devoted to ontologies in radiation therapy. This paper is a compilation of the material presented, and is meant as an introduction to the subject. This is done by means of a didactic introduction to the topic followed by a series of applications in radiation therapy. The goal of this article is to provide the medical physicist and related professionals with sufficient background that they can understand their construction as well as their practical uses.
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Affiliation(s)
- Mark H Phillips
- Department of Radiation Oncology, University of Washington, Seattle, WA 91895, United States.
| | - Lucas M Serra
- Department of Biomedical Informatics, University at Buffalo, 77 Goodell Street, Buffalo, NY 14260, United States
| | - Andre Dekker
- Medical Physics Department, Maastro Clinic, DR. Tanslaan 12, Maastrich 6229 ET, Netherlands
| | - Preetam Ghosh
- Department of Computer Science, Engineering East Hall, Virginia Commonwealth University, Richmond, VA, United States
| | - Samuel M H Luk
- Department of Radiation Oncology, University of Washington, Seattle, WA 91895, United States
| | - Alan Kalet
- Department of Radiation Oncology, University of Washington, Seattle, WA 91895, United States
| | - Charles Mayo
- Radiation Oncology, University of Michigan, 1500 E Medical Center Dr, SPC 5010, Ann Arbor, MI, United States
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10
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Zhang H, Roy U, Tina Lee YT. Enriching Analytics Models with Domain Knowledge for Smart Manufacturing Data Analysis. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2020; 58:10.1080/00207543.2019.1680895. [PMID: 33304000 PMCID: PMC7722260 DOI: 10.1080/00207543.2019.1680895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 05/28/2019] [Indexed: 06/12/2023]
Abstract
Today, data analytics plays an important role in Smart Manufacturing decision making. Domain knowledge is very important to support the development of analytics models. However, in today's data analytics projects, domain knowledge is only documented, but not properly captured and integrated with analytics models. This raises problems in interoperability and traceability of the relevant domain knowledge that is used to develop analytics models. To address these problems, this paper proposes a methodology to enrich analytics models with domain knowledge. To illustrate the proposed methodology, a case study is introduced to demonstrate the utilization of the enriched analytics model to support the development of a Bayesian Network model. The case study shows that the utilization of an enriched analytics model improves the efficiency in developing the Bayesian Network model.
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Affiliation(s)
- Heng Zhang
- Department of Mechanical and Aerospace Engineering, Syracuse University, Syracuse, NY, USA
| | - Utpal Roy
- Department of Mechanical and Aerospace Engineering, Syracuse University, Syracuse, NY, USA
| | - Yung-Tsun Tina Lee
- Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
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Minimum Data Elements for Radiation Oncology: An American Society for Radiation Oncology Consensus Paper. Pract Radiat Oncol 2019; 9:395-401. [PMID: 31445187 DOI: 10.1016/j.prro.2019.07.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 07/31/2019] [Indexed: 11/24/2022]
Abstract
PURPOSE In recent years, the American Society for Radiation Oncology (ASTRO) has received requests for a standard list of data elements from other societies, database architects, Electronic Health Record vendors and, most recently, the pharmaceutical industry. These requests point to a growing interest in capturing radiation oncology data within registries and for quality measurement, interoperability initiatives, and research. Identifying a short and consistent list will lead to improved care coordination, a reduction in data entry by practice staff, and a more complete view of the holistic approach required for cancer treatment. METHODS AND MATERIALS The task force formulated recommendations based on analysis from radiation specific data elements currently in use in registries, accreditation programs, incident learning systems, and electronic health records. The draft manuscript was peer reviewed by 8 reviewers and ASTRO legal counsel and was revised accordingly and posted on the ASTRO website for public comment in April 2019 for 2 weeks. The final document was approved by the ASTRO Board of Directors in June 2019.
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How Much Do Emotional, Behavioral, and Cognitive Factors Actually Impact College Student Attitudes towards English Language Learning? A Quantitative and Qualitative Study. INFORMATION 2019. [DOI: 10.3390/info10050166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Researchers have proposed many multidimensional frameworks to identify significant and potential factors, e.g., educational background, positive feelings and career aspirations, that impact English learning attitude in second language acquisition. Yet, there is still very little research to graphically describe the interactions between these factors and how these factors directly or indirectly impact learning attitude. To this end, a questionnaire survey was conducted in Changchun University of Technology. Statistical measures and Bayesian network analysis were introduced to quantitatively and qualitatively analyze the collected data. Furthermore, the significant attitudinal differences between students majoring in the Liberal Arts or Sciences were investigated for the case study. Studying the interaction between these factors can help explain how they positively affect students’ attitudes toward English language learning. To stimulate interest, teachers may take targeted pedagogical approaches or strategies.
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Luk SMH, Meyer J, Young LA, Cao N, Ford EC, Phillips MH, Kalet AM. Characterization of a Bayesian network‐based radiotherapy plan verification model. Med Phys 2019; 46:2006-2014. [DOI: 10.1002/mp.13515] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2018] [Revised: 03/22/2019] [Accepted: 03/22/2019] [Indexed: 02/02/2023] Open
Affiliation(s)
- Samuel M. H. Luk
- Department of Radiation Oncology University of Washington Medical Center Seattle WA 98195‐6043USA
| | - Juergen Meyer
- Department of Radiation Oncology University of Washington Medical Center Seattle WA 98195‐6043USA
| | - Lori A. Young
- Department of Radiation Oncology University of Washington Medical Center Seattle WA 98195‐6043USA
| | - Ning Cao
- Department of Radiation Oncology University of Washington Medical Center Seattle WA 98195‐6043USA
| | - Eric C. Ford
- Department of Radiation Oncology University of Washington Medical Center Seattle WA 98195‐6043USA
| | - Mark H. Phillips
- Department of Radiation Oncology University of Washington Medical Center Seattle WA 98195‐6043USA
- Department of Biomedical Informatics and Medical Education University of Washington Seattle WA 98019‐4714 USA
| | - Alan M. Kalet
- Department of Radiation Oncology University of Washington Medical Center Seattle WA 98195‐6043USA
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Kalet AM, Luk SMH, Phillips MH. Radiation Therapy Quality Assurance Tasks and Tools: The Many Roles of Machine Learning. Med Phys 2019; 47:e168-e177. [DOI: 10.1002/mp.13445] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 01/14/2019] [Accepted: 02/02/2019] [Indexed: 12/12/2022] Open
Affiliation(s)
- Alan M. Kalet
- Department of Radiation Oncology University of Washington Medical Center Seattle WA 98195 USA
| | - Samuel M. H. Luk
- Department of Radiation Oncology University of Washington Medical Center Seattle WA 98195 USA
| | - Mark H. Phillips
- Department of Radiation Oncology University of Washington Medical Center Seattle WA 98195 USA
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