T. Sandell, Andre Miller, H. Schütze, R. Ivers, Vivega Vijayakumar, Lincoln Dinh
Introduction COVID-19 required health services to be innovative and quickly adapt their health service delivery, including adopting health technology in cancer clinical practice. COVID-19 restrictions forced us to introduce follow-up consultations for many patients via telehealth. At the same time, we adapted an existing Patient Reported Outcome messaging service that linked to the patient’s medical record to allow patients to self-report their health and disease status before their telehealth follow-up consultation. This study aimed to evaluate the feasibility of self-reporting for cancer follow-up care, and determine the patient-clinician level of agreement. Methods Cross-sectional clinical practice study. Patients on radiation oncology follow-up care were sent a text message with a weblink to a survey to self-report their health before their radiation oncologist appointment. Radiation oncologists completed the same set of questions during or within a day of the telehealth follow-up consultation. Descriptive statistics were analysed to evaluate the uptake of self-reporting. Percent agreement and Cohen’s Kappa were used to determine patient-clinician agreement. Results A moderate response rate of 62% was achieved from the 145 patients. There was no difference in the age of patients that were able to complete the assessment. Percent agreement between the patient-reported and the clinician-reported for weight change, appetite, physical performance, side effects was acceptable (>75%). However, percent agreement was moderate for pain and sleep. For most items, Cohen’s Kappa indicated moderate agreement, with pain, side effects, and recurrence being fair. Patients were more likely to report themselves worse than the clinician for all items, except for side effects. Conclusion Patient self-reported health can provide useful information for clinicians to remotely follow-up their patients. This holds promise for future models of follow-up care, particularly for rural and remote patients, and during pandemics and other disasters where clinic attendance is not possible.
{"title":"Patient self-reported follow-up for radiation oncology patients during COVID-19: feasibility and patient-clinician agreement","authors":"T. Sandell, Andre Miller, H. Schütze, R. Ivers, Vivega Vijayakumar, Lincoln Dinh","doi":"10.5166/jroi.12.2.1","DOIUrl":"https://doi.org/10.5166/jroi.12.2.1","url":null,"abstract":"Introduction COVID-19 required health services to be innovative and quickly adapt their health service delivery, including adopting health technology in cancer clinical practice. COVID-19 restrictions forced us to introduce follow-up consultations for many patients via telehealth. At the same time, we adapted an existing Patient Reported Outcome messaging service that linked to the patient’s medical record to allow patients to self-report their health and disease status before their telehealth follow-up consultation. This study aimed to evaluate the feasibility of self-reporting for cancer follow-up care, and determine the patient-clinician level of agreement. \u0000Methods Cross-sectional clinical practice study. Patients on radiation oncology follow-up care were sent a text message with a weblink to a survey to self-report their health before their radiation oncologist appointment. Radiation oncologists completed the same set of questions during or within a day of the telehealth follow-up consultation. Descriptive statistics were analysed to evaluate the uptake of self-reporting. Percent agreement and Cohen’s Kappa were used to determine patient-clinician agreement. \u0000Results A moderate response rate of 62% was achieved from the 145 patients. There was no difference in the age of patients that were able to complete the assessment. Percent agreement between the patient-reported and the clinician-reported for weight change, appetite, physical performance, side effects was acceptable (>75%). However, percent agreement was moderate for pain and sleep. For most items, Cohen’s Kappa indicated moderate agreement, with pain, side effects, and recurrence being fair. Patients were more likely to report themselves worse than the clinician for all items, except for side effects. \u0000Conclusion Patient self-reported health can provide useful information for clinicians to remotely follow-up their patients. This holds promise for future models of follow-up care, particularly for rural and remote patients, and during pandemics and other disasters where clinic attendance is not possible.","PeriodicalId":426862,"journal":{"name":"Journal of Radiation Oncology Informatics","volume":"234 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128202322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alli Jan, Andrew Miller, Peter Wright, Dale Glennan
Purpose: to assess the likelihood of local recurrence of lung malignancies following stereotactic ablative radiotherapy (SABR) by evaluating clinical and radiomic features with machine learning and novel use of deep learning methods. Methods: pre-treatment CT images were attained from seventy patients with primary lung malignancies. The malignancy was segmented by the treating radiation oncologist and 107 radiomic features were extracted from the image. The data underwent feature reduction via Spearman’s correlation and selection with adapted LASSO regression analysis. A random forest model and a multilayer perceptron (MLP) with cost-sensitive classifier were independently used to assess for local recurrence of malignancy. The recurrence likelihood predictions from each of these were used to stratify patients into groups with high and low risk of recurrence. These were assessed for time-to-event predictions using Kaplan-Meier analyses and Gray’s test to evaluate the separation between the high and low-risk groups. The prognostic capacity of the models was evaluated with a concordance index, 95% confidence intervals and bootstrapping (10,000 iterations). Results: the MLP was able to predict the recurrence of malignancy with 100% sensitivity and 91% specificity (AUC 0.95). The MLP predictions showed statistically significant separation of high and low-risk patients, and robust model fit (p=0.04, c=0.79), which out-performed random forest model predictions (p=0.15, c=0.41) that did not reach statistical significance. Conclusions: radiomic data analysis with an MLP showed improved prediction potential within this dataset compared to random forest models for predicting local recurrence of lung cancer.
{"title":"Multilayer Perceptron Analysis of Radiomics to Predict Local Recurrence of Lung Cancer After Radiotherapy","authors":"Alli Jan, Andrew Miller, Peter Wright, Dale Glennan","doi":"10.5166/jroi.12.1.1","DOIUrl":"https://doi.org/10.5166/jroi.12.1.1","url":null,"abstract":"Purpose: to assess the likelihood of local recurrence of lung malignancies following stereotactic ablative radiotherapy (SABR) by evaluating clinical and radiomic features with machine learning and novel use of deep learning methods. \u0000Methods: pre-treatment CT images were attained from seventy patients with primary lung malignancies. The malignancy was segmented by the treating radiation oncologist and 107 radiomic features were extracted from the image. The data underwent feature reduction via Spearman’s correlation and selection with adapted LASSO regression analysis. A random forest model and a multilayer perceptron (MLP) with cost-sensitive classifier were independently used to assess for local recurrence of malignancy. The recurrence likelihood predictions from each of these were used to stratify patients into groups with high and low risk of recurrence. These were assessed for time-to-event predictions using Kaplan-Meier analyses and Gray’s test to evaluate the separation between the high and low-risk groups. The prognostic capacity of the models was evaluated with a concordance index, 95% confidence intervals and bootstrapping (10,000 iterations). \u0000Results: the MLP was able to predict the recurrence of malignancy with 100% sensitivity and 91% specificity (AUC 0.95). The MLP predictions showed statistically significant separation of high and low-risk patients, and robust model fit (p=0.04, c=0.79), which out-performed random forest model predictions (p=0.15, c=0.41) that did not reach statistical significance. \u0000Conclusions: radiomic data analysis with an MLP showed improved prediction potential within this dataset compared to random forest models for predicting local recurrence of lung cancer.","PeriodicalId":426862,"journal":{"name":"Journal of Radiation Oncology Informatics","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132686214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At first sight, shared decision-making and data science seem like two vastly different fields. Yet, despite their differences, both fields could, if combined, reinforce clinical utility for both. Here we describe a new paradigm called data-driven shared decision-making (dSDM), an extension of the existing shared decision-making paradigm. In dSDM, data’s role and its interaction with the patient and doctor are made explicit. Furthermore, we describe the opportunities and challenges of combining data science and shared decision-making into this new paradigm. We believe that dSDM will bridge the gap between the need for patient empowerment and the need for more personalized medicine.
{"title":"Data-driven shared decision-making: a paradigm shift","authors":"R. Fijten, L. Wee, A. Dekker, C. Roumen","doi":"10.5166/JROI.11.2.1","DOIUrl":"https://doi.org/10.5166/JROI.11.2.1","url":null,"abstract":"At first sight, shared decision-making and data science seem like two vastly different fields. Yet, despite their differences, both fields could, if combined, reinforce clinical utility for both. Here we describe a new paradigm called data-driven shared decision-making (dSDM), an extension of the existing shared decision-making paradigm. In dSDM, data’s role and its interaction with the patient and doctor are made explicit. Furthermore, we describe the opportunities and challenges of combining data science and shared decision-making into this new paradigm. We believe that dSDM will bridge the gap between the need for patient empowerment and the need for more personalized medicine.","PeriodicalId":426862,"journal":{"name":"Journal of Radiation Oncology Informatics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129525831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Böhner, L. C. Schmeel, F. Andreas, Scafa Davide, Sarria Gustavo R., Adriana Torres Crigna, D. Koch, S. Garbe, Barbara Link, Dilini Brüser, F. Giordano
As the general public is increasing their online presence and is becoming confident with the digital infrastructure, an opportunity for patient-centered digital care has arisen. Electronic patient-reported outcomes, (e)PRO in short, may facilitate enhanced clinical management of radiation oncology patients. This might enable the physicians to take the initiative and counteract symptoms or undesired side effects before they aggravate and thus, reducing treatment-associated costs. In this article, we review the impetus for and modalities of (e)PRO-based data acquisition and handling in research and routine. We conclude that prospective and technical studies are needed to prove the clinical significance of (e)PROs to pave the way to monetary compensation and widespread application.
{"title":"How to PROceed? Reviewing obstacles and perspectives in patient-centered digital care in radiation oncology","authors":"A. Böhner, L. C. Schmeel, F. Andreas, Scafa Davide, Sarria Gustavo R., Adriana Torres Crigna, D. Koch, S. Garbe, Barbara Link, Dilini Brüser, F. Giordano","doi":"10.5166/jroi.11.1.1","DOIUrl":"https://doi.org/10.5166/jroi.11.1.1","url":null,"abstract":"As the general public is increasing their online presence and is becoming confident with the digital infrastructure, an opportunity for patient-centered digital care has arisen. Electronic patient-reported outcomes, (e)PRO in short, may facilitate enhanced clinical management of radiation oncology patients. This might enable the physicians to take the initiative and counteract symptoms or undesired side effects before they aggravate and thus, reducing treatment-associated costs. In this article, we review the impetus for and modalities of (e)PRO-based data acquisition and handling in research and routine. We conclude that prospective and technical studies are needed to prove the clinical significance of (e)PROs to pave the way to monetary compensation and widespread application.","PeriodicalId":426862,"journal":{"name":"Journal of Radiation Oncology Informatics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122685315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Martin Vogel, H. Fahrner, M. Gainey, M. Schmucker, S. Kirrmann, F. Heinemann
Background: For many years, the oncological doctor's letter has been the pivotal means of information transfer to general practitioners, medical specialists or medical consultants. Yet, both creator and recipient require a high level of abstraction, retentiveness and analysis due to the large number of diagnoses and therapies. In contrast to the commonly used structure of doctor's letters, where all diagnoses and therapies are listed in sequential order with all diagnoses first, it is by no means trivial to establish the important chronological and hierarchical context in the description of oncological cases. Additional aspects of importance are the integration of these letters into existing clinical and departmental information systems (for example via HL7 interface), various export formats (for example PDF, HTML), fax and encrypted email. Moreover these letters need a modern layout that, among others, meets the requirements of corporate design. Methods: The requirements for a doctor's letter system are manifold and can only be represented rudimentarily via a normal word processing system. Due to this deficiency we developed a system that covers all special features and requirements for clinical use. The system is based on a scalable and extensible client-server architecture. We use the programming languages Harbour, C++, PHP and JavaScript, Microsoft SQL database for data storage and the HL7 standard as the interface to other information systems such as hospital information system (HIS). Export formats are PDF, HTML/XML. Layouts are generated with TeX, LaTeX and MikTeX. Results: The aforementioned requirements were resolved with the doctor's letter and finding system IntDok. The hierarchical presentation of diagnoses, histologies and therapies provides the recipient with a first outline of the course of the disease. A strict procedure controls the whole process of document compilation and assists the user with many highly regarded tools such as text blocks, import and export (PDF and HTML/XML including barcodes) functions or HL7 interface to other information systems. The software also provides a sophisticated mail merging. All content from previous letters can easily be inserted into the current document. A TeX-server automatically provides document layout including supreme hyphenation so that uniform and perfect appearance (corporate design) is guaranteed. The documents are saved in a MS-SQL database (almost 230,000 documents since 1991), independent of any proprietary formats such as MS-Word. Conclusion: Creation of documents is fast, simple and well-structured. Sophisticated tools guarantee the optimal use of human resources and time. The system is an important module in our overall digital work environment.
{"title":"Procedural Creation of Medical Reports with Hierarchical Information Processing in Radiation Oncology","authors":"Martin Vogel, H. Fahrner, M. Gainey, M. Schmucker, S. Kirrmann, F. Heinemann","doi":"10.5166/jroi-10-1-2","DOIUrl":"https://doi.org/10.5166/jroi-10-1-2","url":null,"abstract":"Background: For many years, the oncological doctor's letter has been the pivotal means of information transfer to general practitioners, medical specialists or medical consultants. Yet, both creator and recipient require a high level of abstraction, retentiveness and analysis due to the large number of diagnoses and therapies. In contrast to the commonly used structure of doctor's letters, where all diagnoses and therapies are listed in sequential order with all diagnoses first, it is by no means trivial to establish the important chronological and hierarchical context in the description of oncological cases. Additional aspects of importance are the integration of these letters into existing clinical and departmental information systems (for example via HL7 interface), various export formats (for example PDF, HTML), fax and encrypted email. Moreover these letters need a modern layout that, among others, meets the requirements of corporate design. Methods: The requirements for a doctor's letter system are manifold and can only be represented rudimentarily via a normal word processing system. Due to this deficiency we developed a system that covers all special features and requirements for clinical use. The system is based on a scalable and extensible client-server architecture. We use the programming languages Harbour, C++, PHP and JavaScript, Microsoft SQL database for data storage and the HL7 standard as the interface to other information systems such as hospital information system (HIS). Export formats are PDF, HTML/XML. Layouts are generated with TeX, LaTeX and MikTeX. Results: The aforementioned requirements were resolved with the doctor's letter and finding system IntDok. The hierarchical presentation of diagnoses, histologies and therapies provides the recipient with a first outline of the course of the disease. A strict procedure controls the whole process of document compilation and assists the user with many highly regarded tools such as text blocks, import and export (PDF and HTML/XML including barcodes) functions or HL7 interface to other information systems. The software also provides a sophisticated mail merging. All content from previous letters can easily be inserted into the current document. A TeX-server automatically provides document layout including supreme hyphenation so that uniform and perfect appearance (corporate design) is guaranteed. The documents are saved in a MS-SQL database (almost 230,000 documents since 1991), independent of any proprietary formats such as MS-Word. Conclusion: Creation of documents is fast, simple and well-structured. Sophisticated tools guarantee the optimal use of human resources and time. The system is an important module in our overall digital work environment. \u0000","PeriodicalId":426862,"journal":{"name":"Journal of Radiation Oncology Informatics","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117318754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Fahrner, S. Kirrmann, M. Gainey, M. Schmucker, Martin Vogel, F. Heinemann
Background: In 2013, we presented a study entitled “Multimodal document management in radiotherapy”, demonstrating the excellent routine performance of the system about four years after its initiation by evaluating a sample of n=500 documents. During this time the system saw additional developments and significant improvements: the most important innovative step being the automatic document processing. This has been completely reworked, to minimize staff-machine interaction, to increase processing speed and to further simplify the overall document handling. This improved system has been running practically without any problems for several months. Methods: While reworking the automatic document processing, we have developed algorithms that allow us to transfer documents with varying type, within a single scanning procedure, into our departmental system. The system identifies and corrects for any arbitrary order or rotation of scanned pages. Finally, after the transfer into the departmental system, all documents are in the correct order and they are automatically linked to the respective patient record. Results: According to our surveys, the error rate of the system, as in the previous version, is 0%. Compared to manual scanning and mapping of documents, we can quantify a 30-fold increase in the processing speed. In spite of these additional and elaborate processes, code optimizations yielded a processing speed increase of 20%. Pre-sorting of the documents (e.g., medical reports, or documents of informed consents) can be completely dispensed with the automated correction for jumbled documents or document rotations. In this manner 25,000 documents are automatically processed each year in the Department of Radiation Oncology at the University of Freiburg. Conclusion: With the methods presented in this study, and some additional bug fixes, and small improvements, automatic document processing of our departmental system was significantly improved without compromising the error rate. Keywords: Clinic management, documents, workflow, optimisation, efficiency, automation, Mosaiq, oncology informatics
{"title":"Multimodal Document Management in Radiotherapy, an Update","authors":"H. Fahrner, S. Kirrmann, M. Gainey, M. Schmucker, Martin Vogel, F. Heinemann","doi":"10.5166/JROI-10-1-1","DOIUrl":"https://doi.org/10.5166/JROI-10-1-1","url":null,"abstract":"Background: In 2013, we presented a study entitled “Multimodal document management in radiotherapy”, demonstrating the excellent routine performance of the system about four years after its initiation by evaluating a sample of n=500 documents. During this time the system saw additional developments and significant improvements: the most important innovative step being the automatic document processing. This has been completely reworked, to minimize staff-machine interaction, to increase processing speed and to further simplify the overall document handling. This improved system has been running practically without any problems for several months. Methods: While reworking the automatic document processing, we have developed algorithms that allow us to transfer documents with varying type, within a single scanning procedure, into our departmental system. The system identifies and corrects for any arbitrary order or rotation of scanned pages. Finally, after the transfer into the departmental system, all documents are in the correct order and they are automatically linked to the respective patient record. Results: According to our surveys, the error rate of the system, as in the previous version, is 0%. Compared to manual scanning and mapping of documents, we can quantify a 30-fold increase in the processing speed. In spite of these additional and elaborate processes, code optimizations yielded a processing speed increase of 20%. Pre-sorting of the documents (e.g., medical reports, or documents of informed consents) can be completely dispensed with the automated correction for jumbled documents or document rotations. In this manner 25,000 documents are automatically processed each year in the Department of Radiation Oncology at the University of Freiburg. Conclusion: With the methods presented in this study, and some additional bug fixes, and small improvements, automatic document processing of our departmental system was significantly improved without compromising the error rate. \u0000Keywords: Clinic management, documents, workflow, optimisation, efficiency, automation, Mosaiq, oncology informatics \u0000 ","PeriodicalId":426862,"journal":{"name":"Journal of Radiation Oncology Informatics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121002592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Cihoric, I. Igrutinovic, A. Tsikkinis, E. Vlaskou Badra, P. Mackeprang
Clinical guidelines are general recommendations for practicing clinicians regarding prevention, diagnosis and treatment of a given disease. One of the most comprehensive and used guidelines are developed and regularly updated by the National Comprehensive Cancer Network (NCCN). Guidelines are readily available for download in portable document format (PDF). A machine-readable representation of NCCN guidelines is currently not available. In this writing, we argue on the necessity that clinical guidelines should be published in a machine-readable format. After review of the available literature, we describe the most important achievements in the field. Publication of guidelines in a machine-readable form may also be beneficial for other scientific and technical disciplines.
{"title":"A Step Forward in Cancer Informatics—It Is Mandatory to Make Guidelines Machine Readable","authors":"N. Cihoric, I. Igrutinovic, A. Tsikkinis, E. Vlaskou Badra, P. Mackeprang","doi":"10.5166/jroi-9-1-1","DOIUrl":"https://doi.org/10.5166/jroi-9-1-1","url":null,"abstract":"Clinical guidelines are general recommendations for practicing clinicians regarding prevention, diagnosis and treatment of a given disease. One of the most comprehensive and used guidelines are developed and regularly updated by the National Comprehensive Cancer Network (NCCN). Guidelines are readily available for download in portable document format (PDF). A machine-readable representation of NCCN guidelines is currently not available. In this writing, we argue on the necessity that clinical guidelines should be published in a machine-readable format. After review of the available literature, we describe the most important achievements in the field. Publication of guidelines in a machine-readable form may also be beneficial for other scientific and technical disciplines.","PeriodicalId":426862,"journal":{"name":"Journal of Radiation Oncology Informatics","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121090068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The standardising of nomenclature in the radiotherapy planning process has deep implications for the abilityof the profession to examine the adequacy of construction of radiotherapy plans in outcomes research, particularly in relation to disease control and toxicity generation. This paper proposes an interim standardisednomenclature which can be used by any institution as a template for a mappable local standard.The nomenclature is systematically constructed using the Foundational Model of Anatomy, ICRU Report 50 and ICRU report 62. The system foreshadows a XML metadata structure to detail the method of constructionof volumes. Treatment Planning System vendors should build their software with the ability to use this systematic construction technique so that contours and volumes in a radiotherapy plan can be annotated. Thismetadata will allow the investigation of how a radiation plan's construction can affect the therapy outcome.
{"title":"A Rational Informatics-enabled approach to Standardised Nomenclature of Contours and Volumes in Radiation Oncology Planning","authors":"Andrew Miller","doi":"10.5166/JROI-6-1-22","DOIUrl":"https://doi.org/10.5166/JROI-6-1-22","url":null,"abstract":"The standardising of nomenclature in the radiotherapy planning process has deep implications for the abilityof the profession to examine the adequacy of construction of radiotherapy plans in outcomes research, particularly in relation to disease control and toxicity generation. This paper proposes an interim standardisednomenclature which can be used by any institution as a template for a mappable local standard.The nomenclature is systematically constructed using the Foundational Model of Anatomy, ICRU Report 50 and ICRU report 62. The system foreshadows a XML metadata structure to detail the method of constructionof volumes. Treatment Planning System vendors should build their software with the ability to use this systematic construction technique so that contours and volumes in a radiotherapy plan can be annotated. Thismetadata will allow the investigation of how a radiation plan's construction can affect the therapy outcome.","PeriodicalId":426862,"journal":{"name":"Journal of Radiation Oncology Informatics","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117070912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joshua Pratt, V. Pandian, Evan D. Morrison, Andrew Miller
We have been unable to find a verified, published Radiation Oncology Ontology. We undertook the process of verifying a Radiation Oncology Ontology with a mixture of crowd-sourcing and expert-based approaches to verify relationships in the ontology. We used a natural language based approach to portray concepts and relationships, surveying users to assess the relationships between concepts in the Radiation Oncology ontology. The work used a description of a patient's history expressed in XML. The natural language statements relating concepts are available on a website for verification, and readers are invited to complete the survey at http://coi-hs-survey.appspot.com/ to contribute.
{"title":"Developing a tool for crowd-sourcing to Verify a Radiation Oncology Ontology: a Summer Project","authors":"Joshua Pratt, V. Pandian, Evan D. Morrison, Andrew Miller","doi":"10.5166/JROI-6-1-23","DOIUrl":"https://doi.org/10.5166/JROI-6-1-23","url":null,"abstract":"We have been unable to find a verified, published Radiation Oncology Ontology. We undertook the process of verifying a Radiation Oncology Ontology with a mixture of crowd-sourcing and expert-based approaches to verify relationships in the ontology. We used a natural language based approach to portray concepts and relationships, surveying users to assess the relationships between concepts in the Radiation Oncology ontology. The work used a description of a patient's history expressed in XML. The natural language statements relating concepts are available on a website for verification, and readers are invited to complete the survey at http://coi-hs-survey.appspot.com/ to contribute.","PeriodicalId":426862,"journal":{"name":"Journal of Radiation Oncology Informatics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128890640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Image segmentation and registration have been the two major areas of research in the medical imaging community for decades and still are. In the context of radiation oncology, segmentation and registration methods are widely used for target structure definition such as prostate or head and neck lymph node areas. In the past two years, 45% of all articles published in the most important medical imaging journals and conferences have presented either segmentation or registration methods. In the literature, both categories are treated rather separately even though they have much in common. Registration techniques are used to solve segmentation tasks (e.g. atlas based methods) and vice versa (e.g. segmentation of structures used in a landmark based registration). This article reviews the literature on image segmentation methods by introducing a novel taxonomy based on the amount of shape knowledge being incorporated in the segmentation process. Based on that, we argue that all global shape prior segmentation methods are identical to image registration methods and that such methods thus cannot be characterized as either image segmentation or registration methods. Therefore we propose a new class of methods that are able solve both segmentation and registration tasks. We call it regmentation. Quantified on a survey of the current state of the art medical imaging literature, it turns out that 25% of the methods are pure registration methods, 46% are pure segmentation methods and 29% are regmentation methods. The new view on image segmentation and registration provides a consistent taxonomy in this context and emphasizes the importance of regmentation in current medical image processing research and radiation oncology image-guided applications.
{"title":"Regmentation: A New View of Image Segmentation and Registration","authors":"Marius Erdt, S. Steger, G. Sakas","doi":"10.5166/JROI-4-1-19","DOIUrl":"https://doi.org/10.5166/JROI-4-1-19","url":null,"abstract":"Image segmentation and registration have been the two major areas of research in the medical imaging community for decades and still are. In the context of radiation oncology, segmentation and registration methods are widely used for target structure definition such as prostate or head and neck lymph node areas. In the past two years, 45% of all articles published in the most important medical imaging journals and conferences have presented either segmentation or registration methods. In the literature, both categories are treated rather separately even though they have much in common. Registration techniques are used to solve segmentation tasks (e.g. atlas based methods) and vice versa (e.g. segmentation of structures used in a landmark based registration). This article reviews the literature on image segmentation methods by introducing a novel taxonomy based on the amount of shape knowledge being incorporated in the segmentation process. Based on that, we argue that all global shape prior segmentation methods are identical to image registration methods and that such methods thus cannot be characterized as either image segmentation or registration methods. Therefore we propose a new class of methods that are able solve both segmentation and registration tasks. We call it regmentation. Quantified on a survey of the current state of the art medical imaging literature, it turns out that 25% of the methods are pure registration methods, 46% are pure segmentation methods and 29% are regmentation methods. The new view on image segmentation and registration provides a consistent taxonomy in this context and emphasizes the importance of regmentation in current medical image processing research and radiation oncology image-guided applications.","PeriodicalId":426862,"journal":{"name":"Journal of Radiation Oncology Informatics","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115442229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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