Journal of Radiation Oncology Informatics
JROI - Journal of Radiation Oncology Informatics Dealing with all aspects of Informatics in Radiation Oncology
Copyright remains with the author(s). Articles published until 2016 were published under CC BY-NC-ND 2.0.
Patient self-reported follow-up for radiation oncology patients during COVID-19: feasibility and patient-clinician agreement
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.
Copyright (c) 2023 Tiffany Sandell, Andrew Miller, Heike Schütze, Rowena Ivers, Vivega Vijayakumar, Lincoln Dinh http://creativecommons.org/licenses/by/4.0
Multilayer Perceptron Analysis of Radiomics to Predict Local Recurrence of Lung Cancer After Radiotherapy
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.
Copyright (c) 2022 Alli Jan, Andrew Miller, Peter Wright, Dale Glennan http://creativecommons.org/licenses/by/4.0
Radiomics in oncology - uncovering tumor phenotype from medical images: a short introduction
Radiomics is a promising method to quantify and describe the tumor phenotype on medical images. High numbers of image features are extracted from medical images and can be used within a clinical decision support system by integrating this data with clinical and pathological variables. Herein, we give a short introduction into this image analysis method and present an overview on the workflow.
Copyright (c) 2021 Matea Pavic, Janita E. van Timmeren http://creativecommons.org/licenses/by/4.0
Data-driven shared decision-making: a paradigm shift
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.
Copyright (c) 2021 Rianne Fijten, Leonard Wee, Andre Dekker, Cheryl Roumen http://creativecommons.org/licenses/by/4.0
Techniques and software tool for 3D multimodality medical image segmentation
The era of noninvasive diagnostic radiology and image-guided radiotherapy has witnessed burgeoning interest in applying different imaging modalities to stage and localize complex diseases such as atherosclerosis or cancer. It has been observed that using complementary information from multimodality images often significantly improves the robustness and accuracy of target volume definitions in radiotherapy treatment of cancer. In this work, we present techniques and an interactive software tool to support this new framework for 3D multimodality medical image segmentation. To demonstrate this methodology, we have designed and developed a dedicated open source software tool for multimodality image analysis MIASYS. The software tool aims to provide a needed solution for 3D image segmentation by integrating automatic algorithms, manual contouring methods, image preprocessing filters, post-processing procedures, user interactive features and evaluation metrics. The presented methods and the accompanying software tool have been successfully evaluated for different radiation therapy and diagnostic radiology applications.
Copyright (c) 2017 Deshan Yang, Jie Zheng, Ahmad Nofal, Joseph Deasy, Issam M. El Naqa
Automated contrast painting for position verification in radiotherapy
The influence of information technology in medicine has been constantly rising and represents a central part in many medical disciplines, especially radio-oncology. For the proper delivery of radiation treatment, the correct position of the patient is essential. To verify the correct position of the patient radiological images are made. In order to compare positions, contours of structures (often bones) may be used, these need to be identified and painted. The software that was provided with the linear accelerator contains a bitmap paint program, where these structures are painted manually. This manual painting of structures could be replaced by automated algorithms. However, amendments, innovations or customization of the original software are costly and difficult to achieve due to copyright, license and certification issues. The concept described here aims to get around these issues by creating an automated algorithm on the user level, with no interference of the underlying original software. This system uses the Java platform; with the help of the Java Robot class user input can be simulated. The developed tool proved to be time-saving, functional and the development could easily be accomplished and individually tailored to users needs.
Copyright (c) 2017 Paul-Martin Putora, Ludwig Plasswilm, Ludovit Paulis
A Graphical Tool and Methods for Assessing Margin Definition From Daily Image Deformations
Estimating the proper margins for the planning target volume (PTV) could be a challenging task in cases where the organ undergoes significant changes during the course of radiotherapy treatment. Developments in image-guidance and the presence of onboard imaging technologies facilitate the process of correcting setup errors. However, estimation of errors to organ motions remain an open question due to the lack of proper software tools to accompany these imaging technological advances. Therefore, we have developed a new tool for visualization and quantification of deformations from daily images. The tool allows for estimation of tumor coverage and normal tissue exposure as a function of selected margin (isotropic or anisotropic). Moreover, the software allows estimation of the optimal margin based on the probability of an organ being present at a particular location. Methods based on swarm intelligence, specifically Ant Colony Optimization (ACO) are used to provide an efficient estimate of the optimal margin extent in each direction. ACO can provide global optimal solutions in highly nonlinear problems such as margin estimation. The proposed method is demonstrated using cases from gastric lymphoma with daily TomoTherapy megavoltage CT (MVCT) contours. Preliminary results using Dice similarity index are promising and it is expected that the proposed tool will be very helpful and have significant impact for guiding future margin definition protocols.
Copyright (c) 2017 Aditya P. Apte, Rawan Al-Lozi, Gisele Pereira, Matthew Johnson, David Mansur, Issam M. El Naqa
Dodes (diagnostic nodes) for Guideline Manipulation
Background: Treatment recommendations (guidelines) are commonly represented in text form. Based on parameters (questions) recommendations are defined (answers). Objectives: To improve handling, alternative forms of representation are required. Methods: The concept of Dodes (diagnostic nodes) has been developed. Dodes contain answers and questions. Dodes are based on linked nodes and additionally contain descriptive information and recommendations. Dodes are organized hierarchically into Dode trees. Dode categories must be defined to prevent redundancy. Results: A centralized and neutral Dode database can provide standardization, which is a requirement for the comparison of recommendations. Centralized administration of Dode categories can provide information about diagnostic criteria (Dode categories) underutilized in existing recommendations (Dode trees). Conclusions: Representing clinical recommendations in Dode trees improves their manageability, handling and updateability.
Copyright (c) 2017 Paul-Martin Putora, Marcel Blattner, Alexander Papachristofilou, Fabio Mariotti, Beatrice Paoli, Ludwig Plasswilm
Towards Prediction of Radiation Pneumonitis Arising from Lung Cancer Patients Using Machine Learning Approaches
Radiation pneumonitis (RP) is a potentially fatal side effect arising in lung cancer patients who receive radiotherapy as part of their treatment. For the modeling of RP outcomes data, several predictive models based on traditional statistical methods and machine learning techniques have been reported. However, no guidance to variation in performance has been provided to date. In this study, we explore several machine learning algorithms for classification of RP data. The performance of these classification algorithms is investigated in conjunction with several feature selection strategies and the impact of the feature selection strategy on performance is further evaluated. The extracted features include patients demographic, clinical and pathological variables, treatment techniques, and dose-volume metrics. In conjunction, we have been developing an in-house Matlab-based open source software tool, called DREES, customized for modeling and exploring dose response in radiation oncology. This software has been upgraded with a popular classification algorithm called support vector machine (SVM), which seems to provide improved performance in our exploration analysis and has strong potential to strengthen the ability of radiotherapy modelers in analyzing radiotherapy outcomes data.
Copyright (c) 2017 Jung Hun Oh, Aditya Apte, Rawan Al-Lozi, Jeffrey Bradley, Issam M. El Naqa
Pilot Study of a Radiation Oncology Telemedicine Platform
Purpose: A pilot study was undertaken to develop an integrated telemedicine platform for radiation oncology at Memorial Sloan-Kettering Cancer Center (MSKCC) and its regional sites. The platform consisted of a computer system with simultaneous display of multiple live data portals including 1) video-conferencing between physicians, 2) radiology, and 3) radiation treatment-planning system (RTPS). Methods and Materials: Two MSKCC regional centers were set up with a widescreen monitor, a dedicated computer, and a web camera with microphone. Each computer ran a Microsoft operating system, utilized video-conferencing software, and connected to the MSKCC Ethernet. This allowed for access to the health information system, radiology (web-based picture archiving and communication systems), RTPS, shared network drives and the internet. Results: After 3 months, physicians at two MSKCC sites were successfully able to implement the proposed telemedicine platform. A small sample of cases (prostate, breast, head and neck, and anal cases) were tested. Radiology images, radiation treatment volumes and plans, and portal images were reviewed. Side-by-side comparison of contouring techniques was performed. The platform allowed physicians to remotely review details of cases efficiently. The interactions of the telemedicine platform improved clinical understanding of each case and often resulted in contouring changes. Conclusion: From this experience, we feel that telemedicine could have a significant clinical impact on patient care, especially at centers with satellite clinics. The future goal of the system will be the development of a virtual tumor board for radiation oncologists. We envision the simultaneous display of multiple clinical components, including face photo, pathology, tumor images/videos of procedures, radiology, RTPS, and anatomy/contouring databases, on one screen surface.
Copyright (c) 2017 Boris A. Mueller, Ceferino Obcemea, Ceferino Obcemea, James Lee, Sang Sim
Regmentation: A New View of Image Segmentation and Registration
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.
Copyright (c) 2017 Marius Erdt, Sebastian Steger, Georgios Sakas
Determination of the source dwell position of an afterloading device with a detector array
It was the aim to develop and test a measurement technique for the source position of an afterloading system using an electronic two-dimensional detector array (2D array). A "GammaMed plus IX" high dose rate afterloading device (Varian Medical Systems, Palo Alto, USA), and a Seven29 2D detector array (PTW Freiburg GmbH, Freiburg, Germany) have been used. A hollow needle has been connected to the afterloading device. Its outer diameter is 3.0 mm and its length about 220mm. A 14 mm thick Perspex slab was fixed in a reproducible position on the 2D array. Above the 14th detector row, a groove was cut in the slab which accommodates the hollow needle in a fixed position relative to the 2D array. In order to define the position of the source, the signals of the detectors in the 14th detector row have been evaluated. A theoretical curve depending on the source position and strength, has been fitted to the acquired detector signals. It has been shown that the reproducibility of the measurement technique - including the reproducibility of the source placement - was within 0.15mm. This method can not only replace film measurements, it is also more exact and less time consuming.
Copyright (c) 2017 Ferdinand Breithuth, Johann Schiefer, Markus Arn, Samuel Peters, Wolf Seelentag
A Radiation Oncology Based Electronic Health Record in an Integrated Radiation Oncology Network
Purpose: The goal of this ongoing project is to develop and integrate a comprehensive electronic health record (EHR) throughout a multi-facility radiation oncology network to facilitate more efficient workflow and improve overall patient care and safety. Methodology: We required that the EHR provide pre-defined record and verify capability for radiation treatment while still providing a robust clinical health record. In 1996, we began to integrate the Local Area Network Treatment Information System (LANTIS®) across the West Penn Allegheny Radiation Oncology Network (currently including 9 sites). By 2001, we began modifying and expanding the assessment components and creating user-defined templates and have developed a comprehensive electronic health record across our network. Results: In addition to access to the technical record and verify information and imaging obtained for image-guided therapy, we designed and customized 6 modules according to our networks needs to facilitate information acquisition, tracking, and analysis as follows: 1) Demographics/scheduling; 2) Charge codes; 3) Transcription/clinical documents; 4) Clinical/technical assessments; 5) Physician orders 6) Quality assurance pathways. Each module was developed to acquire specific technical/clinical data prospectively in an efficient manner by various staff within the department in a format that facilitates data queries for outcomes/statistical analyses and promotes standardized quality guidelines resulting in a more efficient workflow and improved patient safety and care. Conclusions: Development of a comprehensive EHR across a radiation oncology network is feasible and can be customized to promote clinical/technical standards, facilitate outcomes studies, and improve communication and peer review. The EHR has improved patient care and network integration across a multi-facility radiation oncology system and has markedly reduced the flow and storage of paper across the network.
Copyright (c) 2017 Athanasios Colonias, David S. Parda, Stephen M. Karlovits, Russell Fuhrer, Mark G. Trombetta, Staci Strickland, Marc Luick, Richard Billy, T. Horrigan, E. Day Werts
RadOnc: An R Package for Analysis of Dose-Volume Histogram and Three-Dimensional Structural Data
Purpose/Objectives: Dose volume histogram (DVH) data are generally analyzed within the context of a treatment planning system (TPS) on a per-patient basis, with evaluation of single-plan or comparative dose distributions. However, TPS software generally cannot perform simultaneous comparative dosimetry among a cohort of patients. The same limitations apply to parallel analyses of three-dimensional structures and other clinical data. Materials/Methods: We developed a suite of tools ("RadOnc" package) using R statistical software to better compare pooled DVH data and empower analysis of structure data and clinical correlates. Representative patient data were identified among previously analyzed adult (n=13) and pediatric (n=1) cohorts and these data were used to demonstrate the performance and functionality of the RadOnc package. Results: The RadOnc package facilitates DVH data import from the TPS and includes automated methods for DVH visualization, dosimetric parameter extraction, statistical comparison among multiple DVHs, basic three-dimensional structural processing, and visualization tools to enable customizable production of publication-quality images. Conclusions: The RadOnc package provides a potent clinical research tool with the ability to integrate robust statistical software and dosimetric data from cohorts of patients. It is made freely available to the community for their current use and remains under active development.
Relational database of treatment planning system information
The purpose of the present work was to develop a relational database and associated applications to facilitate retrospective review of data present in radiation treatment plans. The data source was a commercial radiation treatment planning system (Pinnacle3, Philips Medical Systems, Milpitas CA), which is specifically characterized by an open data storage format and internal scripting capability. The database is an open-source, relational database (PostgreSQL, PostgreSQL Global Development Group, http://www.postgresql.org). The data is presented through a web interface in addition to being fully query-accessible using standard tools. A database schema was created to organize the large collection of parameters used to generate treatment plans as well as the parameters that characterized these plans. The system was implemented through a combination of the treatment planning systems internal scripting language and externally executed code. Data is exported in a way that is transparent to the user, through integration into an existing and routinely-used process. The system has been transparently incorporated into our radiation treatment planning workflow. The website-based database interface has allowed users with minimal training to extract information from the database.
Copyright (c) 2017 Michael Eugene Kantor, George Starkschall, Peter Balter
Operations Research Methods for Optimization in Radiation Oncology
Operations Research has a successful tradition of applying mathematical analysis to a wide range of applications, and problems in Medical Physics have been popular over the last couple of decades. The original application was in the optimal design of the uence map for a radiotherapy treatment, a problem that has continued to receive attention. However, Operations Research has been applied to other clinical problems like patient scheduling, vault design, and image alignment. The overriding theme of this article is to present how techniques in Operations Research apply to clinical problems, which we accomplish in three parts. First, we present the perspective from which an operations researcher addresses a clinical problem. Second, we succinctly introduce the underlying methods that are used to optimize a system, and third, we demonstrate how modern software facilitates problem design. Our discussion is supported by several publications to foster continued study. With numerous clinical, medical, and managerial decisions associated with a clinic, operations research has a promising future at improving how radiotherapy treatments are designed and delivered.
Copyright (c) 2017 Matthias Ehrgott, Allen Holder
A Rational Informatics-enabled approach to Standardised Nomenclature of Contours and Volumes in Radiation Oncology Planning
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.
Developing a tool for crowd-sourcing to Verify a Radiation Oncology Ontology: a Summer Project
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.
Copyright (c) 2017 Eleanor Pratt, Vishal Pandian, Evan Morrison, Alexis Andrew Miller http://creativecommons.org/licenses/by/4.0
PROsaiq: A Smart Device-Based and EMR-Integrated System for Patient-Reported Outcome Measurement in Routine Cancer Care
The PROsaiq prototype, which is based on the use of smart devices, was developed to show the technical feasibility of a lean, low-cost ePRO system that integrated with the oncology information system MOSAIQ to provide the potential for benefits in routine patient care, and improved data for clinical research. The system was built with Free & Open Source Software and trialled for a limited number of assessments. The report describes the components used, the decisions made and the hurdles met during the project. An on-line demonstration system is available to showcase PROsaiqs functionality.
Copyright (c) 2017 Thilo Schuler, Alexis Andrew Miller
Multimodal Document Management in Radiotherapy, an Update
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
Copyright (c) 2019 Felix Ernst Heinemann, Harald Fahrner, Stefan Kirrmann, Mark Gainey, Marianne Schmucker, Martin Vogel http://creativecommons.org/licenses/by/4.0
The Informatics of the Planning Target Volume -- why it cannot and should not be changed
The ICRU defined the Planning Target Volume (PTV) as a static and geometrical volume in 1993. Radiation oncologists continue to manually alter PTVs in their daily practice when critical organs at risk (OAR) are too close to high dose PTVs. This practice is examined and shown to be non-standard (defies the ICRU definitions), inaccurate (all DVHs look perfect when the plan is manifestly NOT perfect), and useless for outcomes research (automatically analysed DVHs will find situations where the PTV_unaltered overlaps the OAR_unaltered, but will fail to find situations where an OAR_unaltered would be overlapped by a PTV_unaltered, but is not overlapped by a PTV_altered.
Copyright (c) 2017 Alexis Andrew Miller, Scott Turner
Fully Automatic Danger Zone Determination for SBRT in NSCLC
Lung cancer is the major cause of cancer death worldwide. The most common form of lung cancer is non-small cell lung cancer(NSCLC). Stereotactic body radiation therapy (SBRT) has emerged as a good alternative to surgery in patients with peripheralstage I NSCLC, demonstrating favorable tumor control and low toxicity. Due to spatial relationship to several critical organs atrisk, SBRT of centrally located lesions is associated with more severe toxicity and requires modification in dose application andfractionation, which is currently evaluated in clinical trials. Therefore a classification of lung tumors into central or peripheralis required. In this work we present a novel, highly versatile, mulitmodality tool for tumor classification which requires no userinteraction. Furthermore the tool can automatically segment the trachea, proximal bronchial tree, mediastinum, gross target volumeand internal target volume. The proposed work is evaluated on 19 cases with different image modalities assessing segmentationquality as well as classification accuracy. Experiments showed a good segmentation quality and a classification accuracy of 95 %.These results suggest the use of the proposed tool for clinical trials to assist clinicians in their work and to fasten up the workflowin NSCLC patients treatment.
Copyright (c) 2017 Tobias Fechter, Jose Dolz, Alin Chirindel, Matthias Schlachter, Montserrat Carles, Sonja Adebahr, Michael Mix, Ursula Nestle
A Step Forward in Cancer Informatics—It Is Mandatory to Make Guidelines Machine Readable
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.
Copyright (c) 2018 Nikola Cihoric, Ivan Igrutinovic, Alexandros Tsikkinis, Eugenia Vlaskou Badra, Paul-Henry Mackeprang
Procedural Creation of Medical Reports with Hierarchical Information Processing in Radiation Oncology
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. 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Copyright (c) 2019 Martin Vogel, Harald Fahrner, Mark Gainey, Marianne Schmucker, Stefan Kirrmann, Felix Ernst Heinemann
How to PROceed? Reviewing obstacles and perspectives in patient-centered digital care in radiation oncology
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.
Copyright (c) 2021 Alexander Marc Christian Böhner, Leonard Christopher Schmeel, Feisst Andreas, Scafa Davide, Sarria Gustavo R., Adriana Torres Crigna, David Koch, Stephan Garbe, Barbara Link, Dilini Brüser, Frank A. Giordano http://creativecommons.org/licenses/by/4.0