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Heilemann G, Georg D, Dobiasch M, Widder J, Renner A. Automation of ePROMs in radiation oncology and its impact on patient response and bias. Radiother Oncol 2024; 199:110427. [PMID: 39002570 DOI: 10.1016/j.radonc.2024.110427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/10/2024] [Accepted: 07/04/2024] [Indexed: 07/15/2024]
Abstract
PURPOSE This study evaluates the impact of integrating a novel, in-house developed electronic Patient-Reported Outcome Measures (ePROMs) tool with a commercial Oncology Information System (OIS) on patient response rates and potential biases in real-world data science applications. MATERIALS AND METHODS We designed an ePROMs tool using the NodeJS web application framework, automatically sending e-mail questionnaires to patients based on their treatment schedules in the OIS. The tool is used across various treatment sites to collect PROMs data in a real-world setting. This research examined the effects of increasing automation levels on both recruitment and response rates, as well as potential biases across different patient cohorts. Automation was implemented in three escalating levels, from telephone reminders for missing reports to minimal intervention from study nurses. RESULTS From August 2020 to December 2023, 1,944 patients participated in the PROMs study. Our findings indicate that automating the workflows substantially reduced the patient management workload. However, higher levels of automation led to lower response rates, particularly in collecting late-phase symptoms in breast and head-and-neck cancer cohorts. Additionally, email-based PROMs introduced an age bias when recruiting new patients for the ePROMs study. Nevertheless, age was not a significant predictor of early dropout or missing symptom reports among patients participating. Notably, increased automation was significantly correlated with lower response rates in breast (p = 0.026) and head-and-neck cancer patients (p < 0.001). CONCLUSION Integrating ePROMs within the OIS can significantly reduce workload and personnel resources. However, this efficiency may compromise patient responses in certain groups. A balance must be achieved between workload, resource allocation, and the sensitivity needed to detect clinically significant effects. This may necessitate customized automation levels tailored to specific cancer groups, highlighting a fundamental trade-off between operational efficiency and data quality.
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Affiliation(s)
- G Heilemann
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria; Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University Vienna, Vienna, Austria.
| | - D Georg
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria; Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University Vienna, Vienna, Austria
| | - M Dobiasch
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria
| | - J Widder
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria; Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University Vienna, Vienna, Austria
| | - A Renner
- Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University Vienna, Vienna, Austria; Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University Vienna, Vienna, Austria
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Hessels AC, Visser S, Both S, Korevaar EW, Langendijk JA, Wijsman R. A planning study of proton therapy dose escalation for non-small cell lung cancer. Phys Imaging Radiat Oncol 2024; 31:100616. [PMID: 39157295 PMCID: PMC11327929 DOI: 10.1016/j.phro.2024.100616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 07/19/2024] [Accepted: 07/24/2024] [Indexed: 08/20/2024] Open
Abstract
In non-small-cell lung cancer (NSCLC), improving local control through radiotherapy dose escalation might improve survival. However, a photon-based RCT showed increased organ at risk dose exposure and worse overall survival in the dose escalation arm. In this study, intensity-modulated proton therapy plans with dose escalation to the primary tumour were created for 20 NSCLC patients. The mediastinal envelope was delineated to spare structures around the heart. It was possible to increase primary tumour dose up to 74.0 Gy without a significant increase in organ at risk doses and predicted toxicity.
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Affiliation(s)
- Arno C Hessels
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Sabine Visser
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Stefan Both
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Erik W Korevaar
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Robin Wijsman
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Niezink AGH, van der Schaaf A, Wijsman R, Chouvalova O, van der Wekken AJ, Rutgers SR, Pieterman RM, van Putten JWG, de Hosson SM, van der Leest AHD, Ubbels JF, Woltman-van Iersel M, Widder J, Langendijk JA, Muijs CT. External validation of NTCP-models for radiation pneumonitis in lung cancer patients treated with chemoradiotherapy. Radiother Oncol 2023; 186:109735. [PMID: 37327975 DOI: 10.1016/j.radonc.2023.109735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/16/2023] [Accepted: 06/02/2023] [Indexed: 06/18/2023]
Abstract
PURPOSE Normal tissue complication probability (NTCP) models can be used to estimate the risk of radiation pneumonitis (RP). The aim of this study was to externally validate the most frequently used prediction models for RP, i.e., the QUANTEC and APPELT models, in a large cohort of lung cancer patients treated with IMRT or VMAT. [1-2] METHODS AND MATERIALS: This prospective cohort study, included lung cancer patients treated between 2013 and 2018. A closed testing procedure was performed to test the need for model updating. To improve model performance, modification or removal of variables was considered. Performance measures included tests for goodness of fit, discrimination, and calibration. RESULTS In this cohort of 612 patients, the incidence of RP ≥ grade 2 was 14.5%. For the QUANTEC-model, recalibration was recommended which resulted in a revised intercept and adjusted regression coefficient (from 0.126 to 0.224) of the mean lung dose (MLD),. The APPELT-model needed revision including model updating with modification and elimination of variables. After revision, the New RP-model included the following predictors (and regression coefficients): MLD (B = 0.250), age (B = 0.049, and smoking status (B = 0.902). The discrimination of the updated APPELT-model was higher compared to the recalibrated QUANTEC-model (AUC: 0.79 vs. 0.73). CONCLUSIONS This study demonstrated that both the QUANTEC- and APPELT-model needed revision. Next to changes of the intercept and regression coefficients, the APPELT model improved further by model updating and performed better than the recalibrated QUANTEC model. This New RP-model is widely applicable containing non-tumour site specific variables, which can easily be collected.
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Affiliation(s)
- Anne G H Niezink
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
| | - Arjen van der Schaaf
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Robin Wijsman
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Olga Chouvalova
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Anthonie J van der Wekken
- Department of Pulmonary Diseases, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Steven R Rutgers
- Department of Pulmonology, Treant Hospital Group, Scheper Hospital, Emmen, the Netherlands
| | - Remge M Pieterman
- Department of Pulmonary Diseases, Ommelander Hospital Groningen, Scheemda, the Netherlands
| | - John W G van Putten
- Department of Pulmonary Diseases, Martini Hospital Groningen, Groningen, the Netherlands
| | - Sander M de Hosson
- Department of Pulmonary Diseases, Wilhelmina Hospital Assen, Assen, the Netherlands
| | - Annija H D van der Leest
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jan F Ubbels
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Marleen Woltman-van Iersel
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Joachim Widder
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Radiation Oncology, Comprehensive Cancer Center Vienna, Medical University of Vienna, Austria
| | - Johannes A Langendijk
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Christina T Muijs
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
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Widder J, van der Schaaf A, Lambin P, Marijnen CAM, Pignol JP, Rasch CR, Slotman BJ, Verheij M, Langendijk JA. The Quest for Evidence for Proton Therapy: Model-Based Approach and Precision Medicine. Int J Radiat Oncol Biol Phys 2015; 95:30-36. [PMID: 26684410 DOI: 10.1016/j.ijrobp.2015.10.004] [Citation(s) in RCA: 98] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Accepted: 10/01/2015] [Indexed: 02/07/2023]
Abstract
PURPOSE Reducing dose to normal tissues is the advantage of protons versus photons. We aimed to describe a method for translating this reduction into a clinically relevant benefit. METHODS AND MATERIALS Dutch scientific and health care governance bodies have recently issued landmark reports regarding generation of relevant evidence for new technologies in health care including proton therapy. An approach based on normal tissue complication probability (NTCP) models has been adopted to select patients who are most likely to experience fewer (serious) adverse events achievable by state-of-the-art proton treatment. RESULTS By analogy with biologically targeted therapies, the technology needs to be tested in enriched cohorts of patients exhibiting the decisive predictive marker: difference in normal tissue dosimetric signatures between proton and photon treatment plans. Expected clinical benefit is then estimated by virtue of multifactorial NTCP models. In this sense, high-tech radiation therapy falls under precision medicine. As a consequence, randomizing nonenriched populations between photons and protons is predictably inefficient and likely to produce confusing results. CONCLUSIONS Validating NTCP models in appropriately composed cohorts treated with protons should be the primary research agenda leading to urgently needed evidence for proton therapy.
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Affiliation(s)
- Joachim Widder
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
| | - Arjen van der Schaaf
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology, School for Oncology and Developmental Biology (GROW), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Corrie A M Marijnen
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jean-Philippe Pignol
- Department of Radiation Oncology, Erasmus Medical Center Cancer Institute, Rotterdam, The Netherlands
| | - Coen R Rasch
- Department of Radiation Oncology, Academic Medical Center, Amsterdam, The Netherlands
| | - Ben J Slotman
- Department of Radiation Oncology, VU Medical Center, Amsterdam, The Netherlands
| | - Marcel Verheij
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Wijsman R, Dankers F, Troost EGC, Hoffmann AL, van der Heijden EHFM, de Geus-Oei LF, Bussink J. Multivariable normal-tissue complication modeling of acute esophageal toxicity in advanced stage non-small cell lung cancer patients treated with intensity-modulated (chemo-)radiotherapy. Radiother Oncol 2015; 117:49-54. [PMID: 26341608 DOI: 10.1016/j.radonc.2015.08.010] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Revised: 08/10/2015] [Accepted: 08/11/2015] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE The majority of normal-tissue complication probability (NTCP) models for acute esophageal toxicity (AET) in advanced stage non-small cell lung cancer (AS-NSCLC) patients treated with (chemo-)radiotherapy are based on three-dimensional conformal radiotherapy (3D-CRT). Due to distinct dosimetric characteristics of intensity-modulated radiation therapy (IMRT), 3D-CRT based models need revision. We established a multivariable NTCP model for AET in 149 AS-NSCLC patients undergoing IMRT. MATERIALS AND METHODS An established model selection procedure was used to develop an NTCP model for Grade ⩾2 AET (53 patients) including clinical and esophageal dose-volume histogram parameters. RESULTS The NTCP model predicted an increased risk of Grade ⩾2 AET in case of: concurrent chemoradiotherapy (CCR) [adjusted odds ratio (OR) 14.08, 95% confidence interval (CI) 4.70-42.19; p<0.001], increasing mean esophageal dose [Dmean; OR 1.12 per Gy increase, 95% CI 1.06-1.19; p<0.001], female patients (OR 3.33, 95% CI 1.36-8.17; p=0.008), and ⩾cT3 (OR 2.7, 95% CI 1.12-6.50; p=0.026). The AUC was 0.82 and the model showed good calibration. CONCLUSIONS A multivariable NTCP model including CCR, Dmean, clinical tumor stage and gender predicts Grade ⩾2 AET after IMRT for AS-NSCLC. Prior to clinical introduction, the model needs validation in an independent patient cohort.
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Affiliation(s)
- Robin Wijsman
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Frank Dankers
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Esther G C Troost
- Institute of Radiooncology, Helmholtz-Zentrum Dresden-Rossendorf, Germany; Department of Radiotherapy and Radiooncology, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Germany; OncoRay, National Center for Radiation Research in Oncology, Dresden, Germany
| | - Aswin L Hoffmann
- Institute of Radiooncology, Helmholtz-Zentrum Dresden-Rossendorf, Germany; Department of Radiotherapy and Radiooncology, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Germany
| | | | - Lioe-Fee de Geus-Oei
- Department of Radiology, Leiden University Medical Center, The Netherlands; Biomedical Photonic Imaging Group, MIRA Institute, University of Twente, Enschede, The Netherlands
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
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