Development of a patient-reported questionnaire for collecting toxicity data following prostate brachytherapy

Farnell, Damian J. J.; Mandall, Paula; Anandadas, C.; Routledge, Jacqueline A; Burns, Meriel P; Logue, John P; Wylie, James; Swindell, Ric; Livsey, Jacqueline E; West, Catharine M L; Davidson, Susan E

doi:10.1016/j.radonc.2010.05.011

Cited by 27 publications

(26 citation statements)

References 17 publications

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“…The questionnaires were revised to collect CTCAEv3 data, which incorporated LENT items, for patients with female [14] and male [15] pelvis cancers and produced for head and neck cancer [16]. The questionnaires were validated to check for reliability, compliance and acceptability and were freely available [17].…”

Section: Questionnaires For Collecting Patientreported Quality Of Lifmentioning

confidence: 99%

The REQUITE Project: Validating Predictive Models and Biomarkers of Radiotherapy Toxicity to Reduce Side-effects and Improve Quality of Life in Cancer Survivors

et al. 2014

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The worldwide number of cancer survivors within 5 years of diagnosis was estimated to be about 28.7 million in 2008 [1]. Many must cope with the long-term effects of treatment, which can affect health-related quality of life [2,3]. These issues need to be addressed in a systematic manner in order to enhance the participation of cancer survivors in society and reduce healthcare costs. Sideeffects and quality of life in long-term survivors after radiotherapy are not well researched and the data are rarely collected in a systematic manner in routine follow-up. Also, studies investigating side-effects can be difficult to compare because of the use of different toxicity reporting systems and a lack of pretreatment data, which is important to ensure that side-effects are related to radiotherapy rather than other underlying health problems [4]. Predictive models of radiotherapy toxicity require cross-centre validation for widespread clinical implementation [5]. Genetics is important, but no biomarker has been validated for clinical use. Predictive model research has the potential to not only improve quality of life, but also to increase survival, for example by using isotoxic strategies [6], and to decrease healthcare costs with a more rational use of expensive medical technology such as proton therapy [7]. The REQUITE ProjectREQUITE is funded by the European Union through its Seventh Framework Programme. The objectives of the project are to: (i) carry out a multicentre, cohort study collecting blood samples, epidemiology and treatment data, longitudinal side-effect and quality of life data (before and after treatment, years 1 and 2); (ii) produce a centralised database and biobank of DNA for 5300 patients; (iii) validate published biomarkers of radiosensitivity; (iv) validate clinical predictors of radiotherapy toxicity and incorporate biomarker data; (v) design interventional trials to reduce long-term side-effects; (vi) provide a resource for dissemination and exploitation to the radiotherapy community. The project focuses on cancers of the breast, lung and prostate.

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Section: Questionnaires For Collecting Patientreported Quality Of Lifmentioning

confidence: 99%

The REQUITE Project: Validating Predictive Models and Biomarkers of Radiotherapy Toxicity to Reduce Side-effects and Improve Quality of Life in Cancer Survivors

et al. 2014

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“…The suitability of applying FA to these datasets was evaluated using the Kaiser-Meyer-Olkin (KMO) test and the internal consistency of symptoms was assessed using Cronbach’s alpha with acceptable values ≥ 0.60 (5) and ≥ 0.70 (1), respectively. Symptoms with low prevalence in any dataset were excluded from analysis to prevent spurious results (26).…”

Section: Methodsmentioning

confidence: 99%

“…Patient reported outcomes (PROs) are becoming more common as a means of understanding the details of patient experience following treatment (1–3). In particular, in the field of radiation oncology, extensive questionnaires to measure side effects after radiotherapy (RT) in cancer patients from PROs have been used to supplement objective assessments by healthcare professionals.…”

Section: Introductionmentioning

confidence: 99%

“…In FA, the variance of a factor is represented by an eigenvalue that is computed as the sum of its squared factor loadings for all the items. FA has previously been deemed useful to examine the relationship amongst different adverse effects after RT for various cancers (1, 5–8). In these studies, FA was typically applied to a limited number of items related to treatment characteristics (1, 7, 9), quality of life (6, 8, 9), or symptoms reflecting specific RT-induced injuries (1, 5–7).…”

Section: Introductionmentioning

confidence: 99%

“…FA has previously been deemed useful to examine the relationship amongst different adverse effects after RT for various cancers (1, 5–8). In these studies, FA was typically applied to a limited number of items related to treatment characteristics (1, 7, 9), quality of life (6, 8, 9), or symptoms reflecting specific RT-induced injuries (1, 5–7). FA is likely to be even more useful as the dimensionality of the data increases such as in questionnaires using a clinicmetric atomized approach to document patient-reported outcomes, in which some of the included symptoms are also assumed to be related (10–12).…”

Section: Introductionmentioning

confidence: 99%

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A Factor Analysis Approach for Clustering Patient Reported Outcomes

Thor²,

Olsson³

et al. 2016

Methods Inf Med

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Summary Background In the field of radiation oncology, the use of extensive patient reported outcomes is increasingly common to measure adverse side effects after radiotherapy in cancer patients. Factor analysis has the potential to identify an optimal number of latent factors (i.e., symptom groups). However, the ultimate goal of treatment response modeling is to understand the relationship between treatment variables such as radiation dose and symptom groups resulting from FA. Hence, it is crucial to identify clinically more relevant symptom groups and improved response variables from those symptom groups for a quantitative analysis. Objective The goal of this study is to design a computational method for finding clinically relevant symptom groups from PROs and to test associations between symptom groups and radiation dose. Methods We propose a novel approach where exploratory factor analysis is followed by confirmatory factor analysis to determine the relevant number of symptom groups. We also propose to use a combination of symptoms in a symptom group identified as a new response variable in linear regression analysis to investigate the relationship between the symptom group and dose-volume variables. Results We analyzed patient-reported gastrointestinal symptom profiles from 3 datasets in prostate cancer patients treated with radiotherapy. The final structural model of each dataset was validated using the other two datasets and compared to four other existing FA methods. Our systematic EFA-CFA approach provided clinically more relevant solutions than other methods, resulting in new clinically relevant outcome variables that enabled a quantitative analysis. As a result, statistically significant correlations were found between some dose-volume variables to relevant anatomic structures and symptom groups identified by FA. Conclusion Our proposed method can aid in the process of understanding PROs and provide a basis for improving our understanding of radiation-induced side effects.

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Conventional Machine Learning Methods

Lee

Naqa

2022

Machine and Deep Learning in Oncology, Medical Physics and Radiology

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Development of a patient-reported questionnaire for collecting toxicity data following prostate brachytherapy

Cited by 27 publications

References 17 publications

The REQUITE Project: Validating Predictive Models and Biomarkers of Radiotherapy Toxicity to Reduce Side-effects and Improve Quality of Life in Cancer Survivors

The REQUITE Project: Validating Predictive Models and Biomarkers of Radiotherapy Toxicity to Reduce Side-effects and Improve Quality of Life in Cancer Survivors

A Factor Analysis Approach for Clustering Patient Reported Outcomes

Conventional Machine Learning Methods

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