Systematic Review and Meta-analysis of the Association Between Radiation Therapy Treatment Volume and Patient Outcomes

Kyaw, Jerry Ye Aung; Rendall, Alice; Gillespie, Erin F.; Roques, Tom; Court, Laurence Edward; Lievens, Yolande; Tree, A.; Frampton, Chris; Aggarwal, Ajay

doi:10.1016/j.ijrobp.2023.02.048

Cited by 10 publications

(6 citation statements)

References 60 publications

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“…Finally, our study did not show that treatment of a particular disease site was substantially associated with superior segmentation quality; in fact, it often demonstrated a negative correlation. This seemingly challenges previous findings highlighting the significant role of clinician experience in treatment quality 4–6 . However, the variable did not assess treatment frequency for the specific site, thereby potentially introducing heterogeneity in its interpretation and ultimately diminishing its utility.…”

Section: Discussioncontrasting

confidence: 88%

“…Moreover, the C3RO intake survey — from which demographic variables for our models were derived — was self-reported and requested limited demographic information. For example, direct indicators of treatment volume, which have been shown in previous studies to be strongly correlated to patient outcomes in several disease sites 4 were not collected due to the high potential for recall bias. Similarly, variables related to the routine use of contour guidelines in clinician workflows would have also likely been highly informative but were not collected.…”

Section: Discussionmentioning

confidence: 99%

“…Understanding the characteristics of radiation oncologists associated with superior segmentation performance is of utmost importance, as this knowledge can guide the training of future professionals, inform the design of auto-segmentation tools, and ultimately improve the quality of care provided to cancer patients. While some data do suggest that clinician experience in a particular disease site is associated with improved radiotherapy outcomes [4][5][6] , no studies have directly examined underlying factors related to segmentation quality. Therefore, we aim to investigate how demographic factors of a large number of practicing radiation oncologists are associated with improved segmentation quality through a secondary analysis of the C3RO data across several disease sites.…”

Section: Introductionmentioning

confidence: 99%

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Determining The Role Of Radiation Oncologist Demographic Factors On Segmentation Quality: Insights From A Crowd-Sourced Challenge Using Bayesian Estimation

Wahid,

Sahin,

Kundu

et al. 2023

Preprint

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BACKGROUND: Medical image auto-segmentation is poised to revolutionize radiotherapy workflows. The quality of auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of these clinician-derived segmentations have yet to be fully understood or quantified. Therefore, the purpose of this study was to determine the role of common observer demographic variables on quantitative segmentation performance. METHODS: Organ at risk (OAR) and tumor volume segmentations provided by radiation oncologist observers from the Contouring Collaborative for Consensus in Radiation Oncology public dataset were utilized for this study. Segmentations were derived from five separate disease sites comprised of one patient case each: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and gastrointestinal (GI). Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus gold standard primarily using the Dice Similarity Coefficient (DSC); surface DSC was investigated as a secondary metric. Metrics were stratified into binary groups based on previously established structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Markov chain Monte Carlo Bayesian estimation were used to investigate the association between demographic variables and the binarized segmentation quality for each disease site separately. Variables with a highest density interval excluding zero - loosely analogous to frequentist significance - were considered to substantially impact the outcome measure. RESULTS: After filtering by practicing radiation oncologists, 574, 110, 452, 112, and 48 structure observations remained for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of observations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumor volumes, respectively. Bayesian regression analysis revealed tumor category had a substantial negative impact on binarized DSC for the breast (coefficient mean +- standard deviation: -0.97 +- 0.20), sarcoma (-1.04 +- 0.54), H&N (-1.00 +- 0.24), and GI (-2.95 +- 0.98) cases. There were no clear recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations and wide highest density intervals. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality. Future studies should investigate additional demographic variables, more patients and imaging modalities, and alternative metrics of segmentation acceptability.

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Section: Discussioncontrasting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Determining The Role Of Radiation Oncologist Demographic Factors On Segmentation Quality: Insights From A Crowd-Sourced Challenge Using Bayesian Estimation

Wahid,

Sahin,

Kundu

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, the C3RO intake survey was self-reported and requested limited demographic information. For example, direct indicators of treatment volume, which have been shown in previous studies to be strongly correlated with patient outcomes, 4 were not collected because of the high potential for recall bias. Similarly, variables related to the annotator’s initial clinical training and current workflow, that is, routine use of contour guidelines/resources/software and access to multiple imaging modalities, would have also likely been highly informative but were not collected.…”

Section: Discussionmentioning

confidence: 99%

“…Understanding the characteristics of clinicians associated with superior segmentation performance could help guide training, inform the design of auto-segmentation tools, and ultimately improve the quality of care provided to patients. While some data do suggest that clinician experience is associated with improved radiotherapy outcomes, [4][5][6] no studies have directly examined underlying factors related to segmentation quality. Therefore, we aim to investigate whether demographic factors of a large number of radiation oncologists are associated with improved segmentation quality through a secondary analysis of C3RO.…”

Section: Introductionmentioning

confidence: 99%

Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation

Wahid,

Sahin,

Kundu

et al. 2024

JCO Clin Cancer Inform

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PURPOSE The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors. METHODS Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure. RESULTS Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations. CONCLUSION Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.

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Is Centralisation of Cancer Services Associated With Under‐Treatment of Patients With High‐Risk Prostate Cancer?—A National Population‐Based Study

Han,

Mayne,

Dodkins

et al. 2024

Cancer Medicine

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BackgroundCentralising prostate cancer surgical and radiotherapy services, requires some patients to travel longer to access treatment, but its impact on actual treatment utilisation and outcomes is unknown.MethodsUsing national cancer registry records linked to administrative hospital data, we identified all patients with high risk and locally advanced prostate cancer diagnosed between 1 April 2019 and 31 March 2020 in the English National Health Service (n = 15,971). Estimated travel times from the patient residential areas to the nearest hospital providing surgery or radiotherapy were estimated for journeys by car and by public transport. Multivariable logistic regression was used to model relationships between travel time and receipt of care with adjustment for patient characteristics.Results10,693 (67%) men received radical surgery or radiotherapy (RT) within 12 months of diagnosis. Average travel time to the nearest hospital providing prostatectomy or RT was 23.2 min by private car and 58.2 min by public transport. We found no association between travel time, either by car or public transport and the likelihood of receiving curative treatment. Patients living in the most socially deprived areas, those aged over 70, those with two or more comorbidities, and those of black ethnic origin, were less likely to receive curative treatment (p& =& 0.001 for all associations).ConclusionsThe current configuration of national prostate cancer services is not associated with the likelihood of receiving curative treatment. Further increases in capacity will unlikely improve utilisation rates beyond addressing sociodemographic barriers.

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Systematic Review and Meta-analysis of the Association Between Radiation Therapy Treatment Volume and Patient Outcomes

Cited by 10 publications

References 60 publications

Determining The Role Of Radiation Oncologist Demographic Factors On Segmentation Quality: Insights From A Crowd-Sourced Challenge Using Bayesian Estimation

Determining The Role Of Radiation Oncologist Demographic Factors On Segmentation Quality: Insights From A Crowd-Sourced Challenge Using Bayesian Estimation

Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation

Is Centralisation of Cancer Services Associated With Under‐Treatment of Patients With High‐Risk Prostate Cancer?—A National Population‐Based Study

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