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

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

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.

The impact of transportation mode, socioeconomic deprivation and rurality on travel times to radiotherapy and surgical services for patients with prostate cancer: A national population-based evaluation

BACKGROUND

The distances that patients have to travel can influence their access to cancer treatment. We investigated the determinants of travel time, separately for journeys by car and public transport, to centres providing radical surgery or radiotherapy for prostate cancer.

METHODS

Using national cancer registry records linked to administrative hospital data, we identified patients who had radical surgery or radiotherapy for prostate cancer between January 2017 and December 2018 in the English National Health Service. Estimated travel times from the patients' residential area to the nearest specialist surgical or radiotherapy centre were estimated for journeys by car and by public transport.

RESULTS

We included 13,186 men who had surgery and 26,581 who had radiotherapy. Estimated travel times by public transport (74.4 mins for surgery and 69.4 mins for radiotherapy) were more than twice as long as by car (33.4 mins and 29.1mins, respectively). Patients living in more socially deprived neighbourhoods in rural areas had the longest travel times to the nearest cancer treatment centres by car (62.0 mins for surgery and 52.1 mins for radiotherapy). Conversely patients living in more affluent neighbourhoods in urban conurbations had the shortest (18.7 mins for surgery and 17.9 mins for radiotherapy).

CONCLUSION

Travel times to cancer centres vary widely according to mode of transport, socioeconomic deprivation, and rurality. Policies changing the geographical configuration of cancer services should consider the impact on the expected travel times both by car and by public transport to avoid enhancing existing inequalities in access to treatment and patient outcomes.

The future of cancer care in the UK-time for a radical and sustainable National Cancer Plan

Cancer affects one in two people in the UK and the incidence is set to increase. The UK National Health Service is facing major workforce deficits and cancer services have struggled to recover after the COVID-19 pandemic, with waiting times for cancer care becoming the worst on record. There are severe and widening disparities across the country and survival rates remain unacceptably poor for many cancers. This is at a time when cancer care has become increasingly complex, specialised, and expensive. The current crisis has deep historic roots, and to be reversed, the scale of the challenge must be acknowledged and a fundamental reset is required. The loss of a dedicated National Cancer Control Plan in England and Wales, poor operationalisation of plans elsewhere in the UK, and the closure of the National Cancer Research Institute have all added to a sense of strategic misdirection. The UK finds itself at a crossroads, where the political decisions of governments, the cancer community, and research funders will determine whether we can, together, achieve equitable, affordable, and high-quality cancer care for patients that is commensurate with our wealth, and position our outcomes among the best in the world. In this Policy Review, we describe the challenges and opportunities that are needed to develop radical, yet sustainable plans, which are comprehensive, evidence-based, integrated, patient-outcome focused, and deliver value for money.

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

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.