Validating an algorithm to identify metastatic gastric cancer in the absence of routinely collected TNM staging data

Collecting routine and timely cancer stage at diagnosis by implementing a cancer staging tiered framework: the Western Australian Cancer Registry experience

BACKGROUND

Current processes collecting cancer stage data in population-based cancer registries (PBCRs) lack standardisation, resulting in difficulty utilising diverse data sources and incomplete, low-quality data. Implementing a cancer staging tiered framework aims to improve stage collection and facilitate inter-PBCR benchmarking.

OBJECTIVE

Demonstrate the application of a cancer staging tiered framework in the Western Australian Cancer Staging Project to establish a standardised method for collecting cancer stage at diagnosis data in PBCRs.

METHODS

The tiered framework, developed in collaboration with a Project Advisory Group and applied to breast, colorectal, and melanoma cancers, provides business rules - procedures for stage collection. Tier 1 represents the highest staging level, involving complete American Joint Committee on Cancer (AJCC) tumour-node-metastasis (TNM) data collection and other critical staging information. Tier 2 (registry-derived stage) relies on supplementary data, including hospital admission data, to make assumptions based on data availability. Tier 3 (pathology stage) solely uses pathology reports.

FINDINGS

The tiered framework promotes flexible utilisation of staging data, recognising various levels of data completeness. Tier 1 is suitable for all purposes, including clinical and epidemiological applications. Tiers 2 and 3 are recommended for epidemiological analysis alone. Lower tiers provide valuable insights into disease patterns, risk factors, and overall disease burden for public health planning and policy decisions. Capture of staging at each tier depends on data availability, with potential shifts to higher tiers as new data sources are acquired.

CONCLUSIONS

The tiered framework offers a dynamic approach for PBCRs to record stage at diagnosis, promoting consistency in population-level staging data and enabling practical use for benchmarking across jurisdictions, public health planning, policy development, epidemiological analyses, and assessing cancer outcomes. Evolution with staging classifications and data variable changes will futureproof the tiered framework. Its adaptability fosters continuous refinement of data collection processes and encourages improvements in data quality.

Contemporary trends in the incidence and timing of spinal metastases: A population-based study

Background

Spinal metastases are a significant complication of advanced cancer. In this study, we assess temporal trends in the incidence and timing of spinal metastases and examine underlying patient demographics and primary cancer associations.

Methods

In this population-based retrospective cohort study, health data from 2007 to 2019 in Ontario, Canada were analyzed (n = 37, 375 patients identified with spine metastases). Primary outcomes were annual incidence of spinal metastasis, and time to metastasis after primary diagnosis.

Results

The age-standardized incidence of spinal metastases increased from 229 to 302 cases per million over the 13-year study period. The average annual percent change (AAPC) in incidence was 2.2% (95% CI: 1.4% to 3.0%) with patients aged ≥85 years demonstrating the largest increase (AAPC 5.2%; 95% CI: 2.3% to 8.3%). Lung cancer had the greatest annual incidence, while prostate cancer had the greatest increase in annual incidence (AAPC 6.5; 95% CI: 4.1% to 9.0%). Lung cancer patients were found to have the highest risk of spine metastasis with 10.3% (95% CI: 10.1% to 10.5%) of patients being diagnosed at 10 years. Gastrointestinal cancer patients were found to have the lowest risk of spine metastasis with 1.0% (95% CI: 0.9% to 1.0%) of patients being diagnosed at 10 years.

Conclusions

The incidence of spinal metastases has increased in recent years, particularly among older patients. The incidence and timing vary substantially among different primary cancer types. These findings contribute to the understanding of disease trends and emphasize a growing population of patients who require subspecialty care.

Incidence of psychiatric illness in patients with neuroendocrine tumors: a comparative population-based analysis

PURPOSE

Diversion of tryptophan to tumoral hormonal production has been suggested to result in psychiatric illnesses in neuroendocrine tumors (NET). We measured the occurrence of psychiatric illness after NET diagnosis and compare it to colon cancer (CC).

METHODS

We conducted a population-based retrospective cohort study. Adults with NET were matched 1:1 to CC (2000-2019). Psychiatric illness was defined by mental health diagnoses and mental health care use after a cancer diagnosis, categorized as severe, other, and none. Cumulative incidence functions accounted for death as a competing risk.

RESULTS

A total of 11,223 NETs were matched to CC controls. Five-year cumulative incidences of severe psychiatric illness for NETs vs. CC was 7.7% (95%CI 7.2-8.2%) vs 7.6% (95%CI 7.2-8.2%) (p = 0.50), and that of other psychiatric illness was 32.9% (95%CI 32.0-33.9%) vs 31.6% (95%CI 30.8-32.6%) (p = 0.005). In small bowel and lung NETs, 5-year cumulative incidences of severe (8.1% [95%CI 7.3-8.9%] vs. 7.0% [95%CI 6.3-7.8%]; p = 0.01) and other psychiatric illness (34.7% [95%CI 33.3-36.1%] vs. 31.1% [95%CI 29.7-32.5%]; p < 0.01) were higher than for matched CC. The same was observed for serotonin-producing NETs for both severe (7.9% [95%CI 6.5-9.4%] vs. 6.8% [95%CI 5.5-8.2%]; p = 0.02) and other psychiatric illness (35.4% [95%CI 32.8-38.1%] vs. 31.9% [95%CI 29.3-34.4%]; p = 0.02).

CONCLUSIONS

In all NETs, there was no difference observed in the incidence of psychiatric illness compared to CC. For sub-groups of small bowel and lung NETs and of serotonin-producing NETs, the incidence of psychiatric illness was higher than for CC. These data suggest a signal towards a relationship between those sub-groups of NETs and psychiatric illness.

Textbook Outcome and Survival in Patients With Gastric Cancer: An Analysis of the Population Registry of Esophageal and Stomach Tumours in Ontario (PRESTO)

OBJECTIVE

To examine the association between Textbook Outcome (TO)-a new composite quality measurement-and long-term survival in gastric cancer surgery.

BACKGROUND

Single-quality indicators do not sufficiently reflect the complex and multifaceted nature of perioperative care in patients with gastric adenocarcinoma.

METHODS

All patients undergoing gastrectomy for nonmetastatic gastric adenocarcinoma registered in the Population Registry of Esophageal and Stomach Tumours of Ontario (PRESTO) between 2004 and 2015 were included. TO was defined according to negative margins; >15 lymph nodes sampled; no severe complications; no re-interventions; no unplanned ICU admission; length of stay ≤21 days; no 30-day readmission; and no 30-day mortality. Three-year survival was estimated using the Kaplan-Meier method. A marginal multivariable Cox proportional-hazards model was used to estimate the association between achieving TO metrics and long-term survival. E-value methodology was used to assess for risk of residual confounding.

RESULTS

Of the 1836 patients included in this study, 402 (22%) achieved all TO metrics. TO patients had a higher 3-year survival rate compared to non-TO patients (75% vs 55%, log-rank P < 0.001). After adjustments for covariates and clustering within hospitals, TO was associated with a 41% reduction in mortality (adjusted hazards ratio 0.59, 95% confidence interval 0.48, 0.72, P < 0.001). These results were robust to potential residual confounding.

CONCLUSIONS

Achieving TO is strongly associated with improved long-term survival in gastric cancer patients and merits further focus in surgical quality improvement efforts.

A Systematic Review of Estimating Breast Cancer Recurrence at the Population Level With Administrative Data

BACKGROUND

Exact numbers of breast cancer recurrences are currently unknown at the population level, because they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence. We present the first systematic review and meta-analysis, to our knowledge, of publications estimating breast cancer recurrence at the population level using algorithms based on administrative data.

METHODS

The systematic literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model to obtain a pooled estimate of accuracy.

RESULTS

Seventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%) compared with studies using detection rules without specified model (58.8%). The generalized linear mixed model for all recurrence types reported an accuracy of 92.2% (95% confidence interval = 88.4% to 94.8%).

CONCLUSIONS

Publications reporting algorithms for detecting breast cancer recurrence are limited in number and heterogeneous. A thorough analysis of the existing algorithms demonstrated the need for more standardization and validation. The meta-analysis reported a high accuracy overall, which indicates algorithms as promising tools to identify breast cancer recurrence at the population level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future.

Population Registry of Esophageal and Stomach Tumours in Ontario (PRESTO): protocol for a multicentre clinical and pathological database including 25 000 patients

INTRODUCTION

Oesophagogastric cancers carry a high mortality, economic burden and rising incidence. There is a need to monitor and improve care for this disease. Pathologic information is a cornerstone of cancer diagnosis, treatment and prognosis. Few population-based studies combine pathology information and clinical outcomes. The objective of this study is to develop a clinical and pathological database of oesophagogastric cancers to study practice patterns, resource utilisation and clinical outcomes.

METHODS AND ANALYSIS

The Population Registry of Esophageal and Stomach Tumours in Ontario (PRESTO) will include all patients with oesophagogastric cancer diagnosed from 2002 onwards within the province of Ontario. We estimate that the sample over the first 14 years of the study will include 26 000 patients. Pathologic information from diagnostic procedures, endomucosal resection specimens and surgical resection specimens is being abstracted into a purpose-built database. Pathology information will be linked to administrative data, which capture baseline demographics, patient-reported symptoms, physician billings, hospital visits, hospital characteristics, geography and vital statistics. The registry will be updated prospectively.

ETHICS AND DISSEMINATION

Ethics approval for this study was obtained from the Sunnybrook Health Sciences Centre Research Ethics Board. The PRESTO database will enable the study of oesophagogastric cancer in Ontario under six themes of inquiry: treatment, surgical outcomes, pathology, survival, health system and resource utilisation and cost. This information will be a valuable addition to the global efforts to understand ways to optimise care for these diseases.

Gastrectomy case volume and textbook outcome: an analysis of the Population Registry of Esophageal and Stomach Tumours of Ontario (PRESTO)

OBJECTIVE

To determine the association between gastric cancer surgery case-volume and Textbook Outcome, a new composite quality measurement.

BACKGROUND

Textbook Outcome included (a) negative resection margin, (b) greater than 15 lymph nodes sampled, (c) no severe complication, (d) no re-intervention, (e) no unplanned ICU admission, (f) length of stay of 21 days or less, (g) no 30-day readmission and (h) no 30-day mortality following surgery.

METHODS

All patients undergoing gastrectomy for non-metastatic gastric adenocarcinoma registered in the Population Registry of Esophageal and Stomach Tumours of Ontario between 2004 and 2015 were included. We used multivariable generalized estimating equation (GEE) logistic regression modelling to estimate the association between gastrectomy volume (surgeon and hospital annual volumes) and Textbook Outcome. Volumes were considered as continuous variables and quintiles.

RESULTS

Textbook Outcome was achieved in 378 of 1660 patients (22.8%). The quality metrics least often achieved were inadequate lymph node sampling and presence of severe complications, which occurred in 46.1% and 31.7% of patients, respectively. Accounting for covariates and clustering, neither surgeon volume nor hospital volume were significantly associated with Textbook Outcome. However, hospital volume was associated with adequate lymphadenectomy and fewer unplanned ICU admissions.

CONCLUSIONS

Higher case volume can impact certain measures of quality of care but may not address all care structures necessary for ideal Textbook recovery. Future quality improvement strategies should consider using case-mix adjusted Textbook Outcome rates as a surgical quality metric.

Development of predictive models to identify advanced-stage cancer patients in a US healthcare claims database

BACKGROUND

Although healthcare databases are a valuable source for real-world oncology data, cancer stage is often lacking. We developed predictive models using claims data to identify metastatic/advanced-stage patients with ovarian cancer, urothelial carcinoma, gastric adenocarcinoma, Merkel cell carcinoma (MCC), and non-small cell lung cancer (NSCLC).

METHODS

Patients with ≥1 diagnosis of a cancer of interest were identified in the HealthCore Integrated Research Database (HIRD), a United States (US) healthcare database (2010-2016). Data were linked to three US state cancer registries and the HealthCore Integrated Research Environment Oncology database to identify cancer stage. Predictive models were constructed to estimate the probability of metastatic/advanced stage. Predictors available in the HIRD were identified and coefficients estimated by Least Absolute Shrinkage and Selection Operator (LASSO) regression with cross-validation to control overfitting. Classification error rates and receiver operating characteristic curves were used to select probability thresholds for classifying patients as cases of metastatic/advanced cancer.

RESULTS

We used 2723 ovarian cancer, 6522 urothelial carcinoma, 1441 gastric adenocarcinoma, 109 MCC, and 12,373 NSCLC cases of early and metastatic/advanced cancer to develop predictive models. All models had high discrimination (C > 0.85). At thresholds selected for each model, PPVs were all >0.75: ovarian cancer = 0.95 (95% confidence interval [95% CI]: 0.94-0.96), urothelial carcinoma = 0.78 (95% CI: 0.70-0.86), gastric adenocarcinoma = 0.86 (95% CI: 0.83-0.88), MCC = 0.77 (95% CI 0.68-0.89), and NSCLC = 0.91 (95% CI 0.90 - 0.92).

CONCLUSION

Predictive modeling was used to identify five types of metastatic/advanced cancer in a healthcare claims database with greater accuracy than previous methods.

Gastroesophageal cancer patients need earlier palliative intervention - Using data to inform appropriate care

PURPOSE

To evaluate demographics of survival in patients with gastroesophageal cancer so that it informs nursing practice.

METHOD

Data on 2215 patients diagnosed with gastroesophageal cancer who presented to a specialist referral centre between the years 2000 and 2011 were extracted from a Public Health repository. Survival time was calculated and analysed against clinical and lifestyle factors to reveal whether they had an impact on survival outcomes.

RESULTS

Over 60% of patients had died within the first year, 39% of these died within the first 6 months. Survival outcomes were reduced in advancing age, and in those patients who present as 'emergency' cases. One quarter of patients were seen by a GP, but were not referred urgently through the two week wait system, to specialist care. Thus, gastroesophageal cancer patients need specific and appropriate treatment options, including earlier referrals to palliative care provision. There is also a need for cancer specific education and information at community and clinical levels.

CONCLUSIONS

The globally applied one and five-year statistics applied to cancer survival studies do not adequately capture rates of early demise with gastroesophageal cancer. This study presents a novel approach to statistical analysis, based on patient derived data. It identifies factors linked to earlier deaths. However, rather than a focus on early presentation and diagnosis (which are essential) - it also reveals a significant need to consider early referrals for palliative care and nursing interventions to alleviate pain and suffering in patients with poor prognosis.