Inter-country and ethnic variation in colorectal cancer survival: comparisons between a Philippine population, Filipino-Americans and Caucasians

Outcomes of Robotic versus Laparoscopic versus Open Resection for Rectal Cancer in a Center with a Beginning Robotic Colorectal Surgery Program

Background and Objective

Robotic surgery for rectal malignancies in the Philippines is emerging. Evidence has shown promising results for robot-assisted (R) rectal surgery when compared to the laparoscopic (L) and open (O) approach. This study discussed the clinicopathologic outcomes of the first robotic rectal resections versus laparoscopic and open rectal resections at the Philippine General Hospital (PGH).

Methods

This was a retrospective cohort of 45 consecutive surgical resections for rectal malignancy done at the PGH from March 2019 to October 2019 that compared the outcomes of the first 15 robotic procedures done at the institution versus laparoscopic (n=15) and open (n=15) operations performed during the same time period. One-way ANOVA was done to determine significant differences among variables, while Bonferonni multiple comparison test was done to analyze differences among means.

Results

The 45 patients in the study had a mean age of 56.04 ± 13.45 years. The patients were mostly male (60%). Most of the tumors were located in the low rectum (27/45; 60%). Most of the patients had locally-advanced (at least Stage IIIB) disease (27/45; 60%), and warranted neoadjuvant treatment (41/45; 91.11%). Most patients underwent a sphincter-saving procedure (34/45; 75.56%). All three groups had comparable baseline characteristics. The R-group had the longest operative time (438.07 ± 124.57; p value <0.0001). Blood loss was significantly highest in the R-group (399 ± 133.07 cc; p value - 0.0020) as well, while no statistical difference was observed between the O- and L-groups (p value - 0.75). No conversion to open was noted in the R- and L-groups. Most of the patients had well-differentiated adenocarcinoma (22/45; 48.49%). All patients in the L- and O-groups had an R0 resection There were two R1 resections in the R-group. All patients who underwent an open surgery had a negative circumferential resection margin (CRM); L-group 93.99%, R-group 69.23%. All patients had adequate proximal and distal resection margins. Those who underwent an open surgery had the shortest post-operative length of stay (LOS) (p value - 0.0002). Post-operative ileus (7/45; 15.56%) was the most commonly encountered morbidity, and was seen mostly in the R-group (3/15; 20%). One patient in the R-group underwent a transanal repair of an anastomotic dehiscence and was discharged three days after re-operation. There was no reported mortality.

Conclusion

Our institution with a beginning robotic colorectal surgery program showed promise as its initial outcomes for rectal cancer were compared to the more often-performed open and laparoscopic procedures. The authors expect more favorable clinicopathological outcomes as our staff overcome the prescribed learning curve for robotic surgery.

Global pattern and trends of colorectal cancer survival: a systematic review of population-based registration data

This review will describe the global patterns and trends of colorectal cancer survival, using data from the population-based studies or cancer registration. We performed a systematic search of China National Knowledge Infrastructure (CNKI), Wanfang Data, PubMed, Web of Science, EMBASE, and SEER and collected all population-based survival studies of colorectal cancer (up to June 2020). Estimates of observed and relative survival rates of colorectal cancer by sex, period, and country were extracted from original studies to describe the temporal patterns and trends from the late 1990s to the early 21st century. Globally, 5-year observed survival rates were higher in Seoul, Republic of Korea (1993-1997; 56.8% and 54.3% for colon and rectum cancers, respectively), Zhejiang province (2005-2010; 52.9% for colon cancer), Tianjin (1991-1999; 52.5% for colon cancer), Shanghai (2002-2006; 50.0% for rectum cancer) of China, and in Japan (1993-1996, 59.6% for colorectal cancer). Five-year relative survival rates of colorectal cancer in the Republic of Korea (2010-2014), Queensland, Australia (2005-2012), and the USA (2005-2009) ranked at relatively higher positions compared to other countries. In general, colorectal cancer survival rates are improving over time worldwide. Sex disparities in survival rates were also observed in the colon, rectum, and colorectal cancers in most countries or regions. The poorest age-specific 5-year relative survival rate was observed in patients > 75 years of age. In conclusion, over the past 3 decades, colorectal cancer survival has gradually improved. Geographic variations, sex differences, and age gradients were also observed globally in colorectal cancer survival. Further studies are therefore warranted to investigate the prognostic factors of colorectal cancer.

Imputation of missing prostate cancer stage in English cancer registry data based on clinical assumptions

BACKGROUND

Cancer stage can be missing in national cancer registry records. We explored whether missing prostate cancer stage can be imputed using specific clinical assumptions.

METHODS

Prostate cancer patients diagnosed between 2010 and 2013 were identified in English cancer registry data and linked to administrative hospital and mortality data (n = 139,807). Missing staging items were imputed based on specific assumptions: men with recorded N-stage but missing M-stage have no distant metastases (M0); low/intermediate-risk men with missing N- and/or M-stage have no nodal disease (N0) or metastases; and high-risk men with missing M-stage have no metastases. We tested these clinical assumptions by comparing 4-year survival in men with the same recorded and imputed cancer stage. Multi-variable Cox regression was used to test the validity of the clinical assumptions and multiple imputation.

RESULTS

Survival was similar for men with recorded N-stage but missing M-stage and corresponding men with M0 (89.5% vs 89.6%); for low/intermediate-risk men with missing M-stage and corresponding men with M0 (92.0% vs 93.1%); and for low/intermediate-risk men with missing N-stage and corresponding men with N0 (90.9% vs 93.7%). However, survival was different for high-risk men with missing M-stage and corresponding men with M0. Imputation based on clinical imputation performs as well as statistical multiple imputation.

CONCLUSION

Specific clinical assumptions can be used to impute missing information on nodal involvement and distant metastases in some patients with prostate cancer.

Colorectal cancer stage at diagnosis in migrants versus non-migrants (KoMigra): study protocol of a cross-sectional study in Germany

Background

In Germany, about 20% of the total population have a migration background. Differences exist between migrants and non-migrants in terms of health care access and utilisation. Colorectal cancer is the second most common malignant tumour in Germany, and incidence, staging and survival chances depend, amongst other things, on ethnicity and lifestyle. The current study investigates whether stage at diagnosis differs between migrants and non-migrants with colorectal cancer in an area of high migration and attempts to identify factors that can explain any differences.

Methods/Design

Data on tumour and migration status will be collected for 1,200 consecutive patients that have received a new, histologically verified diagnosis of colorectal cancer in a high migration area in Germany in the previous three months. The recruitment process is expected to take 16 months and will include gastroenterological private practices and certified centres for intestinal diseases. Descriptive and analytical analysis will be performed: the distribution of variables for migrants versus non-migrants and participants versus non-participants will be analysed using appropriate χ2-, t-, F- or Wilcoxon tests. Multivariable, logistic regression models will be performed, with the dependent variable being the dichotomized stage of the tumour (UICC stage I versus more advanced than UICC stage I). Odds ratios and associated 95%-confidence intervals will be calculated. Furthermore, ordered logistic regression models will be estimated, with the exact stage of the tumour at diagnosis as the dependent variable. Predictors used in the ordered logistic regression will be patient characteristics that are specific to migrants as well as patient characteristics that are not. Interaction models will be estimated in order to investigate whether the effects of patient characteristics on stage of tumour at the time of the initial diagnosis is different in migrants, compared to non-migrants.

Discussion

An association of migration status or other socioeconomic variables with stage at diagnosis of colorectal cancer would be an important finding with respect to equal health care access among migrants. It would point to access barriers or different symptom appraisal and, in the long term, could contribute to the development of new health care concepts for migrants.

Trial registration

German Clinical Trials Register DRKS00005056.

Model based period analysis of absolute and relative survival with R: data preparation, model fitting and derivation of survival estimates

Period analysis is increasingly employed in analyses of long-term survival of patients with chronic diseases such as cancer, as it derives more up-to-date survival estimates than traditional cohort based approaches. It has recently been extended with regression modelling using generalized linear models, which increases the precision of the survival estimates and enables to assess and account for effects of additional covariates. This paper provides a detailed presentation how model based period analysis may be used to derive population-based absolute and relative survival estimates using the freely available R language and statistical environment and already available R programs for period analysis. After an introduction of the underlying regression model and a description of the software tools we provide a step-by-step implementation of two regression models in R and illustrate how estimates and a test for trend over time in relative survival may be derived using data from a population based cancer registry.

Imputation of missing values of tumour stage in population-based cancer registration

Background

Missing data on tumour stage information is a common problem in population-based cancer registries. Statistical analyses on the level of tumour stage may be biased, if no adequate method for handling of missing data is applied. In order to determine a useful way to treat missing data on tumour stage, we examined different imputation models for multiple imputation with chained equations for analysing the stage-specific numbers of cases of malignant melanoma and female breast cancer.

Methods

This analysis was based on the malignant melanoma data set and the female breast cancer data set of the cancer registry Schleswig-Holstein, Germany. The cases with complete tumour stage information were extracted and their stage information partly removed according to a MAR missingness-pattern, resulting in five simulated data sets for each cancer entity. The missing tumour stage values were then treated with multiple imputation with chained equations, using polytomous regression, predictive mean matching, random forests and proportional sampling as imputation models. The estimated tumour stages, stage-specific numbers of cases and survival curves after multiple imputation were compared to the observed ones.

Results

The amount of missing values for malignant melanoma was too high to estimate a reasonable number of cases for each UICC stage. However, multiple imputation of missing stage values led to stage-specific numbers of cases of T-stage for malignant melanoma as well as T- and UICC-stage for breast cancer close to the observed numbers of cases. The observed tumour stages on the individual level, the stage-specific numbers of cases and the observed survival curves were best met with polytomous regression or predictive mean matching but not with random forest or proportional sampling as imputation models.

Conclusions

This limited simulation study indicates that multiple imputation with chained equations is an appropriate technique for dealing with missing information on tumour stage in population-based cancer registries, if the amount of unstaged cases is on a reasonable level.