How cancer registries can detect neoplasms in pathology laboratories that code with SNOMED CT terminology? An actual, simple and flexible solution.

BACKGROUND: Pathology laboratories are one of the main information sources for cancer registries and have traditionally been coded with SNOMED; some of them are migrating to SNOMED CT (SCT). Cancer registries encode topography and morphology of neoplasms by the International Classification of Diseases for Oncology (ICD-O). ICD-O updates morphology with WHO Classification of Tumors (Blue-Books). Morphological codes of the ICD-O, Blue-Books and SNOMED (former SNOMEDID) have always coincided. In 2017, SCT removed the SNOMEDID. OBJECTIVES: to define neoplastic and topographic subsets in SCT and map them to ICD-O-3.1/Blue-Books; reduce the original number of SCT concepts; correctly identify neoplasms in the laboratories in accordance with international cancer registry rules. METHODOLOGY: SCT neoplastic concepts were identified by manual revision and SCT resources ("is a", "Associated morphology" relationships; Simple Map Reference Set). Topographic concepts were extracted from the body structure hierarchy of SCT. Both subsets were mapped to ICD-O-3.1/Blue-Books, afterwards. Updating algorithms were designed to automate and update each subset with every SCT release. The process of neoplasms identification was validated in a sample of 5212 specimens with 7378 records from 8 Catalan hospitals. RESULTS: The number of concepts in neoplastic and topographic subsets (16,448 and 32,278) was reduced after the mapping to ICD-O-3.1/Blue-Books (2115 and 330, respectively). Neoplastic subset classified the specimens correctly in the 98.6% of the specimens. CONCLUSIONS: This article presents a flexible tool to exhaustively identify neoplasms in pathology laboratories that code with SCT, following international PBCRs standards and in line with the pathologists, oncologists and epidemiologists' needs.

Definition of a SNOMED CT pathology subset and microglossary, based on 1.17 million biological samples from the Catalan Pathology Registry.

SNOMED CT terminology is not backed by standard norms of encoding among pathologists. The vast number of concepts ordered in hierarchies and axes, together with the lack of rules of use, complicates the functionality of SNOMED CT for coding, extracting, and analyzing the data. Defining subgroups of SNOMED CT by discipline could increase its functionality. The challenge lies in how to choose the concepts to be included in a subset from a total of over 300,000. Besides, SNOMED CT does not cover daily need, as the clinical reality is dynamic and changing. To adapt SNOMED CT to needs in a flexible way, the possibility exists to create extensions. In Catalonia, most pathology departments have been migrating from SNOMED II to SNOMED CT in a bid to advance the development of the Catalan Pathology Registry, which was created in 2014 as a repository for all the pathological diagnoses. This article explains the methodology used to: (a) identify the clinico-pathological entities and the molecular diagnostic procedures not included in SNOMED CT; (b) define the theoretical subset and microglossary of pathology; (c) describe the SNOMED CT concepts used by pathologists of 1.17 million samples of the Catalan Pathology Registry; and (d) adapt the theoretical subset and the microglossary according to the actual use of SNOMED CT. Of the 328,365 concepts available for coding the diagnoses (326,732 in SNOMED CT and 1576 in Catalan extension), only 2% have been used. Combining two axes of SNOMED CT, body structure and clinical findings, has enabled coding most of the morphologies.

Trends in the surgical procedures of women with incident breast cancer in Catalonia, Spain, over a 7-year period (2005-2011).

BACKGROUND: Breast cancer (BC) is the most frequent cancer in women, accounting for 28% of all tumors among women in Catalonia (Spain). Mastectomy has been replaced over time by breast-conserving surgery (BCS) although not as rapidly as might be expected. The aim of this study was to assess the evolution of surgical procedures in incident BC cases in Catalonia between 2005 and 2011, and to analyze variations based on patient and hospital characteristics. METHODS: We processed data from the Catalonian Health Service's Acute Hospital Discharge database (HDD) using ASEDAT software (Analysis, Selection and Extraction of Tumor Data) to identify all invasive BC incident cases according to the codes 174.0-174.9 of the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) that were attended for the one-year periods in 2005, 2008 and 2011. Patients were classified according to surgical procedures (BCS vs mastectomy, and immediate vs delayed reconstruction), and results were compared among periods according to age, stage, comorbidity and hospital level. RESULTS: BC surgical procedures were performed in more than 80% of patients. Surgical cases showed a significant increasing trend in the proportion of women aged 50-69 years, more advanced disease stages, higher comorbidity and they were attended in hospitals of less complexity level throughout the study period. Similar pattern was found for patients treated with BCS, which increased significantly from 67.9% in 2005 to 74.0% in 2011.Simple lymph node removal increased significantly (from 48.8% to 71.4% and from 63.6% to 67.8% for 2005 and 2011 in conservative and radical surgery, respectively). A slightly increase in the proportion of mastectomized young women (from 28% in 2005 to 34% in 2011) was detected, due to multiple factors. About 22% of women underwent post-mastectomy breast reconstruction, this being mostly immediate. CONCLUSIONS: The use of HDD linked to the ASEDAT allowed us to evaluate BC surgical treatment in Catalonia. A consolidating increasing trend of BCS was observed in women aged 50-69 years, which corresponds with the pattern in most European countries. Among the mastectomized patients, immediate breast reconstructions have risen significantly over the period 2005-2011.

REGSTATTOOLS: freeware statistical tools for the analysis of disease population databases used in health and social studies.

BACKGROUND: The repertoire of statistical methods dealing with the descriptive analysis of the burden of a disease has been expanded and implemented in statistical software packages during the last years. The purpose of this paper is to present a web-based tool, REGSTATTOOLShttp://regstattools.net intended to provide analysis for the burden of cancer, or other group of disease registry data. Three software applications are included in REGSTATTOOLS: SART (analysis of disease's rates and its time trends), RiskDiff (analysis of percent changes in the rates due to demographic factors and risk of developing or dying from a disease) and WAERS (relative survival analysis). RESULTS: We show a real-data application through the assessment of the burden of tobacco-related cancer incidence in two Spanish regions in the period 1995-2004. Making use of SART we show that lung cancer is the most common cancer among those cancers, with rising trends in incidence among women. We compared 2000-2004 data with that of 1995-1999 to assess percent changes in the number of cases as well as relative survival using RiskDiff and WAERS, respectively. We show that the net change increase in lung cancer cases among women was mainly attributable to an increased risk of developing lung cancer, whereas in men it is attributable to the increase in population size. Among men, lung cancer relative survival was higher in 2000-2004 than in 1995-1999, whereas it was similar among women when these time periods were compared. CONCLUSIONS: Unlike other similar applications, REGSTATTOOLS does not require local software installation and it is simple to use, fast and easy to interpret. It is a set of web-based statistical tools intended for automated calculation of population indicators that any professional in health or social sciences may require.

BootstRatio: A web-based statistical analysis of fold-change in qPCR and RT-qPCR data using resampling methods.

Real-time quantitative polymerase chain reaction (qPCR) is widely used in biomedical sciences quantifying its results through the relative expression (RE) of a target gene versus a reference one. Obtaining significance levels for RE assuming an underlying probability distribution of the data may be difficult to assess. We have developed the web-based application BootstRatio, which tackles the statistical significance of the RE and the probability that RE>1 through resampling methods without any assumption on the underlying probability distribution for the data analyzed. BootstRatio perform these statistical analyses of gene expression ratios in two settings: (1) when data have been already normalized against a control sample and (2) when the data control samples are provided. Since the estimation of the probability that RE>1 is an important feature for this type of analysis, as it is used to assign statistical significance and it can be also computed under the Bayesian framework, a simulation study has been carried out comparing the performance of BootstRatio versus a Bayesian approach in the estimation of that probability. In addition, two analyses, one for each setting, carried out with data from real experiments are presented showing the performance of BootstRatio. Our simulation study suggests that Bootstratio approach performs better than the Bayesian one excepting in certain situations of very small sample size (N≤12). The web application BootstRatio is accessible through http://regstattools.net/br and developed for the purpose of these intensive computation statistical analyses.

SART ( Statistical Analysis of Rates and Trends): herramienta vía web para el cálculo estadístico de indicadores poblacionales / Statistical Analysis of Rates and Trends (SART): a web-based tool for statistical calculation of population indicators

Se propone una herramienta vía web (SART: http://regstattools.net/sart.html) que automatiza los cálculos para la obtención de distintos indicadores poblacionales importantes para el control de enfermedades o eventos de la salud. Se estructura en cuatro módulos: a) una descriptiva que incluye el cálculo del porcentaje, el número de casos, la tasa cruda, la tasa ajustada, la tasa truncada y la tasa acumulada; b) la estimación del porcentaje de cambio anual de las tasas; c) el cálculo de casos esperados, y d) la razón de incidencia o mortalidad estandarizada. La aplicación solicita unos parámetros de entrada al usuario. Una vez procesados los datos y obtenidos los resultados, éstos se envían por correo electrónico al usuario. Los resultados se obtienen para cada una de las causas de estudio (enfermedades, etnias, zonas geográficas...) y cada uno de los sexos introducidos en el fichero base (AU)

We propose a web-based tool (SART: http://regstattools.net/sart.html) that automates calculations to obtain various population indicators that can be used for the control of diseases or health events. SART has four modules: a) a descriptive module that allows calculation of the number of cases and their percentage, the crude rate, the adjusted rate, the truncated rate and the cumulative rate; b) the estimated annual percentage change of rates; c) calculation of expected cases; and d) the standardized incidence of mortality ratio. SART requests a base file and input parameters from the user before processing the data. The data and the results obtained are processed and then sent by email to the user. The results are provided by sex and for each of the study variables (diseases, ethnic groups, geographic areas...) introduced into the base file (AU)

[Statistical Analysis of Rates and Trends (SART): a web-based tool for statistical calculation of population indicators]. / SART (Statistical Analysis of Rates and Trends): herramienta vía web para el cálculo estadístico de indicadores poblacionales.

We propose a web-based tool (SART: http://regstattools.net/sart.html) that automates calculations to obtain various population indicators that can be used for the control of diseases or health events. SART has four modules: a) a descriptive module that allows calculation of the number of cases and their percentage, the crude rate, the adjusted rate, the truncated rate and the cumulative rate; b) the estimated annual percentage change of rates; c) calculation of expected cases; and d) the standardized incidence of mortality ratio. SART requests a base file and input parameters from the user before processing the data. The data and the results obtained are processed and then sent by email to the user. The results are provided by sex and for each of the study variables (diseases, ethnic groups, geographic areas...) introduced into the base file.

RiskDiff: a web tool for the analysis of the difference due to risk and demographic factors for incidence or mortality data.

BACKGROUND: Analysing the observed differences for incidence or mortality of a particular disease between two different situations (such as time points, geographical areas, gender or other social characteristics) can be useful both for scientific or administrative purposes. From an epidemiological and public health point of view, it is of great interest to assess the effect of demographic factors in these observed differences in order to elucidate the effect of the risk of developing a disease or dying from it. The method proposed by Bashir and Estève, which splits the observed variation into three components: risk, population structure and population size is a common choice at practice. RESULTS: A web-based application, called RiskDiff has been implemented (available at http://rht.iconcologia.net/riskdiff.htm), to perform this kind of statistical analyses, providing text and graphical summaries. Code from the implemented functions in R is also provided. An application to cancer mortality data from Catalonia is used for illustration. CONCLUSIONS: Combining epidemiological with demographical factors is crucial for analysing incidence or mortality from a disease, especially if the population pyramids show substantial differences. The tool implemented may serve to promote and divulgate the use of this method to give advice for epidemiologic interpretation and decision making in public health.

WAERS: an application for Web-assisted estimation of relative survival.

Net cancer survival estimation is usually performed by computing relative survival (RS), which is defined as the ratio between observed and expected survival rates. The mortality of a reference population is required in order to compute the expected survival rate, which can be performed using a variety of statistical packages. A new Web interface to compute RS, called WAERS, has been developed by the Catalan Institute of Oncology. The reference population is first selected, and then the RS of a cohort is computed. A remote server is used for this purpose. A mock example serves to illustrate the use of the tool with a hypothetical cohort, for which RS is estimated based on three different reference populations (a province of Spain, an autonomous community (Region), and the entire Spanish population). At present, only mortality tables for different areas of Spain are available. Future improvements of this application will include mortality tables of Latin American and European Union countries, and stratified (control variable) analysis. This application can be also useful for cohort mortality studies and for registries of several diseases.

[Relative survival computation. Comparison of methods for estimating expected survival]. / Cálculo de la supervivencia relativa. Comparación de métodos de estimación de la supervivencia esperada.

Relative survival is the most commonly used method to determine survival in patients diagnosed with cancer. This method takes into account estimation of expected survival in cancer patients based on the observed mortality in the geographical area to which they belong. The most frequently used methods for estimation of expected survival are the Ederer (I and II) and Hakulinen methods. Survival tables for the geographical areas stratified by age and calendar year are required for these calculations. The present article presents an example of how to perform these estimations and how to choose the most appropriate method for the type of analysis to be performed. This article shows that if the follow-up of the cohort is less than 10 years, any of these methods should give similar results. However, the Hakulinen method is preferred, since it accounts for heterogeneity due to potential withdrawals.

Cálculo de la supervivencia relativa. Comparación de métodos de estimación de la supervivencia esperada / Relative survival computation. Comparison of methods for estimating expected survival

El método más utilizado en el cálculo de la supervivencia de los pacientes diagnosticados de cáncer es la supervivencia relativa. Este método tiene en cuenta la estimación de la supervivencia esperada de dichos pacientes a partir de la mortalidad observada en la zona geográfica de la que proceden. Los métodos utilizados con mayor frecuencia en dicha estimación son los de Ederer (I y II) y el método de Hakulinen. Para dichos cálculos es necesaria la disponibilidad de las tablas de supervivencia estratificadas por edad y por período de calendario. En este trabajo se presenta un ejemplo en el que se muestra cómo realizar dichos cálculos de tal forma que permitan al investigador decidir qué método es el más adecuado según el tipo de análisis que lleve a cabo. Se mostrará que, si el tiempo de seguimiento de la cohorte es inferior a 10 años, cualquiera de los métodos debería dar resultados similares. Sin embargo, el método recomendable es el de Hakulinen, ya que tiene en cuenta la heterogeneidad en el momento de las censuras debidas a posibles pérdidas de seguimiento o abandonos

Relative survival is the most commonly used method to determine survival in patients diagnosed with cancer. This method takes into account estimation of expected survival in cancer patients based on the observed mortality in the geographical area to which they belong. The most frequently used methods for estimation of expected survival are the Ederer (I and II) and Hakulinen methods. Survival tables for the geographical areas stratified by age and calendar year are required for these calculations. The present article presents an example of how to perform these estimations and how to choose the most appropriate method for the type of analysis to be performed. This article shows that if the follow-up of the cohort is less than 10 years, any of these methods should give similar results. However, the Hakulinen method is preferred, since it accounts for heterogeneity due to potential withdrawals

[Automatization of a hospital-based tumor registry]. / Automatización de un registro hospitalario de tumores.

INTRODUCTION: To increase data reliability and reduce the costs associated with the HTR, the Catalan Institute of Oncology programmed the manual procedures of data collection from databases by means of a computer application (ASEDAT). MATERIAL AND METHOD: ASEDAT detects the incident tumors of the registry from the databases of the pathology records (PR) and discharge records (DR) and selects the basic information from both databases. Data from the HTR data was collected for the period 1999-2000 by means of 2 procedures: manual and automatized collection and the results obtained were compared. RESULTS: 10,498 cancer patients were detected. Manual resolution detected 8,309 incident tumors and 2,374 prevalent tumors. ASEDAT automatically detected 8,901 patients (84.8%), in whom 8,367 incident tumors were detected (58 more tumors than the manual procedure). Validation of agreement was performed in the incident tumors detected by both methods (7,063 tumors). In 6,185 tumors (87.6%) the information agreed in all the variables. Of the discordant tumors, 692 (9.8%) were obtained by the RHT staff using manual resolution, and the remainder (186; 2.6%) were obtained by the application (automatic resolution). CONCLUSIONS: Cancer registry automatization is feasible when PR and DR databases are available, coded and automatized.

Automatización de un registro hospitalario de tumores / Automatización de un registro hospitalario de tumores

Introducción: El Instituto Catalán de Oncología automatizó los procedimientos manuales de captación de la información de las bases de datos del alta hospitalaria (AH) y anatomía patológica (APA) mediante una aplicación informática (ASEDAT) con el objetivo de aumentar la fiabilidad de los datos y reducir los costes del Registro Hospitalario de Tumores (RHT). Material y método: ASEDAT detecta los tumores incidentes del centro a partir de las bases de datos de APA y de las AH mediante la selección de la información básica para cada uno de ellos. Se resolvió el RHT para el período 1999-2000 mediante el procedimiento manual y automatizado, y se compararon entre sí los resultados. Resultados: Se detectaron 10.498 pacientes oncológicos. La resolución manual detectó 8.309 tumores incidentes y 2.374 tumores prevalentes. ASEDAT resolvió automáticamente 8.901 pacientes (84,8%), en los cuales se detectaron 8.367 tumores incidentes, 58 tumores más que con el procedimiento manual. La validación de la concordancia se realizó en los tumores incidentes detectados por ambos métodos (7.063 tumores). En 6.185 tumores (87,6%), la información coincidió en todas las variables. De los tumores discordantes, 692 (9,8%) fueron generados por el personal del RHT en la resolución manual y el resto (n = 186; 2,6%) por la aplicación (resolución automática). Conclusiones: La automatización de un registro de cáncer es posible siempre y cuando el centro disponga de las bases de datos de APA y AH codificadas e informatizadas

Introduction: To increase data reliability and reduce the costs associated with the HTR, the Catalan Institute of Oncology programmed the manual procedures of data collection from databases by means of a computer application (ASEDAT). Material and method: ASEDAT detects the incident tumors of the registry from the databases of the pathology records (PR) and discharge records (DR) and selects the basic information from both databases. Data from the HTR data was collected for the period 1999-2000 by means of 2 procedures: manual and automatized collection and the results obtained were compared. Results: 10,498 cancer patients were detected. Manual resolution detected 8,309 incident tumors and 2,374 prevalent tumors. ASEDAT automatically detected 8,901 patients (84.8%), in whom 8,367 incident tumors were detected (58 more tumors than the manual procedure). Validation of agreement was performed in the incident tumors detected by both methods (7,063 tumors). In 6,185 tumors (87.6%) the information agreed in all the variables. Of the discordant tumors, 692 (9.8%) were obtained by the RHT staff using manual resolution, and the remainder (186;2.6%) were obtained by the application (automatic resolution). Conclusions: Cancer registry automatization is feasible when PR and DR databases are available, coded and automatized

[Automatic calculation of relative survival through the web. The WAERS project of the Catalan Institute of Oncology]. / Cálculo automatizado de la supervivencia relativa vía web. El proyecto WAERS del Instituto Catalán de Oncología.

The most commonly used measure to estimate cancer survival is relative survival, defined as the ratio between observed and expected survival. Expected survival is computed on the basis of the mortality of a reference population. Mortality tables for the general population are not always available and their calculation requires specific software. For that purpose, the Catalan Institute of Oncology developed WAERS (Web-Assisted Estimation of Relative Survival), a web-based application that estimates the relative survival for a cohort of patients. The user prepares data in a specific format and sends them to a remote server located at the Catalan Institute of Oncology. This server computes relative survival and returns a file with the results to the electronic address supplied by the user. By means of this application, hospital- and population-based Spanish cancer registries and registries of other diseases can estimate relative survival of their cohorts using their reference population (province or autonomous community). This application could also be useful for cohort mortality studies.

Cálculo automatizado de la supervivencia relativa vía web. El proyecto WAERS del Instituto Catalán de Oncología / Automatic calculation of relative survival through the web. The WAERS project of the Catalan Institute of Oncology

La medida utilizada habitualmente para estimar la supervivencia del cáncer es la supervivencia relativa, definida como el cociente entre la supervivencia observada y la esperada. La supervivencia esperada se calcula a partir de la mortalidad de una población de referencia. La disponibilidad y la preparación de tablas de mortalidad de la población general no es siempre posible y requiere software específico para su cálculo. A tal efecto, el Instituto Catalán de Oncología (ICO)ha desarrollado la aplicación WAERS, una aplicación web que proporciona la estimación de la supervivencia relativa para una cohorte de pacientes. El usuario debe preparar los datos en un formato específico y enviarlos a un servidor remoto que se encuentra en el ICO. Este servidor calcula la supervivencia relativa y devuelve los resultados en un fichero a una dirección que ha indicado el usuario. Mediante esta aplicación, los registros de cáncer de base hospitalaria y poblacional y los registros de otras enfermedades pueden estimar la supervivencia relativa de sus cohortes seleccionando a la población de referencia que consideren (provincia o comunidad autónoma). También puede ser útil para estudios de mortalidad en cohortes

The most commonly used measure to estimate cancer survivalis relative survival, defined as the ratio between observed and expected survival. Expected survival is computed on the basis of the mortality of a reference population. Mortality tables for the general population are not always available and their calculation requires specific software. For that purpose,the Catalan Institute of Oncology developed WAERS (Web-Assisted Estimation of Relative Survival), a web-based application that estimates the relative survival for a cohort of patients. The user prepares data in a specific format and sends them to a remote server located at the Catalan Institute of Oncology. This server computes relative survival and returns afile with the results to the electronic address supplied by theuser. By means of this application, hospital- and population-based Spanish cancer registries and registries of other diseases can estimate relative survival of their cohorts using their reference population (province or autonomous community). This application could also be useful for cohort mortality studies