Accuracy of kidney cancer diagnosis and histological subtype within cancer registry data.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 606-606
Author(s):  
Lori Wood ◽  
Jeff Himmelman ◽  
Kara Thompson ◽  
Jennifer Merrimen
Keyword(s):  
Kidney Cancer ◽  
Cancer Registry ◽  
Clear Cell ◽  
Registry Data ◽  
Pathology Report ◽  
Type I ◽  
Cancer Type ◽  
Histological Subtype ◽  
Cancer Statistics ◽  
Pathology Reports

606 Background: Cancer registries are the mainstay for population-based cancer statistics including incidence and cancer type. In Canada, each province captures this data in provincial registries including the Nova Scotia Cancer Registry (NSCR).The goal of this study was to describe data from the NSCR about method of diagnosis and kidney cancer (KC) pathology and compare it to the actual pathology reports to determine the accuracy of diagnosis and histological subtype assignment. Methods: This retrospective analysis included patients with KC in the NSCR with an ICD-10-CM code C64.9 (malignant neoplasm of unspecified kidney, except renal pelvis) within the largest provincial metropolitan area from 2006-2010. Method of KC diagnosis (clinical, radiologic, histology, or autopsy) was recorded as was the pathological diagnosis based on WHO classification. All non-clear cell KC (nonccKC) diagnosis from the registry were compared to the actual pathology report (and pathology re-review when necessary) for comparison. Results: 733 pts make up the study cohort. 81.2% of patients were diagnosed based on nephrectomy, 11.5% on radiography, 6.5 % biopsy, and 0.8% autopsy. By registry data 53.1% had clear cell, 20.2% KC not otherwise specified (NOS), 12.7% papillary, 3.8% chromophobe, and many other nonccKC. By pathology reports, 62.2% had clear cell, 13.4% papillary, 4.4% chromophobe, only 2% KC NOS (because most radiological diagnosis were classified this way). A large number of pathological diagnoses make up the other nonccKC and discrepancies between registry data and pathology reports will be described and compared in detail. Conclusions: Registry data is commonly used to report cancer statistics. Registry data may not be accurate for the true incidence of KC since 11.5% were based on radiology alone. Clear cell KC made up 53% of registry diagnosis but 62% on pathology report review. Although papillary and chromophobe incidence did not vary a lot, other types of nonccKC did. This registry data did not differentiate between papillary type I and II. NonccKC should not be considered one entity. One must be aware of the gaps in registry data for KC statistics including overall diagnosis, clear cell and nonccKC subtypes.

scholarly journals Accuracy of kidney cancer diagnosis and histological subtype within Canadian cancer registry data

2017 ◽  
Vol 11 (9) ◽  
pp. E326-9
Author(s):  
Jeffrey G. Himmelman ◽  
Jennifer Merrimen ◽  
Kara Matheson ◽  
Chris Theriault ◽  
Lori A. Wood
Keyword(s):  
Cancer Registry ◽  
Clear Cell ◽  
Cancer Registries ◽  
Population Based ◽  
Registry Data ◽  
Pathology Report ◽  
Study Cohort ◽  
Histological Subtype ◽  
Cancer Statistics ◽  
Pathology Reports

Introduction: Provincial/territorial cancer registries (PTCRs) are the mainstay for Canadian population-based cancer statistics. Each jurisdiction captures this data in a population-based registry, including the Nova Scotia Cancer Registry (NSCR). The goal of this study was to describe data from the NSCR regarding renal cell carcinoma (RCC) pathology subtype and method of diagnosis and compare it to the actual pathology reports to determine the accuracy of diagnosis and histological subtype assignment.Methods: This retrospective analysis included patients diagnosed with RCC in the NSCR from 2006‒2010 with an ICD-O-3 code C64.9 seen or treated in the largest NS health district. From the NSCR, method of diagnosis and pathological diagnosis was recorded. All diagnoses of non-clear-cell RCC (nonccRCC) from NSCR were compared to the actual pathology report for descriptive comparison and reasons for discordance.Results: 723 patients make up the study cohort. 81.3% of patients were diagnosed by nephrectomy, 11.1% radiography, 6.9 % biopsy, and 0.7% autopsy. By NSCR data, 52.8% had clear-cell (ccRCC), 20.5% RCC not otherwise specified (NOS), 12.7% papillary, 4% chromophobe, and the rest had other nonccRCC subtypes. By pathology reports, 69.5% had clear-cell, 15% papillary, 5% chromophobe, only 2.7% RCC NOS. There was a discordance rate of 15.4% between NSCR data and diagnosis from pathology report. Reasons for discordance were not enough information by the pathologist in 45.5%, misinterpretation of report by data coder in 22.2%, and true coding error in 32.3%.Conclusions: When using PTCR for RCC incidence data, it is important to understand how the diagnosis is made, as not all are based on pathological confirmation; in this cohort 11% were based on radiology. One must also be aware that clear-cell and non-clearcell subtypes may differ between the PTCR data and pathology reports. In this study, ccRCC made up 52.8% of the registry diagnoses, but increased to 69.6% on pathology report review. Use of synoptic reporting and ongoing education may improve accuracy of registry data.

scholarly journals A COMPARISON OF THE CANCER INCIDENCE RATES BETWEEN THE STATE CANCER STATISTICS AND CANCER REGISTRY DATA IN THE RUSSIAN FEDERATION

2019 ◽  
Vol 18 (5) ◽  
pp. 5-11
Author(s):  
G. V. Petrova ◽  
O. P. Gretsova ◽  
V. V. Starinsky
Keyword(s):  
Cancer Registry ◽  
Cancer Incidence ◽  
Incidence Rates ◽  
The State ◽  
Registry Data ◽  
Cancer Registration ◽  
Registration System ◽  
Cancer Statistics ◽  
Cancer Registry Data ◽  
Cancer Incidence Rates

The purpose of the study was to compare data on the cancer incidence rates for 2016 between the official reports on cancer statistics and federal cancer registry, collected in December 2018.Material and Methods. The study estimated the total data on 18 parameters from 35 regions of Russia, covering 66.3 million people (2016). The database of the Russian cancer registry and the database containing reports on the state cancer statistics were used. The cancer statistics/cancer registry ratio was assessed.Results. No differences in cancer incidence between the official reports on cancer statistics and cancer registry data were found. In the official reports on cancer statistics, the mortality rate, the proportion of posthumously recorded patients per 100 newly diagnosed, the proportion of deaths from diseases not related to cancer per 100 deceased patients, the cancer prevalence and the prevalence rate of unspecified malignant tumors were slightly reduced (to 10 %, 9 %, 5 %, and 4 %, respectively), and the rate of cancer detection, the proportion of histologically verified diagnoses and the proportion of cancers detected in stage III were increased (to 19 %, 10 % and 14 %, respectively) compared to those in cancer registry data.Conclusion. Improvement in the quality and completeness of information about cancer patients is associated rather with increasing the annual report length than with the need to improve the cancer registration system itself.

The Registry Case Finding Engine (CaFE): An automated approach for cancer patient identification from unstructured, free-text pathology reports

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 6080-6080
Author(s):  
D. A. Hanauer ◽  
A. M. Chinnaiyan ◽  
G. Miela ◽  
D. W. Blayney
Keyword(s):  
Cancer Registry ◽  
Basal Cell ◽  
Pathology Report ◽  
Free Text ◽  
Coding System ◽  
Patient Identification ◽  
Time Saving ◽  
Custom Made ◽  
Basal Cell Carcinomas ◽  
Pathology Reports

6080 Background: A vital component to maintaining an accurate cancer registry is the identification of patients with cancer. The University of Michigan Cancer Registry identifies more than 90% of all registry patients by manually reading free-text pathology reports and their associated SNOMED codes. This method is labor and time intensive and is subject to errors of omission. Methods: We created an application to scan free-text pathology reports to identify cases of interest to the registry. It uses a custom-made list of approximately 3,300 words, phrases, and SNOMED codes to positively identify relevant cases and to eliminate non-relevant cases, including those which may mention cancer-related terms. Experienced registrars reviewed 2,451 pathology reports and marked cases of interest to the registry; this served as the gold standard. These reports were also analyzed by the Registry CaFE. The time required for case identification was recorded for both processes. Results: Experienced registrars marked 795 (32.4%) cases as being of interest compared to the CaFE which marked 1,009 (41.1%). The sensitivity of the CaFE was 100% whereas the specificity was 87.1%. An analysis of the 214 errors made by the CaFE revelead that 30 cases (14%) were due to incorrect SNOMED codes assigned by our auto-coding system (Cerner Corporation, Kansas City, MO) and 89 (41.6%) were either skin squamous or basal cell carcinomas (most non-melanomatous skin cancers are not tracked in the registry). Registrars required an average of 21 seconds per pathology report whereas the Registry CaFE processed each report in less than a second. Conclusions: The Registry CaFE identified all relevant cases and correctly eliminated most cases that were not important; it is both effective and time-saving. Future efforts directed at improving the CaFE for squamous and basal cell carcinomas would yield the largest improvement in accuracy. No significant financial relationships to disclose.

The impact of renal cell carcinoma histopathological subtype on disease prognosis: A Canadian multi-institutional analysis.

2015 ◽  
Vol 33 (7_suppl) ◽  
pp. 480-480
Author(s):  
Jennifer Bjazevic ◽  
Jasmir G. Nayak ◽  
Premal Patel ◽  
Anil Kapoor ◽  
Simon Tanguay ◽  
...  
Keyword(s):  
Clear Cell ◽  
Disease Free Survival ◽  
Tumor Grade ◽  
Type I ◽  
Histological Subtype ◽  
Free Survival ◽  
Papillary Type ◽  
The Impact ◽  
Disease Free

480 Background: Renal cell carcinoma (RCC) is divided into several histopathological subtypes, each with significantly different clinical features. However, current data regarding the prognostic value of histological subtype is limited and conflicted. We examined the impact of RCC histology on disease prognosis in a large, multi-institutional Canadian analysis. Methods: The Canadian Kidney Cancer Information System (CKCis), a prospective database from 14 Canadian institutions, was utilized for the study. 1284 patients with non-metastatic RCC, who underwent surgical intervention with curative intent, were included in the study. Patients were stratified according to their primary histology and the Chi-squared test was used to determine associations between histopathology and clinical features. The impact of histology of disease-free survival (DFS) was determined with a multivariate analysis adjusted for age, gender, tumor size, tumor grade, and pathological stage. Results: Clear cell RCC was the most prevalent histological subtype found in 80.5% of patients. Histopathology was significantly associated with patient age, tumor grade, and pathological stage. Advanced stage disease (>T3) was associated with clear cell and papillary type II RCC (p<0.05). 90.7%, 86.7%, 78.5%, and 78.8% of patients with chromophobe, papillary type I, papillary type II, and clear cell RCC respectively, were free of disease after a median follow-up of 1.2 years. On multivariate analysis, histological subtype was a significant predictor of disease-free survival (DFS). When compared to clear cell histology, chromophobe RCC had a significantly higher DFS (HR=0.38, 95% CI 0.15-0.95, p<0.05), and papillary type I RCC had a trend towards a lower rate of disease progression (HR=0.31, 95% CI 0.08-1.28, p=0.05). Conclusions: This study demonstrates that histological subtype impacts disease progression. Histological subtype was independently associated with DFS in surgically treated RCC, specifically chromophobe RCC was shown to have the highest DFS. This may be used to help individualize patient treatment and follow-up based on primary tumor histology.

scholarly journals Mesonephric (clear cell) endometrial cancer

2012 ◽  
Vol 61 (5) ◽  
pp. 85-91 ◽  
Author(s):  
Elena Aleksandrovna Ulrikh ◽  
Dzhamilat Ilyasovna Khalimbekova ◽  
Adiliya Fettekhovna Urmancheyeva ◽  
Dmitriy Yevgenyevich Matsko ◽  
Vakhtang Mikhaylovich Merabishvili
Keyword(s):  
Endometrial Cancer ◽  
Endometrial Carcinoma ◽  
Cancer Registry ◽  
Clear Cell ◽  
Myometrial Invasion ◽  
Registry Data ◽  
High Rate ◽  
Saint Petersburg ◽  
Cancer Registry Data ◽  
Research Institute

Clear Cell Endometrial Cancer is a rare nonendometrioid endometrial carcinoma detected in 1–6 %. The aim of the study was to determine clinical and morphologic features of Clear Cell Endometrial Cancer. Materials: All the cases with Clear Cell Endometrial Cancer treated in the N. N. Petrov Research Institute of Oncology (Saint-Petersburg) were identified from surgical pathology files from 1985 to 2010 years. Saint-Petersburg Population Cancer Registry data (2000–2005 years period) and N. N. Petrov Research Institute of Oncology Hospital Cancer Registry data (1985–2010 years period) were analyzed. The population based study included 3 224 cases of endometrial cancer patients, the hospital study — 3 345 patients. Results: A review of 3 345 cases of endometrial cancer revealed 73 cases (2.2 %) of Clear Cell Endometrial Cancer. Clear Cell Endometrial Cancer registered in elder women with late menopause and atrophic endometrium. The tumor is a highly malignant variant of endometrial carcinoma with deep myometrial invasion (42.5 %), high rate of metastasis (39.1 %) even in cases with superficial invasion (23.0 %) versus endometrioid endometrial cancer: 6.0 %, 14.2 % and 9.0 % respectively. Clear Cell Endometrial Cancer has a poor prognosis with 3-year observed survival 62.7 %, 5-year observed survival — 52.2 % (Saint-Petersburg Population Cancer Registry), 70.9 % (3-year survival) and 61.8 % (5-year survival) according to the N. N. Petrov Research Institute of Oncology Hospital Cancer Registry data. Whereas prognosis in patients with endometrioid endometrial carcinoma is much more favorable: 3-year observed survival 79.4 %, 5-year observed survival — 75.5 %

scholarly journals Hypoxia-Inducible Factor Linked to Differential Kidney Cancer Risk Seen with Type 2A and Type 2B VHL Mutations

2007 ◽  
Vol 27 (15) ◽  
pp. 5381-5392 ◽  
Author(s):  
Lianjie Li ◽  
Liang Zhang ◽  
Xiaoping Zhang ◽  
Qin Yan ◽  
Yoji Andrew Minamishima ◽  
...  
Keyword(s):  
Kidney Cancer ◽  
Clear Cell ◽  
Germ Line ◽  
Hypoxia Inducible Factor ◽  
Suppressor Gene ◽  
Cancer Type ◽  
Missense Mutations ◽  
Von Hippel Lindau ◽  
High Risk Type ◽  
Vhl Disease

ABSTRACT Clear cell carcinoma of the kidney is a major cause of mortality in patients with von Hippel-Lindau (VHL) disease, which is caused by germ line mutations that inactivate the VHL tumor suppressor gene. Biallelic VHL inactivation, due to mutations or hypermethylation, is also common in sporadic clear cell renal carcinomas. The VHL gene product, pVHL, is part of a ubiquitin ligase complex that targets the alpha subunits of the heterodimeric transcription factor hypoxia-inducible factor (HIF) for destruction under well-oxygenated conditions. All VHL mutations linked to classical VHL disease compromise this pVHL function although some missense mutations result in a low risk of kidney cancer (type 2A VHL disease) while others result in a high risk (type 2B VHL disease). We found that type 2A mutants were less defective than type 2B mutants when reintroduced into VHL −/− renal carcinoma cells with respect to HIF regulation. A stabilized version of HIF2α promoted tumor growth by VHL −/− cells engineered to produce type 2A mutants, while knock-down of HIF2α in cells producing type 2B mutants had the opposite effect. Therefore, quantitative differences with respect to HIF deregulation are sufficient to account for the differential risks of kidney cancer linked to VHL mutations.

scholarly journals Investigation Of Geographical Disparities: The Use of An Interpolation Method For Cancer Registry Data

2021 ◽  
Author(s):  
Janis E. Campbell ◽  
Ami Elizabeth Sedani ◽  
Hanh Dung N. Dao ◽  
Ayesha B Sambo ◽  
Mark P. Doescher ◽  
...  
Keyword(s):  
Public Health ◽  
Cancer Registry ◽  
Health Care Professionals ◽  
Interpolation Method ◽  
Low Cost ◽  
Registry Data ◽  
Cancer Type ◽  
Cancer Registry Data ◽  
Public Health Practitioners ◽  
Zip Code

Abstract Purpose: This study aimed to demonstrate a method to systematically illustrate cancer incidence in an accurate and visually attractive, yet straightforward, GIS-based method using ZIP Code level cancer registry data for all cancers combined and four major types of cancer. This analysis will better inform public health researchers by enabling them to empirically assess and describe geographic disparities in cancer for their own areas. Methods: We describe a process of creating smoothed maps with an appropriate, well-described, simple, replicable method. We calculated SIRs for each ZIP Code Tabulation Areas (ZCTAs) for each cancer type. For the IDW, we used the ArcGIS 10.8.1 to create smoothed maps of Oklahoma ZCTA SIR. Conclusion: This study demonstrates a method that public health practitioners can duplicate with minimal skills, no or low-cost applications, and limited data to assist health care professionals and the community in interpreting cancer in their state.

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