Epidemiologie des nichtkleinzelligen Lungenkarzinoms in Deutschland

Kraywinkel, Klaus; Schönfeld, Ina

doi:10.1007/s00761-018-0480-2

Cited by 13 publications

(7 citation statements)

References 10 publications

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“…Lung cancer belongs to the three most frequently diagnosed cancers in Germany and accounts for 24% and 16% of the cancer-related deaths in men and women, respectively [1]. With 80% of all lung cancer diagnoses, non-small cell lung cancer (NSCLC) is the most common histologic subtype, the majority of these being adenocarcinomas (46% in women, 36% in men) or squamous-cell carcinomas (15% in women, 29% in men) [2]. The course of disease in NSCLC is highly heterogeneous, with prognosis as well as therapeutic options depending on the molecular biology of the tumor.…”

Section: Introductionmentioning

confidence: 99%

Biomarker testing in non-small cell lung cancer in routine care: Analysis of the first 3,717 patients in the German prospective, observational, nation-wide CRISP Registry (AIO-TRK-0315)

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Biomarker testing in non-small cell lung cancer in routine care: Analysis of the first 3,717 patients in the German prospective, observational, nation-wide CRISP Registry (AIO-TRK-0315)

et al. 2021

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“…At least 85.2% of all women tested (52 out of 61) were TTF1 positive, but only 78.3% of all men tested were positive (94 out of 120). When tested for the association between sex and TTF1 expression, P = .266 (Chi 2 ), Fisher 0.322. Among non-smokers, the TTF1 rate slightly increased to 85.3% (P = .448; Fisher 0.630).…”

Section: Resultsmentioning

confidence: 99%

“…There are various histological subtypes, with adenocarcinoma (AC) being the most common subtype at approximately 40%. 2 Typical markers of AC include napsin A and thyroid transcription factor 1 ( TTF1 ). 3 TTF1 is particularly relevant as an immunohistochemical marker for the identification of the primary tumor metastasis or differentiation of carcinoma, as it indicates the development of lungs AC.…”

Section: Introductionmentioning

confidence: 99%

Retrospective Data Analysis of Patients With Metastatic Lung Adenocarcinoma With or Without KRAS-Mutation or TTF1-Expression

2022

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Introduction Patients with lung adenocarcinoma not expressing TTF1 and those with a KRAS mutation have worse prognosis. However, available data are limited and sometimes contradictory. Therefore, this retrospective cohort analysis aimed to clarify whether there was a difference in overall survival and progression-free survival between these groups of patients. Methods In total, data derived from 181 patients with metastatic lung adenocarcinoma treated at the Martha-Maria Halle-Dölau Hospital from 2016 to 2019 were analyzed. Kaplan-Meier curves were generated, and associated values, such as median survival and its confidence intervals, were determined using the log-rank test. Results A benefit in overall survival (OS) (8.4 vs 5.8 months; HR, .8; 95% CI, .53-1.19; P = .267) was associated with positive TTF1 expression, but this was not statistically significant. The same trend was shown with the progressive free survival (PFS) (6.5 vs 4.6 months; HR, .76; 95% CI, .51-1.20; P = .162). In patients with a KRAS mutation, there was no difference in OS compared to those with a wildtype KRAS. The median survival was almost identical at 7.5 months ( KRAS mutation, 95% CI, 3.32-11.74) and 7.0 months ( KRAS wildtype, 95% CI, 3.59-10.41). Additionally, in PFS, there was no difference between the 2 groups (5.8 vs 6.3 months). Conclusions Our analysis did not show a worse prognosis in patients with a KRAS mutation or in those with missing TTF1 expression, which is most likely related to the new therapeutic options. As a result of the administration of immunotherapy in patients with a KRAS mutation and the change from a regimen containing pemetrexed to a regimen containing no pemetrexed, the corresponding patients no longer seem to have a worse prognosis.

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“…A histological distinction is made between non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) [ 1 – 3 ]. NSCLC dominates in terms of incidence, accounting for around 80% of all malignant lung tumors [ 1 , 2 , 5 ]. SCLC, by contrast, has a significantly lower incidence rate of 20% [ 1 , 3 , 6 ] but is prognostically even less favorable due to early lymph node and distant metastasis, which is also reflected in significantly lower survival rates compared to NSCLC [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…NSCLC dominates in terms of incidence, accounting for around 80% of all malignant lung tumors [1,2,5]. SCLC, by contrast, has a significantly lower incidence rate of 20% [1,3,6] but is prognostically even less favorable due to early lymph node and distant metastasis, which is also reflected in significantly lower survival rates compared to NSCLC [5,6]. Since most lung cancers are usually only diagnosed at an advanced stage, prompt, targeted therapeutic or palliative intervention is required [7].…”

Section: Introductionmentioning

confidence: 99%

Use of algorithms for identifying patients in a German claims database: learnings from a lung cancer case

Neugebauer

Griesinger

Dippel³

et al. 2022

BMC Health Serv Res

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Background The analysis of statutory health insurance (SHI) data is a little-used approach for understanding treatment and care as well as resource use of lung cancer (LC) patients in Germany. The aims of this observational, retrospective, longitudinal analysis of structured data were to analyze the healthcare situation of LC patients in Germany based on routine data from SHI funds, to develop an algorithm that sheds light on LC types (non-small cell / NSCLC vs. small cell / SCLC), and to gain new knowledge to improve needs-based care. Methods Anonymized billing data of approximately four million people with SHI were analyzed regarding ICD-10 (German modification), documented medical interventions based on the outpatient SHI Uniform Assessment Standard Tariff (EBM) or the inpatient Operations and Procedure Code (OPS), and the dispensing of prescription drugs to outpatients (ATC classification). The study included patients who were members of 64 SHI funds between Jan-1st, 2015 and Dec-31st, 2016 and who received the initial diagnosis of LC in 2015 and 2016. Results The analysis shows that neither the cancer type nor the cancer stage can be unambiguously described by the ICD-10 coding. Furthermore, an assignment based on the prescribed medication provides only limited information: many of the drugs are either approved for both LC types or are used off-label, making it difficult to assign them to a specific LC type. Overall, 25% of the LC patients were unambiguously identifiable as NSCLC vs SCLC based on the ICD-10 code, the drug therapy, and the billing data. Conclusions The current coding system appears to be of limited suitability for drawing conclusions about LC and therefore the SHI patient population. This makes it difficult to analyze the healthcare data with the aim of gathering new knowledge to improve needs-based care. The approach chosen for this study did not allow for development of a LC differentiation algorithm based on the available healthcare data. However, a better overview of patient specific needs could make it possible to modify the range of services provided by the SHI funds. From this perspective, it makes sense, in a first step, to refine the ICD-10 system to facilitate NSCLC vs. SCLC classification.

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Epidemiologie des nichtkleinzelligen Lungenkarzinoms in Deutschland

Cited by 13 publications

References 10 publications

Biomarker testing in non-small cell lung cancer in routine care: Analysis of the first 3,717 patients in the German prospective, observational, nation-wide CRISP Registry (AIO-TRK-0315)

Biomarker testing in non-small cell lung cancer in routine care: Analysis of the first 3,717 patients in the German prospective, observational, nation-wide CRISP Registry (AIO-TRK-0315)

Retrospective Data Analysis of Patients With Metastatic Lung Adenocarcinoma With or Without KRAS-Mutation or TTF1-Expression

Use of algorithms for identifying patients in a German claims database: learnings from a lung cancer case

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