One-year and five-year net survival for adults (15-99) in England diagnosed with one of 29 common cancers, by age and sex.

Haberman data set for cancer survival

Haverman data set is the result of the research conducted between 1958 and 1970 to examine the patient will survived for less than 5 years or grater than equal to five years after operation. The study was held at the University of Chicago's Billings Hospital.

Content

It contains the three features and two classes

1. age - Age of patient

2. year - Year of operation

3. nodes - Positive Lymph Nodes

4. class 1 - Patient survived more than 5 years, class 2 - patient survived less than 5 years

all columns are numerical data

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

A measure of the number of adults diagnosed with breast, lung or colorectal cancer in a year who are still alive five years after diagnosis. ONS still publish survival percentages for individual types of cancers. These can be found at: http://www.ons.gov.uk/ons/rel/cancer-unit/cancer-survival/cancer-survival-in-england--patients-diagnosed-2007-2011-and-followed-up-to-2012/index.html A time series for five-year survival figures for breast, lung and colorectal cancer individually (previous NHS Outcomes Framework indicators 1.4.ii, 1.4.iv and 1.4.vi) is still published and can be found under the link 'Indicator data - previous methodology (.xls)' below. Purpose This indicator attempts to capture the success of the NHS in preventing people from dying once they have been diagnosed with breast, lung or colorectal cancer. Current version updated: May-14 Next version due: To be confirmed

Age-standardised rate of mortality from oral cancer (ICD-10 codes C00-C14) in persons of all ages and sexes per 100,000 population.RationaleOver the last decade in the UK (between 2003-2005 and 2012-2014), oral cancer mortality rates have increased by 20% for males and 19% for females1Five year survival rates are 56%. Most oral cancers are triggered by tobacco and alcohol, which together account for 75% of cases2. Cigarette smoking is associated with an increased risk of the more common forms of oral cancer. The risk among cigarette smokers is estimated to be 10 times that for non-smokers. More intense use of tobacco increases the risk, while ceasing to smoke for 10 years or more reduces it to almost the same as that of non-smokers3. Oral cancer mortality rates can be used in conjunction with registration data to inform service planning as well as comparing survival rates across areas of England to assess the impact of public health prevention policies such as smoking cessation.References:(1) Cancer Research Campaign. Cancer Statistics: Oral – UK. London: CRC, 2000.(2) Blot WJ, McLaughlin JK, Winn DM et al. Smoking and drinking in relation to oral and pharyngeal cancer. Cancer Res 1988; 48: 3282-7. (3) La Vecchia C, Tavani A, Franceschi S et al. Epidemiology and prevention of oral cancer. Oral Oncology 1997; 33: 302-12.Definition of numeratorAll cancer mortality for lip, oral cavity and pharynx (ICD-10 C00-C14) in the respective calendar years aggregated into quinary age bands (0-4, 5-9,…, 85-89, 90+). This does not include secondary cancers or recurrences. Data are reported according to the calendar year in which the cancer was diagnosed.Counts of deaths for years up to and including 2019 have been adjusted where needed to take account of the MUSE ICD-10 coding change introduced in 2020. Detailed guidance on the MUSE implementation is available at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/articles/causeofdeathcodinginmortalitystatisticssoftwarechanges/january2020Counts of deaths for years up to and including 2013 have been double adjusted by applying comparability ratios from both the IRIS coding change and the MUSE coding change where needed to take account of both the MUSE ICD-10 coding change and the IRIS ICD-10 coding change introduced in 2014. The detailed guidance on the IRIS implementation is available at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/impactoftheimplementationofirissoftwareforicd10causeofdeathcodingonmortalitystatisticsenglandandwales/2014-08-08Counts of deaths for years up to and including 2010 have been triple adjusted by applying comparability ratios from the 2011 coding change, the IRIS coding change and the MUSE coding change where needed to take account of the MUSE ICD-10 coding change, the IRIS ICD-10 coding change and the ICD-10 coding change introduced in 2011. The detailed guidance on the 2011 implementation is available at https://webarchive.nationalarchives.gov.uk/ukgwa/20160108084125/http://www.ons.gov.uk/ons/guide-method/classifications/international-standard-classifications/icd-10-for-mortality/comparability-ratios/index.htmlDefinition of denominatorPopulation-years (aggregated populations for the three years) for people of all ages, aggregated into quinary age bands (0-4, 5-9, …, 85-89, 90+)

This release summarises the survival of adults diagnosed with cancer in England between 2016 and 2020 and followed to 2021, and children diagnosed with cancer in England between 2002 and 2020 and followed to 2021. Adult cancer survival estimates are presented by age, deprivation, gender, stage at diagnosis, and geography.

A measure of the number of adults diagnosed with any type of cancer in a year who are still alive five years after diagnosis. Purpose This indicator attempts to capture the success of the NHS in preventing people from dying once they have been diagnosed with any type of cancer. Current version updated: Feb-17 Next version due: Feb-18

A measure of the number of adults diagnosed with any type of cancer in a year who are still alive one year after diagnosis. Purpose This indicator attempts to capture the success of the NHS in preventing people from dying once they have been diagnosed with any type of cancer. Current version updated: Feb-17 Next version due: Feb-18

This dataset presents the mortality rate from cancer among individuals under the age of 75 within the Birmingham and Solihull area. It captures the number of deaths attributed to all cancers (classified under ICD-10 codes C00 to C97) and expresses this as a directly age-standardised rate per 100,000 population. The data is structured in quinary age bands and is available for both single-year and three-year rolling averages, providing a comprehensive view of premature cancer mortality trends in the region.

Rationale Reducing premature mortality from cancer is a key public health priority. This indicator helps track progress in lowering the number of cancer-related deaths among people under 75, supporting efforts to improve early diagnosis, treatment, and prevention strategies.

Numerator The numerator is the number of deaths from all cancers (ICD-10 codes C00 to C97) registered in the respective calendar years, for individuals aged under 75. These figures are aggregated into quinary age bands and sourced from the Death Register.

Denominator The denominator is the population of individuals under 75 years of age, also aggregated into quinary age bands. For single-year rates, the population for that year is used. For three-year rolling averages, the population-years are aggregated across the three years. The source of this data is the 2021 Census.

Caveats Data may not align exactly with published Office for National Statistics (ONS) figures due to differences in postcode lookup versions and the application of comparability ratios in Office for Health Improvement and Disparities (OHID) data. Users should be cautious when comparing this dataset with other national statistics.

External references Further information and related indicators can be found on the OHID Fingertips platform.

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

Update 2 March 2023: Following the merger of NHS Digital and NHS England on 1st February 2023 we are reviewing the future presentation of the NHS Outcomes Framework indicators. As part of this review, the annual publication which was due to be released in March 2023 has been delayed. Further announcements about this dataset will be made on this page in due course. A measure of the number of adults diagnosed with any type of cancer in a year who are still alive five years after diagnosis. This indicator attempts to capture the success of the NHS in preventing people from dying once they have been diagnosed with any type of cancer. As of May 2020, please refer to the data tables published by Public Health England (PHE). This publication is released on an annual basis. A link to the PHE publications, within which the data is held, is available via the resource link below. On the publication page select the ‘Data Tables index of cancer survival 20xx to 20xx’. The data for this indicator is available by applying suitable filters to the dataset contained within the 'Data_Complete’ tab. Legacy unique identifier: P01735

A measure of the number of adults diagnosed with any type of cancer in a year who are still alive one year after diagnosis. This indicator attempts to capture the success of the NHS in preventing people from dying once they have been diagnosed with any type of cancer. As of May 2020, please refer to the data tables published by Public Health England (PHE). This publication is released on an annual basis. A link to the PHE publications, within which the data is held, is available via the resource link below. On the publication page select the ‘Data Tables index of cancer survival 20xx to 20xx’. The data for this indicator is available by applying suitable filters to the dataset contained within the 'Data_Complete’ tab. Legacy unique identifier: P01734

A measure of the number of adults diagnosed with breast, lung or colorectal cancer in a year who are still alive one year after diagnosis. ONS still publish survival percentages for individual types of cancers. These can be found at: http://www.ons.gov.uk/ons/rel/cancer-unit/cancer-survival/cancer-survival-in-england--patients-diagnosed-2007-2011-and-followed-up-to-2012/index.html A time series for one-year survival figures for breast, lung and colorectal cancer individually (previous NHS Outcomes Framework indicators 1.4.i, 1.4.iii and 1.4.v) is still published and can be found under the link 'Indicator data - previous methodology (.xls)' below. Purpose This indicator attempts to capture the success of the NHS in preventing people from dying once they have been diagnosed with breast, lung or colorectal cancer. Current version updated: Feb-14 Next version due: To be confirmed

This dataset helps understand and predict lung cancer risks based on health, environment, and lifestyle factors. It includes details about smoking habits, pollution exposure, healthcare access, and survival chances.

Doctors, researchers, and data scientists can use it to find patterns in lung cancer cases and improve early detection.

Columns Breakdown (25 Features) Country – The country where the patient resides Age – Patient’s age (randomized between 30-90) Gender – Male/Female Smoking_Status – Smoker, Non-Smoker, Former Smoker Second_Hand_Smoke – Yes/No Air_Pollution_Exposure – Low, Medium, High Occupation_Exposure – Yes/No (Factory, Mining, etc.) Rural_or_Urban – Rural/Urban Socioeconomic_Status – Low, Middle, High Healthcare_Access – Good, Limited, Poor Insurance_Coverage – Yes/No Screening_Availability – Yes/No Stage_at_Diagnosis – I, II, III, IV Cancer_Type – NSCLC, SCLC Mutation_Type – EGFR, ALK, KRAS, None Treatment_Access – Full, Partial, None Clinical_Trial_Access – Yes/No Language_Barrier – Yes/No Mortality_Risk – Probability (0.0 - 1.0) 5_Year_Survival_Probability – Probability (0.0 - 1.0) Delay_in_Diagnosis – Yes/No Family_History – Yes/No Indoor_Smoke_Exposure – Yes/No Tobacco_Marketing_Exposure – Yes/No Final_Prediction – Lung Cancer (Yes/No)

Context

In order to produce cancer estimates in Brazil, the governmet, more specificly the National Cancer Intitute (INCA), has systematic centers for collection of data. They are known as RCBP (Cancer Registers with Populational Basis). This data is in accordance with regional laws and can be required by anyone.

Here I translated the variables in order to help in any analysis, but most of the values are not translated due to lazyness. However almost every term is translatable using google or part of a international code system (CID-10 -- classification of diseases -- or CID-O3 -- classification of cancers having in mind topography and morphology). More about the terms can be seen here (unfortunentely this document is in portuguese): www.inca.gov.br/publicacoes/manuais/manual-de-rotinas-e-procedimentos-para-registros-de-cancer-de-base-populacional

Moreover I added estimated populational data of almost all cities in Brazil. This data is produced by IBGE and was organized bt Ricardo Dahis (email: rdahis@basedosdados.org | github_user: rdahis | website: www.ricardodahis.com | ckan_user: rdahis) and can be dowloaded again here https://basedosdados.org/dataset/br-ibge-populacao

Content

This data is quite organized, however it has some flaws: 1) RCBP were added throughout the time 2) People do not always are treated in their state, so ratios can be implicated by it 3) It seems that there is a lack of data from 2013-2019

Even though, this is the best dataset possible in terms of what is happening in cancer in Brazil!

Acknowledgements

This dataset was entirely produced by INCA and I only translated some terms and replaced strings that meant NA for NA.

What should you do with it?

There are some questions that I believe that can be answerd

1) Which cancers are more incident in which population/sub-populations ? 2) Which cancers are had their survival rate enhanced? 3) Do people treat their cancers in their state or they go to other states? is there any trends related to that? 4) Do some centers treat their patients better than others? (is their big differences in outcome depening on where the person was diagnosed) 5) How badly do people fill these forms? (How much NA their is? How much unspecific? Which variables are simply unusable?)

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of cancer (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to cancer (in persons of all ages).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (all ages) with cancer was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with cancer was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with cancer, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have cancerB) the NUMBER of people within that MSOA who are estimated to have cancerAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have cancer, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from cancer, and where those people make up a large percentage of the population, indicating there is a real issue with cancer within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of cancer, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of cancer.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.Population data: Mid-2019 (June 30) Population Estimates for Middle Layer Super Output Areas in England and Wales. © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2020.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021. © Crown Copyright 2020.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Selection of study population: adenocarcinoma (8140-3), carcinosarcoma (8980-3) were selected using International Classification of Diseases in Oncology-3 (ICD-O-3) histology codes. patients between 19 and 100 years of age diagnosed with carcinosarcoma of the gallbladder and with adenocarcinoma of the gallbladder from 2004-2015 were included in the study population.The following were the exclusion standards: 1. no follow-up or vital status information. 2. non-first primary tumor or no clear pathological diagnosis. 3. marital status unknown. 4. race information unknown. 5. t-stage unknown and t0 stage. 6. m-stage unknown. 7. SEER Stage information unknown. 8. incomplete surgical information.

Selection of variables: The following clinical information was obtained for each patient (age, gender, race, marital status, SEER stage, T stage, M stage, AJCC stage, surgery, radiation therapy, chemotherapy, months of survival, and vital status). Patients were evaluated for staging using the American Joint Committee on Cancer (AJCC) 6th edition staging system. The type of surgery was divided into three groups: no surgery (0), undergoing simple cholecystectomy (including local tumor resection or destruction (10-11, 20-27), simple resection of the primary site (30), complete resection of the primary site (40), and tumor reduction (50)), and radical cholecystectomy (60). In this investigation, the sixth edition of the American Joint Committee on Cancer (AJCC) staging manual was used. Overall survival (OS) and cancer-specific survival (CSS) were the main outcomes.Overall survival (OS) was defined as the time from the patient's diagnosis to death from any cause. Cancer-specific survival (CSS) was defined as the interval between the patient's diagnosis of this tumor and death.

ObjectiveTo compare the performance of three machine learning algorithms with the tumor, node, and metastasis (TNM) staging system in survival prediction and validate the individual adjuvant treatment recommendations plan based on the optimal model.MethodsIn this study, we trained three machine learning madel and validated 3 machine learning survival models-deep learning neural network, random forest and cox proportional hazard model- using the data of patients with stage-al3 NSCLC patients who received resection surgery from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database from 2012 to 2017,the performance of survival predication from all machine learning models were assessed using a concordance index (c-index) and the averaged c-index is utilized for cross-validation. The optimal model was externally validated in an independent cohort from Shaanxi Provincial People’s Hospital. Then we compare the performance of the optimal model and TNM staging system. Finally, we developed a Cloud-based recommendation system for adjuvant therapy to visualize survival curve of each treatment plan and deployed on the internet.ResultsA total of 4617 patients were included in this study. The deep learning network performed more stably and accurately in predicting stage-iii NSCLC resected patients survival than the random survival forest and Cox proportional hazard model on the internal test dataset (C-index=0.834 vs. 0.678 vs. 0.640) and better than TNM staging system (C-index=0.820 vs. 0.650) in the external validation. The individual patient who follow the reference from recommendation system had superior survival compared to those who did not. The predicted 5-year-survival curve for each adjuvant treatment plan could be accessed in the recommender system via the browser.ConclusionDeep learning model has several advantages over linear model and random forest model in prognostic predication and treatment recommendations. This novel analytical approach may provide accurate predication on individual survival and treatment recommendations for resected Stage-iii NSCLC patients.

Despite structural and cultural similarities across the Nordic countries, differences in cancer survival remain. With a focus on similarities and differences between the Nordic countries, we investigated the association between socioeconomic position (SEP) and stage at diagnosis, anticancer treatment and cancer survival to describe patterns, explore underlying mechanisms and identify knowledge gaps in the Nordic countries We conducted a systematic review of population based observational studies. A systematic search in PubMed, EMBASE and Medline up till May 2021 was performed, and titles, abstracts and full texts were screened for eligibility by two investigators independently. We extracted estimates of the association between SEP defined as education or income and cancer stage at diagnosis, received anticancer treatment or survival for adult patients with cancer in the Nordic countries. Further, we extracted information on study characteristics, confounding variables, cancer type and results in the available measurements with corresponding confidence intervals (CI) and/or p-values. Results were synthesized in forest plots. From the systematic literature search, we retrieved 3629 studies, which were screened for eligibility, and could include 98 studies for data extraction. Results showed a clear pattern across the Nordic countries of socioeconomic inequality in terms of advanced stage at diagnosis, less favorable treatment and lower cause-specific and overall survival among people with lower SEP, regardless of whether SEP was measured as education or income. Despite gaps in the literature, the consistency in results across cancer types, countries and cancer outcomes shows a clear pattern of systematic socioeconomic inequality in cancer stage, treatment and survival in the Nordic countries. Stage and anticancer treatment explain some, but not all of the observed inequality in overall and cause-specific survival. The need for further studies describing this association may therefore be limited, warranting next step research into interventions to reduce inequality in cancer outcomes. Prospero protocol no: CRD42020166296

The aim of the CCSS is to better understand the long-term effects of childhood cancer and its treatment, as well as determine the factors that increase the risk for adverse outcomes. Participants includes individuals recruited from 31 clinical centres across North America who survived five or more years after a cancer diagnosis during childhood or adolescence (< 21 years) and their siblings, who serve as a comparison group. The original cohort included over 14,000 people who had been diagnosed with cancer between 1970 and 1987, completing baseline assessment in 1994. The expansion cohort was added in 2008, including a further 11,000 people who were diagnosed with cancer between 1987 and 1999. Over 5,000 siblings are included in the study from baseline data collection in 1994. Participants have been followed up for the main study up to 8 times, with the most recent follow up in 2022. There have also been several ancillary studies, in addition to the main questionnaire and biological samples collected.

BackgroundPancreatic cancer (PC) is a highly malignant tumor of the digestive system. The number of elderly patients with PC is increasing, and older age is related to a worse prognosis. Accurate prognostication is crucial in treatment decisions made for people diagnosed with PC. However, an accurate predictive model for the prognosis of these patients is still lacking. We aimed to construct nomograms for predicting the overall survival (OS) of elderly patients with PC.MethodsPatients with PC, older than 65 years old from 2010 to 2015 in the Surveillance, Epidemiology, and End Results database, were selected and randomly divided into training cohort (n = 4,586) and validation cohort (n = 1,966). Data of patients in 2016–2018 (n = 1,761) were used for external validation. Univariable and forward stepwise multivariable Cox analysis was used to determine the independent prognostic factors. We used significant variables in the training set to construct nomograms predicting prognosis. The performance of the models was evaluated for their discrimination and calibration power based on the concordance index (C-index), calibration curve, and the decision curve analysis (DCA).ResultsAge, insurance, grade, surgery, radiation, chemotherapy, T, N, and American Joint Commission on Cancer were independent predictors for OS and thus were included in our nomogram. In the training cohort and validation cohort, the C-indices of our nomogram were 0.725 (95%CI: 0.715–0.735) and 0.711 (95%CI: 0.695–0.727), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves showed similar results. The calibration curves showed a high consensus between observations and predictions. In the external validation cohort, C-index (0.797, 95%CI: 0.778–0.816) and calibration curves also revealed high consistency between observations and predictions. The nomogram-related DCA curves showed better clinical utility compared to tumor-node-metastasis staging. In addition, we have developed an online prediction tool for OS.ConclusionsA web-based prediction model for OS in elderly patients with PC was constructed and validated, which may be useful for prognostic assessment, treatment strategy selection, and follow-up management of these patients.

ObjectiveAutoantibodies have been reported to be associated with cancers. As a biomarker, autoantibodies have been widely used in the early screening of lung cancer. However, the correlation between autoantibodies and the prognosis of lung cancer patients is poorly understood, especially in the Asian population. This retrospective study investigated the association between the presence of autoantibodies and outcomes in patients with lung cancer.MethodsA total of 264 patients diagnosed with lung cancer were tested for autoantibodies in Henan Provincial People’s Hospital from January 2017 to June 2022. The general clinical data of these patients were collected, and after screening out those who met the exclusion criteria, 151 patients were finally included in the study. The Cox proportional hazards model was used to analyze the effect of autoantibodies on the outcomes of patients with lung cancer. The Kaplan-Meier curve was used to analyze the relationship between autoantibodies and the overall survival of patients with lung cancer.ResultsCompared to lung cancer patients without autoantibodies, those with autoantibodies had an associated reduced risk of death (HRs: 0.45, 95% CIs 0.27~0.77), independent of gender, age, smoking history, pathological type, and pathological stage of lung cancer. Additionally, the association was found to be more significant by subgroup analysis in male patients, younger patients, and patients with small cell lung cancer. Furthermore, lung cancer patients with autoantibodies had significantly longer survival time than those without autoantibodies.ConclusionThe presence of autoantibodies is an independent indicator of good prognosis in patients with lung cancer, providing a new biomarker for prognostic evaluation in patients with lung cancer.