Years of life lost due to mortality from lung cancer (ICD-10 C33-C34). Years of life lost (YLL) is a measure of premature mortality. Its primary purpose is to compare the relative importance of different causes of premature death within a particular population and it can therefore be used by health planners to define priorities for the prevention of such deaths. It can also be used to compare the premature mortality experience of different populations for a particular cause of death. The concept of years of life lost is to estimate the length of time a person would have lived had they not died prematurely. By inherently including the age at which the death occurs, rather than just the fact of its occurrence, the calculation is an attempt to better quantify the burden, or impact, on society from the specified cause of mortality. Legacy unique identifier: P00237

This dataset contains Cancer Incidence data for Lung Cancer (All Stages^) including: Age-Adjusted Rate, Confidence Interval, Average Annual Count, and Trend field information for US States for the average 5 year span from 2016 to 2020.Data are segmented by sex (Both Sexes, Male, and Female) and age (All Ages, Ages Under 50, Ages 50 & Over, Ages Under 65, and Ages 65 & Over), with field names and aliases describing the sex and age group tabulated.For more information, visit statecancerprofiles.cancer.govData NotationsState Cancer Registries may provide more current or more local data.TrendRising when 95% confidence interval of average annual percent change is above 0.Stable when 95% confidence interval of average annual percent change includes 0.Falling when 95% confidence interval of average annual percent change is below 0.† Incidence rates (cases per 100,000 population per year) are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84, 85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used for SEER and NPCR incidence rates.‡ Incidence Trend data come from different sources. Due to different years of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. Please refer to the source for each area for additional information.Rates and trends are computed using different standards for malignancy. For more information see malignant.^ All Stages refers to any stage in the Surveillance, Epidemiology, and End Results (SEER) summary stage.Data Source Field Key(1) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(5) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(6) Source: National Program of Cancer Registries SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention (based on the 2022 submission).(7) Source: SEER November 2022 submission.(8) Source: Incidence data provided by the SEER Program. AAPCs are calculated by the Joinpoint Regression Program and are based on APCs. Data are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84,85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used with SEER November 2022 data.Some data are not available, see Data Not Available for combinations of geography, cancer site, age, and race/ethnicity.Data for the United States does not include data from Nevada.Data for the United States does not include Puerto Rico.

Mortality from lung cancer (ICD-10 C33-C34 equivalent to ICD-9 162). To reduce deaths from lung cancer. Legacy unique identifier: P00508

Summary statistics of average lung cancer incidence rates and average daily smokers in percentage in 8 U.S. geographic regions, 1999–2012.

🩺 Lung CT Scans with Metadata for Smoking and Lung Cancer Classification

This dataset contains 1,097 anonymized CT scan images of lungs, originally sourced from the IQ-OTH/NCCD Lung Cancer Dataset (Hamdallah, 2020), which is released under the CC0: Public Domain license.

The dataset has been enhanced with synthetically generated metadata to support research in: - Lung cancer classification using deep learning - The impact of smoking status on cancer diagnosis - Metadata-integrated CNN model training and explainability

📂 Dataset Contents

• images/ – 1,097 CT scan images in JPG format
• metadata/metadata.csv – A structured CSV file with the following fields:
  • patient_id: File name (e.g., Patient (1))
  • age: Simulated age (e.g., 45)
  • gender: Male / Female
  • smoking_status: Never Smoked / Former Smoker / Current Smoker
  • cancer_diagnosis: 0 = Normal, 1 = Lung Cancer

🧠 Use Case

This dataset was prepared for an MSc dissertation titled:

Using Deep Learning to Analyze the Impact of Smoking on Lung Cancer

It is intended for use in: - Deep learning experiments - CNN and metadata fusion models - Medical image classification - Interpretability analysis (e.g., Grad-CAM)

👤 Class Distribution

• 536 patients are normal (label 0)
• 561 patients are cancerous (label 1)

🔖 License

• Images: Public Domain – IQ-OTH/NCCD Dataset on Kaggle
• Metadata: Created by the uploader. Licensed under CC BY 4.0

🔗 Citation

If you use this dataset in research or teaching, please cite:

Guduru, L. N. K. (2025). Lung CT Scans with Metadata for Smoking and Lung Cancer Classification [Dataset]. Kaggle.
Source images from: Hamdallah, A. (2020). IQ-OTH/NCCD Lung Cancer Dataset. Kaggle.

⚠️ Disclaimer

• All metadata was synthetically generated and does not represent real individuals.
• The dataset is intended strictly for academic and research use only.

Abstract Objective: To identify the socioepidemiologic and histopathologic patterns of lung cancer patients in the Middle Euphrates region. Patients and Methods: This study analyzed medical information from lung cancer patients at the Middle Euphrates Cancer Center in Iraq from January 2018 to December 2023. Demographic information (age, gender, residency, and education level) as well as clinical details (histopathological categorization) were obtained. The inclusion criteria included all confirmed lung cancer cases, while cases with inadequate data or non-lung cancer diagnosis were omitted. The data were analyzed using IBM SPSS Statistics (version 26). The data summarized using descriptive statistics, and chi-square tests used to identify correlations between categorical variables at a significance level of p < 0.05. Ethical approval was obtained from the relevant institutional review board. Results: A total of 1162 patients were included with mean age at diagnosis(64.47±11.45) years. Majority of patients are over 60 years (64.4%), followed by (40–60 years), 34%, and the least affected group is under 40 years (1.6%). Males account for the majority of cases (68%), while females about 32%, with male:female ratio that fluctuate around 2:1. Illiterate patients and those with low education levels represent the largest proportion accounting for about 87.9% of the study population. Squamous Cell Carcinoma (SCC) is the most frequent subtype (41.7%), followed closely by Adenocarcinoma (AC) at 37%, and Small Cell Lung Cancer (SCLC), 10.5%. Although SCC is the predominant subtype overall, AC incidence is increasing overtime (from 31.7% in 2018 to 41.4% in 2023) with predominance in females, younger and higher educated groups. While the percentage of SCLC and other less common subgroups remained relatively stable over time, there is a significant reduction in NSCLC-NOS diagnoses (from 11.1% in 2018 to 3.2% in 2023). Conclusions: In Iraq, specifically in the Middle Euphrates region, lung cancer is a major public health issue in the elder age groups. The two main subtypes, SCC and AC, are the main contributors, with obvious increment in AC cases in the recent years. The shifting trends indicate the urgent need for improved screening strategies, focused preventative initiatives, and customized treatment plans in view of changing risk profiles.

The Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) lung cancer dataset was collected in the above-mentioned specialist hospitals over a period of three months in fall 2019. It includes CT scans of patients diagnosed with lung cancer in different stages, as well as healthy subjects. IQ-OTH/NCCD slides were marked by oncologists and radiologists in these two centers. The dataset contains a total of 1190 images representing CT scan slices of 110 cases (see Figure 1). These cases are grouped into three classes: normal, benign, and malignant. of these, 40 cases are diagnosed as malignant; 15 cases diagnosed with benign; and 55 cases classified as normal cases. The CT scans were originally collected in DICOM format. The scanner used is SOMATOM from Siemens. CT protocol includes: 120 kV, slice thickness of 1 mm, with window width ranging from 350 to 1200 HU and window center from 50 to 600 were used for reading. with breath hold at full inspiration. All images were de-identified before performing analysis. Written consent was waived by the oversight review board. The study was approved by the institutional review board of participating medical centers. Each scan contains several slices. The number of these slices range from 80 to 200 slices, each of them represents an image of the human chest with different sides and angles. The 110 cases vary in gender, age, educational attainment, area of residence and living status. Some of them are employees of the Iraqi ministries of Transport and Oil, others are farmers and gainers. Most of them come from places in the middle region of Iraq, particularly, the provinces of Baghdad, Wasit, Diyala, Salahuddin, and Babylon.

Lung Cancer Prediction

Air Pollution, Alcohol, Smoking & Risk of Lung Cancer

About this dataset

This dataset contains information on patients with lung cancer, including their age, gender, air pollution exposure, alcohol use, dust allergy, occupational hazards, genetic risk, chronic lung disease, balanced diet, obesity, smoking, passive smoker, chest pain, coughing of blood, fatigue, weight loss ,shortness of breath ,wheezing ,swallowing difficulty ,clubbing of finger nails and snoring

How to use the dataset

Lung cancer is the leading cause of cancer death worldwide, accounting for 1.59 million deaths in 2018. The majority of lung cancer cases are attributed to smoking, but exposure to air pollution is also a risk factor. A new study has found that air pollution may be linked to an increased risk of lung cancer, even in nonsmokers.

The study, which was published in the journal Nature Medicine, looked at data from over 462,000 people in China who were followed for an average of six years. The participants were divided into two groups: those who lived in areas with high levels of air pollution and those who lived in areas with low levels of air pollution.

The researchers found that the people in the high-pollution group were more likely to develop lung cancer than those in the low-pollution group. They also found that the risk was higher in nonsmokers than smokers, and that the risk increased with age.

While this study does not prove that air pollution causes lung cancer, it does suggest that there may be a link between the two. More research is needed to confirm these findings and to determine what effect different types and levels of air pollution may have on lung cancer risk

Research Ideas

• predicting the likelihood of a patient developing lung cancer
• identifying risk factors for lung cancer
• determining the most effective treatment for a patient with lung cancer

Acknowledgements

License

See the dataset description for more information.

Columns

File: cancer patient data sets.csv | Column name | Description | |:-----------------------------|:--------------------------------------------------------------------| | Age | The age of the patient. (Numeric) | | Gender | The gender of the patient. (Categorical) | | Air Pollution | The level of air pollution exposure of the patient. (Categorical) | | Alcohol use | The level of alcohol use of the patient. (Categorical) | | Dust Allergy | The level of dust allergy of the patient. (Categorical) | | OccuPational Hazards | The level of occupational hazards of the patient. (Categorical) | | Genetic Risk | The level of genetic risk of the patient. (Categorical) | | chronic Lung Disease | The level of chronic lung disease of the patient. (Categorical) | | Balanced Diet | The level of balanced diet of the patient. (Categorical) | | Obesity | The level of obesity of the patient. (Categorical) | | Smoking | The level of smoking of the patient. (Categorical) | | Passive Smoker | The level of passive smoker of the patient. (Categorical) | | Chest Pain | The level of chest pain of the patient. (Categorical) | | Coughing of Blood | The level of coughing of blood of the patient. (Categorical) | | Fatigue | The level of fatigue of the patient. (Categorical) | | Weight Loss | The level of weight loss of the patient. (Categorical) | | Shortness of Breath | The level of shortness of breath of the patient. (Categorical) | | Wheezing | The level of wheezing of the patient. (Categorical) | | Swallowing Difficulty | The level of swallowing difficulty of the patient. (Categorical) | | Clubbing of Finger Nails | The level of clubbing of finger nails of the patient. (Categorical) |

Objective: Lung cancer is one of the most common cancers worldwide and its survival is still poor. The objective of our study was to estimate long-term survival of Hungarian lung cancer patients at first time based on a nationwide review of the National Health Insurance Fund database.Methods: Our retrospective, longitudinal study included patients aged ≥20 years who were diagnosed with lung cancer (ICD-10 C34) between January 1, 2011 and December 31, 2016. Survival rates were evaluated by year of diagnosis, patient gender and age, and morphology of lung cancer.Results: 41,854 newly diagnosed lung cancer patients were recorded. Mean age at diagnosis varied between 64.7 and 65.9 years during study period. One- and 5-year overall survival rates for the total population were 42.2 and 17.9%, respectively. Survival was statistically associated with gender, age and type of lung cancer. Female patients (n = 16,362) had 23% better survival (HR: 0.77, 95% confidence interval (CI): 0.75–0.79; p < 0.001) than males (n = 25,492). The highest survival rates were found in the 20–49 age cohort (5Y = 31.3%) and if the cancer type was adenocarcinoma (5Y = 20.5%). We measured 5.3% improvement (9.2% adjusted) in lung cancer survival comparing the period 2015–2016 to 2011–2012 (HR: 0.95 95% CI: 0.92–0.97; p = 0.003), the highest at females <60 year (0.86 (adjusted HR was 0.79), interaction analysis was significant for age and histology types.Conclusion: Our study provided long-term Lung cancer survival data in Hungary for the first time. We found a 5.3% improvement in 5-year survival in 4 years. Women and young patients had better survival. Survival rates were comparable to–and at the higher end of–rates registered in other East-Central European countries (7.7%–15.7%).

BackgroundLung cancer is a significant health concern in China. There is limited available data of its burden and trends. This study aims to evaluate the trends of lung cancer across different age groups and genders in China and the Group of Twenty (G20) countries, explore the risk factors, and predict the future trends over a 20-year period.MethodsThe data were obtained from the GBD study 2019. The number of cases, age standardized rate (ASR), and average annual percentage changes (AAPC) were used to estimate the trend in lung cancer by age, gender, region and risk factor. The trend of lung cancer was predicted by autoregressive integrated moving average (ARIMA) model by the “xtarimau” command. The joinpoint regression analysis was conducted to identify periods with the highest changes in incidence and mortality. Additionally, the relationship between AAPCs and socio-demographic index (SDI) was explored.ResultsFrom 1990 to 2019, both the incidence and mortality of lung cancer in China and G20 significantly increased, with China experiencing a higher rate of increase. The years with the highest increase in incidence of lung cancer in China were 1998-2004 and 2007-2010. Among the G20 countries, the AAPC in incidence and mortality of lung cancer in the Republic of Korea was the highest, followed closely by China. Although India exhibited similarities, its AAPC in lung cancer incidence and mortality rates was lower than that of China. The prediction showed that the incidence in China will continue to increase. In terms of risk factors, smoking was the leading attributable cause of mortality in all countries, followed by occupational risk and ambient particulate matter pollution. Notably, smoking in China exhibited the largest increase among the G20 countries, with ambient particulate matter pollution ranking second.ConclusionLung cancer is a serious public health concern in China, with smoking and environmental particulate pollution identified as the most important risk factors. The incidence and mortality rates are expected to continue to increase, which places higher demands on China’s lung cancer prevention and control strategies. It is urgent to tailor intervention measures targeting smoking and environmental pollution to contain the burden of lung cancer.

BackgroundLung cancer is one of the cancers with the highest morbidity and mortality. During the last decade, the trends of clinical characteristics, surgical treatments and survival of lung cancer patients in China have remained unclear.MethodsAll lung cancer patients operated on from 2011 to 2020 were identified in a prospectively maintained database of Sun Yat-sen University Cancer Center.ResultsA total of 7,800 lung cancer patients were included in this study. Within the past 10 years, the average age at diagnosis of the patients remained stable, the proportion of asymptomatic, female and nonsmoking patients increased, and the average tumor size decreased from 3.766 to 2.300 cm. In addition, the proportion of early stage and adenocarcinoma increased, while that of squamous cell carcinoma decreased. Among the patients, the proportion of patients having video-assisted thoracic surgery increased. More than 80% of the patients underwent lobectomy and systematic nodal dissection over the 10 years. Additionally, both the average postoperative length of stay and 1-, 3-, and 6-month postoperative mortality decreased. Moreover, the 1-, 3-, and 5-year overall survival (OS) rates of all the operable patients increased from 89.8, 73.9, and 63.8% to 99.6, 90.7, and 80.8%, respectively. The 5-year OS rates of the patients with stage I, II, and III lung cancer were 87.6, 79.9, and 59.9%, respectively, which were higher than those in other published data.ConclusionThere were significant changes in the clinicopathological characteristics, surgical treatments and survival outcomes of the patients with operable lung cancer from 2011 to 2020.

Objective: While Hungary is often reported to have the highest incidence and mortality rates of lung cancer, until 2018 no nationwide epidemiology study was conducted to confirm these trends. The objective of this study was to estimate the occurrence of lung cancer in Hungary based on a retrospective review of the National Health Insurance Fund (NHIF) database.Methods: Our retrospective, longitudinal study included patients aged ≥20 years who were diagnosed with lung cancer (ICD-10 C34) between 1 Jan 2011 and 31 Dec 2016. Age-standardized incidence and mortality rates were calculated using both the 1976 and 2013 European Standard Populations (ESP).Results: Between 2011 and 2016, 6,996 – 7,158 new lung cancer cases were recorded in the NHIF database annually, and 6,045 – 6,465 all-cause deaths occurred per year. Age-adjusted incidence rates were 115.7–101.6/100,000 person-years among men (ESP 1976: 84.7–72.6), showing a mean annual change of − 2.26% (p = 0.008). Incidence rates among women increased from 48.3 to 50.3/100,000 person-years (ESP 1976: 36.9–38.0), corresponding to a mean annual change of 1.23% (p = 0.028). Age-standardized mortality rates varied between 103.8 and 97.2/100,000 person-years (ESP 1976: 72.8–69.7) in men and between 38.3 and 42.7/100,000 person-years (ESP 1976: 27.8–29.3) in women.Conclusion: Age-standardized incidence and mortality rates of lung cancer in Hungary were found to be high compared to Western-European countries, but lower than those reported by previous publications. The incidence of lung cancer decreased in men, while there was an increase in incidence and mortality among female lung cancer patients.

LungCanC2024 Dataset A Comprehensive Multi-Modal Dataset for Lung Cancer Analysis Overview Lung cancer is one of the leading causes of cancer-related deaths worldwide, requiring precise diagnostic and predictive models for improved patient outcomes. The LungCanC2024 Dataset is a large-scale dataset containing 289,010 patient records with imaging, clinical, and genomic features, designed to support research in machine learning, deep learning, federated learning, and personalized medicine.

The dataset includes detailed radiomic features extracted from imaging, patient demographics, smoking history, tumor staging, treatment records, and biomarker expression levels. Additionally, it contains multi-label classification targets, making it suitable for research in cancer detection, subtype classification, stage prediction, and survival analysis.

Dataset Highlights Multi-modal features: Includes radiological, clinical, and genomic data. Large-scale cohort: Comprises 289,010 patient records, enabling deep learning and statistical modeling. Multi-label learning: Supports cancer presence detection, subtype classification, staging, and survival analysis. Federated Learning Ready: Can be used in privacy-preserving distributed training approaches. Suitable for AI-based Precision Medicine: Helps in developing personalized cancer treatment models. Feature Descriptions This dataset consists of three main categories:

1. Imaging Features (Radiomics) Feature Name Description nodule_size_mm Size of detected lung nodules (measured in mm). nodule_texture Texture-based feature derived from radiological analysis. HU_mean Mean Hounsfield Unit (HU) value from CT scans. HU_std Standard deviation of HU values indicating nodule density variations. GLCM_contrast Gray Level Co-occurrence Matrix (GLCM) contrast, measuring texture heterogeneity. GLCM_correlation GLCM correlation metric assessing pattern consistency. PET_SUVmax Maximum Standardized Uptake Value (SUV) from PET scans, indicating metabolic activity. PET_SUVmean Mean SUV value across the tumor region.
2. Clinical & Metadata Features Feature Name Description patient_age Age of the patient (30-90 years). patient_gender Male (70%) / Female (30%). smoking_history Smoking status: Never, Former, or Current smoker. family_history Binary (1 = Family history of lung cancer, 0 = No family history). tumor_location Left Lung / Right Lung (40% vs. 60%). tumor_stage Stage I-IV classification, with imbalanced distribution. radiation_therapy Binary (1 = Received therapy, 0 = No therapy). chemotherapy_received Whether the patient received chemotherapy (Binary). immunotherapy_received Whether the patient received immunotherapy (Binary). targeted_therapy_received Whether the patient received targeted therapy (Binary).
3. Genomic & Biomarker Features Feature Name Description EGFR_mutation_status Binary (1 = EGFR mutation detected, 0 = No mutation). KRAS_mutation_status Binary (1 = KRAS mutation detected, 0 = No mutation). ALK_fusion_status Binary (1 = ALK gene fusion present, 0 = No fusion). PD-L1_expression_level PD-L1 biomarker expression level (0-100%). tumor_mutational_burden Tumor Mutational Burden (TMB), an indicator of genomic instability.
4. Multi-Label Target Variables Feature Name Description cancer_presence Binary (1 = Malignant tumor detected, 0 = No cancer). cancer_subtype Categorical: No Cancer, Adenocarcinoma, Squamous Cell, Small Cell Lung Cancer (SCLC), Other. cancer_stage Categorical: No Cancer, Stage I, Stage II, Stage III, Stage IV. survival_time_months Estimated survival duration (in months). Potential Applications This dataset is highly suitable for various AI, ML, and deep learning applications, including:

✅ Lung Cancer Detection – Identify malignant vs. benign nodules. ✅ Cancer Subtype Classification – Classify tumor histology. ✅ Tumor Stage Prediction – Predict cancer progression. ✅ Survival Analysis – Model patient prognosis based on clinical/genomic factors. ✅ Federated Learning – Train decentralized AI models for privacy-preserving medical research. ✅ Explainable AI (XAI) in Oncology – Investigate feature importance in lung cancer prediction.

ObjectivesOncology has become more reliant on new testing methods and a greater use of electronic medical records, which provide a plethora of information available to physicians and researchers. However, to take advantage of vital clinical and research data for precision medicine, we must initially make an effort to create an infrastructure for the collection, storage, and utilization of this information with uniquely designed disease-specific registries that could support the collection of a large number of patients.Materials and methodsIn this study, we perform an in-depth analysis of a series of lung adenocarcinoma patients (n = 415) with genomic and clinical data in a recently created thoracic patient registry.ResultsOf the 415 patients with lung adenocarcinoma, 59% (n = 245) were female; the median age was 64 (range, 22–92) years with a median OS of 33.29 months (95% CI, 29.77–39.48). The most common actionable alterations were identified in EGFR (n = 177/415 [42.7%]), ALK (n = 28/377 [7.4%]), and BRAF V600E (n = 7/288 [2.4%]). There was also a discernible difference in survival for 222 patients, who had an actionable alteration, with a median OS of 39.8 months as compared to 193 wild-type patients with a median OS of 26.0 months (P<0.001). We identified an unprecedented number of actionable alterations [53.5% (222/415)], including distinct individual alteration rates, as compared with 15.0% and 22.3% in TCGA and GENIE respectively.ConclusionThe use of patient registries, focused genomic panels and the appropriate use of clinical guidelines in community and academic settings may influence cohort selection for clinical trials and improve survival outcomes.

BackgroundAbout 15% of lung cancers in men and 53% in women are not attributable to smoking worldwide. The aim was to develop and validate a simple and non-invasive model which could assess and stratify lung cancer risk in non-smokers in China.MethodsA large-sample size, population-based study was conducted under the framework of the Cancer Screening Program in Urban China (CanSPUC). Data on the lung cancer screening in Henan province, China, from October 2013 to October 2019 were used and randomly divided into the training and validation sets. Related risk factors were identified through multivariable Cox regression analysis, followed by establishment of risk prediction nomogram. Discrimination [area under the curve (AUC)] and calibration were further performed to assess the validation of risk prediction nomogram in the training set, and then validated by the validation set.ResultsA total of 214,764 eligible subjects were included, with a mean age of 55.19 years. Subjects were randomly divided into the training (107,382) and validation (107,382) sets. Elder age, being male, a low education level, family history of lung cancer, history of tuberculosis, and without a history of hyperlipidemia were the independent risk factors for lung cancer. Using these six variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.753, 0.752, and 0.755 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a moderate predictive discrimination, with the AUC was 0.668, 0.678, and 0.685 for the 1-, 3- and 5-year lung cancer risk.ConclusionsWe developed and validated a simple and non-invasive lung cancer risk model in non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in non-smokers.

Number and rate of new cancer cases diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

Background: Paired tumor-normal targeted next-generation sequencing (NGS) is primarily used to identify actionable somatic mutations, but can also detect germline variants including pathogenic germline mutations in DNA mismatch repair (MMR) genes that underlie Lynch syndrome. In the present study we examined paired NGS data from lung cancer patients to identify germline mutations in MMR genes. As lung cancer is not one of the recognized Lynch syndrome-associated neoplasms, we also investigated whether these lung cancer cases are due to Lynch syndrome or are instead sporadic cancers occurring in Lynch syndrome patients.Methods: A retrospective study of 1,179 lung cancer patients with available paired NGS data was performed to identify germline mutations in the MMR genes MLH1, MSH2, MSH6, and PMS2, and evaluate tumor mutation burden (TMB). Microsatellite instability (MSI) testing was done on select cases with MMR gene mutations by either NGS or PCR/capillary electrophoresis approach. Immunohistochemistry (IHC) for MMR proteins was performed in select patients.Results: Pathogenic or likely-pathogenic germline mutations in PMS2, MSH2, or MSH6 were detected in 0.5% (6/1,179) of lung cancer patients; three of the patients had a family history of colon or gastric cancer. The median age at diagnosis of these cases was 68.5 years old. None of these six patients exhibited MSI or loss of MMR protein expression. Among them, no second hit somatic mutations in MMR genes (including single-nucleotide variants, small insertions or deletions and copy number alterations) were detected, and the median TMB was 4.5 muts/MB. Subsequent genetic testing of family members identified new Lynch syndrome cases in two first-degree relatives.Conclusion: These data imply that lung cancers in Lynch syndrome patients are unrelated to the underlying Lynch syndrome diagnosis and occur spontaneously. Nonetheless, paired tumor-normal NGS can identify germline mutations to help reveal Lynch syndrome in cancer patients. This has important implications for cancer screening and risk reduction in these patients and their families.

Background: Lung cancer has inherited susceptibility and show familial aggregation, the characteristics of familial lung cancer exhibit population heterogeneity. Despite previous studies, familial lung cancer in China's Yunnan-Guizhou plateau remains understudied.Methods: Between 2015 and 2017, 1,023 lung cancer patients (residents of Yunnan-Guizhou plateau) were enrolled with no limitation on other parameters, 152 subjects had familial lung cancer. Clinicopathologic parameters were analyzed and compared, 4,754 lung cancer patients from NCI-GDC were used to represent a general population.Results: Familial lung cancer (FLC) subjects showed unique characters: early-onset; increased rate of female, adenocarcinoma, stage IV and other cancer history; unbalance in anatomic sites; all ruling out significant difference in smoking status. Unbalanced distribution of co-existing diseases or symptoms was also discovered. FLC patients were more likely to develop benign lesions (polyps, nodules, cysts) early in life, especially early-growth of multiple pulmonary nodules at higher frequency. Typical diseases with family history like diabetes and hypertension were also increased in FLC population. Compared to GDC data, our subject population was younger: the age peak of our FLC group was in 50–59; our sporadic group had an age peak around 60; while GDC patients' age peak was in 60–69. Importantly, the biggest difference happened in age 40–49: our FLC group and sporadic group had 3 times and 2 times higher ratio than GDC population, respectively. Moreover, the age peaks of our FLC males and FLC females were both in 50–59; while our sporadic females had the age peak in 50–59, much earlier than sporadic males (around 60–69); reflecting gender-specific or age-specific characters in our subject population.Conclusions: Familial lung cancer in China's Yunnan-Guizhou plateau showed unique clinicopathologic characters, differences were found in gender, age, histologic type, TNM stage and co-existing diseases or symptoms. Identification of hereditary factors which lead to increased lung cancer risk will be a challenge of both scientific and clinical significance.

In this study, we developed a method for modeling the progression and detection of lung cancer based on the smoking behavior at an individual level. The model allows obtaining the characteristics of lung cancer in a population at the time of diagnosis. Lung cancer data from Surveillance, Epidemiology and End Results (SEER) database collected between 2004 and 2008 were used to fit the lung cancer progression and detection model. The fitted model combined with a smoking based carcinogenesis model was used to predict the distribution of age, gender, tumor size, disease stage and smoking status at diagnosis and the results were validated against independent data from the SEER database collected from 1988 to 1999. The model accurately predicted the gender distribution and median age of LC patients of diagnosis, and reasonably predicted the joint tumor size and disease stage distribution.

Age standardized rate of cancer incidence, by selected sites of cancer and sex, three-year average, census metropolitan areas.