Lung Cancer Risk Prediction Model Incorporating Lung Function: Development and Validation in the UK Biobank Prospective Cohort Study

Development and evaluation of a risk prediction tool for risk-adapted screening of colorectal cancer in China

Risk prediction tools for colorectal cancer (CRC) have potential to improve the efficiency of population-based screening by facilitating risk-adapted strategies. However, such an applicable tool has yet to be established in the Chinese population. In this study, a risk score was created using data from the China Kadoorie Biobank (CKB), a nationwide cohort study of 409,854 eligible participants. Diagnostic performance of the risk score was evaluated in an independent CRC screening programme, which included 91,575 participants who accepted colonoscopy at designed hospitals in Zhejiang Province, China. Over a median follow-up of 11.1 years, 3136 CRC cases were documented in the CKB. A risk score was created based on nine questionnaire-derived variables, showing moderate discrimination for 10-year CRC risk (C-statistic = 0.68, 95 % CI: 0.67-0.69). In the CRC screening programme, the detection rates of CRC were 0.25 %, 0.82 %, and 1.93 % in low-risk (score <6), intermediate-risk (score: 6-19), and high-risk (score >19) groups, respectively. The newly developed score exhibited a C-statistic of 0.65 (95 % CI: 0.63-0.66), surpassing the widely adopted tools such as the Asia-Pacific Colorectal Screening (APCS), modified APCS, and Korean Colorectal Screening scores (all C-statistics = 0.60). In conclusion, we developed a novel risk prediction tool that is useful to identify individuals at high risk of CRC. A user-friendly online calculator was also constructed to encourage broader adoption of the tool.

Predicting the risk of lung cancer using machine learning: A large study based on UK Biobank

In response to the high incidence and poor prognosis of lung cancer, this study tends to develop a generalizable lung-cancer prediction model by using machine learning to define high-risk groups and realize the early identification and prevention of lung cancer. We included 467,888 participants from UK Biobank, using lung cancer incidence as an outcome variable, including 49 previously known high-risk factors and less studied or unstudied predictors. We developed multivariate prediction models using multiple machine learning models, namely logistic regression, naïve Bayes, random forest, and extreme gradient boosting models. The performance of the models was evaluated by calculating the areas under their receiver operating characteristic curves, Brier loss, log loss, precision, recall, and F1 scores. The Shapley additive explanations interpreter was used to visualize the models. Three were ultimately 4299 cases of lung cancer that were diagnosed in our sample. The model containing all the predictors had good predictive power, and the extreme gradient boosting model had the best performance with an area under curve of 0.998. New important predictive factors for lung cancer were also identified, namely hip circumference, waist circumference, number of cigarettes previously smoked daily, neuroticism score, age, and forced expiratory volume in 1 second. The predictive model established by incorporating novel predictive factors can be of value in the early identification of lung cancer. It may be helpful in stratifying individuals and selecting those at higher risk for inclusion in screening programs.

Effect of postoperative exercise training on physical function and quality of life of lung cancer patients with chronic obstructive pulmonary disease: A randomized controlled trial

BACKGROUND

Postoperative rehabilitation programs consisting of exercise training are considered effective for unselected lung cancer patients. However, whether postoperative exercise is beneficial to lung cancer patients comorbid with chronic obstructive pulmonary disease remains unknown.

METHODS

Eighty-four patients diagnosed with both lung cancer and chronic obstructive pulmonary disease were randomized into the exercise group and control group. Both groups were given standard postoperative rehabilitation for 1 week. After that, oxygen therapy (if needed) and nebulization were given to the control group, while patients in the exercise group started to participate in exercise programs on the basis of receiving oxygen therapy and nebulization as in the control group. The exercise programs consisted of 24 training sessions.

RESULTS

In both groups, the functional status and the results of the pulmonary function test decreased from baseline to the endpoint. However, after surgery and the intervention program, both the maximal oxygen consumption in the cardiopulmonary exercise test and walking distance in the 6-minute walk test in the exercise group were significantly better than those in the control group [15.5 (±1.4) mL/kg/min vs 13.1 (±1.3) mL/kg/min, P = 0.016; 437.4 (±48.6) m vs 381.7 (±40.5) m, P = 0.040]. Force vital capacity and forced expiratory volume in the first second in the exercise group were better than those in the control group, but the differences were not statistically significant [1798.1 (±298.9) mL vs 1664.0 (±329.7) mL, P = 0.254; 1155.7 (±174.3) mL vs 967.4 (±219.4) mL, P = 0.497]. The decline in the standard score of the QLQ-C30 (V3.0) was smaller in the exercise group, but the difference did not meet a statistically significant level [61.7 (±5.7) vs 58.4 (±9.3), P = 0.318].

CONCLUSION

This study demonstrates that a short-term postoperative exercise training program can facilitate the recovery of functional capacity in lung cancer patients with comorbidities of chronic obstructive pulmonary disease.

Personal history of cancer as a risk factor for second primary lung cancer: Implications for lung cancer screening

BACKGROUND

Personal history of cancer is an independent risk factor for lung cancer but is omitted from existing lung cancer screening eligibility criteria. In this study, we assess the lung cancer risk among cancer survivors and discuss potential implications for screening.

METHODS

This was a retrospective, secondary analysis of data from the Surveillance, Epidemiology and End Results (SEER) registry and the MD Anderson Cancer Center (MDACC). We estimated the standardized incidence ratios (SIRs) for lung cancer by site of first primary cancer using data from SEER. We assessed the lung cancer risk among head and neck cancer survivors from MDACC using cumulative incidence and compared the risk ratios (RR) by individuals' screening eligibility status.

RESULTS

Other than first primary lung cancer (SIR: 5.10, 95% CI: 5.01-5.18), cancer survivors in SEER with personal history of head and neck cancer (SIR: 3.71, 95% CI: 3.63-3.80) had the highest risk of developing second primary lung cancer, followed by bladder (SIR: 1.86, 95% CI: 1.81-1.90) and esophageal cancers (SIR: 1.78, 95% CI: 1.61-1.96). Head and neck cancer survivors had higher risk to develop lung cancer compared to the National Lung Screening Trial's subjects, (781 vs. 572 per 100,000 person-years, respectively). Head and neck cancer survivors ineligible for lung cancer screening seen at MDACC had significantly higher lung cancer risk than head and neck cancer survivors from SEER (RR: 1.9, p < 0.001).

CONCLUSION

Personal history of cancer, primarily head and neck cancer, is an independent risk factor for lung cancer and may be considered as an eligibility criterion in future lung cancer screening recommendations.

Detection of the DNA methylation of seven genes contribute to the early diagnosis of lung cancer

BACKGROUND

Low-dose Computed Tomography (CT) is used for the detection of pulmonary nodules, but the ambiguous risk evaluation causes overdiagnosis. Here, we explored the significance of the DNA methylation of 7 genes including TAC1, CDO1, HOXA9, ZFP42, SOX17, RASSF1A and SHOX2 in the blood cfDNA samples in distinguishing lung cancer from benign nodules and healthy individuals.

METHOD

A total of 149 lung cancer patients [72 mass and 77 ground-glass nodules (GGNs)], 5 benign and 48 healthy individuals were tested and analyzed in this study. The lasso-logistic regression model was built for distinguishing cancer and control/healthy individuals or IA lung cancer and non-IA lung cancer cases.

RESULTS

The positive rates of methylation of 7 genes were higher in the cancer group as compared with the healthy group. We constructed a model using age, sex and the ΔCt value of 7 gene methylation to distinguish lung cancer from benign and healthy individuals. The sensitivity, specificity and AUC (area under the curve) were 86.7%, 81.4% and 0.891, respectively. Also, we assessed the significance of 7 gene methylation together with patients' age and sex in distinguishing of GGNs type from the mass type. The sensitivity, specificity and AUC were 77.1%, 65.8% and 0.753, respectively. Furthermore, the methylation positive rates of CDO1 and SHOX2 were different between I-IV stages of lung cancer. Specifically, the positive rate of CDO1 methylation was higher in the non-IA group as compared with the IA group.

CONCLUSION

Collectively, this study reveals that the methylation of 7 genes has a big significance in the diagnosis of lung cancer with high sensitivity and specificity. Also, the 7 genes present with certain significance in distinguishing the GGN type lung cancer, as well as different stages.

Comparing Lung Cancer Screening Strategies in a Nationally Representative US Population Using Transportability Methods for the National Lung Cancer Screening Trial

Importance

The National Lung Screening Trial (NLST) found that screening for lung cancer with low-dose computed tomography (CT) reduced lung cancer-specific and all-cause mortality compared with chest radiography. It is uncertain whether these results apply to a nationally representative target population.

Objective

To extend inferences about the effects of lung cancer screening strategies from the NLST to a nationally representative target population of NLST-eligible US adults.

Design, Setting, and Participants

This comparative effectiveness study included NLST data from US adults at 33 participating centers enrolled between August 2002 and April 2004 with follow-up through 2009 along with National Health Interview Survey (NHIS) cross-sectional household interview survey data from 2010. Eligible participants were adults aged 55 to 74 years, and were current or former smokers with at least 30 pack-years of smoking (former smokers were required to have quit within the last 15 years). Transportability analyses combined baseline covariate, treatment, and outcome data from the NLST with covariate data from the NHIS and reweighted the trial data to the target population. Data were analyzed from March 2020 to May 2023.

Interventions

Low-dose CT or chest radiography screening with a screening assessment at baseline, then yearly for 2 more years.

Main Outcomes and Measures

For the outcomes of lung-cancer specific and all-cause death, mortality rates, rate differences, and ratios were calculated at a median (25th percentile and 75th percentile) follow-up of 5.5 (5.2-5.9) years for lung cancer-specific mortality and 6.5 (6.1-6.9) years for all-cause mortality.

Results

The transportability analysis included 51 274 NLST participants and 685 NHIS participants representing the target population (of approximately 5 700 000 individuals after survey-weighting). Compared with the target population, NLST participants were younger (median [25th percentile and 75th percentile] age, 60 [57 to 65] years vs 63 [58 to 67] years), had fewer comorbidities (eg, heart disease, 6551 of 51 274 [12.8%] vs 1 025 951 of 5 739 532 [17.9%]), and were more educated (bachelor's degree or higher, 16 349 of 51 274 [31.9%] vs 859 812 of 5 739 532 [15.0%]). In the target population, for lung cancer-specific mortality, the estimated relative rate reduction was 18% (95% CI, 1% to 33%) and the estimated absolute rate reduction with low-dose CT vs chest radiography was 71 deaths per 100 000 person-years (95% CI, 4 to 138 deaths per 100 000 person-years); for all-cause mortality the estimated relative rate reduction was 6% (95% CI, -2% to 12%). In the NLST, for lung cancer-specific mortality, the estimated relative rate reduction was 21% (95% CI, 9% to 32%) and the estimated absolute rate reduction was 67 deaths per 100 000 person-years (95% CI, 27 to 106 deaths per 100 000 person-years); for all-cause mortality, the estimated relative rate reduction was 7% (95% CI, 0% to 12%).

Conclusions and Relevance

Estimates of the comparative effectiveness of low-dose CT screening compared with chest radiography in a nationally representative target population were similar to those from unweighted NLST analyses, particularly on the relative scale. Increased uncertainty around effect estimates for the target population reflects large differences in the observed characteristics of trial participants and the target population.

Ethnic differences of genetic risk and smoking in lung cancer: two prospective cohort studies

BACKGROUND

The role of genetic background underlying the disparity of relative risk of smoking and lung cancer between European populations and East Asians remains unclear.

METHODS

To assess the role of ethnic differences in genetic factors associated with smoking-related risk of lung cancer, we first constructed ethnic-specific polygenic risk scores (PRSs) to quantify individual genetic risk of lung cancer in Chinese and European populations. Then, we compared genetic risk and smoking as well as their interactions on lung cancer between two cohorts, including the China Kadoorie Biobank (CKB) and the UK Biobank (UKB). We also evaluated the absolute risk reduction over a 5-year period.

RESULTS

Differences in compositions and association effects were observed between the Chinese-specific PRSs and European-specific PRSs, especially for smoking-related loci. The PRSs were consistently associated with lung cancer risk, but stronger associations were observed in smokers of the UKB [hazard ratio (HR) 1.26 vs 1.15, P  =  0.028]. A significant interaction between genetic risk and smoking on lung cancer was observed in the UKB (RERI, 11.39 (95% CI, 7.01-17.94)], but not in the CKB. Obvious higher absolute risk was observed in nonsmokers of the CKB, and a greater absolute risk reduction was found in the UKB (10.95 vs 7.12 per 1000 person-years, P <0.001) by comparing heavy smokers with nonsmokers, especially for those at high genetic risk.

CONCLUSIONS

Ethnic differences in genetic factors and the high incidence of lung cancer in nonsmokers of East Asian ethnicity were involved in the disparity of smoking-related risk of lung cancer.

Integrated plasma and exosome long noncoding RNA profiling is promising for diagnosing non-small cell lung cancer

OBJECTIVES

Non-small cell lung cancer (NSCLC) accounts for more than 80 % of all lung cancers, and its 5-year survival rate can be greatly improved by early diagnosis. However, early diagnosis remains elusive because of the lack of effective biomarkers. In this study, we aimed to develop an effective diagnostic model for NSCLC based on a combination of circulating biomarkers.

METHODS

Tissue-deregulated long noncoding RNAs (lncRNAs) in NSCLC were identified in datasets retrieved from the Gene Expression Omnibus (GEO, n=727) and The Cancer Genome Atlas (TCGA, n=1,135) databases, and their differential expression was verified in paired local plasma and exosome samples from NSCLC patients. Subsequently, LASSO regression was used to screen for biomarkers in a large clinical population, and a logistic regression model was used to establish a multi-marker diagnostic model. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots, decision curve analysis (DCA), clinical impact curves, and integrated discrimination improvement (IDI) were used to evaluate the efficiency of the diagnostic model.

RESULTS

Three lncRNAs-PGM5-AS1, SFTA1P, and CTA-384D8.35 were consistently expressed in online tissue datasets, plasma, and exosomes from local patients. LASSO regression identified nine variables (Plasma CTA-384D8.35, Plasma PGM5-AS1, Exosome CTA-384D8.35, Exosome PGM5-AS1, Exosome SFTA1P, Log10CEA, Log10CA125, SCC, and NSE) in clinical samples that were eventually included in the multi-marker diagnostic model. Logistic regression analysis revealed that Plasma CTA-384D8.35, exosome SFTA1P, Log10CEA, Exosome CTA-384D8.35, SCC, and NSE were independent risk factors for NSCLC (p<0.01), and their results were visualized using a nomogram to obtain personalized prediction outcomes. The constructed diagnostic model demonstrated good NSCLC prediction ability in both the training and validation sets (AUC=0.97).

CONCLUSIONS

In summary, the constructed circulating lncRNA-based diagnostic model has good NSCLC prediction ability in clinical samples and provides a potential diagnostic tool for NSCLC.

Risk Factors for Recurrent Exacerbations in the General-Practitioner-Based Swiss Chronic Obstructive Pulmonary Disease (COPD) Cohort

BACKGROUND

Patients with chronic obstructive pulmonary disease (COPD) often suffer from acute exacerbations. Our objective was to describe recurrent exacerbations in a GP-based Swiss COPD cohort and develop a statistical model for predicting exacerbation.

METHODS

COPD cohort demographic and medical data were recorded for 24 months, by means of a questionnaire-based COPD cohort. The data were split into training (75%) and validation (25%) datasets. A negative binomial regression model was developed using the training dataset to predict the exacerbation rate within 1 year. An exacerbation prediction model was developed, and its overall performance was validated. A nomogram was created to facilitate the clinical use of the model.

RESULTS

Of the 229 COPD patients analyzed, 77% of the patients did not experience exacerbation during the follow-up. The best subset in the training dataset revealed that lower forced expiratory volume, high scores on the MRC dyspnea scale, exacerbation history, and being on a combination therapy of LABA + ICS (long-acting beta-agonists + Inhaled Corticosteroids) or LAMA + LABA (Long-acting muscarinic receptor antagonists + long-acting beta-agonists) at baseline were associated with a higher rate of exacerbation. When validated, the area-under-curve (AUC) value was 0.75 for one or more exacerbations. The calibration was accurate (0.34 predicted exacerbations vs 0.28 observed exacerbations).

CONCLUSION

Nomograms built from these models can assist clinicians in the decision-making process of COPD care.

Development of lung cancer risk prediction models based on F-18 FDG PET images

OBJECTIVE

We aimed to evaluate whether the degree of F-18 fluorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and to develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET).

METHODS

We retrospectively included 795 healthy individuals who underwent F-18 FDG PET/CT scans for a health check-up. Individuals who developed lung cancer within 5 years of the PET/CT scan were classified into the lung cancer group (n = 136); those who did not were classified into the control group (n = 659). The healthy individuals were then randomly assigned to either the training (n = 585) or validation sets (n = 210). Clinical factors including age, sex, body mass index (BMI), and smoking history were collected. The standardized uptake value ratio (SUVR) and metabolic heterogeneity (MH) index were obtained for the bilateral lungs. Logistic regression models including clinical factors, SUVR, and MH index were generated to quantify the probability of lung cancer development using a training set. The prediction models were validated using a validation set.

RESULTS

The lung SUVR and lung MH index in the lung cancer group were significantly higher than in the control group (p < 0.001 and p < 0.001, respectively). In the combined prediction model 1, age, sex, BMI, smoking history, and lung SUVR were significantly associated with lung cancer development (age: OR 1.07, p < 0.001; male: OR 2.08, p = 0.015; BMI: OR 0.93, p = 0.057; current or past smoker: OR 5.60, p < 0.001; lung SUVR: OR 1.13, p < 0.001). In the combined prediction model 2, age, sex, BMI, smoking history, and lung MH index showed a significant association with lung cancer development (age: OR 1.06, p < 0.001; male: OR 1.87, p = 0.045; BMI: OR 0.93, p = 0.010; current or past smoker: OR 4.78, p < 0.001; lung MH index: OR 1.33, p < 0.001). In the validation data, combined prediction models 1 and 2 exhibited very good discrimination [area under the receiver operator curve (AUC): 0.867 and 0.901, respectively].

CONCLUSIONS

The metabolic parameters on F-18 FDG PET are related to an increased risk of lung cancer. Metabolic parameters can be used as biomarkers to provide information independent of the clinical parameters, related to lung cancer risk.

Nomogram combining clinical and radiological characteristics for predicting the malignant probability of solitary pulmonary nodules measuring ≤ 2 cm

Background

At present, how to identify the benign or malignant nature of small (≤ 2 cm) solitary pulmonary nodules (SPN) are an urgent clinical challenge. This retrospective study aimed to develop a clinical prediction model combining clinical and radiological characteristics for assessing the probability of malignancy in SPNs measuring ≤ 2 cm.

Method

In this study, we included patients with SPNs measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to December 2021. Clinical features, preoperative biomarker results, and computed tomography characteristics were collected. The enrolled patients were randomized at a ratio of 7:3 into a training cohort of 775 and a validation cohort of 331. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. The receiver operating characteristic (ROC) curve was used to evaluate the identification ability of the model. The calibration power was evaluated using the Hosmer-Lemeshow test and calibration curve. The clinical utility of the nomogram was also assessed by decision curve analysis (DCA).

Result

A total of 1,106 patients were included in this study. Among them, the malignancy rate of SPNs was 85.08% (941/1,106). We finally identified the following six independent risk factors by logistic regression: age, carcinoembryonic antigen, nodule shape, calcification, maximum diameter, and consolidation-to-tumor ratio. The area under the ROC curve (AUC) for the training cohort was 0.764 (95% confidence interval [CI]: 0.714-0.814), and the AUC for the validation cohort was 0.729 (95% CI: 0.647-0.811), indicating that the prediction accuracy of nomogram was relatively good. The calibration curve of the predictive model also demonstrated a good calibration in both cohorts. DCA proved that the clinical prediction model was useful in clinical practice.

Conclusion

We developed and validated a predictive model and nomogram for estimating the probability of malignancy in SPNs measuring ≤ 2 cm. With the application of predictive models, thoracic surgeons can make more rational clinical decisions while avoiding overtreatment and wasting medical resources.

[Construction of a Risk Prediction Model for Lung Cancer Based on Lifestyle Behaviors in the UK Biobank Large-Scale Population Cohort]

Objective

To identify the risk factors related to lifestyle behaviors that affect the incidence of lung cancer, to build a lung cancer risk prediction model to identify, in the population, individuals who are at high risk, and to facilitate the early detection of lung cancer.

Methods

The data used in the study were obtained from the UK Biobank, a database that contains information collected from 502 389 participants between March 2006 and October 2010. Based on domestic and international guidelines for lung cancer screening and high-quality research literature on lung cancer risk factors, high-risk population identification criteria were determined. Univariate Cox regression was performed to screen for risk factors of lung cancer and a multifactor lung cancer risk prediction model was constructed using Cox proportional hazards regression. Based on the comparison of Akaike information criterion and Schoenfeld residual test results, the optimal fitted model assuming proportional hazards was selected. The multiple factor Cox proportional hazards regression was performed to consider the survival time and the population was randomly divided into a training set and a validation set by a ratio of 7:3. The model was built using the training set and the performance of the model was internally validated using the validation set. The area under the receiver operating characteristic (ROC) curve ( AUC) was used to evaluate the efficacy of the model. The population was categorized into low-risk, moderate-risk, and high-risk groups based on the probability of occurrence of 0% to <25%, 25% to <75%, and 75% to 100%. The respective proportions of affected individuals in each risk group were calculated.

Results

The study eventually covered 453 558 individuals, and out of the cumulative follow-up of 5 505 402 person-years, a total of 2 330 cases of lung cancer were diagnosed. Cox proportional hazards regression was performed to identify 10 independent variables as predictors of lung cancer, including age, body mass index (BMI), education, income, physical activity, smoking status, alcohol consumption frequency, fresh fruit intake, family history of cancer, and tobacco exposure, and a model was established accordingly. Internal validation results showed that 8 independent variables (all the 10 independent variables screened out except for BMI and fresh fruit intake) were significant influencing factors of lung cancer ( P<0.05). The AUC of the training set for predicting lung cancer occurrence at one year, five years, and ten years were 0.825, 0.785, and 0.777, respectively. The AUC of the validation set for predicting lung cancer occurrence at one year, five years, and ten years were 0.857, 0.782, and 0.765, respectively. 68.38% of the individuals who might develop lung cancer in the future could be identified by screening the high-risk population.

Conclusion

We established, in this study, a model for predicting lung cancer risks associated with lifestyle behaviors of a large population. Showing good performance in discriminatory ability, the model can be used as a tool for developing standardized screening strategies for lung cancer.

The MARS PETCARE BIOBANK protocol: establishing a longitudinal study of health and disease in dogs and cats

BACKGROUND

The veterinary care of cats and dogs is increasingly embracing innovations first applied to human health, including an increased emphasis on preventative care and precision medicine. Large scale human population biobanks have advanced research in these areas; however, few have been established in veterinary medicine. The MARS PETCARE BIOBANK™ (MPB) is a prospective study that aims to build a longitudinal bank of biological samples, with paired medical and lifestyle data, from 20,000 initially healthy cats and dogs (10,000 / species), recruited through veterinary hospitals over a ten-year period. Here, we describe the MPB protocol and discuss its potential as a platform to increase understanding of why and how diseases develop and how to advance personalised veterinary healthcare.

METHODS

At regular intervals, extensive diet, health and lifestyle information, electronic medical records, clinicopathology and activity data are collected, genotypes, whole genome sequences and faecal metagenomes analysed, and blood, plasma, serum, and faecal samples stored for future research.

DISCUSSION

Proposed areas for research include the early detection and progression of age-related disease, risk factors for common conditions, the influence of the microbiome on health and disease and, through genome wide association studies, the identification of candidate loci for disease associated genetic variants. Genomic data will be open access and research proposals for access to data and samples will be considered. Over the coming years, the MPB will provide the longitudinal data and systematically collected biological samples required to generate important insights into companion animal health, identifying biomarkers of disease, supporting earlier identification of risk, and enabling individually tailored interventions to manage disease.

Lung cancer risk score for ever and never smokers in China

BACKGROUND

Most lung cancer risk prediction models were developed in European and North-American cohorts of smokers aged ≥ 55 years, while less is known about risk profiles in Asia, especially for never smokers or individuals aged < 50 years. Hence, we aimed to develop and validate a lung cancer risk estimate tool for ever and never smokers across a wide age range.

METHODS

Based on the China Kadoorie Biobank cohort, we first systematically selected the predictors and explored the nonlinear association of predictors with lung cancer risk using restricted cubic splines. Then, we separately developed risk prediction models to construct a lung cancer risk score (LCRS) in 159,715 ever smokers and 336,526 never smokers. The LCRS was further validated in an independent cohort over a median follow-up of 13.6 years, consisting of 14,153 never smokers and 5,890 ever smokers.

RESULTS

A total of 13 and 9 routinely available predictors were identified for ever and never smokers, respectively. Of these predictors, cigarettes per day and quit years showed nonlinear associations with lung cancer risk (Pnon-linear < 0.001). The curve of lung cancer incidence increased rapidly above 20 cigarettes per day and then was relatively flat until approximately 30 cigarettes per day. We also observed that lung cancer risk declined sharply within the first 5 years of quitting, and then continued to decrease but at a slower rate in the subsequent years. The 6-year area under the receiver operating curve for the ever and never smokers' models were respectively 0.778 and 0.733 in the derivation cohort, and 0.774 and 0.759 in the validation cohort. In the validation cohort, the 10-year cumulative incidence of lung cancer was 0.39% and 2.57% for ever smokers with low (< 166.2) and intermediate-high LCRS (≥ 166.2), respectively. Never smokers with a high LCRS (≥ 21.2) had a higher 10-year cumulative incidence rate than those with a low LCRS (< 21.2; 1.05% vs. 0.22%). An online risk evaluation tool (LCKEY; http://ccra.njmu.edu.cn/lckey/web) was developed to facilitate the use of LCRS.

CONCLUSIONS

The LCRS can be an effective risk assessment tool designed for ever and never smokers aged 30 to 80 years.

Air pollution associated with incidence and progression trajectory of chronic lung diseases: a population-based cohort study

BACKGROUND

No prior study has examined the effects of air pollution on the progression from healthy to chronic lung disease, subsequent chronic lung multimorbidity and further to death.

METHODS

We used data from the UK Biobank of 265 506 adults free of chronic lung disease at recruitment. Chronic lung multimorbidity was defined as the coexistence of at least two chronic lung diseases, including asthma, chronic obstructive pulmonary disease and lung cancer. The concentrations of air pollutants were estimated using land-use regression models. Multistate models were applied to assess the effect of air pollution on the progression of chronic lung multimorbidity.

RESULTS

During a median follow-up of 11.9 years, 13 863 participants developed at least one chronic lung disease, 1055 developed chronic lung multimorbidity and 12 772 died. We observed differential associations of air pollution with different trajectories of chronic lung multimorbidity. Fine particulate matter showed the strongest association with all five transitions, with HRs (95% CI) per 5 µg/m3 increase of 1.31 (1.22 to 1.42) and 1.27 (1.01 to 1.57) for transitions from healthy to incident chronic lung disease and from incident chronic lung disease to chronic lung multimorbidity, and 1.32 (1.21 to 1.45), 1.24 (1.01 to 1.53) and 1.91 (1.14 to 3.20) for mortality risk from healthy, incident chronic lung disease and chronic lung multimorbidity, respectively.

CONCLUSION

Our study provides the first evidence that ambient air pollution could affect the progression from free of chronic lung disease to incident chronic lung disease, chronic lung multimorbidity and death.

The value of blood-based measures of liver function and urate in lung cancer risk prediction: A cohort study and health economic analysis

BACKGROUND

Several studies have reported associations between low-cost blood-based measurements and lung cancer but their role in risk prediction is unclear. We examined the value of expanding lung cancer risk models for targeting low-dose computed tomography (LDCT), including blood measurements of liver function and urate.

METHODS

We analysed a cohort of 388,199 UK Biobank participants with 1873 events and calculated the c-index and fraction of new information (FNI) for models expanded to include combinations of blood measurements, lung function (forced expiratory volume in 1 s - FEV1), alcohol status and waist circumference. We calculated the hypothetical cost per lung cancer case detected by LDCT for different scenarios using a threshold of ≥ 1.51 % risk at 6 years.

RESULTS

The c-index was 0.805 (95 %CI:0.794-0.816) for the model containing conventional predictors. Expanding to include blood measurements increased the c-index to 0.815 (95 %CI: 0.804-0.826;p < 0.0001;FNI:0.06). Expanding to include FEV1, alcohol status, and waist circumference increased the c-index to 0.811 (95 %CI: 0.800-0.822;p < 0.0001;FNI: 0.04). The c-index for the fully expanded model containing all variables was 0.819 (95 %CI:0.808-0.830;p < 0.0001;FNI:0.09). Model expansion had a greater impact on the c-index and FNI for people with a history of smoking cigarettes relative to the full cohort. Compared with the conventional risk model, the expanded models reduced the number of participants meeting the criteria for LDCT screening by 15-21 %, and lung cancer cases detected by 7-8 %. The additional cost per lung cancer case detected relative to the conventional model was £ 1018 for adding blood tests and £ 9775 for the fully expanded model.

CONCLUSION

Blood measurements of liver function and urate made a modest improvement to lung cancer risk prediction compared with a model containing conventional risk factors. There was no evidence that model expansion would improve the cost per lung cancer case detected in UK healthcare settings.

Development, validation, and evaluation of a risk assessment tool for personalized screening of gastric cancer in Chinese populations

BACKGROUND

Effective risk prediction models are lacking for personalized endoscopic screening of gastric cancer (GC). We aimed to develop, validate, and evaluate a questionnaire-based GC risk assessment tool for risk prediction and stratification in the Chinese population.

METHODS

In this three-stage multicenter study, we first selected eligible variables by Cox regression models and constructed a GC risk score (GCRS) based on regression coefficients in 416,343 subjects (aged 40-75 years) from the China Kadoorie Biobank (CKB, development cohort). In the same age range, we validated the GCRS effectiveness in 13,982 subjects from another independent Changzhou cohort (validation cohort) as well as in 5348 subjects from an endoscopy screening program in Yangzhou. Finally, we categorized participants into low (bottom 20%), intermediate (20-80%), and high risk (top 20%) groups by the GCRS distribution in the development cohort.

RESULTS

The GCRS using 11 questionnaire-based variables demonstrated a Harrell's C-index of 0.754 (95% CI, 0.745-0.762) and 0.736 (95% CI, 0.710-0.761) in the two cohorts, respectively. In the validation cohort, the 10-year risk was 0.34%, 1.05%, and 4.32% for individuals with a low (≤ 13.6), intermediate (13.7~30.6), and high (≥ 30.7) GCRS, respectively. In the endoscopic screening program, the detection rate of GC varied from 0.00% in low-GCRS individuals, 0.27% with intermediate GCRS, to 2.59% with high GCRS. A proportion of 81.6% of all GC cases was identified from the high-GCRS group, which represented 28.9% of all the screened participants.

CONCLUSIONS

The GCRS can be an effective risk assessment tool for tailored endoscopic screening of GC in China. Risk Evaluation for Stomach Cancer by Yourself (RESCUE), an online tool was developed to aid the use of GCRS.

Secondary prevention and treatment innovation of early stage non-small cell lung cancer: Impact on diagnostic-therapeutic pathway from a multidisciplinary perspective

Lung cancer (LC) is the leading cause of cancer-related death worldwide, mostly because the lack of a screening program so far. Although smoking cessation has a central role in LC primary prevention, several trials on LC screening through low-dose computed tomography (LDCT) in a high risk population showed a significant reduction of LC related mortality. Most trials showed heterogeneity in terms of selection criteria, comparator arm, detection nodule method, timing and intervals of screening and duration of the follow-up. LC screening programs currently active in Europe as well as around the world will lead to a higher number of early-stage Non Small Cell Lung Cancer (NSCLC) at the diagnosis. Innovative drugs have been recently transposed from the metastatic to the perioperative setting, leading to improvements in terms of resection rates and pathological responses after induction chemoimmunotherapy, and disease free survival with targeted agents and immune checkpoint inhibitors. The present review summarizes available evidence about LC screening, highlighting potential pitfalls and benefits and underlining the impact on the diagnostic therapeutic pathway of NSCLC from a multidisciplinary perspective. Future perspectives in terms of circulating biomarkers under evaluation for patients' risk stratification as well as a focus on recent clinical trials results and ongoing studies in the perioperative setting will be also presented.

Electronic Health Record-Based Absolute Risk Prediction Model for Esophageal Cancer in the Chinese Population: Model Development and External Validation

BACKGROUND

China has the largest burden of esophageal cancer (EC). Prediction models can be used to identify high-risk individuals for intensive lifestyle interventions and endoscopy screening. However, the current prediction models are limited by small sample size and a lack of external validation, and none of them can be embedded into the booming electronic health records (EHRs) in China.

OBJECTIVE

This study aims to develop and validate absolute risk prediction models for EC in the Chinese population. In particular, we assessed whether models that contain only EHR-available predictors performed well.

METHODS

A prospective cohort recruiting 510,145 participants free of cancer from both high EC-risk and low EC-risk areas in China was used to develop EC models. Another prospective cohort of 18,441 participants was used for validation. A flexible parametric model was used to develop a 10-year absolute risk model by considering the competing risks (full model). The full model was then abbreviated by keeping only EHR-available predictors. We internally and externally validated the models by using the area under the receiver operating characteristic curve (AUC) and calibration plots and compared them based on classification measures.

RESULTS

During a median of 11.1 years of follow-up, we observed 2550 EC incident cases. The models consisted of age, sex, regional EC-risk level (high-risk areas: 2 study regions; low-risk areas: 8 regions), education, family history of cancer (simple model), smoking, alcohol use, BMI (intermediate model), physical activity, hot tea consumption, and fresh fruit consumption (full model). The performance was only slightly compromised after the abbreviation. The simple and intermediate models showed good calibration and excellent discriminating ability with AUCs (95% CIs) of 0.822 (0.783-0.861) and 0.830 (0.792-0.867) in the external validation and 0.871 (0.858-0.884) and 0.879 (0.867-0.892) in the internal validation, respectively.

CONCLUSIONS

Three nested 10-year EC absolute risk prediction models for Chinese adults aged 30-79 years were developed and validated, which may be particularly useful for populations in low EC-risk areas. Even the simple model with only 5 predictors available from EHRs had excellent discrimination and good calibration, indicating its potential for broader use in tailored EC prevention. The simple and intermediate models have the potential to be widely used for both primary and secondary prevention of EC.

Clonal Hematopoiesis and Risk of Incident Lung Cancer

PURPOSE

To prospectively examine the association between clonal hematopoiesis (CH) and subsequent risk of lung cancer.

METHODS

Among 200,629 UK Biobank (UKBB) participants with whole-exome sequencing, CH was identified in a nested case-control study of 832 incident lung cancer cases and 3,951 controls (2006-2019) matched on age and year at blood draw, sex, race, and smoking status. A similar nested case-control study (141 cases/652 controls) was conducted among 27,975 participants with whole-exome sequencing in the Mass General Brigham Biobank (MGBB, 2010-2021). In parallel, we compared CH frequency in published data from 5,003 patients with solid tumor (2,279 lung cancer) who had pretreatment blood sequencing performed through Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets.

RESULTS

In UKBB, the presence of CH was associated with increased risk of lung cancer (cases: 12.5% v controls: 8.7%; multivariable-adjusted odds ratio [OR], 1.36; 95% CI, 1.06 to 1.74). The association remained robust after excluding participants with chronic obstructive pulmonary disease. No significant interactions with known risk factors, including polygenic risk score and C-reactive protein, were identified. In MGBB, we observed similar enrichment of CH in lung cancer (cases: 15.6% v controls: 12.7%). The meta-analyzed OR (95% CI) of UKBB and MGBB was 1.35 (1.08 to 1.68) for CH overall and 1.61 (1.19 to 2.18) for variant allele frequencies ≥ 10%. In Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets, CH with a variant allele frequency ≥ 10% was enriched in pretreatment lung cancer compared with other tumors after adjusting for age, sex, and smoking (OR for lung v breast cancer: 1.61; 95% CI, 1.03 to 2.53).

CONCLUSION

Independent of known risk factors, CH is associated with increased risk of lung cancer.

Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges?

As most lung cancer (LC) cases are still detected at advanced and incurable stages, there are increasing efforts to foster detection at earlier stages by low dose computed tomography (LDCT) based LC screening. In this scoping review, we describe current advances in candidate selection for screening (selection phase), technical aspects (screening), and probability evaluation of malignancy of CT-detected pulmonary nodules (PN management). Literature was non-systematically assessed and reviewed for suitability by the authors. For the selection phase, we describe current eligibility criteria for screening, along with their limitations and potential refinements through advanced clinical scores and biomarker assessments. For LC screening, we discuss how the accuracy of computerized tomography (CT) scan reading might be augmented by IT tools, helping radiologists to cope with increasing workloads. For PN management, we evaluate the precision of follow-up scans by semi-automatic volume measurements of CT-detected PN. Moreover, we present an integrative approach to evaluate the probability of PN malignancy to enable safe decisions on further management. As a clear limitation, additional validation studies are required for most innovative diagnostic approaches presented in this article, but the integration of clinical risk models, current imaging techniques, and advancing biomarker research has the potential to improve the LC screening performance generally.

Machine Learning System for Lung Neoplasms Distinguished Based on Scleral Data

Lung cancer remains the most commonly diagnosed cancer and the leading cause of death from cancer. Recent research shows that the human eye can provide useful information about one's health status, but few studies have revealed that the eye's features are associated with the risk of cancer. The aims of this paper are to explore the association between scleral features and lung neoplasms and develop a non-invasive artificial intelligence (AI) method for detecting lung neoplasms based on scleral images. A novel instrument was specially developed to take the reflection-free scleral images. Then, various algorithms and different strategies were applied to find the most effective deep learning algorithm. Ultimately, the detection method based on scleral images and the multi-instance learning (MIL) model was developed to predict benign or malignant lung neoplasms. From March 2017 to January 2019, 3923 subjects were recruited for the experiment. Using the pathological diagnosis of bronchoscopy as the gold standard, 95 participants were enrolled to take scleral image screens, and 950 scleral images were fed to AI analysis. Our non-invasive AI method had an AUC of 0.897 ± 0.041(95% CI), a sensitivity of 0.836 ± 0.048 (95% CI), and a specificity of 0.828 ± 0.095 (95% CI) for distinguishing between benign and malignant lung nodules. This study suggested that scleral features such as blood vessels may be associated with lung cancer, and the non-invasive AI method based on scleral images can assist in lung neoplasm detection. This technique may hold promise for evaluating the risk of lung cancer in an asymptomatic population in areas with a shortage of medical resources and as a cost-effective adjunctive tool for LDCT screening at hospitals.

Tumor organoid biobank-new platform for medical research

Organoids are a new type of 3D model for tumor research, which makes up for the shortcomings of cell lines and xenograft models, and promotes the development of personalized precision medicine. Long-term culture, expansion and storage of organoids provide the necessary conditions for the establishment of biobanks. Biobanks standardize the collection and preservation of normal or pathological specimens, as well as related clinical information. The tumor organoid biobank has a good quality control system, which is conducive to the clinical transformation and large-scale application of tumor organoids, such as disease modeling, new drug development and high-throughput drug screening. This article summarized the common tumor types of patient-derived organoid (PDO) biobanks and the necessary information for biobank construction, such as the number of organoids, morphology, success rate of culture and resuscitation, pathological types. In our results, we found that patient-derived tumor organoid (PDTO) biobanks were being established more and more, with the Netherlands, the United States, and China establishing the most. Biobanks of colorectal, pancreas, breast, glioma, and bladder cancers were established more, which reflected the relative maturity of culture techniques for these tumors. In addition, we provided insights on the precautions and future development direction of PDTO biobank building.

The impact of low-dose CT on smoking behavior among non-smokers, former-smokers, and smokers: A population-based screening cohort in rural China

BACKGROUND

Lung cancer screening may provide a "teachable moment" for the smoking cessation and relapse prevention. However, the impact of lung cancer screening on smoking initiation in non-smokers has not been reported.

METHODS

A baseline smoking behavior survey was conducted in 2000 participants who were screened by low-dose computed tomography (LDCT) from 2014 to 2018. All participants were re-surveyed on their smoking behavior in 2019. Of these, 312 participants were excluded, leaving 1688 participants in the final analysis. The smoking initiation rate in baseline non-smokers, the relapse rate in baseline former smokers, and the abstinence rate in baseline current smokers were calculated, respectively. The associations between screening results, demographic characteristics, and smoking behavior change were analyzed using multivariable logistic regression.

RESULTS

From 2014 to 2019, smoking prevalence significantly decreased from 52.6% to 49.1%. The prevalence of smoking initiation, relapse, and abstinence in baseline non-smokers, former, and current smokers was 16.8%, 22.9%, and 23.7%, respectively. The risk of smoking initiation in baseline non-smokers was significantly higher in those with negative screening result (adjusted OR = 2.97, 95% CI: 1.27-6.94). Compared to smokers who only received baseline screening, the chance of smoking abstinence in baseline current smokers was reduced by over 80% in those who attended 5 rounds of screening (adjusted OR = 0.15, 95% CI:0.08-0.27). No significant associations were found between smoking relapse and prior screening frequency, with at least one positive screening result. Age, gender, occupational exposure, income, and smoking pack years were also associated with smoking behavior changes.

CONCLUSIONS

The overall decreased smoking prevalence indicated an overwhelming effect of "teachable moment" on "license to smoke." A tailored smoking cessation strategy should be integrated into lung cancer screening.

Association between biological aging and lung cancer risk: Cohort study and Mendelian randomization analysis

Chronological age only represents the passage of time, whereas biological age reflects the physiology states and the susceptibility to morbidity and mortality. The association between biological age and lung cancer risk remains controversial. Hence, we conducted a prospective analysis in the UK Biobank study and two-sample Mendelian randomization analysis to investigate this association. Biological aging was evaluated by PhenoAgeAccel, derived from routine clinical biomarkers. Independent of chronological age, PhenoAgeAccel was positively associated with the risk of overall and histological subtypes of lung cancer. There was a joint effect of PhenoAgeAccel and genetics in lung cancer incidence. In Mendelian randomization analysis, the genetically predicted PhenoAgeAccel was associated with the increased risk of overall lung cancer, small cell, and squamous cell carcinoma. Our findings suggest PhenoAgeAccel is an independent risk factor for lung cancer, which could be incorporated with polygenic risk score to identify high-risk individuals for lung cancer.

Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study

BACKGROUND

It is believed that smoking is not the cause of approximately 53% of lung cancers diagnosed in women globally.

OBJECTIVE

The study aimed to develop and validate a simple and noninvasive model that could assess and stratify lung cancer risk in nonsmoking Chinese women.

METHODS

Based on the population-based Cancer Screening Program in Urban China, this retrospective, cross-sectional cohort study was carried out with a vast population base and an immense number of participants. The training set and the validation set were both constructed using a random distribution of the data. Following the identification of associated risk factors by multivariable Cox regression analysis, a predictive nomogram was developed. Discrimination (area under the curve) and calibration were further performed to assess the validation of risk prediction nomogram in the training set, which was then validated in the validation set.

RESULTS

In sum, 151,834 individuals signed up to take part in the survey. Both the training set (n=75,917) and the validation set (n=75,917) were comprised of randomly selected participants. Potential predictors for lung cancer included age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease. We displayed 1-year, 3-year, and 5-year lung cancer risk-predicting nomograms using these 5 factors. In the training set, the 1-year, 3-year, and 5-year lung cancer risk areas under the curve were 0.762, 0.718, and 0.703, respectively. In the validation set, the model showed a moderate predictive discrimination.

CONCLUSIONS

We designed and validated a simple and noninvasive lung cancer risk model for nonsmoking women. This model can be applied to identify and triage people at high risk for developing lung cancers among nonsmoking women.

Development of a multivariable prediction model for anal high-grade squamous intraepithelial lesions in persons living with HIV in Puerto Rico: a cross-sectional study

Background

Persons living with HIV (PLWH) are at high risk of developing anal high-grade squamous intraepithelial lesions (HSIL). We aimed to develop a prediction model for anal HSIL based on individual characteristics of PLWH.

Methods

Cross-sectional study of PLWH aged ≥21 years who attended the Anal Neoplasia Clinic of the University of Puerto Rico Comprehensive Cancer Center from 2016 to 2022. The primary outcome was biopsy-confirmed anal HSIL. For each sex, relations between potential predictors and HSIL were examined using univariate (ULRM) and multivariable (MLRM) logistic regression models. Risk modelling was performed with MLRM and validated with bootstrapping techniques. The area under the ROC Curves (AUC) was estimated with 95% CI.

Findings

HSIL was detected among 45.11% of patients, 68.48% were males, and 59.42% were ≥45 aged. Multivariable analysis showed that, in women, the only significant predictor for HSIL was having a previous abnormal anal cytology (p = 0.01). In men, significant predictors for HSIL were having a previous abnormal anal cytology (p < 0.001) and a history of infection with any gonorrhoea (p = 0.002). Other suggestive predictors for HSIL among women were obesity and smoking. No association between smoking and HSIL among men was observed (p < 0.05). The AUC estimated among women (0.732, 95% CI: 0.651-0.811) was higher than in men (0.689, 95% CI: 0.629-0.748).

Interpretation

Our results support that the inclusion of individual characteristics into the prediction model will adequately predict the presence of HSIL in PLWH.

Funding

This work was supported by the NCI (Grants #U54CA096297, #R25CA240120), the NIGMS (Grant #U54GM133807), and the NIMHD (Grant #U54MD007587).

Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer

Objectives

The purpose of this study was to evaluate whether preoperative radiomics features could meliorate risk stratification for the overall survival (OS) of non-small cell lung cancer (NSCLC) patients.

Methods

After rigorous screening, the 208 NSCLC patients without any pre-operative adjuvant therapy were eventually enrolled. We segmented the 3D volume of interest (VOI) based on malignant lesion of computed tomography (CT) imaging and extracted 1542 radiomics features. Interclass correlation coefficients (ICC) and LASSO Cox regression analysis were utilized to perform feature selection and radiomics model building. In the model evaluation phase, we carried out stratified analysis, receiver operating characteristic (ROC) curve, concordance index (C-index), and decision curve analysis (DCA). In addition, integrating the clinicopathological trait and radiomics score, we developed a nomogram to predict the OS at 1 year, 2 years, and 3 years, respectively.

Results

Six radiomics features, including gradient_glcm_InverseVariance, logarithm_firstorder_Median, logarithm_firstorder_RobustMeanAbsoluteDeviation, square_gldm_LargeDependenceEmphasis, wavelet_HLL_firstorder_Kurtosis, and wavelet_LLL_firstorder_Maximum, were selected to construct the radiomics signature, whose areas under the curve (AUCs) for 3-year prediction reached 0.857 in the training set (n=146) and 0.871 in the testing set (n=62). The results of multivariate analysis revealed that the radiomics score, radiological sign, and N stage were independent prognostic factors in NSCLC. Moreover, compared with clinical factors and the separate radiomics model, the established nomogram exhibited a better performance in predicting 3-year OS.

Conclusions

Our radiomics model may provide a promising non-invasive approach for preoperative risk stratification and personalized postoperative surveillance for resectable NSCLC patients.

Detection of COPD in the SUMMIT Study lung cancer screening cohort using symptoms and spirometry

BACKGROUND

COPD is a major comorbidity in lung cancer screening (LCS) cohorts, with a high prevalence of undiagnosed COPD. Combining symptom assessment with spirometry in this setting may enable earlier diagnosis of clinically significant COPD and facilitate increased understanding of lung cancer risk in COPD. In this study, we wished to understand the prevalence, severity, clinical phenotype and lung cancer risk of individuals with symptomatic undiagnosed COPD in a LCS cohort.

METHODS

16 010 current or former smokers aged 55-77 years attended a lung health check as part of the SUMMIT Study. A respiratory consultation and spirometry were performed alongside LCS eligibility assessment. Those with symptoms, no previous COPD diagnosis and airflow obstruction were labelled as undiagnosed COPD. Baseline low-dose computed tomography (LDCT) was performed in those at high risk of lung cancer (PLCOm2012 score ≥1.3% and/or meeting USPSTF 2013 criteria).

RESULTS

Nearly one in five (19.7%) met criteria for undiagnosed COPD. Compared with those previously diagnosed, those undiagnosed were more likely to be male (59.1% versus 53.2%; p<0.001), currently smoking (54.9% versus 47.6%; p<0.001) and from an ethnic minority group (p<0.001). Undiagnosed COPD was associated with less forced expiratory volume in 1 s impairment (Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades 1 and 2: 85.3% versus 68.4%; p<0.001) and lower symptom/exacerbation burden (GOLD A and B groups: 95.6% versus 77.9%; p<0.001) than those with known COPD. Multivariate analysis demonstrated that airflow obstruction was an independent risk factor for lung cancer risk on baseline LDCT (adjusted OR 2.74, 95% CI 1.73-4.34; p<0.001), with a high risk seen in those with undiagnosed COPD (adjusted OR 2.79, 95% CI 1.67-4.64; p<0.001).

CONCLUSIONS

Targeted case-finding within LCS detects high rates of undiagnosed symptomatic COPD in those most at risk. Individuals with undiagnosed COPD are at high risk for lung cancer.

Construction and Validation of Early Warning Model of Lung Cancer Based on Machine Learning: A Retrospective Study

Background: This study is a retrospective study. The purpose of this study is to construct and validate an early warning model of lung cancer through machine learning. Methods: The CDKN2A gene expression profile and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database and divided into a tumor group and a normal group (n = 57). The top 5 somatic mutation-related genes were extracted from 567 somatic mutation data downloaded from TCGA database using random forest algorithm. Cox proportional hazard model and nomogram were constructed combining CDKN2A, 5 somatic mutation-related genes, gender, and smoking index. Patients were divided into high-risk and low-risk groups according to risk score. The predictability of the model in the prognosis of lung cancer was estimated by Kaplan-Meier survival analysis and receiver operating characteristics curve. Results: We constructed a prognostic model consisting of 5 somatic mutation-related genes (sphingosine 1-phosphate receptor 1 [S1PR1], dedicator of cytokinesis 7 [DOCK7], DEAD-box helicase 4 [DDX4], laminin subunit beta 3 [LAMB3], and importin 5 [IPO5]), cyclin-dependent kinase inhibitor 2A (CDKN2A), gender, and smoking indicators. The high-risk group had a lower overall survival rate compared to the low-risk group (hazard ratio = 2.14, P = 0 .0323). The area under the curve predicted for 3-year, 5-year, and 10-year survival rates are 0.609, 0.673, and 0.698, respectively. The accuracy, sensitivity, and specificity of the model for predicting the 10-year survival rate of lung cancer are 76.19%, 56.71%, and 86.23%. Conclusion: The lung cancer early warning model and nomogram may provide an essential reference for patients with lung cancer management in the clinic.

Personal PM2.5 Elemental Components, Decline of Lung Function, and the Role of DNA Methylation on Inflammation-Related Genes in Older Adults: Results and Implications of the BAPE Study

Epidemiological evidence of the effects of PM_2.5 elements on lung function and DNA methylation is limited. We conducted a longitudinal panel study of 76 healthy older adults aged 60-69 years in Jinan, China, from September 2018 to January 2019. We periodically measured individual 72 h PM_2.5 and element concentrations, lung function, and DNA methylation levels of eight inflammation-related genes. We used linear mixed-effect models to investigate the effects of exposure to personal PM_2.5 elements on the lung function and DNA methylation. Mediation analysis was used to investigate the underlying effect mechanism. Negative changes in the ratio of forced expiratory volume in 1 s to forced vital capacity, ranging from -1.23% [95% confidence interval (CI): -2.11%, -0.35%] to -0.77% (95% CI: -1.49%, -0.04%), were significantly associated with interquartile range (IQR) increases in personal PM_2.5 at different lag periods (7-12, 13-24, 25-48, 0-24, 0-48, and 0-72 h). Arsenic (As), nickel, rubidium (Rb), selenium, and vanadium were significantly associated with at least three lung function parameters, and IQR increases in these elements led to 0.12-5.66% reductions in these parameters. PM_2.5 elements were significantly associated with DNA methylation levels. DNA methylation mediated 7.28-13.02% of the As- and Rb-related reduced lung function. The findings indicate that exposure to elements in personal PM_2.5 contributes to reduced lung function through DNA methylation.

Population attributable fraction of lung cancer due to genetic variants, modifiable risk factors, and their interactions: a nationwide prospective cohort study

BACKGROUND

Genetic variants and modifiable risk factors (including environmental exposure and lifestyle) greatly contribute to the development of lung cancer. The population attributable fraction (PAF) of these risk factors, especially their interactive effects, has not been well quantified.

METHODS

A total of 398,577 participants were included in this analysis. There were 2504 incident lung cancer cases identified over an average 10.4-year follow-up. We applied Cox proportional hazards models to examine the associations between risk factors and incident lung cancer. We further developed a polygenic risk score and evaluated whether environmental factors modified the effect of genetic risk on incident lung cancer. Furthermore, we calculated the PAF for each risk factor, as well as their gene-environment additive interaction, and then combined them to create a weighted PAF that takes into consideration participants with overlapping risk factors.

RESULTS

Our analysis showed that smoking was the leading risk factor for lung cancer with a PAF of 63.73%. We observed additive interactions between smoking, PM_2.5, NO_x, and genetic risk, with PAFs of 17.85% (smoking-high genetic risk interaction), 10.79% (smoking-intermediate genetic risk interaction), 5.30% (NO_x-high genetic risk interaction), 6.55% (PM_2.5-high genetic risk interaction), and 4.99% (PM_2.5-intermediate genetic risk interaction). We estimated that 73.46% of lung cancer cases could be attributable to potentially modifiable risk factors after adjusting for the correlation between them.

CONCLUSION

High genetic risk and several modifiable factors may increase the risk of incident lung cancer. Participants with a high genetic risk may be more vulnerable to developing lung cancer if exposed to smoking and/or high air pollution. Our findings provide evidence that the majority of incident lung cancer cases could be prevented by eliminating modifiable risk factors.

Investigating the Impact of Bank Housing Credit Risk Control Strategy by Blockchain Technology on the Household Consumption Plan

It is essential to solve the problem of information asymmetry in bank housing credit management, thus reducing bank operating costs and credit risk. Therefore, the present work constructs a bank housing credit blockchain alliance system based on the technical characteristics of blockchain to strengthen bank housing credit risk control. First, according to blockchain's elements and system architecture, the application of blockchain to bank housing credit business is analyzed. On this basis, the bank housing credit risk control strategy is proposed. Finally, the mathematical model is used to investigate residents' household consumption to explore the impact of housing credit on the household consumption plan of residents. The results show that when the educational level of the head of household is illiterate or semi-illiterate, the coefficients of participation credit (PC), nonparticipation credit (NPC), and total samples (TSs) of total household consumption are -0.064, 0.067, and 0.174, respectively; when the educational level of the head of household is primary school or junior middle school, the coefficients of PC, NPC, and TS of total household consumption are -0.026, 0.017, and 0.105, respectively; when the educational level of the head of household is high school or above, the coefficients of PC, NPC, and TS of total household consumption are 0.084, 0.073, and 0.064, respectively. The results indicate that the education level of the head of household plays a crucial role in the family consumption plan. Those with a high educational level will compromise on the best consumption decision after constantly weighing the consumption motivation and consumption thinking after obtaining the credit funds. The content reported here is vitally significant in guiding households to clarify the proportion of credit fund distribution and understand their consumption tendency.

Airflow Limitation Increases Lung Cancer Risk in Smokers: The Lifelines Cohort Study

BACKGROUND

The relationship between smoking, airflow limitation, and lung cancer occurrence is unclear. This study aims to evaluate the relationship between airflow limitation and lung cancer, and the effect modification by smoking status.

METHODS

We included participants with spirometry data from Lifelines, a population-based cohort study from the Northern Netherlands. Airflow limitation was defined as FEV1/FVC ratio < 0.7. The presence of pathology-confirmed primary lung cancer during a median follow-up of 9.5 years was collected. The Cox regression model was used and hazard ratios (HR) with 95% confidence interval (95% CI) were reported. Adjusted confounders included age, sex, educational level, smoking, passive smoking, asthma status and asbestos exposure. The effect modification by smoking status was investigated by estimating the relative excess risk due to interaction (RERI) and the ratio of HRs with 95% CI.

RESULTS

Out of 98,630 participants, 14,200 (14.4%) had airflow limitation. In participants with and without airflow limitation, lung cancer incidence was 0.8% and 0.2%, respectively. The adjusted HR between airflow limitation and lung cancer risk was 1.7 (1.4-2.3). The association between airflow limitation and lung cancer differed by smoking status [former smokers: 2.1 (1.4-3.2), current smokers: 2.2 (1.5-3.2)] and never smokers [0.9 (0.4-2.1)]. The RERI and ratio of HRs was 2.1 (0.7-3.4) and 2.5 (1.0-6.5) for former smokers, and 4.6 (95% CI, 1.8-7.4) and 2.5 (95% CI, 1.0-6.3) for current smokers, respectively.

CONCLUSIONS

Airflow limitation increases lung cancer risk and this association is modified by smoking status.

IMPACT

Ever smokers with airflow limitation are an important target group for the prevention of lung cancer.

[Study on the Benefit of Postoperative Exercise Rehabilitation in Patients with Lung Cancer Complicated with Chronic Obstructive Pulmonary Disease]

BACKGROUND

Chronic obstructive pulmonary diseases (COPD) affects 45%-63% of lung cancer patients worldwide. Lung cancer patients complicated with COPD have decreased cardiopulmonary function and increased perioperative risk, and their postoperative exercise endurance and lung function are significantly lower than those with conventional lung cancer. Previous studies have shown that postoperative exercise training can improve the exercise endurance of unselected lung cancer patients, but it is unclear whether lung cancer patients with COPD can also benefit from postoperative exercise training. This study intends to explore the effects of postoperative exercise training on exercise endurance, daily activity and lung function of lung cancer patients with COPD.

METHODS

Seventy-four patients with non-small cell lung cancer (NSCLC) complicated with COPD who underwent pneumonectomy in the lung cancer center of West China Hospital of Sichuan University from August 5, 2020 to August 25, 2021 were prospectively analyzed. They were randomly divided into exercise group and control group; The patients in the two groups received routine postoperative rehabilitation in the first week after operation, and the control group was given routine nursing from the second week. On this basis, the exercise group received postoperative exercise rehabilitation training for two weeks. Baseline evaluation was performed 3 days before operation and endpoint evaluation was performed 3 weeks after operation.

RESULTS

The exercise endurance, daily activity and pulmonary function test results of the two groups decreased from baseline to the end point. However, after the operation and intervention program, the maximum oxygen consumption of Cardiopulmonary Exercise Test and the walking distance of 6-Minute Walking Test in the exercise group were significantly better than those in the control group [(13.09±1.46) mL/kg/min vs (11.89±1.38) mL/kg/min, P=0.033; (297±46) m vs (243±43) m, P=0.041]. The average number of we-chat steps in the exercise group was also significantly better than that in the control group (4,381±397 vs 3,478±342, P=0.035). Forced vital capacity (FVC) and forced expiratory volume in one second (FEV1) in the exercise group were better than those in the control group, but the difference did not reach a statistically significant level [(1.76±0.19) L vs (1.60±0.28) L, P=0.084; (1.01±0.17) L vs (0.96±0.21) L, P=0.467].

CONCLUSIONS

Postoperative exercise rehabilitation training can improve exercise endurance and daily activity ability of patients with lung cancer complicated with COPD and promote postoperative rehabilitation.

Construction and Validation of a Lung Cancer Risk Prediction Model for Non-Smokers in China

Background

About 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.

Methods

A 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.

Results

A 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.

Conclusions

We 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.

Development and validation of a risk prediction model for incident liver cancer

Objective

We aimed to develop and validate a risk prediction model for liver cancer based on routinely available risk factors using the data from UK Biobank prospective cohort study.

Methods

This analysis included 359,489 participants (2,894,807 person-years) without a previous diagnosis of cancer. We used the Fine-Gray regression model to predict the incident risk of liver cancer, accounting for the competing risk of all-cause death. Model discrimination and calibration were validated internally. Decision curve analysis was conducted to quantify the clinical utility of the model. Nomogram was built based on regression coefficients.

Results

Good discrimination performance of the model was observed in both development and validation datasets, with an area under the curve (95% confidence interval) for 5-year risk of 0.782 (0.748-0.816) and 0.771 (0.702-0.840) respectively. The calibration showed fine agreement between observed and predicted risks. The model yielded higher positive net benefits in the decision curve analysis than considering either all participants as being at high or low risk, which indicated good clinical utility.

Conclusion

A new risk prediction model for liver cancer composed of routinely available risk factors was developed. The model had good discrimination, calibration and clinical utility, which may help with the screening and management of liver cancer for general population in the public health field.

Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics

PURPOSE

In the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.

METHODS

A total 255 NSCLC patients (training cohort: n = 170; validation cohort: n = 85) were retrospectively enrolled in the present study. A total of 80 radiomic features were extracted from pretreatment 18F-FDG PET/CT images. Clinicopathologic features were compared between the two cohorts. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. Radiomics signature and clinicopathologic risk factors were incorporated to develop a prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the prognostic factors.

RESULTS

A total of 80 radiomic features were extracted in the training dataset. In the univariate analysis, the expression of PD-L1 in lung tumors was significantly correlated with the radiomic signature, histologic type, Ki-67, SUVmax, MTV, and TLG (p< 0.05, respectively). However, the expression of PD-L1 was not correlated with age, TNM stage, and history of smoking (p> 0.05). Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.

CONCLUSIONS

A prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy.

Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion

Objective

To evaluate the performance of 2D and 3D radiomics features with different machine learning approaches to classify SPLs based on magnetic resonance(MR) T2 weighted imaging (T2WI).

Material and Methods

A total of 132 patients with pathologically confirmed SPLs were examined and randomly divided into training (n = 92) and test datasets (n = 40). A total of 1692 3D and 1231 2D radiomics features per patient were extracted. Both radiomics features and clinical data were evaluated. A total of 1260 classification models, comprising 3 normalization methods, 2 dimension reduction algorithms, 3 feature selection methods, and 10 classifiers with 7 different feature numbers (confined to 3–9), were compared. The ten-fold cross-validation on the training dataset was applied to choose the candidate final model. The area under the receiver operating characteristic curve (AUC), precision-recall plot, and Matthews Correlation Coefficient were used to evaluate the performance of machine learning approaches.

Results

The 3D features were significantly superior to 2D features, showing much more machine learning combinations with AUC greater than 0.7 in both validation and test groups (129 vs. 11). The feature selection method Analysis of Variance(ANOVA), Recursive Feature Elimination(RFE) and the classifier Logistic Regression(LR), Linear Discriminant Analysis(LDA), Support Vector Machine(SVM), Gaussian Process(GP) had relatively better performance. The best performance of 3D radiomics features in the test dataset (AUC = 0.824, AUC-PR = 0.927, MCC = 0.514) was higher than that of 2D features (AUC = 0.740, AUC-PR = 0.846, MCC = 0.404). The joint 3D and 2D features (AUC=0.813, AUC-PR = 0.926, MCC = 0.563) showed similar results as 3D features. Incorporating clinical features with 3D and 2D radiomics features slightly improved the AUC to 0.836 (AUC-PR = 0.918, MCC = 0.620) and 0.780 (AUC-PR = 0.900, MCC = 0.574), respectively.

Conclusions

After algorithm optimization, 2D feature-based radiomics models yield favorable results in differentiating malignant and benign SPLs, but 3D features are still preferred because of the availability of more machine learning algorithmic combinations with better performance. Feature selection methods ANOVA and RFE, and classifier LR, LDA, SVM and GP are more likely to demonstrate better diagnostic performance for 3D features in the current study.

MALAT-1 Expression Correlates with Prognosis in Non-Small-Cell Lung Carcinoma: A Systematic Review and Meta-analysis

Background

Non-small-cell lung carcinoma (abbreviated as NSCLC) progresses rapidly and lacks appropriate biological markers. Recent studies have shown that long noncoding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT-1) has potential application value for clinically diagnosing lung carcinoma. Thus, this study conducted a systematic review and meta-analysis for assessing if MALAT-1 has a relationship to NSCLC outcome.

Methods

This study conducted the search of China National Knowledge Infrastructure, China Science and Technology Journal, SinoMed, EMBASE, Cochrane library, Web of Science, Wanfang database, and PubMed from inception to September, 1, 2021. The published article about MALAT-l expression for NSCLC patients was analyzed. We used combined hazard rates under the confidence interval of 95% for examining the relationship of MALAT-l and NSCLC.

Results

In this meta-analysis, we found that 10 studies were included, and MALAT-1 expressions were distinctly related to an unfavorable overall survival (HR: 2.34 (1.65, 3.33); I2 = 76%). Considering the merger's clinical heterogeneity, for meta-analysis, we used the random-effects method.

Conclusion

Overexpression of MALAT-1 showed correlations to the less effective outcome of NSCLC. MALAT-1 might be a new NSCLC prognosis marker.

A risk prediction model for selecting high-risk population for computed tomography lung cancer screening in China

OBJECTIVE

Two large randomized controlled trials (RCTs) have demonstrated that low dose computed tomography (LDCT) screening reduces lung cancer mortality. Risk-prediction models have been proved to select individuals for lung cancer screening effectively. With the focus on established risk factors for lung cancer routinely available in general cancer screening settings, we aimed to develop and internally validated a risk prediction model for lung cancer.

MATERIALS AND METHODS

Using data from the Cancer Screening Program in Urban China (CanSPUC) in Henan province, China between 2013 and 2019, we conducted a prospective cohort study consisting of 282,254 participants including 126,445 males and 155,809 females. Detailed questionnaire, physical assessment and follow-up were completed for all participants. Using Cox proportional risk regression analysis, we developed the Henan Lung Cancer Risk Models based on simplified questionnaire. Model discrimination was evaluated by concordance statistics (C-statistics), and model calibration was evaluated by the bootstrap sampling, respectively.

RESULTS

By 2020, a total of 589 lung cancer cases occurred in the follow-up yielding an incident density of 64.91/100,000 person-years (pyrs). Age, gender, smoking, history of tuberculosis and history of emphysema were included into the model. The C-index of the model for 1-year lung cancer risk was 0.766 and 0.741 in the training set and validation set, respectively. In stratified analysis, the model showed better predictive power in males, younger participants, and former or current smoking participants. The model calibrated well across the deciles of predicted risk in both the overall population and all subgroups.

CONCLUSIONS

We developed and internally validated a simple risk prediction model for lung cancer, which may be useful to identify high-risk individuals for more intensive screening for cancer prevention.

A new prediction model for lateral cervical lymph node metastasis in patients with papillary thyroid carcinoma: Based on dual-energy CT

PURPOSE

The current study aimed to develop and validate a prediction model to estimate the independent risk factors for lateral cervical lymph node metastasis (LLNM) in papillary thyroid carcinoma (PTC) patients based on dual-energy computed tomography (DECT).

METHOD

This study retrospectively conducted 406 consecutive patients from July 2015 to June 2019 to form the derivation cohorts and performed internal validation. 101 consecutive patients from July 2019 to June 2020 were included to create the external validation cohort. Univariable and multivariable logistic regression analyses were used to evaluate independent risk factors for LLNM. A prediction model based on DECT parameters was built and presented on a nomogram. The internal and external validations were performed.

RESULTS

Iodine concentration (IC) in the arterial phase (OR 2.761, 95% CI 1.028-7.415, P 0.044), IC in venous phase (OR 3.820, 95% CI 1.430-10.209, P 0.008), located in the superior pole (OR 4.181, 95% CI 2.645-6.609, P 0.000), and extrathyroidal extension (OR 4.392, 95% CI 2.142-9.004, P 0.000) were independently associated with LLNM in the derivation cohort. These four predictors were incorporated into the nomogram. The model showed good discrimination in the derivation (AUC, 0.899), internal (AUC, 0.905), and external validation (AUC, 0.912) cohorts. The decision curve revealed that more advantages would be added using the nomogram to estimate LLNM, which implied that the lateral lymph node dissection was recommended.

CONCLUSIONS

DECT parameters could provide independent indicators of LLNM in PTC patients, and the nomogram based on them may be helpful in treatment decision-making.

A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models?

Objective

To discover potential predictors and explore how to build better models by summarizing the existing prognostic prediction models of non-small cell lung cancer (NSCLC).

Background

Research on clinical prediction models of NSCLC has experienced explosive growth in recent years. As more predictors of prognosis are discovered, the choice of predictors to build models is particularly important, and in the background of more applications of next-generation sequencing technology, gene-related predictors are widely used. As it is more convenient to obtain samples and follow-up data, the prognostic model is preferred by researchers.

Methods

PubMed and the Cochrane Library were searched using the items “NSCLC”, “prognostic model”, “prognosis prediction”, and “survival prediction” from 1 January 1980 to 5 May 2021. Reference lists from articles were reviewed and relevant articles were identified.

Conclusions

The performance of gene-related models has not obviously improved. Relative to the innovation and diversity of predictors, it is more important to establish a highly stable model that is convenient for clinical application. Most of the prevalent models are highly biased and referring to PROBAST at the beginning of the study may be able to significantly control the bias. Existing models should be validated in a large external dataset to make a meaningful comparison.

A Modified Calculation Improves the Accuracy of Predicted Postoperative Lung Function Values in Lung Cancer Patients

PURPOSE

Preoperative pulmonary function testing is mandatory for non-small cell lung cancer (NSCLC) surgery. The predicted postoperative FEV1 (ppoFEV1) is used for further risk stratification. We compared the ppoFEV1 with the postoperative FEV1 (postFEV1) in order to improve the calculation of the ppoFEV1.

METHODS

87 patients voluntarily received an FEV1 assessment 1 year after surgery. ppoFEV1 was calculated according to the Brunelli calculation. Baseline characteristics and surgical procedure were compared in a uni- and multivariate analysis between different accuracy levels of the ppoFEV1. Parameters which remained significant in the multinominal regression analysis were evaluated for a modification of the ppoFEV1 calculation.

RESULTS

Independent factors for a more inaccurate ppoFEV1 were preoperative active smoking (odds ratio (OR) 4.1, confidence interval (CI) 3.6-6.41; p = 0.01), packyears (OR 4.1, CI 3.6-6.41; p = 0.008), younger age (OR 1.1, CI 1.01-1.12; p = 0.03), and patients undergoing pneumectomy (OR 5.55, CI 1.35-23.6; p = 0.01). For the customized ppoFEV1 we excluded pneumonectomies. For patients < 60 years, an additional lung segment was added to the calculation. ppoFEV1 = preFEV1 × [Formula: see text]. For actively smoking patients with more than 30 packyears we subtracted one lung segment from the calculation ppoFEV1 = PreFEV1 × [Formula: see text].

CONCLUSION

We were able to enhance the predictability of the ppoFEV1 with modifications. The modified ppoFEV1 (1.828 l ± 0.479 l) closely approximates the postFEV1 of 1.823 l ± 0.476 l, (0.27%) while the original ppoFEV1 calculation is at 1.78 l ± 0.53 (2.19%). However, if patients require pneumectomy, more complex techniques to determine the ppoFEV1 should be included to stratify risk.

Relationship between glucosamine use and the risk of lung cancer: data from a nationwide prospective cohort study

BACKGROUND

Research on glucosamine shows anti-inflammatory and anti-cancer benefits with a minimal adverse effects. We aimed to explore the relationship between use of glucosamine and risk of lung cancer and lung cancer mortality based on data from the large-scale nationwide prospective UK Biobank cohort study.

METHODS

Participants were enrolled between the year 2006 and 2010 and followed up to 2020. Cox proportion hazards model were used to assess the relationship between glucosamine use and risk of lung cancer and lung cancer mortality. Subgroup analyses and sensitivity analyses were performed to explore the potential effect modifications and the robustness of main findings.

RESULTS

A total of 439,393 participants (mean age: 56 years; 53% females) with a mean follow-up of 11 years were included for analyses. There were 82,603 (18.80%) participants reporting regular use of glucosamine at baseline. During follow-up, there were 1,971 (0.45%) lung cancer events documented. Glucosamine use was significantly associated with a decreased risk of lung cancer (hazard ratio=0.84, 95% CI: 0.75-0.92, p<0.001) and lung cancer mortality (hazard ratio=0.88, 95% CI: 0.81-0.96, p=0.002) in fully-adjusted models. A stronger association between glucosamine use and decreased lung cancer risk was observed in participants with a family history of lung cancer when compared to those without a family history.

CONCLUSION

Regular use of glucosamine was significantly related with decreased risk of lung cancer and lung cancer mortality, based on data from this nationwide prospective cohort study.

Individual 5-Year Lung Cancer Risk Prediction Model in Korea Using a Nationwide Representative Database

Early detection of lung cancer by screening has contributed to reduce lung cancer mortality. Identifying high risk subjects for lung cancer is necessary to maximize the benefits and minimize the harms followed by lung cancer screening. In the present study, individual lung cancer risk in Korea was presented using a risk prediction model. Participants who completed health examinations in 2009 based on the Korean National Health Insurance (KNHI) database (DB) were eligible for the present study. Risk scores were assigned based on the adjusted hazard ratio (HR), and the standardized points for each risk factor were calculated to be proportional to the b coefficients. Model discrimination was assessed using the concordance statistic (c-statistic), and calibration ability assessed by plotting the mean predicted probability against the mean observed probability of lung cancer. Among candidate predictors, age, sex, smoking intensity, body mass index (BMI), presence of chronic obstructive pulmonary disease (COPD), pulmonary tuberculosis (TB), and type 2 diabetes mellitus (DM) were finally included. Our risk prediction model showed good discrimination (c-statistic, 0.810; 95% CI: 0.801-0.819). The relationship between model-predicted and actual lung cancer development correlated well in the calibration plot. When using easily accessible and modifiable risk factors, this model can help individuals make decisions regarding lung cancer screening or lifestyle modification, including smoking cessation.

A Novel Diagnosis Method Based on Methylation Analysis of SHOX2 and Serum Biomarker for Early Stage Lung Cancer

Objectives:

Lung cancer (LC) is often accompanied by significant methylation abnormalities. This study aimed to develop a decision tree (DT) accompanied the stature homeobox 2 gene (SHOX2) / prostaglandin E receptor 4 (PTGER4) gene DNA methylation with traditional tumor marker in the differential diagnosis of benign and malignant lung nodule.

Methods:

We performed a study with 104 patients enrolled in the LC group and 36 patients in the benign lung diseases group. All the clinical data of these patients were collected through electronic medical record. Total Methylation (TM) status of both SHOX2 and PTGER4 was defined as methylation levels of SHOX2 plus methylation levels of PTGER4. One-way analysis was used to compare the concentrations of serum samples and t-test was used to compare pairwise mean values between groups. Receiver operating curve (ROC) was used to evaluate the diagnostic value. Furthermore, the strategy was validated in 19 LC patients and 11 patients with benign lung diseases.

Results:

There were significant differences between the concentration of neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), cytokeratin 19 fragments (CYFRA21 -1) and the methylation levels of SHOX2, PTGER4 and TM in lung benign diseases and cancer group. The AUCs of NSE, CEA, CYFRA21 -1, Methylation SHOX2, Methylation PTGER4 and TM were 0.721 (95% CI: 0.627–0.816), 0.753 (95% CI: 0.673–0.833) and 0.778(95% CI: 0.700–0.856), 0.851(0.786-0.916), 0.847(0.780-0.913) and 0.861(0.800-0.922) respectively. We developed a DT model with TM and CYFRA21 -1 used in this study, and the area under the curve (AUC) of DT was 0.921 and the sensitivity up to 0.856. In the validation cohort, the AUC of SHOX2, PTGER4 and TM was also much higher than traditional serum markers.

Conclusions:

Our results indicated that the DT model calculated from the TM and CYFRA21 -1 can accurately classify LC and benign diseases, which showed better diagnostic performance than traditional serum parameter.

Nomogram to assess risk of central cervical lymph node metastasis in patients with cN0 papillary thyroid carcinoma

OBJECTIVE

This study developed and validated an individualized prediction risk model for the need for central cervical lymph node dissection in patients with cN0 papillary thyroid carcinoma (PTC) diagnosed by ultrasound.

METHODS

Upon retrospective review, derivation and internal validation cohorts comprised 1585 consecutive patients with PTC treated from January 2017 to December 2019 at Hospital A. The external validation cohort consisted of 406 consecutive patients treated at Hospital B from January 2016 to June 2020. Independent risk factors for central cervical lymph node metastasis (CLNM) were determined through univariable and multivariable logistic regression analysis. An individualized risk prediction model was constructed and illustrated as a nomogram, which was internally and externally validated.

RESULTS

The following risk factors of CLNM were established: the solitary primary thyroid nodule's diameter, shape, calcification, and capsular abutment-to-lesion perimeter ratio. The areas under the receiver operating characteristic curves of the risk prediction model for the internal and external validation cohorts were 0.921 and 0.923, respectively. The calibration curve showed good agreement between the nomogram-estimated probability of CLNM and the actual CLNM rate in the three cohorts. The decision curve analysis confirmed the clinical usefulness of the nomogram.

CONCLUSION

This study developed and validated a model for predicting risk of CLNM in the individual patient with cN0 PTC, which should be an efficient tool for guiding clinical treatment.

Prediction and clinical utility of a liver cancer risk model in Chinese adults: A prospective cohort study of 0.5 million people

China has made rapid progress in reducing the incidence of HBV infection in the past three decades, along with a rapidly changing lifestyle and aging population. We aimed to develop and validate an up-to-date liver cancer risk prediction model with routinely available predictors and evaluate its applicability for screening guidance. Using data from the China Kadoorie Biobank, we included 486 285 participants in this analysis. Fifteen risk factors were included in the model. Flexible parametric survival models were used to estimate the 10-year absolute risk of liver cancer. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. A total of 2706 participants occurred liver cancer over the 4 814 320 person-years of follow-up. Excellent discrimination of the model was observed in both development and validation datasets, with c-statistics (95% CI) of 0.80 (0.79-0.81) and 0.80 (0.78-0.82) respectively, as well as excellent calibration of observed and predicted risks. Decision curve analysis revealed that use of the model in selecting participants for screening improved benefit at a threshold of 2% 10-year risk, compared to current guideline of screening all HBsAg carriers. Our model was more sensitive than current guideline for cancer screening (28.17% vs 25.96%). We developed and validated a CKB-PLR (Prediction for Liver cancer Risk Based on the China Kadoorie Biobank Study) model to predict the absolute risk of liver cancer for both HBsAg seropositive and seronegative populations. Application of the model is beneficial for precisely identifying the high-risk groups among the general population.