Search

Background: Esophageal cancer (EC) often occurs in the elderly, and approximately 40% of patients are 70 years or older. To investigate the long‐term survival of EC in elderly patients, to provide a theoretical direction for better management and predicting survival of EC based on the hospital‐based multi‐center study in China. Methods: The study was conducted in 18 hospitals including 6 provincial hospitals, 8 municipal hospitals, and 4 county hospitals. We extracted information from medical record homepage, records of admission and discharge, and pathological diagnosis reports from the medical record department of the elderly patients at 70–84 years old to obtain the 3‐year and 5‐year overall survival (OS), and main associated factors, and to analyze the current therapeutic effect of different treatment options for elderly patients. Results: The 3‐year and 5‐year OS rate of the 1013 elderly patients was 44.8% and 32.8%, respectively. Their median survival was 28.00 months. The median survival of patients with squamous cell carcinoma was longer than that of other pathological type (squamous vs. other types: 31.00 vs. 20.00 months, p = 0.018). The median survival of patients with surgery only or combined therapy was longer than that of radiotherapy, chemotherapy, and no therapy (surgery only vs. combined therapy vs. radiotherapy vs. chemotherapy vs. no therapy: 56.00 vs. 33.00 vs. 26.00 vs.18.00 vs. 16.00 months, p < 0.001). The 5‐year OS rate of patients with highly differentiated cancer was higher than that of medium differentiated and poor differentiation/undifferentiated. In multivariate analysis, the older ages, pathological stage, were independent prognostic risk factors for poor EC survival. Treatment method was independent protective factors predictive of a good EC OS. Conclusions: The survival rate of the elderly EC patients was still low in China. Age, therapy method, and pathological stage were mainly associated with the survival rate of EC in elderly patients. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusions Plain Language Summary Breast cancer is a significant contributor to female mortality, exerting a public health burden worldwide, especially in China, where risk‐prediction models with good discriminating accuracy for breast cancer are still scarce.A multicenter screening cohort study was conducted as part of the Cancer Screening Program in Urban China. Dwellers aged 40–74 years were recruited between 2014 and 2019 and prospectively followed up until June 30, 2021. The entire data set was divided by year of enrollment to develop a prediction model and validate it internally. Multivariate Cox regression was used to ascertain predictors and develop a risk‐prediction model. Model performance at 1, 3, and 5 years was evaluated using the area under the curve, nomogram, and calibration curves and subsequently validated internally. The prediction model incorporates selected factors that are assigned appropriate weights to establish a risk‐scoring algorithm. Guided by the risk score, participants were categorized into low‐, medium‐, and high‐risk groups for breast cancer. The cutoff values were chosen using X‐tile plots. Sensitivity analysis was conducted by categorizing breast cancer risk into the low‐ and high‐risk groups. A decision curve analysis was used to assess the clinical utility of the model.Of the 70,520 women enrolled, 447 were diagnosed with breast cancer (median follow‐up, 6.43 [interquartile range, 3.99–7.12] years). The final prediction model included age and education level (high, hazard ratio [HR], 2.01 [95% CI, 1.31–3.09]), menopausal age (≥50 years, 1.34 [1.03–1.75]), previous benign breast disease (1.42 [1.09–1.83]), and reproductive surgery (1.28 [0.97–1.69]). The 1‐year area under the curve was 0.607 in the development set and 0.643 in the validation set. Moderate predictive discrimination and satisfactory calibration were observed for the validation set. The risk predictions demonstrated statistically significant differences between the low‐, medium‐, and high‐risk groups (p < .001). Compared with the low‐risk group, women in the high‐ and medium‐risk groups posed a 2.17‐fold and 1.62‐fold elevated risk of breast cancer, respectively. Similar results were obtained in the sensitivity analyses. A web‐based calculator was developed to estimate risk stratification for women.This study developed and internally validated a risk‐adapted and user‐friendly risk‐prediction model by incorporating easily accessible variables and female factors. The personalized model demonstrated reliable calibration and moderate discriminative ability. Risk‐stratified screening strategies contribute to precisely distinguishing high‐risk individuals from asymptomatic individuals and prioritizing breast cancer screening. Breast cancer remains a burden in China. To enhance breast cancer screening, we need to incorporate population stratification in screening. Accurate risk‐prediction models for breast cancer remain scarce in China. We established and validated a risk‐adapted and user‐friendly risk‐prediction model by incorporating routinely available variables along with female factors. Using this risk‐stratified model helps accurately identify high‐risk individuals, which is of significant importance when considering integrating individual risk assessments into mass screening programs for breast cancer. Current clinical breast cancer screening lacks a constructive clinical pathway and guiding recommendations. Our findings can better guide clinicians and health care providers. Breast cancer remains a burden in China. To enhance breast cancer screening, we need to incorporate population stratification in screening.Accurate risk‐prediction models for breast cancer remain scarce in China. We established and validated a risk‐adapted and user‐friendly risk‐prediction model by incorporating routinely available variables along with female factors.Using this risk‐stratified model helps accurately identify high‐risk individuals, which is of significant importance when considering integrating individual risk assessments into mass screening programs for breast cancer.Current clinical breast cancer screening lacks a constructive clinical pathway and guiding recommendations. Our findings can better guide clinicians and health care providers. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer is a significant contributor to female mortality, exerting a public health burden worldwide, especially in China, where risk-prediction models with good discriminating accuracy for breast cancer are still scarce., Methods: A multicenter screening cohort study was conducted as part of the Cancer Screening Program in Urban China. Dwellers aged 40-74 years were recruited between 2014 and 2019 and prospectively followed up until June 30, 2021. The entire data set was divided by year of enrollment to develop a prediction model and validate it internally. Multivariate Cox regression was used to ascertain predictors and develop a risk-prediction model. Model performance at 1, 3, and 5 years was evaluated using the area under the curve, nomogram, and calibration curves and subsequently validated internally. The prediction model incorporates selected factors that are assigned appropriate weights to establish a risk-scoring algorithm. Guided by the risk score, participants were categorized into low-, medium-, and high-risk groups for breast cancer. The cutoff values were chosen using X-tile plots. Sensitivity analysis was conducted by categorizing breast cancer risk into the low- and high-risk groups. A decision curve analysis was used to assess the clinical utility of the model., Results: Of the 70,520 women enrolled, 447 were diagnosed with breast cancer (median follow-up, 6.43 [interquartile range, 3.99-7.12] years). The final prediction model included age and education level (high, hazard ratio [HR], 2.01 [95% CI, 1.31-3.09]), menopausal age (≥50 years, 1.34 [1.03-1.75]), previous benign breast disease (1.42 [1.09-1.83]), and reproductive surgery (1.28 [0.97-1.69]). The 1-year area under the curve was 0.607 in the development set and 0.643 in the validation set. Moderate predictive discrimination and satisfactory calibration were observed for the validation set. The risk predictions demonstrated statistically significant differences between the low-, medium-, and high-risk groups (p < .001). Compared with the low-risk group, women in the high- and medium-risk groups posed a 2.17-fold and 1.62-fold elevated risk of breast cancer, respectively. Similar results were obtained in the sensitivity analyses. A web-based calculator was developed to estimate risk stratification for women., Conclusions: This study developed and internally validated a risk-adapted and user-friendly risk-prediction model by incorporating easily accessible variables and female factors. The personalized model demonstrated reliable calibration and moderate discriminative ability. Risk-stratified screening strategies contribute to precisely distinguishing high-risk individuals from asymptomatic individuals and prioritizing breast cancer screening., Plain Language Summary: Breast cancer remains a burden in China. To enhance breast cancer screening, we need to incorporate population stratification in screening. Accurate risk-prediction models for breast cancer remain scarce in China. We established and validated a risk-adapted and user-friendly risk-prediction model by incorporating routinely available variables along with female factors. Using this risk-stratified model helps accurately identify high-risk individuals, which is of significant importance when considering integrating individual risk assessments into mass screening programs for breast cancer. Current clinical breast cancer screening lacks a constructive clinical pathway and guiding recommendations. Our findings can better guide clinicians and health care providers., (© 2023 The Authors. Cancer published by Wiley Periodicals LLC on behalf of American Cancer Society.)

Background: Low-dose computed tomography (LDCT) screening is effective in reducing lung cancer mortality in smokers; however, the evidence in nonsmokers is scarce., Objective: This study aimed to evaluate the participant rate and effectiveness of one-off LDCT screening for lung cancer among smokers and nonsmokers., Methods: A population-based prospective cohort study was performed to enroll participants aged between 40 and 74 years from 2013 to 2019 from 4 cities in Zhejiang Province, China. Participants who were evaluated as having a high risk of lung cancer from an established risk score model were recommended to undergo LDCT screening. Follow-up outcomes were retrieved on June 30, 2020. The uptake rate of LDCT screening for evaluated high-risk participants and the detection rate of early-stage lung cancer (stage 0-I) were calculated. The lung cancer incidence, lung cancer mortality, and all-cause mortality were compared between the screened and nonscreened groups., Results: At baseline, 62.56% (18,818/30,079) of smokers and 6% (5483/91,455) of nonsmokers were identified as high risk (P<.001), of whom 41.9% (7885/18,818) and 66.31% (3636/5483) underwent LDCT screening (P<.001), respectively. After a median follow-up of 5.1 years, 1100 lung cancer cases and 456 all-cause death cases (116 lung cancer death cases) were traced. The proportion of early-stage lung cancer among smokers was 60.3% (173/287), which was lower than the proportion of 80.3% (476/593) among nonsmokers (P<.001). Among smokers, a higher proportion was found in the screened group (72/106, 67.9%) than the nonscreened group (56/114, 49.1%; P=.005), whereas no significance was found (42/44, 96% vs 10/12, 83%; P=.20) among nonsmokers. Compared with participants who were not screened, LDCT screening in smokers significantly increased lung cancer incidence (hazard ratio [HR] 1.39, 95% CI 1.09-1.76; P=.007) but reduced lung cancer mortality (HR 0.52, 95% CI 0.28-0.96; P=.04) and all-cause mortality (HR 0.47, 95% CI 0.32-0.69; P<.001). Among nonsmokers, no significant results were found for lung cancer incidence (P=.06), all-cause mortality (P=.89), and lung cancer mortality (P=.17)., Conclusions: LDCT screening effectively reduces lung cancer and all-cause mortality among high-risk smokers. Further efforts to define high-risk populations and explore adequate lung cancer screening modalities for nonsmokers are needed., (©Le Wang, Youqing Wang, Fei Wang, Yumeng Gao, Zhimei Fang, Weiwei Gong, Huizhang Li, Chen Zhu, Yaoyao Chen, Lei Shi, Lingbin Du, Ni Li. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 14.03.2023.)