No abstract

No abstract

Background: Screening with low-dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false-positive rate. Several pulmonary nodules risk prediction models were developed to solve the problem. This systematic review aimed to compare the quality and accuracy of these models. Methods: The keywords "lung cancer," "lung neoplasms," "lung tumor," "risk," "lung carcinoma" "risk," "predict," "assessment," and "nodule" were used to identify relevant articles published before February 2021. All studies with multivariate risk models developed and validated on human LDCT data were included. Informal publications or studies with incomplete procedures were excluded. Information was extracted from each publication and assessed. Results: A total of 41 articles and 43 models were included. External validation was performed for 23.2% (10/43) models. Deep learning algorithms were applied in 62.8% (27/43) models; 60.0% (15/25) deep learning based researches compared their algorithms with traditional methods, and received better discrimination. Models based on Asian and Chinese populations were usually built on single-center or small sample retrospective studies, and the majority of the Asian models (12/15, 80.0%) were not validated using external datasets. Conclusion:The existing models showed good discrimination for identifying highrisk pulmonary nodules, but lacked external validation. Deep learning algorithms are increasingly being used with good performance. More researches are required Zheng Wu and Fei Wang equally contributed to this work.

No abstract

ImportanceAlthough current guidelines highlight the need for earlier screening in women at increased risk of breast cancer in China, data on risk-adapted starting ages of screening are limited.ObjectiveTo explore the risk-adapted starting age of breast cancer screening in China, with comprehensive consideration of breast cancer risk factors.Design, Setting, and ParticipantsA multicenter community-based cohort study was conducted under the framework of the Cancer Screening Program in Urban China. Data were collected from January 1, 2013, to December 31, 2018, for unscreened community-dwelling women aged 40 to 74 years without a history of cancer, kidney dysfunction, or severe heart, brain, or lung disease. Data analysis was performed from October 1, 2021, to August 16, 2022.ExposuresBaseline characteristics associated with breast cancer, including first-degree family history of breast cancer, benign breast disease, breastfeeding, age at menarche, and body mass index.Main Outcomes and MeasuresOutcomes included breast cancer diagnosis and age at diagnosis. Risk-adapted starting age of screening was defined as the age at which women with different levels of breast cancer risk attained a 10-year cumulative risk level similar to women aged 50 years in the general population.ResultsOf the 1 549 988 women enrolled in this study, 3895 had breast cancer (median follow-up, 4.47 [IQR, 3.16-6.35] years). Participants were divided into different risk groups according to breast cancer risk scores (driven by risk factors including first-degree family history of breast cancer, benign breast disease, breastfeeding, age at menarche, and body mass index). Using the 10-year cumulative risk of breast cancer at age 50 years in the general population as a benchmark (2.65% [95% CI, 2.50%-2.76%]), the optimal starting age of screening for women with high, medium, or low risk of breast cancer was identified as 43, 48, or after 55 years, respectively. An online calculator was developed to calculate an individual’s optimal starting age of screening.Conclusions and RelevanceThis study identifies the risk-adapted starting age of breast cancer screening based on the principle of equal management of equal risks, which may inform updates of current screening guidelines.