Lung cancer risk prediction models based on pulmonary nodules: A systematic review

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.

A strategy to reduce the false‐positive rate after low‐dose computed tomography in lung cancer screening: A multicenter prospective cohort study

“…Existing models, which are generally based on American and European populations, may be inefficient when applied to Asian populations because of the differences in population characteristics; the validation of these models in our dataset supports this. Models constructed on Chinese and Asian populations may have bias originating from small sample sizes, retrospective study designs, and single data sources 12 . Models based on deep learning algorithms have shown good discrimination, 27–29 but the demand for large numbers of covariates may limit their clinical application.…”

“…International guidelines recommend the use of prediction models to reduce the FPR of screening 10,11 . However, our previous systematic review suggested that no representative model has been constructed using data from large‐sample, multisource, prospective cohorts in China 12 . At present, the models for the Chinese population are mostly based on small sample, single‐center retrospective research, and extrapolation is limited 13–17 ; existing and widely used models are generally based on European and American populations, 18–23 and may not be appropriate for the screening of Asian and Chinese populations, which have unique demographic characteristics 12 .…”

Comment on “Evaluation of pulmonary nodules by magnetic resonance imaging sequences: which sequence will replace computed tomography?”

“…Aleksandar Georgiev 1,2 * , Lyubomir Chervenkov 1 , Vania Anastasova 3 , Tanya Kitova 4,5 Dear Editor,…”

“…The majority of diagnosed small pulmonary nodules are incidental findings 3 , and globally more than half of lung cancer patients are diagnosed initially in stage IV or more advanced stage 4 . The free survival rate in stage IV and above according to medical literature is approximately 1 year or less, and 5-year survival rates are close to 0% 5,6 .…”

miR-4757-3p Inhibited the Migration and Invasion of Lung Cancer Cell via Targeting Wnt Signaling Pathway

“…Lung cancer remains the most common cancer and the leading cause of cancer death in China. The overall 5year survival rate of lung cancer is between 10% and 20% in most countries [ 2 ]. The final stage of progression of lung cancer is the unrestrained development and division of abnormal cells [ 3 ].…”