CT screening of small pulmonary nodules may help determine cancer risk.
Clinically relevant radiologic features that can be easily scored in the clinical setting may help determine lung cancer risk among participants with small pulmonary nodules (SPNs), according to a study published in Radiology.
Researchers from China and the United States sought to identify features associated with lung cancer risk using data and images from the National Lung Screening Trial (NLST).
The researchers examined radiologic features in SPNs in baseline low-dose CT screening studies that did not meet NLST criteria to be considered a positive screening examination. They identified SPNs for 73 incident case patients who were given a diagnosis of lung cancer at either the first or second follow-up screening study and for 157 control subjects who had undergone three consecutive negative screening studies. Multivariable logistic regression was used to assess the association between radiologic features and lung cancer risk.
The results showed that nine features were significantly different between case patients and control subjects. Using backward elimination followed by bootstrap resampling, the researchers identified a reduced model of highly informative radiologic features with an area under the receiver operating characteristic curve of 0.932, a specificity of 92.38%, and a sensitivity of 76.55% that included total emphysema score, attachment to vessel, nodule location, border definition, and concavity.
The researchers concluded that this set of clinically relevant radiologic features can be easily scored in the clinical setting and may be of use to determine lung cancer risk among participants with SPNs.
What is the Best Use of AI in CT Lung Cancer Screening?
April 18th 2025In comparison to radiologist assessment, the use of AI to pre-screen patients with low-dose CT lung cancer screening provided a 12 percent reduction in mean interpretation time with a slight increase in specificity and a slight decrease in the recall rate, according to new research.
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).
Could Lymph Node Distribution Patterns on CT Improve Staging for Colon Cancer?
April 11th 2025For patients with microsatellite instability-high colon cancer, distribution-based clinical lymph node staging (dCN) with computed tomography (CT) offered nearly double the accuracy rate of clinical lymph node staging in a recent study.