One-quarter of lung cancers found through CT screening are slow-growing or indolent, many of which may have been overdiagnosed, researchers say.
One-quarter of lung cancers found through CT screening are slow-growing or indolent, many of which may have been overdiagnosed, according to findings of a study published in the December 4 issue of Annals of Internal Medicine.
Researchers in Italy investigated whether lung cancer is potentially being over diagnosed, resulting in overtreatment. They conducted a retrospective analysis of 175 patients who had been diagnosed with primary lung cancer. Fifty-five patients had been diagnosed at baseline and 120 later on. The patients were followed for five years.
The researchers measured volume-doubling time (VDT) on low-dose CT (LDCT). This would determine the growth rate; tumors were deemed fast growing (fewer than 400 days), slow-growing (between 400 and 599 days) or indolent (600 days or longer).
Subjects who were found to have nodules that were 5 mm to 8 mm in diameter were rescreened after three months and those who had nodules that were 5 mm or smaller were rescanned after a year. PET/CTs were performed on most subjects who had noncalcified nodules that were larger than 8 mm. Subjects with nodules that were growing or were positive as per PET/CT underwent surgical biopsy and other interventions.
The results showed that among the 120 subjects who were diagnosed later, 19 (15.8 percent) were not visible on earlier scans and their tumors were fast growing, with a median VDT of 52 days. One hundred one subjects (84.2 percent) were progressive, including 70 fast-growing and 31 slow-growing or indolent cancers. Mortality due to lung cancer was significantly higher by 9.2 percent among the subjects with new compared with slow-growing or indolent cancer.
Overall mean VDT for fast-growing cancers was 223 days and for slow-growing or indolent cancers, 545 days.
The authors concluded that while the study was limited because VDT can only indicate overdiagnosis and was estimated for new cancer from one measurement (a diameter of 2 mm assumed the previous year), their findings did indicate that “slow-growing or indolent cancer comprised approximately 25 percent of incident cases, many of which may have been overdiagnosed.” They suggested that overtreatment of these cases could be limited by use of minimally invasive limited resection and nonsurgical treatments.
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.