Low-Dose CT Screening for Lung Cancer: Computer-aided Detection of Missed Lung Cancers

Abstract

Purpose To update information regarding the usefulness of computer-aided detection (CAD) systems with a focus on the most critical category, that of missed cancers at earlier imaging, for cancers that manifest as a solid nodule. Materials and Methods By using a HIPAA-compliant institutional review board-approved protocol where informed consent was obtained, 50 lung cancers that manifested as a solid nodule on computed tomographic (CT) scans in annual rounds of screening (time 1) were retrospectively identified that could, in retrospect, be identified on the previous CT scans (time 0). Four CAD systems were compared, which were referred to as CAD 1, CAD 2, CAD 3, and CAD 4. The total number of accepted CAD-system-detected nodules at time 0 was determined by consensus of two radiologists and the number of CAD-system-detected nodules that were rejected by the radiologists was also documented. Results At time 0 when all the cancers had been missed, CAD system detection rates for the cancers were 56%, 70%, 68%, and 60% (κ = 0.45) for CAD systems 1, 2, 3, and 4, respectively. At time 1, the rates were 74%, 82%, 82%, and 78% (κ = 0.32), respectively. The average diameter of the 50 cancers at time 0 and time 1 was 4.8 mm and 11.4 mm, respectively. The number of CAD-system-detected nodules that were rejected per CT scan for CAD systems 1-4 at time 0 was 7.4, 1.7, 0.6, and 4.5 respectively. Conclusion CAD systems detected up to 70% of lung cancers that were not detected by the radiologist but failed to detect about 20% of the lung cancers when they were identified by the radiologist, which suggests that CAD may be useful in the role of second reader. (©) RSNA, 2016.

Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy

“…While radiomics has already had a variety of applications in many different cancers, and been integrated into routine clinical practice as computer-aided diagnostic tools for some, its usefulness in pancreatic cancer remains less explored but desperately needed [11]. As an extremely lethal cancer with the highest mortality rate of all major cancers in the US [12], pancreatic cancer is a critical global health care problem.…”

Long-Term Active Surveillance of Screening Detected Subsolid Nodules is a Safe Strategy to Reduce Overtreatment

“…In fact, the performance of CAD software is increasing with the advent of deep learning approaches and, as a result, we believe that CAD will eventually be integrated into the reading process for lung cancer screening (e.g., reduction of reading time and increase in sensitivity and negative predictive value). [47][48][49] Second, the number of participants in this screening trial was relatively small compared to the NLST trial. 1 Therefore, we wait for future studies to confirm our results in larger populations.…”

Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

“…51 Another common application is the detection of malignant tumors in medical images, which could serve as a second opinion to detect malignancies that might have been missed by the radiologist. 52,53 In addition to increasing efficiency, deep-learning models could also extract information from image data that is not included in the radiologist report. This would include features that are too complex and time-consuming to extract manually or features that are not currently being used in clinical decision making.…”