The effect of radiation dose reduction on computer-aided detection (CAD) performance in a low-dose lung cancer screening population

Abstract

Purpose Lung cancer screening with low-dose CT has recently been approved for reimbursement, heralding the arrival of such screening services worldwide. Computer-Aided Detection (CAD) tools offer the potential to assist radiologists in detecting nodules in these screening exams. In lung screening, as in all CT exams, there is interest in further reducing radiation dose. However, the effects of continued dose reduction on CAD performance are not fully understood. In this work, we investigated the effect of reducing radiation dose on CAD lung-nodule detection performance in a screening population. Methods The raw projection data files were collected from 481 patients who underwent low-dose screening CT exams at our institution as part of the National Lung Screening Trial (NLST). All scans were performed on a multi-detector scanner (Sensation 64, Siemens Healthcare, Forchheim Germany) according to the NLST protocol, which called for a fixed tube current scan of 25 effective mAs for standard-sized patients and 40 effective mAs for larger patients. The raw projection data were input to a reduced-dose simulation software to create simulated reduced-dose scans corresponding to 50% and 25% of the original protocols. All raw data files were reconstructed at the scanner with 1 mm slice thickness and B50 kernel. The lungs were segmented semi-automatically, and all images and segmentations were input to an in-house CAD algorithm trained on higher-dose scans (75–300 mAs). CAD findings were compared to a reference standard generated by an experienced reader. Nodule- and patient-level sensitivities were calculated along with false positives per scan, all of which were evaluated in terms of the relative change with respect to dose. Nodules were subdivided based on size and solidity into categories analogous to the LungRADS assessment categories, and sub-analyses were performed. Results From the 481 patients in this study, 82 had at least one nodule (prevalence of 17%) and 399 did not (83%). A total of 118 nodules were identified. 27 nodules (23%) corresponded to LungRADS category 4 based on size and composition, while 18 (15%) corresponded to LungRADS category 3 and 73 (61%) corresponded to LungRADS category 2. For solid nodules ≥ 8 mm, patient-level median sensitivities were 100% at all three dose levels, and mean sensitivities were 72%, 63%, and 63% at original, 50%, and 25% dose respectively. Overall mean patient-level sensitivities for nodules ranging from 3 to 45 mm were 38%, 37%, and 38% at original, 50%, and 25% dose due to the prevalence of smaller nodules and non-solid nodules in our reference standard. The mean false-positive rates were 3, 5, and 13 per case. Conclusions CAD sensitivity decreased very slightly for larger nodules as dose was reduced, indicating that reducing the dose to 50% of original levels may be investigated further for use in CT screening. However, the effect of dose was small relative to the effect of the nodule size and solidity characteristics. The number of false positives per scan incr...

Vasculature surrounding a nodule: A novel lung cancer biomarker

“…However, nodule status (i.e., benign or malignant) was determined by subjective visual rating of radiologist and it appears there were more diagnostic CT scans than LDCT scans in the dataset. There is a large variability in radiologists agreement in nodule detection and diagnosis [13, 43–44], which indicates that studies based on radiologists interpretation are based on an approximation of truth. Therefore, matching a radiologist subjective assessment of indeterminate nodules may not be the ultimate target because radiologists often recommend additional follow-up procedures (e.g., follow-up imaging, biopsy) to conclusively determine nodule status.…”

Technical Note: Design and implementation of a high‐throughput pipeline for reconstruction and quantitative analysis of CT image data

“…The total number of reconstructions analyzed was 5112 and 15 quantitative imaging metrics were computed for each reconstruction (all 15 were histogram‐based). The pipeline allowed for this experiment to be conducted approximately 80 times faster, and with substantially less researcher involvement required than the most comparable experiment conducted by our research group, which required approximately 6 months for data generation (i.e., simulated dose reduction and reconstruction) and more for quality assurance and analysis . While a larger cohort was assessed in Ref.…”

“…Simulated dose reduction was performed on the raw data with a noise model, an implementation of which has been validated and utilized for similar, previous experiments . The model adds noise to individual projections considering quantum and electronic noise.…”

“…When cohort sizes are small and only a few reconstructions per subject required, this is a viable approach, however it quickly becomes burdensome. For example, to assemble the dataset used in Young et al . required 6 months (three reconstructions per patient for 481 patients).…”

Technical Note: FreeCT_ICD: An open‐source implementation of a model‐based iterative reconstruction method using coordinate descent optimization for CT imaging investigations

“…These tools have enabled the development of reconstruction pipelines that are not dependent on the availability of clinical CT scanners, can be configured to operate in high‐throughput batch modes, can incorporate simulated dose reduction techniques and therefore can produce a large collection of image datasets that represent a wide range of acquisition and reconstruction settings such as different slice thicknesses and reconstruction kernels used in wFBP. The results of these reconstruction pipelines have contributed to the growing list of investigations evaluating the robustness of quantitative imaging, radiomics, and CAD methods across a range of scanner platforms, acquisition conditions and reconstruction parameters …”