Latest CT technologies in lung cancer screening:protocols and radiation dose reduction

The aim of this review is to provide clinicians and technicians with an overview of the development of CT protocols in lung cancer screening. CT protocols have evolved from pre-fixed settings in early lung cancer screening studies starting in 2004 towards automatic optimized settings in current international guidelines. The acquisition protocols of large lung cancer screening studies and guidelines are summarized. Radiation dose may vary considerably between CT protocols, but has reduced gradually over the years. Ultra-low dose acquisition can be achieved by applying latest dose reduction techniques. The use of low tube current or tin-filter in combination with iterative reconstruction allow to reduce the radiation dose to a submilliSievert level. However, one should be cautious in reducing the radiation dose to ultra-low dose settings since performed studies lacked generalizability. Continuous efforts are made by international radiology organizations to streamline the CT data acquisition and image quality assurance and to keep track of new developments in CT lung cancer screening. Examples like computer-aided diagnosis and radiomic feature extraction are discussed and current limitations are outlined. Deep learning-based solutions in postprocessing of CT images are provided. Finally, future perspectives and recommendations are provided for lung cancer screening CT protocols

Comparison of conventional and higher-resolution reduced-FOV diffusion-weighted imaging of breast tissue

Objective: Reduced FOV-diffusion-weighted imaging (rFOV-DWI) allows for acquisition of a tissue region without back-folding, and may have better fat suppression than conventional DWI imaging (c-DWI). The aim was to compare the ADCs obtained with c-DWI bilateral-breast imaging with single-breast rFOV-DWI. Materials and Methods: Breasts of 38 patients were scanned at 3 T. The mean ADC values obtained for 38 lesions, and fibro-glandular (N = 35) and adipose (N = 38) tissue ROIs were compared between c-DWI and higher-resolution rFOV-DWI (Wilcoxon rank test). Also, the ADCs were compared between the two acquisitions for an oil-only phantom and a combined water/oil phantom. Furthermore, ghost artifacts were assessed. Results: No significant difference in mean ADC was found between the acquisitions for lesions (c-DWI: 1.08 × 10–3 mm2/s, rFOV-DWI: 1.13 × 10–3 mm2/s) and fibro-glandular tissue. For adipose tissue, the ADC using rFOV-DWI (0.31 × 10–3 mm2/s) was significantly higher than c-DWI (0.16 × 10–3 mm2/s). For the oil-only phantom, no difference in ADC was found. However, for the water/oil phantom, the ADC of oil was significantly higher with rFOV-DWI compared to c-DWI. Discussion: Although ghost artifacts were observed for both acquisitions, they appeared to have a greater impact for rFOV-DWI. However, no differences in mean lesions’ ADC values were found, and therefore this study suggests that rFOV can be used diagnostically for single-breast DWI imaging.</p

Breast Tumor Identification in Ultrafast MRI Using Temporal and Spatial Information

Purpose: To investigate the feasibility of using deep learning methods to differentiate benign from malignant breast lesions in ultrafast MRI with both temporal and spatial information. Methods: A total of 173 single breasts of 122 women (151 examinations) with lesions above 5 mm were retrospectively included. A total of 109 out of 173 lesions were benign. Maximum intensity projection (MIP) images were generated from each of the 14 contrast-enhanced T1-weighted acquisitions in the ultrafast MRI scan. A 2D convolutional neural network (CNN) and a long short-term memory (LSTM) network were employed to extract morphological and temporal features, respectively. The 2D CNN model was trained with the MIPs from the last four acquisitions to ensure the visibility of the lesions, while the LSTM model took MIPs of an entire scan as input. The performance of each model and their combination were evaluated with 100-times repeated stratified four-fold cross-validation. Those models were then compared with models developed with standard DCE-MRI which followed the same data split. Results: In the differentiation between benign and malignant lesions, the ultrafast MRI-based 2D CNN achieved a mean AUC of 0.81 ± 0.06, and the LSTM network achieved a mean AUC of 0.78 ± 0.07; their combination showed a mean AUC of 0.83 ± 0.06 in the cross-validation. The mean AUC values were significantly higher for ultrafast MRI-based models than standard DCE-MRI-based models. Conclusion: Deep learning models developed with ultrafast breast MRI achieved higher performances than standard DCE-MRI for malignancy discrimination. The improved AUC values of the combined models indicate an added value of temporal information extracted by the LSTM model in breast lesion characterization

Comparison of conventional and higher-resolution reduced-FOV diffusion-weighted imaging of breast tissue

Objective: Reduced FOV-diffusion-weighted imaging (rFOV-DWI) allows for acquisition of a tissue region without back-folding, and may have better fat suppression than conventional DWI imaging (c-DWI). The aim was to compare the ADCs obtained with c-DWI bilateral-breast imaging with single-breast rFOV-DWI. Materials and Methods: Breasts of 38 patients were scanned at 3 T. The mean ADC values obtained for 38 lesions, and fibro-glandular (N = 35) and adipose (N = 38) tissue ROIs were compared between c-DWI and higher-resolution rFOV-DWI (Wilcoxon rank test). Also, the ADCs were compared between the two acquisitions for an oil-only phantom and a combined water/oil phantom. Furthermore, ghost artifacts were assessed. Results: No significant difference in mean ADC was found between the acquisitions for lesions (c-DWI: 1.08 × 10–3 mm2/s, rFOV-DWI: 1.13 × 10–3 mm2/s) and fibro-glandular tissue. For adipose tissue, the ADC using rFOV-DWI (0.31 × 10–3 mm2/s) was significantly higher than c-DWI (0.16 × 10–3 mm2/s). For the oil-only phantom, no difference in ADC was found. However, for the water/oil phantom, the ADC of oil was significantly higher with rFOV-DWI compared to c-DWI. Discussion: Although ghost artifacts were observed for both acquisitions, they appeared to have a greater impact for rFOV-DWI. However, no differences in mean lesions’ ADC values were found, and therefore this study suggests that rFOV can be used diagnostically for single-breast DWI imaging.</p

Comparison of conventional and higher-resolution reduced-FOV diffusion-weighted imaging of breast tissue

Objective: Reduced FOV-diffusion-weighted imaging (rFOV-DWI) allows for acquisition of a tissue region without back-folding, and may have better fat suppression than conventional DWI imaging (c-DWI). The aim was to compare the ADCs obtained with c-DWI bilateral-breast imaging with single-breast rFOV-DWI. Materials and Methods: Breasts of 38 patients were scanned at 3 T. The mean ADC values obtained for 38 lesions, and fibro-glandular (N = 35) and adipose (N = 38) tissue ROIs were compared between c-DWI and higher-resolution rFOV-DWI (Wilcoxon rank test). Also, the ADCs were compared between the two acquisitions for an oil-only phantom and a combined water/oil phantom. Furthermore, ghost artifacts were assessed. Results: No significant difference in mean ADC was found between the acquisitions for lesions (c-DWI: 1.08 × 10–3 mm2/s, rFOV-DWI: 1.13 × 10–3 mm2/s) and fibro-glandular tissue. For adipose tissue, the ADC using rFOV-DWI (0.31 × 10–3 mm2/s) was significantly higher than c-DWI (0.16 × 10–3 mm2/s). For the oil-only phantom, no difference in ADC was found. However, for the water/oil phantom, the ADC of oil was significantly higher with rFOV-DWI compared to c-DWI. Discussion: Although ghost artifacts were observed for both acquisitions, they appeared to have a greater impact for rFOV-DWI. However, no differences in mean lesions’ ADC values were found, and therefore this study suggests that rFOV can be used diagnostically for single-breast DWI imaging.</p

Cost-effectiveness of abbreviated-protocol MRI screening for women with mammographically dense breasts in a national breast cancer screening program

INTRODUCTION: Magnetic resonance imaging (MRI) has shown the potential to improve the screening effectiveness among women with dense breasts. The introduction of fast abbreviated protocols (AP) makes MRI more feasible to be used in a general population. We aimed to investigate the cost-effectiveness of AP-MRI in women with dense breasts (heterogeneously/extremely dense) in a population-based screening program. METHODS: A previously validated model (SiMRiSc) was applied, with parameters updated for women with dense breasts. Breast density was assumed to decrease with increased age. The base scenarios included six biennial AP-MRI strategies, with biennial mammography from age 50–74 as reference. Fourteen alternative scenarios were performed by varying screening interval (triennial and quadrennial) and by applying a combined strategy of mammography and AP-MRI. A 3% discount rate for both costs and life years gained (LYG) was applied. Model robustness was evaluated using univariate and probabilistic sensitivity analyses. RESULTS: The six biennial AP-MRI strategies ranged from 132 to 562 LYG per 10,000 women, where more frequent application of AP-MRI was related to higher LYG. The optimal strategy was biennial AP-MRI screening from age 50–65 for only women with extremely dense breasts, producing an incremental cost-effectiveness ratio of € 18,201/LYG. At a threshold of € 20,000/LYG, the probability that the optimal strategy was cost-effective was 79%. CONCLUSION: Population-based biennial breast cancer screening with AP-MRI from age 50–65 for women with extremely dense breasts might be a cost-effective alternative to mammography, but is not an option for women with heterogeneously dense breasts

Airflow limitation increases lung cancer risk in smokers:the Lifelines cohort study

BACKGROUND: The relationship between smoking, airflow limitation and lung cancer occurrence is unclear. This study aims to evaluate the relationship between airflow limitation and lung cancer, and the effect modification by smoking status. METHODS: We included participants with spirometry data from Lifelines, a population-based cohort study from the Northern Netherlands. Airflow limitation was defined as FEV1/FVC ratio &lt; 0.7. The presence of pathology-confirmed primary lung cancer during a median follow-up of 9.5 years was collected. The Cox regression model was used and hazard ratios (HR) with 95% confidence interval (95%CI) were reported. Adjusted confounders included age, sex, educational level, smoking, passive smoking, asthma status and asbestos exposure. The effect modification by smoking status was investigated by estimating the relative excess risk due to interaction (RERI) and the ratio of HRs with 95%CI. RESULTS: Out of 98,630 participants, 14,200 (14.4%) had airflow limitation. In participants with and without airflow limitation, lung cancer incidence was 0.8% and 0.2%, respectively. The adjusted HR between airflow limitation and lung cancer risk was 1.7 (1.4-2.3). The association between airflow limitation and lung cancer differed by smoking status [former smokers: 2.1 (1.4 -3.2), current smokers: 2.2 (1.5-3.2)] and never smokers [0.9 (0.4-2.1)]. The RERI and ratio of HRs was 2.1 (0.7-3.4) and 2.5 (1.0-6.5) for former smokers, and 4.6 (95%CI: 1.8-7.4) and 2.5 (95%CI: 1.0-6.3) for current smokers, respectively. CONCLUSIONS: Airflow limitation increases lung cancer risk and this association is modified by smoking status. IMPACT: Ever smokers with airflow limitation are an important target group for the prevention of lung cancer

Comparison of National Comprehensive Cancer Network and European Position Statement protocols for nodule management in low-dose computed tomography lung cancer screening in a general Chinese population

BACKGROUND: Low-dose computed tomography (LDCT) lung cancer screening often refers individuals to unnecessary examinations. This study aims to compare the European Position Statement (EUPS) and National Comprehensive Cancer Network (NCCN) protocols in management of participants at baseline screening round. METHODS: LDCT lung cancer screening was prospectively performed in a Chinese asymptomatic population aged 40–74 years. A total of 1,000 consecutive baseline LDCT scans were read twice independently. All screen-detected lung nodules by the first reader were included. The first reader manually measured the diameter of lung nodules (NCCN protocol), and the second reader semi-automatically measured the volume and diameter (EUPS volume and diameter protocols). The protocols were used to classify the participants into three management groups: next screening round, short-term repeat LDCT scan and referral to a pulmonologist. Groups were compared using Wilcoxon test for paired samples. Number of lung cancers by protocols was provided. RESULTS: Of the 1,000 participants (61.4±6.7 years old), 168 lung nodules in 124 participants were visually detected and manually measured in the first reading, and re-measured semi-automatically. Applying the NCCN protocol, EUPS volume and diameter protocol, the proportion of referrals among all participants was 0.6%, 1.9%, and 1.4%, respectively. The proportion of short-term repeat scans was 4.5%, 9.7% and 4.5%, respectively. Among the 10 lung cancer patients, one would have been diagnosed earlier if the EUPS volume protocol would have been followed. CONCLUSIONS: In a first round screening in a Chinese general population, the lower threshold for referral in the EUPS protocol as compared to the NCCN protocol, leads to more referrals to a pulmonologist, with the potential of earlier cancer diagnosis. The EUPS volume protocol recommends fewer participants to short-term repeat LDCT scan than the EUPS diameter protocol. Follow-up studies should show the impact of both protocols on (interval) cancer diagnosis