Lung cancer screening-don’t forget the chest radiograph

IVC filter - assessing the readability and quality of patient information on the Internet

OBJECTIVE

The internet is an increasingly favorable source of information regarding health-related issues. The aim of this study is to apply appropriate evaluation tools to assess the evidence available online about inferior vena cava (IVC) filters with a focus on quality and readability.

METHODS

A search was performed during December 2022 using three popular search engines, namely Google, Yahoo, and Bing. Websites were categorized into academic, physician, commercial, and unspecified websites according to their content. Information quality was determined using Journal of the American Medical Association (JAMA) criteria, the DISCERN scoring tool, and whether a Health On the Net Foundation certification (HONcode) seal was present. Readability was established using the Flesch Reading Ease Score (FRES) and Flesch-Kincaid Grade Level (FKGL). Statistical significance was accepted as P < .05.

RESULTS

In total, 110 websites were included in our study. The majority of websites were categorized as commercial (25%), followed by hospital (24%), academic (21%), unspecified (16%), and physician (14%). Average scores for all websites using JAMA and DISCERN were 1.93 ± 1.19 (median, 1.5; range, 0-4) and 45.20 ± 12.58 (median, 45.5; range, 21-75), respectively. The highest JAMA mean score of 3.07 ± 1.16 was allocated to physician websites, and the highest DISCERN mean score of 52.85 ± 12.66 was allocated to hospital websites. The HONcode seal appeared on two of the selected websites. Physician, hospital, and unspecified websites had a significantly higher mean JAMA score than academic and commercial websites (all with P < .001). Hospital websites had a significantly higher mean DISCERN score than academic (P = .007), commercial (P < .001), and unspecified websites (P = .017). Readability evaluation generated a mean FRES score of 51.57 ±12.04, which represented a 10th to 12th grade reading level and a mean FKGL score of 8.20 ± 1.70, which represented an 8th to 10th grade reading level. Only 12 sources were found to meet the ≤6th grade target reading level. No significant correlation was found between overall DISCERN score and overall FRES score.

CONCLUSIONS

The study results demonstrate that the quality of online information about IVC filters is suboptimal, and academic and commercial websites, in particular, must enhance their content quality regarding the use of IVC filters. Considering the discontinuation of the HONcode as a standardized quality assessment marker, it is recommended that a similar certification tool be developed and implemented for the accreditation of patient information online.

AI Improves Nodule Detection on Chest Radiographs in a Health Screening Population: A Randomized Controlled Trial

Background The impact of artificial intelligence (AI)-based computer-aided detection (CAD) software has not been prospectively explored in real-world populations. Purpose To investigate whether commercial AI-based CAD software could improve the detection rate of actionable lung nodules on chest radiographs in participants undergoing health checkups. Materials and Methods In this single-center, pragmatic, open-label randomized controlled trial, participants who underwent chest radiography between July 2020 and December 2021 in a health screening center were enrolled and randomized into intervention (AI group) and control (non-AI group) arms. One of three designated radiologists with 13-36 years of experience interpreted each radiograph, referring to the AI-based CAD results for the AI group. The primary outcome was the detection rate, that is, the number of true-positive radiographs divided by the total number of radiographs, of actionable lung nodules confirmed on CT scans obtained within 3 months. Actionable nodules were defined as solid nodules larger than 8 mm or subsolid nodules with a solid portion larger than 6 mm (Lung Imaging Reporting and Data System, or Lung-RADS, category 4). Secondary outcomes included the positive-report rate, sensitivity, false-referral rate, and malignant lung nodule detection rate. Clinical outcomes were compared between the two groups using univariable logistic regression analyses. Results A total of 10 476 participants (median age, 59 years [IQR, 50-66 years]; 5121 men) were randomized to an AI group (n = 5238) or non-AI group (n = 5238). The trial met the predefined primary outcome, demonstrating an improved detection rate of actionable nodules in the AI group compared with the non-AI group (0.59% [31 of 5238 participants] vs 0.25% [13 of 5238 participants], respectively; odds ratio, 2.4; 95% CI: 1.3, 4.7; P = .008). The detection rate for malignant lung nodules was higher in the AI group compared with the non-AI group (0.15% [eight of 5238 participants] vs 0.0% [0 of 5238 participants], respectively; P = .008). The AI and non-AI groups showed similar false-referral rates (45.9% [56 of 122 participants] vs 56.0% [56 of 100 participants], respectively; P = .14) and positive-report rates (2.3% [122 of 5238 participants] vs 1.9% [100 of 5238 participants]; P = .14). Conclusion In health checkup participants, artificial intelligence-based software improved the detection of actionable lung nodules on chest radiographs. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Auffermann in this isssue.

Performance of a Deep Learning Algorithm Compared with Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population

Background The performance of a deep learning algorithm for lung cancer detection on chest radiographs in a health screening population is unknown. Purpose To validate a commercially available deep learning algorithm for lung cancer detection on chest radiographs in a health screening population. Materials and Methods Out-of-sample testing of a deep learning algorithm was retrospectively performed using chest radiographs from individuals undergoing a comprehensive medical check-up between July 2008 and December 2008 (validation test). To evaluate the algorithm performance for visible lung cancer detection, the area under the receiver operating characteristic curve (AUC) and diagnostic measures, including sensitivity and false-positive rate (FPR), were calculated. The algorithm performance was compared with that of radiologists using the McNemar test and the Moskowitz method. Additionally, the deep learning algorithm was applied to a screening cohort undergoing chest radiography between January 2008 and December 2012, and its performances were calculated. Results In a validation test comprising 10 285 radiographs from 10 202 individuals (mean age, 54 years ± 11 [standard deviation]; 5857 men) with 10 radiographs of visible lung cancers, the algorithm's AUC was 0.99 (95% confidence interval: 0.97, 1), and it showed comparable sensitivity (90% [nine of 10 radiographs]) to that of the radiologists (60% [six of 10 radiographs]; P = .25) with a higher FPR (3.1% [319 of 10 275 radiographs] vs 0.3% [26 of 10 275 radiographs]; P < .001). In the screening cohort of 100 525 chest radiographs from 50 070 individuals (mean age, 53 years ± 11; 28 090 men) with 47 radiographs of visible lung cancers, the algorithm's AUC was 0.97 (95% confidence interval: 0.95, 0.99), and its sensitivity and FPR were 83% (39 of 47 radiographs) and 3% (2999 of 100 478 radiographs), respectively. Conclusion A deep learning algorithm detected lung cancers on chest radiographs with a performance comparable to that of radiologists, which will be helpful for radiologists in healthy populations with a low prevalence of lung cancer. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Armato in this issue.

Role of chest radiographs in early lung cancer detection

Background

Lung cancer is the second most common and the most fatal form of cancer. Although annual low-dose computed tomography is used as the primary method of cancer screening, it presents challenges regarding resources as well as potential health risks from radiation exposure. Chest radiography (CXR), though less effective, is used frequently and commonly. Moreover, often in clinical settings, CXR is the first imaging modality used; computed tomography is subsequently performed if abnormalities are detected on CXRs. This study examined whether controlling for distractors and time constraints, as well as side-by-side comparison of multiple CXRs in clinical settings can aid earlier detection of radiological abnormalities indicative of lung cancer lesions.

Methods

Thirty-two attending physicians in the Republic of Korea examined 1,750 radiographs of 50 lung cancer cases. Using “hot spot” technology, participants indicated the possible locations of cancer lesions on each radiograph. Subsequently, the same radiographs, cropped to focus the anatomical regions where lung cancers were diagnosed, were shown side-by-side to the participants. The participants were asked to identify the radiograph which first enabled the diagnosis of lung cancer and which first showed a possible lesion.

Results

Removal of systemic constraints alone significantly improved lesion identification by 221.72±9.69 days. Presenting radiographs side-by-side, cropped to relevant areas, had an additional significant and positive impact on cancer detection in both hidden and open areas on CXRs. Also, lesions were detected at smaller sizes and earlier than when actually diagnosed.

Conclusions

CXR with improved methods and settings provides an easily accessible and low-risk imaging method for earlier detection of lung cancer compared to current clinical imaging settings. Further, this study demonstrates the potential effectiveness of programs that allow side-by-side comparisons of cropped areas of multiple radiographs to detect radiological abnormalities.

Is there an advantage to using computer aided detection for the early detection of pulmonary nodules within chest X-Ray imaging?

OBJECTIVE

Using published literature, this research examines whether Computer-aided Detection (CAD) identifies more Pulmonary Nodules (PN) within Chest X-ray (CXR) systems, compared to radiologist diagnosis without CAD.

KEY FINDINGS

Although the primary papers were pointing to CAD being a beneficial system in the diagnosis of PN detection, a regression analysis of the data available within these papers showed no correlation between the higher sensitivity of CAD against the detrimental high False Positives (FP) of CAD. Findings of the studies were deemed inconclusive.

CONCLUSION

Further research is recommended to review the potential of CAD on CXR PN detection.

IMPLICATIONS FOR PRACTICE

CAD acting as a second reader could potentially reduce interpreter error rate.

Nanomedicines guided nanoimaging probes and nanotherapeutics for early detection of lung cancer and abolishing pulmonary metastasis: Critical appraisal of newer developments and challenges to clinical transition

Lung cancer (LC) is the second most prevalent type of cancer and primary cause of mortality among both men and women, worldwide. The most commonly employed diagnostic modalities for LC include chest X-ray (CXR), magnetic-resonance-imaging (MRI), computed tomography (CT-scan), and fused-positron-emitting-tomography-CT (PET-CT). Owing to several limitations associated with the use of conventional diagnostic tools such as radiation burden to the patient, misleading diagnosis ("missed lung cancer"), false staging and low sensitivity and resolution, contemporary diagnostic regimen needed to be employed for screening of LC. In recent decades, nanotechnology-guided interventions have been transpired as emerging nanoimaging probes for detection of LC at advanced stages, while producing signal amplification, better resolution for surface and deep tissue imaging, and enhanced translocation and biodistribution of imaging probes within the cancerous tissues. Besides enormous potential of nanoimaging probes, nanotechnology-based advancements have also been evidenced for superior efficacy for treatment of LC and abolishing pulmonary metastasis (PM). The success of nanotherapeutics is due to their ability to maximise translocation and biodistribution of anti-neoplastic agents into the tumor tissues, improve pharmacokinetic profiles of anti-metastatic agents, optimise target-specific drug delivery, and control release kinetics of encapsulated moieties in target tissues. This review aims to overview and critically discuss the superiority of nanoimaging probes and nanotherapeutics over conventional regimen for early detection of LC and abolishing PM. Current challenges to clinical transition of nanoimaging probes and therapeutic viability of nanotherapeutics for treatment for LC and PM have also been pondered.

Is chest X-ray screening for lung cancer in smokers cost-effective? Evidence from a population-based study in Italy

Background

After implementation of the PREDICA annual chest X-ray (CXR) screening program in smokers in the general practice setting of Varese-Italy a significant reduction in lung cancer-specific mortality (18 %) was observed. The objective of this study covering July 1997 through December 2006 was to estimate the cost-effectiveness of this intervention.

Methods

We examined detailed information on lung cancer (LC) cases that occurred among smokers invited to be screened in the PREDICA study (Invitation-to-screening Group, n = 5815 subjects) to estimate costs and quality-adjusted life-years (QALYs) from LC diagnosis until death. The control group consisted of 156 screening-eligible smokers from the same area, uninvited and unscreened, who developed LC and were treated by usual care. We calculated the incremental net monetary benefit (INMB) by comparing LC management in screening participants (n = 1244 subjects) and in the Invitation-to-screening group versus control group.

Results

The average number of QALYs since LC diagnosis was 1.7, 1.49 and 1.07, respectively, in screening participants, the invitation-to-screening group, and the control group. The average total cost (screening + management) per LC case was higher in screening participants (€17,516) and the Invitation-to-screening Group (€16,167) than in the control group (€15,503). Assuming a maximum willingness to pay of €30,000/QALY, we found that the intervention was cost-effective with high probability: 79 % for screening participation (screening participants vs. control group) and 95 % for invitation-to-screening (invitation-to-screening group vs. control group).

Conclusions

Based on the PREDICA study, annual CXR screening of high-risk smokers in a general practice setting has high probability of being cost-effective with a maximum willingness to pay of €30,000/QALY.