Computer Vision Tool and Technician as First Reader of Lung Cancer Screening CT Scans

Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis

BACKGROUND

Screening using low-dose computed tomography (LDCT) is a more effective approach and has the potential to detect lung cancer more accurately. We aimed to conduct a meta-analysis to estimate the accuracy of population-based screening studies primarily assessing baseline LDCT screening for lung cancer.

METHODS

MEDLINE, Excerpta Medica Database, and Web of Science were searched for articles published up to April 10, 2022. According to the inclusion and exclusion criteria, the data of true positives, false-positives, false negatives, and true negatives in the screening test were extracted. Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate the quality of the literature. A bivariate random effects model was used to estimate pooled sensitivity and specificity. The area under the curve (AUC) was calculated by using hierarchical summary receiver-operating characteristics analysis. Heterogeneity between studies was measured using the Higgins I2 statistic, and publication bias was evaluated using a Deeks' funnel plot and linear regression test.

RESULTS

A total of 49 studies with 157,762 individuals were identified for the final qualitative synthesis; most of them were from Europe and America (38 studies), ten were from Asia, and one was from Oceania. The recruitment period was 1992 to 2018, and most of the subjects were 40 to 75 years old. The analysis showed that the AUC of lung cancer screening by LDCT was 0.98 (95% CI: 0.96-0.99), and the overall sensitivity and specificity were 0.97 (95% CI: 0.94-0.98) and 0.87 (95% CI: 0.82-0.91), respectively. The funnel plot and test results showed that there was no significant publication bias among the included studies.

CONCLUSIONS

Baseline LDCT has high sensitivity and specificity as a screening technique for lung cancer. However, long-term follow-up of the whole study population (including those with a negative baseline screening result) should be performed to enhance the accuracy of LDCT screening.

The role of computer-assisted radiographer reporting in lung cancer screening programmes

OBJECTIVES

Successful lung cancer screening delivery requires sensitive, timely reporting of low-dose computed tomography (LDCT) scans, placing a demand on radiology resources. Trained non-radiologist readers and computer-assisted detection (CADe) software may offer strategies to optimise the use of radiology resources without loss of sensitivity. This report examines the accuracy of trained reporting radiographers using CADe support to report LDCT scans performed as part of the Lung Screen Uptake Trial (LSUT).

METHODS

In this observational cohort study, two radiographers independently read all LDCT performed within LSUT and reported on the presence of clinically significant nodules and common incidental findings (IFs), including recommendations for management. Reports were compared against a 'reference standard' (RS) derived from nodules identified by study radiologists without CADe, plus consensus radiologist review of any additional nodules identified by the radiographers.

RESULTS

A total of 716 scans were included, 158 of which had one or more clinically significant pulmonary nodules as per our RS. Radiographer sensitivity against the RS was 68-73.7%, with specificity of 92.1-92.7%. Sensitivity for detection of proven cancers diagnosed from the baseline scan was 83.3-100%. The spectrum of IFs exceeded what could reasonably be covered in radiographer training.

CONCLUSION

Our findings highlight the complexity of LDCT reporting requirements, including the limitations of CADe and the breadth of IFs. We are unable to recommend CADe-supported radiographers as a sole reader of LDCT scans, but propose potential avenues for further research including initial triage of abnormal LDCT or reporting of follow-up surveillance scans.

KEY POINTS

• Successful roll-out of mass screening programmes for lung cancer depends on timely, accurate CT scan reporting, placing a demand on existing radiology resources. • This observational cohort study examines the accuracy of trained radiographers using computer-assisted detection (CADe) software to report lung cancer screening CT scans, as a potential means of supporting reporting workflows in LCS programmes. • CADe-supported radiographers were less sensitive than radiologists at identifying clinically significant pulmonary nodules, but had a low false-positive rate and good sensitivity for detection of confirmed cancers.

Rethinking Clinical Trial Radiology Workflows and Student Training: Integrated Virtual Student Shadowing Experience, Education, and Evaluation

There is consistent demand for clinical exposure from students interested in radiology; however, the COVID-19 pandemic resulted in fewer available options and limited student access to radiology departments. Additionally, there is increased demand for radiologists to manage more complex quantification in reports on patients enrolled in clinical trials. We present an online educational curriculum that addresses both of these gaps by virtually immersing students (radiology preprocessors, or RPs) into radiologists' workflows where they identify and measure target lesions in advance of radiologists, streamlining report quantification. RPs switched to remote work at the beginning of the COVID-19 pandemic in our National Institutes of Health (NIH). We accommodated them by transitioning our curriculum on cross-sectional anatomy and advanced PACS tools to a publicly available online curriculum. We describe collaborations between multiple academic research centers and industry through contributions of academic content to this curriculum. Further, we describe how we objectively assess educational effectiveness with cross-sectional anatomical quizzes and decreasing RP miss rates as they gain experience. Our RP curriculum generated significant interest evidenced by a dozen academic and research institutes providing online presentations including radiology modality basics and quantification in clinical trials. We report a decrease in RP miss rate percentage, including one virtual RP over a period of 1 year. Results reflect training effectiveness through decreased discrepancies with radiologist reports and improved tumor identification over time. We present our RP curriculum and multicenter experience as a pilot experience in a clinical trial research setting. Students are able to obtain useful clinical radiology experience in a virtual learning environment by immersing themselves into a clinical radiologist's workflow. At the same time, they help radiologists improve patient care with more valuable quantitative reports, previously shown to improve radiologist efficiency. Students identify and measure lesions in clinical trials before radiologists, and then review their reports for self-evaluation based on included measurements from the radiologists. We consider our virtual approach as a supplement to student education while providing a model for how artificial intelligence will improve patient care with more consistent quantification while improving radiologist efficiency.

How does artificial intelligence in radiology improve efficiency and health outcomes?

Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it will improve health care and reduce costs. Has AI been able to fulfill that promise? We describe six clinical objectives that can be supported by AI: a more efficient workflow, shortened reading time, a reduction of dose and contrast agents, earlier detection of disease, improved diagnostic accuracy and more personalized diagnostics. We provide examples of use cases including the available scientific evidence for its impact based on a hierarchical model of efficacy. We conclude that the market is still maturing and little is known about the contribution of AI to clinical practice. More real-world monitoring of AI in clinical practice is expected to aid in determining the value of AI and making informed decisions on development, procurement and reimbursement.

Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics

Purpose

To test radiomics for prognostication of intrahepatic mass-forming cholangiocarcinoma (IMCC) and to develop a comprehensive risk model.

Methods

Histologically proven IMCC (representing the full range of stages) were retrospectively analyzed by volume segmentation on baseline hepatic venous phase computed tomography (CT), by two readers with different experience (R1 and R2). Morphological CT features included: tumor size, hepatic satellite lesions, lymph node and distant metastases. Radiomic features (RF) were compared across CT protocols and readers. Univariate analysis against overall survival (OS) warranted ranking and selection of RF into radiomic signature (RSign), which was dichotomized into high and low-risk strata (RSign*). Models without and with RSign* (Model 1 and 2, respectively) were compared.

Results

Among 78 patients (median follow-up 262 days, IQR 73–957), 62/78 (79%) died during the study period, 46/78 (59%) died within 1 year. Up to 10% RF showed variability across CT protocols; 37/108 (34%) RF showed variability due to manual segmentation. RSign stratified OS (univariate: HR 1.37 for R1, HR 1.28 for R2), RSign* was different between readers (R1 0.39; R2 0.57). Model 1 showed AUC 0.71, which increased in Model 2: AUC 0.81 (p < 0.001) and AIC 89 for R1, AUC 0.81 (p = 0.001) and AIC 90.2 for R2.

Conclusion

The use of RF into a unified RSign score stratified OS in patients with IMCC. Dichotomized RSign* classified survival strata, its inclusion in risk models showed adjunct yield. The cut-off value of RSign* was different between readers, suggesting that the use of reference values is hampered by interobserver variability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00261-021-03183-9.

Assisted versus Manual Interpretation of Low-Dose CT Scans for Lung Cancer Screening: Impact on Lung-RADS Agreement

Purpose To compare the inter- and intraobserver agreement and reading times achieved when assigning Lung Imaging Reporting and Data System (Lung-RADS) categories to baseline and follow-up lung cancer screening studies by using a dedicated CT lung screening viewer with integrated nodule detection and volumetric support with those achieved by using a standard picture archiving and communication system (PACS)-like viewer. Materials and Methods Data were obtained from the National Lung Screening Trial (NLST). By using data recorded by NLST radiologists, scans were assigned to Lung-RADS categories. For each Lung-RADS category (1 or 2, 3, 4A, and 4B), 40 CT scans (20 baseline scans and 20 follow-up scans) were randomly selected for 160 participants (median age, 61 years; interquartile range, 58-66 years; 61 women) in total. Seven blinded observers independently read all CT scans twice in a randomized order with a 2-week washout period: once by using the standard PACS-like viewer and once by using the dedicated viewer. Observers were asked to assign a Lung-RADS category to each scan and indicate the risk-dominant nodule. Inter- and intraobserver agreement was analyzed by using Fleiss κ values and Cohen weighted κ values, respectively. Reading times were compared by using a Wilcoxon signed rank test. Results The interobserver agreement was moderate for the standard viewer and substantial for the dedicated viewer, with Fleiss κ values of 0.58 (95% CI: 0.55, 0.60) and 0.66 (95% CI: 0.64, 0.68), respectively. The intraobserver agreement was substantial, with a mean Cohen weighted κ value of 0.67. The median reading time was significantly reduced from 160 seconds with the standard viewer to 86 seconds with the dedicated viewer (P < .001). Conclusion Lung-RADS interobserver agreement increased from moderate to substantial when using the dedicated CT lung screening viewer. The median reading time was substantially reduced when scans were read by using the dedicated CT lung screening viewer. Keywords: CT, Thorax, Lung, Computer Applications-Detection/Diagnosis, Observer Performance, Technology Assessment Supplemental material is available for this article. © RSNA, 2021.

Impact of artificial intelligence on clinical radiography practice: Futuristic prospects in a low resource setting

OBJECTIVES

Current trends in clinical radiography practice include the integration of artificial intelligence (AI) and related applications to improve patient care and enhance research. However, in low resource countries there are unique barriers to the process of AI integration. Using Ghana as a case study, this paper seeks to discuss the potential impact of AI on future radiographic practice in low-resource settings. The opportunities, challenges and the way forward to optimise the potential benefits of AI in future practice within these settings have been explored.

KEY FINDINGS

Some of the barriers to AI integration into radiographic practice relate to lack of regulatory and legal policy frameworks and limited resource availability including unreliable internet connectivity and low expert skillset.

CONCLUSION

These barriers notwithstanding, AI presents a great potential to the growth of medical imaging and subsequently improving quality of healthcare delivery in the near future. For example, AI-enabled radiographer reporting has a potential to improving quality of healthcare, especially in low-resource settings like Ghana with an acute shortage of radiologists. In addition, futuristic AI-enabled advancements such as synthetic cross-modality transfer where images from one modality are used as a baseline to generate a corresponding image of another modality without the need for additional scanning will be of particular benefit in low-resource settings.

IMPLICATIONS FOR PRACTICE

The urgent need for inclusion of AI modules for the training of the radiographer of the future has been suggested. Recommendations for development of AI strategies by national societies and regulatory bodies will harmonise the implementation efforts. Finally, there is need for collaboration between clinical practitioners and academia to ensure that the future radiography workforce is well prepared for the AI-enabled clinical environment.

Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT

Background Accurate estimation of the malignancy risk of pulmonary nodules at chest CT is crucial for optimizing management in lung cancer screening. Purpose To develop and validate a deep learning (DL) algorithm for malignancy risk estimation of pulmonary nodules detected at screening CT. Materials and Methods In this retrospective study, the DL algorithm was developed with 16 077 nodules (1249 malignant) collected -between 2002 and 2004 from the National Lung Screening Trial. External validation was performed in the following three -cohorts -collected between 2004 and 2010 from the Danish Lung Cancer Screening Trial: a full cohort containing all 883 nodules (65 -malignant) and two cancer-enriched cohorts with size matching (175 nodules, 59 malignant) and without size matching (177 -nodules, 59 malignant) of benign nodules selected at random. Algorithm performance was measured by using the area under the receiver operating characteristic curve (AUC) and compared with that of the Pan-Canadian Early Detection of Lung Cancer (PanCan) model in the full cohort and a group of 11 clinicians composed of four thoracic radiologists, five radiology residents, and two pulmonologists in the cancer-enriched cohorts. Results The DL algorithm significantly outperformed the PanCan model in the full cohort (AUC, 0.93 [95% CI: 0.89, 0.96] vs 0.90 [95% CI: 0.86, 0.93]; P = .046). The algorithm performed comparably to thoracic radiologists in cancer-enriched cohorts with both random benign nodules (AUC, 0.96 [95% CI: 0.93, 0.99] vs 0.90 [95% CI: 0.81, 0.98]; P = .11) and size-matched benign nodules (AUC, 0.86 [95% CI: 0.80, 0.91] vs 0.82 [95% CI: 0.74, 0.89]; P = .26). Conclusion The deep learning algorithm showed excellent performance, comparable to thoracic radiologists, for malignancy risk estimation of pulmonary nodules detected at screening CT. This algorithm has the potential to provide reliable and reproducible malignancy risk scores for clinicians, which may help optimize management in lung cancer screening. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Tammemägi in this issue.

Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?

Lung cancer computed tomography (CT) screening trials using low-dose CT have repeatedly demonstrated a reduction in the number of lung cancer deaths in the screening group compared to a control group. With various countries currently considering the implementation of lung cancer screening, recurring discussion points are, among others, the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of lung cancer screening. We discuss the performance levels of AI algorithms for various tasks related to the interpretation of lung screening CT scans, how they compare to human experts, and how AI and humans may complement each other. We discuss how AI may be used in the lung cancer CT screening workflow according to the current evidence and describe the additional research that will be required before AI can take a more prominent role in the analysis of lung screening CT scans.

The radiologist's role in lung cancer screening

Lung cancer is still the deadliest cancer in men and women worldwide. This high mortality is related to diagnosis in advanced stages, when curative treatment is no longer an option. Large randomized controlled trials have shown that lung cancer screening (LCS) with low-dose computed tomography (CT) can detect lung cancers at earlier stages and reduce lung cancer-specific mortality. The recent publication of the significant reduction of cancer-related mortality by 26% in the Dutch-Belgian NELSON LCS trial has increased the likelihood that implementation of LCS in Europe will move forward. Radiologists are important stakeholders in numerous aspects of the LCS pathway. Their role goes beyond nodule detection and nodule management. Being part of a multidisciplinary team, radiologists are key players in numerous aspects of implementation of a high quality LCS program. In this non-systematic review we discuss the multifaceted role of radiologists in LCS.

Practical computer vision application to detect hip fractures on pelvic X-rays: a bi-institutional study

Background

Pelvic X-ray (PXR) is a ubiquitous modality to diagnose hip fractures. However, not all healthcare settings employ round-the-clock radiologists and PXR sensitivity for diagnosing hip fracture may vary depending on digital display. We aimed to validate a computer vision algorithm to detect hip fractures across two institutions’ heterogeneous patient populations. We hypothesized a convolutional neural network algorithm can accurately diagnose hip fractures on PXR and a web application can facilitate its bedside adoption.

Methods

The development cohort comprised 4235 PXRs from Chang Gung Memorial Hospital (CGMH). The validation cohort comprised 500 randomly sampled PXRs from CGMH and Stanford’s level I trauma centers. Xception was our convolutional neural network structure. We randomly applied image augmentation methods during training to account for image variations and used gradient-weighted class activation mapping to overlay heatmaps highlighting suspected fracture locations.

Results

Our hip fracture detection algorithm’s area under the receiver operating characteristic curves were 0.98 and 0.97 for CGMH and Stanford’s validation cohorts, respectively. Besides negative predictive value (0.88 Stanford cohort), all performance metrics—sensitivity, specificity, predictive values, accuracy, and F1 score—were above 0.90 for both validation cohorts. Our web application allows users to upload PXR in multiple formats from desktops or mobile phones and displays probability of the image containing a hip fracture with heatmap localization of the suspected fracture location.

Discussion

We refined and validated a high-performing computer vision algorithm to detect hip fractures on PXR. A web application facilitates algorithm use at the bedside, but the benefit of using our algorithm to supplement decision-making is likely institution dependent. Further study is required to confirm clinical validity and assess clinical utility of our algorithm.

Level of evidence

III, Diagnostic tests or criteria.

A Review on Lung Cancer Diagnosis Using Data Mining Algorithms

Due to the serious consequences of lung cancer, medical associations use computer-aided diagnostic procedures to diagnose this disease more accurately. Despite the damaging effects of lung cancer on the body, the lifetime of cancer patients can be extended by early diagnosis. Data mining techniques are practical in diagnosing lung cancer in its first stages. This paper surveys a number of leading data mining-based cancer diagnosis approaches. Moreover, this review draws a comparison between data mining approaches in terms of selection criteria and presents the advantages and disadvantages of each method.

Artificial intelligence in diagnostic imaging: impact on the radiography profession

The arrival of artificially intelligent systems into the domain of medical imaging has focused attention and sparked much debate on the role and responsibilities of the radiologist. However, discussion about the impact of such technology on the radiographer role is lacking. This paper discusses the potential impact of artificial intelligence (AI) on the radiography profession by assessing current workflow and cross-mapping potential areas of AI automation such as procedure planning, image acquisition and processing. We also highlight the opportunities that AI brings including enhancing patient-facing care, increased cross-modality education and working, increased technological expertise and expansion of radiographer responsibility into AI-supported image reporting and auditing roles.

Augmented Radiologist Workflow Improves Report Value and Saves Time: A Potential Model for Implementation of Artificial Intelligence

RATIONALE AND OBJECTIVES

Our primary aim was to improve radiology reports by increasing concordance of target lesion measurements with oncology records using radiology preprocessors (RP). Faster notification of incidental actionable findings to referring clinicians and clinical radiologist exam interpretation time savings with RPs quantifying tumor burden were also assessed.

MATERIALS AND METHODS

In this prospective quality improvement initiative, RPs annotated lesions before radiologist interpretation of CT exams. Clinical radiologists then hyperlinked approved measurements into interactive reports during interpretations. RPs evaluated concordance with our tumor measurement radiologist, the determinant of tumor burden. Actionable finding detection and notification times were also deduced. Clinical radiologist interpretation times were calculated from established average CT chest, abdomen, and pelvis interpretation times.

RESULTS

RPs assessed 1287 body CT exams with 812 follow-up CT chest, abdomen, and pelvis studies; 95 (11.7%) of which had 241 verified target lesions. There was improved concordance (67.8% vs. 22.5%) of target lesion measurements. RPs detected 93.1% incidental actionable findings with faster clinician notification by a median time of 1 hour (range: 15 minutes-16 hours). Radiologist exam interpretation times decreased by 37%.

CONCLUSIONS

This workflow resulted in three-fold improved target lesion measurement concordance with oncology records, earlier detection and faster notification of incidental actionable findings to referring clinicians, and decreased exam interpretation times for clinical radiologists. These findings demonstrate potential roles for automation (such as AI) to improve report value, worklist prioritization, and patient care.

Estimating the Cost-Effectiveness of Lung Cancer Screening with Low-Dose Computed Tomography for High-Risk Smokers in Australia

INTRODUCTION

Health economic evaluations of lung cancer screening with low-dose computed tomography (LDCT) that are underpinned by clinical outcomes are relatively few.

METHODS

We assessed the cost-effectiveness of LDCT lung screening in Australia by applying Australian cost and survival data to the outcomes observed in the U.S. National Lung Screening Trial (NLST), in which a 20% lung cancer mortality benefit was demonstrated for three rounds of annual screening among high-risk smokers age 55 to 74 years. Screening-related costs were estimated from Medicare Benefits Schedule reimbursement rates (2015), lung cancer diagnosis and treatment costs from a 2012 Australian hospital-based study, lung cancer survival rates from the New South Wales Cancer Registry (2005-2009), and other-cause mortality from Australian life tables weighted by smoking status. The health utility outcomes, screening participation rates, and lung cancer rates were those observed in the NLST. Incremental cost effectiveness ratios (ICER) were calculated for a 10-year time horizon.

RESULTS

The cost-effectiveness of LDCT lung screening was estimated at AU$138,000 (80% confidence interval: AU$84,700-AU$353,000)/life-year gained and AU$233,000 (80% confidence interval: AU$128,000-AU$1,110,000)/quality-adjusted life year (QALY) gained. The ICER was more favorable when LDCT screening impact on all-cause mortality was considered, even when the costs of incidental findings were also estimated in sensitivity analyses: AU$157,000/QALY gained. This can be compared to an indicative willingness-to-pay threshold in Australia of AU$30,000 to AU$50,000/QALY.

CONCLUSIONS

LDCT lung screening using NLST selection and implementation criteria is unlikely to be cost-effective in Australia. Future economic evaluations should consider alternative screening eligibility criteria, intervals, nodule management, the impact and cost of new therapies, investigations of incidental findings, and incorporation of smoking cessation interventions.

The impact of trained radiographers as concurrent readers on performance and reading time of experienced radiologists in the UK Lung Cancer Screening (UKLS) trial

Objectives

To compare radiologists’ performance reading CTs independently with their performance using radiographers as concurrent readers in lung cancer screening.

Methods

369 consecutive baseline CTs performed for the UK Lung Cancer Screening (UKLS) trial were double-read by radiologists reading either independently or concurrently with a radiographer. In concurrent reading, the radiologist reviewed radiographer-identified nodules and then detected any additional nodules. Radiologists recorded their independent and concurrent reading times. For each radiologist, sensitivity, average false-positive detections (FPs) per case and mean reading times for each method were calculated.

Results

694 nodules in 246/369 (66.7%) studies comprised the reference standard. Radiologists’ mean sensitivity and average FPs per case both increased with concurrent reading compared to independent reading (90.8 ± 5.6% vs. 77.5 ± 11.2%, and 0.60 ± 0.53 vs. 0.33 ± 0.20, respectively; p < 0.05 for 3/4 and 2/4 radiologists, respectively). The mean reading times per case decreased from 9.1 ± 2.3 min with independent reading to 7.2 ± 1.0 min with concurrent reading, decreasing significantly for 3/4 radiologists (p < 0.05).

Conclusions

The majority of radiologists demonstrated improved sensitivity, a small increase in FP detections and a statistically significantly reduced reading time using radiographers as concurrent readers.

Key Points

• Radiographers as concurrent readers could improve radiologists’ sensitivity in lung nodule detection.

• An increase in false-positive detections with radiographer-assisted concurrent reading occurred.

• The false-positive detection rate was still lower than reported for computer-aided detection.

• Concurrent reading with radiographers was also faster than single reading.

• The time saved per case using concurrently reading radiographers was relatively modest.

Electronic supplementary material

The online version of this article (doi:10.1007/s00330-017-4903-z) contains supplementary material, which is available to authorized users.

Expression and epigenetic regulation of cystatin B in lung cancer and colorectal cancer

AIMS

Dysregulated expression of cystatin B (CSTB) has been implicated in various cancers. The aims of this study were to analyze the CSTB expression and investigate the epigenetic regulation of CSTB in lung and colon cancer cell lines, and also evaluate the clinical outcome of CSTB in primary lung and colorectal tumors.

METHODS

CSTB expression in lung and colon cancer cell lines was analyzed by real-time RT-PCR and western blotting. Epigenetic regulation of CSTB was examined by demethylation, deacetylation tests and bisulfite sequencing (BS). In primary lung and colorectal tumors, the protein expression of CSTB was evaluated by immunohistochemistry on tissue microarray.

RESULTS

CSTB was downregulated in lung cancer cell lines on mRNA and protein levels compared to human bronchial epithelial cells (HBEC). In colon cancer cell lines, CSTB was weakly expressed in Caco2, CX2 and HCT-16 and highly expressed in HT-29, WiDr, SW480 and HRT-18 on mRNA level compared to normal colonic fibroblast cells CCD33Co. After treatment with demethylation agent 5-aza-2'-deoxycytidine, increased CSTB mRNA expression was found in 7 out of 11 lung cancer cell lines including H226, H157, H2170, H1299, COLO677, A549 and H1975, while no obvious alteration was found in colon cancer cell lines. No DNA methylation could be found in the selected CpG islands in two types of cancer cell lines by bisulfite sequencing. In primary tumors, CSTB expression was significantly and inversely correlated with lung tumor stage (pN) and tumor grade (p=0.022 and 0.047, respectively). Kaplan-Meier survival curve revealed a tendency that lung tumors with high CSTB expression had a more favourable prognosis (p=0.062). In colorectal tumors, CSTB was not linked to any clinicopathological parameters including age, size of tumor, lymph node metastasis and tumor grading.

CONCLUSIONS

CSTB might be a potential prognostic marker for patients with primary lung cancer.

Comparing the performance of trained radiographers against experienced radiologists in the UK lung cancer screening (UKLS) trial

OBJECTIVE

To compare the performance of radiographers against that of radiologists for CT lung nodule detection in the UK Lung Cancer Screening (UKLS) pilot trial.

METHODS

Four radiographers, trained in CT nodule detection, and three radiologists were prospectively evaluated. 290 CTs performed for the UKLS were independently read by 2 radiologists and 2 radiographers. The reference standard comprised all radiologist-identified positive nodules after arbitration of discrepancies. For each radiographer and radiologist, relative sensitivity and average false positives (FPs) per case were compared for all cases read, as well as for subsets of cases read by each radiographer-radiologist combination (10 combinations).

RESULTS

599 nodules in 209/290 (72.1%) CT studies comprised the reference standard. The relative mean (±standard deviation) sensitivity of the four radiographers was 71.6 ± 8.5% compared with 83.3 ± 8.1% for the three radiologists. Radiographers were less sensitive and detected more FPs per case than radiologists in 7/10 and 8/10 radiographer-radiologist combinations, respectively (ranges of difference 11.2-33.8% and 0.4-2.6; p < 0.05). In 3/10 and 2/10 combinations, there was no difference in sensitivity and FPs per case between radiographers and radiologists. For nodules ≥100 mm(3) in volume or ≥5 mm in maximum diameter, radiographers were relatively less sensitive than radiologists in only 5/10 radiographer-radiologist combinations (range of difference 16.1-30.6%; p < 0.05) and not significantly different in the remaining 5/10 combinations.

CONCLUSION

Although overall radiographer performance was lower than that of experienced radiologists in this study, some radiographer performances were comparable with that of radiologists.

ADVANCES IN KNOWLEDGE

Overall, radiographers were less sensitive than radiologists reading the same CTs and also displayed higher average FP detections per case when compared with a reference standard derived from radiologist readings. However, some radiographers compared favourably with radiologists, especially when considering larger potentially clinically relevant nodules. Thus, while probably not sensitive enough to function as first readers, radiographers may still be able to fulfil the role of an assistant reader-that is, as a first or concurrent reader, who presents detected nodules for verification to a reading radiologist.