CT screening for lung cancer: comparison of three baseline screening protocols

Reinforcement learning for individualized lung cancer screening schedules: A nested case-control study

BACKGROUND

The current guidelines for managing screen-detected pulmonary nodules offer rule-based recommendations for immediate diagnostic work-up or follow-up at intervals of 3, 6, or 12 months. Customized visit plans are lacking.

PURPOSE

To develop individualized screening schedules using reinforcement learning (RL) and evaluate the effectiveness of RL-based policy models.

METHODS

Using a nested case-control design, we retrospectively identified 308 patients with cancer who had positive screening results in at least two screening rounds in the National Lung Screening Trial. We established a control group that included cancer-free patients with nodules, matched (1:1) according to the year of cancer diagnosis. By generating 10,164 sequence decision episodes, we trained RL-based policy models, incorporating nodule diameter alone, combined with nodule appearance (attenuation and margin) and/or patient information (age, sex, smoking status, pack-years, and family history). We calculated rates of misdiagnosis, missed diagnosis, and delayed diagnosis, and compared the performance of RL-based policy models with rule-based follow-up protocols (National Comprehensive Cancer Network guideline; China Guideline for the Screening and Early Detection of Lung Cancer).

RESULTS

We identified significant interactions between certain variables (e.g., nodule shape and patient smoking pack-years, beyond those considered in guideline protocols) and the selection of follow-up testing intervals, thereby impacting the quality of the decision sequence. In validation, one RL-based policy model achieved rates of 12.3% for misdiagnosis, 9.7% for missed diagnosis, and 11.7% for delayed diagnosis. Compared with the two rule-based protocols, the three best-performing RL-based policy models consistently demonstrated optimal performance for specific patient subgroups based on disease characteristics (benign or malignant), nodule phenotypes (size, shape, and attenuation), and individual attributes.

CONCLUSIONS

This study highlights the potential of using an RL-based approach that is both clinically interpretable and performance-robust to develop personalized lung cancer screening schedules. Our findings present opportunities for enhancing the current cancer screening system.

Personalised follow-up and management schema for patients with screen-detected pulmonary nodules: A dynamic modelling study

BACKGROUND

Selecting the time target for follow-up testing in lung cancer screening is challenging. We aim to devise dynamic, personalized lung cancer screening schema for patients with pulmonary nodules detected through low-dose computed tomography.

METHODS

We developed and validated dynamic models using data of pulmonary nodule patients (aged 55-74 years) from the National Lung Screening Trial. We predicted patient-specific risk profiles at baseline (R0) and updated the risk evaluation results in repeated screening rounds (R1 and R2). We used risk cutoffs to optimize time-dependent sensitivity at an early decision point (3 months) and time-dependent specificity at a late decision point (1 year).

RESULTS

In validation, area under receiver operating characteristic curve for predicting 12-month lung cancer onset was 0.867 (95 % confidence interval: 0.827-0.894) and 0.807 (0.765-0.948) at R0 and R1-R2, respectively. The personalized schema, compared with National Comprehensive Cancer Network (NCCN) guideline and Lung-RADS, yielded lower rates of delayed diagnosis (1.7% vs. 1.7% vs. 6.9 %) and over-testing (4.9% vs. 5.6% vs. 5.6 %) at R0, and lower rates of delayed diagnosis (0.0% vs. 18.2% vs. 18.2 %) and over-testing (2.6% vs. 8.3% vs. 7.3 %) at R2. Earlier test recommendation among cancer patients was more frequent using the personalized schema (vs. NCCN: 29.8% vs. 20.9 %, p = 0.0065; vs. Lung-RADS: 33.2% vs. 22.8 %, p = 0.0025), especially for women, patients aged ≥65 years, and part-solid or non-solid nodules.

CONCLUSIONS

The personalized schema is easy-to-implement and more accurate compared with rule-based protocols. The results highlight value of personalized approaches in realizing efficient nodule management.

Perspective on Management of Low-Dose Computed Tomography Findings on Low-Dose Computed Tomography Examinations for Lung Cancer Screening. From the International Association for the Study of Lung Cancer Early Detection and Screening Committee

Lung cancer screening using low-dose computed tomography (LDCT) carefully implemented has been found to reduce deaths from lung cancer. Optimal management starts with selection of eligibility criteria, counseling of screenees, smoking cessation, selection of the regimen of screening which specifies the imaging protocol, and workup of LDCT findings. Coordination of clinical, radiologic, and interventional teams and ultimately treatment of diagnosed lung cancers under screening determine the benefit of LDCT screening. Ethical considerations of who should be eligible for LDCT screening programs are important to provide the benefit to as many people at risk of lung cancer as possible. Unanticipated diseases identified on LDCT may offer important benefits through early detection of leading global causes of death, such as cardiovascular diseases and chronic obstructive pulmonary disease, as the latter may result from conditions such as emphysema and bronchiectasis, which can be identified early on LDCT. This report identifies the key components of the regimen of LDCT screening for lung cancer which include the need for a management system to provide data for continuous updating of the regimen and provides quality assurance assessment of actual screenings. Multidisciplinary clinical management is needed to maximize the benefit of early detection, diagnosis, and treatment of lung cancer. Different regimens have been evolving throughout the world as the resources and needs may be different, for countries with limited resources. Sharing of results, further knowledge, and incorporation of technologic advances will continue to accelerate worldwide improvements in the diagnostic and treatment approaches.

Measuring the margin distance in pulmonary wedge resection

BACKGROUND

Margin distance contributes to survival and recurrence during wedge resections for early-stage non-small cell lung cancer. The Initiative for Early Lung Cancer Research on Treatment sought to standardize a surgeon-measured margin intraoperatively.

METHODS

Lung cancer patients who underwent wedge resection were reviewed. Margins were measured by the surgeon twice as per a standardized protocol. Intraobserver variability as well as surgeon-pathologist variability were compared.

RESULTS

Forty-five patients underwent wedge resection. Same-surgeon measurement analysis indicated good reliability with a small mean difference and narrow limit of agreement for the two measures. The median surgeon-measured margin was 18.0 mm, median pathologist-measured margin was 16.0 mm and the median difference between the surgeon-pathologist margin was -1.0 mm, ranging from -18.0 to 12.0 mm. Bland-Altman analysis for margin measurements demonstrated a mean difference of 0.65 mm. The limit of agreement for the two approaches were wide, with the difference lying between -16.25 and 14.96 mm.

CONCLUSIONS

A novel protocol of surgeon-measured margin was evaluated and compared with pathologist-measured margin. High intraobserver agreement for repeat surgeon measurements yet low-to-moderate correlation or directionality between surgeon and pathologic measurements were found.

DISCUSSION

A standardized protocol may reduce variability in pathologic assessment. These findings have critical implications considering the impact of margin distance on outcomes.

Lung cancer screening at the VA: Past, present and future

Lung cancer is responsible for more deaths annually in the United States than breast, prostate and colon cancers combined. Lung cancer screening with annual low-dose computed tomography reduces lung cancer mortality in high-risk patients through early detection. The incidence of lung cancer is higher in the veteran population compared to the general population due, in part, to the prevalence of tobacco use. Early detection of lung cancer is therefore an important goal of the Veterans Health Administration (VHA), the largest integrated health care system in the United States. The following will review previous and current initiatives undertaken by the VHA to implement and expand access to lung cancer screening and will highlight target areas of interest to improve uptake and quality of lung cancer screening. Through these initiatives and programs, the VHA aims to provide high quality and equitable access to lung cancer screening for all Veterans that incorporates research that will improve outcomes and potentially inform and optimize the practice of Lung cancer screening across the United States.

Application of logistic regression and convolutional neural network in prediction and diagnosis of high-risk populations of lung cancer

OBJECTIVES

The early detection, early diagnosis, and early treatment of lung cancer are the best strategies to improve the 5-year survival rate. Logistic regression analysis can be a helpful tool in the early detection of high-risk groups of lung cancer. Convolutional neural network (CNN) could distinguish benign from malignant pulmonary nodules, which is critical for early precise diagnosis and treatment. Here, we developed a risk assessment model of lung cancer and a high-precision classification diagnostic model using these technologies so as to provide a basis for early screening of lung cancer and for intelligent differential diagnosis.

METHODS

A total of 355 lung cancer patients, 444 patients with benign lung disease and 472 healthy people from The First Affiliated Hospital of Zhengzhou University were included in this study. Moreover, the dataset of 607 lung computed tomography images was collected from the above patients. The logistic regression method was employed to screen the high-risk groups of lung cancer, and the CNN model was designed to classify pulmonary nodules into benign or malignant nodules.

RESULTS

The area under the curve of the lung cancer risk assessment model in the training set and the testing set were 0.823 and 0.710, respectively. After finely optimizing the settings of the CNN, the area under the curve could reach 0.984.

CONCLUSIONS

This performance demonstrated that the lung cancer risk assessment model could be used to screen for high-risk individuals with lung cancer and the CNN framework was suitable for the differential diagnosis of pulmonary nodules.

Clinical Significance of Lung-RADS Category 3 Lesions in the National Lung Screening Trial

INTRODUCTION

To determine the clinical significance of category 3 (CAT3) abnormalities and the necessity of a 6-month follow-up computed tomography (CT). We also explored features associated with increased lung cancer risk.

METHODS

From the National Lung Screening Trial database, we identified participants with CAT3 lesions at prevalence screen. Rates of lung cancer and lung cancer-specific deaths (LSDs) were compared between those who underwent first follow-up CT before 6 months (early diagnostic group) and those who underwent annual screening (annual diagnostic group). We estimated the change in LSD if the 6-month CT was eliminated. Regression analysis was performed to identify features associated with participants with CAT3 who developed lung cancer.

RESULTS

A total of 1763 CAT3s were identified (6.6% of all participants who had low-dose CT), with 108 lung cancers (6.1%) and 41 LSDs (2.3%) in a 7-year period. Rates of lung cancer (7.5% versus 3.1%) and LSD (4.0% versus 1.0%) were higher in the early diagnostic group than in the annual diagnostic group. We estimated an increase in LSD of 0.6% of all participants with CAT3 (24.4% of all LSDs) if the 6-month CT was not performed. Multivariate regression analysis found that increased age, emphysema, and a part-solid nodule greater than 5 mm were associated with participants with CAT3 who developed lung cancer.

CONCLUSIONS

CAT3 lesions are uncommon, and eliminating the 6-month CT would potentially increase LSD by 0.6% of all patients with CAT3. Age, emphysema, and part-solid nodule greater than 5 mm may be useful in risk prediction models to determine which participants with CAT3 are more likely to develop lung cancer and suggest which patients may need more intense follow-up.

Implementation of low-dose CT screening in two different health care systems: Mount Sinai Healthcare System and Phoenix VA Health Care System

Implementation of lung screening (LS) programs is challenging even among health care organizations that have the motivation, the resources, and more importantly, the goal of providing for life-saving early detection, diagnosis, and treatment of lung cancer. We provide a case study of LS implementation in different healthcare systems, at the Mount Sinai Healthcare System (MSHS) in New York City, and at the Phoenix Veterans Affairs Health Care System (PVAHCS) in Phoenix, Arizona. This will illustrate the commonalities and differences of the LS implementation process in two very different health care systems in very different parts of the United States. Underlying the successful implementation of these LS programs was the use of a comprehensive management system, the Early Lung Cancer Action Program (ELCAP) Management SystemTM. The collaboration between MSHS and PVAHCS over the past decade led to the ELCAP Management SystemTM being gifted by the Early Diagnosis and Treatment Research Foundation to the PVAHCS, to develop a "VA-ELCAP" version. While there remain challenges and opportunities to continue improving LS and its implementation, there is an increasing realization that most patients who are diagnosed with lung cancer as a result of annual LS can be cured, and that of all the possible risks associated with LS, the greater risk of all is for heavy cigarette smokers not to be screened. We identified 10 critical components in implementing a LS program. We provided the details of each of these components for the two healthcare systems. Most importantly, is that continual re-evaluation of the screening program is needed based on the ongoing quality assurance program and database of the actual screenings. At minimum, there should be an annual review and updating. As early diagnosis of lung cancer must be followed by optimal treatment to be effective, treatment advances for small, early lung cancers diagnosed as a result of screening also need to be assessed and incorporated into the entire screening and treatment program.

The Regimen of Computed Tomography Screening for Lung Cancer: Lessons Learned Over 25 Years From the International Early Lung Cancer Action Program

We learned many unanticipated and valuable lessons since we started planning our study of low-dose computed tomography (CT) screening for lung cancer in 1991. The publication of the baseline results of the Early Lung Cancer Action Project (ELCAP) in Lancet 1999 showed that CT screening could identify a high proportion of early, curable lung cancers. This stimulated large national screening studies to be quickly started. The ELCAP design, which provided evidence about screening in the context of a clinical program, was able to rapidly expand to a 12-institution study in New York State (NY-ELCAP) and to many international institutions (International-ELCAP), ultimately working with 82 institutions, all using the common I-ELCAP protocol. This expansion was possible because the investigators had developed the ELCAP Management System for screening, capturing data and CT images, and providing for quality assurance. This advanced registry and its rapid accumulation of data and images allowed continual assessment and updating of the regimen of screening as advances in knowledge and new technology emerged. For example, in the initial ELCAP study, introduction of helical CT scanners had allowed imaging of the entire lungs in a single breath, but the images were obtained in 10 mm increments resulting in about 30 images per person. Today, images are obtained in submillimeter slice thickness, resulting in around 700 images per person, which are viewed on high-resolution monitors. The regimen provides the imaging acquisition parameters, imaging interpretation, definition of positive result, and the recommendations for further workup, which now include identification of emphysema and coronary artery calcifications. Continual updating is critical to maximize the benefit of screening and to minimize potential harms. Insights were gained about the natural history of lung cancers, identification and management of nodule subtypes, increased understanding of nodule imaging and pathologic features, and measurement variability inherent in CT scanners. The registry also provides the foundation for assessment of new statistical techniques, including artificial intelligence, and integration of effective genomic and blood-based biomarkers, as they are developed.

Lung cancer screening by nodule volume in Lung-RADS v1.1: negative baseline CT yields potential for increased screening interval

Objectives

The 2019 Lung CT Screening Reporting & Data System version 1.1 (Lung-RADS v1.1) introduced volumetric categories for nodule management. The aims of this study were to report the distribution of Lung-RADS v1.1 volumetric categories and to analyse lung cancer (LC) outcomes within 3 years for exploring personalized algorithm for lung cancer screening (LCS).

Methods

Subjects from the Multicentric Italian Lung Detection (MILD) trial were retrospectively selected by National Lung Screening Trial (NLST) criteria. Baseline characteristics included selected pre-test metrics and nodule characterization according to the volume-based categories of Lung-RADS v1.1. Nodule volume was obtained by segmentation with dedicated semi-automatic software. Primary outcome was diagnosis of LC, tested by univariate and multivariable models. Secondary outcome was stage of LC. Increased interval algorithms were simulated for testing rate of delayed diagnosis (RDD) and reduction of low-dose computed tomography (LDCT) burden.

Results

In 1248 NLST-eligible subjects, LC frequency was 1.2% at 1 year, 1.8% at 2 years and 2.6% at 3 years. Nodule volume in Lung-RADS v1.1 was a strong predictor of LC: positive LDCT showed an odds ratio (OR) of 75.60 at 1 year (p < 0.0001), and indeterminate LDCT showed an OR of 9.16 at 2 years (p = 0.0068) and an OR of 6.35 at 3 years (p = 0.0042). In the first 2 years after negative LDCT, 100% of resected LC was stage I. The simulations of low-frequency screening showed a RDD of 13.6–21.9% and a potential reduction of LDCT burden of 25.5–41%.

Conclusions

Nodule volume by semi-automatic software allowed stratification of LC risk across Lung-RADS v1.1 categories. Personalized screening algorithm by increased interval seems feasible in 80% of NLST eligible.

Key Points

• Using semi-automatic segmentation of nodule volume, Lung-RADS v1.1 selected 10.8% of subjects with positive CT and 96.87 relative risk of lung cancer at 1 year, compared to negative CT.

• Negative low-dose CT by Lung-RADS v1.1 was found in 80.6% of NLST eligible and yielded 40 times lower relative risk of lung cancer at 2 years, compared to positive low-dose CT; annual screening could be preference sensitive in this group.

• Semi-automatic segmentation of nodule volume and increased screening interval by volumetric Lung-RADS v1.1 could retrospectively suggest a 25.5–41% reduction of LDCT burden, at the cost of 13.6–21.9% rate of delayed diagnosis.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-07275-w) contains supplementary material, which is available to authorized users.

Implementation of the cloud-based computerized interpretation system in a nationwide lung cancer screening with low-dose CT: comparison with the conventional reading system

OBJECTIVES

We aimed to compare the CT interpretation before and after the implementation of a computerized system for lung nodule detection and measurements in a nationwide lung cancer screening program.

METHODS

Our screening program started in April 2017, with 14 participating institutions. Initially, all CTs were interpreted using interpretation systems in each institution and manual nodule measurement (conventional system). A cloud-based CT interpretation system, equipped with semi-automated measurement and CAD (computer-aided detection) for lung nodules (cloud-based system), was implemented during the project. Positive rates and performances for lung cancer diagnosis based on the Lung-RADS version 1.0 were compared between the conventional and cloud-based systems.

RESULTS

A total of 1821 (M:F = 1782:39, mean age 62.7 years, 16 confirmed lung cancers) and 4666 participants (M:F = 4560:106, mean age 62.8 years, 31 confirmed lung cancers) were included in the conventional and cloud-based systems, respectively. Significantly more nodules were detected in the cloud-based system (0.76 vs. 1.07 nodule/participant, p < .001). Positive rate did not differ significantly between the two systems (9.9% vs. 11.0%, p = .211), while their variability across institutions was significantly lower in the cloud-based system (coefficients of variability, 0.519 vs. 0.311, p = .018). The Lung-RADS-based sensitivity (93.8% vs. 93.5%, p = .979) and specificity (90.9% vs. 89.6%, p = .132) did not differ significantly between the two systems.

CONCLUSION

Implementation of CAD and semi-automated measurement for lung nodules in a nationwide lung cancer screening program resulted in increased number of detected nodules and reduced variability in positive rates across institutions.

KEY POINTS

• Computer-aided CT reading detected more lung nodules than radiologists alone in lung cancer screening. • Positive rate in lung cancer screening did not change with computer-aided reading. • Computer-aided CT reading reduced inter-institutional variability in lung cancer screening.

Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was demonstrated in the National Lung Screening Trial (NLST) to reduce mortality from the disease. European mortality data has recently become available from the Nelson randomised controlled trial, which confirmed lung cancer mortality reductions by 26% in men and 39-61% in women. Recent studies in Europe and the USA also showed positive results in screening workers exposed to asbestos. All European experts attending the "Initiative for European Lung Screening (IELS)"-a large international group of physicians and other experts concerned with lung cancer-agreed that LDCT-LCS should be implemented in Europe. However, the economic impact of LDCT-LCS and guidelines for its effective and safe implementation still need to be formulated. To this purpose, the IELS was asked to prepare recommendations to implement LCS and examine outstanding issues. A subgroup carried out a comprehensive literature review on LDCT-LCS and presented findings at a meeting held in Milan in November 2018. The present recommendations reflect that consensus was reached.

Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was demonstrated in the National Lung Screening Trial (NLST) to reduce mortality from the disease. European mortality data has recently become available from the Nelson randomised controlled trial, which confirmed lung cancer mortality reductions by 26% in men and 39-61% in women. Recent studies in Europe and the USA also showed positive results in screening workers exposed to asbestos. All European experts attending the "Initiative for European Lung Screening (IELS)"-a large international group of physicians and other experts concerned with lung cancer-agreed that LDCT-LCS should be implemented in Europe. However, the economic impact of LDCT-LCS and guidelines for its effective and safe implementation still need to be formulated. To this purpose, the IELS was asked to prepare recommendations to implement LCS and examine outstanding issues. A subgroup carried out a comprehensive literature review on LDCT-LCS and presented findings at a meeting held in Milan in November 2018. The present recommendations reflect that consensus was reached.