Quantification of growth patterns of screen-detected lung cancers: The NELSON study

Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation

Background

Lung cancer is one of the most common types of cancer and the leading cause of cancer death in the United Kingdom. Artificial intelligence-based software has been developed to reduce the number of missed or misdiagnosed lung nodules on computed tomography images.

Objective

 To assess the accuracy, clinical effectiveness and cost-effectiveness of using  software with artificial intelligence-derived algorithms to assist in the detection and analysis of lung nodules in computed tomography scans of the chest compared with unassisted reading.

Design

Systematic review and de novo cost-effectiveness analysis.

Methods

Searches were undertaken from 2012 to January 2022. Company submissions were accepted until 31 August 2022. Study quality was assessed using the revised tool for the quality assessment of diagnostic accuracy studies (QUADAS-2), the extension to QUADAS-2 for assessing risk of bias in comparative accuracy studies (QUADAS-C) and the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) checklist. Outcomes were synthesised narratively. Two decision trees were used for cost-effectiveness: (1) a simple decision tree for the detection of actionable nodules and (2) a decision tree reflecting the full clinical pathways for people undergoing chest computed tomography scans. Models estimated incremental cost-effectiveness ratios, cost per correct detection of an actionable nodule, and cost per cancer detected and treated. We undertook scenario and sensitivity analyses.

Results

Twenty-seven studies were included. All were rated as being at high risk of bias. Twenty-four of the included studies used retrospective data sets. Seventeen compared readers with and without artificial intelligence software. One reported prospective screening experiences before and after artificial intelligence software implementation. The remaining studies either evaluated stand-alone artificial intelligence or provided only non-comparative evidence. (1) Artificial intelligence assistance generally improved the detection of any nodules compared with unaided reading (three studies; average per-person sensitivity 0.43-0.68 for unaided and 0.79-0.99 for artificial intelligence-assisted reading), with similar or lower specificity (three studies; 0.77-1.00 for unaided and 0.81-0.97 for artificial intelligence-assisted reading). Nodule diameters were similar or significantly larger with semiautomatic measurements than with manual measurements. Intra-reader and inter-reader agreement in nodule size measurement and in risk classification generally improved with artificial intelligence assistance or were comparable to those with unaided reading. However, the effect on measurement accuracy is unclear. (2) Radiologist reading time generally decreased with artificial intelligence assistance in research settings. (3) Artificial intelligence assistance tended to increase allocated risk categories as defined by clinical guidelines. (4) No relevant clinical effectiveness and cost-effectiveness studies were identified. (5) The de novo cost-effectiveness analysis suggested that for symptomatic and incidental populations, artificial intelligence-assisted computed tomography image analysis dominated the unaided radiologist in cost per correct detection of an actionable nodule. However, when relevant costs and quality-adjusted life-years from the full clinical pathway were included, artificial intelligence-assisted computed tomography reading was dominated by the unaided reader. For screening, artificial intelligence-assisted computed tomography image analysis was cost-effective in the base case and all sensitivity and scenario analyses.

Limitations

Due to the heterogeneity, sparseness, low quality and low applicability of the clinical effectiveness evidence and the major challenges in linking test accuracy evidence to clinical and economic outcomes, the findings presented here are highly uncertain and provide indicators/frameworks for future assessment.

Conclusions

Artificial intelligence-assisted analysis of computed tomography scan images may reduce variability of and improve consistency in the measurement and clinical management of lung nodules. Artificial intelligence may increase nodule and cancer detection but may also increase the number of patients undergoing computed tomography surveillance unnecessarily. No direct comparative evidence was found, and nor was any direct evidence found on clinical outcomes and cost-effectiveness. Artificial intelligence-assisted image analysis may be cost-effective in screening for lung cancer but not for symptomatic populations. However, reliable estimates of cost-effectiveness cannot be obtained with current evidence.

Study registration

This study is registered as PROSPERO CRD42021298449.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135325) and is published in full in Health Technology Assessment; Vol. 29, No. 14. See the NIHR Funding and Awards website for further award information.

Modeling and optimal analysis of lung cancer cell growth and apoptosis with fractional-order dynamics

This study explores the application of fractional-order calculus in modeling lung cancer cell growth dynamics, emphasizing its advantages over traditional integer-order models. Conventional models often fail to capture the complexities of tumor behavior, such as memory effects and long-range interactions. The fractional-order logistic equation provides a more sophisticated framework that integrates intrinsic growth rates and environmental constraints, enabling a nuanced analysis of tumor progression and treatment responses. A key component of this research involves deriving a Laplace domain representation to assess transfer function characteristics, which aids in evaluating stability and response across various frequency domains. An improved fractional-order model was developed to illustrate the interplay between cancer proliferation and immune response mechanisms. The optimization of critical parameters, including the fractional-order ultimate growth rate, has been achieved using a genetic algorithm (GA) optimization. The main findings of this work include the potential of fractional-order modeling to understand, analyze, and determine treatment strategies, ultimately advancing the understanding of cancer dynamics and improving patient outcomes in oncology. Here, it shows the application of fractional-order dynamics to determine the effective treatment procedure concerning all complex parameters involved. This research contributes to the growing body of knowledge on sophisticated mathematical frameworks in cancer research, facilitating the development of tailored therapeutic interventions based on individual patient profiles.

Advancements in lung cancer: a comprehensive perspective on diagnosis, staging, therapy and follow-up from the SAKK Working Group on Imaging in Diagnosis and Therapy Monitoring

In 2015, around 4400 individuals received a diagnosis of lung cancer, and Switzerland recorded approximately 3200 deaths related to lung cancer. Advances in detection, such as lung cancer screening and improved treatments, have led to increased identification of early-stage lung cancer and higher chances of long-term survival. This progress has introduced new considerations in imaging, emphasising non-invasive diagnosis and characterisation techniques like radiomics. Treatment aspects, such as preoperative assessment and the implementation of immune response evaluation criteria in solid tumours (iRECIST), have also seen advancements. For those undergoing curative treatment for lung cancer, guidelines propose follow-up with computed tomography (CT) scans within a specific timeframe. However, discrepancies exist in published guidelines, and there is a lack of universally accepted recommendations for follow-up procedures. This white paper aims to provide a certain standard regarding the use of imaging on the diagnosis, staging, treatment and follow-up of patients with lung cancer.

Lung cancer volume doubling time by computed tomography: A systematic review and meta-analysis

AIM

Lung cancer growth rate influences screening strategies and treatment decisions. This review aims to provide an overview of primary lung cancer growth rate, quantified by volume doubling time (VDT) through computed tomography (CT) measurement.

METHODS

Using PRISMA-DTA guideline, PubMed, EMBASE, and Web of Science were searched until March 2024 for studies reporting CT-measured VDT of pathologically confirmed primary lung cancer before intervention. Summary data were extracted from published reports by two independent researchers. Primary outcomes were pooled mean VDT of lung cancer by nodule type and histology, distribution of indolent lung cancer (defined as VDT>400 days or negative), and correlated factors.

RESULTS

Thirty-three studies were eligible, comprising 3959 patients with primary lung cancer (mean age range:57.6-77.0 years; 60.0 % men). The pooled mean VDT for solid, part-solid, and nonsolid lung cancer were 207, 536, and 669 days, respectively (p < 0.001). When stratified by histology within solid lung cancer, the pooled mean VDT of adenocarcinoma, squamous cell carcinoma, small cell lung cancer, and others were 223, 140, 73, and 178 days, respectively (p < 0.001). Indolent lung cancer was observed in 34.9 % of lung cancer, predominantly in adenocarcinoma (68.9 %). Adenocarcinoma was associated with slower growth, whereas factors such as tumor size, solidity, TNM staging, and smoking history were positively associated with growth rates.

CONCLUSIONS

Pooled mean VDT of solid lung cancer was approximately 207 days, demonstrating significant variability in histology yet remaining under the 400-day referral threshold. Key predictors of growth rate include histology, size, solidity, and smoking history, essential for tailoring early intervention strategies.

TRIAL REGISTRATION NUMBER

CRD42023408069.

Lung Nodule Management in Low-Dose CT Screening for Lung Cancer: Lessons from the NELSON Trial

Screening with low-dose CT (LDCT) in a high-risk population, as defined by age and smoking behavior, reduces lung cancer-related mortality. However, LDCT screening presents a major challenge. Numerous, mostly benign, nodules are seen in the lungs during screening. The question is how to distinguish the malignant from the benign nodules. Various studies use different protocols for nodule management. The Dutch-Belgian NELSON (Nederlands-Leuvens Longkanker Screenings Onderzoek) trial, the largest European lung cancer screening trial, used distinctions based on nodule volumetric assessment and growth rate. This review discusses key findings from the NELSON study regarding the characteristics of screening-detected nodules, including nodule size and its volumetric assessment, growth rate, subtype, and their associated malignancy risk. These results are compared with findings from other screening studies and current recommendations for lung nodule management. By examining differences in nodule management strategies and providing a comprehensive overview of outcomes specific to lung cancer screening, this review aims to contribute to the broader discussion on optimizing lung nodule management in screening programs.

Growth dynamics of lung nodules: implications for classification in lung cancer screening

BACKGROUND

Lung nodules observed in cancer screening are believed to grow exponentially, and their associated volume doubling time (VDT) has been proposed for nodule classification. This retrospective study aimed to elucidate the growth dynamics of lung nodules and determine the best classification as either benign or malignant.

METHODS

Data were analyzed from 180 participants (73.7% male) enrolled in the I-ELCAP screening program (140 primary lung cancer and 40 benign) with three or more annual CT examinations before resection. Attenuation, volume, mass and growth patterns (decelerated, linear, subexponential, exponential and accelerated) were assessed and compared as classification methods.

RESULTS

Most lung cancers (83/140) and few benign nodules (11/40) exhibited an accelerated, faster than exponential, growth pattern. Half (50%) of the benign nodules versus 26.4% of the malignant ones displayed decelerated growth. Differences in growth patterns allowed nodule malignancy to be classified, the most effective individual variable being the increase in volume between two-year-interval scans (ROC-AUC = 0.871). The same metric on the first two follow-ups yielded an AUC value of 0.769. Further classification into solid, part-solid or non-solid, improved results (ROC-AUC of 0.813 in the first year and 0.897 in the second year).

CONCLUSIONS

In our dataset, most lung cancers exhibited accelerated growth in contrast to their benign counterparts. A measure of volumetric growth allowed discrimination between benign and malignant nodules. Its classification power increased when adding information on nodule compactness. The combination of these two meaningful and easily obtained variables could be used to assess malignancy of lung cancer nodules.

Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis

Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. CRITICAL RELEVANCE STATEMENT: Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. KEY POINTS: • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology.

Quantifying the Growth of Glioblastoma Tumors Using Multimodal MRI Brain Images

Predicting the eventual volume of tumor cells, that might proliferate from a given tumor, can help in cancer early detection and medical procedure planning to prevent their migration to other organs. In this work, a new statistical framework is proposed using Bayesian techniques for detecting the eventual volume of cells expected to proliferate from a glioblastoma (GBM) tumor. Specifically, the tumor region was first extracted using a parallel image segmentation algorithm. Once the tumor region was determined, we were interested in the number of cells that could proliferate from this tumor until its survival time. For this, we constructed the posterior distribution of the tumor cell numbers based on the proposed likelihood function and a certain prior volume. Furthermore, we extended the detection model and conducted a Bayesian regression analysis by incorporating radiomic features to discover those non-tumor cells that remained undetected. The main focus of the study was to develop a time-independent prediction model that could reliably predict the ultimate volume a malignant tumor of the fourth-grade severity could attain and which could also determine if the incorporation of the radiomic properties of the tumor enhanced the chances of no malignant cells remaining undetected.

Contextualizing the Role of Volumetric Analysis in Pulmonary Nodule Assessment: AJR Expert Panel Narrative Review

Pulmonary nodules are managed on the basis of their size and morphologic characteristics. Radiologists are familiar with assessing nodule size by measuring diameter using manually deployed electronic calipers. Size may also be assessed with 3D volumetric measurements (referred to as volumetry) obtained with software. Nodule size and growth are more accurately assessed with volumetry than on the basis of diameter, and the evidence supporting clinical use of volumetry has expanded, driven by its use in lung cancer screening nodule management algorithms in Europe. The application of volumetry has the potential to reduce recommendations for imaging follow-up of indeterminate solid nodules without impacting cancer detection. Although changes in scanning conditions and volumetry software packages can lead to variation in volumetry results, ongoing technical advances have improved the reliability of calculated volumes. Volumetry is now the primary method for determining size of solid nodules in the European lung cancer screening position statement and British Thoracic Society recommendations. The purposes of this article are to review technical aspects, advantages, and limitations of volumetry and, by considering specific scenarios, to contextualize the use of volumetry with respect to its importance in morphologic evaluation, its role in predicting malignancy in risk models, and its practical impact on nodule management. Implementation challenges and areas requiring further evidence are also highlighted.

Low-dose computed tomography lung cancer screening: Clinical evidence and implementation research

Lung cancer causes more deaths than breast, cervical, and colorectal cancer combined. Nevertheless, population-based lung cancer screening is still not considered standard practice in most countries worldwide. Early lung cancer detection leads to better survival outcomes: patients diagnosed with stage 1A lung cancer have a >75% 5-year survival rate, compared to <5% at stage 4. Low-dose computed tomography (LDCT) thorax imaging for the secondary prevention of lung cancer has been studied at length, and has been shown to significantly reduce lung cancer mortality in high-risk populations. The US National Lung Screening Trial reported a 20% overall reduction in lung cancer mortality when comparing LDCT to chest X-ray, and the Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) trial more recently reported a 24% reduction when comparing LDCT to no screening. Hence, the focus has now shifted to implementation research. Consequently, the 4-IN-THE-LUNG-RUN consortium based in five European countries, has set up a large-scale multicenter implementation trial. Successful implementation of and accessibility to LDCT lung cancer screening are dependent on many factors, not limited to population selection, recruitment strategy, computed tomography screening frequency, lung-nodule management, participant compliance, and cost effectiveness. This review provides an overview of current evidence for LDCT lung cancer screening, and draws attention to major factors that need to be addressed to successfully implement standardized, effective, and accessible screening throughout Europe. Evidence shows that through the appropriate use of risk-prediction models and a more personalized approach to screening, efficacy could be improved. Furthermore, extending the screening interval for low-risk individuals to reduce costs and associated harms is a possibility, and through the use of volumetric-based measurement and follow-up, false positive results can be greatly reduced. Finally, smoking cessation programs could be a valuable addition to screening programs and artificial intelligence could offer a solution to the added workload pressures radiologists are facing.

Lung cancer in patients with fibrosing interstitial lung diseases – An overview of current knowledge and challenges

Patients with progressive fibrosing interstitial lung diseases (fILD) have increased morbidity and mortality. Lung fibrosis can be associated with lung cancer. The pathogenesis of both diseases shows similarities, although not all mechanisms are understood. The combination of the diseases is challenging, due to the amplified risk of mortality, and also because lung cancer treatment carries additional risks in patients with underlying lung fibrosis. Acute exacerbations in fILD patients are linked to increased mortality, and the risk of acute exacerbations is increased after lung cancer treatment with surgery, chemotherapy or radiotherapy. Careful selection of treatment modalities is crucial to improve survival while maintaining acceptable quality of life in patients with combined lung cancer and fILD. This overview of epidemiology, pathogenesis, treatment and a possible role for antifibrotic drugs in patients with lung cancer and fILD is the summary of a session presented during the virtual European Respiratory Society Congress in 2021. The review summarises current knowledge and identifies areas of uncertainty. Most current data relate to patients with combined idiopathic pulmonary fibrosis and lung cancer. There is a pressing need for additional prospective studies, required for the formulation of a consensus statement or guideline on the optimal care of patients with lung cancer and fILD.

Lung fibrosis can be associated with lung cancer. More and better-designed studies are needed to determine the true incidence/prevalence of lung cancer in fILD. Optimal treatment strategies urgently need to be defined and evaluated.https://bit.ly/37CzTMu

Dynamic Observation of Lung Nodules on Chest CT Before Diagnosis of Early Lung Cancer

Objective

Early recognition and diagnosis of lung cancer can help improve the prognosis of patients. However, early imaging patterns of malignant lung nodules are not fully clear. To understand the early imaging signs of malignant lung cancer nodules, the changes of the lung nodules before diagnosis were dynamically observed and analyzed.

Materials and Methods

This retrospective study observed dynamic changes of lung nodules before pathological confirmation with consecutive regular chest CT examination from January 2003 to December 2018. At least 3 follow-up CT scans were performed in all cases, and the interval between each follow-up was about 1 year. The size, density, and morphological signs of the nodules were evaluated based on the CT axial image, and a reverse line chart or scatter plot with the diagnosis time as coordinate origin was constructed.

Results

A total of 55 lung nodules in 53 patients (mean age, 58.40 years ±11.43 [standard deviation]; 20 women) were accessed. The follow-up time was 5.96 ± 2.68 years. The average diameters in maximum slice of the lesion at baseline and last scan were 6.83 ± 2.92 mm and 16.65 ± 7.34 mm, respectively. According to the reverse line chart, the nodule growth curve segments within 4 years from the last scan showed an ascending shape, and those beyond 4 years showed a flat shape. There are 90.9% (50/55) GGN and 9.1% (5/55) SN when the lesion first appears, and 21.8% (12/55) GGN, 38.2% (21/55) PSN, and 40% (22/55) SN in the last scan. There are 12.7% (7/55) and 98.2% (54/55) nodules with poor morphological signs at baseline and last scan, respectively.

Conclusion

At the time node close to the diagnosis, the growth curve showed an upward pattern; the proportion of PSN and SN rose as the main density types; and the appearance of poor morphological signs of nodules increased. When a persistent lung nodule starts to show a malignant change, a further diagnostic workup is warranted.

Preoperative Changes of Lung Nodule on Computed Tomography and Their Relationship With Pathological Outcomes

Background

Whether changes of lung nodules on computed tomography could bring us helpful information related to their pathological outcomes remained unclear.

Materials and Methods

This retrospective study was carried out among 1,185 cases of lung nodules in Shanghai Chest Hospital from January 2015 to April 2017, which did not shrink or disappear after preoperative follow-up over three months. Their imaging features, changes, and clinical characteristics were collected. A separate analysis was performed in nodules with or without growth in long-axis diameter after follow-up, searching significant changes related to nodule malignancy and the median interval of follow-up for reference. Further study was performed similarly in malignant nodules for discrimination of malignant grading.

Results

Most nodules were stable (n = 885, 75%), whereas others grew (n = 300, 25%). For predicting nodule malignancy, increase in density (&gt;10 Hounsfield units, median follow-up of 549 days) played an important role in growing group whereas it failed in stable group, and the increase in size was less significant in growing group. For discrimination of malignant grading, increase in density (&gt;70 Hounsfield units, median follow-up of 366 days) showed its significance in stable group, and so did increase in size in growing group (maximum diameter growth &gt;3.3 mm, median follow-up of 549 days, or average diameter growth &gt;3.1 mm, median follow-up of 625 days).

Conclusions

There were significant changes of lung nodules by follow-up on computed tomography, related to their pathological outcomes. The predictive power of increase in density or size varied in different situations, whereas all referred to a long-time preoperative follow-up.

Cost-effectiveness of lung cancer screening by low-dose CT in China: a micro-simulation study

Background

The effectiveness of lung cancer screening with low-dose computed tomography (LDCT) has been established. The current study evaluates the cost-effectiveness of lung cancer screening with LDCT in a general population in China.

Methods

A previously validated micro-simulation model was used to simulate a cohort of men and women on a lifetime horizon in the presence and absence of LDCT screening. The modeling data were collected from numerous national and international sources. Simulated screening scenarios included different combinations of screening intervals and start and stop ages. Additional costs (valued in Chinese Yuan, CNY; 1 USD = 6.8976 CNY, 1 EUR = 7.8755 CNY in 2020), life-years gained (LYG) and mortality reduction due to screening were also determined. The costs and life-years were discounted by 3%. All results were scaled to 1,000 individuals. The average cost-effectiveness ratio (ACER) was calculated. A willingness-to-pay threshold of CNY 217.3k / LYG was considered. A healthcare system perspective was adopted.

Results

Compared to no screening, lung cancer screening by LDCT in a general Chinese population yielded 21.0 - 36.7 LYG in men and 9.2 - 16.6 LYG in women across the scenarios. For men, biennial LDCT screening yielded an ACER of CNY 171.4k - 306.3k / LYG relative to no screening. Biennial screening performed between 55 and 75 years of age was optimal at the defined threshold; it resulted in CNY 174.6k / LYG and a lung cancer mortality reduction of 9.1%, and this scenario had a 75% probability of being cost-effective. For women, the ACER ranged from CNY 364.2k to 1193.3k / LYG.

Conclusions

In China, lung cancer screening with LDCT in the general population including never smokers could be cost-effective for men with 75% probability, but not for women. The optimal strategy for men would be performing biennial screening between 55 and 75 years of age.

Lung Cancer Stage Shift as a Result of COVID-19 Lockdowns in New York City, a Brief Report

Introduction

The COVID-19 pandemic reached New York City in early March 2020 resulting in an 11-week lockdown period to mitigate further spread. It has been well documented that cancer care was drastically affected as a result. Given New York City's early involvement, we attempted to identify any stage shift that may have occurred in the diagnoses of non-small cell lung cancer (NSCLC) at our institution as a result of these lockdowns.

Patients and Methods

We conducted a retrospective review of a prospective database of lung cancer patients at our institution from July 1, 2019 until March 31, 2021. Patients were grouped by calendar year quarter in which they received care. Basic demographics and clinical staging were compared across quarters.

Results

Five hundred and fifty four patients were identified that underwent treatment during the time period of interest. During the lockdown period, there was a 50% reduction in the mean number of patients seen (15 ± 3 vs. 28 ± 7, P = .004). In the quarter following easing of restrictions, there was a significant trend towards earlier stage (cStage I/II) disease. In comparison to quarters preceding the pandemic lockdown, there was a significant increase in the proportion of patients with Stage IV disease in the quarters following phased reopening (P = .026).

Conclusion

After a transient but significant increase in Stage I/II disease with easing of restrictions there was a significant increase in patients with Stage IV disease. Extended longitudinal studies must be conducted to determine whether COVID-19 lockdowns will lead to further increases in the proportion of patients with advanced NSCLC.

Clinical Non-Small Cell Lung Cancer Staging and Tumor Length Measurement Results From U.S. Cancer Hospitals

RATIONALE AND OBJECTIVES

Examine the accuracy of clinical non-small cell lung cancer staging and tumor length measurements, which are critical to prognosis and treatment planning.

MATERIALS AND METHODS

Compare clinical and pathological staging and lengths using 10,320 2016 National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) and 559 2010-2018 non-SEER single-institute surgically-treated cases, and analyze modifiable causes of disagreement.

RESULTS

The SEER clinical and pathological group-stages agree only 62.3% ± 0.9% over all stage categories. The lymph node N-stage agrees much better at 83.0% ± 1.0%, but the tumor length-location T-stage agrees only 57.7% ± 0.8% with approximately 29% of the cases having a greater pathology than clinical T-stage. Individual T-stage category agreements with respect to the number of pathology cases are Tis, T1a, T1b, T2a, T2b, T3, T4: 89.9% ± 10.0%; 78.7% ± 1.7%; 51.8% ± 1.9%; 46.1% ± 1.3%; 40.5% ± 3.1%; 44.1% ± 2.2%; 56.4% ± 4.7%, respectively. Most of the single-institute results statistically agree with SEER's. Excluding Tis cases, the mean difference in SEER tumor length is ∼1.18 ± 9.26 mm (confidence interval: 0.97-1.39 mm) with pathological lengths being longer than clinical lengths except for small tumors; the two measurements correlate well (Pearson-r >0.87, confidence interval: 0.86-0.87). Reasons for disagreement include the use of family-category descriptors (e.g., T1) instead of their subcategories (e.g., T1a and T1b), which worsens the T-stage agreement by over 15%. Disagreement is also associated with higher tumor grade, larger resected specimens, higher N-stage, patient age, and periodic biases in clinical and pathological tumor size measurements.

CONCLUSIONS

By including preliminary non-small cell lung cancer clinical stage values in their evaluation, diagnostic radiologists can improve the accuracy of staging and standardize tumor-size measurements, which improves patient care.

Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics

Objectives

To investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma.

Methods

From January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated. Training and validation sets were randomly assigned according to an 8:2 ratio. All cases were divided into fast-growing and slow-growing groups. Researchers extracted 1218 radiomics features from each volumetric region of interest (VOI). Then, radiomics features were selected by repeatability analysis and Analysis of Variance (ANOVA); Based on the Univariate and multivariate analyses, the significant radiographic features is selected in training set. A decision tree algorithm was conducted to establish the radiographic model, radiomics model and the combined radiographic-radiomics model. Model performance was assessed by the area under the curve (AUC) obtained by receiver operating characteristic (ROC) analysis.

Results

Sixty-two radiomics features and one radiographic features were selected for predicting the growth rate of pulmonary nodules. The combined radiographic-radiomics model (AUC 0.78) performed better than the radiographic model (0.727) and the radiomics model (0.710) in the validation set.

Conclusions

The model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model.

Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations

The constantly increasing number of computed tomography (CT) examinations poses major challenges for radiologists. In this article, the additional benefits and potential of an artificial intelligence (AI) analysis platform for chest CT examinations in routine clinical practice will be examined. Specific application examples include AI-based, fully automatic lung segmentation with emphysema quantification, aortic measurements, detection of pulmonary nodules, and bone mineral density measurement. This contribution aims to appraise this AI-based application for value-added diagnosis during routine chest CT examinations and explore future development perspectives.

Early detection of lung cancer in Czech high-risk asymptomatic individuals (ELEGANCE): A study protocol

BACKGROUND

Lung cancer screening in high-risk population increases the proportion of patients diagnosed at a resectable stage.

AIMS

To optimize the selection criteria and quality indicators for lung cancer screening by low-dose CT (LDCT) in the Czech population of high-risk individuals. To compare the influence of screening on the stage of lung cancer at the time of the diagnosis with the stage distribution in an unscreened population. To estimate the impact on life-years lost according to the stage-specific cancer survival and stage distribution in the screened population. To calculate the cost-effectiveness of the screening program.

METHODS

Based on the evidence from large national trials - the National Lung Screening Trial in the USA (NLST), the NELSON study, the recent recommendations of the Fleischner society, the American College of Radiology, and I-ELCAP action group, we developed a protocol for a single-arm prospective study in the Czech Republic for the screening of high-risk asymptomatic individuals. The study commenced in August 2020.

RESULTS

The inclusion criteria are: age 55 to 74 years; smoking: ≥30 pack-years; smoker or ex-smoker <15 years; performance status (0-1). The screening timepoints are at baseline and 1 year. The LDCT acquisition has a target CTDIvol ≤0.5mGy and effective dose ≤0.2mSv for a standard-size patient. The interpretation of findings is primarily based on nodule volumetry, volume doubling time (and related risk of malignancy). The management includes follow-up LDCT, contrast enhanced CT, PET/CT, tissue sampling. The primary outcome is the number of cancers detected at a resectable stage, secondary outcomes include the average cost per diagnosis of lung cancer, the number, cost, complications of secondary examinations, and the number of potentially important secondary findings.

CONCLUSIONS

A study protocol for early detection of lung cancer in Czech high-risk asymptomatic individuals (ELEGANCE) study using LDCT has been described.

Pulmonary nodule radiological diagnostic algorithm in lung cancer screening

Publications of the final results of the two largest randomized lung cancer screening (LCS) trials in the United States and Europe confirmed the reduction in the mortality rate associated with the use of screening with low-dose computed tomography (LDCT). Results of these trials led to widespread acceptance of LCS in properly defined high-risk populations, and its implementation in the clinical practice. Many countries started preparation for national LCS and refreshed still open debate about lung nodule management. Detection of lung cancer in the early stage with a reduction of lung cancer mortality requires dedicated programs with optimized protocols, including a specified pulmonary nodule diagnostic algorithm. The screening protocol should be clear with a precise nodule diameter or volume threshold, based on which a positive screen result is defined. The application of risk-prediction models and the introduction of the semiautomated assessment of detected nodules improves screening accuracy and should be applied in LCS protocols as verified tools to aid radiological diagnosis. In this review, we have summarized recent data about the radiological protocols from the most important LCS programs and pulmonary diagnostic algorithms. These protocols should be taken into consideration in the ongoing and planned LCS programs.

Development of a Model to Estimate the Association Between Delay in Cancer Treatment and Local Tumor Control and Risk of Metastases

IMPORTANCE

The coronavirus disease 2019 (COVID-19) pandemic has led to treatment delays for many patients with cancer. While published guidelines provide suggestions on which cases are appropriate for treatment delay, there are no good quantitative estimates on the association of delays with tumor control or risk of new metastases.

OBJECTIVES

To develop a simplified mathematical model of tumor growth, control, and new metastases for cancers with varying doubling times and metastatic potential and to estimate tumor control probability (TCP) and metastases risk as a function of treatment delay interval.

DESIGN, SETTING, AND PARTICIPANTS

This decision analytical model describes a quantitative model for 3 tumors (ie, head and neck, colorectal, and non-small cell lung cancers). Using accepted ranges of tumor doubling times and metastatic development from the clinical literature from 2001 to 2020, estimates of tumor growth, TCP, and new metastases were analyzed for various treatment delay intervals.

MAIN OUTCOMES AND MEASURES

Risk estimates for potential decreases in local TCP and increases in new metastases with each interval of treatment delay.

RESULTS

For fast-growing head and neck tumors with a 2-month treatment delay, there was an estimated 4.8% (95% CI, 3.4%-6.4%) increase in local tumor control risk and a 0.49% (0.47%-0.51%) increase in new distal metastases risk. A 6-month delay was associated with an estimated 21.3% (13.4-30.4) increase in local tumor control risk and a 6.0% (5.2-6.8) increase in distal metastases risk. For intermediate-growing colorectal tumors, there was a 2.1% (0.7%-3.5%) increase in local tumor control risk and a 2.7% (2.6%-2.8%) increase in distal metastases risk at 2 months and a 7.6% (2.2%-14.2%) increase in local tumor control risk and a 24.7% (21.9%-27.8%) increase in distal metastases risk at 6 months. For slower-growing lung tumors, there was a 1.2% (0.0%-2.8%) increase in local tumor control risk and a 0.19% (0.18%-0.20%) increase in distal metastases risk at 2 months, and a 4.3% (0.0%-10.6%) increase in local tumor control risk and a 1.9% (1.6%-2.2%) increase in distal metastases risk at 6 months.

CONCLUSIONS AND RELEVANCE

This study proposed a model to quantify the association of treatment delays with local tumor control and risk of new metastases. The detrimental associations were greatest for tumors with faster rates of proliferation and metastasis. The associations were smaller, but still substantial, for slower-growing tumors.

The Growth Rate of Subsolid Lung Adenocarcinoma Nodules at Chest CT

Background Confirming that subsolid adenocarcinomas show exponential growth is important because it would justify using volume doubling time to assess their growth. Purpose To test whether the growth of lung adenocarcinomas manifesting as subsolid nodules at chest CT is accurately represented by an exponential model. Materials and Methods Patients with lung adenocarcinomas manifesting as subsolid nodules surgically resected between January 2005 and May 2018, with three or more longitudinal CT examinations before resection, were retrospectively included. Overall volume (for all nodules) and solid component volume (for part-solid nodules) were measured over time. A linear mixed-effects model was used to identify the growth pattern (linear, exponential, quadratic, or power law) that best represented growth. The interactions between nodule growth and clinical, CT morphologic, and pathologic parameters were studied. Results Sixty-nine patients (mean age, 70 years ± 9 [standard deviation]; 48 women) with 74 lung adenocarcinomas were evaluated. Overall growth and solid component growth were better represented by an exponential model (adjusted R² = 0.89 and 0.95, respectively) than by a quadratic model (r² = 0.88 and 0.93, respectively), a linear model (r² = 0.87 and 0.92, respectively), or a power law model (r² = 0.82 and 0.93, respectively). Faster overall volume growth was associated with a history of lung cancer (P < .001), a baseline nodule volume less than 500 mm³ (P = .03), and histologic findings of invasive adenocarcinoma (P < .001). The median volume doubling time of noninvasive adenocarcinoma was significantly longer than that of invasive adenocarcinoma (939 days [interquartile range, 588-1563 days] vs 678 days [interquartile range, 392-916 days], respectively; P = .01). Conclusion The overall volume growth of adenocarcinomas manifesting as subsolid nodules at chest CT was best represented by an exponential model compared with the other tested models. This justifies the use of volume doubling time for the growth assessment of these nodules. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuriyama and Yanagawa in this issue.

The growth feature and its diagnostic value for benign and malignant pulmonary nodules met in routine clinical practice

Background

Growth rate is an independent risk factor for lung cancer in screened pulmonary nodules. This study aimed to clarify growth characteristics of pulmonary nodules in routine clinical practice and examine whether volume doubling time (VDT) can predict the malignancy of these nodules.

Methods

We retrospectively enrolled patients with 5-30-mm-sized pulmonary nodules that had been surgically resected after a follow-up of at least 3 months. Two follow-up computed tomography (CT) images with similar thickness and long interval were obtained. Then, three-dimensional (3D) manual segmentation for all nodules was performed on two follow-up CT scans. Subsequently, VDT was calculated for nodules with a change in volume of at least 25%.

Results

Overall, 305 pulmonary nodules in 305 patients (men, 36.7%; median age, 57) were included. The mean increased diameter, mass, and volume of benign (n=86) and malignant (n=219) nodules were 0.09 vs. 2.37 mm, 0.10 vs. 0.66 g, and 32.74 vs. 1,871.28 mm3, respectively (P<0.05). In total, 24 of 86 benign nodules (28%, 18 grew and 6 shrank) and 121 of 219 malignant nodules (55%, 114 grew and 7 shrank) changed over time. The median VDTs of growing benign and malignant nodules were 389 and 526 days, respectively, (P=0.18), and the area under the receiver operating characteristic (ROC) curve was 0.67 (0.55-0.78), with a sensitivity and specificity of 69% and 58%, respectively. The median VDT for growing nodules was 339 days for inflammatory pseudotumors, 226 days for granulomas, 640 days for benign tumors, 1,541 days for enlarged lymph nodes, 762 days for adenocarcinoma in situ, 954 days for microinvasive adenocarcinoma, 534 days for invasive adenocarcinoma, and 118 days for squamous cell carcinoma.

Conclusions

In routine clinical practice, many malignant nodules could grow slowly or even remain stable over time. Regarding growing nodules, the diagnostic value of VDT was limited.

Persisting new nodules in incidence rounds of the NELSON CT lung cancer screening study

BACKGROUND

The US guidelines recommend low-dose CT (LDCT) lung cancer screening for high-risk individuals. New solid nodules after baseline screening are common and have a high lung cancer probability. Currently, no evidence exists concerning the risk stratification of non-resolving new solid nodules at first LDCT screening after initial detection.

METHODS

In the Dutch-Belgian Randomized Lung Cancer Screening (NELSON) trial, 7295 participants underwent the second and 6922 participants the third screening round. We included participants with solid nodules that were registered as new or <15 mm³ (study detection limit) at previous screens and received additional screening after initial detection, thereby excluding high-risk nodules according to the NELSON management protocol (nodules ≥500 mm³).

RESULTS

Overall, 680 participants with 1020 low-risk and intermediate-risk new solid nodules were included. A total of 562 (55%) new solid nodules were resolving, leaving 356 (52%) participants with a non-resolving new solid nodule, of whom 25 (7%) were diagnosed with lung cancer. At first screening after initial detection, volume doubling time (VDT), volume, and VDT combined with a predefined ≥200 mm³ volume cut-off had high discrimination for lung cancer (VDT, area under the curve (AUC): 0.913; volume, AUC: 0.875; VDT and ≥200 mm³ combination, AUC: 0.939). Classifying a new solid nodule with either ≤590 days VDT or ≥200 mm³ volume positive provided 100% sensitivity, 84% specificity and 27% positive predictive value for lung cancer.

CONCLUSIONS

More than half of new low-risk and intermediate-risk solid nodules in LDCT lung cancer screening resolve. At follow-up, growth assessment potentially combined with a volume limit can be used for risk stratification.

TRIAL REGISTRATION NUMBER

ISRCTN63545820; pre-results.

Challenges and research opportunities for lung cancer screening in China

Following publication of the results of the National Lung Screening Trial in the United States, a randomized controlled trial in Italy (ITALUNG) and two simulation studies in China reported similar findings in 2017 favoring lung cancer screening with low-dose computed tomography among smokers. With such advances in lung cancer screening, worldwide interest has gradually shifted from evaluating whether refining lung cancer screening protocols is effective in preventing deaths. However, there are several practical problems to be resolved, including the balance of enrollment criteria and cost effectiveness, precise measurements to reduce false positive findings, risk-based optimization of screening frequency, challenges associated with cancer heterogeneity, strategies to combine image screening with novel biomarkers, dynamic monitoring of the natural history of cancer, accurate identification and diagnosis of cases among huge populations, and the impact of tobacco control policy and environment protection. As one in three individuals with lung cancer worldwide resides in China, these questions pose great challenges as well as research opportunities for population screening programs in China.

Appropriate screening intervals in low-dose CT lung cancer screening

Lung cancer screening by low-dose chest CT (LDCT) is now being implemented in the United States, and in Europe it was recently recommended to start planning for implementation. Current lung cancer screening programmes include up to 25 annual LDCTs, plus shorter-term follow-up LDCTs when indicated. However, the choice of a yearly CT scan has not been based on biological mechanisms, and it is questionable whether all persons eligible for lung cancer screening require annual screening. A tailored approach in screening programs to balance potential harms and benefits from screening becomes more and more important when lung cancer screening is performed more widespread. If lung cancer screening participants can be identified at mid-high lung cancer risk that can be followed safely by prolonged screening intervals, reduction of possible physiological harms, radiation exposure and costs can be expected. Different randomized controlled lung cancer screening studies have shown that the baseline screen result can be used to identify a subset of participants with a low 2-year lung cancer probability. These participants may be safely followed after a prolonged screening interval with the optimal screening interval probably between 1 and 2 years until their risk profile changes. In case a new pulmonary nodule appears at subsequent screening, or a small baseline nodule starts growing, participants should always return to annual LDCT screening after the appropriate workup.

In vivo growth of 60 non-screening detected lung cancers: a computed tomography study

Current pulmonary nodule management guidelines are based on nodule volume doubling time, which assumes exponential growth behaviour. However, this is a theory that has never been validated in vivo in the routine-care target population. This study evaluates growth patterns of untreated solid and subsolid lung cancers of various histologies in a non-screening setting.Growth behaviour of pathology-proven lung cancers from two academic centres that were imaged at least three times before diagnosis (n=60) was analysed using dedicated software. Random-intercept random-slope mixed-models analysis was applied to test which growth pattern most accurately described lung cancer growth. Individual growth curves were plotted per pathology subgroup and nodule type.We confirmed that growth in both subsolid and solid lung cancers is best explained by an exponential model. However, subsolid lesions generally progress slower than solid ones. Baseline lesion volume was not related to growth, indicating that smaller lesions do not grow slower compared to larger ones.By showing that lung cancer conforms to exponential growth we provide the first experimental basis in the routine-care setting for the assumption made in volume doubling time analysis.

Relationship between nodule count and lung cancer probability in baseline CT lung cancer screening: The NELSON study

OBJECTIVES

To explore the relationship between nodule count and lung cancer probability in baseline low-dose CT lung cancer screening.

MATERIALS AND METHODS

Included were participants from the NELSON trial with at least one baseline nodule (3392 participants [45% of screen-group], 7258 nodules). We determined nodule count per participant. Malignancy was confirmed by histology. Nodules not diagnosed as screen-detected or interval cancer until the end of the fourth screening round were regarded as benign. We compared lung cancer probability per nodule count category.

RESULTS

1746 (51.5%) participants had one nodule, 800 (23.6%) had two nodules, 354 (10.4%) had three nodules, 191 (5.6%) had four nodules, and 301 (8.9%) had>4 nodules. Lung cancer in a baseline nodule was diagnosed in 134 participants (139 cancers; 4.0%). Median nodule count in participants with only benign nodules was 1 (Inter-quartile range [IQR]: 1-2), and 2 (IQR 1-3) in participants with lung cancer (p=NS). At baseline, malignancy was detected mostly in the largest nodule (64/66 cancers). Lung cancer probability was 62/1746 (3.6%) in case a participant had one nodule, 33/800 (4.1%) for two nodules, 17/354 (4.8%) for three nodules, 12/191 (6.3%) for four nodules and 10/301 (3.3%) for>4 nodules (p=NS).

CONCLUSION

In baseline lung cancer CT screening, half of participants with lung nodules have more than one nodule. Lung cancer probability does not significantly change with the number of nodules. Baseline nodule count will not help to differentiate between benign and malignant nodules. Each nodule found in lung cancer screening should be assessed separately independent of the presence of other nodules.