Benefits and harms of CT screening for lung cancer: a systematic review

The American Cancer Society National Lung Cancer Roundtable strategic plan: Current challenges and future directions for shared decision making for lung cancer screening

Shared decision making (SDM) between health care professionals and patients is essential to help patients make well informed choices about lung cancer screening (LCS). Patients who participate in SDM have greater LCS knowledge, reduced decisional conflict, and improved adherence to annual screening compared with patients who do not participate in SDM. SDM tools are acceptable to patients and clinicians. The importance of SDM in LCS is emphasized in recommendations from professional organizations and highlighted as a priority in the 2022 President's Cancer Panel Report. The updated 2022 national coverage determination from the Centers for Medicare & Medicaid Services reaffirms the value of SDM in offering LCS to eligible beneficiaries. The Shared Decision-Making Task Group of the American Cancer Society National Lung Cancer Roundtable undertook a group consensus process to identify priorities for research and implementation related to SDM for LCS and then evaluated current knowledge in these areas. Priority areas included: (1) developing feasible, adaptable SDM training programs for health care professionals; (2) understanding the impact of alternative health system LCS models on SDM practice and outcomes; (3) developing and evaluating new patient decision aids for use with diverse populations and in varied settings; (4) offering conceptual clarity about what constitutes a high-quality decision and developing appropriate quality measures; and (5) studying the use of prediction-augmented screening to support SDM in practice. Gaps in current research in all areas were observed. The authors conclude with a research and implementation agenda to advance the quality and implementation of SDM for persons who might benefit from LCS.

The American Cancer Society National Lung Cancer Roundtable strategic plan: Provider engagement and outreach

The American Cancer Society National Lung Cancer Roundtable strategic plan for provider engagement and outreach addresses barriers to the uptake of lung cancer screening, including lack of provider awareness and guideline knowledge about screening, concerns about potential harms from false-positive examinations, lack of time to implement workflows within busy primary care practices, insufficient infrastructure and administrative support to manage a screening program and patient follow-up, and implicit bias based on sex, race/ethnicity, social class, and smoking status. Strategies to facilitate screening include educational programming, clinical reminder systems within the electronic medical record, decision support aids, and tools to track nodules that can be implemented across a diversity of practices and health care organizational structures. PLAIN LANGUAGE SUMMARY: The American Cancer Society National Lung Cancer Roundtable strategic plan to reduce deaths from lung cancer includes strategies designed to support health care professionals, to better understand lung cancer screening, and to support adults who are eligible for lung cancer screening by providing counseling, referral, and follow-up.

Deep Learning of radiology-genomics integration for computational oncology: A mini review

In the field of computational oncology, patient status is often assessed using radiology-genomics, which includes two key technologies and data, such as radiology and genomics. Recent advances in deep learning have facilitated the integration of radiology-genomics data, and even new omics data, significantly improving the robustness and accuracy of clinical predictions. These factors are driving artificial intelligence (AI) closer to practical clinical applications. In particular, deep learning models are crucial in identifying new radiology-genomics biomarkers and therapeutic targets, supported by explainable AI (xAI) methods. This review focuses on recent developments in deep learning for radiology-genomics integration, highlights current challenges, and outlines some research directions for multimodal integration and biomarker discovery of radiology-genomics or radiology-omics that are urgently needed in computational oncology.

Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data

BACKGROUND

Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data.

METHODS

Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features.

RESULTS

For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer.

CONCLUSION

This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.

Clinical Validation of a Cell-Free DNA Fragmentome Assay for Augmentation of Lung Cancer Early Detection

Lung cancer screening via annual low-dose computed tomography has poor adoption. We conducted a prospective case-control study among 958 individuals eligible for lung cancer screening to develop a blood-based lung cancer detection test that when positive is followed by a low-dose computed tomography. Changes in genome-wide cell-free DNA fragmentation profiles (fragmentomes) in peripheral blood reflected genomic and chromatin characteristics of lung cancer. We applied machine learning to fragmentome features to identify individuals who were more or less likely to have lung cancer. We trained the classifier using 576 cases and controls from study samples and validated it in a held-out group of 382 cases and controls. The validation demonstrated high sensitivity for lung cancer and consistency across demographic groups and comorbid conditions. Applying test performance to the screening eligible population in a 5-year model with modest utilization assumptions suggested the potential to prevent thousands of lung cancer deaths. Significance: Lung cancer screening has poor adoption. Our study describes the development and validation of a novel blood-based lung cancer screening test utilizing a highly affordable, low-coverage genome-wide sequencing platform to analyze cell-free DNA fragmentation patterns. The test could improve lung cancer screening rates leading to substantial public health benefits. See related commentary by Haber and Skates, p. 2025.

Sex-Based Differences in Lung Cancer Incidence: A Retrospective Analysis of Two Large US-Based Cancer Databases

Background/Objectives: Non-small cell lung cancer (NSCLC) has seen a relative rise in incidence among females versus males in recent years, although males still have a higher overall incidence. However, it is unclear whether this trend is consistent across all populations. Therefore, we retrospectively examined this relationship in two large high-risk clinical cohorts. Methods: First, we analyzed lung cancer incidence among individuals with a smoking history of over 40 pack-years in the National Lung Screening Trial (NLST). Then, we investigated the incidence of second primary NSCLC in patients who underwent lobectomy for previous stage I lung cancer using the Surveillance, Epidemiology, and End Results (SEER) database. We performed both univariate and multivariable time-to-event analyses to investigate the relationship between sex and lung cancer incidence. Results: In the NLST cohort (n = 37,627), females had a higher risk of developing primary NSCLC than males (HR = 1.11 [1.007-1.222], p = 0.035) after adjusting for age and pack-year history. In the SEER cohort (n = 19,327), females again exhibited an increased risk of developing a second primary lung cancer (HR = 1.138 [1.02-1.269], p = 0.021), after adjusting for age, race, grade, and histology. Conclusions: Our analysis reveals that females have a modestly higher lung cancer incidence than males in high-risk populations. These findings underscore the importance of further researching the underlying cellular processes that may cause sex-specific differences in lung cancer incidence.

Effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction strength level on the detection and characterization of pulmonary nodules in ultra-low-dose chest CT

OBJECTIVE

To explore the effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction (ASiR-V) strength level on the detection and characterization of pulmonary nodules by an artificial intelligence (AI) software in ultra-low-dose chest CT (ULDCT).

MATERIALS AND METHODS

An anthropomorphic thorax phantom containing 12 spherical simulated nodules (Diameter: 12 mm, 10 mm, 8 mm, 5 mm; CT value: -800HU, -630HU, 100HU) was scanned with three ULDCT protocols: Dose-1 (70kVp:0.11mSv, 100kVp:0.10mSv), Dose-2 (70kVp:0.34mSv, 100kVp:0.32mSv), Dose-3 (70kVp:0.53mSv, 100kVp:0.51mSv). All scanning protocols were repeated five times. CT images were reconstructed using four different strength levels of ASiR-V (0%=FBP, 30%, 50%, 70%ASiR-V) with a slice thickness of 1.25 mm. The characteristics of the physical nodules were used as reference standards. All images were analyzed using a commercially available AI software to identify nodules for calculating nodule detection rate (DR) and to obtain their long diameter and short diameter, which were used to calculate the deformation coefficient (DC) and size measurement deviation percentage (SP) of nodules. DR, DC and SP of different imaging groups were statistically compared.

RESULTS

Image noise decreased with the increase of ASiR-V strength level, and the 70 kV images had lower noise under the same strength level (mean-value 70 kV: 40.14 ± 7.05 (dose 1), 27.55 ± 7.38 (dose 2), 23.88 ± 6.98 (dose 3); 100 kV: 42.36 ± 7.62 (dose 1); 30.78 ± 6.87 (dose 2); 26.49 ± 6.61 (dose 3)). Under the same dose level, there were no differences in DR between 70 kV and 100 kV (dose 1: 58.76% vs. 58.33%; dose 2: 73.33% vs. 70.83%; dose 3: 75.42% vs. 75.42%, all p > 0.05). The DR of GGNs increased significantly at dose 2 and higher (70 kV: 38.12% (dose 1), 60.63% (dose 2), 64.38% (dose 3); 100 kV: 37.50% (dose 1), 59.38% (dose 2), 66.25% (dose 3)). In general, the use of ASiR-V at higher strength levels (> 50%) and 100 kV provided better (lower) DC and SP.

CONCLUSION

Detection rates are similar between 70 kV and 100 kV scans. The 70 kV images have better noise performance under the same ASiR-V level, while images of 100 kV and higher ASiR-V levels are better in preserving the nodule morphology (lower DC and SP); the dose levels above 0.33mSv provide high sensitivity for nodules detection, especially the simulated ground glass nodules.

Gender-specific outcomes of low-dose computed tomography screening for lung cancer detection: A retrospective study in Chinese never-smoker population

OBJECTIVES

Low-dose computed tomography (LDCT) has emerged as a pivotal tool for detecting lung cancer among ever-smokers. This study aims to evaluate the gender-specific outcomes of LDCT screening within the Chinese never-smoking population.

METHODS

We conducted a single-center, retrospective cohort study, which analyzed LDCT screening outcomes for 42,018 asymptomatic participants. Specifically, we focused on assessing gender-specific differences in the prevalence of pulmonary nodules, and the incidence of lung cancer diagnosis among never-smokers.

RESULTS

Among the 42,018 eligible participants, 41.50% were females and 58.50% were males. Most participants were non-smokers (77.57%), with a significantly higher proportion of non-smokers among females than males (99.33% vs. 62.14%). Pulmonary nodules were identified in 2.66% of participants, with a higher prevalence in females (2.99%) than males (2.43%) (p < 0.001). Non-smoking females had a higher incidence of positive nodules than non-smoking males (2.98% vs. 2.38%, p < 0.001). Invasive biopsies were performed in 334 individuals with nodules, confirming lung cancer in 258 cases. The majority of these cancer cases were non-smokers (212), with non-smoking females showing a higher incidence (0.85%) compared to males (0.43%) (p < 0.001). There was no significant difference in the false-positive rates between non-smoking females (0.14%) and males (0.13%). Multivariate analysis showed that never-smoking women were more likely to undergo biopsies (OR 1.65, p = 0.0016) and had a higher, though not statistically significant, probability of lung cancer diagnosis (OR 1.84, p = 0.06).

CONCLUSION

This study elucidates sex-based differences within the Chinese population, revealing a higher prevalence of pulmonary nodules and lung cancers among non-smoking females. These findings offer valuable reference for both clinical practice and future research initiatives.

Glycan Profiling in Small Extracellular Vesicles with a SERS Microfluidic Biosensor Identifies Early Malignant Development in Lung Cancer

Glycosylation is the most common post-translational modification of proteins and regulates a myriad of fundamental biological processes under normal, and pathological conditions. Altered protein glycosylation is linked to malignant transformation, showing distinct glycopatterns that are associated with cancer initiation and progression by regulating tumor proliferation, invasion, metastasis, and therapeutic resistance. The glycopatterns of small extracellular vesicles (sEVs) released by cancer cells are promising candidates for cancer monitoring since they exhibit glycopatterns similar to their cell-of-origin. However, the clinical application of sEV glycans is challenging due to the limitations of current analytical technologies in tracking the trace amounts of sEVs specifically derived from tumors in circulation. Herein, a sEV GLYcan PHenotype (EV-GLYPH) assay that utilizes a microfluidic platform integrated with surface-enhanced Raman scattering for multiplex profiling of sEV glycans in non-small cell lung cancer is clinically validated. For the first time, the EV-GLYPH assay effectively identifies distinct sEV glycan signatures between non-transformed and malignantly transformed lung cells. In a clinical study evaluated on 40 patients, the EV-GLYPH assay successfully differentiates patients with early-stage malignant lung nodules from benign lung nodules. These results reveal the potential to profile sEV glycans for noninvasive diagnostics and prognostics, opening up promising avenues for clinical applications and understanding the role of sEV glycosylation in lung cancer.

Clinical characteristics and prognosis of non-high-risk patients with incidental stage T1 lung cancer: A prospective cohort study

OBJECTIVES

There is currently no evidence documenting the clinical characteristics and prognosis of non-high-risk patients with incidental stage T1 lung cancer (LC). The aim of this study was to investigate the clinical characteristics and prognosis of non-high-risk patients with incidental stage T1 LC.

METHODS

This prospective cohort study included patients with incidental stage T1 LC who were diagnosed pathologically at the First Affiliated Hospital of Chongqing Medical University between 1st Jan 2019 and 31st Dec 2023. The follow-up time for all participants concluded on 31st Jan 2024, or upon death. All included patients were divided into non-high-risk (observation) and high-risk (control) groups based on the 2021 US preventative services task force recommendations. The primary outcomes were overall survival probability and LC-specific survival probability. The secondary outcomes were clinical characteristics, including demographic variables, histological types and TNM staging.

RESULTS

We studied 1876 patients with incidental stage T1 LC. Of these, 1491 (79.48%) non-high-risk patients were included in the observation group, and the remaining 385 (20.52%) high-risk patients composed the control group. The follow-up interval was between 0 and 248 months for all participants, with a median time of 41.64 ± 23.85 months. The patients in the observation group were younger and had smaller tumors, more adenocarcinomas, and earlier disease stages than those in the control group (p ≤ 0.001). The overall survival probability (HR = 0.23, [95% CI: 0.18, 0.31], p < 0.001) and the LC-specific survival probability (HR = 0.23, [95% CI: 0.17, 0.31], p < 0.001) for the patients in the observation group were also both higher than those in the control group. The results appeared to be consistent across important subgroups.

CONCLUSION

In this study, non-high-risk patients with incidental stage T1 LC were younger, had smaller tumors, had more adenocarcinomas, had a lower probability of metastasis, and had longer survival than did high-risk patients.

Autoantibodies in cancer: a systematic review of their clinical role in the most prevalent cancers

Although blood autoantibodies were initially associated with autoimmune diseases, multiple evidence have been accumulated showing their presence in many types of cancer. This has opened their use in clinics, since cancer autoantibodies might be useful for early detection, prognosis, and monitoring of cancer patients. In this review, we discuss the different techniques available for their discovery and validation. Additionally, we discuss here in detail those autoantibody panels verified in at least two different reports that should be more likely to be specific of each of the four most incident cancers. We also report the recent developed kits for breast and lung cancer detection mostly based on autoantibodies and the identification of novel therapeutic targets because of the screening of the cancer humoral immune response. Finally, we discuss unsolved issues that still need to be addressed for the implementation of cancer autoantibodies in clinical routine for cancer diagnosis, prognosis, and/or monitoring.

Construction of lung cancer serum markers based on ReliefF feature selection

Serum miRNAs are available clinical samples for cancer screening. Identifying early serum markers in lung cancer (LC) is essential for patients' early diagnosis and clinical treatment. Expression data of serum miRNAs of lung adenocarcinoma (LUAD) patients and healthy individuals were downloaded from the Gene Expression Omnibus (GEO). These data were normalized and subjected to differential expression analysis to obtain differentially expressed miRNAs (DEmiRNAs). The DEmiRNAs were subsequently subjected to ReliefF feature selection, and subsets closely related to cancer were screened as candidate feature miRNAs. Thereafter, a Gaussian Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifier were constructed based on these candidate feature miRNAs. Then the best diagnostic signature was constructed through NB combined with incremental feature selection (IFS). Thereafter, these samples were subjected to principal component analysis (PCA) based on miRNAs with optimal predictive performance. Finally, the peripheral serum miRNAs of 64 LUAD patients and 59 normal individuals were extracted for qRT-PCR analysis to validate the performance of the diagnostic model in respect of clinical detection. Finally, according to area under the curve (AUC) and accuracy values, the NB classifier composed of miR-5100 and miR-663a manifested the most outstanding diagnostic performance. The PCA results also revealed that the 2-miRNA diagnostic signature could effectively distinguish cancer patients from healthy individuals. Finally, qRT-PCR results of clinical serum samples revealed that miR-5100 and miR-663a expression in tumor samples was remarkably higher than that in normal samples. The AUC of the 2-miRNA diagnostic signature was 0.968. In summary, we identified markers (miR-5100 and miR-663a) in serum for early LUAD screening, providing ideas for developing early LUAD diagnostic models.

Early Detection and Interception of Lung Cancer

Recent advances in lung cancer treatment have led to dramatic improvements in 5-year survival rates. And yet, lung cancer remains the leading cause of cancer-related mortality, in large part, because it is often diagnosed at an advanced stage, when cure is no longer possible. Lung cancer screening (LCS) is essential for intercepting the disease at an earlier stage. Unfortunately, LCS has been poorly adopted in the United States, with less than 5% of eligible patients being screened nationally. This article will describe the data supporting LCS, the obstacles to LCS implementation, and the promising opportunities that lie ahead.

Participant factors associated with psychosocial impacts of lung cancer screening: A systematic review

BACKGROUND

Psychosocial impacts of lung cancer screening (LCS) can cause both harm to individuals and serve as barriers to screening participation and adherence. Early data suggest that the psychosocial impacts of LCS are moderated by certain factors (e.g. sociodemographic characteristics and beliefs), but evidence synthesis is lacking. This systematic review aimed to understand individual-level risk factors for psychosocial burden during LCS as a precursor to developing strategies to identify and support participants, and improve LCS engagement.

METHODS

Four databases were searched for full-text articles published in English reporting any association between participant factors and psychosocial outcomes experienced during LCS. Study quality was assessed by two independent investigators; findings were synthesised narratively. The review was pre-registered with PROSPERO and adhered to PRISMA guidelines.

RESULTS

Thirty-five articles were included; most (33/35) studies were assessed at high or moderate risk of bias. Study designs were pre-post (n = 13), cross-sectional (n = 13), qualitative (n = 8) and mixed-methods (n = 1) and conducted primarily in the United States (n = 17). Psychological burden in LCS varied, and was often associated with younger age, female gender, current smoking status or increased smoking history, lower education, lower socio-economic group, not being married or co-habiting and experience with cancer. However, results were mixed, and non-significant associations were also reported across all factors. Beliefs (e.g. fatalism, stigma and expectation of LDCT results) and comorbid psychological burden were also linked to psychosocial outcomes, but evidence was sparse. Associations between risk perception, other participant factors and other psychosocial outcomes was inconclusive, likely reflecting individual biases in risk conceptualisation.

CONCLUSION(S)

Several participant factors are consistently reported to be associated with psychosocial impacts of LCS, though study heterogeneity and high risk of bias necessitate more robust evaluation. Further research on how perceptions, beliefs and expectations can be used to improve psychosocial outcomes during LCS is needed.

Distribution of Solid Lung Nodules Presence and Size by Age and Sex in a Northern European Nonsmoking Population

Background Most of the data regarding prevalence and size distribution of solid lung nodules originates from lung cancer screening studies that target high-risk populations or from Asian general cohorts. In recent years, the identification of lung nodules in non-high-risk populations, scanned for clinical indications, has increased. However, little is known about the presence of solid lung nodules in the Northern European nonsmoking population. Purpose To study the prevalence and size distribution of solid lung nodules by age and sex in a nonsmoking population. Materials and Methods Participants included nonsmokers (never or former smokers) from the population-based Imaging in Lifelines study conducted in the Northern Netherlands. Participants (age ≥ 45 years) with completed lung function tests underwent chest low-dose CT scans. Seven trained readers registered the presence and size of solid lung nodules measuring 30 mm3 or greater using semiautomated software. The prevalence and size of lung nodules (≥30 mm3), clinically relevant lung nodules (≥100 mm3), and actionable nodules (≥300 mm3) are presented by 5-year categories and by sex. Results A total of 10 431 participants (median age, 60.4 years [IQR, 53.8-70.8 years]; 56.6% [n = 5908] female participants; 46.1% [n = 4812] never smokers and 53.9% [n = 5619] former smokers) were included. Of these, 42.0% (n = 4377) had at least one lung nodule (male participants, 47.5% [2149 of 4523]; female participants, 37.7% [2228 of 5908]). The prevalence of lung nodules increased from age 45-49.9 years (male participants, 39.4% [219 of 556]; female participants, 27.7% [236 of 851]) to age 80 years or older (male participants, 60.7% [246 of 405]; female participants, 50.9% [163 of 320]). Clinically relevant lung nodules were present in 11.1% (1155 of 10 431) of participants, with prevalence increasing with age (male participants, 8.5%-24.4%; female participants, 3.7%-15.6%), whereas actionable nodules were present in 1.1%-6.4% of male participants and 0.6%-4.9% of female participants. Conclusion Lung nodules were present in a substantial proportion of all age groups in the Northern European nonsmoking population, with slightly higher prevalence for male participants than female participants. © RSNA, 2024 Supplemental material is available for this article.

Prevalence and management of pulmonary nodules: a systematic review and meta-analysis

Background

Pulmonary nodules are small, focal lesions often identified via computed tomography (CT) scans. Although the majority are benign, a small percentage of them may be malignant or potentially become malignant, underscoring the importance of early detection and effective management. This study systematically reviews the epidemiology, risk factors, and management strategies for pulmonary nodules, comparing findings across Chinese and non-Chinese populations to better inform the actuarial calculations for predicting the demand of medical services for patients with pulmonary nodules.

Methods

We performed a systematic analysis of the PubMed and China Knowledge Infrastructure (CNKI) databases for studies reporting the detection rate of pulmonary nodules through CT scans. Both cross-sectional studies and the baseline data from longitudinal studies were included. A modified version of the Newcastle-Ottawa Scale was used to assess the risk of bias and random effect models were used to estimate the overall prevalence.

Results

We identified 32 studies and included 24 of them in our meta-analysis. Pooled analysis showed that the overall prevalence of pulmonary nodules was 0.27 (95% confidence interval: 0.25-0.29) after outliers removal. Subgroup analysis showed that there was no significant difference for prevalence between Chinese and non-Chinese populations. Males (0.38) were shown to have slightly higher prevalence compared to females (0.36), but not significant (P=0.88). Age and smoking are the most frequently reported risk factors by studies.

Conclusions

Overall, 27% of participants were positive for pulmonary nodules. Advancing age and smoking were consistently identified as a key risk factor for the incidence of pulmonary nodules. Although the management strategies are different across studies, recent guidelines recommend personalized management strategies, prioritizing nodule size, characteristics, and individual risk factors to optimize outcomes.

The pros and cons of lung cancer screening

Several trials have shown that low-dose computed tomography-based lung cancer screening (LCS) allows a substantial reduction in lung cancer-related mortality, carrying the potential for other clinical benefits. There are, however, some uncertainties to be clarified and several aspects to be implemented to optimize advantages and minimize the potential harms of LCS. This review summarizes current evidence on LCS, discussing some of the well-established and potential benefits, including lung cancer (LC)-related mortality reduction and opportunity for smoking cessation interventions, as well as the disadvantages of LCS, such as overdiagnosis and overtreatment. CLINICAL RELEVANCE STATEMENT: Different perspectives are provided on LCS based on the updated literature. KEY POINTS: Lung cancer is a leading cancer-related cause of death and screening should reduce associated mortality. This review summarizes current evidence related to LCS. Several aspects need to be implemented to optimize benefits and minimize potential drawbacks of LCS.

Sucrose-Powered Liposome Nanosensors for Urinary Glucometer-Based Monitoring of Cancer

Timely detection of early-stage cancer holds immense potential in enhancing prognostic outcomes. There is an increasing desire for versatile tools to enable simple, sensitive, and cost-effective cancer detection. By exploiting the extraintestinal metabolic inertness and efficiency renal clearance of sucrose, we designed a liposome nanosensor using sucrose as a messenger to convert tumor-specific esterase activity into glucose meter readout, enabling economical and sensitive urinalysis for cancer detection in point-of-care testing (POCT). Our results demonstrate that the nanosensors exhibited significant signal differences between tumor-bearing and healthy mice in both orthotopic and metastatic tumor models. Additionally, efficient elimination of the nanosensors through the hepatobiliary pathway was observed with no significant toxicity. Such a non-invasive diagnostic modality significantly assists in personalized pharmacological treatment and follow-up efficacy assessment. We envision that this modular liposome nanosensor platform might be applied for economically detecting diverse diseases via a simple urinary test.

Advances in proteomics methods for the analysis of exhaled breath condensate

The analysis of exhaled breath condensate (EBC) demonstrates a promising avenue of minimally invasive biopsies for diagnostics. EBC is obtained by cooling exhaled air and collecting the condensation to be utilized for downstream analysis using various analytical methods. The aqueous phase of breath contains a large variety of miscible small compounds including polar electrolytes, amino acids, cytokines, chemokines, peptides, small proteins, metabolites, nucleic acids, and lipids/eicosanoids-however, these analytes are typically present at minuscule levels in EBC, posing a considerable technical challenge. Along with recent improvements in devices for breath collection, the sensitivity and resolution of liquid chromatography coupled to online mass spectrometry-based proteomics has attained subfemtomole sensitivity, vastly enhancing the quality of EBC sample analysis. As a result, proteomics analysis of EBC has been expanding the field of breath biomarker research. We present an au courant overview of the achievements in proteomics of EBC, the advancement of EBC collection devices, and the current and future applications for EBC biomarker analysis.

Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs

OBJECTIVES

To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs.

METHODS

To develop a deep learning-based prognostic model using chest radiographs (DLPM), the patients diagnosed with IPF during 2011-2021 were retrospectively collected and were divided into training (n = 1007), validation (n = 117), and internal test (n = 187) datasets. Up to 10 consecutive radiographs were included for each patient. For external testing, three cohorts from independent institutions were collected (n = 152, 141, and 207). The discrimination performance of DLPM was evaluated using areas under the time-dependent receiver operating characteristic curves (TD-AUCs) for 3-year survival and compared with that of forced vital capacity (FVC). Multivariable Cox regression was performed to investigate whether the DLPM was an independent prognostic factor from FVC. We devised a modified gender-age-physiology (GAP) index (GAP-CR), by replacing DLCO with DLPM.

RESULTS

DLPM showed similar-to-higher performance at predicting 3-year survival than FVC in three external test cohorts (TD-AUC: 0.83 [95% CI: 0.76-0.90] vs. 0.68 [0.59-0.77], p < 0.001; 0.76 [0.68-0.85] vs. 0.70 [0.60-0.80], p = 0.21; 0.79 [0.72-0.86] vs. 0.76 [0.69-0.83], p = 0.41). DLPM worked as an independent prognostic factor from FVC in all three cohorts (ps < 0.001). The GAP-CR index showed a higher 3-year TD-AUC than the original GAP index in two of the three external test cohorts (TD-AUC: 0.85 [0.80-0.91] vs. 0.79 [0.72-0.86], p = 0.02; 0.72 [0.64-0.80] vs. 0.69 [0.61-0.78], p = 0.56; 0.76 [0.69-0.83] vs. 0.68 [0.60-0.76], p = 0.01).

CONCLUSIONS

A deep learning model successfully predicted survival in patients with IPF from chest radiographs, comparable to and independent of FVC.

CLINICAL RELEVANCE STATEMENT

Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity.

KEY POINTS

• A deep learning-based prognostic model for idiopathic pulmonary fibrosis was developed using 6063 radiographs. • The prognostic performance of the model was comparable-to-higher than forced vital capacity, and was independent from FVC in all three external test cohorts. • A modified gender-age-physiology index replacing diffusing capacity for carbon monoxide with the deep learning model showed higher performance than the original index in two external test cohorts.

University of Pennsylvania Telehealth Research Center of Excellence

Drawing from insights from communication science and behavioral economics, the University of Pennsylvania Telehealth Research Center of Excellence (Penn TRACE) is designing and testing telehealth strategies with the potential to transform access to care, care quality, outcomes, health equity, and health-care efficiency across the cancer care continuum, with an emphasis on understanding mechanisms of action. Penn TRACE uses lung cancer care as an exemplar model for telehealth across the care continuum, from screening to treatment to survivorship. We bring together a diverse and interdisciplinary team of international experts and incorporate rapid-cycle approaches and mixed methods evaluation in all center projects. Our initiatives include a pragmatic sequential multiple assignment randomized trial to compare the effectiveness of telehealth strategies to increase shared decision-making for lung cancer screening and 2 pilot projects to test the effectiveness of telehealth to improve cancer care, identify multilevel mechanisms of action, and lay the foundation for future pragmatic trials. Penn TRACE aims to produce new fundamental knowledge and advance telehealth science in cancer care at Penn and nationally.

Health Economic Evaluation of Lung Cancer Screening Using a Diagnostic Blood Test: The Early Detection of Cancer of the Lung Scotland (ECLS)

BACKGROUND

Diagnostic blood tests have the potential to identify lung cancer in people at high risk. We assessed the cost-effectiveness of a lung cancer screening intervention, using the EarlyCDT®-Lung Test (ECLS) with subsequent X-ray and low-dose chest CT scans (LDCT) for patients with a positive test result, compared to both usual care and LDCT screening for the target population.

METHODS

We conducted a model-based lifetime analysis from a UK NHS and personal social services perspective. We estimated incremental net monetary benefit (NMB) for the ECLS intervention compared to no screening and to LDCT screening.

RESULTS

The incremental NMB of ECLS intervention compared to no screening was GBP 33,179 (95% CI: -GBP 81,396, GBP 147,180) and GBP 140,609 (95% CI: -GBP 36,255, GBP 316,612), respectively, for a cost-effectiveness threshold of GBP 20,000 and GBP 30,000 per quality-adjusted life year. The same figures compared with LDCT screening were GBP 162,095 (95% CI: GBP 52,698, GBP 271,735) and GBP 52,185 (95% CI: -GBP 115,152, GBP 219,711).

CONCLUSIONS

The ECLS intervention is the most cost-effective screening alternative, with the highest probability of being cost-effective, when compared to no screening or LDCT screening. This result may change with modifications of the parameters, suggesting that the three alternatives considered in the main analysis are potentially cost-effective.

Increased Utilization of Low-Dose CT for Lung Cancer Screening at an Arkansas Community Oncology Clinic

BACKGROUND

Low-dose CT (LDCT) is underused in Arkansas for lung cancer screening, a rural state with a high incidence of lung cancer. The objective was to determine whether offering free LDCT increased the number of high-risk individuals screened in a rural catchment area.

METHODS

There were 5,402 patients enrolled in screening at Highlands Oncology, a community oncology clinic in Northwest Arkansas, from 2013 to 2020. Screenings were separated into time periods: period 1 (10 months for-fee), period 2 (10 months free with targeted advertisements and primary care outreach), and period 3 (62 months free with only primary care outreach). In all, 5,035 high-risk participants were eligible for analysis based on National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology. Enrollment rates, incidence densities (IDs), Cox proportional hazard models, and Kaplan-Meier curves were performed to investigate differences between enrollment periods and high-risk groups.

RESULTS

Patient volume increased drastically once screenings were offered free of charge (period 1 = 4.6 versus period 2 = 66.0 and period 3 = 69.8 average patients per month). Incidence density per 1,000 person-years increased through each period (IDPeriod 1 = 17.2; IDPeriod 2 = 20.8; IDPeriod 3 = 25.5 cases). Cox models revealed significant differences in lung cancer risk between high-risk groups (P = .012) but not enrollment periods (P = .19). Kaplan-Meier lung cancer-free probabilities differed significantly between high-risk groups (log-rank P = .00068) but not enrollment periods (log-rank P = .18).

CONCLUSIONS

This study suggests that eligible patients are more receptive to free LDCT screening, despite most insurances not having a required copay for eligible patients.

An analysis of the influencing factors of false negative autoantibodies in patients with non-small cell lung cancer

Objectives

To analyze the clinical significance of seven autoantibodies (P53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGE, and CAGE) in patients with non-small cell lung cancer (NSCLC) and the factors that influence false-negative results.

Methods

Seven autoantibodies were measured in the serum of 502 patients with non-small cell lung cancer (NSCLC) using ELISA, and their correlations with age, sex, smoking history, pathological type, clinical stage, and PD-L1 gene expression were analyzed. The clinicopathological data of the false-negative and positive groups for the seven autoantibodies were compared to determine the influencing factors.

Results

P53 antibody expression level was correlated with lobulation sign, PGP9.5 antibody expression level with sex and vascular convergence; SOX2 antibody expression level with pathological type, clinical stage, and enlarged lymph nodes; and MAGE antibody expression level with the pathological type (P<0.05). False-negative autoantibodies are prone to occur in lung cancer patients with ground-glass nodules, no enlarged lymph nodes, no vascular convergence, and PD-L1 gene expression <1% (P <0.05).

Conclusion

Detection of seven autoantibodies was clinically significant in patients with NSCLC. However, poor sensitivity should be considered in clinical diagnoses to prevent missed diagnoses.

Primary lung cancer in children and adolescents

PURPOSE

Primary lung cancer is extremely rare in children and adolescents. The aim of this study is to clarify clinical features and outcomes of primary lung cancer in children and adolescents.

METHODS

Young patients (aged ≤ 20 years) diagnosed as primary lung cancer between 2012 and 2023 were retrospective reviewed. According to radiological appearance of the nodules, they were divided into solid nodule (SN) group and ground glass opacity (GGO) group.

RESULTS

A total of 74 patients were identified, with a median age at diagnosis of 18 years old (range: 11-20), including 7 patients in SN group and 67 patients in GGO group. In the GGO group, none of the nodules enlarged or changed during an average surveillance period of 10.8 months before surgery, except one. Wedge resection was the most common procedure (82.1%), followed by segmentectomy (16.4%) and lobectomy (1.5%). Histopathological analysis revealed that 64.2% of GGO nodules were adenocarcinoma in situ and minimally invasive adenocarcinomas, while the remaining 35.8% were invasive adenocarcinomas. Mutational analysis was performed in nine patients, with mutations identified in all cases. After a mean follow-up period of 1.73 ± 1.62 years, two patients in the SN group died due to multiple distant metastases, while all patients in the GGO group survived without recurrence. The overall survival (100%) of the GGO group was significantly higher than SN group (66.7%).

CONCLUSIONS

Primary lung cancer in children and adolescents are rare and histopathological heterogeneous. Persistent GGO nodules may indicate early-stage lung adenocarcinoma in children and adolescents.

Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning

BACKGROUND

Lung cancer is the leading cause of cancer-related death worldwide. This study aimed to establish novel multiclassification prediction models based on machine learning (ML) to predict the probability of malignancy in pulmonary nodules (PNs) and to compare with three published models.

METHODS

Nine hundred fourteen patients with PNs were collected from four medical institutions (A, B, C and D), which were organized into tables containing clinical features, radiologic features and laboratory test features. Patients were divided into benign lesion (BL), precursor lesion (PL) and malignant lesion (ML) groups according to pathological diagnosis. Approximately 80% of patients in A (total/male: 632/269, age: 57.73 ± 11.06) were randomly selected as a training set; the remaining 20% were used as an internal test set; and the patients in B (total/male: 94/53, age: 60.04 ± 11.22), C (total/male: 94/47, age: 59.30 ± 9.86) and D (total/male: 94/61, age: 62.0 ± 11.09) were used as an external validation set. Logical regression (LR), decision tree (DT), random forest (RF) and support vector machine (SVM) were used to establish prediction models. Finally, the Mayo model, Peking University People's Hospital (PKUPH) model and Brock model were externally validated in our patients.

RESULTS

The AUC values of RF model for MLs, PLs and BLs were 0.80 (95% CI: 0.73-0.88), 0.90 (95% CI: 0.82-0.99) and 0.75 (95% CI: 0.67-0.88), respectively. The weighted average AUC value of the RF model for the external validation set was 0.71 (95% CI: 0.67-0.73), and its AUC values for MLs, PLs and BLs were 0.71 (95% CI: 0.68-0.79), 0.98 (95% CI: 0.88-1.07) and 0.68 (95% CI: 0.61-0.74), respectively. The AUC values of the Mayo model, PKUPH model and Brock model were 0.68 (95% CI: 0.62-0.74), 0.64 (95% CI: 0.58-0.70) and 0.57 (95% CI: 0.49-0.65), respectively.

CONCLUSIONS

The RF model performed best, and its predictive performance was better than that of the three published models, which may provide a new noninvasive method for the risk assessment of PNs.

Characteristics of Singapore lung cancer patients who miss out on lung cancer screening recommendations

INTRODUCTION

The National Lung Screening Trial (NLST) identified individuals at high risk for lung cancer and showed that serial low-dose helical computed tomography could identify lung cancer at an earlier stage, leading to mortality reduction. However, there is little evidence regarding the effectiveness of the NLST criteria for the Asian population.

METHODS

We performed a retrospective audit in our hospital from January 2018 to December 2018, with the aim to describe the characteristics of patients diagnosed with lung cancer and to identify patients who would miss out on lung cancer screening when the NLST criteria was applied.

RESULTS

We found that only 38.1% of our cohort who were diagnosed with lung cancer met the NLST criteria strictly by age and smoking status. Patients who met the screening criteria would have derived significant benefits from it, as 85.4% of our patients had presented at an advanced stage and 54.6% died within 1 year. When the United States Preventive Services Task Force criteria was applied, it increased the sensitivity of lung cancer diagnosis to 58.7%. Only 15.5% of the female patients with lung cancer met the NLST criteria; their low smoking quantity was a significant contributing factor for exclusion.

CONCLUSION

The majority of Singapore patients diagnosed with lung cancer, especially females, would not have been identified with the NLST criteria. However, those who met the inclusion criteria would have benefited greatly from screening. Extending the screening age upper limit may yield benefits and improved sensitivity in the Singapore context.

Safety net hospital risk model demonstrates stronger, population-specific applicability in characterizing lung cancer risk

Background

Determining lung cancer (LC) risk using personalized risk stratification may improve screening effectiveness. While the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) is a well-established stratification model for LC screening, it was derived from a predominantly Caucasian population and its effectiveness in a safety net hospital (SNH) population is unknown. We have developed a model more tailored to the SNH population and compared its performance to the PLCO model in a SNH setting.

Methods

Retrospective dataset was compiled from patients screened for LC at SNH from 2015 to 2019. Descriptive statistics were calculated using the following variables: age, sex, race, education, body mass index (BMI), smoking history, personal cancer history, family LC history, chronic obstructive pulmonary disease (COPD), and emphysema. Variables distribution was compared using t- and chi-square tests. LC risk scores were calculated using SNH and PLCO models and categorized as low (scores <0.65%), moderate (0.65-1.49%), and high (>1.5%). Linear regression was applied to evaluate the relationship between models and covariates.

Results

Of 896 individuals, 38 were diagnosed with LC. Data reflected the SNH patient demographics, which predominantly were African American (53.5%), current smokers (69.9%), and with emphysema (70.1%). Among the non-LC cohort, SNH model most frequently categorized patients as low risk, while PLCO model most frequently classified patients as moderate risk. Among the LC cohort, there was no significant difference between mean scores or risk stratification. SNH model showed 92.1% sensitivity and 96.8% specificity while PLCO model showed 89.4% sensitivity and 26.1% specificity. Emphysema demonstrated a strong association in SNH model (P<0.001) while race showed no relation.

Conclusions

SNH model demonstrated greater specificity for characterizing LC risk in a SNH population. The results demonstrated the importance of study sample representation when identifying risk factors in a stratification model.

Risk-based lung cancer screening performance in a universal healthcare setting

Globally, lung cancer is the leading cause of cancer death. Previous trials demonstrated that low-dose computed tomography lung cancer screening of high-risk individuals can reduce lung cancer mortality by 20% or more. Lung cancer screening has been approved by major guidelines in the United States, and over 4,000 sites offer screening. Adoption of lung screening outside the United States has, until recently, been slow. Between June 2017 and May 2019, the Ontario Lung Cancer Screening Pilot successfully recruited 7,768 individuals at high risk identified by using the PLCOm2012noRace lung cancer risk prediction model. In total, 4,451 participants were successfully screened, retained and provided with high-quality follow-up, including appropriate treatment. In the Ontario Lung Cancer Screening Pilot, the lung cancer detection rate and the proportion of early-stage cancers were 2.4% and 79.2%, respectively; serious harms were infrequent; and sensitivity to detect lung cancers was 95.3% or more. With abnormal scans defined as ones leading to diagnostic investigation, specificity was 95.5% (positive predictive value, 35.1%), and adherence to annual recall and early surveillance scans and clinical investigations were high (>85%). The Ontario Lung Cancer Screening Pilot provides insights into how a risk-based organized lung screening program can be implemented in a large, diverse, populous geographic area within a universal healthcare system.

Lipidomics reveals new lipid-based lung adenocarcinoma early diagnosis model

Lung adenocarcinoma (LUAD) continues to pose a significant mortality risk with a lack of dependable biomarkers for early noninvasive cancer detection. Here, we find that aberrant lipid metabolism is significantly enriched in lung cancer cells. Further, we identified four signature lipids highly associated with LUAD and developed a lipid signature-based scoring model (LSRscore). Evaluation of LSRscore in a discovery cohort reveals a robust predictive capability for LUAD (AUC: 0.972), a result further validated in an independent cohort (AUC: 0.92). We highlight one lipid signature biomarker, PE(18:0/18:1), consistently exhibiting altered levels both in cancer tissue and in plasma of LUAD patients, demonstrating significant predictive power for early-stage LUAD. Transcriptome analysis reveals an association between increased PE(18:0/18:1) levels and dysregulated glycerophospholipid metabolism, which consistently displays strong prognostic value across two LUAD cohorts. The combined utility of LSRscore and PE(18:0/18:1) holds promise for early-stage diagnosis and prognosis of LUAD.

Detection of lung cancer and stages via breath analysis using a self-made electronic nose device

BACKGROUND

Breathomics is an emerging area focusing on monitoring and diagnosing pulmonary diseases, especially lung cancer. This research aims to employ metabolomic methods to create a breathprint in human-expelled air to rapidly identify lung cancer and its stages.

RESEARCH DESIGN AND METHODS

An electronic nose (e-nose) system with five metal oxide semiconductor (MOS) gas sensors, a microcontroller, and machine learning algorithms was designed and developed for this application. The volunteers in this study include 114 patients with lung cancer and 147 healthy controls to understand the clinical potential of the e-nose system to detect lung cancer and its stages.

RESULTS

In the training phase, in discriminating lung cancer from controls, the XGBoost classifier model with 10-fold cross-validation gave an accuracy of 91.67%. In the validation phase, the XGBoost classifier model correctly identified 35 out of 42 patients with lung cancer samples and 44 out of 51 healthy control samples providing an overall sensitivity of 83.33% and specificity of 86.27%.

CONCLUSIONS

These results indicate that the exhaled breath VOC analysis method may be developed as a new diagnostic tool for lung cancer detection. The advantages of e-nose based diagnostics, such as an easy and painless method of sampling, and low-cost procedures, will make it an excellent diagnostic method in the future.

Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches

BACKGROUND

Low-dose CT screening can reduce lung cancer-related mortality. However, most screen-detected pulmonary abnormalities do not develop into cancer and it often remains challenging to identify malignant nodules, particularly among indeterminate nodules. We aimed to develop and assess prediction models based on radiological features to discriminate between benign and malignant pulmonary lesions detected on a baseline screen.

METHODS

Using four international lung cancer screening studies, we extracted 2060 radiomic features for each of 16 797 nodules (513 malignant) among 6865 participants. After filtering out low-quality radiomic features, 642 radiomic and 9 epidemiological features remained for model development. We used cross-validation and grid search to assess three machine learning (ML) models (eXtreme Gradient Boosted Trees, random forest, least absolute shrinkage and selection operator (LASSO)) for their ability to accurately predict risk of malignancy for pulmonary nodules. We report model performance based on the area under the curve (AUC) and calibration metrics in the held-out test set.

RESULTS

The LASSO model yielded the best predictive performance in cross-validation and was fit in the full training set based on optimised hyperparameters. Our radiomics model had a test-set AUC of 0.93 (95% CI 0.90 to 0.96) and outperformed the established Pan-Canadian Early Detection of Lung Cancer model (AUC 0.87, 95% CI 0.85 to 0.89) for nodule assessment. Our model performed well among both solid (AUC 0.93, 95% CI 0.89 to 0.97) and subsolid nodules (AUC 0.91, 95% CI 0.85 to 0.95).

CONCLUSIONS

We developed highly accurate ML models based on radiomic and epidemiological features from four international lung cancer screening studies that may be suitable for assessing indeterminate screen-detected pulmonary nodules for risk of malignancy.

Development, validation, and simplification of a scanner-specific CT simulator

BACKGROUND

Simulated computed tomography (CT) images allow for knowledge of the underlying ground truth and for easy variation of imaging conditions, making them ideal for testing and optimization of new applications or algorithms. However, simulating all processes that affect CT images can result in simulations that are demanding in terms of processing time and computer memory. Therefore, it is of interest to determine how much the simulation can be simplified while still achieving realistic results.

PURPOSE

To develop a scanner-specific CT simulation using physics-based simulations for the position-dependent effects and shift-invariant image corruption methods for the detector effects. And to investigate the impact on image realism of introducing simplifications in the simulation process that lead to faster and less memory-demanding simulations.

METHODS

To make the simulator realistic and scanner-specific, the spatial resolution and noise characteristics, and the exposure-to-detector output relationship of a clinical CT system were determined. The simulator includes a finite focal spot size, raytracing of the digital phantom, gantry rotation during projection acquisition, and finite detector element size. Previously published spectral models were used to model the spectrum for the given tube voltage. The integrated energy at each element of the detector was calculated using the Beer-Lambert law. The resulting angular projections were subsequently corrupted by the detector modulation transfer function (MTF), and by addition of noise according to the noise power spectrum (NPS) and signal mean-variance relationship, which were measured for different scanner settings. The simulated sinograms were reconstructed on the clinical CT system and compared to real CT images in terms of CT numbers, noise magnitude using the standard deviation, noise frequency content using the NPS, and spatial resolution using the MTF throughout the field of view (FOV). The CT numbers were validated using a multi-energy CT phantom, the noise magnitude and frequency were validated with a water phantom, and the spatial resolution was validated with a tungsten wire. These metrics were compared at multiple scanner settings, and locations in the FOV. Once validated, the simulation was simplified by reducing the level of subsampling of the focal spot area, rotation and of detector pixel size, and the changes in MTFs were analyzed.

RESULTS

The average relative errors for spatial resolution within and across image slices, noise magnitude, and noise frequency content within and across slices were 3.4%, 3.3%, 4.9%, 3.9%, and 6.2%, respectively. The average absolute difference in CT numbers was 10.2 HU and the maximum was 22.5 HU. The simulation simplification showed that all subsampling can be avoided, except for angular, while the error in frequency at 10% MTF would be maximum 16.3%.

CONCLUSION

The simulation of a scanner-specific CT allows for the generation of realistic CT images by combining physics-based simulations for the position-dependent effects and image-corruption methods for the shift-invariant ones. Together with the available ground truth of the digital phantom, it results in a useful tool to perform quantitative analysis of reconstruction or post-processing algorithms. Some simulation simplifications allow for reduced time and computer power requirements with minimal loss of realism.

A literature review of a meta-analysis of BRAF mutations in non-small cell lung cancer

BACKGROUND

The research on the relationship between the Braf Proto-oncogene (BRAF) mutation and lung cancer has generated conflicting findings. Nevertheless, there is an argument suggesting that assessing the BRAF status could offer benefits in terms of managing and prognosing individuals with non-small cell lung cancer (NSCLC). To present a comprehensive overview of this subject, we undertook an up-to-date meta-analysis of pertinent publications.

METHODS

We conducted an extensive literature search utilizing Medical Subject Headings keywords, namely "BRAF", "mutation", "lung", "tumor", "NSCLC", and "neoplasm", across multiple databases, including PubMed, EMBASE, ISI Science Citation Index, and CNKI. For each study, we calculated and evaluated the odds ratio and confidence interval, focusing on the consistency of the eligible research.

RESULTS

The meta-analysis unveiled a noteworthy correlation between BRAF mutation and lung cancer. No significant evidence was found regarding the connection between smoking and staging among individuals with BRAF mutations. Furthermore, a substantial disparity in the rate of BRAF mutations was observed between males and females.

CONCLUSION

Our meta-analysis revealed a significant correlation between BRAF mutations and NSCLC. Moreover, we observed a higher incidence of BRAF lung mutations in females compared to males. Additionally, the BRAFV600E mutation was found to be more prevalent among female patients and nonsmokers.

Evaluation of Pulmonary Nodules by Radiologists vs. Radiomics in Stand-Alone and Complementary CT and MRI

Increased attention has been given to MRI in radiation-free screening for malignant nodules in recent years. Our objective was to compare the performance of human readers and radiomic feature analysis based on stand-alone and complementary CT and MRI imaging in classifying pulmonary nodules. This single-center study comprises patients with CT findings of pulmonary nodules who underwent additional lung MRI and whose nodules were classified as benign/malignant by resection. For radiomic features analysis, 2D segmentation was performed for each lung nodule on axial CT, T2-weighted (T2w), and diffusion (DWI) images. The 105 extracted features were reduced by iterative backward selection. The performance of radiomics and human readers was compared by calculating accuracy with Clopper-Pearson confidence intervals. Fifty patients (mean age 63 +/- 10 years) with 66 pulmonary nodules (40 malignant) were evaluated. ACC values for radiomic features analysis vs. radiologists based on CT alone (0.68; 95%CI: 0.56, 0.79 vs. 0.59; 95%CI: 0.46, 0.71), T2w alone (0.65; 95%CI: 0.52, 0.77 vs. 0.68; 95%CI: 0.54, 0.78), DWI alone (0.61; 95%CI:0.48, 0.72 vs. 0.73; 95%CI: 0.60, 0.83), combined T2w/DWI (0.73; 95%CI: 0.60, 0.83 vs. 0.70; 95%CI: 0.57, 0.80), and combined CT/T2w/DWI (0.83; 95%CI: 0.72, 0.91 vs. 0.64; 95%CI: 0.51, 0.75) were calculated. This study is the first to show that by combining quantitative image information from CT, T2w, and DWI datasets, pulmonary nodule assessment through radiomics analysis is superior to using one modality alone, even exceeding human readers' performance.

Diagnostic Potential of Serum Glycome Analysis in Lung Cancer: A Glycopattern Study

Lung cancer is the leading cause of cancer-related death, with high morbidity and mortality rates due to the lack of reliable methods for diagnosing lung cancer at an early stage. Low-dose computed tomography can help detect abnormal areas in the lungs, but only 16% of cases are diagnosed early. Tests for lung cancer markers are often employed to determine genetic expression or mutations in lung carcinogenesis. Serum glycome analysis is a promising new method for early lung cancer diagnosis as glycopatterns exhibit significant differences in lung cancer patients. In this study, we employed a solid-phase chemoenzymatic method to systematically compare glycopatterns in benign cases, adenocarcinoma before and after surgery, and advanced stages of adenocarcinoma. Our findings indicate that serum high-mannose levels are elevated in both benign cases and adenocarcinoma, while complex N-glycans, including fucose and 2,6-linked sialic acid, are downregulated in the serum. Subsequently, we developed an algorithm that utilizes 16 altered N-glycans, 7 upregulated and 9 downregulated, to generate a score based on their intensity. This score can predict the stages of cancer progression in patients through glycan characterization. This methodology offers a potential means of diagnosing lung cancer through serum glycome analysis.

Rates of Downstream Procedures and Complications Associated With Lung Cancer Screening in Routine Clinical Practice : A Retrospective Cohort Study

BACKGROUND

Lung cancer screening (LCS) using low-dose computed tomography (LDCT) reduces lung cancer mortality but can lead to downstream procedures, complications, and other potential harms. Estimates of these events outside NLST (National Lung Screening Trial) have been variable and lacked evaluation by screening result, which allows more direct comparison with trials.

OBJECTIVE

To identify rates of downstream procedures and complications associated with LCS.

DESIGN

Retrospective cohort study.

SETTING

5 U.S. health care systems.

PATIENTS

Individuals who completed a baseline LDCT scan for LCS between 2014 and 2018.

MEASUREMENTS

Outcomes included downstream imaging, invasive diagnostic procedures, and procedural complications. For each, absolute rates were calculated overall and stratified by screening result and by lung cancer detection, and positive and negative predictive values were calculated.

RESULTS

Among the 9266 screened patients, 1472 (15.9%) had a baseline LDCT scan showing abnormalities, of whom 140 (9.5%) were diagnosed with lung cancer within 12 months (positive predictive value, 9.5% [95% CI, 8.0% to 11.0%]; negative predictive value, 99.8% [CI, 99.7% to 99.9%]; sensitivity, 92.7% [CI, 88.6% to 96.9%]; specificity, 84.4% [CI, 83.7% to 85.2%]). Absolute rates of downstream imaging and invasive procedures in screened patients were 31.9% and 2.8%, respectively. In patients undergoing invasive procedures after abnormal findings, complication rates were substantially higher than those in NLST (30.6% vs. 17.7% for any complication; 20.6% vs. 9.4% for major complications).

LIMITATION

Assessment of outcomes was retrospective and was based on procedural coding.

CONCLUSION

The results indicate substantially higher rates of downstream procedures and complications associated with LCS in practice than observed in NLST. Diagnostic management likely needs to be assessed and improved to ensure that screening benefits outweigh potential harms.

PRIMARY FUNDING SOURCE

National Cancer Institute and Gordon and Betty Moore Foundation.

Screening approaches for lung cancer by blood-based biomarkers: Challenges and opportunities

Lung cancer (LC) is one of the leading causes for cancer-related deaths in the world, accounting for 28% of all cancer deaths in Europe. Screening for lung cancer can enable earlier detection of LC and reduce lung cancer mortality as was demonstrated in several large image-based screening studies such as the NELSON and the NLST. Based on these studies, screening is recommended in the US and in the UK a targeted lung health check program was initiated. In Europe lung cancer screening (LCS) has not been implemented due to limited data on cost-effectiveness in the different health care systems and questions on for example the selection of high-risk individuals, adherence to screening, management of indeterminate nodules, and risk of overdiagnosis. Liquid biomarkers are considered to have a high potential to address these questions by supporting pre- and post- Low Dose CT (LDCT) risk-assessment thereby improving the overall efficacy of LCS. A wide variety of biomarkers, including cfDNA, miRNA, proteins and inflammatory markers have been studied in the context of LCS. Despite the available data, biomarkers are currently not implemented or evaluated in screening studies or screening programs. As a result, it remains an open question which biomarker will actually improve a LCS program and do this against acceptable costs. In this paper we discuss the current status of different promising biomarkers and the challenges and opportunities of blood-based biomarkers in the context of lung cancer screening.

Benefit-to-radiation-risk of low-dose computed tomography lung cancer screening

BACKGROUND

The US National Lung Screening Trial (NLST) and Dutch-Belgian NELSON randomized controlled trials have shown significant mortality reductions from low-dose computed tomography (CT) lung cancer screening (LCS). NLST, ITALUNG, and COSMOS trials have provided detailed dosimetry data for LCS.

METHODS

LCS trial mortality benefit results, organ dose and effective dose data, and Biological Effects of Ionizing Radiation, Report VII (BEIR VII) organ dose-to-cancer-mortality risk data are used to estimate benefit-to-radiation-risk ratios of the NLST, ITALUNG, and COSMOS trials. Data from those trials also are used to estimate benefit-to-radiation-risk ratios for longer-term LCS corresponding to scenarios recommended by United States Preventive Services Task Force and the American Cancer Society.

RESULTS

Including only screening doses, NLST benefit-to-radiation-risk ratios are 12:1 for males, 19:1 for females, and 16:1 overall. Including both screening and estimated follow-up doses, benefit-to-radiation-risk ratios for NLST are 9:1 for males, 13:1 for females, and 12:1 overall. For the ITALUNG trial, the benefit-to-radiation-risk ratio is 58-63:1. For the COSMOS trial, assuming sex-specific mortality benefits like those of the NELSON trial, the benefit-to-radiation-risk ratio is 23:1. Assuming a conservative 20% mortality benefit, annual screening in people 50-79 years old with a 20+ pack-year history of smoking has benefit-to-radiation-risk ratios of 23:1 (with follow-up doses adding 40% to screening doses) to 29:1 (with follow-up adding 10%) based on COSMOS dose data.

CONCLUSIONS

Based on linear, no threshold BEIR VII dose-risk estimates, benefit-to-radiation-risk ratios for LCS are highly favorable. Results emphasize the importance of using modern CT technologies, maintaining low diagnostic follow-up rates, and minimizing both screening and diagnostic follow-up doses.

PLAIN LANGUAGE SUMMARY

The benefits of lung cancer screening significantly outweigh estimates of future harms associated with exposure to radiation during screening and diagnostic follow-up examinations. Our findings emphasize the importance of lung cancer screening practices using state-of-the-art computed tomography scanners and specialized low-dose lung screening and diagnostic follow-up techniques.

Outcome of Incidental Pulmonary Nodules in a Real-World Setting

OBJECTIVES

Early diagnosis of lung cancer is imperative to improve survival. Incidental pulmonary nodules (IPN) may represent early stages of lung cancer and appropriate follow-up and management of these nodules is important, but also very resource demanding. We aim to describe the results of the CT-based follow-up on a cohort of patients with IPN in terms of detected malignancies, the proportion undergoing invasive procedures, and the subsequent outcome.

MATERIALS AND METHODS

Retrospective cohort study of patients in a CT IPN follow-up program who underwent a needle biopsy of the lung from 2018 to 2021 at Aarhus University Hospital.

RESULTS

A total of 4181 patients with IPN were followed with CT control scans. Out of these 249 (6%) were diagnosed with lung cancer of which 224 (90%) were diagnosed as a result of the IPN follow-up. Seventy-five percent of the patients were diagnosed in stages I to II and curable treatment was possible in 77.9% of the patients. In the CT IPN follow-up program 449 patients underwent a CT guided needle biopsy. Out of these 190 patients underwent biopsy without the detection of malignancy, corresponding to 4.5% of the entire IPN population.

CONCLUSION

The cumulated incidence of lung cancer in our population in the IPN follow-up program was 6%. The probability of malignancy when undergoing an invasive procedure on an IPN was 55.7% of which lung cancer was vastly predominant. The majority of lung cancers were diagnosed in an early and potentially curable stage.

Investigation of the quarter-dose 18 F-FDG total-body PET in routine clinical practice and its clinical value

OBJECTIVE

The purpose of the study was to evaluate the routine clinical application of total-body PET with quarter-dose 18 F-FDG.

METHODS

The contrast recovery coefficient (CRC) and coefficient of variation (COV) were evaluated among full-, half-, and quarter-dose groups with an acquisition duration of 10-, 5-, 3-, and 1-min in the NEMA (IQ) phantom test. Fifty patients undergoing total-body PET/CT with quarter-dose (0.925MBq/kg) of 18 F-FDG were included in the prospective study. The acquisition time was 10 min, divided into duration groups of 5-, 3-, and 1-min, referred to as G10, G5, G3, and G1. Visual scores were assessed based on overall visual assessment, noise scoring, and lesion conspicuity. Lesion SUV max and TBR were evaluated in semi-quantitative analysis. G10 was used as the gold reference to evaluate lesion detectability.

RESULTS

In the phantom study, the COV value of the images with quarter-dose 18 F-FDG and 10-min acquisition time was 11.52%. For spheres with 10 mm diameter, the CRC of quarter-dose PET images was relatively stable compared to that of full-dose groups with all acquisition durations. In the human study, the visual score in G10, G5, and G3 was significantly higher than that in G1. The differences in lesion SUV max and TBR for G1-G10 were significantly higher than that for G5-G10 and G3-G10. All lesions in G10 could be identified in G5 and G3.

CONCLUSION

The phantom and human findings demonstrated the feasibility of quarter-dose 18 F-FDG PET with 3-min acquisition time, which can maintain image quality with reduced radiation dose.

Diagnostic value of circulating miRNA in the benign and malignant lung nodules: A systematic review and meta-analysis

BACKGROUND

Lung cancer is the leading cause of death worldwide, and its diagnosis remains a significant challenge. Identifying effective methods to differentiate benign from malignant lung nodules is of paramount importance. This meta-analysis aimed to evaluate the clinical utility of circulating microRNAs (miRNAs) for the differential diagnosis of benign and malignant lung nodules.

METHODS

This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A comprehensive search was conducted across 4 electronic databases, without any temporal restrictions. The inclusion and exclusion criteria were strictly applied to assess the clinical applications of circulating miRNAs. A robust and transparent quality assessment was performed using the quality assessment of diagnostic accuracy studies-2 tool, and rigorous statistical analyses were conducted to synthesize the various diagnostic measures.

RESULTS

In the meta-analysis of 11 studies, quality assessment of diagnostic accuracy studies-2 assessment revealed < 5% high-risk methodologies, ensuring robustness. Sensitivity and Specificity were consolidated at 0.83 (95% confidence interval [CI]: 0.72-0.90) and 0.81 (95% CI: 0.73-0.88), respectively. The positive likelihood ratio and negative likelihood ratio were 4.45 (95% CI: 3.03-6.54) and 0.21 (95% CI: 0.12-0.35), respectively. The diagnostic odds ratio was 21.31 (95% CI: 10.25-44.30) and area under the receiver operating characteristic curve was 0.89 (95% CI: 0.86-0.91). Subgroup analysis highlighted significant variations in diagnostic accuracy by ethnicity and miRNA source, with non-Asian populations and serum-based tests showing higher diagnostic accuracy.

CONCLUSION

This meta-analysis demonstrated that circulating miRNAs hold substantial diagnostic value in distinguishing between benign and malignant lung nodules.

Incidence of Clinically Relevant Solitary Pulmonary Nodules Utilizing a Universal Health Care System

INTRODUCTION

Solitary pulmonary nodules (SPNs) are common, but the clinical relevance of these nodules is unknown. Utilizing current screening guidelines, we sought to better characterize the national incidence of clinically important SPNs within the largest universal health care system in the nation.

MATERIALS AND METHODS

TRICARE data were queried to identify SPNs for ages 18-64 years. SPNs that had been diagnosed within a year with no prior oncologic history were included to ensure true incidence. A proprietary algorithm was applied to determine clinically significant nodules. Further analysis characterized incidence by age grouping, gender, region, military branch, and beneficiary status.

RESULTS

A total of 229,552 SPNs were identified with a 60% reduction seen after application of the clinical significance algorithm (N = 88,628). The incidence increased in each decade of life (all P < 0.01). Adjusted incident rate ratios were significantly higher for SPNs detected in the Midwest and Western regions. The incident rate ratio was also higher in females (1.05, confidence interval [CI] 1.018, P = 0.001) as well as non-active duty members (dependents = 1.4 and retired = 1.6, respectively, CIs 1.383-1.492 and 1.591-1.638, P < 0.01). The incidence calculated per 1,000 patients overall was 3.1/1,000. Ages 44-54 years had an incidence of 5.5/1,000 patients, which is higher than the previously reported incidence of < 5.0 nationally for the same age group.

CONCLUSIONS

This analysis represents the largest evaluation of SPNs to date combined with clinical relevance adjustment. These data suggest a higher incidence of clinically significant SPNs starting at an age of 44 years in nonmilitary or retired women localized to the Midwest and Western regions of the United States.

A Novel Artificial Intelligence Based Denoising Method for Ultra-Low Dose CT Used for Lung Cancer Screening

RATIONALE AND OBJECTIVES

To assess ultra-low-dose (ULD) computed tomography as well as a novel artificial intelligence-based reconstruction denoising method for ULD (dULD) in screening for lung cancer.

MATERIALS AND METHODS

This prospective study included 123 patients, 84 (70.6%) men, mean age 62.6 ± 5.35 (55-75), who had a low dose and an ULD scan. A fully convolutional-network, trained using a unique perceptual loss was used for denoising. The network used for the extraction of the perceptual features was trained in an unsupervised manner on the data itself by denoising stacked auto-encoders. The perceptual features were a combination of feature maps taken from different layers of the network, instead of using a single layer for training. Two readers independently reviewed all sets of images.

RESULTS

ULD decreased average radiation-dose by 76% (48%-85%). When comparing negative and actionable Lung-RADS categories, there was no difference between dULD and LD (p = 0.22 RE, p > 0.999 RR) nor between ULD and LD scans (p = 0.75 RE, p > 0.999 RR). ULD negative likelihood ratio (LR) for the readers was 0.033-0.097. dULD performed better with a negative LR of 0.021-0.051. Coronary artery calcifications (CAC) were documented on the dULD scan in 88(74%) and 81(68%) patients, and on the ULD in 74(62.2%) and 77(64.7%) patients. The dULD demonstrated high sensitivity, 93.9%-97.6%, with an accuracy of 91.7%. An almost perfect agreement between readers was noted for CAC scores: for LD (ICC = 0.924), dULD (ICC = 0.903), and for ULD (ICC = 0.817) scans.

CONCLUSION

A novel AI-based denoising method allows a substantial decrease in radiation dose, without misinterpretation of actionable pulmonary nodules or life-threatening findings such as aortic aneurysms.

Access to Lung Cancer Screening

Rural and racial/ethnic minority communities experience higher risk and mortality from lung cancer. Lung cancer screening with low-dose computed tomography reduces mortality. However, disparities persist in the uptake of lung cancer screening, especially in marginalized communities. Barriers to lung cancer screening are multilevel and include patient, provider, and system-level barriers. This discussion highlights the key barriers faced by rural and racial/ethnic minority communities.

Functional Diversity in Radiolabeled Nanoceramics and Related Biomaterials for the Multimodal Imaging of Tumors

Nanotechnology advances have the potential to assist toward the earlier detection of diseases, giving increased accuracy for diagnosis and helping to personalize treatments, especially in the case of noncommunicative diseases (NCDs) such as cancer. The main advantage of nanoparticles, the scaffolds underpinning nanomedicine, is their potential to present multifunctionality: synthetic nanoplatforms for nanomedicines can be tailored to support a range of biomedical imaging modalities of relevance for clinical practice, such as, for example, optical imaging, computed tomography (CT), magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), and positron emission tomography (PET). A single nanoparticle has the potential to incorporate myriads of contrast agent units or imaging tracers, encapsulate, and/or be conjugated to different combinations of imaging tags, thus providing the means for multimodality diagnostic methods. These arrangements have been shown to provide significant improvements to the signal-to-noise ratios that may be obtained by molecular imaging techniques, for example, in PET diagnostic imaging with nanomaterials versus the cases when molecular species are involved as radiotracers. We surveyed some of the main discoveries in the simultaneous incorporation of nanoparticulate materials and imaging agents within highly kinetically stable radio-nanomaterials as potential tracers with (pre)clinical potential. Diversity in function and new developments toward synthesis, radiolabeling, and microscopy investigations are explored, and preclinical applications in molecular imaging are highlighted. The emphasis is on the biocompatible materials at the forefront of the main preclinical developments, e.g., nanoceramics and liposome-based constructs, which have driven the evolution of diagnostic radio-nanomedicines over the past decade.

Lung Cancer Screening in Asia: An Expert Consensus Report

INTRODUCTION

The incidence and mortality of lung cancer are highest in Asia compared with Europe and USA, with the incidence and mortality rates being 34.4 and 28.1 per 100,000 respectively in East Asia. Diagnosing lung cancer at early stages makes the disease amenable to curative treatment and reduces mortality. In some areas in Asia, limited availability of robust diagnostic tools and treatment modalities, along with variations in specific health care investment and policies, make it necessary to have a more specific approach for screening, early detection, diagnosis, and treatment of patients with lung cancer in Asia compared with the West.

METHOD

A group of 19 advisors across different specialties from 11 Asian countries, met on a virtual Steering Committee meeting, to discuss and recommend the most affordable and accessible lung cancer screening modalities and their implementation, for the Asian population.

RESULTS

Significant risk factors identified for lung cancer in smokers in Asia include age 50 to 75 years and smoking history of more than or equal to 20 pack-years. Family history is the most common risk factor for nonsmokers. Low-dose computed tomography screening is recommended once a year for patients with screening-detected abnormality and persistent exposure to risk factors. However, for high-risk heavy smokers and nonsmokers with risk factors, reassessment scans are recommended at an initial interval of 6 to 12 months with subsequent lengthening of reassessment intervals, and it should be stopped in patients more than 80 years of age or are unable or unwilling to undergo curative treatment.

CONCLUSIONS

Asian countries face several challenges in implementing low-dose computed tomography screening, such as economic limitations, lack of efforts for early detection, and lack of specific government programs. Various strategies are suggested to overcome these challenges in Asia.

Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline

A growing body of evidence on a wide spectrum of adverse cardiac events following oncologic therapies has led to the emergence of cardio-oncology as an increasingly relevant interdisciplinary specialty. This also calls for better risk-stratification for patients undergoing cancer treatment. Machine learning (ML), a popular branch discipline of artificial intelligence that tackles complex big data problems by identifying interaction patterns among variables, has seen increasing usage in cardio-oncology studies for risk stratification. The objective of this comprehensive review is to outline the application of ML approaches in cardio-oncology, including deep learning, artificial neural networks, random forest and summarize the cardiotoxicity identified by ML. The current literature shows that ML has been applied for the prediction, diagnosis and treatment of cardiotoxicity in cancer patients. In addition, role of ML in gender and racial disparities for cardiac outcomes and potential future directions of cardio-oncology are discussed. It is essential to establish dedicated multidisciplinary teams in the hospital and educate medical professionals to become familiar and proficient in ML in the future.

Utilization of Diagnostic Procedures After Lung Cancer Screening in the National Lung Screening Trial

OBJECTIVE

To examine utilization patterns of diagnostic procedures after lung cancer screening among participants enrolled in the National Lung Screening Trial.

METHODS

Using a sample of National Lung Screening Trial participants with abstracted medical records, we assessed utilization of imaging, invasive, and surgical procedures after lung cancer screening. Missing data were imputed using multiple imputation by chained equations. For each procedure type, we examined utilization within a year after the screening or until the next screen, whichever came first, across arms (low-dose CT [LDCT] versus chest X-ray [CXR]) and by screening results. We also explored factors associated with having these procedures using multivariable negative binomial regressions.

RESULTS

After baseline screening, our sample had 176.5 and 46.7 procedures per 100 person-years for those with a false-positive and negative result, respectively. Invasive and surgical procedures were relatively infrequent. Among those who screened positive, follow-up imaging and invasive procedures were 25% and 34% less frequent in those screened with LDCT, compared with CXR. Postscreening utilization of invasive and surgical procedures was 37% and 34% lower at the first incidence screen compared with baseline. Participants with positive results at baseline were six times more likely to undergo additional imaging than those with normal findings.

DISCUSSION

Use of imaging and invasive procedures to evaluate abnormal findings varied by screening modality, with a lower rate for LDCT than CXR. Invasive and surgical workup were less prevalent after subsequent screening examinations compared with baseline screening. Utilization was associated with older age but not gender, race or ethnicity, insurance status, or income.

Development of lung cancer risk prediction models based on F-18 FDG PET images

OBJECTIVE

We aimed to evaluate whether the degree of F-18 fluorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and to develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET).

METHODS

We retrospectively included 795 healthy individuals who underwent F-18 FDG PET/CT scans for a health check-up. Individuals who developed lung cancer within 5 years of the PET/CT scan were classified into the lung cancer group (n = 136); those who did not were classified into the control group (n = 659). The healthy individuals were then randomly assigned to either the training (n = 585) or validation sets (n = 210). Clinical factors including age, sex, body mass index (BMI), and smoking history were collected. The standardized uptake value ratio (SUVR) and metabolic heterogeneity (MH) index were obtained for the bilateral lungs. Logistic regression models including clinical factors, SUVR, and MH index were generated to quantify the probability of lung cancer development using a training set. The prediction models were validated using a validation set.

RESULTS

The lung SUVR and lung MH index in the lung cancer group were significantly higher than in the control group (p < 0.001 and p < 0.001, respectively). In the combined prediction model 1, age, sex, BMI, smoking history, and lung SUVR were significantly associated with lung cancer development (age: OR 1.07, p < 0.001; male: OR 2.08, p = 0.015; BMI: OR 0.93, p = 0.057; current or past smoker: OR 5.60, p < 0.001; lung SUVR: OR 1.13, p < 0.001). In the combined prediction model 2, age, sex, BMI, smoking history, and lung MH index showed a significant association with lung cancer development (age: OR 1.06, p < 0.001; male: OR 1.87, p = 0.045; BMI: OR 0.93, p = 0.010; current or past smoker: OR 4.78, p < 0.001; lung MH index: OR 1.33, p < 0.001). In the validation data, combined prediction models 1 and 2 exhibited very good discrimination [area under the receiver operator curve (AUC): 0.867 and 0.901, respectively].

CONCLUSIONS

The metabolic parameters on F-18 FDG PET are related to an increased risk of lung cancer. Metabolic parameters can be used as biomarkers to provide information independent of the clinical parameters, related to lung cancer risk.