Assessment of Barriers and Challenges to Screening, Diagnosis, and Biomarker Testing in Early-Stage Lung Cancer

A novel composite model for distinguishing benign and malignant pulmonary nodules

Previous studies have demonstrated that a four-protein marker panel (4MP), consisting of Pro-SFTPB, CA125, Cyfra21-1, and CEA could be used to identify benign and malignant lung nodules. This study aims to improve the 4MP's performance by combining clinical characteristics and low-dose chest computed tomography (LDCT) screening features. This study involved 380 patients with pulmonary nodules, diagnosing 91 benign and 289 early-stage lung cancer via postoperative histopathology. Serum levels of Pro-SFTPB, CA125, Cyfra21-1, and CEA were assessed using an immunofluorescence assay. Clinical features were selected using the LassoCV method. A new diagnostic model was developed using logistic regression, incorporating 4MP, clinical characteristics, and LDCT features. The model's diagnostic performance was compared to the lung cancer biomarker panel (LCBP) nodule risk model, and evaluated through sensitivity, specificity, and the AUC value. The AUC values for distinguishing between benign and malignant pulmonary nodules were 0.612 for the 4MP model. We screened out 7 factors of patient clinical information and CT features of nodules. The composite model (4MP + age + gender + BMI + family history of cancer + nodule size + nodule margin + nodule density) achieved an AUC of 0.808, especially for small nodules (AUC = 0.835 for nodules ≤ 6 mm). Furthermore, within the same validation cohort, the performance of the composite model (AUC = 0.680) surpassed that of the LCBP nodule risk model (AUC = 0.599). The novel composite model accurately diagnoses malignant pulmonary nodules, especially small ones, helping to stratify patients by lung cancer risk.

Simultaneous Detection of MMP-1 and MMP-12 Using Au-Se Nanoprobe for Advancing NSCLC Diagnosis

This study presents the development and characterization of Au-Se nanoprobes (NPs) for the specific and simultaneous detection of matrix metalloproteinase-1 and -12 (MMP-1/12), key biomarkers in non-small-cell lung cancer (NSCLC). Leveraging the stability of Au-Se bonds against biological thiols, such as glutathione (GSH), the NPs exhibit remarkable resistance to interference, maintaining its fluorescence signal across a wide range of temperatures, pH levels, and in the presence of high concentrations of GSH. The synthesized NPs demonstrate high specificity and sensitivity toward MMP-1/12 in vitro, with optimal imaging achieved after 4 h of incubation in NSCLC A549 cells. Furthermore, confocal imaging experiments successfully distinguished between NSCLC A549 cells and normal lung epithelial cells (Beas-2b), underscoring the potential of these NPs in early NSCLC diagnosis and the study of tumor microenvironments. This work not only introduces a reliable tool for cancer biomarker detection but also contributes to the advancement of nanomaterial applications in NSCLC diagnosis.

Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review

Artificial intelligence (AI) is revolutionizing early cancer detection by enhancing the sensitivity, efficiency, and precision of screening programs for breast, colorectal, and lung cancers. Deep learning algorithms, such as convolutional neural networks, are pivotal in improving diagnostic accuracy by identifying patterns in imaging data that may elude human radiologists. AI has shown remarkable advancements in breast cancer detection, including risk stratification and treatment planning, with models achieving high specificity and precision in identifying invasive ductal carcinoma. In colorectal cancer screening, AI-powered systems significantly enhance polyp detection rates during colonoscopies, optimizing the adenoma detection rate and improving diagnostic workflows. Similarly, low-dose CT scans integrated with AI algorithms are transforming lung cancer screening by increasing the sensitivity and specificity of early-stage cancer detection, while aiding in accurate lesion segmentation and classification. This review highlights the potential of AI to streamline cancer diagnosis and treatment by analyzing vast datasets and reducing diagnostic variability. Despite these advancements, challenges such as data standardization, model generalization, and integration into clinical workflows remain. Addressing these issues through collaborative research, enhanced dataset diversity, and improved explainability of AI models will be critical for widespread adoption. The findings underscore AI's potential to significantly impact patient outcomes and reduce cancer-related mortality, emphasizing the need for further validation and optimization in diverse healthcare settings.

Barriers to lung cancer screening

INTRODUCTION

Despite efforts to improve national lung cancer screening rates, only 4 % of eligible high-risk patients pursue this opportunity annually.

OBJECTIVE

The goal of this study was to better understand the system, provider, and patient-level barriers to lung cancer screening METHODS: 300 high-risk patients in the southern Nevada region who met the criteria for lung cancer screening were given a questionnaire during their outpatient visit for low dose chest CT from August 2023 through February 2024.

RESULTS

The most common reported barriers were absence of symptoms (38 %), not wishing to know that they had cancer (30 %), and not having awareness of the eligibility for CT screening (24 %).

CONCLUSION

A multifactorial approach is indicated to provide education for high-risk patients to clarify the importance of early diagnosis, demonstrate accessibility and low cost for lung cancer screening, and absolve any misconceptions about lung cancer screening.

Healthcare disparities, screening, and molecular testing in the changing landscape of non-small cell lung cancer in the United States: a review

Inequitable access to care continues to hinder improvements in diagnosis and treatment of lung cancer. This review describes healthcare disparities in the changing landscape of non-small cell lung cancer (NSCLC) in the United States, focusing on racial, ethnic, sex-based, and socioeconomic trends. Furthermore, strategies to address disparities, overcome challenges, and improve patient outcomes are proposed. Barriers exist across lung cancer screening, diagnosis, and treatment regimens, varying by sex, age, race and ethnicity, geography, and socioeconomic status. Incidence and mortality rates of lung cancer are higher among Black men than White men, and incidences in young women are substantially greater than in young men. Disparities may be attributed to geographic differences in screening access, with correlating higher incidence and mortality rates in rural versus urban areas. Lower socioeconomic status is also linked to lower survival rates. Several strategies could help reduce disparities and improve outcomes. Current guidelines could improve screening eligibility by incorporating sex, race, and socioeconomic status variables. Patient and clinician education on screening guidelines and patient-level barriers to care are key, and biomarker testing is critical since ~ 70% of patients with NSCLC have an actionable biomarker. Timely diagnosis, staging, and comprehensive biomarker testing, including cell-free DNA liquid biopsy, may provide valuable treatment guidance for patients with NSCLC. Efforts to improve lung cancer screening and biomarker testing access, decrease bias, and improve education about screening and testing are needed to reduce healthcare disparities in NSCLC.

The clinical effect of thoracoscopic segmentectomy in the treatment of lung malignancies less than 2CM in diameter

OBJECTIVE

To investigate the clinical effect of thoracoscopic segmentectomy in the treatment of lung malignancies less than 2CM in diameter.

METHODS

In this retrospective study, a total of 103 patients with lung cancer who received outpatient or inpatient treatment from December 2020 to May 2022 were selected and divided into the lobectomy group (n = 48) and the segmentectomy group (n = 55) according to different surgical methods. The lobectomy group was treated with thoracoscopic lobectomy, while the segmentectomy group was treated with thoracoscopic segmentectomy. The prognostic effect, complications, blood gas level and respiratory function indexes of the two groups were observed and compared.

RESULTS

The general data of the two groups of patients, such as gender, age, course of disease, body mass index, lesion diameter, lesion site and pathological type, were analyzed by statistical software. There was no statistical significance in the operation time and the number of lymph node dissection between the two groups (P > 0.05), while the drainage volume and intraoperative blood loss in the segmentectomy group were lower than those in the lobectomy group, and the drainage time and hospital stay were shorter than those in the lobectomy group, with statistical significance (P < 0.05). Before treatment, there were no statistically significant differences in various lung function indexes between the two groups (P > 0.05). After treatment, the values of FVC, FEV1 and FEV1/FVC in each group had different amplitude changes, and the values of FVC, FEV1 and FEV1/FVC in the segmentectomy group were significantly higher than those in the lobectomy group, with statistical significance (P < 0.05). Thoracoscopic segmentectomy showed a lower incidence of respiratory complications (P = 0.042) and higher pulmonary air leak (P = 0.023) than thoracoscopic lobectomy. After propensity score-matched analysis, respiratory complications remained significantly higher in thoracoscopic segmentectomy (P = 0.017). However, the difference in the total complication rate between the two groups was not statistically significant (P > 0.05). There were no differences during the 2-year follow-up (median follow-up in months: 18.4; interquartile range, 14.8-21.3) in terms of overall survival (P = 0.49) and disease-free survival (P = 0.34) between groups (P > 0.05).

CONCLUSIONS

For patients with lung cancer less than 2 cm in diameter, thoracoscopic segmentectomy can achieve good short-term efficacy, with rapid postoperative recovery and little impact on lung function, which may be helpful to improve patients' postoperative quality of life.

Understanding the public knowledge, attitude, and practice toward screening and risk factors of lung cancer in Saudi Arabia: A cross-sectional study

CONTEXT

Early detection of lung cancer through screening can improve outcomes; yet public knowledge, attitudes, and practices regarding lung cancer screening in Saudi Arabia are limited.

AIMS

The aim is to assess knowledge, attitudes, and practices toward lung cancer risk factors and screening, and understand the impact of demographic factors on these variables.

SETTINGS AND DESIGN

An observational cross-sectional study was conducted from October 2023 to March 2024, involving 708 participants.

METHODS

A validated questionnaire from a previous Malaysian study, translated into Arabic, was distributed to participants.

STATISTICAL ANALYSIS USED

Descriptive statistics, Shapiro-Wilk test, one-way analysis of variance, Tukey's test, and logistic regression were used.

RESULTS

Most participants (95.9%) recognized smoking as a major risk factor. Common misconceptions included viewing lung cancer as infectious (84.0%) or affecting only men (14.4%). Nonetheless, 66.4% expressed willingness for future screening. Younger participants (18-40 years) and those with higher education demonstrated better knowledge scores (mean score: 11.33 ± 2.97 for ages 18-40; 11.42 ± 2.88 for those with master's or doctorate degrees), with significant differences based on age (P = 0.007) and education level (P = 0.025). No significant differences were observed based on gender, region of residence, or monthly family income.

CONCLUSIONS

There is a positive inclination toward lung cancer screening among the Saudi public, but there are significant knowledge gaps, particularly regarding nonsmoking-related risk factors and misconceptions, suggesting a need for enhanced public education and screening programs.

Nanoscale Extracellular Vesicle-Enabled Liquid Biopsy: Advances and Challenges for Lung Cancer Detection

Lung cancer is responsible for the death of over a million people worldwide every year. With its high mortality rate and exponentially growing number of new cases, lung cancer is a major threat to public health. The high mortality and poor survival rates of lung cancer patients can be attributed to its stealth progression and late diagnosis. For a long time, intrusive tissue biopsy has been considered the gold standard for lung cancer diagnosis and subtyping; however, the intrinsic limitations of tissue biopsy cannot be overlooked. In addition to being invasive and costly, it also suffers from limitations in sensitivity and specificity, is not suitable for repeated sampling, provides restricted information about the tumor and its molecular landscape, and is inaccessible in several cases. To cope with this, advancements in diagnostic technologies, such as liquid biopsy, have shown great prospects. Liquid biopsy is an innovative non-invasive approach in which cancer-related components called biomarkers are detected in body fluids, such as blood, urine, saliva and others. It offers a less invasive alternative with the potential for applications such as routine screening, predicting treatment outcomes, evaluating treatment effectiveness, detecting residual disease, or disease recurrence. A large number of research articles have indicated extracellular vesicles (EVs) as ideal biomarkers for liquid biopsy. EVs are a heterogeneous collection of membranous nanoparticles with diverse sizes, contents, and surface markers. EVs play a critical role in pathophysiological states and have gained prominence as diagnostic and prognostic biomarkers for multiple diseases, including lung cancer. In this review, we provide a detailed overview of the potential of EV-based liquid biopsy for lung cancer. Moreover, it highlights the strengths and weaknesses of various contemporary techniques for EV isolation and analysis in addition to the challenges that need to be addressed to ensure the widespread clinical application of EV-based liquid biopsies for lung cancer. In summary, EV-based liquid biopsies present interesting opportunities for the development of novel diagnostic and prognostic platforms for lung cancer, one of the most abundant cancers responsible for millions of cancer-related deaths worldwide.

Detecting mir-155-3p through a Molecular Beacon Bead-Based Assay

Lung cancer (LC) is recognized as one of the most prevalent and lethal cancers worldwide, underscoring an urgent need for innovative diagnostic and therapeutic approaches. MicroRNAs (miRNAs) have emerged as promising biomarkers for several diseases and their progression, such as LC. However, traditional methods for detecting and quantifying miRNAs, such as PCR, are time-consuming and expensive. Herein, we used a molecular beacon (MB) bead-based assay immobilized in a microfluidic device to detect miR-155-3p, which is frequently overexpressed in LC. The assay relies on the fluorescence enhancement of the MB upon binding to the target miRNA via Watson and Crick complementarity, resulting in a conformational change from a stem-loop to a linear structure, thereby bringing apart the fluorophores at each end. This assay was performed on a microfluidic platform enabling rapid and straightforward target detection. We successfully detected miR-155-3p in a saline solution, obtaining a limit of detection (LOD) of 42 nM. Furthermore, we evaluated the method's performance in more complex biological samples, including A549 cells' total RNA and peripheral blood mononuclear cells (PBMCs) spiked with the target miRNA. We achieved satisfactory recovery rates, especially in A549 cells' total RNA.

Precision nanomedicine to treat non-small cell lung cancer

Lung cancer is a major cause of death worldwide, being often detected at a later stage due to the non-appearance of early symptoms. Therefore, specificity of the treatment is of utmost importance for its effective treatment. Precision medicine is a personalized therapy based on the genomics of the patient to design a suitable drug approach. Genetic mutations render the tumor resistant to specific mutations and the therapy is in vain even though correct medications are prescribed. Therefore, Precision medicine needs to be explored for the treatment of Non-small cell lung cancer (NSCLC). Nanoparticles are widely explored to give personalized interventions to treat lung cancer due to their various advantages like the ability to reach cancer cells, enhanced permeation through tissues, specificity, increased bioavailability, etc. Various nanoparticles (NPs) including gold nanoparticles, carbon nanotubes, aptamer-based NPs etc. were conjugated with biomarkers/diagnostic agents specific to cancer type and were delivered. Various biomarker genes have been identified through precision techniques for the diagnosis and treatment of NSCLC like EGFR, RET, KRAS, ALK, ROS-1, NTRK-1, etc. By incorporating of drug with the nanoparticle through bioconjugation, the specificity of the treatment can be enhanced with this revolutionary treatment. Additionally, integration of theranostic cargos in the nanoparticle would allow diagnosis as well as treatment by targeting the site of disease progression. Therefore, to target NSCLC effectively precision nanomedicine has been adopted in recent times. Here, we present different nanoparticles that are used as precision nanomedicine and their effectiveness against NSCLC disease.

Medical Relevance, State-of-the-Art and Perspectives of "Sweet Metacode" in Liquid Biopsy Approaches

This review briefly introduces readers to an area where glycomics meets modern oncodiagnostics with a focus on the analysis of sialic acid (Neu5Ac)-terminated structures. We present the biochemical perspective of aberrant sialylation during tumourigenesis and its significance, as well as an analytical perspective on the detection of these structures using different approaches for diagnostic and therapeutic purposes. We also provide a comparison to other established liquid biopsy approaches, and we mathematically define an early-stage cancer based on the overall prognosis and effect of these approaches on the patient's quality of life. Finally, some barriers including regulations and quality of clinical validations data are discussed, and a perspective and major challenges in this area are summarised.

Description of an activity-based enzyme biosensor for lung cancer detection

BACKGROUND

Lung cancer is associated with the greatest cancer mortality as it typically presents with incurable distributed disease. Biomarkers relevant to risk assessment for the detection of lung cancer continue to be a challenge because they are often not detectable during the asymptomatic curable stage of the disease. A solution to population-scale testing for lung cancer will require a combination of performance, scalability, cost-effectiveness, and simplicity.

METHODS

One solution is to measure the activity of serum available enzymes that contribute to the transformation process rather than counting biomarkers. Protease enzymes modify the environment during tumor growth and present an attractive target for detection. An activity based sensor platform sensitive to active protease enzymes is presented. A panel of 18 sensors was used to measure 750 sera samples from participants at increased risk for lung cancer with or without the disease.

RESULTS

A machine learning approach is applied to generate algorithms that detect 90% of cancer patients overall with a specificity of 82% including 90% sensitivity in Stage I when disease intervention is most effective and detection more challenging.

CONCLUSION

This approach is promising as a scalable, clinically useful platform to help detect patients who have lung cancer using a simple blood sample. The performance and cost profile is being pursued in studies as a platform for population wide screening.

Making Sense of the Nonalcoholic Fatty Liver Disease Clinical Practice Guidelines: What Clinicians Need to Know

Standards of care summarized in clinical practice guidelines for nonalcoholic fatty liver disease (NAFLD) offer clinicians a streamlined diagnostic and management approach based on the best available evidence. These recommendations have changed a great deal in recent years; today, there is a clear focus on screening for the early identification and risk stratification of patients at high risk of steatohepatitis and clinically significant fibrosis to promote timely referrals to specialty care when needed. This article reviews and provides the rationale for current guidelines for NAFLD screening, diagnosis, treatment, and monitoring and addresses barriers to providing evidence-based NAFLD care and how to overcome them. The current paradigm of care calls for primary care clinicians and specialists to work together, within a multidisciplinary care team familiar with obesity and diabetes care, to provide comprehensive management of these complex patients.

Biofluid Metabolic Profiling for Lung Cancer Screening via Reactive Matrix-Assisted Laser Desorption Ionization Mass Spectrometry

Lung cancer (LC) has the highest mortality rate among various cancer diseases. Developing an early screening method for LC with high classification accuracy is essential. Herein, 2-hydrazinoquinoline (2-HQ) is utilized as a dual-mode reactive matrix for metabolic fingerprint analysis and LC screening via matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS). Metabolites in both positive mode and negative mode can be detected using 2-HQ as the matrix, and derivative analysis of aldehyde and ketone compounds can be achieved simultaneously. Hundreds of serum and urine samples from LC patients and healthy volunteers were analyzed. Combined with machine learning, LC patients and healthy volunteers were successfully distinguished with a high area under the curve value (0.996 for blind serum samples and 0.938 for urine). The MS signal was identified for metabolic profiling, and dysregulated metabolites of the LC group were analyzed. The above results showed that this method has great potential for rapid screening of LC.

Clinical Network Systems Biology: Traversing the Cancer Multiverse

In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance. Through the union of clinical medicine and basic sciences, there has been a revolution in the development and approval of cancer therapeutic drug options including tyrosine kinase inhibitors, antibody-drug conjugates, and immunotherapy. This 'Team Medicine' approach within the cancer systems biology framework can be further improved upon through the development of high-throughput clinical trial models that utilize machine learning models, rapid sample processing to grow patient tumor cell cultures, test multiple therapeutic options and assign appropriate therapy to individual patients quickly and efficiently. The integration of systems biology into the clinical network would allow for rapid advances in personalized medicine that are often hindered by a lack of drug development and drug testing.