Early Detection Of Lung Cancer Research Articles

Abstract A132: Missed Opportunities? Regional availability of Low Dose CT lung cancer screening facility locations in the year prior to diagnoses: A descriptive comparison of American Indian and Non-Hispanic White lung cancer patients in North Carolina

Abstract INTRODUCTION American Indians (AI) have much higher incidence rates of lung cancer (LC) compared with non-Hispanic Whites (NHW) in the US. While low-dose computed tomography (LDCT) screening facilitates early detection of lung cancer, it is unknown how available LDCT screenings are to racially and geographically diverse populations in North Carolina (NC). To address this gap in knowledge, we conducted a descriptive study to characterize incident LC cases among AIs and NHWs, the distance from their location at diagnosis to the nearest LDCT screening facility and explored whether distance was associated with late-stage LC diagnoses. Methods Data were obtained from the Cancer Information and Population Health Resource (CIPHR), which comprise data from NC Central Cancer Registry (CCR) as well as public and private health insurance claims for the state. We identified ZIP codes and dates of rendered service for facilities providing LDCT by searching through claims from 2015-20. In addition, we identified incident LC cases among AIs and NHWs aged ≥18 years, from 2015-19. We assembled lists of LDCT facilities that existed in the year prior to each incident LC case. We calculated the minimum centroid to centroid distance to all facilities. Clinical attributes included age at diagnosis, sex, histology, stage at diagnosis, and rurality as measured by Rural Urban Commuting Area. Risk ratios were calculated for late-stage LC diagnoses across a range of thresholds for patient’s distance to nearest LDCT screening facility for AIs and NHWs. Results We identified 388 AI and 26,867 NHW incident LC cases in NC between 2015-20. Compared to NHWs, the median age at diagnosis for AIs was 5 years younger (AI: 65 vs. NHW: 69); more frequently male (AI: 54% vs NHW: 51%); and almost twice as likely to reside in rural areas (AI: 52% vs. NHW: 28%). There were no differences in late-stage LC diagnoses (AI: 54% vs. NHW: 52%). For distance (in miles) to the nearest LDCT screening facility, the median [Interquartile range] was twice as high for AIs (9.9 miles [0.8, 16.30]) compared to NHWs (5.3 miles [0, 12.1]). Among NHWs, there were statistically significant associations between distance and late-stage LC diagnosis at the shortest distance threshold of 5 miles (RR=1.04; 95%CL: 1.02, 1.07) through the threshold of 30 miles (RR=1.10 95%CL: 1.05, 1.15). There was no association across any distance threshold among AI lung cancer patients. Conclusions The large difference in accessibility of LDCT screening was not strongly associated with metastatic lung cancer diagnoses, possibly because so many patients were diagnosed late, reflecting poor early detection across all populations. We found that 80% of AIs with LC lived more than 10 miles away from LDCT screening facilities. This geographic distance may increase difficulty accessing cancer care geographically, and contribute to suboptimal LC screening rates. If efforts are to be made to increase early detection and improve survival, accessibility needs to be addressed on different dimensions. Citation Format: Bradford E. Jackson, Marc Emerson, Daniel Carrizosa, Chris Baggett, Lisa Spees, Joel Begay, Ana Salas, Yadurshirni Raveendran, Tomi Akinyemiju, Rachel Denlinger-Apte, Stephanie Wheeler, Ronny Bell. Missed Opportunities? Regional availability of Low Dose CT lung cancer screening facility locations in the year prior to diagnoses: A descriptive comparison of American Indian and Non-Hispanic White lung cancer patients in North Carolina [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A132.

Advances in molecular imaging for early detection of lung cancer

Lung cancer remains the second most commonly diagnosed cancer globally and the leading cause of cancer-related deaths, a trend consistent in the United States as of 2023. One of the key reasons for the high mortality rate of lung cancer is its poor prognosis, with 75% of patients diagnosed at middle and advanced stages. Early detection of subclinical lung cancer, metastases, and their fibrotic stroma is crucial for enabling timely treatment, reducing reoccurrence, and stratifying patients. Current diagnostic methods, such as lung biopsy for patients with small nodules, are highly invasive and technically challenging. The radiological gold standard, computed tomography (CT), is associated with ionizing radiation. However, positron emission tomography (PET) and magnetic resonance imaging (MRI) have emerged as promising methodologies for lung cancer diagnosis. PET tracers with a variety of targeting mechanisms are currently under development in human trials. With advancements in hardware and software over the past decades, radiation-free MRI has been clinically and preclinically validated as an alternative to CT. Moreover, novel-targeted MRI contrast agents have been tested in animal models and show strong translational potential. In this review, we summarize the state-of-the-art progress in molecular imaging for the early detection of lung cancer and its potential biomarkers.

Economic Evaluation of a Novel Lung Cancer Diagnostic in a Population of Patients with a Positive Low-Dose Computed Tomography Result

Background: Early detection of lung cancer is crucial for improving patient outcomes. Although advances in diagnostic technologies have significantly enhanced the ability to identify lung cancer in earlier stages, there are still limitations. The alarming rate of false positives has resulted in unnecessary utilization of medical resources and increased risk of adverse events from invasive procedures. Consequently, there is a critical need for advanced diagnostics after an initial low-dose computed tomography (LDCT) scan. Objectives: This study evaluated the potential cost savings for US payers of CyPath® Lung, a novel diagnostic tool utilizing flow cytometry and machine learning for the early detection of lung cancer, in patients with positive LDCT scans with indeterminate pulmonary nodules (IPNs) ranging from 6 to 29 mm. Methods: A cost offset model was developed to evaluate the net expected savings associated with the use of CyPath® Lung relative to the current standard of care for individuals whose IPNs range from 6 to 29 mm. Perspectives from both Medicare and private payers in a US setting are included, with a 1-year time horizon. Cost calculations included procedure expenses, complication costs, and diagnostic assessment costs per patient. Primary outcomes of this analysis include cost savings per cohort and cost savings per patient. Results: Our analysis showed positive cost savings from a private payer’s perspective, with expected savings of $895 202 311 per cohort and $6460 per patient, across all patients. Scenario analysis resulted in cost savings of $890 829 889 per cohort, and $6429 per patient. Similarly, savings of $378 689 020 per cohort or $2733 per patient were yielded for Medicare payers, across all patients. In addition, scenario analysis accounting for false negative patients from a Medicare payer perspective yielded savings of $376 902 203 per cohort and $2720 per patient. Discussion: The results suggest substantial cost savings, primarily due to reductions in follow-up diagnostic assessments and procedures, and highlight the importance of accurate diagnostic tools in reducing unnecessary healthcare expenditures. Conclusion: CyPath® Lung utilization yields savings for private and Medicare payers relative to the current standard of care in a US setting for individuals with 6 to 20 mm IPNs.

Modeling different strategies towards control of lung cancer: leveraging early detection and anti-cancer cell measures

The global population has encountered significant challenges throughout history due to infectious diseases. To comprehensively study these dynamics, a novel deterministic mathematical model, TCD I L 2 Z, is developed for the early detection and treatment of lung cancer. This model incorporates I L 2 cytokine and anti-PD-L1 inhibitors, enhancing the immune system’s anticancer response within five epidemiological compartments. The TCD I L 2 Z model is analyzed qualitatively and quantitatively, emphasizing local stability given the limited data-a critical component of epidemic modeling. The model is systematically validated by examining essential elements such as equilibrium points, the reproduction number ( R 0 ), stability, and sensitivity analysis. Next-generation techniques based on R 0 that track disease transmission rates across the sub-compartments are fed into the system. At the same time, sensitivity analysis helps model how a particular parameter affects the dynamics of the system. The stability on the global level of such therapy agents retrogrades individuals with immunosuppression or treated with I L 2 and anti-PD-L1 inhibitors admiring the Lyapunov functions’ applications. NSFD scheme based on the implicit method is used to find the exact value and is compared with Euler’s method and RK4, which guarantees accuracy. Thus, the simulations were conducted in the MATLAB environment. These simulations present the general symptomatic and asymptomatic consequences of lung cancer globally when detected in the middle and early stages, and measures of anticancer cells are implemented including boosting the immune system for low immune individuals. In addition, such a result provides knowledge about real-world control dynamics with I L 2 and anti-PD-L1 inhibitors. The studies will contribute to the understanding of disease spread patterns and will provide the basis for evidence-based intervention development that will be geared toward actual outcomes.

Serum-Based Biomarker Panels are Crucial Tools for the Early Detection of Lung Cancer in High-Risk Individuals in North Indian Population

Serum-Based Biomarker Panels are Crucial Tools for the Early Detection of Lung Cancer in High-Risk Individuals in North Indian Population

Nose-on-Chip Nanobiosensors for Early Detection of Lung Cancer Breath Biomarkers.

Lung cancer remains a global health concern, demanding the development of noninvasive, prompt, selective, and point-of-care diagnostic tools. Correspondingly, breath analysis using nanobiosensors has emerged as a promising noninvasive nose-on-chip technique for the early detection of lung cancer through monitoring diversified biomarkers such as volatile organic compounds/gases in exhaled breath. This comprehensive review summarizes the state-of-the-art breath-based lung cancer diagnosis employing chemiresistive-module nanobiosensors supported by theoretical findings. It unveils the fundamental mechanisms and biological basis of breath biomarker generation associated with lung cancer, technological advancements, and clinical implementation of nanobiosensor-based breath analysis. It explores the merits, challenges, and potential alternate solutions in implementing these nanobiosensors in clinical settings, including standardization, biocompatibility/toxicity analysis, green and sustainable technologies, life-cycle assessment, and scheming regulatory modalities. It highlights nanobiosensors' role in facilitating precise, real-time, and on-site detection of lung cancer through breath analysis, leading to improved patient outcomes, enhanced clinical management, and remote personalized monitoring. Additionally, integrating these biosensors with artificial intelligence, machine learning, Internet-of-things, bioinformatics, and omics technologies is discussed, providing insights into the prospects of intelligent nose-on-chip lung cancer sniffing nanobiosensors. Overall, this review consolidates knowledge on breathomic biosensor-based lung cancer screening, shedding light on its significance and potential applications in advancing state-of-the-art medical diagnostics to reduce the burden on hospitals and save human lives.

A Hybrid VGG 19 and Capsule Network Based Deep Learning Model for Lung Cancer Diagnosis using CT S can Images

Objectives: Lung cancer is one of the most prevalent causes of carcinoma-related fatalities globally; precise and effective diagnostic instruments are desperately needed. Researchers and physicians continue to encounter difficulties when aiming to employ deep learning models in healthcare environments for recognizing lung cancer and to achieve higher sensitivity and accuracy on large data sets, despite several advancements in this field. Our study builds upon an extensive review of existing lung cancer detection methods, highlighting their strengths and weaknesses. The performance of this model deteriorates when there are variances in Lung CT image features such as rotation, tiling, and other aberrant image orientations. This study aims to address the issues associated with variations in lung CT images. Methods: A hybrid deep learning model that blends the potency of CNNs with the cutting-edge design of capsule networks is proposed. Inspired by recent advancements, our model integrates the VGG19 and Capsule Network architectures to address orientation-related challenges often encountered in traditional CNN-based approaches. The creation, training, and assessment of this hybrid model for lung nodule identification and categorization are our main research goals. Findings: We anticipate that our research has yielded several valuable outcomes, including improved nodule classification accuracy of 99.20%, reduced false positive rates, and minimized training times. Novelty: This research employing a hybrid deep learning model that combines the capabilities of the VGG-19 and capsule network could lead to more widespread utilization in cancer diagnosis by enhancing early lung cancer detection and developing the field of medical image analysis. The shortcomings of convolutional neural networks are addressed with Capsule Network. Leveraging the knowledge and insights gained from lung cancer detection, our research also investigates the potential applicability of our methodology to identify other types of medical image classification such as breast and ovarian cancer. Keywords: Computed Tomography, Lung cancer, Deep Learning, CNN, VGG19, Capsule Network

The Role of circHIPK3 in Tumorigenesis and Its Potential as a Biomarker in Lung Cancer.

Lung cancer treatment and detection can be improved by the identification of new biomarkers. Novel approaches in investigating circular RNAs (circRNAs) as biomarkers have yielded promising results. A circRNA molecule circHIPK3 was found to be widely expressed in non-small-cell lung cancer (NSCLC) cells, where it plays a crucial role in lung cancer tumorigenesis. CircHIPK3 promotes lung cancer progression by sponging oncosuppressive miRNAs such as miR-124, miR-381-3p, miR-149, and miR-107, which results in increased cell proliferation, migration, and resistance to therapies. Inhibiting circHIPK3 has been demonstrated to suppress tumour growth and induce apoptosis, which suggests its potential use in the development of new lung cancer treatment strategies targeting circHIPK3-related pathways. As a biomarker, circHIPK3 shows promise for early detection and monitoring of lung cancer. CircHIPK3 increased expression levels in lung cancer cells, and its potential link to metastasis risk highlights its clinical relevance. Given the promising preliminary findings, more clinical trials are needed to validate circHIPK3 efficacy as a biomarker. Moreover, future research should determine if the mechanisms discovered in NSCLC apply to small cell lung cancer (SCLC) to investigate circHIPK3-targeted therapies for SCLC.

Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model

Cancer seems to have a vast number of deaths due to its heterogeneity, aggressiveness, and significant propensity for metastasis. The predominant categories of cancer that may affect males and females and occur worldwide are colon and lung cancer. A precise and on-time analysis of this cancer can increase the survival rate and improve the appropriate treatment characteristics. An efficient and effective method for the speedy and accurate recognition of tumours in the colon and lung areas is provided as an alternative to cancer recognition methods. Earlier diagnosis of the disease on the front drastically reduces the chance of death. Machine learning (ML) and deep learning (DL) approaches can accelerate this cancer diagnosis, facilitating researcher workers to study a vast majority of patients in a limited period and at a low cost. This research presents Histopathological Imaging for the Early Detection of Lung and Colon Cancer via Ensemble DL (HIELCC-EDL) model. The HIELCC-EDL technique utilizes histopathological images to identify lung and colon cancer (LCC). To achieve this, the HIELCC-EDL technique uses the Wiener filtering (WF) method for noise elimination. In addition, the HIELCC-EDL model uses the channel attention Residual Network (CA-ResNet50) model for learning complex feature patterns. Moreover, the hyperparameter selection of the CA-ResNet50 model is performed using the tuna swarm optimization (TSO) technique. Finally, the detection of LCC is achieved by using the ensemble of three classifiers such as extreme learning machine (ELM), competitive neural networks (CNNs), and long short-term memory (LSTM). To illustrate the promising performance of the HIELCC-EDL model, a complete set of experimentations was performed on a benchmark dataset. The experimental validation of the HIELCC-EDL model portrayed a superior accuracy value of 99.60% over recent approaches.

Evaluation and integration of cell-free DNA signatures for detection of lung cancer

Cell-free DNA (cfDNA) analysis has shown potential in detecting early-stage lung cancer based on non-genetic features. To distinguish patients with lung cancer from healthy individuals, peripheral blood were collected from 926 lung cancer patients and 611 healthy individuals followed by cfDNA extraction. Low-pass whole genome sequencing and targeted methylation sequencing were conducted and various features of cfDNA were evaluated. With our customized algorithm using the most optimal features, the ensemble stacked model was constructed, called ESim-seq (Early Screening tech with Integrated Model). In the independent validation cohort, the ESim-seq model achieved an area under the curve (AUC) of 0.948 (95 % CI: 0.915–0.981), with a sensitivity of 79.3 % (95 % CI: 71.5–87.0 %) across all stages at a specificity of 96.0 % (95 % CI: 90.6–100.0 %). Specifically, the sensitivity of the ESim-seq model was 76.5 % (95 % CI: 67.3–85.8 %) in stage I patients, 100 % (95 % CI: 100.0–100.0 %) in stage II patients, 100 % (95 % CI: 100.0–100.0 %) in stage III patients and 87.5 % (95 % CI: 64.6%–100.0 %) in stage IV patients in the independent validation cohort. Besides, we constructed LCSC model (Lung Cancer Subtype multiple Classification), which was able to accurately distinguish patients with small cell lung cancer from those with non-small cell lung cancer, achieving an AUC of 0.961 (95 % CI: 0.949–0.957). The present study has established a framework for assessing cfDNA features and demonstrated the benefits of integrating multiple features for early detection of lung cancer.

Vision Transformer Based Effective Model for Early Detection and Classification of Lung Cancer

Vision Transformer Based Effective Model for Early Detection and Classification of Lung Cancer

Lung cancer masquerading as COVID-19 in a young non-smoking woman: case report

Introduction and importance: The clinical and radiological similarities between COVID- 19 and lung cancer pose diagnostic challenges, particularly in young, non-smoking individuals. Ground glass opacities (GGO) on imaging, often associated with COVID- 19, can also indicate lung cancer. Distinguishing between these conditions is crucial but complex, requiring a systematic approach. Case Presentation: We present a case of a 31-year-old non-smoking woman initially suspected of COVID-19 due to cough, dyspnea, and GGO on chest CT. Despite negative RT-PCR and inconclusive bronchial aspiration, symptoms persisted, prompting further investigation. A PET scan revealed hypermetabolic consolidation, leading to a biopsy confirming adenocarcinoma. Clinical Discussion: Lung cancer can mimic COVID-19 symptoms, complicating diagnosis, especially in young, non-smoking patients. While smoking remains the primary risk factor, lung cancer in non-smokers, particularly young individuals, is increasingly recognized. GGO, commonly associated with COVID-19, should prompt consideration of malignancy, emphasizing the importance of a comprehensive differential diagnosis. Conclusion: Early detection of lung cancer in young, non-smoking individuals is vital yet challenging. Clinicians should maintain a high index of suspicion, promptly investigating persistent or worsening symptoms, even in the absence of traditional risk factors. Timely biopsy and intervention are critical for improving outcomes in this population.

AI-Enhanced Lung Cancer Prediction: A Hybrid Model's Precision Triumph.

Lung cancer is considered one of the most dangerous cancers, with a 5-year survival rate, ranking the disease among the top three deadliest cancers globally. Effectively combating lung cancer requires early detection for timely targeted interventions. However, ensuring early detection poses a major challenge, giving rise to innovative approaches. The emergence of artificial intelligence offers revolutionary solutions for predicting cancer. While marking a significant healthcare shift, the imperative to enhance artificial intelligence models remains a focus, particularly in precision medicine. This study introduces a hybrid deep learning model, incorporating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Networks (BiLSTM), designed for lung cancer detection from patients' medical notes. Comparative analysis with the MIMIC IV dataset reveals the model's superiority, achieving an MCC of 96.2% with an Accuracy of 98.1%, and outperforming LSTM and BioBERT with an MCC of 93.5 %, an accuracy of 97.0% and MCC of 95.5 with an accuracy of 98.0% respectively. Another comprehensive comparison was conducted with state-of-the-art results using the Yelp Review Polarity dataset. Remarkably, our model significantly outperforms the compared models, showcasing its superior performance and potential impact in the field. This research signifies a significant stride toward precise and early lung cancer detection, emphasizing the ongoing necessity for Artificial Intelligence model refinement in precision medicine.

Innovations in Early Lung Cancer Detection: Tracing the Evolution and Advancements in Screening.

Lung cancer mortality rates, particularly non-small cell lung cancer (NSCLC), continue to present a significant global health challenge, and the adoption of lung cancer screening remains limited, often influenced by inequities in access to healthcare. Despite clinical evidence demonstrating the efficacy of annual screening with low-dose computed tomography (LDCT) and recommendations from medical organizations including the U.S. Preventive Services Task Force (USPSTF), the national lung cancer screening uptake remains around 5% among eligible individuals. Advancements in the clinical management of NSCLC have recently become more personalized with the implementation of blood-based biomarker testing. Extensive research into tumor-derived cell-free DNA (cfDNA) through fragmentation offers a novel method for improving early lung cancer detection. This review assesses the screening landscape, explores obstacles to lung cancer screening, and discusses how a plasma whole genome fragmentome test (pWGFrag-Lung) can improve lung cancer screening participation and adherence.

Retrospective evaluation of plasma protein tumour markers for early lung cancer detection

BackgroundBlood-based biomarkers might help lung cancer diagnosis. A panel of serum tumour markers (TM) has been validated for hospital referrals due to clinical suspicion of lung cancer. We have compared plasma from a cohort enriched for early-stage lung cancer, including controls from a healthy population cohort.MethodsTM assays for CEA, CYFRA 21-1, CA15.3, ProGRP and SCC were run on a Roche Elecsys 2010 Immunoassay Analyser for a retrospective, nested case-control cohort from the Liverpool Lung Project. The primary endpoints were the sensitivity and specificity of a pre-defined TM panel using published thresholds.ResultsExcept for ProGRP, TM levels were significantly higher in cases and ROC AUC values demonstrated significant discriminant power. Accuracy and levels were higher for late-stage cancers, except for ProGRP which was highest in stage 1 cancers. Although providing similar sensitivity (82.4% vs 88.5%), TMs performed worse for specificity (39.3% vs 82%) and overall (Youden’s Index 0.22 vs 0.77) and this was not improved by threshold optimisation or binary logistic regression.ConclusionsAlthough the TMs were associated with lung cancer status and discriminatory with a high sensitivity when combined, performance was compromised in early-stage disease, which casts some doubt on utility in the screening setting.

In-training and private physicians' knowledge, attitudes, practices and perceived barriers on the utilization of low dose chest CT scan as lung cancer screening for high-risk patients in a tertiary hospital: Single center experience.

150 Background: Lung cancer is the leading cause of cancer-related deaths worldwide. Aside from smoking cessation efforts and guidelines from all proficient panels, utilizing Low dose CT scan (LDCT) of the chest as lung cancer screening (LCS) has been poorly adopted. Methods: This was conducted after institutional review board approval. The respondents were handed hard copy or electronic consent forms and survey questionnaire depending on the platform of their choice. Results: Majority of the respondents were in-training female physicians with median age of 31 years old who specialized in Internal Medicine practicing between 1-5 years who catered to more than 10 patients per week in outpatient clinics. Nearly half of the respondents answered that the recommended frequency for lung cancer screening (LCS) for high-risk individuals is done bi-annually. Majority agreed that lung cancer screening is effective in decreasing lung cancer mortality and morbidity. Most respondents have initiated discussion about the risks and benefits of low dose chest CT scan (LDCT) as lung cancer screening among eligible high-risk patients. However, most of the respondents disagreed that lung cancer screening is cost effective. The top three perceived barriers on the utilization of Low dose chest CT scan as lung cancer screening reported in descending order revealed that it is not cost-effective, some forgets to screen patients and lung cancer screening has the potential to cause mental anguish or anxiety to patients. Conclusions: This study recommends conducting further studies exploring the socio-economic burden and impact assessment of lung cancer in the Philippines to enable translation of more meaningful strategies for local and national lung cancer screening access, health policies and financing. Promoting continuous lung cancer screening forums, lectures and information dissemination of annual LDCT of the chest among high risk patients will encourage and strengthen interdepartmental and multidisciplinary referral and collaboration in increasing earlier detection of lung cancer and decreasing related morbidity and mortality.

(Invited) Multi-Modal Solid-State Gas Sensors-Based Breath Analysis System with Convolutional Neural Network (CNN) for Early Detection of Lung Cancer

Currently, digital healthcare technologies based on Internet of Things (IOT) are emerging, and the market for digital healthcare was estimated to be 151.5 billion dollars by 2020. Among the digital healthcare technologies, disease diagnosis is attracting maximum attention. Traditional diagnostic methods, such as radiology and human biological material analysis, have limitations of long study time, high cost, radiation exposure, and pain. Therefore, non-invasive diagnostics methods, for early diagnosis of specific diseases and personal healthcare management, have attracted attention.Breath sensors are one of the promising candidates for the realization of non-invasive diagnosis owing to their ease of use, safety, and cost effectiveness. The exhaled breath analysis technique holds great promise as a non-invasive method for diagnosing lung cancer. In the breath of lung cancer patients, various volatile organic compounds (VOCs) markers are generated during metabolic processes. Analyzing these VOCs markers presents a compelling avenue for early and non-invasive lung cancer diagnosis. Solid state gas sensors capable of measuring specific VOCs are currently being researched and utilized as commercial gas sensors for this purpose. However, the concentration of lung cancer VOCs markers in exhaled breath is commonly very low, and individual commercial gas sensors often lack the necessary sensitivity and selectivity to accurately diagnose lung cancer. In this study, we propose a multimodal solid state electronic biosensor system designed to differentiate lung cancer VOCs characteristics among multiple VOCs, enabling exhalation-based lung cancer diagnosis. Initially, we developed multimodal biosensors using more than 20 gas sensors, consisting of metal oxide semiconductor sensors, electrochemical sensors, photoionized detectors, metalorganic frame sensors and so on. And each exhibiting varying sensitivity profiles for complex gas mixtures. Additionally, we designed a classification algorithm based on convolutional neural networks (CNN) and multi-layered perceptron (MLP) to distinguish between lung cancer patients and healthy individuals using the output from these diverse multimodal gas sensors. To evaluate the system's performance, we collected exhaled air samples from 74 lung cancer patients and 107 normal subjects, conducting clinical trials with our developed system. We did model evaluation with confusion matrix and ROC curve. The results of these tests confirmed that our system achieved a high predictive accuracy of over 95% in classifying lung cancer patients. The simplicity of the sensor configuration in our developed system makes it suitable for large-scale screening tests to identify potential candidates for precise lung cancer diagnosis. This technology has the potential to contribute to reduced national welfare costs and improved patient survival rates through early diagnosis.

Machine learning-based SERS label-free detection of plasma and exosome binding in early-stage lung cancer

Lung cancer is extremely lethal and early screening can be an effective treatment for lung cancer. Liquid biopsies allow for rapid, non-destructive testing and are promising for early screening. Plasma is rich in biomarkers and has been widely used for cancer diagnosis. Exosomes derived from plasma are considered a more promising biomarker. This paper reports a label-free detection strategy for plasma and exosomes of cancer-free (HC), adenocarcinoma in situ (AIS) and early-stage invasive adenocarcinoma (IAC) using surface-enhanced Raman spectroscopy (SERS). SERS spectra of plasma and exosomes were trained one to each other using four machine learning algorithms. Notably, this work breaks new ground by proposing a SERS “Combined Spectrum” of plasma combined with exosomes. The SERS “Combined Spectrum” synthesizes SERS spectra of plasma and exosomes from the same case sample into a completely new spectrum for training purposes. Calculated by each of the four machine learning algorithms, the plasma and exosome SERS “Combined Spectrum” were calculated with an accuracy higher than 97 % and an AUC value higher than 0.9500. Based on these results, the combined SERS “Combined Spectrum” of plasma and exosome can be used for the early detection of lung cancer, and it also has a promising future for application in the detection of other diseases.

Assessing the diagnostic utility of serum tumor markers for lung cancer detection in patients with interstitial pneumonia.

The prevalence of lung cancer among individuals afflicted with interstitial pneumonia (IP) stands at approximately 20%. The early detection of lung cancer via chest computed tomography (CT) surveillance proves challenging in IP patients. Our investigation sought to identify a potential biomarker capable of providing early indications of the presence of lung tumors in such patients. We examined the attributes of serum tumor markers, imaging characteristics, and histological findings in individuals diagnosed with IP, both with and without concurrent lung cancer. 106 patients diagnosed with IP were included in the study, comprising 36 individuals with concurrent lung cancer and 70 patients solely diagnosed with IP. Serum concentrations of CEA and CA12-5 were notably elevated in IP patients with lung cancer, compared to those with IP alone. Logistic regression analyses revealed that, in comparison to IP patients within the first quartile of CEA levels, the relative risk of developing lung cancer associated with IP escalated by 4.0-fold, 3.1-fold, 11.0-fold, and 13.3-fold in the second, third, fourth, and fifth quartiles, respectively. Upon controlling for gender and age, statistical significance in risk was observed solely for the fourth and fifth quartiles. Receiver operating characteristic (ROC) curve analysis conducted in patients diagnosed with ILD-CA identified a CEA cutoff point of 6.9ng/mL, demonstrating sensitivities of 61.1% and specificities of 78.5%. The area under the curve was calculated as 0.7(95% CI: 0.63-0.81). The serum levels of CEA were notably elevated in IP patients with concurrent lung cancer in contrast to those who were just suffering from IP. The heightened serum CEA levels correlate with an escalated risk of cancer occurrence among IP patients, suggesting that serum CEA levels could potentially serve as an indicative marker for the presence of cancer in IP patients.

Circulating tumour DNA analysis for early detection of lung cancer: a systematic review.

Circulating tumor DNA (ctDNA) analysis has been applied in cancer diagnostics including lung cancer. Specifically for the early detection purpose, various modalities of ctDNA analysis have demonstrated their potentials. Such analyses have showed diverse performance across different studies. We performed a systematic review of original studies published before 1 January 2023. Studies that evaluated ctDNA alone and in combination with other biomarkers for early detection of lung cancer were included. The systematic review analysis included 56 original studies that were aimed for early detection of lung cancer. There were 39 studies for lung cancer only and 17 for pan-cancer early detection. Cancer and control cases included were heterogenous across studies. Different molecular features of ctDNA have been evaluated, including 7 studies on cell-free DNA concentration, 17 on mutation, 29 on methylation, 5 on hydroxymethylation and 8 on fragmentation patterns. Among these 56 studies, 17 have utilised different combinations of the above-mentioned ctDNA features and/or circulation protein markers. For all the modalities, lower sensitivities were reported for the detection of early-stage cancer. The systematic review suggested the clinical utility of ctDNA analysis for early detection of lung cancer, alone or in combination with other biomarkers. Future validation with standardised testing protocols would help integration into clinical care.