Early Lung Cancer Detection

High-resolution computed tomography in clinical T1 N0 M0 adenocarcinoma of the lung

Lung cancer remains one of the deadliest cancers worldwide, with survival rates significantly dependent on early detection. Traditional diagnostic methods, while effective, often identify lung cancer in advanced stages, limiting treatment options. Recently, artificial intelligence (AI) and deep learning (DL) models have gained attention for their potential to assist in early lung cancer diagnosis through radiological data, particularly computed tomography (CT) and X-ray imaging. This review examines recent advancements in AI and DL models specifically applied to early lung cancer detection, offering a comprehensive look at model architecture, data preprocessing techniques, and performance metrics. AI models, particularly those using convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown significant promise in improving diagnostic accuracy. In studies published over the past two years, CNN-based models have achieved sensitivity rates upwards of 90% when applied to large datasets, such as the LIDC-IDRI, a publicly available database containing thousands of annotated lung scans. Furthermore, hybrid approaches combining AI models with radiologist expertise have demonstrated reduced false positives, a frequent challenge in automated image analysis. A critical factor in these advancements has been data quality and volume. Large, annotated datasets with high-resolution images have enabled more robust training and validation, essential for refining these models. However, challenges remain, particularly regarding the standardization of data across institutions, variations in scanner quality, and ethical concerns surrounding patient privacy. The review also highlights studies exploring multi-modal approaches, integrating radiological data with clinical records and genetic markers to create more personalized diagnostic tools. These multi-modal methods have the potential to improve predictive accuracy further, though they currently require more extensive validation. Overall, while AI and DL technologies offer transformative potential for early lung cancer detection, widespread implementation depends on continued research in model accuracy, data standardization, and ethical safeguards. This paper concludes by emphasizing the need for collaboration across disciplines—AI researchers, radiologists, and oncologists—to refine these models into reliable tools that can be integrated seamlessly into clinical workflows for timely and accurate lung cancer diagnosis.

Exosomal lipids for classifying early and late stage non-small cell lung cancer

BackgroundCurrently, immunotherapy is widely used in the treatment of various stages of non-small cell lung cancer. According to clinical experience and results of previous studies, immunotherapy as neoadjuvant therapy seems to exhibit better efficacy against early resectable non-small cell lung cancer as compared to advanced lung cancer, which is often defined as unresectable non-small cell lung cancer. However, this observation has not been established in clinical studies. This systematic review aimed to evaluate the efficacy of immunotherapy in early and late lung cancer, wherein objective response rate (ORR) and disease control rate (DCR) were used as evaluation indexes. The present study also evaluated the safety of immunotherapy in early and late lung cancer, wherein the rate of treatment-related adverse reactions (TRAEs) was used as an indicator.MethodsElectronica databases, including PubMed, Cochrane Library, Embase, and other databases, were searched to identify relevant studies. Besides this, all the available reviews, abstracts, and meeting reports from the main international lung cancer meetings were searched manually. ORR, DCR, and TRAEs were extracted as the primary outcomes.ResultsA total of 52 randomized controlled trials involving 13,660 patients were shortlisted. It was observed that immunotherapy alone significantly improved DCR in early lung cancer in comparison to advanced lung cancer. Importantly, the improvement in ORR was not to the same extent as reported in the case of advanced lung cancer. The combination of immunotherapy with other therapies, especially immunochemotherapy, significantly improved ORR and DCR in early lung cancer. In terms of safety, immunotherapy either alone or in combination with other therapies exhibited a better safety profile in early lung cancer than in advanced lung cancer.ConclusionsAltogether, the benefits of immunotherapy in early lung cancer appeared to be better than those observed in advanced lung cancer, especially with the regard to the regimen of immunotherapy in combination with chemotherapy. In terms of safety, both immunotherapy alone and its combination with chemotherapy were found to be safer in early lung cancer as compared to advanced lung cancer.

Carcinogenesis.- Inhalation Carcinogenesis: An Overview.- Lung Carcinogenesis During in Vivo Cigarette Smoking and Radon Daughter Exposure in Rats.- Epithelial Lesions Induced by Alpha Particles and Cigarette Smoke Condensates in Organotypic Culture of Human Bronchus.- Asbestos Carcinogenesis: Asbestos Interactions and Epithelial Lesions in Cultured Human Tracheobronchial Tissues and Cells.- Histogenesis.- The Contribution of Uranium Miners to Lung Cancer Histogenesis.- Pathogenesis of Bronchial Carcinoma, with Special Reference to Morphogenesis and the Influence on the Bronchial Mucosa of 20-Methylcholanthrene and Cigarette Smoking.- Lung Cancer Histogenesis Following in Vivo Bronchial Injections of 20-Methylcholanthrene in Dogs.- Localization of Occult Lung Cancer.- Bronchoscopie Localization of Radiologically Occult Cancer.- Hematoporphyrin Derivative as a Tumor Marker in the Detection and Localization of Pulmonary Malignancy.- Fluorescence Bronchoscopy for Localizing Early Bronchial Cancer and Carcinoma in Situ.- Fluorescence Fiberoptic Bronchoscopy in the Diagnosis of Early Stage Lung Cancer.- Results of Early Lung Cancer Detection Programs.- Results of a Lung Cancer Detection Program in an Asbestos Industry.- Lung Cancer Mortality in Males Screened by Chest X-ray and Cytologic Sputum Examination: A Preliminary Report.- Sputum Cytology and Asbestos Exposure: A Preliminary Report.- Lung Cancer Screening Programs in Canadian Uranium Mines.- Detection of Early and Roentgenologically Occult Bronchogenic Carcinoma: Preliminary Report of the Sabbatsberg Hospital Sputum Cytologic Screening Study.- Results of Lung Cancer Screening Programs in Japan.- Results of the Memorial Sloan-Kettering Lung Project.- Results of the Mayo Lung Project: An Interim Report.

Lung cancer remains the leading cause of mortality from cancer. This chapter describes the evolution and results of screening studies using these techniques and the controversies that developed surrounding the results of these studies. It explores several emerging technologies for early lung cancer detection, with a special emphasis on low-dose spiral computed tomography (CT). Screening studies for lung cancer date to the 1950s and 1960s, when several studies were undertaken using a variety of screening protocols that combined chest radiography and sputum analysis. Low-dose spiral CT has been explored as a tool for detecting early lung cancer in asymptomatic individuals at risk for the disease, with encouraging preliminary results. The light-induced fluorescence endoscopy device, which was designed to capitalize on differences in autofluorescence properties in order to aid in the detection and localization of preinvasive lung cancer, has been approved by the FDA for the detection of early lung cancer.

Treatment of "early" stage lung cancer should offer the patient the best chance for cure. Disease-free survival after surgical resection of lung carcinoma in situ has been reported as over 90%. For "microinvasive" lung cancer it may be similar. After resection of stage IA non-small cell lung cancer, survival at 5 years is approximately 60 to 70%. If endoscopic or bronchoscopic treatments of early stage lung cancer can offer similar disease-free survival with less perioperative mortality, morbidity, and cost, then they may be alternative front-line therapies. Regardless of therapeutic choice, the initial hurdle is developing a practical detection method for early stage disease. This article reviews early stage lung cancer detection by fluorescence bronchoscopy and potential treatment by the endoscopic techniques of photodynamic therapy, brachytherapy, neodymium:yttrium-aluminum-garnet (Nd:YAG) laser, electrocautery, and cryotherapy.

Lung cancer remains one of the leading causes of death worldwide, with many sufferers unaware of their condition until it is too late for treatment. Therefore, high-accuracy prediction methods are urgently needed for early detection of lung cancer. This research uses the Random Forest algorithm, known for its excellent performance in medical data classification. In this study, modeling was optimized by implementing hyperparameter optimization using Optuna. The results of the generated model show an accuracy rate of 98.6%, which is highly significant in the context of early lung cancer detection. Additionally, this algorithm demonstrated 100% recall for the positive class and 97% for the negative class, indicating that the model is highly effective in identifying patients who truly have lung cancer. Another advantage of this model is seen in the AUC (Area Under the Curve) value reaching 1, indicating 100% accurate predictions. With these results, this research affirms the importance of using the Random Forest algorithm in developing early detection systems for lung cancer. This not only can improve treatment success rates but also significantly reduce mortality rates from lung cancer.

P1.11-15 Feasibility of a Lung Health Clinic for Early Lung Cancer Identification in High-Risk Individuals in South-East London

Lung cancer remains the leading cause of cancer-related mortality worldwide, highlighting the urgent need for earlier detection within real-world screening and patient management pathways. Recent advances in multi-omics technologies have created new opportunities for identifying biomarkers associated with early-stage lung cancer, particularly in high-risk populations under clinical surveillance. This review systematically evaluates early diagnostic biomarkers across multiple omics layers, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics. It also summarises the application of artificial intelligence (AI), particularly machine learning and deep learning approaches, for integrating and analysing complex multi-omics datasets to support biomarker discovery and clinical decision-making. Multi-omics strategies are accelerating the identification of molecular signatures relevant to early lung cancer detection. AI-driven methods enable the extraction of latent patterns from high-dimensional data, facilitating risk stratification, diagnostic refinement, histological subtyping and treatment planning. The review highlights the clinical utility of these biomarkers and their potential incorporation into screening algorithms, as well as the development of AI-based clinical decision support systems (CDSS) aligned with real-world clinical workflows. However, major barriers to clinical translation remain, including multi-centre data heterogeneity, limited model interpretability affecting clinical trust, regulatory and cost-effectiveness challenges and insufficient validation in prospective cohorts. Emerging technologies, such as single-cell and spatial multi-omics, along with federated learning frameworks, offer promising solutions to bridge the gap between computational discovery and clinical implementation. The integration of AI and multi-omics approaches has the potential to advance risk-adapted and personalised early detection strategies for lung cancer.

PURPOSE: Screening for lung cancer using a biomarker blood test has the potential to overcome the currently low adherence rates in the US, offering patients a convenient and accessible option. Integrating routine blood draws into lung cancer screening could boost participation, mirroring the success of non-invasive tests in colorectal screening. Numerous proof-of-concept studies indicate that analyzing genome-wide cfDNA fragmentation patterns can effectively distinguish between individuals with and without lung cancer. We conducted a multi-institutional case-control study to train and validate a fragmentome based cfDNA classifier for lung cancer early detection. METHODS: The DELFI-L101 study (NCT04825834) is a prospective, multi-site, observational case-control study designed to develop and validate an early detection test for lung cancer. The DELFI (DNA Evaluation of Fragments for Early Interception) method utilizes low-pass whole genome sequencing, a cost-effective approach that examines cell-free DNA (cfDNA) fragmentation patterns associated with lung cancer. Enrolled participants were aged ≥50 y, smoking history of ≥20 pack-years, and had undergone or had planned chest CT imaging. Medical histories were documented, and blood samples were collected for DELFI analysis. A classifier, developed through supervised machine learning, was created to distinguish between samples from individuals with lung cancer and those without. Performance was assessed through repeated 10-fold cross-validation in the training set and then subsequently assessed in an independent split-study clinical validation set. The classifier was optimized for the sensitivity detection of lung cancer in the screening-eligible population. Performance was evaluated across patient and tumor characteristics, adjusting the stage distribution to match that of LDCT screening populations. RESULTS: In an analysis of 958 participants from both the lung cancer and non-cancer control groups, the sensitivity of the test remained consistent across various participant demographics, comorbidities, and tumor characteristics, increasing with higher group, T, and N stages. The stage weighted sensitivity for the screening population was 80% (95% CI: 75%-86%), with a specificity of 53% (95% CI: 43%-64%). At a prevalence of 0.7%, the negative predictive value was 99.7%. CONCLUSIONS: Clinical validation of the DELFI-based blood test demonstrates its potential to detect lung cancer within the screening eligible population. A negative result is associated with a high negative predictive value. Given the limited adoption of low-dose computed tomography (LDCT) screening, especially among socio-economically disadvantaged individuals, this clinically validated, blood-based cfDNA test could significantly increase LDCT screening participation. Citation Format: Peter Mazzone, Peter B. Bach, Lindsey B. Cotton, Caitlin A. Schonewolf. Clinical validation: A blood-based biomarker for early lung cancer detection based on circulating DNA fragmentomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1062.

Background: Improving prediction models to timely detect lung cancer is paramount. Our aim is to develop and validate prediction models for early detection of lung cancer in primary care, based on free-text consultation notes, that exploit the order and context among words and sentences. Methods: Data of all patients enlisted in 49 general practices between 2002 and 2021 were assessed, and we included those older than 30 years with at least one free-text note. We developed two models using a hierarchical architecture that relies on attention and bidirectional long short-term memory networks. One model used only text, while the other combined text with clinical variables. The models were trained on data excluding the five months leading up to the diagnosis, using target replication and a tuning set, and were tested on a separate dataset for discrimination, PPV, and calibration. Results: A total of 250,021 patients were enlisted, with 1507 having a lung cancer diagnosis. Included in the analysis were 183,012 patients, of which 712 had the diagnosis. From the two models, the combined model showed slightly better performance, achieving an AUROC on the test set of 0.91, an AUPRC of 0.05, and a PPV of 0.034 (0.024, 0.043), and showed good calibration. To early detect one cancer patient, 29 high-risk patients would require additional diagnostic testing. Conclusions: Our models showed excellent discrimination by leveraging the word and sentence structure. Including clinical variables in addition to text slightly improved performance. The number needed to treat holds promise for clinical practice. Investigating external validation and model suitability in clinical practice is warranted.

Early Detection of Lung Cancer with Meso Tetra (4-Carboxyphenyl) Porphyrin-Labeled Sputum

Surface-enhanced Raman spectroscopy (SERS) provides a rapid and nondestructive method for biological plasma analysis, offering unparalleled sensitivity and specificity. However, most current studies predominantly employ the drop-cast method, where liquid samples are dried on the SERS substrate for spectral recording. While effective, this method is both time-consuming and inconsistent. Additionally, the Raman spectral signal of uric acid in plasma often dominates, overshadowing other crucial biomarkers necessary for disease differentiation. In this study, we present the application of label-free SERS detection for distinguishing early stage lung cancer (LC) from healthy controls (HC) using plasma samples. Plasma was diluted, adjusted to a weakly acidic pH, and combined with silver nanoparticles (Ag NPs) for SERS analysis. To maintain the plasma in a liquid state and prevent evaporation, liquid paraffin was employed. Our findings indicate that a 10-fold dilution of plasma under weakly acidic conditions significantly enhanced diagnostic performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.975. This method is featured by its simplicity and high efficiency, presenting a promising strategy for the minimally invasive diagnosis of early stage LC using SERS technology.

The Center for Global Early Detection at MD Anderson has initiated two longitudinal early detection studies: LEAP (Lung Cancer Early Detection and Prevention, 2013-2019) for lung cancer screening and MERIT (Mammography Early Detection Risk Assessment and Imaging Technologies, 2017-ongoing) for breast cancer screening. Both cohorts enrolled subjects based on US standard-of-care guidelines at the time of enrollment for low-dose CT screening and mammography, respectively. The cohorts collected detailed health data from questionnaires, imaging data, and blood specimens at the time of screening. In addition to information on the screened-detected cancers, follow-up information was collected on any other types of cancers that are diagnosed after enrollment. Here, we evaluate the incidence of other cancers without available screening to assess the potential impact of multi-cancer early detection studies for these two types of screening populations. The two studies enrolled approximately 9,300 subjects (N = 2853 for LEAP; N = 6,400 for MERIT) with median follow up times of 2.99 years (IQR: 1.09-4.08) for LEAP and 2.05 years (IQR: 0.09-3.01) for MERIT. Currently, there are more than 800,000 aliquots of banked biospecimens from both cohorts. The incidence of lung cancer for the LEAP cohort undergoing low-dose CT screening was 0.91% per year. Cancers other than lung cancer were also diagnosed after enrollment including prostate, breast, bladder, head, and neck, colorectal, lymphoma, endocrine, gynecologic, and kidney cancers. Overall, the incidence of other cancers in LEAP was high for those cancers without available screening - 0.49% per year excluding breast, prostate, and colorectal cancers. The incidence of breast cancer for women in LEAP was lower than expected, 0.17% per year, likely a result of underscreening by mammography. For the MERIT cohort, the incidence of breast cancer was 0.89% per year. Cancers other than breast were diagnosed in MERIT after enrollment including gynecologic, melanoma, lung, colorectal, endocrine, bladder, and gastrointestinal cancers. The incidence of other cancers in MERIT was 0.55% per year, excluding colorectal cancer. We conclude that the incidence of cancers without a current screening modality in LEAP and MERIT is sufficiently high (0.49-0.55% per year) to justify the application of multi-cancer screening. Even for cancers with standard-of-care screening, a multi-cancer blood-based biomarker test could be useful given the possibility of underscreening, as we observed for breast cancer in the LEAP cohort. Citation Format: Jennifer Dennison, Ehsan Irajizad, Edwin Ostrin, Myrna Godoy, Olena Weaver, Jessica Leung, Samir Hanash. Application of multi-cancer early detection to lung and breast cancer screening populations. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P057.