Diagnosis Of Early Stage Lung Cancer Research Articles

Multi-center study on the application potential of Siaα-2,6Gal in early and differential diagnosis of lung cancer.

This study aimed to investigate the application potential of the abnormal glycan structure Siaα-2,6Gal in the early and differential diagnosis of lung cancer. Clinical data and serum samples from 730 patients and 120 healthy individuals participating in clinical trials on Siaα-2,6Gal were collected at three medical centers between January 2022 and June 2023. The levels of Siaα-2,6Gal, carcinoembryonic antigen (CEA), cytokeratin 19 fragment antigen (CYFRA21-1), squamous cell carcinoma antigen (SCC), neuron-specific enolase (NSE), and pro-gastrin-releasing peptide (ProGRP) in serum were measured. The application potentials of these markers in the early and differential diagnosis, classification, and staging of lung cancer were explored. (1) Serum Siaα-2,6Gal levels in the lung cancer group were 2,606 (1,970-3,458) U/mL, significantly higher than those in the benign lung disease, miscellaneous malignant tumor, miscellaneous benign disease, and healthy individual groups at 1,359 (950-1,528), 1,252 (903-1,532), 1,196 (850-1,490), and 1,210 (1,100-1,287) U/mL (P < 0.0001). (2) Serum Siaα-2,6Gal levels in the early-stage lung cancer (stages 0-II) group were 2,576 (1,929-3,338) U/mL, significantly higher than those in the benign pulmonary nodule group at 1,419 (1,105-1,820) U/mL (P < 0.0001). (3) Receiver operating characteristic curves showed that Siaα-2,6Gal had a high diagnostic efficiency for lung cancer (area under the curve (AUC) = 0.9217), significantly superior to CEA, CYFRA21-1, SCC, NSE, and ProGRP (AUCs of 0.6618, 0.6605, 0.5783, 0.5985, and 0.6381). Siaα-2,6Gal is a promising biomarker for lung cancer diagnosis and may offer superior differential diagnosis of early-stage lung cancer from benign pulmonary nodules compared to traditional tumor markers.

P2.04B.11 Factors Associated with Early-Stage Lung Cancer Diagnosis (ESLCD) In Brazil - 2013 - 2019: A Multivariable Analysis

P2.04B.11 Factors Associated with Early-Stage Lung Cancer Diagnosis (ESLCD) In Brazil - 2013 - 2019: A Multivariable Analysis

Clinical application of convolutional neural network lung nodule detection software: An Australian quaternary hospital experience.

Early-stage lung cancer diagnosis through detection of nodules on computed tomography (CT) remains integral to patient survivorship, promoting national screening programmes and diagnostic tools using artificial intelligence (AI) convolutional neural networks (CNN); the software of AI-Rad Companion™ (AIRC), capable of self-optimising feature recognition. This study aims to demonstrate the practical value of AI-based lung nodule detection in a clinical setting; a limited body of research. One hundred and eighty-three non-contrast CT chest studies from a single centre were assessed for AIRC software analysis. Prospectively collected data from AIRC detection and characterisation of lung nodules (size: ≥3 mm) were assessed against the reference standard; reported findings of a blinded consultant radiologist. One hundred and sixty-seven CT chest studies were included; 52% indicated for nodule or lung cancer surveillance. Of 289 lung nodules, 219 (75.8%) nodules (mean size: 10.1 mm) were detected by both modalities, 28 (9.7%) were detected by AIRC alone and 42 (14.5%) by radiologist alone. Solid nodules missed by AIRC were larger than those missed by radiologist (11.5 mm vs 4.7 mm, P < 0.001). AIRC software sensitivity was 87.3%, with significant false positive and negative rates demonstrating 12.5% specificity (PPV 0.6, NPV 0.4). In a population of high nodule prevalence, AIRC lung nodule detection software demonstrates sensitivity comparable to that of consultant radiologist. The clinical significance of larger sized nodules missed by AIRC software presents a barrier to current integration in practice. We consider this research highly relevant in providing focus for ongoing software development, potentiating the future success of AI-based tools within diagnostic radiology.

Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection.

As a lung cancer biomarker, exosomes were utilized for in vitro diagnosis to overcome the lack of sensitivity of conventional imaging and the potential harm caused by tissue biopsy. However, given the inherent heterogeneity of exosomes, the challenge of accurately and reliably recognizing subtle differences in the composition of exosomes from clinical samples remains significant. Herein, we report an artificial intelligence-assisted surface-enhanced Raman spectroscopy (SERS) strategy for label-free profiling of plasma exosomes for accurate diagnosis of early-stage lung cancer. Specifically, we build a deep learning model using exosome spectral data from lung cancer cell lines and normal cell lines. Then, we extracted the features of cellular exosomes by training a convolutional neural network (CNN) model on the spectral data of cellular exosomes and used them as inputs to a support vector machine (SVM) model. Eventually, the spectral features of plasma exosomes were combined to effectively distinguish adenocarcinoma in situ (AIS) from healthy controls (HC). Notably, the approach demonstrated significant performance in distinguishing AIS from HC samples, with an area under the curve (AUC) of 0.84, sensitivity of 83.3%, and specificity of 83.3%. Together, the results demonstrate the utility of exosomes as a biomarker for the early diagnosis of lung cancer and provide a new approach to prescreening techniques for lung cancer.

Early stage lung cancer detection from speech sounds in natural environments

In the diagnosis of early-stage lung cancer, conventional methods often rely on periodic imaging techniques using medical devices. However, recent studies suggest that speech sounds could offer valuable insights into the diagnosis of disease. This study investigates the different characteristics of speech sounds recorded in natural environments between individuals diagnosed with lung cancer and those who are healthy. Using signal processing techniques and a self-supervised contrastive learning approach, we investigate the classification of these speech sounds for the diagnosis of early-stage lung cancer. Our results show that it is possible to utilize naturally recorded speech sounds. Using the Graph Attention Transformer Fine-Tuning Contrastive Learning (GAT-ftCL) model, which leverages graph neural networks to capture complex relationships in data and fine-tunes the learning process through contrastive learning, we achieve an accuracy of 90.90% in distinguishing individuals diagnosed with lung cancer from healthy individuals. Furthermore, the model achieves a remarkable accuracy of 92.85% in specifically identifying individuals with early stage (stage 1) lung cancer within the stage 1 group and healthy individuals. These results underline the diagnostic potential of natural speech sounds, especially in the detection of early-stage lung cancer.

Factors associated with early-stage lung cancer diagnosis (ESLCD) in Brazil, 2013-2019: A multivariable analysis.

e20091 Background: Lung cancer has the highest mortality rate among the leading deadly cancers worldwide. Despite improvements in tobacco cessation and treatment over the years, disparities in access to care, timely diagnosis, and proper treatment remain prevalent. This study aims to identify the factors associated (FA) with Early-Stage Lung Cancer Diagnosis (ESLCD) in Brazil. Methods: This retrospective cohort study includes individuals diagnosed with lung cancer from January 2013 to December 2019, registered in the Brazilian hospital-based Cancer Registry (HBCR). Descriptive and bivariate analysis was performed, and T-tests and Chi-square were used to compare early vs late-stage groups. Multivariable backward stepwise logistic regression identified FA with ESLCD at a 95% confidence interval (CI). Results: The study was conducted with data on 86,558 individuals registered in the HBCR; 56.6% were men, 64 years old on average, and 54.8% were white. We found a high percentage of patients without education information (23.9%), and 70% had completed high school. As for behavioral characteristics, only 20% of the patients had never smoked; the others were current smokers (40%) or former smokers (40%). Almost half of the patients were from the Southeast region of Brazil (44.6%), 27% from the South, 19.9% from the Northeast, 3.9% from the North and 3.9% from the Midwest. The vast majority of the individuals were diagnosed at a late stage (85.6%), while 14.4% were ESLCD. In the bivariate analysis, being less than 65 years old, being female, white, never smoking, having a college degree, and living in the south or southeast regions were associated with ESLCD. In the final multivariable model the Factors Associated with ESLCD were having less than 65 years (OR=1.28 [95% CI: 1.22-1.34]), of the female gender (OR=1.26 [95% CI: 1.22-1.32]), having education greater than a college degree (OR=1.49 [95% CI: 1.38-1.61]), and living on the southeast (OR=1.22 [95% CI: 1.15-1.30]); and in the south region (OR=1.21 [95% CI: 1.18-1.32]); which are the wealthiest regions in the country. Conclusions: The education level was the most decisive factor associated with ESLCD. Geographic location played an important role in ESLCD, indicating issues related to wealth and access to health services. Improving access to lung cancer diagnosis, especially for vulnerable populations, can reduce disparities in the rates of early-stage diagnosis and improve overall survival. [Table: see text]

Accurate identification of lung cancer in the early phase by serum exosomal miR-1271-5p.

e20029 Background: Exosomal miRNAs are associated with the proliferation, migration, and invasion of lung cancer cells. We aim to investigate the clinical significance of serum exosomal miR-1271-5p in early-stage lung cancer (ESLC) mainly characterized by pulmonary nodules to push forward the prevention and treatment of lung cancer. Methods: The serum exosome (sEVs) miR-1271-5p was obtained from a total of 134 subjects from the patients with ESLC and the benign nodule (BLN) and healthy controls, and the expression level of miR-1271-5p were detected by RT-qPCR. Through analysis the clinical feature and CT (Computed Tomography) imaging finding of the subjects confirmed by pathology, independent risk factors for ESLC were obtained according to univariate and multivariate logistic regression analysis. R software was incorporated to build a mathematical prediction model of nomogram, and the clinical significance of sEVs-miR-1271-5p in the prediction and diagnosis of ESLC was verified by ROC curve. Results: The relative expression of sEVs-miR-1271-5p was lower in the patients with ESLC than the patients with BLN and healthy controls. The AUC value of the ESLC predicted by sEVs-miR-1271-5p was 0.9845 (95%CI: 0.9649-1.000, P &lt; 0.0001). The results of multi-factor logistic regression analysis showed that gender (OR = 0.213, p = 0.046), nodule diameter (OR = 0.794, p = 0.031), lobulation (OR = 0.013, p &lt; 0.0001), and sEVs-miR-1271-5p (OR = 0.014, p = 0.037) were independent risk factors for ESLC. Based on these independent risk factors, a prediction model for ESLC was established, with an AUC value of 0.8810 (95% CI: 0.8076-0.9553, P &lt; 0.0001), the sensitivity, specificity, positive and negative predictive value and accuracy of the model were 85%, 80.43%, 79.07%, 86.05% and 82.56% respectively. Conclusions: Serum exosomal miR-1271-5p may break through the early diagnosis of lung cancer and provide important therapeutic targets.

Plasma Metabolite Profiling in the Search for Early-Stage Biomarkers for Lung Cancer: Some Important Breakthroughs.

Lung cancer is the leading cause of cancer-related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster, and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already been shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterized by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation to their metabolic pathways and in light of the altered cancer metabolism.

Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning.

Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.

Incidental diagnosis of lung cancer on chest CT scan performed for suspected or documented COVID-19 infection

ContextRecent studies have shown a benefit of chest computed tomography (CT scan) in lung cancer screening. The COVID-19 pandemic has led to many chest CT scan performed on a large population. The objective of this study was to describe the incidence and characteristics of lung cancer detected on chest CT scan, outside the framework of a clinical trial, for a suspected or documented COVID-19 infection. MethodsWe conducted a multicenter study, carried out from the analysis of data from the prospective COVID-19 database of the Clinical Data Warehouse of the Greater Paris University Hospitals (AP-HP). We identified the patients who had been diagnosed with a lung cancer, due to a chest CT scan done for a suspected or confirmed COVID-19 infection. The study period was limited to the first two epidemic lockdowns: (03/01/20 - 05/31/20) and (10/10/20 - 11/30/20). ResultsOver the study period, 24 390 patients had at least one chest CT scan. Among them, 72 lung cancer diagnoses were made (incidence 0.30 %; median age 67.4 years old, 50.0 % current smokers, 55.6 % adenocarcinoma). Half of the lung cancer patients (n = 36) did not meet the National Lung Screening Trial inclusion criteria. Twenty-six patients (36.1 %) were diagnosed at an early stage, 25 (34.7 %) of whom received radical curative treatment. Twenty-six patients died during the follow-up (36.1 %) but none in early stages. The median overall survival in lung cancer patients was 693 days [532 – NA]. ConclusionsA large-scale chest CT scan strategy for suspected or documented COVID-19 infection has allowed a significant proportion of early-stage lung cancer diagnosis, all of which have benefited from curative treatment.

A collection of multiregistry data on patients at high risk of lung cancer-a Danish retrospective cohort study of nearly 40,000 patients.

Lung cancer (LC) is the leading cause of cancer related deaths, and several countries are implementing screening programs. Risk models have been introduced to refine the LC screening criteria, but the use of real-world data for this task demands a robust data infrastructure and quality. In this retrospective cohort study, we aim to address the different relevant risk factors in terms of data sources, descriptive statistics, completeness and quality. Data on comorbidity, prescription medication, smoking history, consultations, symptoms, familial predispositions, exposures, laboratory data among others were collected for all patients examined on a risk of LC over a 10-year period in the Region of Southern Denmark. Data were delivered from the regional data warehouse as well as the Danish Lung Cancer Registry. Associations between LC and non-LC groups were examined through Chi-squared test (categorical variables) and Wilcoxon signed-rank test (continuous variables that were non-parametric). These associations were investigated on both the original datasets and the subset of patients with complete data. The number of examined individuals increased over the study period and more patients were diagnosed with LC in stage I-II, from 18% in 2009 to 31% in 2018. LC patients were more likely to be older, smoker, with a registered prescription of the included medication. They also exhibited differences in laboratory analysis indicating inflammation and hyponatremia. Weight loss, fatigue and pain were more prevalent in the LC group, while hemoptysis and fever were more common among the non-LC patients. Advanced-stage LC patients experienced a higher rate of symptoms compared to those in the low stages. Within the sub-cohort with complete dataset results, most observed trends persisted, although data on comorbidities were susceptibility to change. This study provides key insights into LC risk assessment using a robust dataset of patients examined for suspected LC. A consistent positive trend in early-stage LC diagnosis was observed throughout the study period. LC patients exhibited distinct smoking behaviors, medication patterns, variations in lab results, and specific symptoms. These discoveries have the potential to enhance discrimination in machine learning-based prediction models, particularly those capable of handling complex distributions. Serving as a detailed account of real-world data collection and processing, the study establishes a foundation for future development of prediction models aimed at facilitating the early referral of LC patients.

Prevalence and timing of chronic obstructive pulmonary disease (COPD) diagnosis among patients newly diagnosed with lung cancer in the US.

118 Background: COPD is one of the most common comorbidities among patients with lung cancer, and an important determinant of their survival. While COPD is frequently under diagnosed in the general population, less is known about the occurrence of COPD underdiagnosis in patients with lung cancer. Objectives: 1) To estimate prevalence and under diagnosis of COPD among patients newly diagnosed with lung cancer. 2) To quantify the association between timing of COPD diagnosis and stage of lung cancer at diagnosis and whether this association is modified by sex, race, and socioeconomic status (SES). Methods: A secondary data analysis was conducted using the SEER-Medicare database. We included Medicare beneficiaries aged 66+years with primary invasive lung cancer diagnosed from 2008 to 2017 who were alive at the time of cancer diagnosis and had continuous Medicare coverage 12 months prior and 3 months after lung cancer diagnosis. Exposure: Comorbid COPD was determined using Medicare claims and classified as early COPD (evidence of COPD related healthcare utilization (HCU) &gt;3 months before lung cancer diagnosis) versus late COPD (evidence of COPD related HCU only during the +/- 3 months from lung cancer diagnosis). Outcome: Early (localized) versus late (regional and distant) stage of lung cancer diagnosis. Statistical analysis: We used generalized linear models to estimate adjusted absolute (prevalence difference PD) and relative (prevalence ratio PR) association. Results: In a population of 185,405 Medicare beneficiaries with lung cancer, 131,230 (71%) had evidence of COPD. Compared to lung cancer patients without COPD, those with COPD were more likely to be younger, male, White, of lower SES, have more comorbidities, higher HCU, and be diagnosed with early stage lung cancer. Patients with early COPD diagnosis constitute 66,867 (51%) of patients with COPD and lung cancer. They were more likely to be older, female, Non-Hispanic White, of lower SES, and to have more comorbidities, acute exacerbation of COPD, and more early-stage lung cancer compared to patients with late COPD diagnosis. We observed a significant positive association between early COPD diagnosis and early-stage lung cancer diagnosis (PR= 1.27, 95% CI= 1.24 to 1.30; PD= 0.062, 95% CI= 0.057 to 0.066) in models adjusted for age, sex, race, SES, HCU, comorbidities, year of cancer diagnosis, SEER region, and histology. In subgroup analyses, this association was stronger for males, Non-Hispanic Black, American Indian Alaskan Native, and patients of lower SES. Conclusions: Approximately seven out of 10 patients with lung cancer have COPD, and late COPD diagnosis is common. Earlier COPD diagnosis is associated with diagnosis of lung cancer at earlier stages. These findings highlight the need for more structured screening of COPD among individuals at high risk for lung cancer.

Deep Convolutional Generative Adversarial Networks for Automated Segmentation and Detection of Lung Adenocarcinoma Using Red Deer Optimization Algorithm

The diagnosis of early-stage lung cancer can be challenging due to several factors. Firstly, the asymptomatic nature of the disease means that it may not present any noticeable symptoms until it has progressed to later stages. Additionally, the use of computed tomography, which can be expensive and involves repetitive radiation exposure, can further complicate the diagnostic process. Even specialists may encounter difficulties when examining lung CT imagery to identify pulmonary nodules, particularly in the case of cell lung adenocarcinoma lesions.This paper suggests a unique deep learning-based Deep Convolutional Generative Adversarial Networks (DCGAN) model for lung cancer classification. The dataset utilized for the experimental purpose is accessed from the LUNA16 challenge database. This comprises 888 CT scans of the lungs. These images are initially segmented using Quick-CapsNet (QCN) model and applied with Red Deer Optimization (RDO) algorithm to extract the optimized features. Furthermore, the categorization between benign and malignant tumors is carried out using the DC-GAN model. The pulmonary nodule detection accuracy of the proposed model is 98.65%, indicating early-stage lung cancer. It is discovered to be superior to other existing techniques, such as sophisticated deep learning, straightforward machine learning, and hybrid methods applied to lung CT scans for nodule diagnosis. According to experimental findings, the suggested way can significantly help radiologists spot early lung cancer and facilitate prompt patient management.

A machine learning-based PET/CT model for automatic diagnosis of early-stage lung cancer.

The aim of this study was to develop a machine learning-based automatic analysis method for the diagnosis of early-stage lung cancer based on positron emission tomography/computed tomography (PET/CT) data. A retrospective cohort study was conducted using PET/CT data from 187 cases of non-small cell lung cancer (NSCLC) and 190 benign pulmonary nodules. Twelve PET and CT features were used to train a diagnosis model. The performance of the machine learning-based PET/CT model was tested and validated in two separate cohorts comprising 462 and 229 cases, respectively. The standardized uptake value (SUV) was identified as an important biochemical factor for the early stage of lung cancer in this model. The PET/CT diagnosis model had a sensitivity and area under the curve (AUC) of 86.5% and 0.89, respectively. The testing group comprising 462 cases showed a sensitivity and AUC of 85.7% and 0.87, respectively, while the validation group comprising 229 cases showed a sensitivity and AUC of 88.4% and 0.91, respectively. Additionally, the proposed model improved the clinical discrimination ability for solid pulmonary nodules (SPNs) in the early stage significantly. The feature data collected from PET/CT scans can be analyzed automatically using machine learning techniques. The results of this study demonstrated that the proposed model can significantly improve the accuracy and positive predictive value (PPV) of SPNs at the early stage. Furthermore, this algorithm can be optimized into a robotic and less biased PET/CT automatic diagnosis system.

Natural Language Processing for the Identification of Incidental Lung Nodules in Computed Tomography Reports: A Quality Control Tool.

To evaluate the diagnostic performance of a natural language processing (NLP) model in detecting incidental lung nodules (ILNs) in unstructured chest computed tomography (CT) reports. All unstructured consecutive reports of chest CT scans performed at a tertiary hospital between 2020 and 2021 were retrospectively reviewed (n = 21,542) to train the NLP tool. Internal validation was performed using reference readings by two radiologists of both CT scans and reports, using a different external cohort of 300 chest CT scans. Second, external validation was performed in a cohort of all random unstructured chest CT reports from 57 different hospitals conducted in May 2022. A review by the same thoracic radiologists was used as the gold standard. The sensitivity, specificity, and accuracy were calculated. Of 21,542 CT reports, 484 mentioned at least one ILN (mean age, 71 ± 17.6 [standard deviation] years; women, 52%) and were included in the training set. In the internal validation (n = 300), the NLP tool detected ILN with a sensitivity of 100.0% (95% CI, 97.6 to 100.0), a specificity of 95.9% (95% CI, 91.3 to 98.5), and an accuracy of 98.0% (95% CI, 95.7 to 99.3). In the external validation (n = 977), the NLP tool yielded a sensitivity of 98.4% (95% CI, 94.5 to 99.8), a specificity of 98.6% (95% CI, 97.5 to 99.3), and an accuracy of 98.6% (95% CI, 97.6 to 99.2). Twelve months after the initial reports, 8 (8.60%) patients had a final diagnosis of lung cancer, among which 2 (2.15%) would have been lost to follow-up without the NLP tool. NLP can be used to identify ILNs in unstructured reports with high accuracy, allowing a timely recall of patients and a potential diagnosis of early-stage lung cancer that might have been lost to follow-up.

CTCs Detection and Whole-exome Sequencing Might Be Used to Differentiate Benign and Malignant Pulmonary Nodules.

Low-density computed tomography (LDCT) improved early lung cancer diagnosis but introduces an excess of false-positive pulmonary nodules data. Hence, accurate diagnosis of early-stage lung cancer remains challenging. The purpose of the study was to assess the feasibility of using circulating tumour cells (CTCs) to differentiate malignant from benign pulmonary nodules. 122 patients with suspected malignant pulmonary nodules detected on chest CT in preparation for surgery were prospectively recruited. Peripheral blood samples were collected before surgery, and CTCs were identified upon isolation by size of epithelial tumour cells and morphological analysis. Laser capture microdissection, MALBAC amplification, and whole-exome sequencing were performed on 8 samples. The diagnostic efficacy of CTCs counting, and the genomic variation profile of benign and malignant CTCs samples were analysed. Using 2.5 cells/5 mL as the cut-off value, the area under the receiver operating characteristic curve was of 0.651 (95% confidence interval: 0.538-0.764), with a sensitivity and specificity of 0.526 and 0.800, respectively, and positive and negative predictive values of 91.1% and 30.3%, respectively. Distinct sequence variations differences in DNA damage repair-related and driver genes were observed in benign and malignant samples. TP53 mutations were identified in CTCs of four malignant cases; in particular, g.7578115T>C, g.7578645C>T, and g.7579472G>C were exclusively detected in all four malignant samples. CTCs play an ancillary role in the diagnosis of pulmonary nodules. TP53 mutations in CTCs might be used to identify benign and malignant pulmonary nodules.

Analysis of the value of dynamic computed tomography (CT) examination in the diagnosis of early lung cancer.

Abstract. Early diagnosis and treatment are vital to improving lung cancer patients’ quality of life and survival rate. This study aimed to investigate the value of dynamic enhanced scanning examination by computed tomography (CT) in early lung cancer diagnosis. One hundred and twenty patients with isolated lung nodules were selected to analyze this diagnostic method, using pathological diagnostic results of cancer as the gold standard. Of the 120 pa-tients with isolated pulmonary nodules, the diagnosis was confirmed by patho-logical examination in 96 patients with early lung cancer (adenocarcinoma of the lung) and 24 patients with benign lung lesions. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of CT dynamic enhancement scans for the diagnosis of early-stage lung cancer were 93.75%, 83.33%, 91.67%, 95.74%, and 76.92%, respectively. Early-stage lung cancer had significantly less blood volume and a noticeably shorter mean time to passage than benign lung lesions (p&lt;0.01). Blood flow and surface permeability were higher in early-stage lung cancer than in benign lung lesions (p&lt;0.05). The ar-eas under the receiver operating characteristic (ROC) curves for blood volume, blood flow, surface permeability, and mean time to passage for the diagnosis of early-stage lung cancer were 0.737, 0.724, 0.779, and 0.946, respectively. In conclusion, CT dynamic enhancement scan has good application value in diag-nosing early lung cancer and is worth promoting in clinical practice.

Development of a novel blood-based RNA gene expression platform for early-stage lung cancer diagnosis.

8557 Background: Blood-based methods using circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA) are under development for early and less invasive detection of lung cancer, although detection of the earliest stage cancers (stages 0-II) using these modalities is suboptimal. We hypothesized that a machine learning approach using RNA gene expression may offer important information on the biology of the patient, allowing for gene expression profiles to be used as a surrogate measurement of cancer disease phenotype and as a promising direction for early detection of lung cancer. In a previous study, 23 miRNA biomarkers were successfully discovered and validated for the non-invasive diagnostic classification of lung adenocarcinoma, achieving 97.7% sensitivity, 98.7% specificity in blood obtained from 383 clinical subjects. The aim of this study was to train a machine learning algorithm, from the 23 miRNA features, to test the signature for early lung cancer detection. Methods: A large and diverse clinical cohort was obtained from the NIH Gene Expression Omnibus database, GEO Accession Number GSE137140 ( n=3,744), comprised of miRNA extracted from serum samples consisting of subjects with pre-operative lung cancer ( n=1,566) and non-cancer controls ( n=2,178). Our analytic plan leveraged machine learning methods derived from XGBoost classification, a popular supervised-learning algorithm that uses sequentially built shallow decision trees to provide accurate results and avoidance of overfitting. The algorithm was trained using XGBoost 1.4.1.1 R library programmed with R v3.6.3. Results: The lung cancer cohort was heavily weighted towards early-stage lung cancer (87.7% stage I/II), including representation across prevalent histologic types (adenocarcinoma 77.8%, non-adenocarcinoma 22.2%) and those who self-reported as never smokers (37.9%). The 23-miRNA signature achieved 98% sensitivity, 89% specificity in the held-out test set (Table). When incorporating age and gender, the 23-miRNA signature achieved 95.5% sensitivity, 90.3% specificity. Conclusions: A machine learning approach using RNA gene expression in patient serum achieved high sensitivity and specificity in a large, predominantly early-stage, lung cancer cohort. A multi-analyte, multimodal approach that leverages machine learning algorithms with RNA gene expression profiles and available demographics and clinical risk-factors, represents the possibility to accurately detect lung cancer in the earliest stages. This approach has successfully been translated from microarray to PCR instrumentation, with further validation of this machine learning method and approach currently underway. [Table: see text]

Retracted: Evaluation of lncRNA FOXD3-AS1 as a Biomarker for Early-Stage Lung Cancer Diagnosis and Subtype Identification.

[This retracts the article DOI: 10.1155/2022/5702014.].

An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network.

The diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients.