A novel serum based biomarker panel has complementary ability to preclude presence of early lung cancer for low dose CT (LDCT)

Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis

Lung cancer (LC) causes few symptoms in the earliest stages, leading to one of the highest mortality rates among cancers. Low-dose computerised tomography (LDCT) is used to screen high-risk individuals, reducing the mortality rate by 20%. However, LDCT results in a high number of false positives and is associated with unnecessary follow-up and cost. Biomarkers with high sensitivities and specificities could assist in the early detection of LC, especially in patients with high-risk features. Carcinoembryonic antigen (CEA), cytokeratin 19 fragments and cancer antigen 125 have been found to be highly expressed during the later stages of LC but have low sensitivity in the earliest stages. We determined the best biomarkers for the early diagnosis of LC, using a systematic review of eight databases. We identified 98 articles that focussed on the identification and assessment of diagnostic biomarkers and achieved a pooled area under curve of 0.85 (95% CI 0.82-0.088), indicating that the diagnostic performance of these biomarkers when combined was excellent. Of the studies, 30 focussed on single/antigen panels, 22 on autoantibodies, 31 on miRNA and RNA panels, and 15 suggested the use of circulating DNA combined with CEA or neuron-specific enolase (NSE) for early LC detection. Verification of blood biomarkers with high sensitivities (Ciz1, exoGCC2, ITGA2B), high specificities (CYFR21-1, antiHE4, OPNV) or both (HSP90α, CEA) along with miR-15b and miR-27b/miR-21 from sputum may improve early LC detection. Further assessment is needed using appropriate sample sizes, control groups that include patients with non-malignant conditions, and standardised cut-off levels for each biomarker.

Assessment of circulating biomarkers for detection of lung cancer in a high-risk cohort

BACKGROUND

There is an urgent need for early detection of lung cancer. Screening with low-dose computed tomography (LDCT) is now implemented in the US. Supplementary use of a lung cancer biomarker with high specificity is desirable.

OBJECTIVE

To assess the diagnostic properties of a biomarker panel consisting of cytokeratin 19 fragment (CYFRA 21-1), carcinoembryonic antigen (CEA) and cancer antigen 125 (CA125).

METHODS

A cohort of 250 high-risk patients was investigated on suspicion of lung cancer. Ahead of diagnostic work-up, blood samples taken. Cross-validated prediction models were computed to assess lung cancer detection properties.

RESULTS

In total 32% (79/250) of patients were diagnosed with lung cancer. Area under the curve (AUC) for the three biomarkers was of 0.795, with sensitivity/specificity of 57%/93% and negative predictive value of 83%. When combining the biomarkers with US screening criteria, the AUC was 0.809, while applying only US screening criteria on the cohort, yielded an AUC of 0.62. The ability of the biomarkers to detect stage I-II lung cancer was substantially lower; AUC 0.54.

CONCLUSIONS

In a high-risk cohort, the detection properties of the three biomarkers were acceptable compared to current LDCT screening criteria. However, the ability to detect early stage lung cancer was low.

Development and validation of a screening model for lung cancer using machine learning: A large-scale, multi-center study of biomarkers in breath

Objectives

Lung cancer (LC) is the largest single cause of death from cancer worldwide, and the lack of effective screening methods for early detection currently results in unsatisfactory curative treatments. We herein aimed to use breath analysis, a noninvasive and very simple method, to identify and validate biomarkers in breath for the screening of lung cancer.

Materials and methods

We enrolled a total of 2308 participants from two centers for online breath analyses using proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS). The derivation cohort included 1007 patients with primary LC and 1036 healthy controls, and the external validation cohort included 158 LC patients and 107 healthy controls. We used eXtreme Gradient Boosting (XGBoost) to create a panel of predictive features and derived a prediction model to identify LC. The optimal number of features was determined by the greatest area under the receiver‐operating characteristic (ROC) curve (AUC).

Results

Six features were defined as a breath-biomarkers panel for the detection of LC. In the training dataset, the model had an AUC of 0.963 (95% CI, 0.941–0.982), and a sensitivity of 87.1% and specificity of 93.5% at a positivity threshold of 0.5. Our model was tested on the independent validation dataset and achieved an AUC of 0.771 (0.718–0.823), and sensitivity of 67.7% and specificity of 73.0%.

Conclusion

Our results suggested that breath analysis may serve as a valid method in screening lung cancer in a borderline population prior to hospital visits. Although our breath-biomarker panel is noninvasive, quick, and simple to use, it will require further calibration and validation in a prospective study within a primary care setting.

Deep Learning-Based Chest CT Image Features in Diagnosis of Lung Cancer

This study was to evaluate the diagnostic value of deep learning-optimized chest CT in the patients with lung cancer. 90 patients who were diagnosed with lung cancer by surgery or puncture in hospital were selected as the research subjects. The Mask Region Convolutional Neural Network (Mask-RCNN) model was a typical end-to-end image segmentation model, and Dual Path Network (DPN) was used in nodule detection. The results showed that the accuracy of DPN algorithm model in detecting lung lesions in lung cancer patients was 88.74%, the accuracy of CT diagnosis of lung cancer was 88.37%, the sensitivity was 82.91%, and the specificity was 87.43%. Deep learning-based CT examination combined with serum tumor detection, factoring into Neurospecific enolase (N S E), cytokeratin 19 fragment (CYFRA21), Carcinoembryonic antigen (CEA), and squamous cell carcinoma (SCC) antigen, improved the accuracy to 97.94%, the sensitivity to 98.12%, and the specificity to 100%, all showing significant differences (P < 0.05). In conclusion, this study provides a scientific basis for improving the diagnostic efficiency of CT imaging in lung cancer and theoretical support for subsequent lung cancer diagnosis and treatment.

Nanoscale Technologies in Highly Sensitive Diagnosis of Cardiovascular Diseases

Cardiovascular diseases (CVD) are the leading cause of death and morbidity in the world and are a major contributor to healthcare costs. Although enormous progress has been made in diagnosing CVD, there is an urgent need for more efficient early detection and the development of novel diagnostic tools. Currently, CVD diagnosis relies primarily on clinical symptoms based on molecular imaging (MOI) or biomarkers associated with CVDs. However, sensitivity, specificity, and accuracy of the assay are still challenging for early-stage CVDs. Nanomaterial platform has been identified as a promising candidate for improving the practical usage of diagnostic tools because of their unique physicochemical properties. In this review article, we introduced cardiac biomarkers and imaging techniques that are currently used for CVD diagnosis. We presented the applications of various nanotechnologies on diagnosis within cardiac immunoassays (CIAs) and molecular imaging. We also summarized and compared different cardiac immunoassays based on their sensitivities and working ranges of biomarkers.

Circulating DNA, a Potentially Sensitive and Specific Diagnostic Tool for Future Medicine

Liquid biopsy has the great potential of detecting early diseases before deterioration and is valued for screening abnormalities at early stage. In oncology, circulating DNA derived from shed cancer cells reflects the tissue of origin, so it could be used to locate tissue sites during early screening. However, the heterogenous parameters of different types limit the clinical application, making it inaccessible to encompass all the cancer types. Instead, for reproducible scenario as pregnancy, fetal cell-free DNA has been well utilized for screening aneuploidies. Noninvasive and convenient as is, it would be of great value in the next decades far more than early diagnosis. This review recapitulates the discovery and development of tumor and fetal cell-free DNA. The common factors are also present that could be taken into consideration when collecting, transporting, and preserving samples. Meanwhile, several protocols used for purifying cell-free DNA, either classic ones or through commercial kits, are compared carefully. In addition, the development of technologies for analyzing cell-free DNA have been summarized and discussed in detail, especially some up-to-date approaches. At the end, the potential prospect of circulating DNA is bravely depicted. In summary, although there would be a lot of efforts before it’s prevalent, cell-free DNA remains a promising tool in point-of-care diagnostic medicine.

Application of Real-Time Cell Electronic Analysis System in Modern Pharmaceutical Evaluation and Analysis

Objective: We summarized the progress of the xCELLigence real-time cell analysis (RTCA) technology application in recent years for the sake of enriching and developing the application of RTCA in the field of Chinese medicine. Background: The RTCA system is an established electronic cellular biosensor. This system uses micro-electronic biosensor technology that is confirmed for real-time, label-free, dynamic and non-offensive monitoring of cell viability, migration, growth, spreading, and proliferation. Methods: We summarized the relevant experiments and literature of RTCA technology from the principles, characteristics, applications, especially from the latest application progress. Results and conclusion: RTCA is attracting more and more attention. Now it plays an important role in drug screening, toxicology, Chinese herbal medicine and so on. It has wide application prospects in the area of modern pharmaceutical evaluation and analysis.

Blood based biomarkers beyond genomics for lung cancer screening

While there is considerable interest at the present time in the development of so-called liquid biopsy approaches for cancer detection based notably on circulating tumor DNA, there are other types of potential biomarkers that show promise for lung cancer screening and early detection. Here we review approaches and some of the promising markers based on proteomics, metabolomics and the immune response to tumor antigens in the form of autoantibodies.