The combination of CTCs and CEA can help guide the management of patients with SPNs suspected of being lung cancer

Value of circulating tumor cell assisting low-dose computed tomography in screening pulmonary nodules based on existing liquid biopsy techniques: a systematic review with meta-analysis and trial sequential analysis

OBJECTIVE

This study aims to assess the diagnostic utility of circulating tumor cells (CTCs) in conjunction with low-dose computed tomography (LDCT) for differentiating between benign and malignant pulmonary nodules and to substantiate the foundation for their integration into clinical practice.

METHODS

A systematic literature review was performed independently by two researchers utilizing databases including PubMed, Web of Science, The Cochrane Library, Embase, and Medline, to collate studies up to September 15, 2023, that investigated the application of CTCs in diagnosing pulmonary nodules. A meta-analysis was executed employing Stata 15.0 and Revman 5.4 to calculate the pooled sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC). Additionally, trial sequential analysis was conducted using dedicated TSA software.

RESULTS

The selection criteria identified 16 studies, encompassing a total of 3409 patients. The meta-analysis revealed that CTCs achieved a pooled sensitivity of 0.84 (95% CI 0.80 to 0.87), specificity of 0.80 (95% CI 0.73 to 0.86), PLR of 4.23 (95% CI 3.12 to 5.72), NLR of 0.20 (95% CI 0.16 to 0.25), DOR of 20.92 (95% CI 13.52 to 32.36), and AUC of 0.89 (95% CI 0.86 to 0.93).

CONCLUSIONS

Circulating tumor cells demonstrate substantial diagnostic accuracy in distinguishing benign from malignant pulmonary nodules. The incorporation of CTCs into the diagnostic protocol can significantly augment the diagnostic efficacy of LDCT in screening for malignant lung diseases.

Novel Isolating Approaches to Circulating Tumor Cell Enrichment Based on Microfluidics: A Review

Circulating tumor cells (CTCs), derived from the primary tumor and carrying genetic information, contribute significantly to the process of tumor metastasis. The analysis and detection of CTCs can be used to assess the prognosis and treatment response in patients with tumors, as well as to help study the metastatic mechanisms of tumors and the development of new drugs. Since CTCs are very rare in the blood, it is a challenging problem to enrich CTCs efficiently. In this paper, we provide a comprehensive overview of microfluidics-based enrichment devices for CTCs in recent years. We explore in detail the methods of enrichment based on the physical or biological properties of CTCs; among them, physical properties cover factors such as size, density, and dielectric properties, while biological properties are mainly related to tumor-specific markers on the surface of CTCs. In addition, we provide an in-depth description of the methods for enrichment of single CTCs and illustrate the importance of single CTCs for performing tumor analyses. Future research will focus on aspects such as improving the separation efficiency, reducing costs, and increasing the detection sensitivity and accuracy.

Recent Progress in Enhanced Cancer Diagnosis, Prognosis, and Monitoring Using a Combined Analysis of the Number of Circulating Tumor Cells (CTCs) and Other Clinical Parameters

Analysis of circulating tumor cells (CTCs) holds promise to diagnose cancer or monitor its development. Among the methods, counting CTC numbers in blood samples could be the simplest way to implement it. Nevertheless, its clinical utility has not yet been fully accepted. The reasons could be due to the rarity and heterogeneity of CTCs in blood samples that could lead to misleading results from assays only based on single CTC counts. To address this issue, a feasible direction is to combine the CTC counts with other clinical data for analysis. Recent studies have demonstrated the use of this new strategy for early detection and prognosis evaluation of cancers, or even for the distinguishment of cancers with different stages. Overall, this approach could pave a new path to improve the technical problems in the clinical applications of CTC counting techniques. In this review, the information relevant to CTCs, including their characteristics, clinical use of CTC counting, and technologies for CTC enrichment, were first introduced. This was followed by discussing the challenges and new perspectives of CTC counting techniques for clinical applications. Finally, the advantages and the recent progress in combining CTC counts with other clinical parameters for clinical applications have been discussed.

Genetic susceptibility loci of lung cancer are associated with malignant risk of pulmonary nodules and improve malignancy diagnosis based on CEA levels

Objective

The heightened prevalence of pulmonary nodules (PN) has escalated its significance as a public health concern. While the precise identification of high-risk PN carriers for malignancy remains an ongoing challenge, genetic variants hold potentials as determinants of disease susceptibility that can aid in diagnosis. Yet, current understanding of the genetic loci associated with malignant PN (MPN) risk is limited.

Methods

A frequency-matched case-control study was performed, comprising 247 MPN cases and 412 benign NP (BNP) controls. We genotyped 11 established susceptibility loci for lung cancer in a Chinese cohort. Loci associated with MPN risk were utilized to compute a polygenic risk score (PRS). This PRS was subsequently incorporated into the diagnostic evaluation of MPNs, with emphasis on serum tumor biomarkers.

Results

Loci rs10429489G>A, rs17038564A>G, and rs12265047A>G were identified as being associated with an increased risk of MPNs. The PRS, formulated from the cumulative risk effects of these loci, correlated with the malignant risk of PNs in a dose-dependent fashion. A high PRS was found to amplify the MPN risk by 156% in comparison to a low PRS [odds ratio (OR)=2.56, 95% confidence interval (95% CI), 1.40-4.67]. Notably, the PRS was observed to enhance the diagnostic accuracy of serum carcinoembryonic antigen (CEA) in distinguishing MPNs from BPNs, with diagnostic values rising from 0.716 to 0.861 across low- to high-PRS categories. Further bioinformatics investigations pinpointed rs10429489G>A as an expression quantitative trait locus.

Conclusions

Loci rs10429489G>A, rs17038564A>G, and rs12265047A>G contribute to MPN risk and augment the diagnostic precision for MPNs based on serum CEA concentrations.

Evaluation of C-reactive protein and fibrinogen in comparison to CEA and CA72-4 as diagnostic biomarkers for colorectal cancer

Carcinoembryonic antigen (CEA) and carbohydrate antigen 72-4 (CA72-4) are commonly used markers for colorectal cancer (CRC) in clinical applications. However, low positivity rate and sensitivity limits their clinical effectiveness. In this study, we explored the potential of C-reactive protein (CRP) and fibrinogen to improve the diagnostic efficiency of traditional biomarkers of CRC. The concentrations of CRP and fibrinogen in plasma were significantly higher in CRC patients compared with benign or healthy controls. The area under the ROC curves (AUCs) showed that the diagnostic efficacy of CRP and fibrinogen was 0.745 (95% CI: 0.712-0.779) and 0.699 (95% CI: 0.663-0.734), respectively. AUC increased to 0.750 (95% CI: 0.716-0.784) when CRP and fibrinogen were combined. It also further improved to 0.889 (95% CI: 0.866-0.913) when CRP and fibrinogen were integrated with CEA and CA72-4. Moreover, this combination increased the maximum area under AUC to 0.857 (95% CI: 0.830-0.883), which effective differentiated CRC from benign disease. Overall, this study found that CRP and fibrinogen were highly expressed in the plasma of CRC patients, suggesting their potential to improve the diagnostic efficiency of traditional biomarkers of CRC.

Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges?

As most lung cancer (LC) cases are still detected at advanced and incurable stages, there are increasing efforts to foster detection at earlier stages by low dose computed tomography (LDCT) based LC screening. In this scoping review, we describe current advances in candidate selection for screening (selection phase), technical aspects (screening), and probability evaluation of malignancy of CT-detected pulmonary nodules (PN management). Literature was non-systematically assessed and reviewed for suitability by the authors. For the selection phase, we describe current eligibility criteria for screening, along with their limitations and potential refinements through advanced clinical scores and biomarker assessments. For LC screening, we discuss how the accuracy of computerized tomography (CT) scan reading might be augmented by IT tools, helping radiologists to cope with increasing workloads. For PN management, we evaluate the precision of follow-up scans by semi-automatic volume measurements of CT-detected PN. Moreover, we present an integrative approach to evaluate the probability of PN malignancy to enable safe decisions on further management. As a clear limitation, additional validation studies are required for most innovative diagnostic approaches presented in this article, but the integration of clinical risk models, current imaging techniques, and advancing biomarker research has the potential to improve the LC screening performance generally.

Carcinoembryonic Antigen-Related Cell Adhesion Molecule Type 5 Receptor-Targeted Fluorescent Intraoperative Molecular Imaging Tracer for Lung Cancer: A Nonrandomized Controlled Trial

Importance

Localization of subcentimeter ground glass opacities during minimally invasive thoracoscopic lung cancer resections is a significant challenge in thoracic oncology. Intraoperative molecular imaging has emerged as a potential solution, but the availability of suitable fluorescence agents is a limiting factor.

Objective

To evaluate the suitability of SGM-101, a carcinoembryonic antigen-related cell adhesion molecule type 5 (CEACAM5) receptor-targeted near-infrared fluorochrome, for molecular imaging-guided lung cancer resections, because glycoprotein is expressed in more than 80% of adenocarcinomas.

Design, Setting, and Participants

For this nonrandomized, proof-of-principal, phase 1 controlled trial, patients were divided into 2 groups between August 1, 2020, and January 31, 2022. Patients with known CEACAM5-positive gastrointestinal tumors suggestive of lung metastasis were selected as proof-of-principle positive controls. The investigative group included patients with lung nodules suggestive of primary lung malignant neoplasms. Patients 18 years or older without significant comorbidities that precluded surgical exploration with suspicious pulmonary nodules requiring surgical biopsy were included in the study.

Interventions

SGM-101 (10 mg) was infused up to 5 days before index operation, and pulmonary nodules were imaged using a near-infrared camera system with a dedicated thoracoscope.

Main Outcomes and Measures

SGM-101 localization to pulmonary nodules and its correlation with CEACAM5 glycoprotein expression by the tumor as quantified by tumor and normal pulmonary parenchymal fluorescence.

Results

Ten patients (5 per group; 5 male and 5 female; median [IQR] age, 66 [58-69] years) with 14 total lesions (median [range] lesion size, 0.91 [0.90-2.00] cm) were enrolled in the study. In the control group of 4 patients (1 patient did not undergo surgical resection because of abnormal preoperative cardiac clearance findings that were not deemed related to SGM-101 infusion), the mean (SD) lesion size was 1.33 (0.48) cm, 2 patients had elevated serum CEA markers, and 2 patients had normal serum CEA levels. Of the 4 patients who underwent surgical intervention, those with 2+ and 3+ tissue CEACAM5 expression had excellent tumor fluorescence, with a mean (SD) tumor to background ratio of 3.11 (0.45). In the patient cohort, the mean (SD) lesion size was 0.68 (0.22) cm, and no elevations in serum CEA levels were found. Lack of SGM-101 fluorescence was associated with benign lesions and with lack of CEACAM5 staining.

Conclusions and Relevance

This in-human proof-of-principle nonrandomized controlled trial demonstrated SGM-101 localization to CEACAM5-positive tumors with the detection of real-time near-infrared fluorescence in situ, ex vivo, and by immunofluorescence microscopy. These findings suggest that SGM-101 is a safe, receptor-specific, and feasible intraoperative molecular imaging fluorochrome that should be further evaluated in randomized clinical trials.

Trial Registration

ClinicalTrials.gov identifier: NCT04315467.

Value of dual-layer spectral detector computed tomography in the diagnosis of benign/malignant solid solitary pulmonary nodules and establishment of a prediction model

Objective

This study aimed to investigate the role of spectral detector computed tomography (SDCT) quantitative parameters and their derived quantitative parameters combined with lesion morphological information in the differential diagnosis of solid SPNs.

Methods

This retrospective study included basic clinical data and SDCT images of 132 patients with pathologically confirmed SPNs (102 and 30 patients in the malignant and benign groups, respectively). The morphological signs of SPNs were evaluated and the region of interest (ROI) was delineated from the lesion to extract and calculate the relevant SDCT quantitative parameters, and standardise the process. Differences in qualitative and quantitative parameters between the groups were statistically analysed. A receiver operating characteristic (ROC) curve was constructed to evaluate the efficacy of the corresponding parameters in the diagnosis of benign and malignant SPNs. Statistically significant clinical data, CT signs and SDCT quantitative parameters were analysed using multivariate logistic regression to determine the independent risk factors for predicting benign and malignant SPNs, and the best multi-parameter regression model was established. Inter-observer repeatability was assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots.

Results

Malignant SPNs differed from benign SPNs in terms of size, lesion morphology, short spicule sign, and vascular enrichment sign (P< 0.05). The SDCT quantitative parameters and their derived quantitative parameters of malignant SPNs (SAR_40keV, SAR_70keV, Δ_40keV, Δ_70keV, CER_40keV, CER_70keV, NEF_40keV, NEF_70keV, λ, NIC, NZ_eff) were significantly higher than those of benign SPNs (P< 0.05). In the subgroup analysis, most parameters could distinguish between benign and adenocarcinoma groups (SAR_40keV, SAR_70keV, Δ_40keV, Δ_70keV, CER_40keV, CER_70keV, NEF_40keV, NEF_70keV, λ, NIC, and NZ_eff), and between benign and squamous cell carcinoma groups (SAR_40keV, SAR70_keV, Δ_40keV, Δ_70keV, NEF_40keV, NEF_70keV, λ, and NIC). However, there were no significant differences between the parameters in the adenocarcinoma and squamous cell carcinoma groups. ROC curve analysis indicated that NIC, NEF_70keV, and NEF_40keV had higher diagnostic efficacy for differentiating benign and malignant SPNs (area under the curve [AUC]:0.869, 0.854, and 0.853, respectively), and NIC was the highest. Multivariate logistic regression analysis showed that size (OR=1.138, 95% CI 1.022-1.267, P=0.019), Δ_70keV (OR=1.060, 95% CI 1.002-1.122, P=0.043), and NIC (OR=7.758, 95% CI 1.966-30.612, P=0.003) were independent risk factors for the prediction of benign and malignant SPNs. ROC curve analysis showed that the AUC of size, Δ_70keV, NIC, and a combination of the three for differential diagnosis of benign and malignant SPNs were 0.636, 0.846, 0.869, and 0.903, respectively. The AUC for the combined parameters was the largest, and the sensitivity, specificity, and accuracy were 88.2%, 83.3% and 86.4%, respectively. The SDCT quantitative parameters and their derived quantitative parameters in this study exhibited satisfactory inter-observer repeatability (ICC: 0.811-0.997).

Conclusion

SDCT quantitative parameters and their derivatives can be helpful in the differential diagnosis of benign and malignant solid SPNs. The quantitative parameter, NIC, is superior to the other relevant quantitative parameters and when NIC is combined with lesion size and Δ_70keV value for comprehensive diagnosis, the efficacy could be further improved.

Blood protein biomarkers in lung cancer

Lung cancer has consistently ranked first as the cause of cancer-associated mortality. The 5-year survival rate has risen slowly, and the main obstacle to improving the prognosis of patients has been that lung cancer is usually diagnosed at an advanced or incurable stage. Thus, early detection and timely intervention are the most effective ways to reduce lung cancer mortality. Tumor-specific molecules and cellular elements are abundant in circulation, providing real-time information in a noninvasive and cost-effective manner during lung cancer development. These circulating biomarkers are emerging as promising tools for early detection of lung cancer and can be used to supplement computed tomography screening, as well as for prognosis prediction and treatment response monitoring. Serum and plasma are the main sources of circulating biomarkers, and protein biomarkers have been most extensively studied. In this review, we summarize the research progress on three most common types of blood protein biomarkers (tumor-associated antigens, autoantibodies, and exosomal proteins) in lung cancer. This review will potentially guide researchers toward a more comprehensive understanding of candidate lung cancer protein biomarkers in the blood to facilitate their translation to the clinic.

CT and CEA-based machine learning model for predicting malignant pulmonary nodules

Computed tomography (CT), an efficient radiological technology, is used to detect lung cancer in the clinic. Carcinoembryonic antigen (CEA), a common tumor biomarker, is applied in the detection of various tumors. To highlight the advantages of two-dimensional techniques and assist clinicians in optimizing lung cancer diagnostic schemes, we established a favorable model combining CT and CEA. In the study, univariate analysis was performed to screen independent predictors in a training cohort of 271 patients with malignant pulmonary nodules (MPNs) and 92 with benign pulmonary nodules (BPNs). Six machine learning-based models involving five CT predictors (mediastinal lymph node enlargement, lobulation, vascular notch sign, spiculation, and nodule number) and lnCEA were constructed and validated in an independent cohort of 129 participants (92 MPNs and 37 BPNs) by SPSS Modeler. A nomogram and the Delong test were generated by R software. Finally, the model established by logistic regression had highest diagnostic efficiency (area under the curve [AUC] = 0.912). Moreover, the diagnostic ability of the logistic model in the validation cohort (AUC = 0.882, 80.4% sensitivity, 75.7% specificity) was higher than that of the Peking University model (AUC = 0.712, 68.5% sensitivity, 70.3% specificity) and the Mayo model (AUC = 0.745, 62.0% sensitivity, 75.7% specificity). Interestingly, for the participants with intermediate (10-30 mm) and CEA-negative nodule, the model reached an AUC of 0.835 (72.3% sensitivity, 83.3% specificity). The AUC for the early lung cancer was as high as 0.822 with 67.3% sensitivity and 78.9% specificity. As a conclusion, this promising model presents a new diagnostic strategy for the clinic to distinguish MPNs from BPNs.

The use of carcinoembryonic antigen levels to predict lung nodule malignancy: a meta-analysis

OBJECTIVES

To assess the diagnostic value of serum carcinoembryonic antigen (CEA) as a diagnostic biomarker that can be used to differentiate between benign and malignant lung nodules (LNs).

METHODS

PubMed, Cochrane Library, and Embase were reviewed from January 2000 to April 2020 for eligible studies. Stata v12.0 was used to conduct this meta-analysis.

RESULTS

Our initial literature search identified 511 potentially relevant studies, of which 11 were ultimately included in the present meta-analysis. Ten studies were retrospective and only 1 study was prospective. Overall these studies incorporated 2760 patients and 2760 total LNs (1733 malignant, 1027 benign). Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) values for these studies were 0.33 (95% CI: 0.20-0.49), 0.92 (95% CI: 0.85-0.96), 3.96 (95% CI: 2.84-5.54), 0.73 (95% CI: 0.62-0.87), and 5.42 (95% CI: 3.77-7.78), respectively. The area under curve (AUC) value was 0.77, consistent with moderate diagnostic accuracy. We detected significant heterogeneity when calculating pooled sensitivity (I² = 95.9%, P = 0.00), specificity (I² = 92.0%, P = 0.00), PLR (I² = 61.7%, P = 0.00), NLR (I² = 92.8%, P = 0.00), and DOR (I² = 93.8%, P = 0.00). No significant evidence of publication bias was detected via Deeks' funnel plot asymmetry test (P = 0.371). Meta-regression analysis revealed different reference standards to be closely associated with both sensitivity and specificity.

CONCLUSIONS

Serum CEA can achieve moderate diagnostic performance as a means of differentiating between malignant and benign LNs.

Has_Circ_0002490 Circular RNA: A Potential Novel Biomarker for Lung Cancer

Background: Lung cancer (LC) is ranked as a leading cause of cancer-related death worldwide. However, there are still few reliable screening biomarkers for daily clinical practice in LC. Circular RNAs (circRNAs) have been suggested as valuable diagnostic biomarkers in various cancers. In this study, the expression and diagnostic potential of several circRNAs for LC were explored. Methods: Seventy-two pairs of LC tissues and adjacent normal lung tissues were collected to measure the relative expression level of circRNAs using quantitative reverse transcription-polymerase chain reaction. In addition, the relationships between circRNAs and the clinicopathological features of LC patients were analyzed. Furthermore, the sensitivities and specificities of the circRNAs were evaluated by receiver operating characteristic (ROC) analysis. Results: The expression levels of has_circ_0002490, has_circ_0087357, has_circ_0004891, has_circ_0074368, and has_circ_0000896 were downregulated in LC tissues compared with adjacent normal lung tissues. The lower levels of has_circ_0002490, has_circ_0087357, has_circ_0004891, and has_circ_0000896 were significantly correlated with advanced disease stages. The area under the ROC curves of has_circ_0002490, has_circ_0087357, has_circ_0074368, has_circ_0004891, and has_circ_0000896 were 0.833, 0.793, 0.773, 0.730, and 0.645, respectively. Conclusions: Has_circ_0002490, has_circ_0087357, has_circ_0074368, has_circ_0004891, and has_circ_0000896 are capable of distinguishing LC tissues from normal lung tissues. Besides, the biggest area under the ROC curve value of has_circ_000249 suggests it appears to be a better diagnosis marker for LC patients.

Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma

SIMPLE SUMMARY

Studies have shown that about 30% of kidney cancer patients will have metastasis, and lymph node metastasis (LNM) may be related to a poor prognosis. Our retrospective study aims to provide a reliable machine learning-based model to predict the occurrence of LNM in kidney cancer. We screened the pathological grade, liver metastasis, M staging, primary site, T staging, and tumor size from the training group (n=39016) formed by the SEER database and the validation group (n=771) formed by the medical center. Independent predictors of LNM in cancer patients. Using six different algorithms to build a prediction model, it is found that the prediction performance of the XGB model in the training group and the validation group is significantly better than any other machine learning model. The results show that prediction tools based on machine learning can accurately predict the probability of LNM in patients with kidney cancer and have satisfactory clinical application prospects.

BACKGROUND

Lymph node metastasis (LNM) is associated with the prognosis of patients with kidney cancer. This study aimed to provide reliable machine learning-based (ML-based) models to predict the probability of LNM in kidney cancer.

METHODS

Data on patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology and Outcomes (SEER) database from 2010 to 2017, and variables were filtered by least absolute shrinkage and selection operator (LASSO), univariate and multivariate logistic regression analyses. Statistically significant risk factors were used to build predictive models. We used 10-fold cross-validation in the validation of the model. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Correlation heat maps were used to investigate the correlation of features using permutation analysis to assess the importance of predictors. Probability density functions (PDFs) and clinical utility curves (CUCs) were used to determine clinical utility thresholds.

RESULTS

The training cohort of this study included 39,016 patients, and the validation cohort included 771 patients. In the two cohorts, 2544 (6.5%) and 66 (8.1%) patients had LNM, respectively. Pathological grade, liver metastasis, M stage, primary site, T stage, and tumor size were independent predictive factors of LNM. In both model validation, the XGB model significantly outperformed any of the machine learning models with an AUC value of 0.916.A web calculator (https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py) were built based on the XGB model. Based on the PDF and CUC, we suggested 54.6% as a threshold probability for guiding the diagnosis of LNM, which could distinguish about 89% of LNM patients.

CONCLUSIONS

The predictive tool based on machine learning can precisely indicate the probability of LNM in kidney cancer patients and has a satisfying application prospect in clinical practice.

Prognostic significance of postoperative longitudinal change of serum carcinoembryonic antigen level in patients with stage I lung adenocarcinoma completely resected by single-port video-assisted thoracic surgery: a retrospective study

Background

Patients with stage I lung adenocarcinoma (LUAD) have varying postoperative prognosis. This study aimed to investigate the prognostic significance of postoperative longitudinal change of serum carcinoembryonic antigen (CEA) level in patients with stage I LUAD.

Methods

The study cohort comprised 241 patients with stage I LUAD completely resected with single-port video-assisted thoracic surgery (VATS). The patients were categorized into 4 groups according to the postoperative longitudinal change of serum CEA levels measured in the third and sixth months after surgery: the NN group (continuously normal), HN group (increase first and then decrease), NH group (decrease first and then increase), and HH group (continuously high). Recurrence-free survival (RFS) was analyzed by the Kaplan-Meier method and compared by log-rank test. A nomogram was developed to predict recurrence in the stage I LUAD patients.

Results

In univariate analysis, differentiation (P<0.001), visceral pleural invasion (VPI) (P=0.025), tumor diameter (P<0.001), tumor-node-metastasis (TNM) stage (P=0.008), preoperative CEA levels (≥10.0 vs. <10.0 ng/mL, P<0.001), and postoperative CEA grouping (NH/HH vs. NN/HN, P<0.001) were significant prognostic factors for stage I LUAD patients. Multivariate analysis showed that tumor diameter (P=0.009) and postoperative CEA grouping (P<0.001) were considered to be independent prognostic factors of postoperative recurrence of stage I LUAD. Tumor diameter (≥20 mm) and postoperative CEA (NH/HH vs. NN/HN) were associated with worse RFS. Receiver operating characteristic (ROC) curve analysis showed that postoperative CEA (NH/HH vs. NN/HN) have high sensitivity (64.7%) and specificity (83.2%) for early prediction of postoperative recurrence of stage I LUAD. The area under curve (AUC) value was 0.745. The nomogram based on multivariate Cox regression had a concordance index (value of 0.789). The calibration plot showed that the predicted probabilities closely matched the observed probabilities.

Conclusions

Longitudinal change in serum CEA level after surgery was found to be an independent unfavorable prognostic factor in completely resected stage I LUAD patients. The NH group and HH group were significantly associated with worse RFS. A nomogram was established to predict the postoperative recurrence of patients with stage I LUAD.

Cross-Scale Integration of Nano-Sized Extracellular Vesicle-Based Biomarker and Radiomics Features for Predicting Suspected Sub-Solid Pulmonary Nodules

Sub-solid nodules (SSN) are common radiographic findings. Due to possibility of malignancy, further evaluation is urgentlyneeded for prevention and management of lung cancer (LC). This current study enrolled patients with SSN, including LC, benign nodules (BN), and healthy individuals as a control, to discover small extracellular vesicles (sEVs) differentially expressed miRNAs (DEMs) as biomarker by next-generation sequencing (NGS) and validation by RT-qPCR. Through cross-scale integration of validated small-molecule and macro-imaging, the prediction model was developed by logistic algorithms and further interpreted into an easy-to-use Nomogram by Cox-proportional hazards modeling. Present study has discovered various sEVs DEMs and sEVs-miR-424-5p that were selected and validated as novel potential biomarkers for cancerous nodule, namely LC. Furthermore, the 10 radiomics signs and 4 clinical features of SSN were merged with sEVs-miR-424-5p and proceeded in multivariate logistic regression analysis to develop the cross-scale integrated modeling, which yielded a significantly higher area under the curve (AUC). Finally, visualization of an easy-to-use nomogram was invented to potentially predict suspected SSN. sEVs-miR-424-5p could be a novel biomarker for distinguishing SSN from LC and BN populations. Its association with cross-scale fusion of radiomics-clinical features will provide great potential to be an errorless prediction of malignant SSN.

Combination of CT and telomerase+ circulating tumor cells improves diagnosis of small pulmonary nodules

BACKGROUND

Early diagnosis and treatment are key to the long-term survival of lung cancer patients. Although CT has significantly contributed to the early diagnosis of lung cancer, there are still consequences of excessive or delayed treatment. By improving the sensitivity and specificity of circulating tumor cell (CTC) detection, a solution was proposed for differentiating benign from malignant pulmonary nodules.

METHODS

In this study, we used telomerase reverse transcriptase–based (TERT-based) CTC detection (TBCD) to distinguish benign from malignant pulmonary nodules < 2 cm and compared this method with the pathological diagnosis as the gold standard. FlowSight and FISH were used to confirm the CTCs detected by TBCD.

RESULTS

Our results suggest that CTCs based on TBCD can be used as independent biomarkers to distinguish benign from malignant nodules and are significantly superior to serum tumor markers. When the detection threshold was 1, the detection sensitivity and specificity of CTC diagnosis were 0.854 and 0.839, respectively. For pulmonary nodules ≤ 1 cm and 1–2 cm, the sensitivity and specificity of CTCs were both higher than 77%. Additionally, the diagnostic ability of CTC-assisted CT was compared by CT detection. The results show that CT combined with CTCs could significantly improve the differentiation ability of benign and malignant nodules in lung nodules < 2 cm and that the sensitivity and specificity could reach 0.899 and 0.839, respectively.

CONCLUSION

TBCD can effectively diagnose pulmonary nodules and be used as an effective auxiliary diagnostic scheme for CT diagnosis.

FUNDING

National Key Research and Development Project grant nos. 2019YFC1315700 and 2017YFC1308702, CAMS Initiative for Innovative Medicine grant no. 2017-I2M-1-005, and National Natural Science Foundation of China grant no. 81472013.

Enhancing Prediction Performance by Add-On Combining Circulating Tumor Cell Count, CD45neg EpCAMneg Cell Count on Colorectal Cancer, Advance, and Metastasis

Simple Summary

Information describing circulating tumor cells (CTCs) holds promise for clinical applications. However, conventional CTCs enumeration could ignore the CTCs more relevant to cancer metastasis. Thus, negative selection CTC enumeration was proposed, by which information on the numbers of CTCs and CD45^neg EpCAM^neg cells can be obtained. By combining this approach with the conventional biomarker carcinoembryonic antigen (CEA), this study aimed to explore whether any combination of these biomarkers could improve the predictive performance for colorectal cancer (CRC) or its status. Results revealed that a combination of the two cell populations showed improved performance (AUROC: 0.893) for CRC prediction over the use of only one population. Compared with CEA alone, the combination of the three biomarkers increased the performance (AUROC) for advanced CRC prediction from 0.643 to 0.727. Compared with that of CEA alone for metastatic CRC prediction, the AUROC was increased from 0.780 to 0.837 when the CTC count was included.

Abstract

Conventional circulating tumor cell (CTC) enumeration could ignore the CTCs more relevant to cancer metastasis. Thus, negative selection CTC enumeration was proposed, by which information on two cellular biomarkers (numbers of CTCs and CD45^neg EpCAM^neg cells) can be obtained. By combining this approach with the conventional biomarker carcinoembryonic antigen (CEA), this study aimed to explore whether any combination of these biomarkers could improve the predictive performance for colorectal cancer (CRC) or its status. In this work, these two cell populations in healthy donors and CRC patients were quantified. Results revealed that enumeration of these two cell populations was able to discriminate healthy donors from CRC patients, even patients with non-advanced CRC. Moreover, the combination of the two cell populations showed improved performance (AUROC: 0.893) for CRC prediction over the use of only one population. Compared with CEA alone, the combination of the three biomarkers increased the performance (AUROC) for advanced CRC prediction from 0.643 to 0.727. Compared with that of CEA alone for metastatic CRC prediction, the AUROC was increased from 0.780 to 0.837 when the CTC count was included. Overall, this study demonstrated that the combination of these two cellular biomarkers with CEA improved the predictive performance for CRC and its status.

Liquid biopsies to distinguish malignant from benign pulmonary nodules

Over the past decades, low-dose computed tomography (LD-CT) screening has been widely used for the early detection of lung cancer. Increasing numbers of indeterminate pulmonary nodules are now being discovered. However, it remains challenging to distinguish malignant from benign pulmonary nodules, especially those considered to be small or ground-glass (GGN) nodules. Liquid biopsies have been successfully applied in the diagnosis of advanced lung cancer, and the potential value for early detection of lung cancer has made great progress. Recent studies have demonstrated the value of various blood-based tumor biomarkers in determining the nature of pulmonary nodules, including cell-free DNA (cfDNA), microRNAs (miRNAs), circulating tumor cells (CTCs) and tumor-associated autoantibodies (AAbs). In this review, we summarize the latest progress of liquid biopsies, and their potential applications and challenges in the diagnosis of malignant pulmonary nodules.

Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode

Background

Materials and Methods

Results

Conclusion