The Past, Present, and Future (Liquid Biopsy) of Serum Tumor Markers in Lung Cancer: A Primer for the Radiologist

Ultrasensitive detection of NSE employing a novel electrochemical immunosensor based on a conjugated copolymer

In the current study a simple and highly specific label-free impedimetric neuron specific enolase (NSE) immunosensor based on a copolymer matrix-coated disposable electrode was designed and tested. The copolymer matrix was prepared using a very conductive EDOT monomer and semi-conductive thiophene-bearing epoxy groups (ThEp), and the combination of the two monomers enhanced the conductivity and protein loading capacity of the electrode surface. The P(ThEp-co-EDOT) copolymer matrix was prepared via a drop-casting process and anti-NSE recognition biomolecules were immobilized directly on the epoxy groups of the copolymer. After the coupling of NSE molecules on the P(ThEp-co-EDOT) copolymer matrix-coated electrode surface, the charge transfer resistance (Rct) of the biosensor changed dramatically. These changes in Rct were proportional to the NSE molecule amounts captured by anti-NSE molecules. Under optimized experimental conditions, the increment in the Rct value was proportional to the NSE concentration over a range of 0.01 to 25 pg mL-1 with a detection limit (LOD) of 2.98 × 10-3 pg mL-1. This copolymer-coated electrode provided a lower LOD than the other biosensors. In addition, the suggested electrochemical immuno-platform showed good selectivity, superior reproducibility, long-term stability, and high recovery of NSE in real serum (95.64-102.20%) and saliva (95.28-105.35%) samples. These results showed that the present system had great potential for electrochemical biosensing applications.

Clinical perspectives on serum tumor marker use in predicting prognosis and treatment response in advanced non-small cell lung cancer

The optimal positioning and usage of serum tumor markers (STMs) in advanced non-small cell lung cancer (NSCLC) care is still unclear. This review aimed to provide an overview of the potential use and value of STMs in routine advanced NSCLC care for the prediction of prognosis and treatment response. Radiological imaging and clinical symptoms have shown not to capture a patient's entire disease status in daily clinical practice. Since STM measurements allow for a rapid, minimally invasive, and safe evaluation of the patient's tumor status in real time, STMs can be used as companion decision-making support tools before start and during treatment. To overcome the limited sensitivity and specificity associated with the use of STMs, tests should only be applied in specific subgroups of patients and different test characteristics should be defined per clinical context in order to answer different clinical questions. The same approach can similarly be relevant when developing clinical applications for other (circulating) biomarkers. Future research should focus on the approaches described in this review to achieve STM test implementation in advanced NSCLC care.

A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma

BACKGROUND

Invasive lung adenocarcinoma with MPP/SOL components has a poor prognosis and often shows a tendency to recurrence and metastasis. This poor prognosis may require adjustment of treatment strategies. Preoperative identification is essential for decision-making for subsequent treatment.

OBJECTIVE

This study aimed to preoperatively predict the probability of MPP/SOL components in lung adenocarcinomas by a comprehensive model that includes radiomics features, clinical characteristics, and serum tumor biomarkers.

DESIGN

A retrospective case control, diagnostic accuracy study.

METHODS

This study retrospectively recruited 273 patients (males: females, 130: 143; mean age ± standard deviation, 63.29 ± 10.03 years; range 21-83 years) who underwent resection of invasive lung adenocarcinoma. Sixty-one patients (22.3%) were diagnosed with lung adenocarcinoma with MPP/SOL components. Radiomic features were extracted from CT before surgery. Clinical, radiomic, and combined models were developed using the logistic regression algorithm. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC). Studies were scored according to the Radiomics Quality Score and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines.

RESULTS

The radiomics model achieved the best AUC values of 0.858 and 0.822 in the training and test cohort, respectively. Tumor size (T_size), solid tumor size (ST_size), consolidation-to-tumor ratio (CTR), years of smoking, CYFRA 21-1, and squamous cell carcinoma antigen were used to construct the clinical model. The clinical model achieved AUC values of 0.741 and 0.705 in the training and test cohort, respectively. The nomogram showed higher AUCs of 0.894 and 0.843 in the training and test cohort, respectively.

CONCLUSION

This study has developed and validated a combined nomogram, a visual tool that integrates CT radiomics features with clinical indicators and serum tumor biomarkers. This innovative model facilitates the differentiation of micropapillary or solid components within lung adenocarcinoma and achieves a higher AUC, indicating superior predictive accuracy.

Recent advances in the use of liquid biopsy to fight central nervous system tumors

Brain tumors are considered one of the deadliest types of cancer, being challenging to treat, especially due to the blood-brain barrier, which has been linked to treatment resistance. The genomic classification of brain tumors has been helping in the diagnostic precision, however tumor heterogeneity in addition to the difficulties to obtain tissue biopsies, represent a challenge. The biopsies are usually obtained either via neurosurgical removal or stereotactic tissue biopsy, which can be risky procedures for the patient. To overcome these challenges, liquid biopsy has become an interesting option by constituting a safer procedure than conventional biopsy, which may offer valuable cellular and molecular information representative of the whole organism. Besides, it is relatively easy to obtain such as in the case of blood (venipuncture) and urine sample collection. In the present comprehensive review, we discuss the newest information regarding liquid biopsy in the brain tumors' field, methods employed, the different sources of bio-fluids and their potential circulating targets.

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma

Purpose

Exploring a non-invasive method to accurately differentiate peripheral small cell lung cancer (PSCLC) and peripheral lung adenocarcinoma (PADC) could improve clinical decision-making and prognosis.

Methods

This retrospective study reviewed the clinicopathological and imaging data of lung cancer patients between October 2017 and March 2022. A total of 240 patients were enrolled in this study, including 80 cases diagnosed with PSCLC and 160 with PADC. All patients were randomized in a seven-to-three ratio into the training and validation datasets (170 vs. 70, respectively). The least absolute shrinkage and selection operator regression was employed to generate radiomics features and univariate analysis, followed by multivariate logistic regression to select significant clinical and radiographic factors to generate four models: clinical, radiomics, clinical-radiographic, and clinical-radiographic-radiomics (comprehensive). The Delong test was to compare areas under the receiver operating characteristic curves (AUCs) in the models.

Results

Five clinical-radiographic features and twenty-three selected radiomics features differed significantly in the identification of PSCLC and PADC. The clinical, radiomics, clinical-radiographic and comprehensive models demonstrated AUCs of 0.8960, 0.8356, 0.9396, and 0.9671 in the validation set, with the comprehensive model having better discernment than the clinical model (P=0.036), the radiomics model (P=0.006) and the clinical-radiographic model (P=0.049).

Conclusions

The proposed model combining clinical data, radiographic characteristics and radiomics features could accurately distinguish PSCLC from PADC, thus providing a potential non-invasive method to help clinicians improve treatment decisions.

Association of CEA, NSE, CYFRA 21-1, SCC-Ag, and ProGRP with Clinicopathological Characteristics and Chemotherapeutic Outcomes of Lung Cancer

Objective

The aim of this study was to investigate the association of serum carcinoembryonic antigen (CEA), nerve-specific enolase (NSE), cytokeratin 19 fragment (CYFRA21-1), squamous cell carcinoma antigen (SCC-Ag), and pro-gastrin-releasing peptide (ProGRP) with the clinicopathological characteristics and chemotherapeutic outcomes of patients with lung cancer.

Methods

A total of 189 patients with lung cancer (lung cancer group) diagnosed at the Fourth Affiliated Hospital of Anhui Medical University from January 2020 to December 2021 were included. During the same period, 199 patients with benign lung disorders were included as the benign lung disease group and 75 healthy people were selected as the control group. The serum concentrations of CEA, NSE, CYFRA21-1, SCC-Ag, and ProGRP in all the 3 groups were analyzed and compared in patients with different lung cancer tumor-node-metastasis (TNM) stages and pathological classifications. A total of 11 patients with small cell lung cancer (SCLC) and 18 patients with lung adenocarcinoma (LAC) were further evaluated for the dynamic changes of CEA, NSE, CYFRA21-1, SCC-Ag, and ProGRP before chemotherapy and during the 6 courses of chemotherapy, and the outcome of chemotherapy was evaluated every 2 courses.

Results

The serum concentrations of CEA, NSE, CYFRA21-1, SCC-Ag, and ProGRP in the lung cancer group were significantly higher than those in the control group (P < .05). We found statistically significant differences in serum CEA, NSE, CYFRA 21-1, SCC-Ag, and ProGRP among patients with different pathological types (LAC, squamous cell carcinoma, or SCLC) and different stages (I–IV). The ProGRP and NSE had the highest expression in SCLC, CEA showed the highest expression in LAC, whereas CYFRA21-1 and SCC-Ag showed the highest expression in lung squamous cell carcinoma (LSCC). The concentrations of all the markers were elevated in the advanced pathological stages. The receiver operating characteristic curve analysis showed that the diagnostic value of the combined detection of CEA, NSE, CYFRA 21-1, SCC-Ag, and ProGRP for lung cancer was significantly higher than using a single biomarker (P < .05). Our dynamic monitoring results show that ProGRP progressively decreased in remission cases of SCLC and CEA progressively decreased in LAC remission cases.

Conclusion

CEA, NSE, CYFRA 21-1, SCC-Ag, and ProGRP have good clinical value in the early diagnosis, differential diagnosis, and progression monitoring of lung cancer. The ProGRP and CEA concentrations are beneficial for evaluating the outcome of chemotherapy in SCLC and LAC. The combined detection of multiple biomarkers shows improved clinical value in the early diagnosis of lung cancer.