18F-FDG PET/CT and circulating tumor cells in treatment-naive patients with non-small-cell lung cancer

Establishment of a prognosis prediction model for lung squamous cell carcinoma related to PET/CT: basing on immunogenic cell death-related lncRNA

BACKGROUND

Immunogenic cell death (ICD) stimulates adaptive immunity and holds significant promise in cancer therapy. Nevertheless, the influence of ICD-associated long non-coding RNAs (lncRNAs) on the prognosis of patients with lung squamous cell carcinoma (LUSC) remains unexplored.

METHODS

We employed data from the The Cancer Genome Atlas (TCGA)database to identify ICD-related lncRNAs associated with the prognosis of LUSC using univariate Cox regression analysis. Subsequently, we utilized the LOSS regression model to construct a predictive risk model for assessing the prognosis of LUSC patients based on ICD-related lncRNAs. Our study randomly allocated187 TCGA patients into a training group and 184 patients for testing the predictive model. Furthermore, we conducted quantitative polymerase chain reaction (qPCR) analysis on 43 tumor tissues from LUSC patients to evaluate lncRNA expression levelsPearson correlation analysis was utilized to analyze the correlation of risk scores with positron emission tomography/computed tomography (PET/CT) parameters among LUSC patients.

RESULTS

The findings from the univariate Cox regression revealed 16 ICD-associated lncRNAs linked to LUSC prognosis, with 12 of these lncRNAs integrated into our risk model utilizing the LOSS regression. Survival analysis indicated a markedly higher overall survival time among patients in the low-risk group compared to those in the high-risk group. The area under the Receiver operating characteristic (ROC) curve to differentiate high-risk and low-risk patients was 0.688. Additionally, the overall survival rate was superior in the low-risk group compared to the high-risk group. Correlation analysis demonstrated a positive association between the risk score calculated based on the ICD-lncRNA risk model and the maximum standard uptake value (SUVmax) (r = 0.427, P = 0.0043) as well as metabolic volume (MTV)of PET-CT (r = 0.360, P = 0.0177) in 43 LUSC patients.

CONCLUSION

We have successfully developed a risk model founded on ICD-related lncRNAs that proves effective in predicting the overall survival of LUSC patients.

Circulating tumour cells for early detection of clinically relevant cancer

Given that cancer mortality is usually a result of late diagnosis, efforts in the field of early detection are paramount to reducing cancer-related deaths and improving patient outcomes. Increasing evidence indicates that metastasis is an early event in patients with aggressive cancers, often occurring even before primary lesions are clinically detectable. Metastases are usually formed from cancer cells that spread to distant non-malignant tissues via the blood circulation, termed circulating tumour cells (CTCs). CTCs have been detected in patients with early stage cancers and, owing to their association with metastasis, might indicate the presence of aggressive disease, thus providing a possible means to expedite diagnosis and treatment initiation for such patients while avoiding overdiagnosis and overtreatment of those with slow-growing, indolent tumours. The utility of CTCs as an early diagnostic tool has been investigated, although further improvements in the efficiency of CTC detection are required. In this Perspective, we discuss the clinical significance of early haematogenous dissemination of cancer cells, the potential of CTCs to facilitate early detection of clinically relevant cancers, and the technological advances that might improve CTC capture and, thus, diagnostic performance in this setting.

Advances in liquid biopsy-based markers in NSCLC

Lung cancer is the second most-frequently occurring cancer and the leading cause of cancer-associated deaths worldwide. Non-small cell lung cancer (NSCLC), the most common type of lung cancer is often diagnosed in middle or advanced stages and have poor prognosis. Diagnosis of disease at an early stage is a key factor for improving prognosis and reducing mortality, whereas, the currently used diagnostic tools are not sufficiently sensitive for early-stage NSCLC. The emergence of liquid biopsy has ushered in a new era of diagnosis and management of cancers, including NSCLC, since analysis of circulating tumor-derived components, such as cell-free DNA (cfDNA), circulating tumor cells (CTCs), cell-free RNAs (cfRNAs), exosomes, tumor-educated platelets (TEPs), proteins, and metabolites in blood or other biofluids can enable early cancer detection, treatment selection, therapy monitoring and prognosis assessment. There have been great advances in liquid biopsy of NSCLC in the past few years. Hence, this chapter introduces the latest advances on the clinical application of cfDNA, CTCs, cfRNAs and exosomes, with a particular focus on their application as early markers in the diagnosis, treatment and prognosis of NSCLC.

Predicting pathological highly invasive lung cancer from preoperative [18F]FDG PET/CT with multiple machine learning models

PURPOSE

The efficacy of sublobar resection of primary lung cancer have been proven in recent years. However, sublobar resection for highly invasive lung cancer increases local recurrence. We developed and validated multiple machine learning models predicting pathological invasiveness of lung cancer based on preoperative [¹⁸F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) radiomic features.

METHODS

Overall, 873 patients who underwent lobectomy or segmentectomy for primary lung cancer were enrolled. Radiomics features were extracted from preoperative PET/CT images with the PyRadiomics package. Seven machine learning models and an ensemble of all models (ENS) were evaluated after 100 iterations. In addition, the probability of highly invasive lung cancer was calculated in a nested cross-validation to assess the calibration plot and clinical usefulness and to compare to consolidation tumour ratio (CTR) on CT images, one of the generally used diagnostic criteria.

RESULTS

In the training set, when PET and CT features were combined, all models achieved an area under the curve (AUC) of ≥ 0.880. In the test set, ENS showed the highest mean AUC of 0.880 and smallest standard deviation of 0.0165, and when the cutoff was 0.5, accuracy of 0.804, F1 of 0.851, precision of 0.821, and recall of 0.885. In the nested cross-validation, the AUC of 0.882 (95% CI: 0.860-0.905) showed a high discriminative ability, and the calibration plot indicated consistency with a Brier score of 0.131. A decision curve analysis showed that the ENS was valid with a threshold probability ranging from 3 to 98%. Accuracy showed an improvement of more than 8% over the CTR.

CONCLUSION

The machine learning model based on preoperative [¹⁸F]FDG PET/CT images was able to predict pathological highly invasive lung cancer with high discriminative ability and stability. The calibration plot showed good consistency, suggesting its usefulness in quantitative risk assessment.

Metabolic activity via 18F-FDG PET/CT is predictive of microsatellite instability status in colorectal cancer

Purpose

Identification of microsatellite instability high (MSI-H) colorectal cancer (CRC) is crucial for screening patients most likely to benefit from immunotherapy. We aim to investigate whether the metabolic characteristics is related to MSI status and can be used to predict the MSI-H CRC.

Methods

A retrospective analysis was conducted on 420 CRC patients who were identified via [¹⁸F]fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/computed tomography(CT) prior to therapy. Maximum standardized uptake (SUV_max), mean standardized uptake (SUV_mean), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of the primary tumor were calculated and compared between MSI-H and microsatellite stability (MSS). Predictive factors of MSI status were selected from metabolic parameters and clinicopathological profiles via a multivariate analysis.

Results

Of 420 colorectal cancers, 44 exhibited a high incidence of MSI. Both MTV and TLG were significantly higher in MSI-H group compared with the MSS group (P = 0.004 and P = 0.010, respectively). Logistic regression analysis indicated that CRC with MSI-H were related to younger age (P = 0.013), primary lesion located at right hemi-colon (P < 0.001) and larger MTV on PET/CT imaging (P = 0.019). MTV more than 32.19 of colorectal cancer was linked to the presence of MSI (P = 0.019).

Conclusion

Tumor metabolic burden were higher in MSI-H CRC which may be useful for predicting the MSI status of CRC patient and thus aid in determination of immunotherapy for patients with CRC.

Distribution and Clinical Significance of IL-17A in Tumor-Infiltrating Lymphocytes of Non-Small Cell Lung Cancer Patients

Objective: To investigate the distribution of IL-17A and its clinical significance in tumor infiltrating lymphocytes (TILs) of patients with non-small cell lung cancer (NSCLC).

Methods: Expression level of IL-17A in TILs of 3 paired NSCLC and paracancerous specimens was measured by qRT-PCR. The distribution of IL-17A in immune cell subsets of 15 paired NSCLC and paracancerous specimens was examined by flow cytometry. The correlation between IL-17A and clinical features of NSCLC was identified.

Results: IL-17A was significantly upregulated in TILs of NSCLC specimens than those of paracancerous ones (p < 0.0001). Meanwhile, T helper 17 cells (Th17 cells, p < 0.001), IL-17-secreting CD8⁺ T cells (Tc17 cells, p < 0.001) and IL-17-producing cells (γδT17 cells, p < 0.0001) were significantly abundant in TILs of NSCLC specimens than those of controls, and the higher abundance of the latter was much pronounced than that of the former two. Moreover, γδT17 cells in TILs were significantly correlated with lymphatic metastasis and CYFRA 21-1 level of NSCLC patients (p < 0.05).

Conclusion: Tumor infiltrated γδT cells are the main source of IL-17 in early-stage NSCLC, and IL-17 may be a vital regulator involved in the development of NSCLC.

Progress and future prospective of FDG-PET/CT imaging combined with optimized procedures in lung cancer: toward precision medicine

With a 5-year overall survival of approximately 20%, lung cancer has always been the number one cancer-specific killer all over the world. As a fusion of positron emission computed tomography (PET) and computed tomography (CT), PET/CT has revolutionized cancer imaging over the past 20 years. In this review, we focused on the optimization of the function of ¹⁸F-flurodeoxyglucose (FDG)-PET/CT in diagnosis, prognostic prediction and therapy management of lung cancers by computer programs. FDG-PET/CT has demonstrated a surprising role in development of therapeutic biomarkers, prediction of therapeutic responses and long-term survival, which could be conducive to solving existing dilemmas. Meanwhile, novel tracers and optimized procedures are also developed to control the quality and improve the effect of PET/CT. With the continuous development of some new imaging agents and their clinical applications, application value of PET/CT has broad prospects in this area.