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

Radiomics analysis of 18F-FDG PET/CT for visceral pleural invasion in non-small cell lung cancer with pleural attachment

AIM

This study aimed to establish and validate a preoperative model that integrates clinical factors and radiomic features from 2-[18F]-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) for predicting visceral pleural invasion (VPI) in non-small cell lung cancer (NSCLC) with radiological pleural attachment.

MATERIALS AND METHODS

A total of 974 NSCLC patients (408 with VPI-present and 566 with VPI-absent) were retrospectively included from two medical centres. Clinical data and PET/CT radiomic features were collected. The optimal predictors from these radiomic features were selected to create the radiomics score (Rad-score) for the PET/CT radiomics model. Significant clinical factors and Rad-scores were incorporated into a combined PET/CT radiomics-clinical model. The predictive performance of the models was assessed using receiver operating characteristic (ROC) analysis.

RESULTS

The combined PET/CT radiomics-clinical model predicted VPI status with areas under the ROC curve (AUCs) of 0.869, 0.858, and 0.863 in the training set (n=569), internal validation set (n=245), and external validation set (n=160), respectively. These were significantly higher than the AUCs of the PET/CT radiomics model, which were 0.828, 0.782, and 0.704 (all P<0.001). In patients with a maximum tumour diameter (Dmax) ≤ 3 cm (n=537) and in patients with adenocarcinoma (n=659), the AUCs of the combined model were 0.876 and 0.877, respectively. A nomogram based on the combined model was developed, with well-fitted calibration curves.

CONCLUSION

The combined PET/CT radiomics-clinical model provides an advantage in predicting VPI status in NSCLC with pleural attachment.

Development and validation of a radiogenomics prognostic model integrating PET/CT radiomics and glucose metabolism-related gene signatures for non-small cell lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) is a highly heterogeneous malignancy characterized by altered glucose metabolism. Integration of PET/CT radiomics with glucose metabolism-related genomic signatures could provide a more comprehensive approach for prognosis and treatment guidance.

METHODS

Radiomics features were extracted from PET/CT images of 156 NSCLC patients from The Cancer Imaging Archive (TCIA) database, and glucose metabolism-related gene signatures were obtained from TCGA and GEO databases. We developed a multimodal radiogenomics prognostic model (RGC-score) using least absolute shrinkage and selection operator (LASSO) regression, combining PET/CT radiomics, glucose metabolism-related genes (GMR-genes). Functional enrichment analysis, immune infiltration assessment, and drug sensitivity analysis were performed to investigate the biological significance of glucose metabolism-related genes (GMR-genes).

RESULTS

The RGC-score model effectively stratified NSCLC patients into distinct high- and low-risk groups with significant differences in survival outcomes (P < 0.001), demonstrating excellent predictive performance (1-year AUC = 0.907, 5-year AUC = 0.968).GMR-genes are mainly involved in the process of metabolic remodeling of tumors, which is closely related to the immune microenvironment (especially CD8+ T cell infiltration) and immune checkpoint molecule expression. Additionally, significant differences in drug sensitivity were identified between glucose metabolism subtypes.

CONCLUSION

The RGC-score robustly predicts NSCLC prognosis and informs metabolic-immune interactions for personalized therapy. Limitations include the retrospective design and modest validation cohort size, necessitating prospective multicenter trials.

Correlation of 18 F-FDG PET/CT metabolic parameters with Ki-67 expression and tumor staging in nasopharyngeal carcinoma

PURPOSE

The study aimed to investigate the imaging parameters of 18F-fluorodeoxyglucose ( 18 F-FDG) PET/computed tomography (PET/CT) in nasopharyngeal carcinoma (NPC), specifically examining the relationship between mean standardized uptake value (SUVmean), maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) with Ki-67 expression, T-stage, and tumor node metastasis (TNM) stage.

METHODS

A retrospective analysis was conducted on 143 consecutive NPC patients from January 2015 to December 2023 who underwent 18 F-FDG PET/CT for initial disease assessment. SUVmax, SUVmean, MTV, and TLG were quantified from PET/CT images. Immunohistochemical staining was used to assess Ki-67 protein expression. Correlations between 18 F-FDG PET/CT metabolic parameters, Ki-67 expression, T-stage, and TNM-stage were evaluated using statistical methods, with significance set at P  < 0.05.

RESULTS

All primary NPC lesions demonstrated elevated 18 F-FDG uptake. Significant positive correlations were observed between SUVmax ( r  = 0.234, P  = 0.005), SUVmean ( r  = 0.223, P  = 0.007), MTV ( r  = 0.218, P  = 0.009), and TLG ( r  = 0.232, P  = 0.005) with Ki-67 labeling index. The univariate analysis indicated that all the parameters (SUVmax, SUVmean, MTV, and TLG) in the group with Ki-67 ≥ 50% were significantly higher than those in the group with Ki-67 < 50% ( P  = 0.001). Additionally, binary logistic regression analysis revealed that SUVmax was an independent risk factor for the group with Ki-67 ≥ 50% ( P  = 0.003). The univariate analysis revealed that all parameters (SUVmax, SUVmean, MTV, and TLG) in the T3-4 group and clinical stage IV group were significantly higher than those in the T1-2 group and stages I-III group ( P both <0.05), respectively. Furthermore, binary logistic regression analysis demonstrated that MTV was an independent risk factor for both comparisons ( P both <0.05).

CONCLUSION

The metabolic parameters derived from 18 F-FDG PET/CT in NPC indirectly reflect tumor biological behavior, suggesting their potential utility in guiding individualized comprehensive treatment strategies.

Predictive value of 18F-FDG PET/CT metabolic parameters for lymph node metastasis of non-small cell lung cancer

BACKGROUND

Lymph node metastasis is closely associated with the prognosis of patients with non-small cell lung cancer (NSCLC). This study aimed to evaluate the role of preoperative 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) parameters in mediastinal lymph node metastasis in NSCLC.

METHODS

One hundred patients with NSCLC who underwent surgery, systematic lymph node dissection, who had undergone 18FFDG PET/CT for initial staging were divided into two groups: lymph node metastasis and non-metastasis. The maximum standardized uptake value (SUVmax), average standardized uptake value (SUVmean), SUV in the liver (SURliver), mediastinal blood pool (SURblood), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were detected in both groups. Receiver operating characteristic (ROC) curves were used to evaluate the parameters for predicting the diagnostic efficacy.

RESULTS

The SUVmax, SUVmean, SURblood, SURliver, MTV, and TLG were higher in the group with lymph node metastasis than in the group without lymph node metastasis. The ROC analysis showed that 18F-FDG PET/CT demonstrated acceptable predictive ability with AUC of 0.964 (95% CI, 0.930-0.998).

CONCLUSIONS

The relative 18F-FDG PET/CT primary uptake and substitution parameters showed acceptable predictive efficacy for mediastinal lymph node metastasis in patients with NSCLC. Additional, SURblood has potential for clinical application.

Combination of circulating tumor cells and 18F-FDG PET/CT for precision diagnosis in patients with non-small cell lung cancer

PURPOSE

To investigate the value of 2-deoxy-18f-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) and circulating tumor cells (CTCs) for the differential diagnosis of patients with benign lung diseases and those with NSCLC. To explore the phenotypic heterogeneity of CTCs and their correlation with FDG uptake in patients with Stage I-IV NSCLC.

METHODS

Blood specimens from patients with benign lung diseases and patients with primary NSCLC were collected for the detection of CTCs and their subtypes (epithelial, mixed, and mesenchymal) and analyzed for 18F-FDG PET/CT tumor metabolic parameters, including the maximum standardized uptake value (SUVmax), standard uptake value (SUL), metabolic tumor volume of primary lesion (MTV), total lesion glycolysis of primary lesion (TLG). Clinical data including age, gender, smoking history, tumor size, TNM stage and pathology type were also collected. The value of the two method alone and in combination for the differential diagnosis of benign and malignant was comparatively analyzed. Finally, the differences in CTC and its subtypes in different stages of NSCLC were compared, and FDG metabolic parameters were correlated with CTC subtypes.

RESULTS

There were a total of 65 patients with pulmonary diseases, including 12 patients with benign pulmonary diseases and 53 patients with NSCLC. The mean age was 67 ± 10 (38-89 years), 27 were females and 38 were males. 31 (22 males and 9 females) had a long history of smoking. The mean size of the largest diameter of all single lesions was 36 ± 22 mm with a range of 10-108 mm. Seven out of 12 benign diseases were inflammatory granulomatous lesions and 5 were inflammatory pseudotumours. Twenty-four out of 53 NSCLC were adenocarcinomas and 29 were squamous carcinomas. Twelve out of 53 patients with NSCLC were in Stage I, 10 were in Stage II, 17 were in Stage III and 14 were in Stage IV. SUVmax, SUL, MTV, TLG, total CTCs, epithelial CTCs, and mixed CTCs were all valuable in the differential diagnosis of benign and malignant. TLG combined with mixed CTCs was statistically different from all other diagnostic methods (p < 0.05) and higher than any other diagnostic criteria. In the differential diagnosis of benign and Stage I NSCLC, only total CTC (Z = -2.188 p = 0.039) and mixed CTCs (Z = -3.020 p = 0.014) had certain diagnostic efficacy, and there was no statistical difference between them (p = 0.480). Only mesenchymal CTCs differed in Stage I-IV NSCLC, with a higher number of those who developed distant metastases than those who had non-distant metastases. Epithelial CTCs correlated with SUVmax (r = 0.333, p = 0.015) and SUL (r = 0.374, p = 0.006). Mmesenchymal CTCs correlated with MTV (r = 0.342, p = 0.018) and TLG (r = 0.319, p = 0.02). Further subgroup analyses revealed epithelial CTCs were correlated with SUVmax (r = 0.543, p = 0.009) and SUL (r = 0.552, p = 0.008), and the total CTCs was correlated with SUVmax (r = 0.622, p = 0.003), SUL (r = 0.652, p = 0.003), MTV (r = 0.460, p = 0.031), and TLG (r = 0.472, p = 0.027) in the early group (Stage I-II). Only mesenchymal CTCs was associated with MTV (r = 0.369, p = 0.041), and TLG (r = 0.415, p = 0.02) in the intermediate-late group (Stage III-IV).

CONCLUSION

Both FDG PET metabolic parameters and CTCs demonstrated diagnostic value for NSCLC, and combining TLG with mixed CTCs could enhance their diagnostic efficacy. The total CTCs and mixed CTCs showed greater diagnostic value than FDG PET in distinguishing benign lesions from Stage I NSCLC. In NSCLC patients, the epithelial CTCs exhibited a positive correlation with SUVmax and SUL, while mesenchymal CTCs correlated with MTV, and TLG. Besides, epithelial CTCs showed stronger correlations with SUVmax and SUL, and total CTCs showed stronger correlations with SUVmax, SUL, MTV, and TLG in Stage I-II NSCLC. Only mesenchymal CTCs in Stage III-IV NSCLC showed correlations with MTV and TLG. Stage IV NSCLC cases displayed a higher number of mesenchymal CTCs.

Advances in circulating tumor cells for early detection, prognosis and metastasis reduction in lung cancer

Globally, lung cancer stands as the leading type of cancer in terms of incidence and is the major source of mortality attributed to cancer. We have outlined the molecular biomarkers for lung cancer that are available clinically. Circulating tumor cells (CTCs) spread from the original location, circulate in the bloodstream, extravasate, and metastasize, forming secondary tumors by invading and establishing a favorable environment. CTC analysis is considered a common liquid biopsy method for lung cancer. We have enumerated both in vivo and ex vivo techniques for CTC separation and enrichment, examined the advantages and limitations of these methods, and also discussed the detection of CTCs in other bodily fluids. We have evaluated the value of CTCs, as well as CTCs in conjunction with other biomarkers, for their utility in the early detection and prognostic assessment of patients with lung cancer. CTCs engage with diverse cells of the metastatic process, interfering with the interaction between CTCs and various cells in metastasis, potentially halting metastasis and enhancing patient prognosis.

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.