Prognostic Significance of SUVmax Combined With Lactate Dehydrogenase in Advanced Lung Cancer Patients Treated With Immune Checkpoint Inhibitor Plus Chemotherapy: A Retrospective Study

Predictive classification of lung cancer pathological based on PET/CT radiomics

OBJECTIVES

To develop and validate a combined clinical and radiomics model for non-invasive prediction of lung cancer (LC) pathological types (lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung cancer) based on patients' pre-treatment FDG PET/CT images and clinical data, as a complementary tool to aid in the diagnosis of LC pathological histological classification.

METHODS

In total, 896 patients with pathological confirmation of lung cancer were part of this retrospective study. The training and test groups included 819 patients who underwent scanning using scanner 1. The independent validation group included 77 patients who using scanner 2. The optimal features were retained by least absolute shrinkage and selection operator algorithm dimensionality reduction screening of the collected radiomics features, clinical parameters, and PET metabolic parameters. Five models were established to predict the lung cancer pathological types by the k-nearest neighbor classification (KNN) algorithm. The performance of the prediction model was assessed by calculating the area under the curve (AUC) from the receiver operator characteristic curve (ROC).

RESULTS

Of all five predictive models (the PET-only radiomics model, the CT-only radiomics model, the PET/CT radiomics model, the clinical-only model and the combined clinical and PET/CT radiomics model), the clinical combined PET/CT radiomics model exhibited best performance. The macro-AUC for the training, test and independent validation groups were 0.974, 0.931, 0.960, the micro-AUC were 0.976, 0.940, 0.970, and the accuracy were 0.963, 0.914, and 0.961, respectively.

CONCLUSIONS

Our model combined radiomics and clinical data and showed higher performance in non-invasively predicting the LC pathological types, which suggesting that PET/CT radiomics may be a promising technique for predicting LC histopathology.

Predicting programmed death-ligand 1 (PD-L1) expression with fluorine-18 fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) metabolic parameters in resectable non-small cell lung cancer

BACKGROUND

Programmed death-ligand 1 (PD-L1) expression is a predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). PD-L1 and glucose transporter 1 expression are closely associated, and studies demonstrate correlation of PD-L1 with glucose metabolism.

AIM

The aim of this study was to investigate the association of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG-PET/CT) metabolic parameters with PD-L1 expression in primary lung tumour and lymph node metastases in resected NSCLC.

METHODS

We conducted a retrospective analysis of 210 patients with node-positive resectable stage IIB-IIIB NSCLC. PD-L1 tumour proportion score (TPS) was determined using the DAKO 22C3 immunohistochemical assay. Semi-automated techniques were used to analyse pre-operative [18F]FDG-PET/CT images to determine primary and nodal metabolic parameter scores (including max, mean, peak and peak adjusted for lean body mass standardised uptake values (SUV), metabolic tumour volume (MTV), total lesional glycolysis (TLG) and SUV heterogeneity index (HISUV)).

RESULTS

Patients were predominantly male (57%), median age 70 years with non-squamous NSCLC (68%). A majority had negative primary tumour PD-L1 (TPS < 1%; 53%). Mean SUVmax, SUVmean, SUVpeak and SULpeak values were significantly higher (p < 0.05) in those with TPS ≥ 1% in primary tumour (n = 210) or lymph nodes (n = 91). However, ROC analysis demonstrated only moderate separability at the 1% PD-L1 TPS threshold (AUCs 0.58-0.73). There was no association of MTV, TLG and HISUV with PD-L1 TPS.

CONCLUSION

This study demonstrated the association of SUV-based [18F]FDG-PET/CT metabolic parameters with PD-L1 expression in primary tumour or lymph node metastasis in resectable NSCLC, but with poor sensitivity and specificity for predicting PD-L1 positivity ≥ 1%.

CLINICAL RELEVANCE STATEMENT

Whilst SUV-based fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography metabolic parameters may not predict programmed death-ligand 1 positivity ≥ 1% in the primary tumour and lymph nodes of resectable non-small cell lung cancer independently, there is a clear association which warrants further investigation in prospective studies.

TRIAL REGISTRATION

Non-applicable KEY POINTS: • Programmed death-ligand 1 immunohistochemistry has a predictive role in non-small cell lung cancer immunotherapy; however, it is both heterogenous and dynamic. • SUV-based fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG-PET/CT) metabolic parameters were significantly higher in primary tumour or lymph node metastases with positive programmed death-ligand 1 expression. • These SUV-based parameters could potentially play an additive role along with other multi-modal biomarkers in selecting patients within a predictive nomogram.

18 F-FDG PET/CT for evaluation of metastases in nonsmall cell lung cancer on the efficacy of immunotherapy

OBJECTIVE

This study aimed to investigate the relationship between 18 F-fluorodeoxyglucose PET/computed tomography ( 18 F-FDG PET/CT) metabolic parameters and clinical benefit and prognosis in nonsmall cell lung cancer (NSCLC).

METHODS

In total, 34 advanced NSCLC patients who received 18 F-FDG PET/CT before immunotherapy were retrospectively included in this study. All patients were divided into two groups, the clinical benefit (CB) group and the no-clinical benefit (no-CB) group, based on the efficacy of evaluation after 6 months of treatment. Also clinical information, characteristics of metastases, survival, PD-L1 expression level and glucose metabolic parameters were evaluated.

RESULTS

Finally, 24 patients were in the CB group, and 10 patients were in the no-CB group. There was a significant difference between the CB group and the no-CB group in TNM stages ( P  = 0.005), visceral and bone metastasis ( P  = 0.031), metabolic tumor volume of primary lesion (MTV-P; P  = 0.003), the metabolic tumor volume of whole-body (MTVwb; P  = 0.005) and total lesion glycolysis of whole-body (TLGwb, P  = 0.015). However, for patient outcomes, the independent prognostic factors associated with progression free survival were TNM stage (HR = 0.113; 95% CI, 0.029-0.439; P  = 0.002), TLG-P (HR = 0.085; 95% CI, 0.018-0.402; P  = 0.002) and TLG-LN (HR = 0.068; 95% CI, 0.015-0.308; P  = 0.000), and the TLG-LN (HR = 0.242; 95% CI, 0.066-0.879; P  = 0.002) was the independent prognostic factor associated with overall survival.

CONCLUSIONS

Metastatic lesion burden evaluated by 18 F-FDG PET/ CT can predict response to immunotherapy in advanced NSCLC patients, in which lymph node metastasis lesion metabolic burden is a meaningful predictor, but a large multicenter trial is still needed to validate this conclusion.

Targeting lactate metabolism for cancer immunotherapy - a matter of precision

Immune checkpoint inhibitors and adoptive T cell therapies have been valuable additions to the toolbox in the fight against cancer. These treatments have profoundly increased the number of patients with a realistic perspective toward a return to a cancer-free life. Yet, in a number of patients and tumor entities, cancer immunotherapies have been ineffective so far. In solid tumors, immune exclusion and the immunosuppressive tumor microenvironment represent substantial roadblocks to successful therapeutic outcomes. A major contributing factor to the depressed anti-tumor activity of immune cells in tumors is the harsh metabolic environment. Hypoxia, nutrient competition with tumor and stromal cells, and accumulating noxious waste products, including lactic acid, pose massive constraints to anti-tumor immune cells. Numerous strategies are being developed to exploit the metabolic vulnerabilities of tumor cells in the hope that these would also alleviate metabolism-inflicted immune suppression. While promising in principle, especially in combination with immunotherapies, these strategies need to be scrutinized for their effect on tumor-fighting immune cells, which share some of their key metabolic properties with tumor cells. Here, we provide an overview of strategies that seek to tackle lactate metabolism in tumor or immune cells to unleash anti-tumor immune responses, thereby opening therapeutic options for patients whose tumors are currently not treatable.

The association between albumin levels and survival in patients treated with immune checkpoint inhibitors: A systematic review and meta-analysis

Background: The albumin levels may potentially be used as a prognostic biomarker in patients with cancertreated with immune checkpoint inhibitors (ICIs) due to its close relationship with nutritional and inflammatory status. However, the available data is limited with heterogeneous patient cohorts, sample sizes and variable cut-offs. Therefore, we conducted a systematic review and meta-analysis to evaluate the association between survival outcomes and albumin levels in patients treated with ICIs. Methods: We conducted a systematic review using the PubMed, Web of Science, and Embase databases to filter the published studies up to 1 June 2022. The meta-analyses were performed with the generic inverse-variance method with a random-effects model due to the high degree of heterogeneity. The primary outcome measure was hazard ratio (HR) with 95% confidence intervals (CI). The study protocol was registered with the PROSPERO registry (Registration Number: CRD42022337746). Results: Thirty-six studies encompassing 8406 cancer patients with advanced disease were included in the meta-analyses. Almost half of the studies were conducted in NSCLC cohorts (n = 15), and 3.5 gr/dL was the most frequently used albumin cut-off in the included studies (n = 20). Patients with lower albumin levels had a significantly increased risk of death (HR: 1.65, 95% CI: 1.52-1.80, p < 0.0001) than patients with higher albumin levels. Subgroup analyses for study location, sample size, tumor type and albumin cut-off were demonstrated consistent results. Furthermore, in the subgroup analysis of eight studies using albumin levels as a continuous prognostic factor, every 1 gr/dL decrease in albumin levels was associated with significantly increased risk of death by a factor of 10% (HR: 1.10, 95% CI: 1.05-1.16, p = 0.0002). Similar to analyses with overall survival, the patients with lower albumin levels had an increased risk of progression or death compared to patients with higher albumin levels (HR: 1.76, 95% CI: 1.40-2.21, p < 0.001). Conclusion: The available evidence demonstrates that albumin levels may be a prognostic biomarker in advanced cancer patients treated with ICIs. Further research is needed to delineate the role of albumin levels in patients treated with ICIs in the adjuvant setting, as well as the possible benefit of therapeutic approaches to improve hypoalbuminemia.

The Prognostic Value of Gastric Immune Prognostic Index in Gastric Cancer Patients Treated With PD-1/PD-L1 Inhibitors

Objective: This study aimed to investigate the prognostic value of the gastric immune prognostic index (GIPI) in gastric cancer patients treated with programmed death 1/programmed death-ligand 1 (PD-1/PD-L1) inhibitors.

Methods: This study was conducted to elucidate the role of GIPI using the data from 146 gastric cancer patients treated with PD-1/PD-L1 inhibitors between August 2016 and December 2020 in Harbin Medical University Cancer Hospital. The GIPI calculation was based on dNLR and LDH. Patients were categorized into three groups: 1) GIPI good (LDH ≤250 U/L and dNLR ≤3); 2) GIPI intermediate (LDH &gt;250 U/L and NLR &gt;3); 3) GIPI poor (LDH &gt;250 U/L and dNLR &gt;3). The correlations between GIPI and clinicopathologic characteristics were determined by the Chi-square test or the Fisher’s exact test. The Kaplan–Meier analysis and log-rank test were used to calculate and compare progression-free survival (PFS) and overall survival (OS). The univariate and multivariate Cox proportional hazards regression model was used to detect prognostic and predictive factors of PFS and OS.

Results: 146 patients treated with PD-1/PD-L1 inhibitors were included in this study, of which, 72.6% were GIPI good, 23.3% were GIPI intermediate, and 4.1% were GIPI poor. The GIPI was associated with the common blood parameters, including neutrophils and lymphocytes. The multivariate analysis showed that platelet, TNM stage, and treatment were the independent prognostic factors for PFS and OS. Patients with GIPI intermediate/poor were associated with shorter PFS (median: 24.63 vs. 32.50 months; p = 0.078) and OS (median: 28.37 months vs. not reached; p = 0.033) than those with GIPI good. GIPI intermediate/poor was correlated with shorter PFS and OS than GIPI good, especially in subgroups of patients with ICI treatment and patients with PD-1/PD-L1 positive status.

Conclusions: The GIPI correlated with poor outcomes for PD-1/PD-L1 expression status and may be useful for identifying gastric cancer patients who are unlikely to benefit from treatment.

Immunotherapy Monitoring with Immune Checkpoint Inhibitors Based on [18F]FDG PET/CT in Metastatic Melanomas and Lung Cancer

Immunotherapy with checkpoint inhibitors has prompted a major change not only in cancer treatment but also in medical imaging. In parallel with the implementation of new drugs modulating the immune system, new response criteria have been developed, aiming to overcome clinical drawbacks related to the new, unusual, patterns of response characterizing both solid tumors and lymphoma during the course of immunotherapy. The acknowledgement of pseudo-progression, hyper-progression, immune-dissociated response and so forth, has become mandatory for all imagers dealing with this clinical scenario. A long list of acronyms, i.e., irRC, iRECIST, irRECIST, imRECIST, PECRIT, PERCIMT, imPERCIST, iPERCIST, depicts the enormous effort made by radiology and nuclear medicine physicians in the last decade to optimize imaging parameters for better prediction of clinical benefit in immunotherapy regimens. Quite frequently, a combination of clinical-laboratory data with imaging findings has been tested, proving the ability to stratify patients into various risk groups. The next steps necessarily require a large scale validation of the most robust criteria, as well as the clinical implementation of immune-targeting tracers for immuno-PET or the exploitation of radiomics and artificial intelligence as complementary tools during the course of immunotherapy administration. For the present review article, a summary of PET/CT role for immunotherapy monitoring will be provided. By scrolling into various cancer types and applied response criteria, the reader will obtain necessary information for better understanding the potentials and limitations of the modality in the clinical setting.