PD-L1 expression correlation with metabolic parameters of FDG PET/CT and clinicopathological characteristics in non-small cell lung cancer

The effect of smoking on tumor immunoediting: Friend or foe?

The recognition of smoking as an independent risk factor for lung cancer has become a widely accepted within the realm of respiratory medicine. The emergence of tumor immunotherapy has notably enhanced the prognosis for numerous late-stage cancer patients. Nevertheless, some studies have noted a tendency for lung cancer patients who smoke to derive greater benefit from immunotherapy. This observation has sparked increased interest in the interaction between smoking and the immune response to tumors in lung cancer. The concept of cancer immunoediting has shed light on the intricate and nuanced relationship between the immune system and tumors. Starting from the perspectives of immune surveillance, immune equilibrium, and immune evasion, this narrative review explores how smoking undermines the immune response against tumor cells and induces the generation of tumor neoantigens, and examines other behaviors that trigger tumor immune evasion. By elucidating these aspects, the review concludes that smoking is not conducive to tumor immunoediting.

Deep learning radiomics model based on PET/CT predicts PD-L1 expression in non-small cell lung cancer

Purpose

Programmed cell death protein-1 ligand (PD-L1) is an important prognostic predictor for immunotherapy of non-small cell lung cancer (NSCLC). This study aimed to develop a non-invasive deep learning and radiomics model based on positron emission tomography and computed tomography (PET/CT) to predict PD-L1 expression in NSCLC.

Methods

A total of 136 patients with NSCLC between January 2021 and September 2022 were enrolled in this study. The patients were randomly divided into the training dataset and the validation dataset in a ratio of 7:3. Radiomics feature and deep learning feature were extracted from their PET/CT images. The Mann-whitney U-test, Least Absolute Shrinkage and Selection Operator algorithm and Spearman correlation analysis were used to select the top significant features. Then we developed a radiomics model, a deep learning model, and a fusion model based on the selected features. The performance of three models were compared by the area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value.

Results

Of the patients, 42 patients were PD-L1 negative and 94 patients were PD-L1 positive. A total of 2446 radiomics features and 4096 deep learning features were extracted per patient. In the training dataset, the fusion model achieved a highest AUC (0.954, 95% confident internal [CI]: 0.890-0.986) compared with the radiomics model (0.829, 95%CI: 0.738-0.898) and the deep learning model (0.935, 95%CI: 0.865-0.975). In the validation dataset, the AUC of the fusion model (0.910, 95% CI: 0.779-0.977) was also higher than that of the radiomics model (0.785, 95% CI: 0.628-0.897) and the deep learning model (0.867, 95% CI: 0.724-0.952).

Conclusion

The PET/CT-based deep learning radiomics model can predict the PD-L1 expression accurately in NSCLC patients, and provides a non-invasive tool for clinicians to select positive PD-L1 patients.

Clinical feasibility study of early 30-minute dynamic FDG-PET scanning protocol for patients with lung lesions

PURPOSE

This study aimed to evaluate the clinical feasibility of early 30-minute dynamic 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) positron emission tomography (PET) scanning protocol for patients with lung lesions in comparison to the standard 65-minute dynamic FDG-PET scanning as a reference.

METHODS

Dynamic 18F-FDG PET images of 146 patients with 181 lung lesions (including 146 lesions confirmed by histology) were analyzed in this prospective study. Dynamic images were reconstructed into 28 frames with a specific temporal division protocol for the scan data acquired 65 min post-injection. Ki images and quantitative parameters Ki based on two different acquisition durations [the first 30 min (Ki-30 min) and 65 min (Ki-65 min)] were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. The two acquisition durations were compared for Ki image quality (including visual score analysis and number of lesions detected) and Ki value (including accuracy of Ki, the value of differential diagnosis of lung lesions and prediction of PD-L1 status) by Wilcoxon's rank sum test, Spearman's rank correlation analysis, receiver operating characteristic (ROC) curve, and the DeLong test. The significant testing level (alpha) was set to 0.05.

RESULTS

The quality of the Ki-30 min images was not significantly different from the Ki-65 min images based on visual score analysis (P > 0.05). In terms of Ki value, among 181 lesions, Ki-65 min was statistically higher than Ki-30 min (0.027 ± 0.017 ml/g/min vs. 0.026 ± 0.018 ml/g/min, P < 0.05), while a very high correlation was obtained between Ki-65 min and Ki-30 min (r = 0.977, P < 0.05). In the differential diagnosis of lung lesions, ROC analysis was performed on 146 histologically confirmed lesions, the area under the curve (AUC) of Ki-65 min, Ki-30 min, and SUVmax was 0.816, 0.816, and 0.709, respectively. According to the Delong test, no significant differences in the diagnostic accuracies were found between Ki-65 min and Ki-30 min (P > 0.05), while the diagnostic accuracies of Ki-65 min and Ki-30 min were both significantly higher than that of SUVmax (P < 0.05). In 73 (NSCLC) lesions with definite PD-L1 expression results, the Ki-65 min, Ki-30 min, and SUVmax in PD-L1 positivity were significantly higher than that in PD-L1 negativity (P < 0.05). And no significant differences in predicting PD-L1 positivity were found among Ki-65 min, Ki-30 min, and SUVmax (AUC = 0.704, 0.695, and 0.737, respectively, P > 0.05), according to the results of ROC analysis and Delong test.

CONCLUSIONS

This study indicates that an early 30-minute dynamic FDG-PET acquisition appears to be sufficient to provide quantitative images with good-quality and accurate Ki values for the assessment of lung lesions and prediction of PD-L1 expression. Protocols with a shortened early 30-minute acquisition time may be considered for patients who have difficulty with prolonged acquisitions to improve the efficiency of clinical acquisitions.

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.

The Prognostic Role of [18F]FDG PET/CT in Patients with Advanced Cutaneous Squamous Cell Carcinoma Submitted to Cemiplimab Immunotherapy: A Single-Center Retrospective Study

Background: Baseline 2-deoxy-2[18F]fluoro-d-glucose ([18F]FDG) positron emission tomography (PET)-derived parameters and 12-week metabolic response were investigated as prognostic factors in advanced cutaneous squamous cell carcinoma (cSCC) submitted to cemiplimab immunotherapy. Materials and Methods: Clinical records of 25 cSCC patients receiving cemiplimab, submitted to [18F]FDG positron emission tomography/computed tomography (PET/CT) at baseline and after ∼12 weeks, were retrospectively reviewed. The Kaplan-Meier (KM) method was applied to analyze differences in event-free survival (EFS), and Cox regression analysis was employed to identify the prognostic factors. Results: At the 12-week PET/CT evaluation, 16 patients (64%) were classified as responders (complete or partial response) and 9 (36%) as nonresponders ("unconfirmed progressive metabolic disease") according to immune PET Response Criteria in Solid Tumors (iPERCIST). By KM analysis, baseline metabolic tumor volume (MTV) and total lesion glycolysis (TLG) significantly correlated with the EFS (p < 0.05). Furthermore, the KM analysis showed that the lack of metabolic response at 12 weeks was associated with meaningfully shorter EFS (7.2 ± 1 months in nonresponders vs. 20.3 ± 2.3 months in responders). In Cox multivariate analysis, metabolic response at 12 weeks remained the only predictor of the EFS (p < 0.05). Conclusions: Baseline tumor load (i.e., MTV and TLG) and metabolic response at 12 weeks may have a prognostic impact in cSCC patients treated with cemiplimab.

[Construction of A Nomogram Prediction Model for PD-L1 Expression in Non-small Cell Lung Cancer Based on 18F-FDG PET/CT Metabolic Parameters]

BACKGROUND

In recent years, immunotherapy represented by programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunosuppressants has greatly changed the status of non-small cell lung cancer (NSCLC) treatment. PD-L1 has become an important biomarker for screening NSCLC immunotherapy beneficiaries, but how to easily and accurately detect whether PD-L1 is expressed in NSCLC patients is a difficult problem for clinicians. The aim of this study was to construct a Nomogram prediction model of PD-L1 expression in NSCLC patients based on 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography/conputed tomography (PET/CT) metabolic parameters and to evaluate its predictive value.

METHODS

Retrospective collection of 18F-FDG PET/CT metabolic parameters, clinicopathological information and PD-L1 test results of 155 NSCLC patients from Inner Mongolia People's Hospital between September 2016 and July 2021. The patients were divided into the training group (n=117) and the internal validation group (n=38), and another 51 cases of NSCLC patients in our hospital between August 2021 and July 2022 were collected as the external validation group according to the same criteria. Then all of them were categorized according to the results of PD-L1 assay into PD-L1+ group and PD-L1- group. The metabolic parameters and clinicopathological information of patients in the training group were analyzed by univariate and binary Logistic regression, and a Nomogram prediction model was constructed based on the screened independent influencing factors. The effect of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) in both the training group and the internal and external validation groups.

RESULTS

Binary Logistic regression analysis showed that metabolic tumor volume (MTV), gender and tumor diameter were independent influences on PD-L1 expression. Then a Nomogram prediction model was constructed based on the above independent influences. The ROC curve for the model in the training group shows an area under the curve (AUC) of 0.769 (95%CI: 0.683-0.856) with an optimal cutoff value of 0.538. The AUC was 0.775 (95%CI: 0.614-0.936) in the internal validation group and 0.752 (95%CI: 0.612-0.893) in the external validation group. The calibration curves were tested by the Hosmer-Lemeshow test and showed that the training group (χ2=0.040, P=0.979), the internal validation group (χ2=2.605, P=0.271), and the external validation group (χ2=0.396, P=0.820) were well calibrated. The DCA curves show that the model provides clinical benefit to patients over a wide range of thresholds (training group: 0.00-0.72, internal validation group: 0.00-0.87, external validation group: 0.00-0.66).

CONCLUSIONS

The Nomogram prediction model constructed on the basis of 18F-FDG PET/CT metabolic parameters has greater application value in predicting PD-L1 expression in NSCLC patients.

18F-FAPI-04 PET/CT parameters predict PD-L1 expression in esophageal squamous cell carcinoma

Purpose

This prospective study examined whether metabolism parameters obtained using the tracer 18F-AlFNOTA-fibroblast activation protein inhibitor (FAPI)-04 (denoted as 18F-FAPI-04) in positron emission tomography/computed tomography (PET/CT) can predict programmed death ligand-1 (PD-L1) expression in patients with locally advanced esophageal squamous cell carcinoma (LA-ESCC).

Patients and methods

The 24 enrolled LA-ESCC patients underwent an 18F-FAPI-04 PET/CT scan. The maximum, mean, peak and standard deviation standard uptake values (SUVmax, SUVmean, SUVpeak and SUVsd), metabolic tumor volume (MTV), and total lesion FAP (TLF) expression of the primary tumor were collected. Additionally, we evaluated PD-L1 expression on cancer cells by immunohistochemistry and immunofluorescence methods. Patients were divided into negative and positive expressions according to the expression of PD-L1 (CPS < 10 and CPS ≥ 10), and the variables were compared between the two groups.

Results

The SUVmax, SUVmean, SUVpeak and SUVsd were significantly higher in patients with positive expression than in negative expression (all p < 0.05). Receiver operating characteristic curve analysis identified SUVmean (area under the curve [AUC] = 0.882, p = 0.004), SUVsd (AUC = 0.874, p = 0.005), SUVpeak (AUC = 0.840, p = 0.010) and SUVmax (AUC = 0.765, p = 0.045) as significant predictors of the PD-L1 positive expression, with cutoff values of 9.67, 1.90, 9.67 and 13.71, respectively. On univariate logistic regression analysis, SUVmean (p = 0.045), SUVsd (p = 0.024), and SUVpeak (p = 0.031) were significantly correlated with the PD-L1 positive expression. On multivariable logistic regression analysis, SUVsd (p = 0.035) was an optimum predictor factor for PD-L1 positive expression.

Conclusion

18F-FAPI-04 PET/CT parameters, including SUVmean, SUVpeak, and SUVsd, correlated with PD-L1 expression in patients with LA-ESCC, and thus SUVsd was an optimum predictor for PD-L1 positive expression, which could help to explore the existence of immune checkpoints and select ESCC candidates for immunotherapy.

Retrospective Analysis of the Predictive Value of 18F-FDG PET/CT Metabolic Parameters for PD-L1 Expression in Cervical Cancer

BACKGROUND

Immunotherapy targeting PD-1/PD-L1 has been proven to be effective for cervical cancer treatment. To explore non-invasive examinations for assessing the PD-L1 status in cervical cancer, we performed a retrospective study to investigate the predictive value of ¹⁸F-FDG PET/CT.

METHODS

The correlations between PD-L1 expression, clinicopathological characteristics and ¹⁸F-FDG PET/CT metabolic parameters were evaluated in 74 cervical cancer patients. The clinicopathological characteristics included age, histologic type, tumor differentiation, FIGO stage and tumor size. The metabolic parameters included maximum standard uptake (SUVmax), mean standard uptake (SUVmean), total lesion glycolysis (TLG) and tumor metabolic volume (MTV).

RESULTS

In univariate analysis, SUVmax, SUVmean, TLG, tumor size and tumor differentiation were obviously associated with PD-L1 status. SUVmax (rs = 0.42) and SUVmean (rs = 0.40) were moderately positively correlated with the combined positive score (CPS) for PD-L1 in Spearman correlation analysis. The results of multivariable analysis showed that the higher SUVmax (odds ratio = 2.849) and the lower degree of differentiation (Odds Ratio = 0.168), the greater probability of being PD-L1 positive. The ROC curve analysis demonstrated that when the cut-off values of SUVmax, SUVmean and TLG were 10.45, 6.75 and 143.4, respectively, the highest accuracy for predicting PD-L1 expression was 77.0%, 71.6% and 62.2%, respectively. The comprehensive predictive ability of PD-L1 expression, assessed by combining SUVmax with tumor differentiation, showed that the PD-L1-negative rate was 100% in the low probability group, whereas the PD-L1-positive rate was 84.6% in the high probability group. In addition, we also found that the H-score of HIF-1α was moderately positively correlated with PD-L1 CPS (rs = 0.51).

CONCLUSIONS

The SUVmax and differentiation of the primary lesion were the optimum predictors for PD-L1 expression in cervical cancer. There was a great potential for ¹⁸F-FDG PET/CT in predicting PD-L1 status and selecting cervical cancer candidates for PD1/PD-L1 immune checkpoint therapy.

The radiological appearances of lung cancer treated with immunotherapy

Therapy and prognosis of several solid and hematologic malignancies, including non-small cell lung cancer (NSCLC), have been favourably impacted by the introduction of immune checkpoint inhibitors (ICIs). Their mechanism of action relies on the principle that some cancers can evade immune surveillance by expressing surface inhibitor molecules, known as "immune checkpoints". ICIs aim to conceal tumoural checkpoints on the cell surface and reinvigorate the ability of the host immune system to recognize tumour cells, triggering an antitumoural immune response.In this review, we will focus on the imaging patterns of different responses occurring in patients treated by ICIs. We will also discuss imaging findings of immune-related adverse events (irAEs), along with current and future perspectives of metabolic imaging. Finally, we will explore the role of radiomics in the setting of ICI-treated patients.

Predicting PD-L1 expression status in patients with non-small cell lung cancer using [18F]FDG PET/CT radiomics

BACKGROUND

In recent years, immune checkpoint inhibitor (ICI) therapy has greatly changed the treatment prospects of patients with non-small cell lung cancer (NSCLC). Among the available ICI therapy strategies, programmed death-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors are the most widely used worldwide. At present, immunohistochemistry (IHC) is the main method to detect PD-L1 expression levels in clinical practice. However, given that IHC is invasive and cannot reflect the expression of PD-L1 dynamically and in real time, it is of great clinical significance to develop a new noninvasive, accurate radiomics method to evaluate PD-L1 expression levels and predict and filter patients who will benefit from immunotherapy. Therefore, the aim of our study was to assess the predictive power of pretherapy [¹⁸F]-fluorodeoxyglucose ([¹⁸F]FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics features for PD-L1 expression status in patients with NSCLC.

METHODS

A total of 334 patients with NSCLC who underwent [¹⁸F]FDG PET/CT imaging prior to treatment were analyzed retrospectively from September 2016 to July 2021. The LIFEx7.0.0 package was applied to extract 63 PET and 61 CT radiomics features. In the training group, the least absolute shrinkage and selection operator (LASSO) regression model was employed to select the most predictive radiomics features. We constructed and validated a radiomics model, clinical model and combined model. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to evaluate the predictive performance of the three models in the training group and validation group. In addition, a radiomics nomogram to predict PD-L1 expression status was established based on the optimal predictive model.

RESULTS

Patients were randomly assigned to a training group (n = 233) and a validation group (n = 101). Two radiomics features were selected to construct the radiomics signature model. Multivariate analysis showed that the clinical stage (odds ratio [OR] 1.579, 95% confidence interval [CI] 0.220-0.703, P < 0.001) was a significant predictor of different PD-L1 expression statuses. The AUC of the radiomics model was higher than that of the clinical model in the training group (0.706 vs. 0.638) and the validation group (0.761 vs. 0.640). The AUCs in the training group and validation group of the combined model were 0.718 and 0.769, respectively.

CONCLUSION

PET/CT-based radiomics features demonstrated strong potential in predicting PD-L1 expression status and thus could be used to preselect patients who may benefit from PD-1/PD-L1-based immunotherapy.

PET/CT molecular imaging in the era of immune-checkpoint inhibitors therapy

Cancer immunotherapy, especially immune-checkpoint inhibitors (ICIs), has paved a new way for the treatment of many types of malignancies, particularly advanced-stage cancers. Accumulating evidence suggests that as a molecular imaging modality, positron emission tomography/computed tomography (PET/CT) can play a vital role in the management of ICIs therapy by using different molecular probes and metabolic parameters. In this review, we will provide a comprehensive overview of the clinical data to support the importance of 18F-fluorodeoxyglucose PET/CT (18F-FDG PET/CT) imaging in the treatment of ICIs, including the evaluation of the tumor microenvironment, discovery of immune-related adverse events, evaluation of therapeutic efficacy, and prediction of therapeutic prognosis. We also discuss perspectives on the development direction of 18F-FDG PET/CT imaging, with a particular emphasis on possible challenges in the future. In addition, we summarize the researches on novel PET molecular probes that are expected to potentially promote the precise application of ICIs.

The predictive value of total-body PET/CT in non-small cell lung cancer for the PD-L1 high expression

Purpose

Total-body positron emission tomography/computed tomography (PET/CT) provides faster scanning speed, higher image quality, and lower injected dose. To compensate for the shortcomings of the maximum standard uptake value (SUVmax), we aimed to normalize the values of PET parameters using liver and blood pool SUV (SUR-L and SUR-BP) to predict programmed cell death-ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC) patients.

Materials and methods

A total of 138 (104 adenocarcinoma and 34 squamous cell carcinoma) primary diagnosed NSCLC patients who underwent ¹⁸F-FDG-PET/CT imaging were analyzed retrospectively. Immunohistochemistry (IHC) analysis was performed for PD-L1 expression on tumor cells and tumor-infiltrating immune cells with 22C3 antibody. Positive PD-L1 expression was defined as tumor cells no less than 50% or tumor-infiltrating immune cells no less than 10%. The relationships between PD-L1 expression and PET parameters (SUVmax, SUR-L, and SUR-BP) and clinical variables were analyzed. Statistical analysis included χ² test, receiver operating characteristic (ROC), and binary logistic regression.

Results

There were 36 patients (26%) expressing PD-L1 positively. Gender, smoking history, Ki-67, and histologic subtype were related factors. SUVmax, SUR-L, and SUR-BP were significantly higher in the positive subset than those in the negative subset. Among them, the area under the curve (AUC) of SUR-L on the ROC curve was the biggest one. In NSCLC patients, the best cutoff value of SUR-L for PD-L1-positive expression was 4.84 (AUC = 0.702, P = 0.000, sensitivity = 83.3%, specificity = 54.9%). Multivariate analysis confirmed that age and SUR-L were correlated factors in adenocarcinoma (ADC) patients.

Conclusion

SUVmax, SUR-L, and SUR-BP had utility in predicting PD-L1 high expression, and SUR-L was the most reliable parameter. PET/CT can offer reference to screen patients for first-line atezolizumab therapy.

18F FDG PET/CT and Novel Molecular Imaging for Directing Immunotherapy in Cancer

Immunotherapy has transformed the treatment landscape of many cancers, with durable responses in disease previously associated with a poor prognosis. Patient selection remains a challenge, with predictive biomarkers an urgent unmet clinical need. Current predictive biomarkers, including programmed death-ligand 1 (PD-L1) (measured with immunohistochemistry), are imperfect. Promising biomarkers, including tumor mutation burden and tumor infiltrating lymphocyte density, fail to consistently predict response and have yet to translate to routine clinical practice. Heterogeneity of immune response within and between lesions presents a further challenge where fluorine 18 fluorodeoxyglucose PET/CT has a potential role in assessing response, stratifying treatment, and detecting and monitoring immune-related toxicities. Novel radiopharmaceuticals also present a unique opportunity to define the immune tumor microenvironment to better predict which patients may respond to therapy, for example by means of in vivo whole-body PD-L1 and CD8+ T cell expression imaging. In addition, longitudinal molecular imaging may help further define dynamic changes, particularly in cases of immunotherapy resistance, helping to direct a more personalized therapeutic approach. This review highlights current and emerging applications of molecular imaging to stratify, predict, and monitor molecular dynamics and treatment response in areas of clinical need.

Correlation of epidermal growth factor receptor mutation status and PD-L1 expression with [18F]FDG PET using volume-based parameters in non-small cell lung cancer

OBJECTIVE

We investigated the relationship between 2-deoxy-2-[18F]fluoro-D-glucose (FDG) PET using volume-based parameters and epidermal growth factor receptor (EGFR) mutation status, programmed death-ligand-1 (PD-L1) expression level, and their combination, in pretreated non-small cell lung cancer (NSCLC).

METHODS

FDG PET findings and EGFR mutation status and PD-L1 expression level were investigated retrospectively in 93 patients with newly diagnosed NSCLC (77 adenocarcinomas, 16 squamous cell carcinomas). Tumors were divided into six groups: EGFR mutant/negative PD-L1, EGFR mutant/low PD-L1, EGFR mutant/high PD-L1, EGFR wild/negative PD-L1, EGFR wild/low PD-L1, and EGFR wild/high PD-L1. The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) for primary tumor were measured from PET images. The EGFR mutation status and PD-L1 expression level were estimated in tumor tissue specimens and compared with the PET parameters.

RESULTS

None of the PET parameters differed significantly between EGFR-mutated and wild-type EGFR. According to the PD-L1 level, significant differences were detected in SUVmax (P = 0.001) and TLG (P = 0.016), but not MTV. Comparing all six groups, significant difference was detected in only SUVmax (P = 0.011).

CONCLUSION

Based on the preliminary results of this study, FDG PET may help in the prediction of PD-L1 expression level, but not EGFR mutation status, in patients with newly diagnosed NSCLC. The SUVmax rather than MTV or TLG, may be of value in predicting the six groups according to the combination of EGFR mutation status and PD-L1 expression level.

Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics

PURPOSE

In the present study, we aimed to evaluate the expression of programmed death-ligand 1 (PD-L1) in patients with non-small cell lung cancer (NSCLC) by radiomic features of 18F-FDG PET/CT and clinicopathological characteristics.

METHODS

A total 255 NSCLC patients (training cohort: n = 170; validation cohort: n = 85) were retrospectively enrolled in the present study. A total of 80 radiomic features were extracted from pretreatment 18F-FDG PET/CT images. Clinicopathologic features were compared between the two cohorts. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. Radiomics signature and clinicopathologic risk factors were incorporated to develop a prediction model by using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve was used to assess the prognostic factors.

RESULTS

A total of 80 radiomic features were extracted in the training dataset. In the univariate analysis, the expression of PD-L1 in lung tumors was significantly correlated with the radiomic signature, histologic type, Ki-67, SUVmax, MTV, and TLG (p< 0.05, respectively). However, the expression of PD-L1 was not correlated with age, TNM stage, and history of smoking (p> 0.05). Moreover, the prediction model for PD-L1 expression level over 1% and 50% that combined the radiomic signature and clinicopathologic features resulted in an area under the curve (AUC) of 0.762 and 0.814, respectively.

CONCLUSIONS

A prediction model based on PET/CT images and clinicopathological characteristics provided a novel strategy for clinicians to screen the NSCLC patients who could benefit from the anti-PD-L1 immunotherapy.

Primary tumor location in lung cancer: the evaluation and administration

ABSTRACT

Lung cancer continues to be the leading cause of cancer-related death in the world, which is classically subgrouped into two major histological types: Non-small cell lung cancer (NSCLC) (85% of patients) and small-cell lung cancer (SCLC) (15%). Tumor location has been reported to be associated with the prognosis of various solid tumors. Several types of cancer often occur in a specific region and are more prone to spread to predilection locations, including colorectal cancer, prostate cancer, gastric cancer, ovarian cancer, cervical cancer, bladder cancer, lung tumor, and so on. Besides, tumor location is also considered as a risk factor for lung neoplasm with chronic obstructive pulmonary disease/emphysema. Additionally, the primary lung cancer location is associated with specific lymph node metastasis. And the recent analysis has shown that the primary location may affect metastasis pattern in metastatic NSCLC based on a large population. Numerous studies have enrolled the "location" factor in the risk model. Anatomy location and lobe-specific location are both important in prognosis. Therefore, it is important for us to clarify the characteristics about tumor location according to various definitions. However, the inconsistent definitions about tumor location among different articles are controversial. It is also a significant guidance in multimode therapy in the present time. In this review, we mainly aim to provide a new insight about tumor location, including anatomy, clinicopathology, and prognosis in patients with lung neoplasm.

Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images

BACKGROUND

Currently, only a fraction of patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs) experience a durable clinical benefit (DCB). According to NCCN guidelines, Programmed death-ligand 1 (PD-L1) expression status determined by immunohistochemistry (IHC) of biopsies is the only clinically approved companion biomarker to trigger the use of ICI therapy. Based on prior work showing a relationship between quantitative imaging and gene expression, we hypothesize that quantitative imaging (radiomics) can provide an alternative surrogate for PD-L1 expression status in clinical decision support.

METHODS

¹⁸F-FDG-PET/CT images and clinical data were curated from 697 patients with NSCLC from three institutions and these were analyzed using a small-residual-convolutional-network (SResCNN) to develop a deeply learned score (DLS) to predict the PD-L1 expression status. This developed model was further used to predict DCB, progression-free survival (PFS), and overall survival (OS) in two retrospective and one prospective test cohorts of ICI-treated patients with advanced stage NSCLC.

RESULTS

The PD-L1 DLS significantly discriminated between PD-L1 positive and negative patients (area under receiver operating characteristics curve ≥0.82 in the training, validation, and two external test cohorts). Importantly, the DLS was indistinguishable from IHC-derived PD-L1 status in predicting PFS and OS, suggesting the utility of DLS as a surrogate for IHC. A score generated by combining the DLS with clinical characteristics was able to accurately (C-indexes of 0.70-0.87) predict DCB, PFS, and OS in retrospective training, prospective testing and external validation cohorts.

CONCLUSION

Hence, we propose DLS as a surrogate or substitute for IHC-determined PD-L1 measurement to guide individual pretherapy decisions pending in larger prospective trials.

Usefulness of 18F-FDG PET/CT in treatment-naive patients with thymic squamous cell carcinoma

OBJECTIVE

Thymic squamous cell carcinoma (TSCC) is very rare. This study aims to investigate the clinical utility of fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) in treatment-naive patients with TSCC.

METHODS

The tumor metabolic parameters of ¹⁸F-FDG PET/CT, including maximum standard uptake value (SUVmax), metabolic tumor volume of primary lesion (MTV-P) and combination of primary lesion and metastases (MTV-C), and total lesion glycolysis of primary lesion (TLG-P) and combination of primary lesion and metastases (TLG-C) were collected. Age, sex, smoking, serum tumor markers, tumor size, Masaoka-Koga stage, TNM stage, contrast-enhanced CT scan, and tumor immunity were also reviewed. Moreover, progression-free survival (PFS) and overall survival (OS) of these patients were analyzed.

RESULTS

Forty-two treatment-naive patients with TSCC were enrolled in this study. All primary tumors were FDG-avid with the average SUVmax of 10.0 ± 4.5 (range, 1.5-20.4). Higher SUVmax, MTV-C, and TLG-C were observed in advanced Masaoka-Koga stage than early stage, and higher SUVmax was found in advanced TNM stage than early stage. Next, 36 out of 42 patients performed chest contrast-enhanced CT scan, which showed SUVmax associated with the enhancement degree of CT. Moreover, 27 out of 42 lesions were assessed tumor immunity, and the detective rates of PD-L1, PD-1, CD4, CD8, and Foxp3 were 59.3%, 37.0%, 59.3%, 100%, and 77.8%, respectively. Higher SUVmax was observed in lesions with lower CD4-positive tumor-infiltrating lymphocytes. Furthermore, 12- and 24-month PFS and OS rates were 62.0% vs 32.8% and 84.5% vs 68.9%, respectively. Multivariate Cox regression analysis showed that only MTV-C was an independent predictor of PFS.

CONCLUSION

¹⁸F-FDG PET/CT is useful in evaluating tumor staging, assessing CT enhancement degree, and detecting tumor immunity of TSCC before treatment. ¹⁸F-FDG PET/CT could also be a promising tool to provide prognostic information for treatment-naive patients with TSCC.

Utility of Volumetric Metabolic Parameters on Preoperative FDG PET/CT for Predicting Tumor Lymphovascular Invasion in Non-Small Cell Lung Cancer

Background: Lymphovascular invasion (LVI) is an adverse prognostic indicator in non-small cell lung cancer (NSCLC) and serves as an indication for postoperative adjuvant chemotherapy recommendation after resection. Objective: To assess the utility of clinicopathologic factors and volumetric metabolic parameters from preoperative FDG PET/CT in predicting primary tumor LVI in NSCLC. Methods: This retrospective study included 161 patients (mean age, 61.8±8.1 years; 111 men, 50 women) with surgically-confirmed NSCLC who underwent preoperative FDG PET/CT between January 2018 and November 2020. Two nuclear medicine physicians used software to place automated volumes of interest delineating each tumor to record metabolic indices (SUVmax, SUVmean, and metabolictumor volume [MTV]), which in turn were used to calculate total lesion glycolysis (TLG). Measurements were first performed independently to determine interobserver agreement using intraclass correlation coefficients (ICCs) and then repeated in consensus. Associations of clinicopathologic and metabolic parameters with tumor LVI status were assessed using t test, Mann-Whitney U test, and chi-squared test. Diagnostic performance was assessed using ROC analysis. Multivariable logistic regression analysis was performed to identify independent predictors of tumor LVI. Results: A total of 23.6% (38/161) of patients had LVI. Interobserver agreement was ICC=1.000 for SUVmax, ICC=0.997 for SUVmean, and 0.999 for MTV. Tumors with LVI, compared with tumors without LVI, exhibited higher SUVmax (15.4±5.9 vs 11.7±7.5, p=.006), SUVmean (6.0±1.6 vs 5.1±2.0, p=.009), MTV (median 15.8 cm3 vs 5.5 cm3, p<.001), and TLG (median 88.8 vs 24.5, p<.001). Among the metabolic parameters, AUC was highest for MTV (0.704), with optimal MTV cutoff of 6.4 cm3 yielding sensitivity 92.1% (35/38), specificity 56.1% (69/123), PPV 39.3% (35/89), and NPV 95.8% (69/72) for LVI. Independent predictors (p<.05) of LVI were MTV (≥6.4 cm3, odds ratio [OR]=6.5), N1 (OR=6.4) or N2 (OR=4.0) disease, and T2 disease (OR=3.6). These factors combined achieved AUC of 0.854 for LVI. Conclusion: The volumetric metabolic parameter MTV from preoperative FDG PET/CT is an independent predictor of tumor LVI in NSCLC. Clinical Impact: Further studies are warranted to assess the potential role of preoperative prediction of LVI using FDG PET/CT to help guide clinical decision making in NSCLC.

Radiomic biomarkers of tumor immune biology and immunotherapy response

Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen - for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistance mechanisms. For the most part, imaging data have remained an untapped, yet abundant, resource to address these challenges. In recent years, quantitative image analyses have highlighted the utility of medical imaging in predicting tumor phenotypes, prognosis, and therapeutic response. These studies have been fueled by an explosion of resources in high-throughput mining of image features (i.e. radiomics) and artificial intelligence. In this review, we highlight current progress in radiomics to understand tumor immune biology and predict clinical responses to immunotherapies. We also discuss limitations in these studies and future directions for the field, particularly if high-dimensional imaging data are to play a larger role in precision medicine.

Obesity is a risk factor for intrahepatic cholangiocarcinoma progression associated with alterations of metabolic activity and immune status

Body mass index (BMI) is well known to be associated with poor prognosis in several cancers. The relationship between BMI and the long-term outcomes of patients with intrahepatic cholangiocarcinoma (ICC) is incompletely understood. This study investigated the relationships of BMI with clinicopathological characteristics and patient outcomes, focusing on metabolic activity and immune status. The relationship between BMI and the maximum standardized uptake value (SUVmax) on fluorine-18 fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) was analyzed. In addition, immunohistochemistry was performed for programmed cell death-ligand 1 (PD-L1), cluster of differentiation 8 (CD8), and forkhead box protein P3 (Foxp3). Seventy-four patients with ICC were classified into normal weight (BMI < 25.0 kg/m², n = 48) and obesity groups (BMI ≥ 25.0 kg/m², n = 26), respectively. Serum carbohydrate antigen 19–9 levels were higher in the obesity group than in the normal weight group. Tumor size and the intrahepatic metastasis rate were significantly larger in the obesity group. Patients in the obesity group had significantly worse prognoses than those in the normal weight group. Moreover, BMI displayed a positive correlation with SUVmax on ¹⁸F-FDG PET/CT (n = 46, r = 0.5152). Patients with high ¹⁸F-FDG uptake had a significantly higher rate of PD-L1 expression, lower CD8 + tumor-infiltrating lymphocyte (TIL) counts, and higher Foxp3 + TIL counts. The elevated BMI might predict the outcomes of patients with ICC. Obesity might be associated with ICC progression, possibly through alterations in metabolic activity and the immune status.