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

Assessment of PD-L1 Expression in Non-Small Cell Lung Cancers Using [68Ga]Ga-DOTA-WL12 PET/CT

Assessing programmed death ligand-1 (PD-L1) expression in non-small cell lung cancer (NSCLC), particularly in metastatic cases, remains challenging. In this study, surface plasmon resonance (SPR) analysis and [68Ga]Ga-DOTA-WL12 micro-PET/CT imaging are performed. [68Ga]Ga-DOTA-WL12 PET/CT and [18F]FDG PET/CT are performed on a cohort of 20 patients with NSCLC. Semi-quantitative assessments include SUVmax, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and target-to-background ratio (TBR). DOTA-WL12 exhibits robust PD-L1 binding with a KD value of 0.2 nM. Subsequent human studies reveal significant correlations between PD-L1 expression and the [68Ga]Ga-DOTA-WL12 SUVmax in primary and metastatic lesions, surpassing the [18F]FDG results (r = 0.8889, p <0.0001 vs r = 0.0469, p = 0.8127). Notably, [68Ga]Ga-DOTA-WL12 imaging discerned SUVmax and TBR differences between PD-L1 TPS ≤1% and PD-L1 TPS > 1% groups (p all <0.001). In an NSCLC patient with brain metastases, [68Ga]Ga-DOTA-WL12 shows a SUVmean of 0.04 in the brain background, with TBR values of 17 and 23, underscoring its potential for detecting brain metastases. The study provides initial evidence for the clinical utility of [68Ga]Ga-DOTA-WL12 PET/CT for lesion detection, immunotherapy selection, and therapeutic efficacy evaluation in PD-L1-expressing NSCLC, demonstrating its potential as a valuable tool in NSCLC research and management.

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.

Artificial intelligence in immunotherapy PET/SPECT imaging

OBJECTIVE

Immunotherapy has dramatically altered the therapeutic landscape for oncology, but more research is needed to identify patients who are likely to achieve durable clinical benefit and those who may develop unacceptable side effects. We investigated the role of artificial intelligence in PET/SPECT-guided approaches for immunotherapy-treated patients.

METHODS

We performed a scoping review of MEDLINE, CENTRAL, and Embase databases using key terms related to immunotherapy, PET/SPECT imaging, and AI/radiomics through October 12, 2022.

RESULTS

Of the 217 studies identified in our literature search, 24 relevant articles were selected. The median (interquartile range) sample size of included patient cohorts was 63 (157). Primary tumors of interest were lung (n = 14/24, 58.3%), lymphoma (n = 4/24, 16.7%), or melanoma (n = 4/24, 16.7%). A total of 28 treatment regimens were employed, including anti-PD-(L)1 (n = 13/28, 46.4%) and anti-CTLA-4 (n = 4/28, 14.3%) monoclonal antibodies. Predictive models were built from imaging features using univariate radiomics (n = 7/24, 29.2%), radiomics (n = 12/24, 50.0%), or deep learning (n = 5/24, 20.8%) and were most often used to prognosticate (n = 6/24, 25.0%) or describe tumor phenotype (n = 5/24, 20.8%). Eighteen studies (75.0%) performed AI model validation.

CONCLUSION

Preliminary results suggest broad potential for the application of AI-guided immunotherapy management after further validation of models on large, prospective, multicenter cohorts.

CLINICAL RELEVANCE STATEMENT

This scoping review describes how artificial intelligence models are built to make predictions based on medical imaging and explores their application specifically in the PET and SPECT examination of immunotherapy-treated cancers.

KEY POINTS

• Immunotherapy has drastically altered the cancer treatment landscape but is known to precipitate response patterns that are not accurately accounted for by traditional imaging methods. • There is an unmet need for better tools to not only facilitate in-treatment evaluation but also to predict, a priori, which patients are likely to achieve a good response with a certain treatment as well as those who are likely to develop side effects. • Artificial intelligence applied to PET/SPECT imaging of immunotherapy-treated patients is mainly used to make predictions about prognosis or tumor phenotype and is built from baseline, pre-treatment images. Further testing is required before a true transition to clinical application can be realized.

Integrating Artificial Intelligence and PET Imaging for Drug Discovery: A Paradigm Shift in Immunotherapy

The integration of artificial intelligence (AI) and positron emission tomography (PET) imaging has the potential to become a powerful tool in drug discovery. This review aims to provide an overview of the current state of research and highlight the potential for this alliance to advance pharmaceutical innovation by accelerating the development and deployment of novel therapeutics. We previously performed a scoping review of three databases (Embase, MEDLINE, and CENTRAL), identifying 87 studies published between 2018 and 2022 relevant to medical imaging (e.g., CT, PET, MRI), immunotherapy, artificial intelligence, and radiomics. Herein, we reexamine the previously identified studies, performing a subgroup analysis on articles specifically utilizing AI and PET imaging for drug discovery purposes in immunotherapy-treated oncology patients. Of the 87 original studies identified, 15 met our updated search criteria. In these studies, radiomics features were primarily extracted from PET/CT images in combination (n = 9, 60.0%) rather than PET imaging alone (n = 6, 40.0%), and patient cohorts were mostly recruited retrospectively and from single institutions (n = 10, 66.7%). AI models were used primarily for prognostication (n = 6, 40.0%) or for assisting in tumor phenotyping (n = 4, 26.7%). About half of the studies stress-tested their models using validation sets (n = 4, 26.7%) or both validation sets and test sets (n = 4, 26.7%), while the remaining six studies (40.0%) either performed no validation at all or used less stringent methods such as cross-validation on the training set. Overall, the integration of AI and PET imaging represents a paradigm shift in drug discovery, offering new avenues for more efficient development of therapeutics. By leveraging AI algorithms and PET imaging analysis, researchers could gain deeper insights into disease mechanisms, identify new drug targets, or optimize treatment regimens. However, further research is needed to validate these findings and address challenges such as data standardization and algorithm robustness.

Prediction of Tumor PD-L1 Expression in Resectable Non-Small Cell Lung Cancer by Machine Learning Models Based on Clinical and Radiological Features: Performance Comparison With Preoperative Biopsy

OBJECTIVE

We investigated if PD-L1 expression can be predicted by machine learning using clinical and imaging features.

METHODS

We included 117 patients with c-stage I/II non-small cell lung cancer who underwent radical resection. A total of 3951 radiomic features were extracted by defining the tumor (within tumor contour), rim (contour ±3 mm) and exterior (contour +10 mm) on preoperative contrast computed tomography. After feature selection by Boruta algorithm, prediction models of tumor PD-L1 expression (22C3: ≥1%, <1%) of resected specimens were constructed using Random Forest: radiomics, clinical, and combined models. Their performance was evaluated by 5-fold cross-validation, and AUCs were compared using Delong test. Next, study groups were categorized as patients without biopsy (training set), and those with biopsy (test set). Predictive ability of biopsy was compared to each prediction model.

RESULTS

Of 117 patients (66 ± 10 years old, 48% male), 33 (28.2%) had PD-L1≥1%. Mean AUC of PD-L1≥1% for the validation set in radiomics, clinical, and combined models were 0.80, 0.80, and 0.83 (P = .32 vs. clinical model), respectively. The diagnosis of malignancy was made in 22 of 38 (58%) patients with attempted biopsies, and PD-L1 was measurable in 19 of 38 (50%) patients. Diagnostic accuracies of PD-L1≥1% from 19 determinable biopsies and 38 all attempted biopsies were 0.68 and 0.34, respectively. These were out performed by machine learning: 0.71, 0.71, and 0.74 for radiomics, clinical, and combined models, respectively.

CONCLUSIONS

Our machine learning could be an adjunctive tool in estimating PD-L1 expression prior to neoadjuvant treatment, particularly when PD-L1 is indeterminable with biopsy.

Current status and prospect of PET-related imaging radiomics in lung cancer

Lung cancer is highly aggressive, which has a high mortality rate. Major types encompass lung adenocarcinoma, lung squamous cell carcinoma, lung adenosquamous carcinoma, small cell carcinoma, and large cell carcinoma. Lung adenocarcinoma and lung squamous cell carcinoma together account for more than 80% of cases. Diverse subtypes demand distinct treatment approaches. The application of precision medicine necessitates prompt and accurate evaluation of treatment effectiveness, contributing to the improvement of treatment strategies and outcomes. Medical imaging is crucial in the diagnosis and management of lung cancer, with techniques such as fluoroscopy, computed radiography (CR), digital radiography (DR), computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET)/CT, and PET/MRI being essential tools. The surge of radiomics in recent times offers fresh promise for cancer diagnosis and treatment. In particular, PET/CT and PET/MRI radiomics, extensively studied in lung cancer research, have made advancements in diagnosing the disease, evaluating metastasis, predicting molecular subtypes, and forecasting patient prognosis. While conventional imaging methods continue to play a primary role in diagnosis and assessment, PET/CT and PET/MRI radiomics simultaneously provide detailed morphological and functional information. This has significant clinical potential value, offering advantages for lung cancer diagnosis and treatment. Hence, this manuscript provides a review of the latest developments in PET-related radiomics for lung cancer.

[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.

Role of sex and sex hormones in PD-L1 expression in NSCLC: clinical and therapeutic implications

Currently, immunotherapy based on PD-1/PD-L1 pathway blockade has improved survival of non-small cell lung cancer (NSCLC) patients. However, differential responses have been observed by sex, where men appear to respond better than women. Additionally, adverse effects of immunotherapy are mainly observed in women. Studies in some types of hormone-dependent cancer have revealed a role of sex hormones in anti-tumor response, tumor microenvironment and immune evasion. Estrogens mainly promote immune tolerance regulating T-cell function and modifying tumor microenvironment, while androgens attenuate anti-tumor immune responses. The precise mechanism by which sex and sex hormones may modulate immune response to tumor, modify PD-L1 expression in cancer cells and promote immune escape in NSCLC is still unclear, but current data show how sexual differences affect immune therapy response and prognosis. This review provides update information regarding anti-PD-1/PD-L immunotherapeutic efficacy in NSCLC by sex, analyzing potential roles for sex hormones on PD-L1 expression, and discussing a plausible of sex and sex hormones as predictive response factors to immunotherapy.

PD-1/PD-L1 inhibitor treatment and its impact on clinical imaging in non-small cell lung cancer: a systematic review and meta-analysis of immune-related adverse events

Background

In the contemporary era of cancer treatment, lung cancer (LC) holds the unenviable position of being the primary contributor to cancer-induced mortality worldwide. Although immunotherapy has expanded the therapeutic landscape for metastatic non-small cell lung cancer (NSCLC), the advent of immune checkpoint inhibitors has been accompanied by a concomitant increase in immune-related adverse events (irAEs). Timely detection of irAEs is pivotal for efficacious management and enhanced patient outcomes. Diagnostic imaging, encompassing x-ray and CT scans, can facilitate the identification and supervision of irAEs, thereby ensuring the prompt recognition of associated patterns and alterations for expeditious treatment.

Methods

The present inquiry undertook a systematic exploration of multiple databases, incorporating a diverse array of studies such as randomized controlled trials and observational analyses. Patient demographics, imaging outcomes, and risk of bias were extracted from the data. Meta-analysis was executed utilizing R Statistical Software, with the results of the risk of bias assessment summarized accordingly.

Findings

The analysis unveiled a higher prevalence of irAEs in patients receiving first-line treatment for NSCLC compared to those receiving subsequent treatments, with a statistically significant distinction observed for both high- and low-grade irAEs (p < 0.001). Pneumonitis, thyroiditis, and colitis emerged as the most frequently reported irAEs, whereas hepatitis and pancolitis were less commonly documented. This investigation signifies a crucial advancement in elucidating the function of imaging in the treatment of NSCLC with PD-1/PD-L1 inhibitors and emphasizes the imperative for ongoing research in this domain.

Assessment of androgen receptor expression in breast cancer patients using 18 F-FDG PET/CT radiomics and clinicopathological characteristics

OBJECTIVE

In the present study, we mainly aimed to predict the expression of androgen receptor (AR) in breast cancer (BC) patients by combing radiomic features and clinicopathological factors in a non-invasive machine learning way.

MATERIALS AND METHODS

A total of 48 BC patients, who were initially diagnosed by ¹⁸F-FDG PET/CT, were retrospectively enrolled in this study. LIFEx software was used to extract radiomic features based on PET and CT data. The most useful predictive features were selected by the LASSO (least absolute shrinkage and selection operator) regression and t-test. Radiomic signatures and clinicopathologic characteristics were incorporated to develop a prediction model using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve, Hosmer-Lemeshow (H-L) test, and decision curve analysis (DCA) were conducted to assess the predictive efficiency of the model.

RESULTS

In the univariate analysis, the metabolic tumor volume (MTV) was significantly correlated with the expression of AR in BC patients (p < 0.05). However, there only existed feeble correlations between estrogen receptor (ER), progesterone receptor (PR), and AR status (p = 0.127, p = 0.061, respectively). Based on the binary logistic regression method, MTV, SHAPE_Sphericity_CT (CT Sphericity from SHAPE), and GLCM_Contrast_CT (CT Contrast from grey-level co-occurrence matrix) were included in the prediction model for AR expression. Among them, GLCM_Contrast_CT was an independent predictor of AR status (OR = 9.00, p = 0.018). The area under the curve (AUC) of ROC in this model was 0.832. The p-value of the H-L test was beyond 0.05.

CONCLUSIONS

A prediction model combining radiomic features and clinicopathological characteristics could be a promising approach to predict the expression of AR and noninvasively screen the BC patients who could benefit from anti-AR regimens.

Radiomics for the non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to non-small cell lung cancer

BACKGROUND

The expression level of the programmed cell death ligand 1 (PD-L1) appears to be a predictor for response to immunotherapy using checkpoint inhibitors in patients with non-small cell lung cancer (NSCLC). As differences in terms of PD-L1 expression levels in the extracranial primary tumor and the brain metastases may occur, a reliable method for the non-invasive assessment of the intracranial PD-L1 expression is, therefore of clinical value. Here, we evaluated the potential of radiomics for a non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to NSCLC.

PATIENTS AND METHODS

Fifty-three NSCLC patients with brain metastases from two academic neuro-oncological centers (group 1, n = 36 patients; group 2, n = 17 patients) underwent tumor resection with a subsequent immunohistochemical evaluation of the PD-L1 expression. Brain metastases were manually segmented on preoperative T1-weighted contrast-enhanced MRI. Group 1 was used for model training and validation, group 2 for model testing. After image pre-processing and radiomics feature extraction, a test-retest analysis was performed to identify robust features prior to feature selection. The radiomics model was trained and validated using random stratified cross-validation. Finally, the best-performing radiomics model was applied to the test data. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analyses.

RESULTS

An intracranial PD-L1 expression (i.e., staining of at least 1% or more of tumor cells) was present in 18 of 36 patients (50%) in group 1, and 7 of 17 patients (41%) in group 2. Univariate analysis identified the contrast-enhancing tumor volume as a significant predictor for PD-L1 expression (area under the ROC curve (AUC), 0.77). A random forest classifier using a four-parameter radiomics signature, including tumor volume, yielded an AUC of 0.83 ± 0.18 in the training data (group 1), and an AUC of 0.84 in the external test data (group 2).

CONCLUSION

The developed radiomics classifiers allows for a non-invasive assessment of the intracranial PD-L1 expression in patients with brain metastases secondary to NSCLC with high accuracy.

PET Radiomics and Response to Immunotherapy in Lung Cancer: A Systematic Review of the Literature

The aim of this review is to provide a comprehensive overview of the existing literature concerning the applications of positron emission tomography (PET) radiomics in lung cancer patient candidates or those undergoing immunotherapy.

MATERIALS AND METHODS

A systematic review was conducted on databases and web sources. English-language original articles were considered. The title and abstract were independently reviewed to evaluate study inclusion. Duplicate, out-of-topic, and review papers, or editorials, articles, and letters to editors were excluded. For each study, the radiomics analysis was assessed based on the radiomics quality score (RQS 2.0). The review was registered on the PROSPERO database with the number CRD42023402302.

RESULTS

Fifteen papers were included, thirteen were qualified as using conventional radiomics approaches, and two used deep learning radiomics. The content of each study was different; indeed, seven papers investigated the potential ability of radiomics to predict PD-L1 expression and tumor microenvironment before starting immunotherapy. Moreover, two evaluated the prediction of response, and four investigated the utility of radiomics to predict the response to immunotherapy. Finally, two papers investigated the prediction of adverse events due to immunotherapy.

CONCLUSIONS

Radiomics is promising for the evaluation of TME and for the prediction of response to immunotherapy, but some limitations should be overcome.

Relationship between SUVmax on 18F-FDG PET and PD-L1 expression in liver metastasis lesions after colon radical operation

PURPOSE

Our study was to investigate the correlation correlation between FDG uptake and PD-L1 expression of liver metastasis in patients with colon cancer, and to determine the value of FDG-PET in predicting PD-L1 expression in liver metastasis of colon cancer.

METHODS

A total of 72 patients with confirmed liver metastasis of colon cancer were included in this retrospective study. The PD-L1 expression and immune cell infiltrating of tumors were determined through immunohistochemistry staining. The SUVmax of liver metastasis lesions were assessed using ¹⁸ F-FDG PET/CT. The correlation between PD-L1 expression and the clinicopathological were evaluated by the Cox proportional hazards model and the Kaplan-Meier survival analysis.

RESULTS

PD-L1 expression was significantly correlated with FDG uptake (SUVmax), tumor size, differentiation, survival and cytotoxic T cells infiltration in liver metastasis of colon cancer (P < 0.05). And liver metastases with high counts of infiltrating cytotoxic T cells showed greater FDG uptake than those with low counts of infiltrating cytotoxic T cells. The SUVmax of liver metastases and the degree of differentiation of metastases were closely related to PD-L1 expression, and were independent risk factors.The combined assessment of SUVmax values and tthe degree of differentiation of metastase can help determine PD-L1 expression in liver metastasis of colon cancer.

CONCLUSIONS

FDG uptake in liver metastasis of colon cancer was positively correlated with the PD-L1 expression and the number of cytotoxic T cells infiltration. The joint evaluation of two parameters, SUVmax and degree of differentiation, can predict PD-L1 expression in liver metastases.

Radiomics: A review of current applications and possibilities in the assessment of tumor microenvironment

With the recent success in the application of immunotherapy for treating various advanced cancers, the tumor microenvironment has rapidly become an important field of research. The tumor microenvironment is complex and its characteristics strongly influence disease biology and potentially responses to systemic therapy. Accurate preoperative assessment of tumor microenvironment is of great significance for the formulation of an immunotherapy strategy and evaluation of patient prognosis. As a research hotspot in medical image analysis technology, radiomics has been applied in the auxiliary diagnosis of the tumor microenvironment. This article reviews the current status of radiomics in the elective application on tumor microenvironment and discusses potential prospects.

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.

Predictive Value of 18F-Fluorodeoxyglucose Positron-Emission Tomography Metabolic and Volumetric Parameters for Systemic Metastasis in Tonsillar Cancer

Although the prognosis of tonsillar cancer (human papillomavirus-positive oropharyngeal squamous cell carcinoma) is improving, disease control failure (distant metastasis) still occurs in some cases. We explored whether several 18F-fluorodeoxyglucose (FDG) positron-emission tomography (PET) parameters can predict metastasis. We retrospectively reviewed the medical records of 55 patients with tonsil squamous cell carcinoma who underwent pretreatment 18F-FDG positron-emission tomography/computed tomography (PET/CT) followed by primary surgery. During the follow-up period, systemic metastases were found in 7 of the 55 patients. The most common sites were the lungs (33%), bone (22%), brain/skull base (22%), small bowel (11%), and liver (11%). Pathologically, P53 mutation was less common in patients with systemic metastasis (41.7% vs. 14.3%, p = 0.054) than without systemic metastasis. In terms of PET parameters, the metabolic tumor volume (MTV2.5) and total lesion glycolysis (TLG2.5) values were lower in the primary tumor, and higher in the metastatic lymph nodes, of human papillomavirus (HPV)-positive compared to HPV-negative patients (all p < 0.05). The MTV2.5, TLG2.5, and tumor−to−liver uptake ratio were 36.07 ± 54.24 cm3, 183.46 ± 298.62, and 4.90 ± 2.77, respectively, in the systemic metastasis group, respectively; all of these values were higher than those of the patients without systemic metastasis (all p < 0.05). The MTV2.5 value was significantly different between the groups even when the values for the primary tumor and metastatic lymph nodes were summed (53.53 ± 57.78 cm3, p = 0.036). The cut-off value, area under the curve (95% confidence interval), sensitivity, and specificity of MTV2.5 for predicting systemic metastasis were 11.250 cm3, 0.584 (0.036−0.832), 0.571, and 0.565, respectively. The MTV2.5 of metastatic lymph nodes and summed MTV2.5 values of the primary tumor and metastatic lymph nodes were significantly higher in tonsillar cancer patients with than without systemic metastases. We suggest PET/CT scanning for pre-treatment cancer work-up and post-treatment surveillance to consider additional systemic therapy in patients with a high risk of disease control failure.

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.

Relationship between standardized uptake value on 18F-FDG PET and PD-L1 expression in clear cell renal cell carcinoma

Purpose

Partial clear cell renal cell carcinoma (CCRCC) may be sensitive to immune checkpoint inhibitor treatment targeting the programmed cell death 1 (PD-1)/programmed cell death 1 ligand 1 (PD-L1) pathway. Assessing the levels of PD-L1 using non-invasive imaging is useful to select immunotherapy-sensitive patients. Currently, whether PD-L1 levels in CCRCC correlate with 18F fluorodeoxyglucose (18F-FDG) uptake is unknown. This study aimed to assess whether 18F-FDG-positron emission tomography (PET) imaging could be used to infer PD-L1 levels in CCRCC.

Methods

Immunohistochemistry (IHC) was used to assess PD-L1 levels in samples of tumors obtained retrospectively from a cohort of 58 patients with CCRCC who also received 18F-FDG PET/CT imaging. The IHC scores for PD-L1 were compared with the 18F-FDG maximum standardized uptake value (SUVmax), and the mean standardized uptake value (SUVmean) value, with the clinical characteristics of CCRCC, and with the IHC scores of enzymes related to glucose metabolism (glucose transporter type 1 (GLUT1), hexokinase 2 (HK2), lactate dehydrogenase A (LDHA)), and Von Hippel-Lindau tumor suppressor (VHL).

Results

Increased renal venous invasion, lymph node metastasis, tumor size, SUVmean, and SUVmax correlated significantly with higher PD-L1 levels (P &lt; 0.05). The IHC scores of VHL and LDHA correlated positively with those of PD-L1 (P = 0.035, P = 0.011, respectively). Significant correlations between PD-L1 levels and SUVmean and lymph node metastasis were observed upon multivariate analysis. SUVmean combined with lymph node metastasis predicted that 20.59% of the low probability group would express PD-L1, 29.41% of the medium probability group would express PD-L1, and 71.43% of the high probability group would express PD-L1.

Conclusion

The status of lymph node metastasis, SUVmax, and SUVmean of the primary lesion correlated with PD-L1 levels in CCRCC. A combination of lymph node metastasis status and SUVmean could be utilized to predict PD-L1 levels, thus allowing monitoring of a tumor’s immunotherapy response.

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.

Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy

Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology’s role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57–180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10–16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.

[18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications

Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [¹⁸F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([¹⁸F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [¹⁸F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [¹⁸F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [¹⁸F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.

Multi-Omics Approaches for the Prediction of Clinical Endpoints after Immunotherapy in Non-Small Cell Lung Cancer: A Comprehensive Review

Immune checkpoint inhibitors (ICI) have revolutionized the management of locally advanced and advanced non-small lung cancer (NSCLC). With an improvement in the overall survival (OS) as both first- and second-line treatments, ICIs, and especially programmed-death 1 (PD-1) and programmed-death ligands 1 (PD-L1), changed the landscape of thoracic oncology. The PD-L1 level of expression is commonly accepted as the most used biomarker, with both prognostic and predictive values. However, even in a low expression level of PD-L1, response rates remain significant while a significant number of patients will experience hyperprogression or adverse events. The dentification of such subtypes is thus of paramount importance. While several studies focused mainly on the prediction of the PD-L1 expression status, others aimed directly at the development of prediction/prognostic models. The response to ICIs depends on a complex physiopathological cascade, intricating multiple mechanisms from the molecular to the macroscopic level. With the high-throughput extraction of features, omics approaches aim for the most comprehensive assessment of each patient. In this article, we will review the place of the different biomarkers (clinical, biological, genomics, transcriptomics, proteomics and radiomics), their clinical implementation and discuss the most recent trends projecting on the future steps in prediction modeling in NSCLC patients treated with ICI.

Smoking-, Alcohol-, and Age-Related Alterations of Blood Monocyte Subsets and Circulating CD4/CD8 T Cells in Head and Neck Cancer

Head and neck squamous cell carcinoma (HNSCC) represents a heterogeneous malignant disease of the oral cavity, pharynx, and larynx. Although cigarette smoking, alcohol abuse, and aging are well-established associated factors for HNSCC, their respective influence on immunologic alterations of monocyte subsets or T-cell compositions in the peripheral blood has not yet been fully unveiled. Using flow cytometry, whole blood measurements of CD14/CD16 monocyte subsets and analyses of T-cell subsets in isolated PBMC fractions were carried out in 64 HNSCC patients in view of their tobacco and alcohol consumption, as well as their age, in comparison to healthy volunteers. Flow cytometric analysis revealed significantly increased expression of monocytic CD11b, as well as significantly decreased expression levels of CX3CR1 on classical and intermediate monocyte subsets in smoking-related and in alcohol-related HNSCC patients compared to healthy donors. Peripheral monocytes revealed an age-correlated significant decrease in PD-L1 within the entirety of the HNSCC cohort. Furthermore, we observed significantly decreased abundances of CD8+ effector memory T cells in active-smoking HNSCC patients and significantly increased percentages of CD8+ effector T cells in alcohol-abusing patients compared to the non-smoking/non-drinking patient cohort. Our data indicate an enhanced influence of smoking and alcohol abuse on the dynamics and characteristics of circulating monocyte subsets and CD4/CD8 T-cell subset proportions, as well as an age-related weakened immunosuppression in head and neck cancer patients.