18F-FDG Pet Parameters and Radiomics Features Analysis in Advanced Nsclc Treated with Immunotherapy as Predictors of Therapy Response and Survival

PET/CT imaging for evaluation of multimodal treatment efficacy and toxicity in advanced NSCLC-current state and future directions

PURPOSE

The advent of immune checkpoint inhibitors (ICIs) has revolutionized the treatment of advanced NSCLC, leading to a string of approvals in recent years. Herein, a narrative review on the role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in the ever-evolving treatment landscape of advanced NSCLC is presented.

METHODS

This comprehensive review will begin with an introduction into current treatment paradigms incorporating ICIs; the evolution of CT-based criteria; moving onto novel phenomena observed with ICIs and the current state of hybrid imaging for diagnosis, treatment planning, evaluation of treatment efficacy and toxicity in advanced NSCLC, also taking into consideration its limitations and future directions.

CONCLUSIONS

The advent of ICIs marks the dawn of a new era bringing forth new challenges particularly vis-à-vis treatment response assessment and observation of novel phenomena accompanied by novel systemic side effects. While FDG PET/CT is widely adopted for tumor volume delineation in locally advanced disease, response assessment to immunotherapy based on current criteria is of high clinical value but has its inherent limitations. In recent years, modifications of established (PET)/CT criteria have been proposed to provide more refined approaches towards response evaluation. Not only a comprehensive inclusion of PET-based response criteria in prospective randomized controlled trials, but also a general harmonization within the variety of PET-based response criteria is pertinent to strengthen clinical implementation and widespread use of hybrid imaging for response assessment in NSCLC.

Immune Checkpoint Inhibitors in Advanced NSCLC: [18F]FDG PET/CT as a Troubleshooter in Treatment Response

Introduction: The aim of this study was to investigate whether [¹⁸F]FDG PET/CT-derived semi-quantitative parameters can predict immunotherapy treatment response in non-small cell lung cancer (NSCLC) patients. Secondly, immune-related adverse events (irAEs) and lymphoid cell-rich organs activation were evaluated. Materials and Methods: Twenty-eight patients who underwent [¹⁸F]FDG PET/CT scans before and at first restaging therapy with immuno-checkpoint inhibitors (ICIs) were retrospectively analyzed. PET-based semi-quantitative parameters extracted from both scans were respectively: SUV_max and SUV_peak of the target lesion, whole-body metabolic tumor volume (MTV_WB), and whole-body total lesion glycolysis (TLG_WB), as well as their interval changes (ΔSUV_maxTL, ΔSUV_peakTL, ΔMTV_WB, ΔTLG_WB). These PET-derived parameters were correlated to controlled disease (CD) assessed by RECIST 1.1. IrAEs, if present, were also described and correlated with clinical benefit (CB). SUV_max of the spleen and bone marrow at restaging scans were also correlated to CB. Results: The CD was achieved in 54% of patients. Out of 28 eligible patients, 13 (46%) experienced progressive disease (PD), 7 showed SD, 7 had PR, and only in one patient CR was achieved. ΔSUV_maxTL (p = 0.002) and ΔSUV_peakTL (p < 0.001) as well as ΔMTV_WB (p < 0.001) and ΔTLG_WB (p < 0.005) were significantly associated with PD vs. non-PD. IrAEs and lymphoid cell-rich organs activation did not correlate with CB. Conclusions: [¹⁸F]FDG PET/CT by using interval changes of PET-derived semi-quantitative parameters could represent a reliable tool in immunotherapy treatment response evaluation in NSCLC patients.

NSCLC Biomarkers to Predict Response to Immunotherapy with Checkpoint Inhibitors (ICI): From the Cells to In Vivo Images

Lung cancer remains the leading cause of cancer-related death, and it is usually diagnosed in advanced stages (stage III or IV). Recently, the availability of targeted strategies and of immunotherapy with checkpoint inhibitors (ICI) has favorably changed patient prognosis. Treatment outcome is closely related to tumor biology and interaction with the tumor immune microenvironment (TME). While the response in molecular targeted therapies relies on the presence of specific genetic alterations in tumor cells, accurate ICI biomarkers of response are lacking, and clinical outcome likely depends on multiple factors that are both host and tumor-related. This paper is an overview of the ongoing research on predictive factors both from in vitro/ex vivo analysis (ranging from conventional pathology to molecular biology) and in vivo analysis, where molecular imaging is showing an exponential growth and use due to technological advancements and to the new bioinformatics approaches applied to image analyses that allow the recovery of specific features in specific tumor subclones.

Response Prediction and Evaluation Using PET in Patients with Solid Tumors Treated with Immunotherapy

Simple Summary

In cancer treatment, immunotherapy is increasingly becoming important as a component of first-line treatment and has improved the prognosis of patients since its introduction. A large group of patients, however, do not respond to immunotherapy, and predicting a treatment response remains challenging. Furthermore, evaluating a response using conventional computed tomography (CT) scans is not straightforward due to the different mechanism of action of immunotherapy compared to chemotherapy. This review provides an overview of positron emission tomography (PET) in predicting and evaluating treatment response to immunotherapy.

Abstract

In multiple malignancies, checkpoint inhibitor therapy has an established role in the first-line treatment setting. However, only a subset of patients benefit from checkpoint inhibition, and as a result, the field of biomarker research is active. Molecular imaging with the use of positron emission tomography (PET) is one of the biomarkers that is being studied. PET tracers such as conventional ¹⁸F-FDG but also PD-(L)1 directed tracers are being evaluated for their predictive power. Furthermore, the use of artificial intelligence is under evaluation for the purpose of response prediction. Response evaluation during checkpoint inhibitor therapy can be challenging due to the different response patterns that can be observed compared to traditional chemotherapy. The additional information provided by PET can potentially be of value to evaluate a response early after the start of treatment and provide the clinician with important information about the efficacy of immunotherapy. Furthermore, the use of PET to stratify between patients with a complete response and those with a residual disease can potentially guide clinicians to identify patients for which immunotherapy can be discontinued and patients for whom the treatment needs to be escalated. This review provides an overview of the use of positron emission tomography (PET) to predict and evaluate treatment response to immunotherapy.

A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory Study

Baseline clinical prognostic factors for recurrent and/or metastatic (RM) head and neck squamous cell carcinoma (HNSCC) treated with immunotherapy are lacking. CT-based radiomics may provide additional prognostic information. A total of 85 patients with RM-HNSCC were enrolled for this study. For each tumor, radiomic features were extracted from the segmentation of the largest tumor mass. A pipeline including different feature selection steps was used to train a radiomic signature prognostic for 10-month overall survival (OS). Features were selected based on their stability to geometrical transformation of the segmentation (intraclass correlation coefficient, ICC > 0.75) and their predictive power (area under the curve, AUC > 0.7). The predictive model was developed using the least absolute shrinkage and selection operator (LASSO) in combination with the support vector machine. The model was developed based on the first 68 enrolled patients and tested on the last 17 patients. Classification performance of the radiomic risk was evaluated accuracy and the AUC. The same metrics were computed for some baseline predictors used in clinical practice (volume of largest lesion, total tumor volume, number of tumor lesions, number of affected organs, performance status). The AUC in the test set was 0.67, while accuracy was 0.82. The performance of the radiomic score was higher than the one obtainable with the clinical variables (largest lesion volume: accuracy 0.59, AUC = 0.55; number of tumoral lesions: accuracy 0.71, AUC 0.36; number of affected organs: accuracy 0.47; AUC 0.42; total tumor volume: accuracy 0.59, AUC 0.53; performance status: accuracy 0.41, AUC = 0.47). Radiomics may provide additional baseline prognostic value compared to the variables used in clinical practice.

Additional Value of PET/CT-Based Radiomics to Metabolic Parameters in Diagnosing Lynch Syndrome and Predicting PD1 Expression in Endometrial Carcinoma

Purpose

We aim to compare the radiomic features and parameters on 2-deoxy-2-[fluorine-18] fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) between patients with endometrial cancer with Lynch syndrome and those with endometrial cancer without Lynch syndrome. We also hope to explore the biologic significance of selected radiomic features.

Materials and Methods

We conducted a retrospective cohort study, first using the 18F-FDG PET/CT images and clinical data from 100 patients with endometrial cancer to construct a training group (70 patients) and a test group (30 patients). The metabolic parameters and radiomic features of each tumor were compared between patients with and without Lynch syndrome. An independent cohort of 23 patients with solid tumors was used to evaluate the value of selected radiomic features in predicting the expression of the programmed cell death 1 (PD1), using 18F-FDG PET/CT images and RNA-seq genomic data.

Results

There was no statistically significant difference in the standardized uptake values on PET between patients with endometrial cancer with Lynch syndrome and those with endometrial cancer without Lynch syndrome. However, there were significant differences between the 2 groups in metabolic tumor volume and total lesion glycolysis (p < 0.005). There was a difference in the radiomic feature of gray level co-occurrence matrix entropy (GLCMEntropy; p < 0.001) between the groups: the area under the curve was 0.94 in the training group (sensitivity, 82.86%; specificity, 97.14%) and 0.893 in the test group (sensitivity, 80%; specificity, 93.33%). In the independent cohort of 23 patients, differences in GLCMEntropy were related to the expression of PD1 (r_s =0.577; p < 0.001).

Conclusions

In patients with endometrial cancer, higher metabolic tumor volumes, total lesion glycolysis values, and GLCMEntropy values on 18F-FDG PET/CT could suggest a higher risk for Lynch syndrome. The radiomic feature of GLCMEntropy for tumors is a potential predictor of PD1 expression.

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.

Baseline total metabolic tumour volume on 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography-computed tomography as a promising biomarker in patients with advanced non-small cell lung cancer treated with first-line pembrolizumab

INTRODUCTION

Immune checkpoint inhibitors (ICIs) have become the standard of care in the management of advanced non-small cell lung cancer (NSCLC). Nevertheless, only a small proportion of patients benefit from ICIs. The aim of the present study is to assess whether 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography-computed tomography ([18F]FDG-PET/CT)-derived parameters may be used as biomarkers in patients with advanced NSCLC receiving first-line pembrolizumab.

MATERIALS AND METHODS

This is a monocentric retrospective cohort study including patients with advanced NSCLC (stage IV) and Programmed death-ligand 1 (PD-L1) expression ≥50% treated with pembrolizumab. A control group of patients treated with epidermal growth factor receptor (EGFR) inhibitors for EGFR-mutated NSCLC was also enrolled. Only patients with a positive [18F]18F-FDG PET/CT result within 60 days from treatment initiation were included.Total metabolic tumour volume (tMTV) was calculated for each lesion using a dedicated software (PET VCAR; GE Healthcare), which semiautomatically delineates the tumour's contours with a maximum standardised uptake value (SUVmax) threshold of 42% within the lesion. tMTV was obtained summing each lesion's MTV. Potential prognostic parameters for overall survival (OS) were analysed (tMTV, SUVmax, bone/liver metastasis, neutrophil:lymphocyte ratio ≥4, Eastern Cooperative Oncology Group performance status ≥2, lactate dehydrogenase above the upper limit of normal).

RESULTS

Overall, 34 patients treated with first line-pembrolizumab and 40 patients treated with EGFR tyrosine kinase inhibitors were included. In the pembrolizumab group, the median follow-up was 20.3, while the median OS was 4.7 months (95% confidence interval [CI] = 0.3-9.1) for patients with tMTV ≥75 cm³ vs not reached (NR) for patients with tMTV <75 cm³ (95% CI = NR-NR; hazard ratio [HR] = 5.37; 95% CI = 1.72-16.77; p = 0.004). No difference was found in the control group (HR = 1.43; 95% CI = 0.61-3.34; p = 0.411).

CONCLUSION

Our data suggest that tMTV ≥75cm³ can be used as a prognostic biomarker of poor outcomes in patients with PD-L1-high advanced NSCLC treated with first-line pembrolizumab. This information could be useful for the selection of patients who may require the addition of chemotherapy to pembrolizumab.

Radiomics Analysis of PET and CT Components of 18F-FDG PET/CT Imaging for Prediction of Progression-Free Survival in Advanced High-Grade Serous Ovarian Cancer

Objective

To investigate radiomics features extracted from PET and CT components of ¹⁸F-FDG PET/CT images integrating clinical factors and metabolic parameters of PET to predict progression-free survival (PFS) in advanced high-grade serous ovarian cancer (HGSOC).

Methods

A total of 261 patients were finally enrolled in this study and randomly divided into training (n=182) and validation cohorts (n=79). The data of clinical features and metabolic parameters of PET were reviewed from hospital information system(HIS). All volumes of interest (VOIs) of PET/CT images were semi-automatically segmented with a threshold of 42% of maximal standard uptake value (SUVmax) in PET images. A total of 1700 (850×2) radiomics features were separately extracted from PET and CT components of PET/CT images. Then two radiomics signatures (RSs) were constructed by the least absolute shrinkage and selection operator (LASSO) method. The RSs of PET (PET_RS) and CT components(CT_RS) were separately divided into low and high RS groups according to the optimum cutoff value. The potential associations between RSs with PFS were assessed in training and validation cohorts based on the Log-rank test. Clinical features and metabolic parameters of PET images (PET_MP) with P-value <0.05 in univariate and multivariate Cox regression were combined with PET_RS and CT_RS to develop prediction nomograms (Clinical, Clinical+ PET_MP, Clinical+ PET_RS, Clinical+ CT_RS, Clinical+ PET_MP + PET_RS, Clinical+ PET_MP + CT_RS) by using multivariate Cox regression. The concordance index (C-index), calibration curve, and net reclassification improvement (NRI) was applied to evaluate the predictive performance of nomograms in training and validation cohorts.

Results

In univariate Cox regression analysis, six clinical features were significantly associated with PFS. Ten PET radiomics features were selected by LASSO to construct PET_RS, and 1 CT radiomics features to construct CT_RS. PET_RS and CT_RS was significantly associated with PFS both in training (P <0.00 for both RSs) and validation cohorts (P=0.01 for both RSs). Because there was no PET_MP significantly associated with PFS in training cohorts. Only three models were constructed by 4 clinical features with P-value <0.05 in multivariate Cox regression and RSs (Clinical, Clinical+ PET_RS, Clinical+ CT_RS). Clinical+ PET_RS model showed higher prognostic performance than other models in training cohort (C-index=0.70, 95% CI 0.68-0.72) and validation cohort (C-index=0.70, 95% CI 0.66-0.74). Calibration curves of each model for prediction of 1-, 3-year PFS indicated Clinical +PET_RS model showed excellent agreements between estimated and the observed 1-, 3-outcomes. Compared to the basic clinical model, Clinical+ PET_MS model resulted in greater improvement in predictive performance in the validation cohort.

Conclusion

PET_RS can improve diagnostic accuracy and provide complementary prognostic information compared with the use of clinical factors alone or combined with CT_RS. The newly developed radiomics nomogram is an effective tool to predict PFS for patients with advanced HGSOC.

The Future of Cancer Diagnosis, Treatment and Surveillance: A Systemic Review on Immunotherapy and Immuno-PET Radiotracers

Immunotherapy is an effective therapeutic option for several cancers. In the last years, the introduction of checkpoint inhibitors (ICIs) has shifted the therapeutic landscape in oncology and improved patient prognosis in a variety of neoplastic diseases. However, to date, the selection of the best patients eligible for these therapies, as well as the response assessment is still challenging. Patients are mainly stratified using an immunohistochemical analysis of the expression of antigens on biopsy specimens, such as PD-L1 and PD-1, on tumor cells, on peritumoral immune cells and/or in the tumor microenvironment (TME). Recently, the use and development of imaging biomarkers able to assess in-vivo cancer-related processes are becoming more important. Today, positron emission tomography (PET) with 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) is used routinely to evaluate tumor metabolism, and also to predict and monitor response to immunotherapy. Although highly sensitive, FDG-PET in general is rather unspecific. Novel radiopharmaceuticals (immuno-PET radiotracers), able to identify specific immune system targets, are under investigation in pre-clinical and clinical settings to better highlight all the mechanisms involved in immunotherapy. In this review, we will provide an overview of the main new immuno-PET radiotracers in development. We will also review the main players (immune cells, tumor cells and molecular targets) involved in immunotherapy. Furthermore, we report current applications and the evidence of using [18F]FDG PET in immunotherapy, including the use of artificial intelligence (AI).

Metabolic Parameters as Biomarkers of Response to Immunotherapy and Prognosis in Non-Small Cell Lung Cancer (NSCLC): A Real World Experience

Immune-checkpoint inhibitors (ICIs) have been proven to have great efficacy in non-small cell lung cancer (NSCLC) as single agents or in combination therapy, being capable to induce deep and durable remission. However, severe adverse events may occur and about 40% of patients do not benefit from the treatment. Predictive factors of response to ICIs are needed in order to customize treatment. The aim of this study is to evaluate the correlation between quantitative positron emission tomography (PET) parameters defined before starting ICI therapy and responses to treatment and patient outcome. We retrospectively analyzed 92 NSCLC patients treated with nivolumab, pembrolizumab or atezolizumab. Basal PET/computed tomography (CT) scan parameters (whole-body metabolic tumor volume-wMTV, total lesion glycolysis-wTLG, higher standardized uptake volume maximum and mean-SUVmax and SUVmean) were calculated for each patient and correlated with outcomes. Patients who achieved disease control (complete response + partial response + stable disease) had significantly lower MTV median values than patients who had not (progressive disease) (77 vs. 160.2, p = 0.039). Furthermore, patients with MTV and TLG values lower than the median values had improved OS compared to patients with higher MTV and TLG (p = 0.03 and 0.05, respectively). No relation was found between the other parameters and outcome. In conclusion, baseline metabolic tumor burden, measured with MTV, might be an independent predictor of treatment response to ICI and a prognostic biomarker in NSCLC patients.

The Value of 18F-FDG PET/CT in Predicting the Response to PD-1 Blocking Immunotherapy in Advanced NSCLC Patients with High-Level PD-L1 Expression

BACKGROUND

The objective of this study was to evaluate if ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT)-derived parameters are useful in predicting response and survival after programmed cell death protein 1 (PD-1) blocking immunotherapy in patients with advanced NSCLC characterized by a high programmed death-ligand 1 (PD-L1) expression (≥50%) on immunohistochemistry.

PATIENTS AND METHODS

In 30 patients with advanced stage IV non-small-cell lung cancer (NSCLC) and high PD-L1 expression, ¹⁸F-FDG PET/CT parameters before start of treatment with PD-1 blocking immunotherapy were evaluated retrospectively. In 24 out of the 30 patients, ¹⁸F-FDG PET/CT was available 8 to 9 weeks after start of the treatment. Response Evaluation Criteria in Solid Tumors (RECIST 1.1) and metabolic responses assessed on ¹⁸F-FDG PET/CT were compared.

RESULTS

Median follow-up was 20 months (range, 4.2-37.6). Median PD-L1 expression was 80%. The objective response rate with RECIST 1.1 was 53.3%. Median progression-free survival (PFS) was 12.4 months (95% confidence interval [CI], 1.0-37.8), and median overall survival (OS) was 14.9 months (95% CI, 2.4-38.2). Baseline ¹⁸F-FDG PET/CT parameters did not differ between responders and non-responders (all P > .05). The maximum standardized uptake value (SUVmax) was the only ¹⁸F-FDG PET/CT parameter associated with PFS (P = .04), with a trend for OS (P = .06). At first evaluation, response according to total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) were associated with PFS and OS (both P < .0001). This was not the case for RECIST 1.1 (P = .29 for PFS and P = .38 for OS).

CONCLUSION

Clinical response and survival were independent from metabolic tumor volume at baseline. Reduction of metabolic tumor volume after 8 to 9 weeks of treatment was a better predictor for prolonged survival than RECIST 1.1.

Diagnostic Value of Conventional PET Parameters and Radiomic Features Extracted from 18F-FDG-PET/CT for Histologic Subtype Classification and Characterization of Lung Neuroendocrine Neoplasms

Aim: To evaluate if conventional Positron emission tomography (PET) parameters and radiomic features (RFs) extracted by 18F-FDG-PET/CT can differentiate among different histological subtypes of lung neuroendocrine neoplasms (Lu-NENs). Methods: Forty-four naïve-treatment patients on whom 18F-FDG-PET/CT was performed for histologically confirmed Lu-NEN (n = 46) were retrospectively included. Manual segmentation was performed by two operators allowing for extraction of four conventional PET parameters (SUVmax, SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG)) and 41 RFs. Lu-NENs were classified into two groups: lung neuroendocrine tumors (Lu-NETs) vs. lung neuroendocrine carcinomas (Lu-NECs). Lu-NETs were classified according to histological subtypes (typical (TC)/atypical carcinoid (AC)), Ki67-level, and TNM staging. The least absolute shrink age and selection operator (LASSO) method was used to select the most predictive RFs for classification and Pearson correlation analysis was performed between conventional PET parameters and selected RFs. Results: PET parameters, in particular, SUVmax (area under the curve (AUC) = 0.91; cut-off = 5.16) were higher in Lu-NECs vs. Lu-NETs (p < 0.001). Among RFs, HISTO_Entropy_log10 was the most predictive (AUC = 0.90), but correlated with SUVmax/SUVmean (r = 0.95/r = 0.94, respectively). No statistical differences were found between conventional PET parameters and RFs (p > 0.05) and TC vs. AC classification. Conventional PET parameters were correlated with N+ status in Lu-NETs. Conclusion: In our study, conventional PET parameters were able to distinguish Lu-NECs from Lu-NETs, but not TC from AC. RFs did not provide additional information.

Immunotherapy, cancer and PET

The treatment of cancer by immunotherapy has been a revolution, as it is the first strategy that manages to control the disease for prolonged periods of time. Its efficacy is associated with different imaging response patterns and the appearance of new toxicities. We would highlight two patterns of tumour response: pseudoprogression, or growth of tumour lesions after the start of immunotherapy treatment, followed by a significant reduction in lesions, and hyperprogression, acceleration of tumour progression and metastasis early after the start of treatment. The emergence of such patterns has generated new metabolic response criteria, such as PECRIT, PERCIMT, imPERCIST and IPERCIST. Of particular interest are the new immunoPET-specific biomarkers, as they allow the identification of patients presenting the tumour target and are useful for predicting response to immunotherapy.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

The role of immune checkpoint inhibitor therapy in advanced adrenocortical carcinoma revisited: review of literature

PURPOSE

Adrenocortical carcinoma (ACC) is a rare disease with few therapeutic options. There is an urgency of new effective therapeutic options for these patients. The role of immune checkpoint inhibitors (ICI) in advanced ACC patients is still unclear.

METHODS

We conducted a MEDLINE search using the following string: adrenocortical carcinoma and immunotherapy or checkpoint inhibitors.

RESULTS

We found four case series comprising 10 patients, and four prospective studies totaling 115 patients. The response rate (RR) in the group of 10 patients was 1 complete response, 3 partial response (PR), 4 stable disease (SD), and 2 progressive disease (PD). The median progression-free survival (mPFS) ranged from 2 to 31 months and the median overall survival (mOS) ranged from 4.3 to 31 months. The results in the 115 patients from prospective trials was variable, the PR ranged from 6 to 23%, the SD ranged from 18 to 50% and overall disease control rate ranged from 30 to 64%. The mPFS reported varied from 1.8 to 2.6 months while the mOS varied from 10.6 to 24.9 months. There were five patients with sustained response for more than 24 months. The most common treatment-related adverse event (TRAE) was the increase in liver enzymes. No treatment-related deaths were reported. Better results in terms of RR and survival were observed in studies that used pembrolizumab. No predictive biomarker of response was found up to now.

CONCLUSION

ICI, mainly pembrolizumab, is a potential therapeutic option, which is safe and associated with prolonged OS benefit, in selected patients with advanced ACC.

Association of the Metabolic Score Using Baseline FDG-PET/CT and dNLR with Immunotherapy Outcomes in Advanced NSCLC Patients Treated with First-Line Pembrolizumab

Background: We aimed to assess the clinical utility of a previously published score combining the total metabolic tumor volume (TMTV) on baseline FDG-PET/CT and pretreatment derived from the neutrophils to lymphocytes ratio (dNLR) for prognostication in NSCLC patients undergoing first-line immunotherapy (IT). Methods: In this multicenter retrospective study, 63 advanced NSCLC patients with a PD-L1 tumor proportion score (TPS) ≥50%, who underwent FDG-PET/CT before first-line IT, treated from January 2017 to September 2019, were enrolled. Associations between this score and the progression-free survival (PFS), overall survival (OS), disease control rate (DCR), and overall response rate (ORR) were evaluated. Results: The median (m) PFS and mOS were 7.7 (95% CI 4.9–10.6) and 12.1 (8.6–15.6) months, respectively, and DCR and ORR were 65% and 58%, respectively. mOS was 17.9 months (14.6 not reached) for the good group versus 13.8 (95%CI 8.4–18.9) and 6.6 (CI 2.0–11.2) months for the intermediate and poor groups, respectively. mPFS was 15.1 (95%CI 12.1–20.0) months for the good group versus 5.2 (1.9–8.5) and 1.9 (95%CI 1.3–2.5) months for the intermediate and poor groups, respectively. The poor prognosis group was associated with DCR and ORR (p < 0.05). Conclusions: The metabolic score combining TMTV on the baseline FDG-PET/CT scan and pretreatment dNLR was associated with the survival and response in a cohort of advanced NSCLC patients with ≥50% PD-L1 receiving frontline IT.

Utility of [18F] Fluoro-Deoxyglucose Positron Emission Tomography/Computed Tomography for Staging and Therapy Response Evaluation in Pediatric Rhabdomyosarcoma: A Case Series and Literature Review

Background: The role of [¹⁸F] fluoro-deoxyglucose [[¹⁸F] FDG] positron emission tomography (PET)/computed tomography (CT) in pediatric rhabdomyosarcoma (RMS) is not well-established. This manuscript explores the role of staging and therapy response evaluation of PET/CT in a series of patients with RMS.

Methods: Thirteen consecutive patients with pathologically proven RMS underwent baseline PET/CT scan and a second PET/CT for evaluation of therapy response. Maximum standardized uptake value (SUV_max), mean standardized uptake value (SUV_mean), highest standardized uptake peak value (SUV_peak), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained from baseline PET/CT and were used as potential predictors for evaluation of metabolic treatment response.

Results: On baseline PET/CT, most RMSs are located in the pelvic cavity, and upper arms ranked second. The primary lesions were large and showed invasion to the surrounding tissues. Lymph node metastases were seen in eight patients, and eight patients showed distant metastasis to the lung, liver, and bone. The median SUV_max, SUV_mean, and SUV_peak of primary sites were 7.1, 4.0, and 5.9, respectively. The median MTV and TLG were 196.6 cm³ and 780.2, respectively. After therapy, six patients received complete metabolic response (CMR) and non-CMR occurred in seven patients on the second PET/CT. SUV_max, SUV_peak, MTV, and TLG in patients with CMR were significantly lower than those in patients with non-CMR.

Conclusions: Primary sites and metastatic lesions of RMS demonstrate increased glycolytic activity, which may allow them to be imaged using [¹⁸F] FDG PET/CT. Metabolic parameters derived from the baseline PET/CT have potential value for predicting CMR to therapy in pediatric RMS.

[18F]FDG PET immunotherapy radiomics signature (iRADIOMICS) predicts response of non-small-cell lung cancer patients treated with pembrolizumab

Background

Immune checkpoint inhibitors have changed the paradigm of cancer treatment; however, non-invasive biomarkers of response are still needed to identify candidates for non-responders. We aimed to investigate whether immunotherapy [¹⁸F]FDG PET radiomics signature (iRADIOMICS) predicts response of metastatic non-small-cell lung cancer (NSCLC) patients to pembrolizumab better than the current clinical standards.

Patients and methods

Thirty patients receiving pembrolizumab were scanned with [¹⁸F]FDG PET/CT at baseline, month 1 and 4. Associations of six robust primary tumour radiomics features with overall survival were analysed with Mann-Whitney U-test (MWU), Cox proportional hazards regression analysis, and ROC curve analysis. iRADIOMICS was constructed using univariate and multivariate logistic models of the most promising feature(s). Its predictive power was compared to PD-L1 tumour proportion score (TPS) and iRECIST using ROC curve analysis. Prediction accuracies were assessed with 5-fold cross validation.

Results

The most predictive were baseline radiomics features, e.g. Small Run Emphasis (MWU, p = 0.001; hazard ratio = 0.46, p = 0.007; AUC = 0.85 (95% CI 0.69–1.00)). Multivariate iRADIOMICS was found superior to the current standards in terms of predictive power and timewise with the following AUC (95% CI) and accuracy (standard deviation): iRADIOMICS (baseline), 0.90 (0.78–1.00), 78% (18%); PD-L1 TPS (baseline), 0.60 (0.37–0.83), 53% (18%); iRECIST (month 1), 0.79 (0.62–0.95), 76% (16%); iRECIST (month 4), 0.86 (0.72–1.00), 76% (17%).

Conclusions

Multivariate iRADIOMICS was identified as a promising imaging biomarker, which could improve management of metastatic NSCLC patients treated with pembrolizumab. The predicted non-responders could be offered other treatment options to improve their overall survival.