The Value of FDG PET/CT in Treatment Response Assessment, Follow-Up, and Surveillance of Lung Cancer

Value of [18F]AlF-NOTA-FAPI-04 PET/CT for predicting pathological response and survival in patients with locally advanced pancreatic ductal adenocarcinoma receiving neoadjuvant chemotherapy

OBJECTIVES

This study aimed to evaluate the predictive value of [18F]AlF-NOTA-FAPI-04 PET/CT for pathological response to neoadjuvant chemotherapy (NCT) and prognosis in patients with locally advanced pancreatic ductal adenocarcinoma (LAPDAC).

METHODS

This study included 34 patients with histopathologically and radiologically confirmed LAPDAC who received [18F]AlF-NOTA-FAPI-04 PET/CT scans before NCT. After 4-6 cycles of NCT, these patients underwent radical resection. Pathological response to NCT was assessed by pathological tumor regression grades (TRG) based on the Evans system. PET/CT parameters were evaluated for their association with TRG, recurrence-free survival (RFS) and overall survival (OS) after NCT, including the maximum standardized uptake value (SUVmax), FAPI-avid tumor volume (FTV), total lesion FAP expression (TLF) of primary tumor, total FAPI-avid pancreatic volume (FPV) and total pancreatic FAP expression (TPF) of total pancreas.

RESULTS

Of 34 patients with LAPDAC, 14 patients had a pathologic good response (PGR, Evans III-IV), and 20 patients had a pathologic poor response (PPR, Evans I-II). Both the primary tumor SUVmax, FTV and TLF, and total pancreas FPV and TPF in the PGR groups were significantly lower than those in the PPR groups. Furthermore, SUVmax and TLF were higher in poorly differentiated LAPDAC than in well-moderately differentiated neoplasms. The FTV, TLF, FPV and TPF were closely associated with RFS and OS. On multivariate analysis, patients with FTV > 54.21 and TLF > 290.21 had a worse RFS and OS, respectively (HR = 3.24, P = 0.014 and HR = 3.35, P = 0.019) and OS (HR = 7.35, P = 0.002 and HR = 7.09, P = 0.004) in LAPDAC after NCT.

CONCLUSIONS

The parameters of [18F]AlF-NOTA-FAPI-04 PET/CT had the excellent performance for predicting pathologic TRG after NCT in LAPDAC. FTV and TLF were independent postoperative prognostic factors for RFS and OS for LAPDAC.

Uncertainty-aware automatic TNM staging classification for [18F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learning

BACKGROUND

[18F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can currently only be categorised by experts manually reading them. Pre-trained transformer-based language models (PLMs) have shown success in extracting complex linguistic features from text. Accordingly, we developed a multi-task 'TNMu' classifier to classify the presence/absence of tumour, node, metastasis ('TNM') findings (as defined by The Eight Edition of TNM Staging for Lung Cancer). This is combined with an uncertainty classification task ('u') to account for studies with ambiguous TNM status.

METHODS

2498 reports were annotated by a nuclear medicine physician and split into train, validation, and test datasets. For additional evaluation an external dataset (n = 461 reports) was created, and annotated by two nuclear medicine physicians with agreement reached on all examples. We trained and evaluated eleven publicly available PLMs to determine which is most effective for PET-CT reports, and compared multi-task, single task and traditional machine learning approaches.

RESULTS

We find that a multi-task approach with GatorTron as PLM achieves the best performance, with an overall accuracy (all four tasks correct) of 84% and a Hamming loss of 0.05 on the internal test dataset, and 79% and 0.07 on the external test dataset. Performance on the individual TNM tasks approached expert performance with macro average F1 scores of 0.91, 0.95 and 0.90 respectively on external data. For uncertainty an F1 of 0.77 is achieved.

CONCLUSIONS

Our 'TNMu' classifier successfully extracts TNM staging information from internal and external PET-CT reports. We concluded that multi-task approaches result in the best performance, and better computational efficiency over single task PLM approaches. We believe these models can improve PET-CT services by assisting in auditing, creating research cohorts, and developing decision support systems. Our approach to handling uncertainty represents a novel first step but has room for further refinement.

Molecular Imaging Biomarkers for Early Cancer Detection: A Systematic Review of Emerging Technologies and Clinical Applications

BACKGROUND

Early cancer detection is crucial for improving patient outcomes. Molecular imaging biomarkers offer the potential for non-invasive, early-stage cancer diagnosis.

OBJECTIVES

To evaluate the effectiveness and accuracy of molecular imaging biomarkers for early cancer detection across various imaging modalities and cancer types.

METHODS

A comprehensive search of PubMed/MEDLINE, Embase, Web of Science, Cochrane Library, and Scopus was performed, covering the period from January 2010 to December 2023. Eligibility criteria included original research articles published in English on molecular imaging biomarkers for early cancer detection in humans. The risk of bias for included studies was evaluated using the QUADAS-2 tool. The findings were synthesized through narrative synthesis, with quantitative analysis conducted where applicable.

RESULTS

In total, 50 studies were included. Positron emission tomography (PET)-based biomarkers showed the highest sensitivity (mean: 89.5%, range: 82-96%) and specificity (mean: 91.2%, range: 85-100%). Novel tracers such as [68Ga]-PSMA for prostate cancer and [18F]-FES for breast cancer demonstrated promising outcomes. Optical imaging techniques showed high specificity in intraoperative settings.

CONCLUSIONS

Molecular imaging biomarkers show significant potential for improving early cancer detection. Integration into clinical practice could lead to earlier interventions and improved outcomes. Further research is needed to address standardization and cost-effectiveness.

Assessing the Effectiveness of Selective RET Inhibitors in RET-Positive Cancers through Fluorodeoxyglucose Uptake Analysis

Selective RET inhibitors, such as selpercatinib and pralsetinib, have revolutionized the treatment of cancers with RET gene alterations. These inhibitors have shown remarkable clinical efficacy, particularly in RET-driven lung cancer, medullary thyroid cancer, and other solid tumors driven by RET gene fusions. The assessment of treatment response in oncology has been greatly enhanced by Fluorodeoxyglucose Positron Emission Tomography (FDG-PET), a valuable tool that measures tumor metabolism and provides early indicators of treatment effectiveness. This work explores the effectiveness of selective RET inhibitors in targeting RET-positive cancers and investigates the utility of FDG-PET in assessing treatment response. The paper includes insightful case studies that highlight the successful application of RET inhibitors in the treatment of RET-positive cancers. The findings suggest that FDG-PET has the potential to serve as a non-invasive biomarker for monitoring treatment response in patients with RET-positive cancers. However, further research is required to establish standardized criteria for interpreting FDG-PET scans in the context of selective RET inhibitors and to uncover the broader applications of FDG-PET in precision oncology.

Multitask connected U-Net: automatic lung cancer segmentation from CT images using PET knowledge guidance

Lung cancer is a predominant cause of cancer-related mortality worldwide, necessitating precise tumor segmentation of medical images for accurate diagnosis and treatment. However, the intrinsic complexity and variability of tumor morphology pose substantial challenges to segmentation tasks. To address this issue, we propose a multitask connected U-Net model with a teacher-student framework to enhance the effectiveness of lung tumor segmentation. The proposed model and framework integrate PET knowledge into the segmentation process, leveraging complementary information from both CT and PET modalities to improve segmentation performance. Additionally, we implemented a tumor area detection method to enhance tumor segmentation performance. In extensive experiments on four datasets, the average Dice coefficient of 0.56, obtained using our model, surpassed those of existing methods such as Segformer (0.51), Transformer (0.50), and UctransNet (0.43). These findings validate the efficacy of the proposed method in lung tumor segmentation tasks.

Radiation Therapy for Lung Cancer: Imaging Appearances and Pitfalls

Radiation therapy is part of a multimodality treatment approach to lung cancer. The radiologist must be aware of both the expected and the unexpected imaging findings of the post-radiation therapy patient, including the time course for development of post- radiation therapy pneumonitis and fibrosis. In this review, a brief discussion of radiation therapy techniques and indications is presented, followed by an image-heavy differential diagnostic approach. The review focuses on computed tomography imaging examples to help distinguish normal postradiation pneumonitis and fibrosis from alternative complications, such as infection, local recurrence, or radiation-induced malignancy.

Positron emission tomography in the diagnosis and management of primary pediatric lung tumors

Primary pediatric lung tumors are uncommon and have many overlapping clinical and imaging features. In contrast to adult lung tumors, these rare pediatric neoplasms have a relatively broad histologic spectrum. Informed by a single-institution 13-year retrospective record review, we present an overview of the most common primary pediatric lung neoplasms, with a focus on the role of positron emission tomography (PET), specifically 18F-fluorodeoxyglucose (FDG) PET and 68Ga-DOTATATE PET, in the management of primary pediatric lung tumors. In addition to characteristic conventional radiographic and cross-sectional imaging findings, knowledge of patient age, underlying cancer predisposition syndromes, and PET imaging features may help narrow the differential. While metastases from other primary malignancies remain the most commonly encountered pediatric lung malignancy, the examples presented in this pictorial essay highlight many of the important conventional radiologic and PET imaging features of primary pediatric lung malignancies.

Clinical [18F]FSPG Positron Emission Tomography Imaging Reveals Heterogeneity in Tumor-Associated System xc- Activity

BACKGROUND

(4S)-4-(3-[18F]fluoropropyl)-L-glutamic acid ([18F]FSPG) positron emission tomography/computed tomography (PET/CT) provides a readout of system xc- transport activity and has been used for cancer detection in clinical studies of different cancer types. As system xc- provides the rate-limiting precursor for glutathione biosynthesis, an abundant antioxidant, [18F]FSPG imaging may additionally provide important prognostic information. Here, we performed an analysis of [18F]FSPG radiotracer distribution between primary tumors, metastases, and normal organs from cancer patients. We further assessed the heterogeneity of [18F]FSPG retention between cancer types, and between and within individuals.

METHODS

This retrospective analysis of prospectively collected data compared [18F]FSPG PET/CT in subjects with head and neck squamous cell cancer (HNSCC, n = 5) and non-small-cell lung cancer (NSCLC, n = 10), scanned at different institutions. Using semi-automated regions of interest drawn around tumors and metastases, the maximum standardized uptake value (SUVmax), SUVmean, SUV standard deviation and SUVpeak were measured. [18F]FSPG time-activity curves (TACs) for normal organs, primary tumors and metastases were subsequently compared to 18F-2-fluoro-2-deoxy-D-glucose ([18F]FDG) PET/CT at 60 min post injection (p.i.).

RESULTS

The mean administered activity of [18F]FSPG was 309.3 ± 9.1 MBq in subjects with NSCLC and 285.1 ± 11.3 MBq in those with HNSCC. The biodistribution of [18F]FSPG in both cohorts showed similar TACs in healthy organs from cancer patients. There was no statistically significant overall difference in the average SUVmax of tumor lesions at 60 min p.i. for NSCLC (8.1 ± 7.1) compared to HNSCC (6.0 ± 4.1; p = 0.29) for [18F]FSPG. However, there was heterogeneous retention between and within cancer types; the SUVmax at 60 min p.i. ranged from 1.4 to 23.7 in NSCLC and 3.1-12.1 in HNSCC.

CONCLUSION

[18F]FSPG PET/CT imaging from both NSCLC and HNSCC cohorts showed the same normal-tissue biodistribution, but marked tumor heterogeneity across subjects and between lesions. Despite rapid elimination through the urinary tract and low normal-background tissue retention, the diagnostic potential of [18F]FSPG was limited by variability in tumor retention. As [18F]FSPG retention is mediated by the tumor's antioxidant capacity and response to oxidative stress, this heterogeneity may provide important insights into an individual tumor's response or resistance to therapy.

Need for Objective Task-Based Evaluation of Image Segmentation Algorithms for Quantitative PET: A Study with ACRIN 6668/RTOG 0235 Multicenter Clinical Trial Data

Reliable performance of PET segmentation algorithms on clinically relevant tasks is required for their clinical translation. However, these algorithms are typically evaluated using figures of merit (FoMs) that are not explicitly designed to correlate with clinical task performance. Such FoMs include the Dice similarity coefficient (DSC), the Jaccard similarity coefficient (JSC), and the Hausdorff distance (HD). The objective of this study was to investigate whether evaluating PET segmentation algorithms using these task-agnostic FoMs yields interpretations consistent with evaluation on clinically relevant quantitative tasks. Methods: We conducted a retrospective study to assess the concordance in the evaluation of segmentation algorithms using the DSC, JSC, and HD and on the tasks of estimating the metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary tumors from PET images of patients with non-small cell lung cancer. The PET images were collected from the American College of Radiology Imaging Network 6668/Radiation Therapy Oncology Group 0235 multicenter clinical trial data. The study was conducted in 2 contexts: (1) evaluating conventional segmentation algorithms, namely those based on thresholding (SUVmax40% and SUVmax50%), boundary detection (Snakes), and stochastic modeling (Markov random field-Gaussian mixture model); (2) evaluating the impact of network depth and loss function on the performance of a state-of-the-art U-net-based segmentation algorithm. Results: Evaluation of conventional segmentation algorithms based on the DSC, JSC, and HD showed that SUVmax40% significantly outperformed SUVmax50%. However, SUVmax40% yielded lower accuracy on the tasks of estimating MTV and TLG, with a 51% and 54% increase, respectively, in the ensemble normalized bias. Similarly, the Markov random field-Gaussian mixture model significantly outperformed Snakes on the basis of the task-agnostic FoMs but yielded a 24% increased bias in estimated MTV. For the U-net-based algorithm, our evaluation showed that although the network depth did not significantly alter the DSC, JSC, and HD values, a deeper network yielded substantially higher accuracy in the estimated MTV and TLG, with a decreased bias of 91% and 87%, respectively. Additionally, whereas there was no significant difference in the DSC, JSC, and HD values for different loss functions, up to a 73% and 58% difference in the bias of the estimated MTV and TLG, respectively, existed. Conclusion: Evaluation of PET segmentation algorithms using task-agnostic FoMs could yield findings discordant with evaluation on clinically relevant quantitative tasks. This study emphasizes the need for objective task-based evaluation of image segmentation algorithms for quantitative PET.

How Imaging Advances Are Defining the Future of Precision Radiation Therapy

Radiation therapy is fundamental in the treatment of cancer. Imaging has always played a central role in radiation oncology. Integrating imaging technology into irradiation devices has increased the precision and accuracy of dose delivery and decreased the toxic effects of the treatment. Although CT has become the standard imaging modality in radiation therapy, the development of recently introduced next-generation imaging techniques has improved diagnostic and therapeutic decision making in radiation oncology. Functional and molecular imaging techniques, as well as other advanced imaging modalities such as SPECT, yield information about the anatomic and biologic characteristics of tumors for the radiation therapy workflow. In clinical practice, they can be useful for characterizing tumor phenotypes, delineating volumes, planning treatment, determining patients' prognoses, predicting toxic effects, assessing responses to therapy, and detecting tumor relapse. Next-generation imaging can enable personalization of radiation therapy based on a greater understanding of tumor biologic factors. It can be used to map tumor characteristics, such as metabolic pathways, vascularity, cellular proliferation, and hypoxia, that are known to define tumor phenotype. It can also be used to consider tumor heterogeneity by highlighting areas at risk for radiation resistance for focused biologic dose escalation, which can impact the radiation planning process and patient outcomes. The authors review the possible contributions of next-generation imaging to the treatment of patients undergoing radiation therapy. In addition, the possible roles of radio(geno)mics in radiation therapy, the limitations of these techniques, and hurdles in introducing them into clinical practice are discussed. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Stereotactic body radiation therapy and thermal ablation for treatment of NSCLC: A systematic literature review and meta-analysis

RATIONALE

Stereotactic body radiation therapy (SBRT) is the standard of care for inoperable early stage non-small cell lung cancer (NSCLC). Use of image guided thermal ablation (IGTA; including microwave ablation [MWA] and radiofrequency ablation [RFA]) has increased in NSCLC, however there are no studies comparing all three.

OBJECTIVE

To compare the efficacy of IGTA (including MWA and RFA) and SBRT for the treatment of NSCLC.

METHODS

Published literature databases were systematically searched for studies assessing MWA, RFA, or SBRT. Local tumor progression (LTP), disease-free survival (DFS), and overall survival (OS) were assessed with single-arm pooled analyses and meta-regressions in NSCLC patients and a stage IA subgroup. Study quality was assessed with a modified methodological index for non-randomized studies (MINORS) tool.

RESULTS

Forty IGTA study-arms (2,691 patients) and 215 SBRT study-arms (54,789 patients) were identified. LTP was lowest after SBRT at one and two years in single-arm pooled analyses (4% and 9% vs. 11% and 18%) and at one year in meta-regressions when compared to IGTA (OR = 0.2, 95%CI = 0.07-0.63). MWA patients had the highest DFS of all treatments in single-arm pooled analyses. In meta-regressions at two and three-years, DFS was significantly lower for RFA compared to MWA (OR = 0.26, 95%CI = 0.12-0.58; OR = 0.33, 95%CI = 0.16-0.66, respectively). OS was similar across modalities, timepoints, and analyses. Older age, male patients, larger tumors, retrospective studies, and non-Asian study region were also predictors of worse clinical outcomes. In high-quality studies (MINORS score ≥ 7), MWA patients had better clinical outcomes than the overall analysis. Stage IA MWA patients had lower LTP, higher OS, and generally lower DFS, compared to the main analysis of all NSCLC patients.

CONCLUSIONS

NSCLC patients had comparable outcomes after SBRT and MWA, which were better than those with RFA.

Deep learning-based dynamic PET parametric Ki image generation from lung static PET

OBJECTIVES

PET/CT is a first-line tool for the diagnosis of lung cancer. The accuracy of quantification may suffer from various factors throughout the acquisition process. The dynamic PET parametric K_i provides better quantification and improve specificity for cancer detection. However, parametric imaging is difficult to implement clinically due to the long acquisition time (~ 1 h). We propose a dynamic parametric imaging method based on conventional static PET using deep learning.

METHODS

Based on the imaging data of 203 participants, an improved cycle generative adversarial network incorporated with squeeze-and-excitation attention block was introduced to learn the potential mapping relationship between static PET and K_i parametric images. The image quality of the synthesized images was qualitatively and quantitatively evaluated by using several physical and clinical metrics. Statistical analysis of correlation and consistency was also performed on the synthetic images.

RESULTS

Compared with those of other networks, the images synthesized by our proposed network exhibited superior performance in both qualitative and quantitative evaluation, statistical analysis, and clinical scoring. Our synthesized K_i images had significant correlation (Pearson correlation coefficient, 0.93), consistency, and excellent quantitative evaluation results with the K_i images obtained in standard dynamic PET practice.

CONCLUSIONS

Our proposed deep learning method can be used to synthesize highly correlated and consistent dynamic parametric images obtained from static lung PET.

KEY POINTS

• Compared with conventional static PET, dynamic PET parametric K_i imaging has been shown to provide better quantification and improved specificity for cancer detection. • The purpose of this work was to develop a dynamic parametric imaging method based on static PET images using deep learning. • Our proposed network can synthesize highly correlated and consistent dynamic parametric images, providing an additional quantitative diagnostic reference for clinicians.

Preclinical PET Imaging of Tumor Cell Death following Therapy Using Gallium-68-Labeled C2Am

There is an unmet clinical need for imaging agents capable of detecting early evidence of tumor cell death, since the timing, extent, and distribution of cell death in tumors following treatment can give an indication of treatment outcome. We describe here 68Ga-labeled C2Am, which is a phosphatidylserine-binding protein, for imaging tumor cell death in vivo using positron emission tomography (PET). A one-pot synthesis of 68Ga-C2Am (20 min, 25 °C, >95% radiochemical purity) has been developed, using a NODAGA-maleimide chelator. The binding of 68Ga-C2Am to apoptotic and necrotic tumor cells was assessed in vitro using human breast and colorectal cancer cell lines, and in vivo, using dynamic PET measurements in mice implanted subcutaneously with the colorectal tumor cells and treated with a TRAIL-R2 agonist. 68Ga-C2Am showed predominantly renal clearance and low retention in the liver, spleen, small intestine, and bone and generated a tumor-to-muscle (T/m) ratio of 2.3 ± 0.4, at 2 h post probe administration and at 24 h following treatment. 68Ga-C2Am has the potential to be used in the clinic as a PET tracer for assessing early treatment response in tumors.

Predictive value of 18F-FDG PET/CT for evaluating the response to hypofractionated radiotherapy combined with PD-1 blockade in non-small cell lung cancer

Purpose

This retrospective study aimed to investigate ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG)-positron emission tomography/computed tomography (PET/CT) as a predictor of response to hypofractionated radiotherapy (HFRT) combined with programmed cell death-1 (PD-1) blockade for lung cancer.

Methods

We included 41 patients with advanced non-small cell lung cancer (NSCLC) in this study. PET/CT was performed before (SCAN-0) and one month (SCAN-1), three months (SCAN-2), and six months (SCAN-3) after treatment. Using the European Organization for Research and Treatment of Cancer 1999 criteria and PET response criteria in solid tumors, treatment responses were classified as complete metabolic response (CMR), partial metabolic response (PMR), stable metabolic disease (SMD), or progressive metabolic disease (PMD). Patients were further categorized as those with metabolic benefits (MB; SMD, PMR, and CMR) and those without MBs (NO-MB; PMD). We analyzed the prognosis and overall survival (OS) of patients with new visceral/bone lesions during treatment. Based on the findings, we generated a nomogram to predict survival. Receiver operating characteristics and calibration curves were used to evaluate the accuracy of the prediction model.

Results

The mean OS based on SCANs 1, 2, and 3 was significantly higher in patients with MB and those without new visceral/bone lesions. The prediction nomogram for survival had a high area under the curve and a high predictive value based on the receiver operating characteristics and calibration curves.

Conclusion

¹⁸FDG-PET/CT has the potential to predict the outcomes of HFRT combined with PD-1 blockade in NSCLC. Therefore, we recommend using a nomogram to predict patient survival.

The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges

Objectives

Lung cancer has been widely characterized through radiomics and artificial intelligence (AI). This review aims to summarize the published studies of AI based on positron emission tomography/computed tomography (PET/CT) radiomics in non-small-cell lung cancer (NSCLC).

Materials and methods

A comprehensive search of literature published between 2012 and 2022 was conducted on the PubMed database. There were no language or publication status restrictions on the search. About 127 articles in the search results were screened and gradually excluded according to the exclusion criteria. Finally, this review included 39 articles for analysis.

Results

Classification is conducted according to purposes and several studies were identified at each stage of disease:1) Cancer detection (n=8), 2) histology and stage of cancer (n=11), 3) metastases (n=6), 4) genotype (n=6), 5) treatment outcome and survival (n=8). There is a wide range of heterogeneity among studies due to differences in patient sources, evaluation criteria and workflow of radiomics. On the whole, most models show diagnostic performance comparable to or even better than experts, and the common problems are repeatability and clinical transformability.

Conclusion

AI-based PET/CT Radiomics play potential roles in NSCLC clinical management. However, there is still a long way to go before being translated into clinical application. Large-scale, multi-center, prospective research is the direction of future efforts, while we need to face the risk of repeatability of radiomics features and the limitation of access to large databases.

Development and Validation of the Random Forest Model via Combining CT-PET Image Features and Demographic Data for Distant Metastases among Lung Cancer Patients

The work aimed at developing and validating a random forest model of CT-PET image features combined with demographic data to diagnose distant metastases among lung cancer patients. This study involved lung cancer patients from The Cancer Genome Atlas lung adenocarcinoma (TCGA-LUAD) dataset, the lung PET-CT dataset, the lung squamous cell carcinoma (LSCC) dataset, and the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma (CPTAC-LUAD) dataset and collected the information on 178 CT, 178 PET, and the patients' age, history of smoking, and gender. We conducted image processing and feature extraction. Finally, 4 computed tomography (CT) image features and 2 positron emission tomography (PET) image features were extracted. Four prediction models based on CT image features, PET image features, and demographic data were developed, and the area under the receiver operating characteristic (ROC) curve was used to evaluate the performance of prediction models. A total of 178 eligible samples were randomly divided into a training set (n = 134) and a testing set (n = 44) at a ratio of 3 : 1, with 2021 as a random number. ROC analyses illustrated that the predictive performance for distant metastases of combining CT-PET image features and demographic data for training and testing were 0.923 (95% confidence interval (CI): 0.873-0.973) and 0.873 (95% CI: 0.757-0.990). In addition, the predictive performance of the combined model in the testing set was significantly better than that of the CT-demographic data model (0.716, 95% CI: 0.531-0.902), PET-demographic data model (0.802, 95% CI: 0.633-0.970), and CT-PET model (0.797, 95% CI: 0.666-0.928). The random forest model via combining CT-PET image features and demographic data could have great performance in predicting distant metastases among lung cancer patients.

Changing Role of PET/CT in Cancer Care With a Focus on Radiotherapy

Positron emission tomography (PET) integrated with computed tomography (CT) has brought revolutionary changes in improving cancer care (CC) for patients. These include improved detection of previously unrecognizable disease, ability to identify oligometastatic status enabling more aggressive treatment strategies when the disease burden is lower, its use in better defining treatment targets in radiotherapy (RT), ability to monitor treatment responses early and thus improve the ability for early interventions of non-responding tumors, and as a prognosticating tool as well as outcome predicting tool. PET/CT has enabled the emergence of new concepts such as radiobiotherapy (RBT), radioimmunotherapy, theranostics, and pharmaco-radiotherapy. This is a rapidly evolving field, and this primer is to help summarize the current status and to give an impetus to developing new ideas, clinical trials, and CC outcome improvements.

Radiolabelling small and biomolecules for tracking and monitoring

Radiolabelling small molecules with beta-emitters has been intensively explored in the last decades and novel concepts for the introduction of radionuclides continue to be reported regularly. New catalysts that induce carbon/hydrogen activation are able to incorporate isotopes such as deuterium or tritium into small molecules. However, these established labelling approaches have limited applicability for nucleic acid-based drugs, therapeutic antibodies, or peptides, which are typical of the molecules now being investigated as novel therapeutic modalities. These target molecules are usually larger (significantly >1 kDa), mostly multiply charged, and often poorly soluble in organic solvents. However, in preclinical research they often require radiolabelling in order to track and monitor drug candidates in metabolism, biotransformation, or pharmacokinetic studies. Currently, the most established approach to introduce a tritium atom into an oligonucleotide is based on a multistep synthesis, which leads to a low specific activity with a high level of waste and high costs. The most common way of tritiating peptides is using appropriate precursors. The conjugation of a radiolabelled prosthetic compound to a functional group within a protein sequence is a commonly applied way to introduce a radionuclide or a fluorescent tag into large molecules. This review highlights the state-of-the-art in different radiolabelling approaches for oligonucleotides, peptides, and proteins, as well as a critical assessment of the impact of the label on the properties of the modified molecules. Furthermore, applications of radiolabelled antibodies in biodistribution studies of immune complexes and imaging of brain targets are reported.

Head‑to‑head assessment of [68Ga]Ga-DOTA-FAPI-04 PET/CT vs [18F]FDG PET/CT in fibroblastic tumors

OBJECTIVES

We aimed to evaluate [⁶⁸Ga]Ga-DOTA-FAPI-04 versus [¹⁸F]FDG PET/CT in the application of fibroblastic tumors.

METHODS

Twenty participants with 6 subtypes of fibroblastic tumors prospectively underwent ¹⁸F-FDG and [⁶⁸Ga]Ga-DOTA-FAPI-04 PET/CT examinations to evaluate the lesions. PET/CT findings were confirmed by surgical pathology of fifteen participants, puncture biopsy of two participants, or imaging follow-up of three participants. Two independent sample t tests were used to compare the uptake of [¹⁸F]FDG vs [⁶⁸Ga]Ga-DOTA-FAPI-04 in primary, recurrent and metastatic lesions. One-way ANOVA was used to compare the uptake of [¹⁸F]FDG or [⁶⁸Ga]Ga-DOTA-FAPI-04 among primary, recurrent, and metastatic lesions. The uptake of [⁶⁸Ga]Ga-DOTA-FAPI-04 vs [¹⁸F]FDG in different histopathological lesions was compared by two independent sample t tests.

RESULTS

Twenty participants were confirmed to have 38 lesions. Although there was no significant difference in the detection of lesions between [⁶⁸Ga]Ga-DOTA-FAPI-04 and [¹⁸F]FDG PET/CT (38 vs 36, p = 0.493), the uptake of [⁶⁸Ga]Ga-DOTA-FAPI-04 in lesions was significantly higher than that of [¹⁸F]FDG (p < 0.001), including primary (p < 0.001), recurrent (p = 0.018) and metastatic (p < 0.001) lesions. The SUVmax of [⁶⁸Ga]Ga-DOTA-FAPI-04 in primary and recurrent lesions was higher than that in metastasis (p = 0.034 and p = 0.015, respectively). The SUVmax of [⁶⁸Ga]Ga-DOTA-FAPI-04 in primary and recurrent malignant lesions was significantly higher than that of the intermediate (p < 0.001). The SUVmax of [⁶⁸Ga]Ga-DOTA-FAPI-04 in one participant of recurrent SFT with 5 lesions was significantly lower after treatment than before treatment (p = 0.016).

CONCLUSIONS

[⁶⁸Ga]Ga-DOTA-FAPI-04 outperformed [¹⁸F]FDG PET/CT in displaying the primary, recurrent and metastatic lesions of fibroblastic tumors.

State of the Art: Lung Cancer Staging Using Updated Imaging Modalities

Lung cancer is among the most common mortality causes worldwide. This scientific article is a comprehensive review of current knowledge regarding screening, subtyping, imaging, staging, and management of treatment response for lung cancer. The traditional imaging modality for screening and initial lung cancer diagnosis is computed tomography (CT). Recently, a dual-energy CT was proven to enhance the categorization of variable pulmonary lesions. The National Comprehensive Cancer Network (NCCN) recommends usage of fluorodeoxyglucose positron emission tomography (FDG PET) in concert with CT to properly stage lung cancer and to prevent fruitless thoracotomies. Diffusion MR is an alternative to FDG PET/CT that is radiation-free and has a comparable diagnostic performance. For response evaluation after treatment, FDG PET/CT is a potent modality which predicts survival better than CT. Updated knowledge of lung cancer genomic abnormalities and treatment regimens helps to improve the radiologists’ skills. Incorporating the radiologic experience is crucial for precise diagnosis, therapy planning, and surveillance of lung cancer.

Joint EANM/SNMMI/ESTRO practice recommendations for the use of 2-[18F]FDG PET/CT external beam radiation treatment planning in lung cancer V1.0

PURPOSE

2-[18F]FDG PET/CT is of utmost importance for radiation treatment (RT) planning and response monitoring in lung cancer patients, in both non-small and small cell lung cancer (NSCLC and SCLC). This topic has been addressed in guidelines composed by experts within the field of radiation oncology. However, up to present, there is no procedural guideline on this subject, with involvement of the nuclear medicine societies.

METHODS

A literature review was performed, followed by a discussion between a multidisciplinary team of experts in the different fields involved in the RT planning of lung cancer, in order to guide clinical management. The project was led by experts of the two nuclear medicine societies (EANM and SNMMI) and radiation oncology (ESTRO).

RESULTS AND CONCLUSION

This guideline results from a joint and dynamic collaboration between the relevant disciplines for this topic. It provides a worldwide, state of the art, and multidisciplinary guide to 2-[18F]FDG PET/CT RT planning in NSCLC and SCLC. These practical recommendations describe applicable updates for existing clinical practices, highlight potential flaws, and provide solutions to overcome these as well. Finally, the recent developments considered for future application are also reviewed.

Predicting cancer outcomes with radiomics and artificial intelligence in radiology

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.

Role of PET/CT in post-therapeutic assessment of bronchogenic carcinoma

Background

Lung cancer is the most common among all kinds of cancers. It still constitutes the leading cause of cancer-related deaths worldwide, even with major advancements in prevention and treatments available. More than 85% of the cases are of non-small cell lung cancer (NSCLC), while less than 15% are of small cell lung cancers (SCLCs).

Patients and methods

This is a prospective study of 20 patients confirmed histopathologically to have bronchogenic carcinoma, who came for assessment of therapeutic response. All patients underwent positron emission tomography/computed tomography (PET/CT) before and after therapy. Semiquantitative assessment was used to determine maximum standardized uptake value (SUVmax). Treatment response evaluation was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST) criteria.

Results

Comparison of the pre- and post-treatment SUVmax in the responder and non-responder groups revealed that the post-treatment SUV was significantly lower than the baseline SUV in the responder group (P = 0.008). The responder post-treatment SUV and ∆ SUV were significantly lower than the non-responder values (P = 0.014 and 0.0004 respectively). The optimum threshold values of post-treatment SUV and ∆ SUV threshold defined by the receiver operating characteristic (ROC) curve analysis were ≤ 8 and ≤ −48.3 respectively. The sensitivity, specificity, PPV, NPV, and AUC of post-treatment SUV for predicting tumor response were 100%, 66.67%, 66.7%, 100%, and 0.833 respectively. The sensitivity, specificity, PPV, NPV, and AUC of ∆ SUV for predicting tumor response were 100%, 91.67%, 88.9%, 100%, and 0.979% respectively.

Conclusion

PET/CT proved itself as useful, efficient, and reliable tool in follow-up of lung cancer patients as it gives an early and accurate metabolic response assessment before any CT changes, leading to early modification of therapy or confirmation of its efficiency.

The usefulness of 18F-FDG PET/CT in follow-up and recurrence detection for patients with lung carcinoma and its impact on the survival outcome

Background

We assess the usefulness of 18F-FDG PET/CT for detection of recurrent or residual tumor in post treatment patients with NSCLC and comparing the results with CECT, and we evaluate its impact to the clinical assessment and overall survival of lung cancer patients.

Results

A prospective study of 63 patients with potentially resectable NSCLC, were divided into 2 groups according to the initial staging of the disease and the early response to treatment.

Group A (n=29) patients were treated by curative treatment, and group B (n=34) patients were treated by palliative treatment.

Evaluation of patients was done during the follow-up period clinically every 3 months and by 18F-FDG PET/CT and CECT imaging at 6 months intervals.

In group A, 18F-FDG PET/CT correctly diagnosed all recurrent or residual tumors (n=7) whereas CECT diagnosed only 5 with 2 false negative cases.

In group B, 18F-FDG PET/CT correctly diagnosed all recurrent or residual tumors (n=23) whereas CECT diagnosed 16 patients with 7 false negative cases.

By comparison of 18F-FDG PET/CT and CECT in detection of residual or recurrent lung cancer (n=30), the sensitivity, specificity, PPV, NPV, and accuracy of 18F-FDG PET/CT were 100%, 92%, 92%, 100%, and 96% respectively, while of CECT were 72%, 95%, 94%, 79%, and 84% respectively in correlation with reference standard data. The calculated SUV max ranged from 2.1 to 4.9.

There was a significant difference in overall survival between patients in routine scan who had positive 18F FDG PET/CT result (median survival 18 months) and those who had negative result (median survival 45 months) (P<0.0001).

Conclusion

18 F-FDG PET/CT plays an important role in distinguishing post treatment changes from tumor recurrence in patients with lung cancer. Follow-up or surveillance 18 F-FDG PET/CT is a prognostic indicator for overall survival of patients.

Macrovasculature and positron emission tomography (PET) standardized uptake value in patients with lung cancer

PURPOSE

To investigate the relationship between macrovasculature features and the standardized uptake value (SUV) of positron emission tomography (PET), which is a surrogate for the metabolic activity of a lung tumor.

METHODS

We retrospectively analyzed a cohort of 90 lung cancer patients who had both chest CT and PET-CT examinations before receiving cancer treatment. The SUVs in the medical reports were used. We quantified three macrovasculature features depicted on CT images (i.e., vessel number, vessel volume, and vessel tortuosity) and several tumor features (i.e., volume, maximum diameter, mean diameter, surface area, and density). Tumor size (e.g., volume) was used as a covariate to adjust for possible confounding factors. Backward stepwise multiple regression analysis was performed to develop a model for predicting PET SUV from the relevant image features. The Bonferroni correction was used for multiple comparisons.

RESULTS

PET SUV was positively correlated with vessel volume (R = 0.44, p < 0.001) and vessel number (R = 0.44, p < 0.001) but not with vessel tortuosity (R = 0.124, p > 0.05). After adjusting for tumor size, PET SUV was significantly correlated with vessel tortuosity (R = 0.299, p = 0.004) and vessel number (R = 0.224, p = 0.035), but only marginally correlated with vessel volume (R = 0.187, p = 0.079). The multiple regression model showed a performance with an R-Squared of 0.391 and an adjusted R-Squared of 0.355 (p < 0.001).

CONCLUSIONS

Our investigations demonstrate the potential relationship between macrovasculature and PET SUV and suggest the possibility of inferring the metabolic activity of a lung tumor from chest CT images.

Pitfalls on PET/CT Due to Artifacts and Instrumentation

PET/CT has become a preferred imaging modality over PET-only scanners in clinical practice. However, along with the significant improvement in diagnostic accuracy and patient throughput, pitfalls on PET/CT are reported as well. This review provides a general overview on the potential influence of the limitations with respect to PET/CT instrumentation and artifacts associated with the modality integration on the image appearance and quantitative accuracy of PET. Approaches proposed in literature to address the limitations or minimize the artifacts are discussed as well as their current challenges for clinical applications. Although the CT component can play an important role in assisting clinical diagnosis, we concentrate on the imaging scenarios where CT is used to provide auxiliary information for attenuation compensation and scatter correction in PET.

Predictive Value of Folate Receptor-Positive Circulating Tumor Cells for the Preoperative Diagnosis of Lymph Node Metastasis in Patients with Lung Adenocarcinoma

Noninvasive assessments of the risk of lymph node metastasis (LNM) in patients with lung adenocarcinoma (LAD) are of great value for selecting individualized treatment options. However, the diagnostic accuracies of different preoperative LN evaluation methods in routine clinical practice are not satisfactory. Here, an assessment to detect folate receptor (FR)-positive circulating tumor cells (CTCs) based on ligand-targeted enzyme-linked polymerization is established. FR-positive CTCs have the potential to improve the specificity and sensitivity of diagnosing LNM in lung cancer patients. The addition of CTC level improved the diagnostic efficiency of the initial prediction model that comprises other clinical parameters. A nomogram for predicting preoperative LNM is established, which showed good prediction and calibration capacities and achieved an average area under the curve of 0.786. Good correlations are observed between the CTC level and nodal classifications, such as the number of positive LNs and the ratio of the number of positive LNs to removed LNs (LN ratio or LNR). The ligand-targeted enzyme-linked polymerization-assisted assessment of CTCs enables noninvasive detection and has a useful predictive value for the preoperative diagnosis of LNM in patients with LAD.

18F-FLT PET/CT Adds Value to 18F-FDG PET/CT for Diagnosing Relapse After Definitive Radiotherapy in Patients with Lung Cancer: Results of a Prospective Clinical Trial

Diagnosing relapse after radiotherapy for lung cancer is challenging. The specificity of both CT and ¹⁸F-FDG PET/CT is low because of radiation-induced changes. 3'-deoxy-3'-¹⁸F-fluorothymidine (¹⁸F-FLT) PET has previously demonstrated higher specificity for malignancy than ¹⁸F-FDG PET. We investigated the value of ¹⁸F-FLT PET/CT for diagnosing relapse in irradiated lung cancer. Methods: Patients suspected of relapse of lung cancer after definitive radiotherapy (conventional fractionated radiotherapy [cRT] or stereotactic body radiotherapy [SBRT]) were included. Sensitivity and specificity were analyzed both within the irradiated high-dose volume (HDV) and on a patient basis. Marginal differences and interobserver agreement were assessed. Results: Sixty-three patients who had received radiotherapy in 70 HDVs (34 cRT; 36 SBRT) were included. The specificity of ¹⁸F-FLT PET/CT was higher than that of ¹⁸F-FDG PET/CT (HDV, 96% [95% CI, 87-100] vs. 71% [95% CI, 57-83] [P = 0.0039]; patient-based, 90% [95% CI, 73-98] vs. 55% [95% CI, 36-74] [P = 0.0020]). The difference in specificity between ¹⁸F-FLT PET/CT and ¹⁸F-FDG PET/CT was higher after cRT than after SBRT. The sensitivity of ¹⁸F-FLT PET/CT was lower than that of ¹⁸F-FDG PET/CT (HDV, 69% [95% CI, 41-89] vs. 94% [95% CI, 70-100] [P = 0.1250]; patient-based, 70% [95% CI, 51-84] vs. 94% [95% CI, 80-99] [P = 0.0078]). Adding ¹⁸F-FLT PET/CT when ¹⁸F-FDG PET/CT was positive or inconclusive improved the diagnostic value compared with ¹⁸F-FDG PET/CT alone. In cRT HDVs, the probability of malignancy increased from 67% for ¹⁸F-FDG PET/CT alone to 100% when both tracers were positive. Conclusion: ¹⁸F-FLT PET/CT adds diagnostic value to ¹⁸F-FDG PET/CT in patients with suspected relapse. The diagnostic impact of ¹⁸F-FLT PET/CT was highest after cRT. We suggest adding ¹⁸F-FLT PET/CT when ¹⁸F-FDG PET/CT is inconclusive or positive within the previously irradiated volume to improve diagnostic value in patients for whom histologic confirmation is not easily obtained.

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

The dynamics and prognostic value of FDG PET-metrics in weekly monitoring of (chemo)radiotherapy for NSCLC

PURPOSE

To test if the relative change in FDG-PET SUV_max over the course of treatment was associated with disease progression and overall survival. Additionally, the prognostic values of other first-order PET-metric changes were investigated.

METHODS

The study included 38 patients with stage II-III NSCLC, who underwent concurrent chemoradiotherapy. Patients received two pre-treatment FDG-PET scans and four during-treatment scans at weekly intervals. SUV_max was normalized to the start of treatment and analyzed using linear regression. Linear regression coefficients of other first order PET-metrics were grouped according to dissimilarity. Associations to patient outcome were analyzed using Cox hazard ratio.

RESULTS

Twenty-eight patients satisfied the criteria for analysis. All PET-metrics demonstrated a strong linear correlation with time during treatment [median R-range: -0.87: -0.97]. No strong associations (p > 0.10) were found for the relative slope of SUV_max to patient outcomes. Other first-order metrics did correlate with outcome but the single imaging time-point maximizing the association of PET response with outcome varied per PET metric and outcome parameter.

CONCLUSION

All investigated FDG PET metrics linearly decreased during treatment. Relative change in SUV_max was not associated to patient outcome while several other first order PET-metrics were related to patient outcome. A single optimal imaging time-point could not be identified.

Just another "Clever Hans"? Neural networks and FDG PET-CT to predict the outcome of patients with breast cancer

BACKGROUND

Manual quantification of the metabolic tumor volume (MTV) from whole-body ¹⁸F-FDG PET/CT is time consuming and therefore usually not applied in clinical routine. It has been shown that neural networks might assist nuclear medicine physicians in such quantification tasks. However, little is known if such neural networks have to be designed for a specific type of cancer or whether they can be applied to various cancers. Therefore, the aim of this study was to evaluate the accuracy of a neural network in a cancer that was not used for its training.

METHODS

Fifty consecutive breast cancer patients that underwent ¹⁸F-FDG PET/CT were included in this retrospective analysis. The PET-Assisted Reporting System (PARS) prototype that uses a neural network trained on lymphoma and lung cancer ¹⁸F-FDG PET/CT data had to detect pathological foci and determine their anatomical location. Consensus reads of two nuclear medicine physicians together with follow-up data served as diagnostic reference standard; 1072 ¹⁸F-FDG avid foci were manually segmented. The accuracy of the neural network was evaluated with regard to lesion detection, anatomical position determination, and total tumor volume quantification.

RESULTS

If PERCIST measurable foci were regarded, the neural network displayed high per patient sensitivity and specificity in detecting suspicious ¹⁸F-FDG foci (92%; CI = 79-97% and 98%; CI = 94-99%). If all FDG-avid foci were regarded, the sensitivity degraded (39%; CI = 30-50%). The localization accuracy was high for body part (98%; CI = 95-99%), region (88%; CI = 84-90%), and subregion (79%; CI = 74-84%). There was a high correlation of AI derived and manually segmented MTV (R² = 0.91; p < 0.001). AI-derived whole-body MTV (HR = 1.275; CI = 1.208-1.713; p < 0.001) was a significant prognosticator for overall survival. AI-derived lymph node MTV (HR = 1.190; CI = 1.022-1.384; p = 0.025) and liver MTV (HR = 1.149; CI = 1.001-1.318; p = 0.048) were predictive for overall survival in a multivariate analysis.

CONCLUSION

Although trained on lymphoma and lung cancer, PARS showed good accuracy in the detection of PERCIST measurable lesions. Therefore, the neural network seems not prone to the clever Hans effect. However, the network has poor accuracy if all manually segmented lesions were used as reference standard. Both the whole body and organ-wise MTV were significant prognosticators of overall survival in advanced breast cancer.

Oncologic significance of unexpected osseous foci on FDG-PET without correlative CT abnormalities

PURPOSE

Our purpose was to explore the clinical significance of unexpected osseous foci on 18F-FDG-PET without correlative CT abnormalities (FWCT) in patients referred for oncologic evaluation. The significance of FDG-avid foci without correlative CT abnormalities has been previously explored in tissues such as breast, lung, liver, and prostate; however, osseous foci without correlative CT abnormalities continue to present challenges in diagnostic interpretations.

METHODS

This study is a retrospective review of 120 osseous FWCT, reported in 91 patients, and their corresponding clinical follow-up. We included only patients with at least 6 months of clinical follow-up leading to a final diagnosis, reviewing bone biopsy results, follow-up imaging, and clinical notes. We excluded those patients on active chemotherapy at the time of the scan. For reports describing > 3 foci, we only analyzed the one with highest maximum standardized uptake value (SUVmax). As a measure of uptake intensity, we obtained focus-to-liver ratios (F/L) utilizing their SUVmax and corresponding hepatic 3D SUVmean.

RESULTS

Of 91 patients, 74 (81%) had biopsy-confirmed primary malignancies and 17 (19%) had suspicious findings on diagnostic imaging, but no proven primary malignancy. 50 of 120 (42%) osseous foci were malignant and 70 (58%) were benign. 49 of 120 (41%) foci were solitary and 71 (59%) were 0 with other foci (non-solitary). Malignancy resulted from 15/49 (31%) solitary foci and 35/71 (49%) non-solitary foci. Malignant lesions had a mean F/L 2.37 ± 0.397 and benign lesions a mean F/L 1.49 ± 0.169. Osseous malignancy correlated with a higher uptake intensity (Spearman = 0.408; P < 0.01) and was significantly associated with F/L ≥ 2.0 (P < 0.001). Osseous FWCT led to restaging and management modification in 12/91 (13%) patients.

CONCLUSION

Osseous FWCT frequently represent early stages of malignancy. A higher index of suspicion is warranted for osseous FWCT associated with underlying myeloproliferative neoplasms, breast and lung cancer, and moderate (F/L 1.0-2.0) or high (F/L > 2.0) uptake intensity. Interpreting physicians encountering these variables can recommend interval follow-up with 18F-FDG-PET/CT or correlation with contrast-enhanced MRI or tissue biopsy. In patients with an altered biodistribution of 18F-FDG in the bone marrow (e.g., recent chemotherapy cycle), follow-up FDG-PET can be obtained at an appropriate time interval following oncologic treatment.

Impact of [18F]FDG-PET and [18F]FLT-PET-Parameters in Patients with Suspected Relapse of Irradiated Lung Cancer

Radiation-induced changes may cause a non-malignant high 2-deoxy-2-[¹⁸F]fluoro-d-glucose (FDG)-uptake. The 3′-deoxy-3′-[¹⁸F]fluorothymidine (FLT)-PET/CT performs better in the differential diagnosis of inflammatory changes and lung lesions with a higher specificity than FDG-PET/CT. We investigated the association between post-radiotherapy FDG-PET-parameters, FLT-PET-parameters, and outcome. Sixty-one patients suspected for having a relapse after definitive radiotherapy for lung cancer were included. All the patients had FDG-PET/CT and FLT-PET/CT. FDG-PET- and FLT-PET-parameters were collected from within the irradiated high-dose volume (HDV) and from recurrent pulmonary lesions. For associations between PET-parameters and relapse status, respectively, the overall survival was analyzed. Thirty patients had a relapse, of these, 16 patients had a relapse within the HDV. FDG-SUV_max and FLT-SUV_max were higher in relapsed HDVs compared with non-relapsed HDVs (median FDG-SUV_max: 12.8 vs. 4.2; p < 0.001; median FLT-SUV_max 3.9 vs. 2.2; p < 0.001). A relapse within HDV had higher FDG-SUV_peak (median FDG-SUV_peak: 7.1 vs. 3.5; p = 0.014) and was larger (median metabolic tumor volume (MTV_50%): 2.5 vs. 0.7; 0.014) than the relapsed lesions outside of HDV. The proliferative tumor volume (PTV_50%) was prognostic for the overall survival (hazard ratio: 1.07 pr cm³ [1.01–1.13]; p = 0.014) in the univariate analysis, but not in the multivariate analysis. FDG-SUV_max and FLT-SUV_max may be helpful tools for differentiating the relapse from radiation-induced changes, however, they should not be used definitively for relapse detection.

PET/CT implication on bronchogenic carcinoma TNM staging and follow-up using RECIST/PERCIST criteria: a comparative study with CT

Background

To evaluate the role of PET/CT on bronchogenic carcinoma staging as well as treatment response evaluation on follow-up compared to CT study alone.

Methods

A prospective study of 60 patients confirmed histopathologically to have non-small cell bronchogenic carcinoma, 30 of them came for staging (group T) while the rest 30 came for follow-up (group F) to assess therapy response. All patients underwent PET/CT with data analysis done using the eighth edition tumor, nodal, metastatic staging (TNM) staging for group T and RECIST/PERCIST criteria for group F. The CT data alone transferred to a blind radiologist for analysis using the same parameters. The results were collected and compared.

Results

Regarding group T, 12 patients showed different TNM staging between PET/CT and CT alone, 5 cases with different T stagings, 4 cases with different N stagings, and 5 cases with different M stagings. Also, 8 cases showed different surgical stagings. Regarding group F, 9 cases showed a difference between RECIST obtained by CT and PERCIST obtained by PET/CT with most of the cases (6 cases) showed change from partial or stable response to progressive response.

Conclusion

PET/CT has a significant role in TNM staging of bronchogenic carcinoma more at T2 staging due to its ability to differentiate the tumoral mass from the nearby pulmonary reaction. Also, PET/CT makes a difference in tumoral follow-up by its ability to detect the functional changes even before structural changes. Finally, PET/CT is a very important tool in management strategy.

Diagnostic and Predictive Values of 18F-FDG PET/CT Metabolic Parameters in EGFR-Mutated Advanced Lung Adenocarcinoma

Purpose

The clinical implications of the metabolic parameters of ¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography (¹⁸F-FDG PET/CT) in epidermal growth factor receptor (EGFR)-mutated lung cancer are not fully understood. The aim of this study was to evaluate the diagnostic and prognostic utility of the parameters in EGFR-mutated lung cancer patients.

Patients and Methods

We retrospectively enrolled 134 patients with advanced lung adenocarcinoma (72 EGFR-negative and 62 EGFR-positive). We evaluated the correlation between EGFR mutational status and the maximum standardized uptake value (SUVmax), as well as the associations between treatment outcomes in EGFR-mutated patients and various metabolic parameters of primary tumors. For the best predictive parameters, we calculated the metabolic tumor volume (MTV) and total lesion glycolysis (TLG) using two SUV cutoffs: 1.5 (MTV_1.5, TLG_1.5) and 2.5 (MTV_2.5, TLG_2.5).

Results

Mean SUVmax was lower for EGFR-mutated tumors compared with EGFR wild-type (6.11 vs 10.41, p < 0.001) tumors. Low SUVmax was significantly associated with positive EGFR mutation (odds ratio = 1.74). Multivariate analysis for survival demonstrated that high MTV_1.5, TLG_1.5, MTV_2.5, and TLG_2.5 were independently associated with shorter progression-free survival (PFS) and overall survival (OS), and the highest hazard ratios were found in TLG_1.5 (3.26 for PFS and 4.62 for OS).

Conclusion

SUVmax may be predictive for EGFR mutational status, and MTV and TLG of primary tumors may be promising prognostic parameters; ¹⁸F-FDG PET/CT has potential utility for the risk stratification of EGFR-mutated patients treated with targeted therapy.

Predictive value of interim 18F-FDG-PET in patients with non-small cell lung cancer treated with definitive radiation therapy

PURPOSE

We evaluated that early metabolic response determined by 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) during radiotherapy (RT), predicts outcomes in non-small cell lung cancer.

MATERIAL AND METHODS

Twenty-eight patients evaluated using pretreatment 18F-FDG-PET/CT (PETpre) and interim 18F-FDG-PET/CT (PETinterim) after 11 fractions of RT were retrospectively reviewed. Maximum standardized uptake value (SUVmax) was calculated for primary lesion. Predictive value of gross tumor volume (ΔGTV) and SUVmax (ΔSUVmax) changes was evaluated for locoregional control (LRC), distant failure (DF), and overall survival (OS). Metabolic responders were patients with ΔSUVmax >40%.

RESULTS

Metabolic responders showed better trends in 1-year LRC (90.9%) than non-responders (47.1%) (p = 0.086). Patients with large GTVpre (≥120 cc) demonstrated poor LRC (hazard ratio 4.14, p = 0.022), while metabolic non-responders with small GTVpre (<120 cc) and metabolic responders with large GTVpre both had 1-year LRC rates of 75.0%. Reduction of 25% in GTV was not associated with LRC; however, metabolic responders without a GTV response showed better 1-year LRC (83.3%) than metabolic non-responders with a reduction in GTV (42.9%). Metabolic responders showed lower 1-year DF (16.7%) than non-responders (50.0%) (p = 0.025). An ΔSUVmax threshold of 40% yielded accuracy of 64% for predicting LRC, 75% for DF, and 54% for OS. However, ΔGTV > 25% demonstrated inferior diagnostic values than metabolic response.

CONCLUSIONS

Changes in tumor metabolism diagnosed using PETinterim during RT better predicted treatment responses, recurrences, and prognosis than other factors historically used.

Assessment of the role of Ga-68 PSMA I&T PET/CT in response evaluation to docetaxel therapy in castration resistant prostate cancer patients

OBJECTIVE

There have been only few studies investigating the role of PSMA ligands in the therapy response assessment of metastasized castration resistant prostate cancer (mCRPC) cases. In this study we aimed at evaluating the capability of ⁶⁸Ga- prostate-specific membrane antigen (PSMA) I&T positron emission tomography/computerized tomography (PET/CT) in the assessment of therapeutic response in patients under docetaxel therapy for prostate cancer (PCa).

MATERIAL AND METHODS

The clinical records of all mCRPC patients treated with docetaxel and referred to our department for ⁶⁸Ga-PSMA I&T PET/CT imaging were retrospectively analysed. Sixteen patients (mean age 69 years, range 52-82 years) with castration-resistant prostate cancer patients receiving palliative docetaxel therapy and had undergone ⁶⁸Ga-PSMA I&T PET/CT scan were included in the study. ⁶⁸Ga-PSMA I&T PET/CT imaging was done and prostate specific antigen (PSA) levels were measured at baseline before administration of docetaxel (PET1) and after at least 3 cycles (range 4-12) of chemotherapy (PET2). Patient-based as well as lesion-based comparison of PET2 findings with PET1 findings were done.

RESULTS

The change (decrease) observed in lymph node and prostate gland/prostatic bed SUVmax values after treatment compared to pretreatment was found to be statistically significant (P=.033). 3/16 patients (19%) were classified as progressive disease (PD), 4/16 (25%) as stable disease (SD), 9/16 (56%) as partial remission (PR) radiologically. An increasing PSA trend (IT) was observed in 4 patients (25%) and a decreasing PSA trend (DT) in 3 patients (18%). Nine patients showed a PSA response of ≥ 50% (56%). Of the 4 patients showing SD, 3 had IT, 3 had BR. Of the 9 patients who showed PR on PET studies, 8 patients showed BR and 1 patient showed DT.

CONCLUSION

Imaging with ⁶⁸Ga-PSMA PET/CT showed great concordance with biochemical response evaluation in terms of PSA levels, especially in patients showing good response to therapy. ⁶⁸Ga-PSMA PET/CT was also successful in identifying progressive disease in patients showing paradoxical decline in PSA levels.

Benefits of positron emission tomography scans for the evaluation of radiotherapy

The assessment of tumour response during and after radiotherapy determines the subsequent management of patients (adaptation of treatment plan, monitoring, adjuvant treatment, rescue treatment or palliative care). In addition to its role in extension assessment and therapeutic planning, positron emission tomography combined with computed tomography provides useful functional information for the evaluation of tumour response. The objective of this article is to review published data on positron emission tomography combined with computed tomography as a tool for evaluating external radiotherapy for cancers. Data on positron emission tomography combined with computed tomography scans acquired at different times (during, after initial and after definitive [chemo-]radiotherapy, during post-treatment follow-up) in solid tumours (lung, head and neck, cervix, oesophagus, prostate and rectum) were collected and analysed. Recent recommendations of the National Comprehensive Cancer Network are also reported. Positron emission tomography combined with computed tomography with (¹⁸F)-labelled fluorodeoxyglucose has a well-established role in clinical routine after chemoradiotherapy for locally advanced head and neck cancers, particularly to limit the number of neck lymph node dissection. This imaging modality also has a place for the evaluation of initial chemoradiotherapy of oesophageal cancer, including the detection of distant metastases, and for the post-therapeutic evaluation of cervical cancer. Several radiotracers for positron emission tomography combined with computed tomography, such as choline, are also recommended for patients with prostate cancer with biochemical failure. (¹⁸F)-fluorodeoxyglucose positron emission tomography combined with computed tomography is optional in many other circumstances and its clinical benefits, possibly in combination with MRI, to assess response to radiotherapy remain a very active area of research.

ICOS Is an Indicator of T-cell-Mediated Response to Cancer Immunotherapy

Immunotherapy is innovating clinical cancer management. Nevertheless, only a small fraction of patient's benefit from current immunotherapies. To improve clinical management of cancer immunotherapy, it is critical to develop strategies for response monitoring and prediction. In this study, we describe inducible T-cell costimulator (ICOS) as a conserved mediator of immune response across multiple therapy strategies. ICOS expression was evaluated by flow cytometry, ⁸⁹Zr-DFO-ICOS mAb PET/CT imaging was performed on Lewis lung cancer models treated with different immunotherapy strategies, and the change in tumor volume was used as a read-out for therapeutic response. ImmunoPET imaging of ICOS enabled sensitive and specific detection of activated T cells and early benchmarking of immune response. A STING (stimulator of interferon genes) agonist was identified as a promising therapeutic approach in this manner. The STING agonist generated significantly stronger immune responses as measured by ICOS ImmunoPET and delayed tumor growth compared with programmed death-1 checkpoint blockade. More importantly, ICOS ImmunoPET enabled early and robust prediction of therapeutic response across multiple treatment regimens. These data show that ICOS is an indicator of T-cell-mediated immune response and suggests ICOS ImmunoPET as a promising strategy for monitoring, comparing, and predicting immunotherapy success in cancer. SIGNIFICANCE: ICOS ImmunoPET is a promising strategy to noninvasively predict and monitor immunotherapy response.See related commentary by Choyke, p. 2975.

Surveillance With PET/CT and Liquid Biopsies of Stage I-III Lung Cancer Patients After Completion of Definitive Therapy: A Randomized Controlled Trial (SUPER)

Despite increased focus on prevention as well as improved treatment possibilities, lung cancer remains among the most frequent and deadliest cancer diagnoses worldwide. Even lung cancer patients treated with curative intent have a high risk of relapse, leading to a dismal prognosis. More knowledge on the efficacy of surveillance with both current and new technologies as well as on the impact on patient treatment, quality of life, and survival are urgently needed. We therefore designed a randomized phase 3 trial. In one arm, every other computed tomography (CT) scan is replaced by positron emission tomography/CT, the other arm is the standard follow-up scheme with CT. The standard arm is identical to the current national Danish follow-up program. The primary endpoint is to compare the number of relapses treatable with curative intent in the 2 arms. We aim to include 750 patients over a 3-year period. Additionally, we will test the feasibility of noninvasive lung cancer diagnostics and surveillance in the form of circulating tumor DNA analysis. For this purpose, blood samples are collected before treatment and at each following control. The blood samples are stored in a biobank for later analysis and will not be used for guiding patient treatment decisions.

18F-FDG PET/CT in Restaging and Evaluation of Response to Therapy in Lung Cancer: State of the Art

BACKGROUND

Metabolic information provided by 18F-FDG PET/CT are useful for initial staging, therapy planning, response evaluation, and to a lesser extent for the follow-up of non-small cell lung cancer (NSCLC). To date, there are no established clinical guidelines in treatment response and early detection of recurrence.

OBJECTIVE

To provide an overview of 18F-FDG PET/CT in NSCLC and in particular, to discuss its utility in treatment response evaluation and restaging of lung cancer.

METHODS

A comprehensive search was used based on PubMed results. From all studies published in English those that explored the role of 18F-FDG PET/CT in the treatment response scenario were selected.

RESULTS

Several studies have demonstrated that modifications in metabolic activity, expressed by changes in SUV both in the primary tumor as well as in regional lymph nodes, are associated with tumor response and survival. Beside SUV, other metabolic parameters (i.e. MTV, TLG, and percentage changes) are emerging to be helpful for predicting clinical outcomes.

CONCLUSION

18F-FDG parameters appear to be promising factors for evaluating treatment response and for detecting recurrences, although larger prospective trials are needed to confirm these evidences and to determine optimal cut-off values.

Prognostic factors of EGFR-mutated metastatic adenocarcinoma of lung

PURPOSE

The first-line treatment of metastatic lung adenocarcinoma with epidermal growth factor receptor (EGFR) mutation is tyrosine kinase inhibitor (TKI). This study aimed to evaluate potential factors affecting the progression-free survival under TKI treatment.

METHODS

Forty one patients with EGFR-mutated metastatic lung adenocarcinoma under first-line TKI treatment were retrospectively evaluated. Ten factors potentially influencing the progression-free survival were studied: patients' age, gender, smoking history, number of comorbidities, performance status, tumor mutation site, maximum of standardized uptake value (SUV_max) of primary tumor in FDG PET/CT, serum CEA level, number of metastatic organs and presence of pleural/pericardial effusion. Mantel-Cox tests and waterfall plots were performed for statistical analyses.

RESULTS

Statistical evaluation demonstrated that primary SUV_max, serum CEA level, gender and smoking history were important prognostic factors, with corresponding p values of 0.001, 0.023, 0.034 and 0.041 respectively in Mantel-Cox analyses.

CONCLUSION

Low primary SUV_max, low serum CEA level, female and never smoker were four prognostic factors suggestive of good response to TKI in mutated EGFR metastatic lung adenocarcinoma. SUV_max is probably the most important among the four factors.

Association of Receipt of Positron Emission Tomography-Computed Tomography With Non-Small Cell Lung Cancer Mortality in the Veterans Affairs Health Care System

IMPORTANCE

Positron emission tomography-computed tomography (PET-CT) has been increasingly used in the management of lung cancer, but its association with survival has not been convincingly documented.

OBJECTIVE

To examine the association of the use of PET-CT with non-small cell lung cancer (NSCLC) mortality in the US Department of Veterans Affairs (VA) health care system from 2000 to 2013.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included 64 103 veterans receiving care in the VA health care system who were diagnosed with incident NSCLC between September 2000 and December 2013. Data analysis took place in October 2018.

EXPOSURE

Use of PET-CT before and/or after diagnosis.

MAIN OUTCOMES AND MEASURES

All-cause and NSCLC-specific 5-year mortality; secondary outcome was receipt of stage-appropriate treatment.

RESULTS

A total of 64 103 veterans with the diagnosis of NSCLC were evaluated; 62 838 (98.0%) were men, and 50 584 (78.9%) were white individuals. Among these, 51 844 (80.9%) had a PET-CT performed: 25 735 (40.1%) in the 12 months before diagnosis and 41 242 (64.3%) in the 5 years after diagnosis. Increased PET-CT use (597 of 978 veterans [59.2%] in 2000 vs 3649 of 3915 [93.2%] in 2013) and decreased NSCLC-specific 5-year mortality (879 of 978 veterans [89.9%] in 2000 vs 3226 of 3915 veterans [82.4%] in 2013) were found over time. Increased use of stage-appropriate therapy was also seen over time, from 346 of 978 veterans (35.4%) in 2000 to 2062 of 3915 (52.7%) in 2013 (P < .001). Increased PET-CT use was associated with higher-complexity level VA facilities (26 127 veterans [82.3%] at level 1a vs 1289 [75.2%] at level 3 facilities; P < .001) and facilities with on-site PET-CT compared with facilities without on-site PET-CT (33 081 [82.2%] vs 17 443 [80.3%]; P < .001). Use of PET-CT before diagnosis was associated with increased likelihood of stage-appropriate treatment for all stages of NSCLC (eg, veterans with stage 1 disease: 4837 of 7870 veterans [61.5%] who received PET-CT underwent surgical resection vs 4042 of 7938 veterans [50.9%] who did not receive PET-CT; P < .001) and decreased mortality in a risk-adjusted model among all participants and among veterans undergoing stage-appropriate treatment (all-cause mortality: hazard ratio [HR], 0.78; 95% CI, 0.77-0.79; NSCLC-specific mortality: HR, 0.78; 95% CI, 0.76-0.80). Facilities with on-site PET-CT and higher-complexity level facilities were associated with a mortality benefit, with 16% decreased mortality at level 1a vs level 3 facilities (HR, 0.84; 95% CI, 0.80-0.89) and a 3% decrease in all-cause mortality in facilities with on-site PET-CT (HR, 0.97; 95% CI, 0.96-0.99).

CONCLUSIONS

In this study, the use of PET-CT among veterans with NSCLC significantly increased from 2000 to 2013, coinciding with decreased 5-year mortality and an increase in stage-appropriate treatment. Variation in use of PET-CT was found, with the highest use at higher-complexity level facilities and those with PET-CT on-site. These facilities were associated with reduced all-cause and NSCLC-specific mortality.