PET/Computed Tomography in Treatment Response Assessment in Cancer

Organ-specific accuracy of [18F]FDG-PET/CT in identifying immune-related adverse events in patients with high-risk melanoma treated with adjuvant immune checkpoint inhibitor

PURPOSE

This study aimed to determine the organ-specific accuracy of [18F]FDG-PET/CT in identifying immune-related adverse events (irAEs) in patients with high-risk (stage III/IV) surgically resected melanoma treated with an adjuvant immune checkpoint inhibitor (ICI) and determine the incidence of irAEs within the first year after starting treatment.

MATERIALS AND METHODS

This registry-based study included individuals who had undergone surgical removal of melanoma and were undergoing adjuvant ICI treatment (either nivolumab or pembrolizumab). The study specifically enrolled patients who had undergone both a baseline and at least one subsequent follow-up [18F]FDG-PET/CT scan. Follow-up scans were performed every third month in the first year after surgery to screen for disease recurrence. We retrospectively compared the follow-up scans with baseline scans to identify irAEs. Clinical information on irAEs was obtained from medical records and served as a reference standard for determining the accuracy of [18F]FDG-PET/CT.

RESULTS

A total of 123 patients with 363 [18F]FDG-PET/CT scans were included, and 65 patients (52.8%) developed irAEs. In decreasing order, the organ-specific incidences of irAEs were: skin 26/65 (40%), muscle and joints 21/65 (32.3%), intestines 13/65 (20%), thyroid gland 12/65 (18.5%), lungs 4/65 (6.2%), and heart 2/65 (3.1%). The sensitivities and specificities of [18F]FDG-PET/CT for diagnosing irAEs were: skin 19% (95% CI: 7-39%) and 95% (88-98%), muscles and joints 71% (48-89%) and 83% (75-90%), intestines 100% (75-100%) and 85% (77-91%); thyroid gland 92% (62-99%) and 95% (89-98%), lungs 75% (19-99%) and 90% (83-95%), and heart 50% (13-99%) and 97% (92-99%), respectively.

CONCLUSION

[18F]FDG-PET/CT generally had moderate to high sensitivities (except for skin and heart) and specificities in diagnosing irAEs in patients receiving adjuvant ICI; this could be suggested to be systematically assessed and reported in scan reports.

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.

PET-CTBased Quantitative Parameters for Assessment of Treatment Response and Disease Activity in Cancer and Noncancerous Disorders

The various semiquantitative and quantitative PET-CT parameters provide measurement of disease activity and assessment of treatment response in the PET-CT studies. These include standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG), and total metabolic tumor volume (TMTV). Thresholding and adaptive thresholding methods are commonly used algorithms for the evaluation of global disease activity. Readily available commercial software frequently in-built with the current generation PET-CT scanners for providing easy, less time consuming, highly reproducible, and more accurate measurement of global disease activity on PET-CT imaging in evaluation of malignant as well as benign disorders.

Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

Reassessing Patterns of Response to Immunotherapy with PET: From Morphology to Metabolism

Cancer demands precise evaluation and accurate and timely assessment of response to treatment. Imaging must be performed early during therapy to allow adjustments to the course of treatment. For decades, cross-sectional imaging provided these answers, showing responses to the treatment through changes in tumor size. However, with the emergence of immune checkpoint inhibitors, complex immune response patterns were revealed that have quickly highlighted the limitations of this approach. Patterns of response beyond tumor size have been recognized and include cystic degeneration, necrosis, hemorrhage, and cavitation. Furthermore, new unique patterns of response have surfaced, like pseudoprogression and hyperprogression, while other patterns were shown to be deceptive, such as unconfirmed progressive disease. This evolution led to new therapeutic evaluation criteria adapted specifically for immunotherapy. Moreover, inflammatory adverse effects of the immune checkpoint blockade were identified, many of which were life threatening and requiring prompt intervention. Given complex concepts like tumor microenvironment and novel therapeutic modalities in the era of personalized medicine, increasingly sophisticated imaging techniques are required to address the intricate patterns of behavior of different neoplasms. Fluorine 18-fluorodeoxyglucose PET/CT has rapidly emerged as one such technique that spans both molecular biology and immunology. This imaging technique is potentially capable of identifying and tracking prognostic biomarkers owing to its combined use of anatomic and metabolic imaging, which enables it to characterize biologic processes in vivo. This tailored approach may provide whole-body quantification of the metabolic burden of disease, providing enhanced prediction of treatment response and improved detection of adverse events. ^©RSNA, 2020.

Assessing Immunotherapy with Functional and Molecular Imaging and Radiomics

Immunotherapy is changing the treatment paradigm for cancer and has introduced new challenges in medical imaging. Because not all patients benefit from immunotherapy, pretreatment imaging should be performed to identify not only prognostic factors but also factors that allow prediction of response to immunotherapy. Follow-up studies must allow detection of nonresponders, without confusion of pseudoprogression with real progression to prevent premature discontinuation of treatment that can benefit the patient. Conventional imaging techniques and classic tumor response criteria are limited for the evaluation of the unusual patterns of response that arise from the specific mechanisms of action of immunotherapy, so advanced imaging methods must be developed to overcome these shortcomings. The authors present the fundamentals of the tumor immune microenvironment and immunotherapy and how they influence imaging findings. They also discuss advances in functional and molecular imaging techniques for the assessment of immunotherapy in clinical practice, including their use to characterize immune phenotypes, assess patient prognosis and response to therapy, and evaluate immune-related adverse events. Finally, the development of radiomics and radiogenomics in these therapies and the future role of imaging biomarkers for immunotherapy are discussed. Online supplemental material is available for this article. ^©RSNA, 2020.