Perspectives on joint EANM/SNMMI/ANZSNM practice guidelines/procedure standards for [18F]FDG PET/CT imaging during immunomodulatory treatments in patients with solid tumors.

Response assessment in the context of immunomodulatory treatments represents a major challenge for the medical imaging community and requires a multidisciplinary approach with involvement of oncologists, radiologists, and nuclear medicine specialists. There is evolving evidence that [18F]FDG PET/CT is a useful diagnostic modality for this purpose. The clinical indications for, and the principal aspects of its standardization in this context have been detailed in the recently published "Joint EANM/SNMMI/ANZSNM practice guidelines/procedure standards on recommended use of [18F]FDG PET/CT imaging during immunomodulatory treatments in patients with solid tumors version 1.0". These recommendations arose from a fruitful collaboration between international nuclear medicine societies and experts in cancer treatment. In this perspective, the key elements of the initiative are reported, summarizing the core aspects of the guidelines for radiologists and nuclear medicine physicians. Beyond the previous guidelines, this perspective adds further commentary on how this technology can advance development of novel therapeutic approaches and guide management of individual patients.

Joint EANM/SNMMI/ANZSNM practice guidelines/procedure standards on recommended use of [18F]FDG PET/CT imaging during immunomodulatory treatments in patients with solid tumors version 1.0.

PURPOSE: The goal of this guideline/procedure standard is to assist nuclear medicine physicians, other nuclear medicine professionals, oncologists or other medical specialists for recommended use of [18F]FDG PET/CT in oncological patients undergoing immunotherapy, with special focus on response assessment in solid tumors. METHODS: In a cooperative effort between the EANM, the SNMMI and the ANZSNM, clinical indications, recommended imaging procedures and reporting standards have been agreed upon and summarized in this joint guideline/procedure standard. CONCLUSIONS: The field of immuno-oncology is rapidly evolving, and this guideline/procedure standard should not be seen as definitive, but rather as a guidance document standardizing the use and interpretation of [18F]FDG PET/CT during immunotherapy. Local variations to this guideline should be taken into consideration. PREAMBLE: The European Association of Nuclear Medicine (EANM) is a professional non-profit medical association founded in 1985 to facilitate worldwide communication among individuals pursuing clinical and academic excellence in nuclear medicine. The Society of Nuclear Medicine and Molecular Imaging (SNMMI) is an international scientific and professional organization founded in 1954 to promote science, technology and practical application of nuclear medicine. The Australian and New Zealand Society of Nuclear Medicine (ANZSNM), founded in 1969, represents the major professional society fostering the technical and professional development of nuclear medicine practice across Australia and New Zealand. It promotes excellence in the nuclear medicine profession through education, research and a commitment to the highest professional standards. EANM, SNMMI and ANZSNM members are physicians, technologists, physicists and scientists specialized in the research and clinical practice of nuclear medicine. All three societies will periodically put forth new standards/guidelines for nuclear medicine practice to help advance the science of nuclear medicine and improve service to patients. Existing standards/guidelines will be reviewed for revision or renewal, as appropriate, on their fifth anniversary or sooner, if indicated. Each standard/guideline, representing a policy statement by the EANM/SNMMI/ANZSNM, has undergone a thorough consensus process, entailing extensive review. These societies recognize that the safe and effective use of diagnostic nuclear medicine imaging requires particular training and skills, as described in each document. These standards/guidelines are educational tools designed to assist practitioners in providing appropriate and effective nuclear medicine care for patients. These guidelines are consensus documents based on current knowledge. They are not intended to be inflexible rules or requirements of practice, nor should they be used to establish a legal standard of care. For these reasons and those set forth below, the EANM, SNMMI and ANZSNM caution against the use of these standards/guidelines in litigation in which the clinical decisions of a practitioner are called into question. The ultimate judgment regarding the propriety of any specific procedure or course of action must be made by medical professionals considering the unique circumstances of each case. Thus, there is no implication that an action differing from what is laid out in the guidelines/procedure standards, standing alone, is below standard of care. To the contrary, a conscientious practitioner may responsibly adopt a course of action different from that set forth in the standards/guidelines when, in the reasonable judgment of the practitioner, such course of action is indicated by the condition of the patient, limitations of available resources or advances in knowledge or technology subsequent to publication of the guidelines/procedure standards. The practice of medicine involves not only the science, but also the art of dealing with the prevention, diagnosis, alleviation and treatment of disease. The variety and complexity of human conditions make it impossible for general guidelines to consistently allow for an accurate diagnosis to be reached or a particular treatment response to be predicted. Therefore, it should be recognized that adherence to these standards/ guidelines will not ensure a successful outcome. All that should be expected is that practitioners follow a reasonable course of action, based on their level of training, current knowledge, clinical practice guidelines, available resources and the needs/context of the patient being treated. The sole purpose of these guidelines is to assist practitioners in achieving this objective. The present guideline/procedure standard was developed collaboratively by the EANM, the SNMMI and the ANZSNM, with the support of international experts in the field. They summarize also the views of the Oncology and Theranostics and the Inflammation and Infection Committees of the EANM, as well as the procedure standards committee of the SNMMI, and reflect recommendations for which the EANM and SNMMI cannot be held responsible. The recommendations should be taken into the context of good practice of nuclear medicine and do not substitute for national and international legal or regulatory provisions.

Risk of relapse after anti-PD1 discontinuation in patients with Hodgkin lymphoma.

INTRODUCTION: Patients with relapsed/refractory Hodgkin lymphoma (R/R HL) experience high response rates upon anti-PD1 therapy. In these patients, the optimal duration of treatment and the risk of relapse after anti-PD1 discontinuation are unknown. METHODS: We retrospectively analyzed patients with R/R HL who responded to anti-PD1 monotherapy and discontinued the treatment either because of unacceptable toxicity or prolonged remission. A machine learning algorithm based on 17 candidate variables was trained and validated to predict progression-free survival (PFS) landmarked at the time of discontinuation of anti-PD1 therapy. RESULTS: Forty patients from 14 centers were randomly assigned to training (n = 25) and validation (n = 15) sets. At the time of anti-PD1 discontinuation, patients had received treatment for a median duration of 11.2 (range, 0-time to best response was not statistically significant in discriminating patients with PFS lesser or greater than 12 months). Considering PFS status as a binary variable (alive or dead) at a specific time point (12 months) is convenient, intuitive and allows for comparing the value of potential predicting variables in these two groups of patients. Nonetheless, this approach has two drawbacks: first, it binarizes outcome; second, it excludes patients alive with a time to last follow up lesser 12 months. Therefore, it is less powerful to demonstrate statistically significant association with PFS even if it exists 5 months. Patients discontinued anti-PD1 treatment either because of prolonged remission (N = 27, 67.5%) or unacceptable toxicity (N = 13, 32.5%). Most patients were in CR (N = 35, 87.5%) at the time of anti-PD1 discontinuation. In the training set, the machine learning algorithm identified that the most important variables to predict PFS were patients' age, time to best response, and presence or absence of CR. The performance observed in the training set was validated in the validation set. CONCLUSION: In this pilot, proof of concept study using a machine learning algorithm, we identified biomarkers capable of predicting the risk of relapse after anti-PD1 discontinuation (age, time to best response, quality of response). Once confirmed, these simple biomarkers will represent useful tools to guide the management of these patients.

A Simple Formula to Estimate Parathyroid Weight on 4D-CT, Predict Pathologic Weight, and Diagnose Parathyroid Adenoma in Patients with Primary Hyperparathyroidism.

BACKGROUND AND PURPOSE: Parathyroid gland weight is a clinically relevant parameter used to diagnose parathyroid adenomas intraoperatively. We evaluated the accuracy of a formula to estimate parathyroid weight on preoperative 4D-CT. MATERIALS AND METHODS: A single-institution retrospective study was performed in patients with primary hyperparathyroidism who underwent 4D-CT between January 2013 and December 2014 with subsequent parathyroidectomy and surgical cure. All patients had correct localization of a solitary parathyroid adenoma. The longest 3 dimensions of all identified parathyroid glands were measured on CT, and weight was estimated using the formula: weight4D-CT (mg) = 1 mg/mm3 × Length (mm) × Width (mm) × Height (mm) × π/6. We correlated weight4D-CT with pathology specimen weight (weightpathology). Using receiver operating characteristic analysis, we estimated the performance of weight4D-CT to discriminate a parathyroid adenoma from normal glands on 4D-CT and determined the optimal threshold based on the Youden index. RESULTS: One hundred sixteen patients (85 women, 31 men) were evaluated. Weight4D-CT was shown to be strongly correlated with weightpathology as demonstrated by Spearman ρ = 0.73 (P < .01), concordance correlation coefficient = 0.92 (95% CI, 0.89-0.94), and Cronbach α = 0.96. The performance of weight4D-CT for the diagnosis of parathyroid adenoma was excellent, with an area under the curve of 0.955 (95% CI, 0.925-0.985; P < .001). Based on the Youden index, the optimal threshold was >50 mg, with a sensitivity of 96.7% and a specificity of 95.7%. CONCLUSIONS: Radiologists can accurately estimate parathyroid adenoma weight on 4D-CT. This metric is highly correlated with pathologic weight, and a threshold cutoff of >50 mg can be used to distinguish parathyroid adenoma from normal glands.

[Computational medical imaging (radiomics) and potential for immuno-oncology]. / Imagerie médicale computationnelle (radiomique) et potentiel en immuno-oncologie.

The arrival of immunotherapy has profoundly changed the management of multiple cancers, obtaining unexpected tumour responses. However, until now, the majority of patients do not respond to these new treatments. The identification of biomarkers to determine precociously responding patients is a major challenge. Computational medical imaging (also known as radiomics) is a promising and rapidly growing discipline. This new approach consists in the analysis of high-dimensional data extracted from medical imaging, to further describe tumour phenotypes. This approach has the advantages of being non-invasive, capable of evaluating the tumour and its microenvironment in their entirety, thus characterising spatial heterogeneity, and being easily repeatable over time. The end goal of radiomics is to determine imaging biomarkers as decision support tools for clinical practice and to facilitate better understanding of cancer biology, allowing the assessment of the changes throughout the evolution of the disease and the therapeutic sequence. This review will develop the process of computational imaging analysis and present its potential in immuno-oncology.

Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology.

Medical image processing and analysis (also known as Radiomics) is a rapidly growing discipline that maps digital medical images into quantitative data, with the end goal of generating imaging biomarkers as decision support tools for clinical practice. The use of imaging data from routine clinical work-up has tremendous potential in improving cancer care by heightening understanding of tumor biology and aiding in the implementation of precision medicine. As a noninvasive method of assessing the tumor and its microenvironment in their entirety, radiomics allows the evaluation and monitoring of tumor characteristics such as temporal and spatial heterogeneity. One can observe a rapid increase in the number of computational medical imaging publications-milestones that have highlighted the utility of imaging biomarkers in oncology. Nevertheless, the use of radiomics as clinical biomarkers still necessitates amelioration and standardization in order to achieve routine clinical adoption. This Review addresses the critical issues to ensure the proper development of radiomics as a biomarker and facilitate its implementation in clinical practice.