ENETS standardized (synoptic) reporting for molecular imaging studies in neuroendocrine tumours

Dotatate PET/CT and 225Ac-Dotatate Therapy for Somatostatin Receptor-expressing Metastatic Breast Cancer

Background Somatostatin receptors, and specifically somatostatin receptor type 2 (SSTR2), have primarily been associated with neuroendocrine tumors and have revolutionized the imaging and therapy of patients with these tumors. SSTR2 is expressed on other tumors at lower prevalence. Purpose To evaluate the potential of SSTR2-targeted imaging and therapy in patients with breast cancer. Materials and Methods In a preclinical experiment, SSTR2 expression was assessed in tissue microarrays of breast cancer samples using H-score analysis. H-scores higher than 50 (0-300 scale) were considered positive. Then, a prospective phase 2 clinical trial of SSTR2-targeted tetraazacyclododecane tetraacetic acid octreotate (Dotatate) PET/CT was performed in participants with biopsy-proven estrogen receptor (ER)-positive breast cancer from January to August 2023. A positive Dotatate PET/CT scan was defined as tumors with a Krenning score of 3 (avidity greater than liver) or 4 (avidity greater than spleen). The proportion of positive scans and the 95% CI were calculated. One participant with metastatic ER-positive breast cancer and a Krenning 4 Dotatate PET/CT result underwent treatment with SSTR2-targeted actinium 225 (225Ac) Dotatate. Results Preclinical microarrays demonstrated that 63 of 123 ER-positive breast cancer tissue samples (51% [95% CI: 42, 60]) but only 22 of 121 ER-negative breast cancer tissue samples (18% [95% CI: 12, 26]) were enriched for SSTR2 (P < .001). Thirty female participants (mean age, 66 years ± 15) with metastatic ER-positive breast cancer were accrued to the phase 2 SSTR2-targeted imaging trial and underwent Dotatate PET/CT. Dotatate PET/CT demonstrated that nine of 30 participants (30% [95% CI: 15, 49]) had tumors with Krenning scores of 3 or 4, indicating strong SSTR2 expression. SSTR2-targeted therapy with alpha-emitting 225Ac-Dotatate resulted in a near complete response in a heavily pretreated participant with metastatic ER-positive breast cancer and a Krenning 4 Dotatate PET result. Conclusion Molecular imaging targeting SSTR2 and radioligand therapy with SSTR2-targeted 225Ac-Dotatate enables a new therapeutic option for patients with metastatic breast cancer. Clinical trial registration no. NCT05880394 © RSNA, 2024 See also the editorial by Lin and Choyke in this issue.

Recent advances and future challenges in the diagnosis of neuroendocrine neoplasms

Neuroendocrine neoplasms (NEN) are a heterogeneous group of malignancies with increasing incidence, whose diagnosis is usually delayed, negatively impacting on patients' prognosis. The latest advances in pathological classifications, biomarker identification and imaging techniques may provide early detection, leading to personalized treatment strategies. In this narrative review the recent developments in diagnosis of NEN are discussed including progresses in pathological classifications, biomarker and imaging. Furthermore, the challenges that lie ahead are investigated. By discussing the limitations of current approaches and addressing potential roadblocks, we hope to guide future research directions in this field. This article is proposed as a valuable resource for clinicians and researchers involved in the management of NEN. Update of pathological classifications and the availability of standardized templates in pathology and radiology represent a substantially improvement in diagnosis and communication among clinicians. Additional immunohistochemistry markers may now enrich pathological classifications, as well as miRNA profiling. New and multi-analytical circulating biomarkers, as liquid biopsy and NETest, are being proposed for diagnosis but their validation and availability should be improved. Radiological imaging strives for precise, non-invasive and less harmful technique to improve safety and quality of life in NEN patient. Nuclear medicine may benefit of somatostatin receptors' antagonists and membrane receptor analogues. Diagnosis in NEN still represents a challenge due to their complex biology and variable presentation. Further advancements are necessary to obtain early and minimally invasive diagnosis to improve patients' outcomes.

Management of patients with rare adult solid cancers: objectives and evaluation of European reference networks (ERN) EURACAN

About 500,000 patients with rare adult solid cancers (RASC) are diagnosed yearly in Europe. Delays and unequal quality of management impact negatively their survival. Since 2017, European reference networks (ERN) aim to improve the quality of care of patients with rare disease. The steering committee of EURACAN, including physicians, researchers and patients review here the previous actions, present objectives of the ERN EURACAN dedicated to RASC. EURACAN promoted management in reference centres, and equal implementation of excellence and innovation in Europe and developed 22 clinical practice guidelines (CPGs). Additionally, fourteen information brochures translated in 24 EU languages were developed in collaboration with patient advocacy groups (ePAGs) and seventeen training session were organized. Nevertheless, connections to national networks in the 26 participating countries (106 centres), simplification of cross-border healthcare, international multidisciplinary tumour boards, registries and monitoring of the quality of care are still required. In this Health Policy, evaluation criteria of the performances of the network and of health care providers are proposed.

Quantitative uptake in 99m Tc-EDDA/HYNIC-TOC somatostatin receptor imaging - the effect of long-acting release somatostatin analogue therapy

PURPOSE

Withdrawal of long-acting release somatostatin analogue (LAR-SSA) treatment before somatostatin receptor imaging is based on empirical reasoning that it may block uptake at receptor sites. This study aims to quantify differences in uptake of 99m Tc-EDDA/HYNIC-TOC between patients receiving LAR-SSA and those who were not.

METHODS

Quantification of 177 patients (55 on LAR-SSA) imaged with 99m Tc-EDDA/HYNIC-TOC was performed, with analysis of pathological tissue and organs with physiological uptake using thresholded volumes of interest. Standardised uptake values (SUVs) and tumour/background (T/B) ratios were calculated and compared between the two patient groups.

RESULTS

SUVs were significantly lower for physiological organ uptake for patients on LAR-SSA (e.g. spleen: SUV max 13.3 ± 5.9 versus 33.9 ± 9.0, P  < 0.001); there was no significant difference for sites of pathological uptake (e.g. nodal metastases: SUV max 19.2 ± 13.0 versus 17.4 ± 11.5, P  = 0.552) apart from bone metastases (SUV max 14.1 ± 13.5 versus 7.7 ± 8.0, P  = 0.017) where it was significantly higher.

CONCLUSION

LAR-SSA has an effect only on physiological organ uptake of 99m Tc-EDDA/HYNIC-TOC, reducing uptake. It has no significant effect on pathological uptake for most sites of primary and metastatic disease. This should be taken into account if making quantitative measurements, calculating T/B ratios or assigning Krenning Scores. There is the potential for improved dosimetric results in Peptide Receptor Radionuclide Therapy by maintaining patients on LAR-SSA.

Artificial Intelligence in Oncological Hybrid Imaging

BACKGROUND

 Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.

METHODS AND RESULTS

 The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.

CONCLUSION

 AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.

KEY POINTS

  · Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..

Validation of the SSTR-RADS 1.0 for the structured interpretation of SSTR-PET/CT and treatment planning in neuroendocrine tumor (NET) patients

OBJECTIVES

The recently proposed standardized reporting and data system for somatostatin receptor (SSTR)-targeted PET/CT SSTR-RADS 1.0 showed promising first results in the assessment of diagnosis and treatment planning with peptide receptor radionuclide therapy (PRRT) in neuroendocrine tumors (NET). This study aimed to determine the intra- and interreader agreement of SSTR-RADS 1.0.

METHODS

SSTR-PET/CT scans of 100 patients were independently evaluated by 4 readers with different levels of expertise according to the SSTR-RADS 1.0 criteria at 2 time points within 6 weeks. For each scan, a maximum of five target lesions were freely chosen by each reader (not more than three lesions per organ) and stratified according to the SSTR-RADS 1.0 criteria. Overall scan score and binary decision on PRRT were assessed. Intra- and interreader agreement was determined using the intraclass correlation coefficient (ICC).

RESULTS

Interreader agreement using SSTR-RADS 1.0 for identical target lesions (ICC ≥ 0.91) and overall scan score (ICC ≥ 0.93) was excellent. The decision to state "functional imaging fulfills requirements for PRRT and qualifies patient as potential candidate for PRRT" also demonstrated excellent agreement among all readers (ICC ≥ 0.86). Intrareader agreement was excellent even among different experience levels when comparing target lesion-based scores (ICC ≥ 0.98), overall scan score (ICC ≥ 0.93), and decision for PRRT (ICC ≥ 0.88).

CONCLUSION

SSTR-RADS 1.0 represents a highly reproducible and accurate system for stratifying SSTR-targeted PET/CT scans with high intra- and interreader agreement. The system is a promising approach to standardize the diagnosis and treatment planning in NET patients.

KEY POINTS

• SSTR-RADS 1.0 offers high reproducibility and accuracy. • SSTR-RADS 1.0 is a promising method to standardize diagnosis and treatment planning for patients with NET.

Artificial Intelligence in Oncological Hybrid Imaging

BACKGROUND

Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.

METHODS AND RESULTS

The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.

CONCLUSION

AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.

KEY POINTS

· Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..

CITATION FORMAT

· Feuerecker B, Heimer M, Geyer T et al. Artificial Intelligence in Oncological Hybrid Imaging. Fortschr Röntgenstr 2023; 195: 105 - 114.

ENETS standardized (synoptic) reporting for neuroendocrine tumour pathology

In recent years the WHO classification of neuroendocrine neoplasms (NEN) has evolved. Nomenclature as well as thresholds for grading have changed leading to potential confusion and lack of comparability of tumour reports. Therefore, the European Neuroendocrine Tumour Society (ENETS) has set-up an interdisciplinary working group to develop templates for a pathology data set for standardised reporting of NEN. Experts of various disciplines, members of the ENETS Advisory Board, formed a taskforce that discussed and decided on the structure, content and the number of templates needed for reporting the most common NEN. The selection of the required items was based on the WHO classification of digestive system tumours, the WHO classification of tumours of the lung and mediastinum and on "ENETS standard of care" reports. The final proposal of the working group was approved by the ENETS Advisory Board. Templates for synoptic reporting were created for the seven most common NEN primary sites, that is, stomach, duodenum, jejunum-ileum, appendix, colon-rectum, pancreas, lung and mediastinum. In addition, a general template for reporting biopsies was designed. The templates allow the recording of the essential items on differentiation, proliferation (Ki-67 and mitosis), neuroendocrine features (positivity for chromogranin A and synaptophysin) and stage as well as several optional markers especially helpful for the distinction of neuroendocrine tumours (NET) from neuroendocrine carcinomas (NEC). In summary, this paper presents the content and development of synoptic reports for most sites of NEN by a multidisciplinary team of international experts in the field, which could help to improve unambiguous reporting of NEN.

ENETS standardized (synoptic) reporting for endoscopy in neuroendocrine tumors

Despite efforts from various endoscopy societies, reporting in the field of endoscopy remains extremely heterogeneous. Harmonisation of clinical practice in endoscopy has been highlighted by application of many clinical practice guidelines and standards pertaining to the endoscopic procedures and reporting are underlined. The aim of the proposed "standardised reporting" is to (1) facilitate recognition of gastrointestinal neuroendocrine neoplasms (NEN) on initial endoscopy, (2) to enable interdisciplinary decision making for treatment by a multidisciplinary team, (3) to provide a basis for a standardised endoscopic follow-up which allows detection of recurrence or progression reliably, (4) to make endoscopic reports on NEN comparable between different units, and (5) to allow research collaboration between NEN centres in terms of consistency of their endoscopic data. The ultimate goal is to improve disease management, patient outcome and reduce the diagnostic burden on the side of the patient by ensuring the highest possible diagnostic accuracy and validity of endoscopic exams and possibly interventions.

ENETS standardized (synoptic) reporting for radiological imaging in neuroendocrine tumours

This expert consensus document represents an initiative by the European Neuroendocrine Tumor Society (ENETS) to provide guidance for synoptic reporting of radiological examinations critical to the diagnosis, grading, staging and treatment of neuroendocrine neoplasms (NENs). Template drafts for initial tumor staging and follow-up by computed tomography (CT) and magnetic resonance imaging (MRI) were established, based on existing institutional and organisational reporting templates relevant for NEN imaging, and applying the RadLex lexicon of radiological information (Radiological Society of North America), for consistency regarding the radiological terms. During the ENETS Scientific Advisory Board meeting 2018, the template drafts were subject to iterative interdisciplinary discussions among experts in imaging, surgery, gastroenterology, oncology and pathology. Members of the imaging group stated a strong preference for a combination of limited and standardised options by way of drop-down menus. Separate templates were produced for the initial work-up and for follow-up, respectively. To provide a detailed description of the radiological findings of the primary tumor and its local extension and spread, different templates were developed for bronchial, pancreatic and gastrointestinal NENs for CT and MRI, respectively. Each template was structured in 10 sections: clinical details, comparative imaging modality, acquisition technique, primary tumor findings, regional lymph node metastases, distant metastases, TNM classification, reference lesions according to RECIST 1.1, additional findings and conclusion. Two templates were developed for follow-up, for CT and MRI, respectively, and were specifically focused on assessment of therapy response. These included a qualitative response assessment, such as decrease of vascularisation and presence of necrosis, and a quantitative assessment according to RECIST 1.1 and the modified RECIST (mRECIST) for assessing tumor response following transarterial chemoembolisation.

Synoptic reporting of echocardiography in carcinoid heart disease (ENETS Carcinoid Heart Disease Task Force)

BACKGROUND

This European Neuroendocrine Tumor Society (ENETS) Expert Consensus document aims to provide practical guidance and standardization for echocardiography in the screening and follow-up of carcinoid heart disease (CHD) in patients with a neuroendocrine tumour (NET) and carcinoid syndrome.

METHODS

NET experts within the ENETS Carcinoid Heart Disease Task Force reviewed both general reporting guidelines and specialized scoring systems for transthoracic echocardiography (TTE) in CHD. Based on this review, a dedicated template report was designed by the multidisciplinary working group of cardiologists, oncologists, endocrinologists, gastroenterologists, surgeons and radiologists.

RESULTS

We propose a Synoptic Reporting of Echocardiography in Carcinoid Heart Disease which represents an agreed peer reviewed proforma to capture information at the time of referral and enable a detailed outcome of CHD assessment. This includes a systematic and detailed list of structures to evaluate data to capture at the time of reporting of TTE.

CONCLUSIONS

Adherence to these reporting guidelines aims to promote homogeneous and detailed evaluation of CHD to secure accurate assessment and allow comparison of studies performed intra- and inter-individually. These guidelines could also facilitate CHD assessment as part of prospective clinical trials to enable standardization of the findings seen in response to therapy.

Radiolabeled Somatostatin Analogues for Diagnosis and Treatment of Neuroendocrine Tumors

Simple Summary

Neuroendocrine neoplasms (NENs) are rare and heterogeneous tumors, presenting in often challenging clinical scenarios, and require multidisciplinary discussion for optimal care. The theranostic approach (DOTA peptides labelled with ⁶⁸Ga for imaging well-differentiated neuroendocrine tumors NETs, and labelled with ⁹⁰Y or ¹⁷⁷Lu for therapy) plays a crucial role in the management of NENs to assess disease extension and criteria for peptide receptor radionuclide therapy (PRRT) eligibility of based on somatostatin receptor (SSTR) expression. The present paper is an overview of currently employed radiolabeled SSTR analogues used for both diagnosis and therapy of NENs. Further emerging radiopharmaceuticals targeting SSTRs (e.g., fluorinated SSTR agonists, radiolabeled SSTR antagonists) as well as strategies to improve PRRT efficacy (by means of implementation of personalized treatment schemes, dosimetry, amelioration of response assessment strategies, and optimization of treatment sequencing) are also discussed. Finally, although very preliminary, some studies employing radiomic features in various kinds of NET are reported.

Abstract

Neuroendocrine neoplasms (NENs) are rare and heterogeneous tumors that require multidisciplinary discussion for optimal care. The theranostic approach (DOTA peptides labelled with ⁶⁸Ga for diagnosis and with ⁹⁰Y or ¹⁷⁷Lu for therapy) plays a crucial role in the management of NENs to assess disease extension and as a criteria for peptide receptor radionuclide therapy (PRRT) eligibility based on somatostatin receptor (SSTR) expression. On the diagnostic side, [⁶⁸Ga]Ga-DOTA peptides PET/CT (SSTR PET/CT) is the gold standard for imaging well-differentiated SSTR-expressing neuroendocrine tumors (NETs). [¹⁸F]FDG PET/CT is useful in higher grade NENs (NET G2 with Ki-67 > 10% and NET G3; NEC) for more accurate disease characterization and prognostication. Promising emerging radiopharmaceuticals include somatostatin analogues labelled with ¹⁸F (to overcome the limits imposed by ⁶⁸Ga), and SSTR antagonists (for both diagnosis and therapy). On the therapeutic side, the evidence gathered over the past two decades indicates that PRRT is to be considered as an effective and safe treatment option for SSTR-expressing NETs, and is currently included in the therapeutic algorithms of the main scientific societies. The positioning of PRRT in the treatment sequence, as well as treatment personalization (e.g., tailored dosimetry, re-treatment, selection criteria, and combination with other alternative treatment options), is warranted in order to improve its efficacy while reducing toxicity. Although very preliminary (being mostly hampered by lack of methodological standardization, especially regarding feature selection/extraction) and often including small patient cohorts, radiomic studies in NETs are also presented. To date, the implementation of radiomics in clinical practice is still unclear. The purpose of this review is to offer an overview of radiolabeled SSTR analogues for theranostic use in NENs.