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Vocino Trucco G, Righi L, Volante M, Papotti M. Updates on lung neuroendocrine neoplasm classification. Histopathology 2024; 84:67-85. [PMID: 37794655 DOI: 10.1111/his.15058] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 09/08/2023] [Accepted: 09/14/2023] [Indexed: 10/06/2023]
Abstract
Lung neuroendocrine neoplasms (NENs) are a heterogeneous group of pulmonary neoplasms showing different morphological patterns and clinical and biological characteristics. The World Health Organisation (WHO) classification of lung NENs has been recently updated as part of the broader attempt to uniform the classification of NENs. This much-needed update has come at a time when insights from seminal molecular characterisation studies revolutionised our understanding of the biological and pathological architecture of lung NENs, paving the way for the development of novel diagnostic techniques, prognostic factors and therapeutic approaches. In this challenging and rapidly evolving landscape, the relevance of the 2021 WHO classification has been recently questioned, particularly in terms of its morphology-orientated approach and its prognostic implications. Here, we provide a state-of-the-art review on the contemporary understanding of pulmonary NEN morphology and the potential contribution of artificial intelligence, the advances in NEN molecular profiling with their impact on the classification system and, finally, the key current and upcoming prognostic factors.
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Affiliation(s)
| | - Luisella Righi
- Department of Oncology, University of Turin, Turin, Italy
| | - Marco Volante
- Department of Oncology, University of Turin, Turin, Italy
| | - Mauro Papotti
- Department of Oncology, University of Turin, Turin, Italy
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Mirshahvalad SA, Eisazadeh R, Shahbazi-Akbari M, Pirich C, Beheshti M. Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [ 18F]F-FDG Tracers - Part I. PSMA, Choline, and DOTA Radiotracers. Semin Nucl Med 2024; 54:171-180. [PMID: 37752032 DOI: 10.1053/j.semnuclmed.2023.08.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 08/29/2023] [Indexed: 09/28/2023]
Abstract
Artificial intelligence (AI) has evolved significantly in the past few decades. This thriving trend has also been seen in medicine in recent years, particularly in the field of imaging. Machine learning (ML), deep learning (DL), and their methods (eg, SVM, CNN), as well as radiomics, are the terminologies that have been introduced to this field and, to some extent, become familiar to the expert clinicians. PET is one of the modalities that has been enhanced via these state-of-the-art algorithms. This robust imaging technique further merged with anatomical modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), to provide reliable hybrid modalities, PET/CT and PET/MRI. Applying AI-based algorithms on the different components (PET, CT, and MRI) has resulted in promising results, maximizing the value of PET imaging. However, [18F]F-FDG, the most commonly utilized tracer in molecular imaging, has been mainly in the spotlight. Thus, we aimed to look into the less discussed tracers in this review, moving beyond [18F]F-FDG. The novel non-[18F]F-FDG agents also showed to be valuable in various clinical tasks, including lesion detection and tumor characterization, accurate delineation, and prognostic impact. Regarding prostate patients, PSMA-based models were highly accurate in determining tumoral lesions' location and delineating them, particularly within the prostate gland. However, they also could assess whole-body images to detect extra-prostatic lesions in a patient automatically. Considering the prognostic value of prostate-specific membrane antigen (PSMA) PET using AI, it could predict response to treatment and patient survival, which are crucial in patient management. Choline imaging, another non-[18F]F-FDG tracer, similarly showed acceptable results that may be of benefit in the clinic, though the current evidence is significantly more limited than PSMA. Lastly, different subtypes of DOTA ligands were found to be valuable. They could diagnose tumoral lesions in challenging sites and even predict histopathology grade, being a highly advantageous noninvasive tool. In conclusion, the current limited investigations have shown promising results, leading us to a bright future for AI in molecular imaging beyond [18F]F-FDG.
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Affiliation(s)
- Seyed Ali Mirshahvalad
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada
| | - Roya Eisazadeh
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Malihe Shahbazi-Akbari
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research Center for Nuclear Medicine, Department of Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Christian Pirich
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Mohsen Beheshti
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
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Gålne A, Enqvist O, Sundlöv A, Valind K, Minarik D, Trägårdh E. AI-based quantification of whole-body tumour burden on somatostatin receptor PET/CT. Eur J Hybrid Imaging 2023; 7:14. [PMID: 37544941 PMCID: PMC10404578 DOI: 10.1186/s41824-023-00172-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 06/28/2023] [Indexed: 08/08/2023] Open
Abstract
BACKGROUND Segmenting the whole-body somatostatin receptor-expressing tumour volume (SRETVwb) on positron emission tomography/computed tomography (PET/CT) images is highly time-consuming but has shown value as an independent prognostic factor for survival. An automatic method to measure SRETVwb could improve disease status assessment and provide a tool for prognostication. This study aimed to develop an artificial intelligence (AI)-based method to detect and quantify SRETVwb and total lesion somatostatin receptor expression (TLSREwb) from [68Ga]Ga-DOTA-TOC/TATE PET/CT images. METHODS A UNet3D convolutional neural network (CNN) was used to train an AI model with [68Ga]Ga-DOTA-TOC/TATE PET/CT images, where all tumours were manually segmented with a semi-automatic method. The training set consisted of 148 patients, of which 108 had PET-positive tumours. The test group consisted of 30 patients, of which 25 had PET-positive tumours. Two physicians segmented tumours in the test group for comparison with the AI model. RESULTS There were good correlations between the segmented SRETVwb and TLSREwb by the AI model and the physicians, with Spearman rank correlation coefficients of r = 0.78 and r = 0.73, respectively, for SRETVwb and r = 0.83 and r = 0.81, respectively, for TLSREwb. The sensitivity on a lesion detection level was 80% and 79%, and the positive predictive value was 83% and 84% when comparing the AI model with the two physicians. CONCLUSION It was possible to develop an AI model to segment SRETVwb and TLSREwb with high performance. A fully automated method makes quantification of tumour burden achievable and has the potential to be more widely used when assessing PET/CT images.
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Affiliation(s)
- Anni Gålne
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden.
- Department of Translational Medicine, Faculty of Medicine, Lund University, Malmö, Sweden.
- WCMM Wallenberg Centre for Molecular Medicine, Lund, Sweden.
| | - Olof Enqvist
- Eigenvision AB, Malmö, Sweden
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Anna Sundlöv
- Department of Clinical Sciences, Oncology and Pathology, Lund University, Lund, Sweden
| | - Kristian Valind
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
- Department of Translational Medicine, Faculty of Medicine, Lund University, Malmö, Sweden
- WCMM Wallenberg Centre for Molecular Medicine, Lund, Sweden
| | - David Minarik
- Department of Translational Medicine, Faculty of Medicine, Lund University, Malmö, Sweden
- WCMM Wallenberg Centre for Molecular Medicine, Lund, Sweden
- Radiation Physics, Skåne University Hospital, Malmö, Sweden
| | - Elin Trägårdh
- Department of Translational Medicine, Faculty of Medicine, Lund University, Malmö, Sweden
- WCMM Wallenberg Centre for Molecular Medicine, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Malmö, Sweden
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Abstract
Machine learning methods have been growing in prominence across all areas of medicine. In pathology, recent advances in deep learning (DL) have enabled computational analysis of histological samples, aiding in diagnosis and characterization in multiple disease areas. In cancer, and particularly endocrine cancer, DL approaches have been shown to be useful in tasks ranging from tumor grading to gene expression prediction. This review summarizes the current state of DL research in endocrine cancer histopathology with an emphasis on experimental design, significant findings, and key limitations.
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Affiliation(s)
- Siddhi Ramesh
- Department of Medicine, Section of Hematology/Oncology, University of Chicago Medical Center, 5841 South Maryland Avenue, MC 2115, Chicago, IL 60637, USA; The University of Chicago Medicine & Biological Sciences, 5841 South Maryland Avenue, Chicago, IL, USA
| | - James M Dolezal
- Department of Medicine, Section of Hematology/Oncology, University of Chicago Medical Center, 5841 South Maryland Avenue, MC 2115, Chicago, IL 60637, USA; The University of Chicago Medicine & Biological Sciences, 5841 South Maryland Avenue, Chicago, IL, USA
| | - Alexander T Pearson
- Department of Medicine, Section of Hematology/Oncology, University of Chicago Medical Center, 5841 South Maryland Avenue, MC 2115, Chicago, IL 60637, USA; University of Chicago Comprehensive Cancer Center, Chicago, IL, USA; The University of Chicago Medicine & Biological Sciences, 5841 South Maryland Avenue, Chicago, IL, USA.
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Diagnostic Management of Gastroenteropancreatic Neuroendocrine Neoplasms: Technique Optimization and Tips and Tricks for Radiologists. Tomography 2023; 9:217-246. [PMID: 36828370 PMCID: PMC9958666 DOI: 10.3390/tomography9010018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/22/2023] [Accepted: 01/23/2023] [Indexed: 01/31/2023] Open
Abstract
Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) comprise a heterogeneous group of neoplasms, which derive from cells of the diffuse neuroendocrine system that specializes in producing hormones and neuropeptides and arise in most cases sporadically and, to a lesser extent, in the context of complex genetic syndromes. Furthermore, they are primarily nonfunctioning, while, in the case of insulinomas, gastrinomas, glucagonomas, vipomas, and somatostatinomas, they produce hormones responsible for clinical syndromes. The GEP-NEN tumor grade and cell differentiation may result in different clinical behaviors and prognoses, with grade one (G1) and grade two (G2) neuroendocrine tumors showing a more favorable outcome than grade three (G3) NET and neuroendocrine carcinoma. Two critical issues should be considered in the NEN diagnostic workup: first, the need to identify the presence of the tumor, and, second, to define the primary site and evaluate regional and distant metastases. Indeed, the primary site, stage, grade, and function are prognostic factors that the radiologist should evaluate to guide prognosis and management. The correct diagnostic management of the patient includes a combination of morphological and functional evaluations. Concerning morphological evaluations, according to the consensus guidelines of the European Neuroendocrine Tumor Society (ENETS), computed tomography (CT) with a contrast medium is recommended. Contrast-enhanced magnetic resonance imaging (MRI), including diffusion-weighted imaging (DWI), is usually indicated for use to evaluate the liver, pancreas, brain, and bones. Ultrasonography (US) is often helpful in the initial diagnosis of liver metastases, and contrast-enhanced ultrasound (CEUS) can solve problems in characterizing the liver, as this tool can guide the biopsy of liver lesions. In addition, intraoperative ultrasound is an effective tool during surgical procedures. Positron emission tomography (PET-CT) with FDG for nonfunctioning lesions and somatostatin analogs for functional lesions are very useful for identifying and evaluating metabolic receptors. The detection of heterogeneity in somatostatin receptor (SSTR) expression is also crucial for treatment decision making. In this narrative review, we have described the role of morphological and functional imaging tools in the assessment of GEP-NENs according to current major guidelines.
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Pavlidis ET, Pavlidis TE. Molecular factors, diagnosis and management of gastrointestinal tract neuroendocrine tumors: An update. World J Clin Cases 2022; 10:9573-9587. [PMID: 36186187 PMCID: PMC9516923 DOI: 10.12998/wjcc.v10.i27.9573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/16/2022] [Accepted: 08/17/2022] [Indexed: 02/05/2023] Open
Abstract
The prevalence of gastrointestinal neuroendocrine tumors (GI-NETs) is increasing, and despite recent advances in their therapy, it remains inadequate in patients with advanced well-differentiated neuroendocrine tumors. These tumors present many challenges concerning the molecular basis and genomic profile, pathophysiology, clinicopathological features, histopathologic classification, diagnosis and treatment. There has been an ongoing debate on diagnostic criteria and clinical behavior, and various changes have been made over the last few years. Neuroendocrine carcinoma of the gastrointestinal system is a rare but highly malignant neoplasm that is genetically distinct from gastrointestinal system neuroendocrine tumors (NETs). The diagnosis and management have changed over the past decade. Emerging novel biomarkers and metabolic players in cancer cells are useful and promising new diagnostic tools. Progress in positron emission tomography-computerized tomography and scintigraphy with new radioactive agents (64Cu-DOTATATE or 68Ga-DOTATATE) replacing enough octreoscan, has improved further the current diagnostic imaging. Promising results provide targeted therapies with biological agents, new drugs, chemotherapy and immunotherapy. However, the role of surgery is important, since it is the cornerstone of management. Simultaneous resection of small bowel NETs with synchronous liver metastases is a surgical challenge. Endoscopy offers novel options not only for diagnosis but also for interventional management. The therapeutic option should be individualized based on current multidisciplinary information.
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Affiliation(s)
- Efstathios Theodoros Pavlidis
- Department of 2nd Surgical Propedeutic, Hippocration Hospital, Aristotle University of Thessaloniki, School of Medicine, Thessaloniki 54642, Greece
| | - Theodoros Efstathios Pavlidis
- Department of 2nd Surgical Propedeutic, Aristotle University of Thessaloniki, School of Medicine, Thessaloniki 54642, Greece
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