1
|
K Nazar A, Basu S. Radiolabeled Somatostatin Analogs for Cancer Imaging. Semin Nucl Med 2024:S0001-2998(24)00058-8. [PMID: 39122608 DOI: 10.1053/j.semnuclmed.2024.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Accepted: 07/01/2024] [Indexed: 08/12/2024]
Abstract
Somatostatin receptors (SSTR) are expressed by many tumours especially those related to neuro-endocrine origin and molecular functional imaging of SSTR expression using radiolabelled somatostatin analogs have revolutionized imaging of patients with these group of malignancies. Coming a long way from the first radiolabelled somatostatin analog 123I-Tyr-3-octreotide, there has been significant developments in terms of radionuclides used, the ligands and somatostatin derivatives. 111In-Pentetreotide extensively employed for imaging NETs at the beginning has now been replaced by 68Ga-SSA based PET-CT. SSA-PET/CT performs superior to conventional imaging modalities and has evolved in the mainframe for NET imaging. The advantages were multiple: (i) superior spatial resolution of PET versus SPECT, (ii) quantitative capabilities of PET aiding in disease activity and treatment response monitoring with better precision, (iii) shorter scan time and (iv) less patient exposure to radiation. The modality is indicated for staging, detecting the primary in CUP-NETs, restaging, treatment planning (along with FDG: the concept of dual-tracer PET-CT) as well as treatment response evaluation and follow-up of NETs. SSA PET/CT has also been incorporated in the guidelines for imaging of Pheochromocytoma-Paraganglioma, Medullary carcinoma thyroid, Meningioma and Tumor induced osteomalacia. At present, there is rising interest on (a) 18F-labelled SSA, (b) 64Cu-labelled SSA, and (c) somatostatin antagonists. 18F offers excellent imaging properties, 64Cu makes delayed imaging feasible which has implications in dosimetry and SSTR antagonists bind with the SST receptors with high affinity and specificity, providing high contrast images with less background, which can be translated to theranostics effectively. SSTR have been demonstrated in non-neuroendocrine tumours as well in the peer-reviewed literature, with studies demonstrating the potential of SSA PET/CT in Neuroblastoma, Nasopharyngeal carcinoma, carcinoma prostate (neuroendocrine differentiation) and lymphoma. This review will focus on the currently available SSAs and their history, different SPECT/PET agents, SSTR antagonists, comparison between the various imaging tracers, and their utility in both neuroendocrine and non-neuroendocrine tumors.
Collapse
Affiliation(s)
- Aamir K Nazar
- Radiation Medicine Centre, Bhabha Atomic Research Centre, Tata Memorial Centre Annexe, Mumbai; Homi Bhabha National Institute, Mumbai
| | - Sandip Basu
- Radiation Medicine Centre, Bhabha Atomic Research Centre, Tata Memorial Centre Annexe, Mumbai; Homi Bhabha National Institute, Mumbai.
| |
Collapse
|
2
|
Santo G, Di Santo G, Virgolini I. Peptide Receptor Radionuclide Therapy of Neuroendocrine Tumors: Agonist, Antagonist and Alternatives. Semin Nucl Med 2024; 54:557-569. [PMID: 38490913 DOI: 10.1053/j.semnuclmed.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/14/2024] [Accepted: 02/14/2024] [Indexed: 03/17/2024]
Abstract
Peptide receptor radionuclide therapy (PRRT) today is a well-established treatment strategy for patients with neuroendocrine tumors (NET). First performed already more than 30 years ago, PRRT was incorporated only in recent years into the major oncology guidelines, based on its proven efficacy and safety in clinical trials. Following the phase 3 NETTER-1 trial, which led to the final registration of the radiopharmaceutical Luthatera® for G1/G2 NET patients in 2017, the long-term results of the phase 3 NETTER-2 trial may pave the way for a new treatment option also for advanced G2/G3 patients as first-line therapy. The growing knowledge about the synergistic effect of combined therapies could also allow alternative (re)treatment options for NET patients, in order to create a tailored treatment strategy. The evolving thera(g)nostic concept could be applied for the identification of patients who might benefit from different image-guided treatment strategies. In this scenario, the use of dual tracer PET/CT in NET patients, using both [18F]F-FDG/[68Ga]Ga-DOTA-somatostatin analog (SSA) for diagnosis and follow-up, is under discussion and could also result in a powerful prognostic tool. In addition, alternative strategies based on different metabolic pathways, radioisotopes, or combinations of different medical approaches could be applied. A number of different promising "doors" could thus open in the near future for the treatment of NET patients - and the "key" will be thera(g)nostic!
Collapse
Affiliation(s)
- Giulia Santo
- Department of Nuclear Medicine, Medical University of Innsbruck, Innsbruck, Austria; Department of Experimental and Clinical Medicine, "Magna Graecia" University of Catanzaro, Catanzaro, Italy
| | - Gianpaolo Di Santo
- Department of Nuclear Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - Irene Virgolini
- Department of Nuclear Medicine, Medical University of Innsbruck, Innsbruck, Austria.
| |
Collapse
|
3
|
Levenson RM, Singh Y, Rieck B, Hathaway QA, Farrelly C, Rozenblit J, Prasanna P, Erickson B, Choudhary A, Carlsson G, Sarkar D. Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology. J Transl Med 2024; 104:102060. [PMID: 38626875 DOI: 10.1016/j.labinv.2024.102060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 05/19/2024] Open
Abstract
Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative and, increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able to obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.
Collapse
Affiliation(s)
- Richard M Levenson
- Department of Pathology and Laboratory Medicine, University of California Davis, Davis, California.
| | - Yashbir Singh
- Department of Radiology, Mayo Clinic, Rochester, Minnesota.
| | - Bastian Rieck
- Helmholtz Munich and Technical University of Munich, Munich, Germany
| | - Quincy A Hathaway
- Department of Medical Education, West Virginia University, Morgantown, West Virginia
| | | | | | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | | | | | - Gunnar Carlsson
- Department of Mathematics, Stanford University, Stanford, California
| | - Deepa Sarkar
- Institute of Genomic Health, Ichan school of Medicine, Mount Sinai, New York
| |
Collapse
|
4
|
Smith TAD. Gene Abnormalities and Modulated Gene Expression Associated with Radionuclide Treatment: Towards Predictive Biomarkers of Response. Genes (Basel) 2024; 15:688. [PMID: 38927624 PMCID: PMC11202453 DOI: 10.3390/genes15060688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 05/22/2024] [Accepted: 05/23/2024] [Indexed: 06/28/2024] Open
Abstract
Molecular radiotherapy (MRT), also known as radioimmunotherapy or targeted radiotherapy, is the delivery of radionuclides to tumours by targeting receptors overexpressed on the cancer cell. Currently it is used in the treatment of a few cancer types including lymphoma, neuroendocrine, and prostate cancer. Recently reported outcomes demonstrating improvements in patient survival have led to an upsurge in interest in MRT particularly for the treatment of prostate cancer. Unfortunately, between 30% and 40% of patients do not respond. Further normal tissue exposure, especially kidney and salivary gland due to receptor expression, result in toxicity, including dry mouth. Predictive biomarkers to select patients who will benefit from MRT are crucial. Whilst pre-treatment imaging with imaging versions of the therapeutic agents is useful in demonstrating tumour binding and potentially organ toxicity, they do not necessarily predict patient benefit, which is dependent on tumour radiosensitivity. Transcript-based biomarkers have proven useful in tailoring external beam radiotherapy and adjuvant treatment. However, few studies have attempted to derive signatures for MRT response prediction. Here, transcriptomic studies that have identified genes associated with clinical radionuclide exposure have been reviewed. These studies will provide potential features for seeding multi-component biomarkers of MRT response.
Collapse
Affiliation(s)
- Tim A D Smith
- Nuclear Futures Institute, School of Computer Science and Engineering, Bangor University, Dean Street, Bangor LL57 1UT, UK
| |
Collapse
|
5
|
Yan Q, Chen Y, Liu C, Shi H, Han M, Wu Z, Huang S, Zhang C, Hou B. Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis. Front Oncol 2024; 14:1332387. [PMID: 38725633 PMCID: PMC11080013 DOI: 10.3389/fonc.2024.1332387] [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: 11/02/2023] [Accepted: 04/02/2024] [Indexed: 05/12/2024] Open
Abstract
Background Accurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients' prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis. Method A systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool. Results A total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%. Conclusion AI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications. Systematic Review Registration https://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.
Collapse
Affiliation(s)
- Qian Yan
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Yubin Chen
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Chunsheng Liu
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Hexian Shi
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Mingqian Han
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Zelong Wu
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Shanzhou Huang
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Chuanzhao Zhang
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Baohua Hou
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
- Department of General Surgery, Heyuan People’s Hospital, Heyuan, China
| |
Collapse
|
6
|
Ma J, Wang X, Tang M, Zhang C. Preoperative prediction of pancreatic neuroendocrine tumor grade based on 68Ga-DOTATATE PET/CT. Endocrine 2024; 83:502-510. [PMID: 37715934 PMCID: PMC10850018 DOI: 10.1007/s12020-023-03515-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 08/29/2023] [Indexed: 09/18/2023]
Abstract
OBJECTIVE To establish a prediction model for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs) based on 68Ga-DOTATATE PET/CT. METHODS Clinical data of 41 patients with PNETs were included in this study. According to the pathological results, they were divided into grade 1 and grade 2/3. 68Ga-DOTATATE PET/CT images were collected within one month before surgery. The clinical risk factors and significant radiological features were filtered, and a clinical predictive model based on these clinical and radiological features was established. 3D slicer was used to extracted 107 radiomic features from the region of interest (ROI) of 68Ga-dotata PET/CT images. The Pearson correlation coefficient (PCC), recursive feature elimination (REF) based five-fold cross validation were adopted for the radiomic feature selection, and a radiomic score was computed subsequently. The comprehensive model combining the clinical risk factors and the rad-score was established as well as the nomogram. The performance of above clinical model and comprehensive model were evaluated and compared. RESULTS Adjacent organ invasion, N staging, and M staging were the risk factors for PNET grading (p < 0.05). 12 optimal radiomic features (3 PET radiomic features, 9 CT radiomic features) were screen out. The clinical predictive model achieved an area under the curve (AUC) of 0.785. The comprehensive model has better predictive performance (AUC = 0.953). CONCLUSION We proposed a comprehensive nomogram model based on 68Ga-DOTATATE PET/CT to predict grade 1 and grade 2/3 of PNETs and assist personalized clinical diagnosis and treatment plans for patients with PNETs.
Collapse
Affiliation(s)
- Jiao Ma
- Department of Nuclear Medicine, The Affilliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, PR China
| | - Xiaoyong Wang
- Department of Radiology, The Affilliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, PR China
| | - Mingsong Tang
- Department of Radiology, The Affilliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, PR China
| | - Chunyin Zhang
- Department of Nuclear Medicine, The Affilliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, PR China.
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, 646000, Sichuan, PR China.
- Academician (expert) Workstation of Sichuan Province, Luzhou, 646000, Sichuan, PR China.
| |
Collapse
|
7
|
Yazdani E, Geramifar P, Karamzade-Ziarati N, Sadeghi M, Amini P, Rahmim A. Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens. Diagnostics (Basel) 2024; 14:181. [PMID: 38248059 PMCID: PMC10814892 DOI: 10.3390/diagnostics14020181] [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: 11/23/2023] [Revised: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 01/23/2024] Open
Abstract
Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.
Collapse
Affiliation(s)
- Elmira Yazdani
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
- Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran 14117-13135, Iran; (P.G.); (N.K.-Z.)
| | - Najme Karamzade-Ziarati
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran 14117-13135, Iran; (P.G.); (N.K.-Z.)
| | - Mahdi Sadeghi
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
- Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Payam Amini
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran 14496-14535, Iran;
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC V5Z 1L3, Canada
| |
Collapse
|
8
|
Ronot M, Soyer P. Can radiomics outperform pathology for tumor grading? Diagn Interv Imaging 2024; 105:3-4. [PMID: 37714731 DOI: 10.1016/j.diii.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 09/01/2023] [Indexed: 09/17/2023]
Affiliation(s)
- Maxime Ronot
- Department of Radiology, Hôpital Beaujon, AP-HP, 92110, Clichy, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France.
| | - Philippe Soyer
- Université Paris Cité, Faculté de Médecine, 75006, Paris, France; Department of Diagnostic and Interventional Imaging, AP-HP, Hôpital Cochin, 75014, Paris, France
| |
Collapse
|
9
|
Ingenerf M, Grawe F, Winkelmann M, Karim H, Ruebenthaler J, Fabritius MP, Ricke J, Seidensticker R, Auernhammer CJ, Zacherl MJ, Seidensticker M, Schmid-Tannwald C. Neuroendocrine liver metastases treated using transarterial radioembolization: Identification of prognostic parameters at 68Ga-DOTATATE PET/CT. Diagn Interv Imaging 2024; 105:15-25. [PMID: 37453859 DOI: 10.1016/j.diii.2023.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 07/18/2023]
Abstract
PURPOSE To identify prognostic clinical and imaging parameters for patients with neuroendocrine liver metastases (NELMs) undergoing transarterial radioembolization (TARE). MATERIALS AND METHODS Forty-seven patients (27 men; mean age, 64 years) with NELMs who received TARE, along with pre-procedure liver MRI and 68Ga-DOTATATE positron emission tomography/computed tomography were included. Apparent diffusion coefficient and standardized uptake value (SUV) of three liver metastases, normal spleen and liver were measured. SUVmax or SUVmean were used for the calculation of tumor-to-organ ratios (tumor-to-spleen and tumor-to-liver ratios) using all possible combinations (including SUVmax/SUVmax, SUVmax/SUVmean, and SUVmean/SUVmean). Clinical parameters (hepatic tumor-burden, presence of extra-hepatic metastases, chromograninA, Ki-67 and bilirubin levels) were assessed. Overall survival, progression-free survival (PFS) and hepatic progression-free survival (HPFS) were calculated using Kaplan-Meier curves. RESULTS Median overall survival, PFS and HPFS were 49.6, 13.1 and 28.3 months, respectively. In multivariable Cox regression analysis, low Ki-67 (≤ 5%), low hepatic tumor-burden (< 10%), absence of extrahepatic metastases, and increased Tmean/Lmax ratio were significant prognostic factors of longer overall survival and HPFS. High baseline chromograninA (> 1330 ng/mL) was associated with shorter HPFS. Tmean/Lmax > 1.9 yielded a median overall survival of 69 vs. 33 months (P < 0.04), and a median HPFS of 30 vs. 19 months (P = 0.09). For PFS, high baseline SUVmax of NELMs was the single significant parameter in the multivariable model. SUVmax > 28 resulted in a median PFS of 16.9 vs. 6.5 months, respectively (P = 0.001). CONCLUSION High preinterventional Tmean/Lmax ratios, and high SUVmax on 68Ga-DOTATATE positron emission tomography/computed tomography seem to have prognostic value in patients with NELMs undergoing TARE, potentially aiding patient selection and management alongside conventional variables.
Collapse
Affiliation(s)
- Maria Ingenerf
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany.
| | - Freba Grawe
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; Department of Nuclear Medicine, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Michael Winkelmann
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Homeira Karim
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Johannes Ruebenthaler
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; Interdisciplinary Centre of Neuroendocrine Tumors of the GastroEnteroPancreatic System (GEPNET-KUM), University Hospital, LMU Munich, 81377 Munich, Germany
| | | | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; Interdisciplinary Centre of Neuroendocrine Tumors of the GastroEnteroPancreatic System (GEPNET-KUM), University Hospital, LMU Munich, 81377 Munich, Germany
| | | | - Christoph Josef Auernhammer
- Department of Internal Medicine 4, University Hospital, LMU Munich, 81377 Munich, Germany; Interdisciplinary Centre of Neuroendocrine Tumors of the GastroEnteroPancreatic System (GEPNET-KUM), University Hospital, LMU Munich, 81377 Munich, Germany
| | | | - Max Seidensticker
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; Interdisciplinary Centre of Neuroendocrine Tumors of the GastroEnteroPancreatic System (GEPNET-KUM), University Hospital, LMU Munich, 81377 Munich, Germany
| | - Christine Schmid-Tannwald
- Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; Interdisciplinary Centre of Neuroendocrine Tumors of the GastroEnteroPancreatic System (GEPNET-KUM), University Hospital, LMU Munich, 81377 Munich, Germany
| |
Collapse
|
10
|
Lee H, Kipnis ST, Niman R, O’Brien SR, Eads JR, Katona BW, Pryma DA. Prediction of 177Lu-DOTATATE Therapy Outcomes in Neuroendocrine Tumor Patients Using Semi-Automatic Tumor Delineation on 68Ga-DOTATATE PET/CT. Cancers (Basel) 2023; 16:200. [PMID: 38201627 PMCID: PMC10778298 DOI: 10.3390/cancers16010200] [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: 12/11/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Treatment of metastatic neuroendocrine tumors (NET) with 177Lu-DOTATATE peptide receptor radionuclide therapy (PRRT) results in favorable response only in a subset of patients. We investigated the prognostic value of quantitative pre-treatment semi-automatic 68Ga-DOTATATE PET/CT analysis in NET patients treated with PRRT. METHODS The medical records of 94 NET patients who received at least one cycle of PRRT at a single institution were retrospectively reviewed. On each pre-treatment 68Ga-DOTATATE PET/CT, the total tumor volume (TTV), maximum tumor standardized uptake value for the patient (SUVmax), and average uptake in the lesion with the lowest radiotracer uptake (SUVmin) were determined with a semi-automatic tumor delineation method. Progression-free survival (PFS) and overall survival (OS) among the patients were compared based on optimal cutoff values for the imaging parameters. RESULTS On Kaplan-Meier analysis and univariate Cox regression, significantly shorter PFS was observed in patients with lower SUVmax, lower SUVmin, and higher TTV. On multivariate Cox regression, lower SUVmin and higher TTV remained predictive of shorter PFS. Only higher TTV was found to be predictive of shorter OS on Kaplan-Meier and Cox regression analyses. In a post hoc Kaplan-Meier analysis, patients with at least one high-risk feature (low SUVmin or high TTV) showed shorter PFS and OS, which may be the most convenient parameter to measure in clinical practice. CONCLUSIONS The tumor volume and lowest lesion uptake on 68Ga-DOTATATE PET/CT can predict disease progression following PRRT in NET patients, with the former also predictive of overall survival. NET patients at risk for poor outcomes following PRRT can be identified with semi-automated quantitative analysis of 68Ga-DOTATATE PET/CT.
Collapse
Affiliation(s)
- Hwan Lee
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Sarit T. Kipnis
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Medicine, Georgetown University, Washington, DC 20007, USA
| | - Remy Niman
- MIM Software Inc., Cleveland, OH 44122, USA
| | - Sophia R. O’Brien
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jennifer R. Eads
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Bryson W. Katona
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Daniel A. Pryma
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
11
|
Rogasch JMM, Shi K, Kersting D, Seifert R. Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET). Nuklearmedizin 2023; 62:361-369. [PMID: 37995708 PMCID: PMC10667066 DOI: 10.1055/a-2198-0545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 10/25/2023] [Indexed: 11/25/2023]
Abstract
AIM Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction. METHODS A systematic search for original articles was run on PubMed. All articles were rated according to 17 criteria proposed by the authors. Criteria with >2 rating categories were binarized into "adequate" or "inadequate". The association between the number of "adequate" criteria per article and the date of publication was examined. RESULTS One hundred articles were identified (published between 07/2017 and 09/2023). The median proportion of articles per criterion that were rated "adequate" was 65% (range: 23-98%). Nineteen articles (19%) mentioned neither a test cohort nor cross-validation to separate training from testing. The median number of criteria with an "adequate" rating per article was 12.5 out of 17 (range, 4-17), and this did not increase with later dates of publication (Spearman's rho, 0.094; p = 0.35). In 22 articles (22%), less than half of the items were rated "adequate". Only 8% of articles published the source code, and 10% made the dataset openly available. CONCLUSION Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.
Collapse
Affiliation(s)
- Julian Manuel Michael Rogasch
- Department of Nuclear Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital University Hospital Bern, Bern, Switzerland
| | - David Kersting
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Robert Seifert
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| |
Collapse
|
12
|
Feuerecker B, Heimer MM, Geyer T, Fabritius MP, Gu S, Schachtner B, Beyer L, Ricke J, Gatidis S, Ingrisch M, Cyran CC. Artificial Intelligence in Oncological Hybrid Imaging. Nuklearmedizin 2023; 62:296-305. [PMID: 37802057 DOI: 10.1055/a-2157-6810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
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..
Collapse
Affiliation(s)
- Benedikt Feuerecker
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
- German Cancer Research Center (DKFZ), Partner site Munich, DKTK German Cancer Consortium, Munich, Germany
| | - Maurice M Heimer
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Thomas Geyer
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | | | - Sijing Gu
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | | | - Leonie Beyer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Sergios Gatidis
- Department of Radiology, University Hospital Tübingen, Tübingen, Germany
- MPI, Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Clemens C Cyran
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| |
Collapse
|
13
|
Fiscone C, Rundo L, Lugaresi A, Manners DN, Allinson K, Baldin E, Vornetti G, Lodi R, Tonon C, Testa C, Castelli M, Zaccagna F. Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis. Sci Rep 2023; 13:16239. [PMID: 37758804 PMCID: PMC10533494 DOI: 10.1038/s41598-023-42914-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023] Open
Abstract
Multiple Sclerosis (MS) is an autoimmune demyelinating disease characterised by changes in iron and myelin content. These biomarkers are detectable by Quantitative Susceptibility Mapping (QSM), an advanced Magnetic Resonance Imaging technique detecting magnetic properties. When analysed with radiomic techniques that exploit its intrinsic quantitative nature, QSM may furnish biomarkers to facilitate early diagnosis of MS and timely assessment of progression. In this work, we explore the robustness of QSM radiomic features by varying the number of grey levels (GLs) and echo times (TEs), in a sample of healthy controls and patients with MS. We analysed the white matter in total and within six clinically relevant tracts, including the cortico-spinal tract and the optic radiation. After optimising the number of GLs (n = 64), at least 65% of features were robust for each Volume of Interest (VOI), with no difference (p > .05) between left and right hemispheres. Different outcomes in feature robustness among the VOIs depend on their characteristics, such as volume and variance of susceptibility values. This study validated the processing pipeline for robustness analysis and established the reliability of QSM-based radiomics features against GLs and TEs. Our results provide important insights for future radiomics studies using QSM in clinical applications.
Collapse
Affiliation(s)
- Cristiana Fiscone
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy
| | - Alessandra Lugaresi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- UOSI Riabilitazione Sclerosi Multipla, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - David Neil Manners
- Department for Life Quality Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Kieren Allinson
- Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Elisa Baldin
- Epidemiology and Statistics Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Gianfranco Vornetti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Claudia Testa
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy.
| | - Mauro Castelli
- NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312, Lisbon, Portugal
| | - Fulvio Zaccagna
- Department of Imaging, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Investigative Medicine Division, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
14
|
Ronot M, Dioguardi Burgio M, Gregory J, Hentic O, Vullierme MP, Ruszniewski P, Zappa M, de Mestier L. Appropriate use of morphological imaging for assessing treatment response and disease progression of neuroendocrine tumors. Best Pract Res Clin Endocrinol Metab 2023; 37:101827. [PMID: 37858478 DOI: 10.1016/j.beem.2023.101827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Neuroendocrine tumors (NETs) are relatively rare neoplasms displaying heterogeneous clinical behavior, ranging from indolent to aggressive forms. Patients diagnosed with NETs usually receive a varied array of treatments, including somatostatin analogs, locoregional treatments (ablation, intra-arterial therapy), cytotoxic chemotherapy, peptide receptor radionuclide therapy (PRRT), and targeted therapies. To maximize therapeutic efficacy while limiting toxicity (both physical and economic), there is a need for accurate and reliable tools to monitor disease evolution and progression and to assess the effectiveness of these treatments. Imaging morphological methods, primarily relying on computed tomography (CT) and magnetic resonance imaging (MRI), are indispensable modalities for the initial evaluation and continuous monitoring of patients with NETs, therefore playing a pivotal role in gauging the response to treatment. The primary goal of assessing tumor response is to anticipate and weigh the benefits of treatments, especially in terms of survival gain. The World Health Organization took the pioneering step of introducing assessment criteria based on cross-sectional imaging. This initial proposal standardized the measurement of lesion sizes, laying the groundwork for subsequent criteria. The Response Evaluation Criteria in Solid Tumors (RECIST) subsequently refined and enhanced these standards, swiftly gaining acceptance within the oncology community. New treatments were progressively introduced, targeting specific features of NETs (such as tumor vascularization or expression of specific receptors), and achieving significant qualitative changes within tumors, although associated with minimal or paradoxical effects on tumor size. Several alternative criteria, adapted from those used in other cancer types and focusing on tumor viability, the slow growth of NETs, or refining the existing size-based RECIST criteria, have been proposed in NETs. This review article aims to describe and discuss the optimal utilization of CT and MRI for assessing the response of NETs to treatment; it provides a comprehensive overview of established and emerging criteria for evaluating tumor response, along with comparative analyses. Molecular imaging will not be addressed here and is covered in a dedicated article within this special issue.
Collapse
Affiliation(s)
- Maxime Ronot
- Université Paris-Cité, Centre de Recherche sur l'Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France; Université Paris-Cité, Department of Radiology, Beaujon Hospital (APHP.Nord), Clichy, France.
| | - Marco Dioguardi Burgio
- Université Paris-Cité, Centre de Recherche sur l'Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France; Université Paris-Cité, Department of Radiology, Beaujon Hospital (APHP.Nord), Clichy, France
| | - Jules Gregory
- Université Paris-Cité, Centre de Recherche sur l'Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France; Université Paris-Cité, Department of Radiology, Beaujon Hospital (APHP.Nord), Clichy, France
| | - Olivia Hentic
- Université Paris-Cité, Department of Pancreatology and Digestive Oncology, Beaujon Hospital (APHP.Nord), Clichy, France
| | | | - Philippe Ruszniewski
- Université Paris-Cité, Centre de Recherche sur l'Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France; Université Paris-Cité, Department of Pancreatology and Digestive Oncology, Beaujon Hospital (APHP.Nord), Clichy, France
| | - Magaly Zappa
- Department of Radiology, Cayenne University Hospital, Cayenne, Guyanne, France
| | - Louis de Mestier
- Université Paris-Cité, Centre de Recherche sur l'Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France; Université Paris-Cité, Department of Pancreatology and Digestive Oncology, Beaujon Hospital (APHP.Nord), Clichy, France
| |
Collapse
|
15
|
Balma M, Laudicella R, Gallio E, Gusella S, Lorenzon L, Peano S, Costa RP, Rampado O, Farsad M, Evangelista L, Deandreis D, Papaleo A, Liberini V. Applications of Artificial Intelligence and Radiomics in Molecular Hybrid Imaging and Theragnostics for Neuro-Endocrine Neoplasms (NENs). Life (Basel) 2023; 13:1647. [PMID: 37629503 PMCID: PMC10455722 DOI: 10.3390/life13081647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 07/12/2023] [Accepted: 07/25/2023] [Indexed: 08/27/2023] Open
Abstract
Nuclear medicine has acquired a crucial role in the management of patients with neuroendocrine neoplasms (NENs) by improving the accuracy of diagnosis and staging as well as their risk stratification and personalized therapies, including radioligand therapies (RLT). Artificial intelligence (AI) and radiomics can enable physicians to further improve the overall efficiency and accuracy of the use of these tools in both diagnostic and therapeutic settings by improving the prediction of the tumor grade, differential diagnosis from other malignancies, assessment of tumor behavior and aggressiveness, and prediction of treatment response. This systematic review aims to describe the state-of-the-art AI and radiomics applications in the molecular imaging of NENs.
Collapse
Affiliation(s)
- Michele Balma
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100 Cuneo, Italy; (S.P.); (A.P.); (V.L.)
| | - Riccardo Laudicella
- Unit of Nuclear Medicine, Biomedical Department of Internal and Specialist Medicine, University of Palermo, 90133 Palermo, Italy; (R.L.); (R.P.C.)
| | - Elena Gallio
- Medical Physics Unit, A.O.U. Città Della Salute E Della Scienza Di Torino, Corso Bramante 88/90, 10126 Torino, Italy; (E.G.); (O.R.)
| | - Sara Gusella
- Nuclear Medicine, Central Hospital Bolzano, 39100 Bolzano, Italy; (S.G.); (M.F.)
| | - Leda Lorenzon
- Medical Physics Department, Central Bolzano Hospital, 39100 Bolzano, Italy;
| | - Simona Peano
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100 Cuneo, Italy; (S.P.); (A.P.); (V.L.)
| | - Renato P. Costa
- Unit of Nuclear Medicine, Biomedical Department of Internal and Specialist Medicine, University of Palermo, 90133 Palermo, Italy; (R.L.); (R.P.C.)
| | - Osvaldo Rampado
- Medical Physics Unit, A.O.U. Città Della Salute E Della Scienza Di Torino, Corso Bramante 88/90, 10126 Torino, Italy; (E.G.); (O.R.)
| | - Mohsen Farsad
- Nuclear Medicine, Central Hospital Bolzano, 39100 Bolzano, Italy; (S.G.); (M.F.)
| | - Laura Evangelista
- Department of Biomedical Sciences, Humanitas University, 20089 Milan, Italy;
| | - Desiree Deandreis
- Department of Nuclear Medicine and Endocrine Oncology, Gustave Roussy and Université Paris Saclay, 94805 Villejuif, France;
| | - Alberto Papaleo
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100 Cuneo, Italy; (S.P.); (A.P.); (V.L.)
| | - Virginia Liberini
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100 Cuneo, Italy; (S.P.); (A.P.); (V.L.)
| |
Collapse
|
16
|
Bourdeleau P, Couvelard A, Ronot M, Lebtahi R, Hentic O, Ruszniewski P, Cros J, de Mestier L. Spatial and temporal heterogeneity of digestive neuroendocrine neoplasms. Ther Adv Med Oncol 2023; 15:17588359231179310. [PMID: 37323185 PMCID: PMC10262621 DOI: 10.1177/17588359231179310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/11/2023] [Indexed: 06/17/2023] Open
Abstract
Neuroendocrine neoplasms (NENs) are initially monoclonal neoplasms that progressively become polyclonal, with very different genotypic and phenotypic characteristics leading to biological differences, including the Ki-67 proliferation index, morphology, or sensitivity to treatments. Whereas inter-patient heterogeneity has been well described, intra-tumor heterogeneity has been little studied. However, NENs present a high degree of heterogeneity, both spatially within the same location or between different lesions, and through time. This can be explained by the emergence of tumor subclones with different behaviors. These subpopulations can be distinguished by the Ki-67 index, but also by the expression of hormonal markers or by differences in the intensity of uptake on metabolic imaging, such as 68Ga-somatostatin receptor and Fluorine-18 fluorodeoxyglucose positron emission tomography. As these features are directly related to prognosis, it seems mandatory to move toward a standardized, improved selection of the tumor areas to be studied to be as predictive as possible. The temporal evolution of NENs frequently leads to changes in tumor grade over time, with impact on prognosis and therapeutic decision-making. However, there is no recommendation regarding systematic biopsy of NEN recurrence or progression, and which lesion to sample. This review aims to summarize the current state of knowledge, the main hypotheses, and the main implications regarding intra-tumor spatial and temporal heterogeneity in digestive NENs.
Collapse
Affiliation(s)
- Pauline Bourdeleau
- Department of Pancreatology and Digestive Oncology, Beaujon Hospital (APHP.Nord), Université Paris-Cité, Clichy, France
| | - Anne Couvelard
- Department of Pathology, Beaujon/Bichat Hospitals (APHP.Nord), Université Paris-Cité, Clichy/Paris, France
- Centre de Recherche sur l’Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France
| | - Maxime Ronot
- Department of Radiology, Beaujon Hospital (APHP.Nord), Université Paris-Cité, Clichy, France, and Centre de Recherche sur l’Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France
| | - Rachida Lebtahi
- Department of Nuclear Medicine, Beaujon Hospital (APHP.Nord), Université Paris-Cité, Clichy, France Centre de Recherche sur l’Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France
| | - Olivia Hentic
- Department of Pancreatology and Digestive Oncology, Beaujon Hospital (APHP.Nord), Université Paris-Cité, Clichy, France
| | - Philippe Ruszniewski
- Department of Pancreatology and Digestive Oncology, Beaujon Hospital (APHP.Nord), Université Paris-Cité, Clichy, France
- Centre de Recherche sur l’Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France
| | - Jérôme Cros
- Department of Pathology, Beaujon/Bichat Hospitals (APHP.Nord), Université Paris-Cité, Clichy/Paris, France
- Centre de Recherche sur l’Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France
| | | |
Collapse
|
17
|
Dai J, Wang H, Xu Y, Chen X, Tian R. Clinical application of AI-based PET images in oncological patients. Semin Cancer Biol 2023; 91:124-142. [PMID: 36906112 DOI: 10.1016/j.semcancer.2023.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/28/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.
Collapse
Affiliation(s)
- Jiaona Dai
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hui Wang
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yuchao Xu
- School of Nuclear Science and Technology, University of South China, Hengyang City 421001, China
| | - Xiyang Chen
- Division of Vascular Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Rong Tian
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China.
| |
Collapse
|
18
|
Feuerecker B, Heimer MM, Geyer T, Fabritius MP, Gu S, Schachtner B, Beyer L, Ricke J, Gatidis S, Ingrisch M, Cyran CC. Artificial Intelligence in Oncological Hybrid Imaging. ROFO-FORTSCHR RONTG 2023; 195:105-114. [PMID: 36170852 DOI: 10.1055/a-1909-7013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
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.
Collapse
Affiliation(s)
- Benedikt Feuerecker
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.,German Cancer Research Center (DKFZ), Partner site Munich, DKTK German Cancer Consortium, Munich, Germany
| | - Maurice M Heimer
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Thomas Geyer
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | | | - Sijing Gu
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | | | - Leonie Beyer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Sergios Gatidis
- Department of Radiology, University Hospital Tübingen, Tübingen, Germany.,MPI, Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Clemens C Cyran
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| |
Collapse
|
19
|
Li C, Wang S, Li C, Yin Y, Feng F, Fu H, Wang H, Chen S. Improved risk stratification by PET-based intratumor heterogeneity in children with high-risk neuroblastoma. Front Oncol 2022; 12:896593. [PMID: 36353561 PMCID: PMC9637983 DOI: 10.3389/fonc.2022.896593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 09/20/2022] [Indexed: 11/12/2023] Open
Abstract
PURPOSE The substratification of high-risk neuroblastoma is challenging, and new predictive imaging biomarkers are warranted for better patient selection. The aim of the study was to evaluate the prognostic role of PET-based intratumor heterogeneity and its potential ability to improve risk stratification in neuroblastoma. METHODS Pretreatment 18F-FDG PET/CT scans from 112 consecutive children with newly diagnosed neuroblastoma were retrospectively analyzed. The primary tumor was segmented in the PET images. SUVs, volumetric parameters including metabolic tumor volume (MTV) and total lesion glycolysis (TLG), and texture features were extracted. After the exclusion of imaging features with poor and moderate reproducibility, the relationships between the imaging indices and clinicopathological factors, as well as event-free survival (EFS), were assessed. RESULTS The median follow-up duration was 33 months. Multivariate analysis showed that PET-based intratumor heterogeneity outperformed clinicopathological features, including age, stage, and MYCN, and remained the most robust independent predictor for EFS [training set, hazard ratio (HR): 6.4, 95% CI: 3.1-13.2, p < 0.001; test set, HR: 5.0, 95% CI: 1.8-13.6, p = 0.002]. Within the clinical high-risk group, patients with a high metabolic heterogeneity showed significantly poorer outcomes (HR: 3.3, 95% CI: 1.6-6.8, p = 0.002 in the training set; HR: 4.4, 95% CI: 1.5-12.9, p = 0.008 in the test set) compared to those with relatively homogeneous tumors. Furthermore, intratumor heterogeneity outran the volumetric indices (MTVs and TLGs) and yielded the best performance of distinguishing high-risk patients with different outcomes with a 3-year EFS of 6% vs. 47% (p = 0.001) in the training set and 9% vs. 51% (p = 0.004) in the test set. CONCLUSION PET-based intratumor heterogeneity was a strong independent prognostic factor in neuroblastoma. In the clinical high-risk group, intratumor heterogeneity further stratified patients with distinct outcomes.
Collapse
Affiliation(s)
- Chao Li
- Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shaoyan Wang
- Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Can Li
- Department of Pathology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yafu Yin
- Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fang Feng
- Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongliang Fu
- Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hui Wang
- Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Suyun Chen
- Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|
20
|
Lee ONY, Tan KV, Tripathi V, Yuan H, Chan WWL, Chiu KWH. The Role of 68 Ga-DOTA-SSA PET/CT in the Management and Prediction of Peptide Receptor Radionuclide Therapy Response for Patients With Neuroendocrine Tumors : A Systematic Review and Meta-analysis. Clin Nucl Med 2022; 47:781-793. [PMID: 35485851 DOI: 10.1097/rlu.0000000000004235] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE The aim of this study was to identify and evaluate the role of 68 Ga-DOTA-somatostatin analog (SSA) PET/CT in guiding treatment for patients with neuroendocrine tumors (NETs) based on published literature, with specific focus on the ability of PET/CT to impact clinical management and predict peptide receptor radionuclide therapy (PRRT) response. PATIENTS AND METHODS A systematic literature search of articles up to December 2021 was performed using PubMed and Scopus. Eligible studies included ≥10 patients with confirmed or suspected NETs who had undergone pretreatment staging 68 Ga-DOTA-SSA PET/CT. A meta-analysis using the random-effects model was conducted to determine the overall change in management after PET/CT, whereas PET/CT-derived parameters that correlated with PRRT outcome were summarized from studies that assessed its predictive capabilities. RESULTS A total of 39 studies were included in this systemic review, of which 2266 patients from 24 studies were included for meta-analysis. We showed that PET/CT resulted in a change in clinical management in 36% (95% confidence interval, 31%-41%; range, 3%-66%) of patients. Fifteen studies consisting of 618 patients examined the prognostic ability of 68 Ga-DOTA-SSA PET/CT for PRRT. Of those, 8 studies identified a higher pretreatment SUV to favor PRRT, and 4 identified PET-based radiomic features for somatostatin receptor heterogeneity to be predictive of PRRT response. CONCLUSIONS Along with its diagnostic abilities, 68 Ga-DOTA-SSA PET/CT can impact treatment decision-making and may predict PRRT response in patients with NETs. More robust studies should be conducted to better elucidate the prognostic role of somatostatin receptor PET/CT in optimizing treatment for clinical outcome.
Collapse
Affiliation(s)
- Osher Ngo Yung Lee
- From the Edinburgh Medical School, The University of Edinburgh, Edinburgh, United Kingdom
| | - Kel Vin Tan
- Department of Oncology, The University of Oxford, Oxford, United Kingdom
| | - Vrijesh Tripathi
- Department of Mathematics and Statistics, The University of the West Indies, St. Augustine Campus, Trinidad and Tobago
| | - Hui Yuan
- Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | | | - Keith Wan Hang Chiu
- Department of Diagnostic and Interventional Radiology, Kwong Wah Hospital, Hong Kong
| |
Collapse
|
21
|
Caruso D, Polici M, Rinzivillo M, Zerunian M, Nacci I, Marasco M, Magi L, Tarallo M, Gargiulo S, Iannicelli E, Annibale B, Laghi A, Panzuto F. CT-based radiomics for prediction of therapeutic response to Everolimus in metastatic neuroendocrine tumors. Radiol Med 2022; 127:691-701. [PMID: 35717429 PMCID: PMC9308597 DOI: 10.1007/s11547-022-01506-4] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 05/20/2022] [Indexed: 12/17/2022]
Abstract
Abstract
Aim
To test radiomic approach in patients with metastatic neuroendocrine tumors (NETs) treated with Everolimus, with the aim to predict progression-free survival (PFS) and death.
Materials and methods
Twenty-five patients with metastatic neuroendocrine tumors, 15/25 pancreatic (60%), 9/25 ileal (36%), 1/25 lung (4%), were retrospectively enrolled between August 2013 and December 2020. All patients underwent contrast-enhanced CT before starting Everolimus, histological diagnosis, tumor grading, PFS, overall survival (OS), death, and clinical data collected. Population was divided into two groups: responders (PFS ≤ 11 months) and non-responders (PFS > 11 months). 3D segmentation was performed on whole liver of naïve CT scans in arterial and venous phases, using a dedicated software (3DSlicer v4.10.2). A total of 107 radiomic features were extracted and compared between two groups (T test or Mann–Whitney), radiomics performance assessed with receiver operating characteristic curve, Kaplan–Meyer curves used for survival analysis, univariate and multivariate logistic regression performed to predict death, and interobserver variability assessed. All significant radiomic comparisons were validated by using a synthetic external cohort. P < 0.05 is considered significant.
Results
15/25 patients were classified as responders (median PFS 25 months and OS 29 months) and 10/25 as non-responders (median PFS 4.5 months and OS 23 months). Among radiomic parameters, Correlation and Imc1 showed significant differences between two groups (P < 0.05) with the best performance (internal cohort AUC 0.86–0.84, P < 0.0001; external cohort AUC 0.84–0.90; P < 0.0001). Correlation < 0.21 resulted correlated with death at Kaplan–Meyer analysis (P = 0.02). Univariate analysis showed three radiomic features independently correlated with death, and in multivariate analysis radiomic model showed good performance with AUC 0.87, sensitivity 100%, and specificity 66.7%. Three features achieved 0.77 ≤ ICC < 0.83 and one ICC = 0.92.
Conclusions
In patients affected by metastatic NETs eligible for Everolimus treatment, radiomics could be used as imaging biomarker able to predict PFS and death.
Collapse
Affiliation(s)
- Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, "Sapienza"-University of Rome, Sant'Andrea University Hospital, AOU Sant'Andrea, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
- Radiology Unit, Sant'Andrea University Hospital, AOU Sant'Andrea, 00189, Rome, Italy
| | - Michela Polici
- Department of Medical Surgical Sciences and Translational Medicine, "Sapienza"-University of Rome, Sant'Andrea University Hospital, AOU Sant'Andrea, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
- Radiology Unit, Sant'Andrea University Hospital, AOU Sant'Andrea, 00189, Rome, Italy
| | - Maria Rinzivillo
- Digestive Disease Unit, Sant'Andrea University Hospital, AOU Sant'Andrea, 00189, Rome, Italy
- ENETS Center of Excellence of Rome, Sant'Andrea University Hospital, AOU Sant'Andrea, 00189, Rome, Italy
| | - Marta Zerunian
- Department of Medical Surgical Sciences and Translational Medicine, "Sapienza"-University of Rome, Sant'Andrea University Hospital, AOU Sant'Andrea, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
- Radiology Unit, Sant'Andrea University Hospital, AOU Sant'Andrea, 00189, Rome, Italy
| | - Ilaria Nacci
- Department of Medical Surgical Sciences and Translational Medicine, "Sapienza"-University of Rome, Sant'Andrea University Hospital, AOU Sant'Andrea, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
- Radiology Unit, Sant'Andrea University Hospital, AOU Sant'Andrea, 00189, Rome, Italy
| | - Matteo Marasco
- Department of Medical Surgical Sciences and Translational Medicine, "Sapienza"-University of Rome, Sant'Andrea University Hospital, AOU Sant'Andrea, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
- Digestive Disease Unit, Sant'Andrea University Hospital, AOU Sant'Andrea, 00189, Rome, Italy
| | - Ludovica Magi
- Department of Medical Surgical Sciences and Translational Medicine, "Sapienza"-University of Rome, Sant'Andrea University Hospital, AOU Sant'Andrea, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
- Digestive Disease Unit, Sant'Andrea University Hospital, AOU Sant'Andrea, 00189, Rome, Italy
| | - Mariarita Tarallo
- Department of Surgery "Pietro Valdoni", Sapienza University of Rome, 00161, Rome, Italy
| | - Simona Gargiulo
- Department of Medical Surgical Sciences and Translational Medicine, "Sapienza"-University of Rome, Sant'Andrea University Hospital, AOU Sant'Andrea, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Elsa Iannicelli
- Department of Medical Surgical Sciences and Translational Medicine, "Sapienza"-University of Rome, Sant'Andrea University Hospital, AOU Sant'Andrea, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
- Radiology Unit, Sant'Andrea University Hospital, AOU Sant'Andrea, 00189, Rome, Italy
| | - Bruno Annibale
- Department of Medical Surgical Sciences and Translational Medicine, "Sapienza"-University of Rome, Sant'Andrea University Hospital, AOU Sant'Andrea, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
- Digestive Disease Unit, Sant'Andrea University Hospital, AOU Sant'Andrea, 00189, Rome, Italy
| | - Andrea Laghi
- Department of Medical Surgical Sciences and Translational Medicine, "Sapienza"-University of Rome, Sant'Andrea University Hospital, AOU Sant'Andrea, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
- Radiology Unit, Sant'Andrea University Hospital, AOU Sant'Andrea, 00189, Rome, Italy.
| | - Francesco Panzuto
- Digestive Disease Unit, Sant'Andrea University Hospital, AOU Sant'Andrea, 00189, Rome, Italy
- ENETS Center of Excellence of Rome, Sant'Andrea University Hospital, AOU Sant'Andrea, 00189, Rome, Italy
| |
Collapse
|
22
|
Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061330. [PMID: 35741139 PMCID: PMC9222024 DOI: 10.3390/diagnostics12061330] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 12/04/2022] Open
Abstract
The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.
Collapse
Affiliation(s)
- David Morland
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| |
Collapse
|
23
|
Ambrosini V, Zanoni L, Filice A, Lamberti G, Argalia G, Fortunati E, Campana D, Versari A, Fanti S. Radiolabeled Somatostatin Analogues for Diagnosis and Treatment of Neuroendocrine Tumors. Cancers (Basel) 2022; 14:cancers14041055. [PMID: 35205805 PMCID: PMC8870358 DOI: 10.3390/cancers14041055] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/10/2022] [Accepted: 02/17/2022] [Indexed: 02/04/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Valentina Ambrosini
- Department of Experimental Diagnostic and Specialized Medicine, University of Bologna, 40138 Bologna, Italy; (V.A.); (G.L.); (G.A.); (E.F.); (D.C.); (S.F.)
- Nuclear Medicine Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Lucia Zanoni
- Nuclear Medicine Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
- Correspondence:
| | - Angelina Filice
- Nuclear Medicine Unit, Azienda USL-IRCCS di Reggio Emilia, 42123 Reggio Emilia, Italy; (A.F.); (A.V.)
| | - Giuseppe Lamberti
- Department of Experimental Diagnostic and Specialized Medicine, University of Bologna, 40138 Bologna, Italy; (V.A.); (G.L.); (G.A.); (E.F.); (D.C.); (S.F.)
- Division of Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Giulia Argalia
- Department of Experimental Diagnostic and Specialized Medicine, University of Bologna, 40138 Bologna, Italy; (V.A.); (G.L.); (G.A.); (E.F.); (D.C.); (S.F.)
| | - Emilia Fortunati
- Department of Experimental Diagnostic and Specialized Medicine, University of Bologna, 40138 Bologna, Italy; (V.A.); (G.L.); (G.A.); (E.F.); (D.C.); (S.F.)
| | - Davide Campana
- Department of Experimental Diagnostic and Specialized Medicine, University of Bologna, 40138 Bologna, Italy; (V.A.); (G.L.); (G.A.); (E.F.); (D.C.); (S.F.)
- Division of Medical Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Annibale Versari
- Nuclear Medicine Unit, Azienda USL-IRCCS di Reggio Emilia, 42123 Reggio Emilia, Italy; (A.F.); (A.V.)
| | - Stefano Fanti
- Department of Experimental Diagnostic and Specialized Medicine, University of Bologna, 40138 Bologna, Italy; (V.A.); (G.L.); (G.A.); (E.F.); (D.C.); (S.F.)
- Nuclear Medicine Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| |
Collapse
|
24
|
Imaging of Neuroendocrine Neoplasms: Monitoring Treatment Response—AJR Expert Panel Narrative Review. AJR Am J Roentgenol 2022; 218:767-780. [DOI: 10.2214/ajr.21.27159] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
25
|
Guerrisi A, Russillo M, Loi E, Ganeshan B, Ungania S, Desiderio F, Bruzzaniti V, Falcone I, Renna D, Ferraresi V, Caterino M, Solivetti FM, Cognetti F, Morrone A. Exploring CT Texture Parameters as Predictive and Response Imaging Biomarkers of Survival in Patients With Metastatic Melanoma Treated With PD-1 Inhibitor Nivolumab: A Pilot Study Using a Delta-Radiomics Approach. Front Oncol 2021; 11:704607. [PMID: 34692481 PMCID: PMC8529867 DOI: 10.3389/fonc.2021.704607] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 09/08/2021] [Indexed: 01/08/2023] Open
Abstract
In the era of artificial intelligence and precision medicine, the use of quantitative imaging methodological approaches could improve the cancer patient’s therapeutic approaches. Specifically, our pilot study aims to explore whether CT texture features on both baseline and first post-treatment contrast-enhanced CT may act as a predictor of overall survival (OS) and progression-free survival (PFS) in metastatic melanoma (MM) patients treated with the PD-1 inhibitor Nivolumab. Ninety-four lesions from 32 patients treated with Nivolumab were analyzed. Manual segmentation was performed using a free-hand polygon approach by drawing a region of interest (ROI) around each target lesion (up to five lesions were selected per patient according to RECIST 1.1). Filtration-histogram-based texture analysis was employed using a commercially available research software called TexRAD (Feedback Medical Ltd, London, UK; https://fbkmed.com/texrad-landing-2/) Percentage changes in texture features were calculated to perform delta-radiomics analysis. Texture feature kurtosis at fine and medium filter scale predicted OS and PFS. A higher kurtosis is correlated with good prognosis; kurtosis values greater than 1.11 for SSF = 2 and 1.20 for SSF = 3 were indicators of higher OS (fine texture: 192 HR = 0.56, 95% CI = 0.32–0.96, p = 0.03; medium texture: HR = 0.54, 95% CI = 0.29–0.99, p = 0.04) and PFS (fine texture: HR = 0.53, 95% CI = 0.29–0.95, p = 0.03; medium texture: HR = 0.49, 209 95% CI = 0.25–0.96, p = 0.03). In delta-radiomics analysis, the entropy percentage variation correlated with OS and PFS. Increasing entropy indicates a worse outcome. An entropy variation greater than 5% was an indicator of bad prognosis. CT delta-texture analysis quantified as entropy predicted OS and PFS. Baseline CT texture quantified as kurtosis also predicted survival baseline. Further studies with larger cohorts are mandatory to confirm these promising exploratory results.
Collapse
Affiliation(s)
- Antonino Guerrisi
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Rome, Italy
| | - Michelangelo Russillo
- Medical Oncology Unit 1, Department of Clinical and Cancer Research IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Emiliano Loi
- Medical Physics and Expert Systems Laboratory, 3 Department of Research and Advanced Technologies, Istituti Fisioterapici Ospitalieri - IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, Imaging Department, University College Hospital, London, United Kingdom
| | - Sara Ungania
- Medical Physics and Expert Systems Laboratory, 3 Department of Research and Advanced Technologies, Istituti Fisioterapici Ospitalieri - IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Flora Desiderio
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Rome, Italy
| | - Vicente Bruzzaniti
- Medical Physics and Expert Systems Laboratory, 3 Department of Research and Advanced Technologies, Istituti Fisioterapici Ospitalieri - IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Italia Falcone
- Medical Oncology Unit 1, Department of Clinical and Cancer Research IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Davide Renna
- Medical Oncology 1, IRCCS-Regina Elena National Cancer Institute, Rome, Italy
| | - Virginia Ferraresi
- Medical Oncology Unit 1, Department of Clinical and Cancer Research IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Mauro Caterino
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Rome, Italy
| | - Francesco Maria Solivetti
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Rome, Italy
| | - Francesco Cognetti
- Medical Oncology Unit 1, Department of Clinical and Cancer Research IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Aldo Morrone
- Scientific Director, San Gallicano Dermatological Institute IRCCS, Rome, Italy
| |
Collapse
|