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Shen K, Su W, Liang C, Shi D, Sun J, Yu R. Differentiating small (< 2 cm) pancreatic ductal adenocarcinoma from neuroendocrine tumors with multiparametric MRI-based radiomic features. Eur Radiol 2024:10.1007/s00330-024-10837-x. [PMID: 38869639 DOI: 10.1007/s00330-024-10837-x] [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: 03/19/2024] [Revised: 04/08/2024] [Accepted: 05/02/2024] [Indexed: 06/14/2024]
Abstract
OBJECTIVES To assess MR-based radiomic analysis in preoperatively discriminating small (< 2 cm) pancreatic ductal adenocarcinomas (PDACs) from neuroendocrine tumors (PNETs). METHODS A total of 197 patients (146 in the training cohort, 51 in the validation cohort) from two centers were retrospectively collected. A total of 7338 radiomics features were extracted from T2-weighted, diffusion-weighted, T1-weighted, arterial phase, portal venous phase and delayed phase imaging. The optimal features were selected by the Mann-Whitney U test, Spearman's rank correlation test and least absolute shrinkage and selection operator method and used to construct the radiomic score (Rad-score). Conventional radiological and clinical features were also assessed. Multivariable logistic regression was used to construct a radiological model, a radiomic model and a fusion model. RESULTS Nine optimal features were identified and used to build the Rad-score. The radiomic model based on the Rad-score achieved satisfactory results with AUCs of 0.905 and 0.930, sensitivities of 0.780 and 0.800, specificities of 0.906 and 0.952 and accuracies of 0.836 and 0.863 for the training and validation cohorts, respectively. The fusion model, incorporating CA19-9, tumor margins, pancreatic duct dilatation and the Rad-score, exhibited the best performance with AUCs of 0.977 and 0.941, sensitivities of 0.914 and 0.852, specificities of 0.954 and 0.950, and accuracies of 0.932 and 0.894 for the training and validation cohorts, respectively. CONCLUSIONS The MR-based Rad-score is a novel image biomarker for discriminating small PDACs from PNETs. A fusion model combining radiomic, radiological and clinical features performed very well in differentially diagnosing these two tumors. CLINICAL RELEVANCE STATEMENT A fusion model combining MR-based radiomic, radiological, and clinical features could help differentiate between small pancreatic ductal adenocarcinomas and pancreatic neuroendocrine tumors. KEY POINTS Preoperatively differentiating small pancreatic ductal adenocarcinomas (PDACs) and pancreatic neuroendocrine tumors (PNETs) is challenging. Multiparametric MRI-based Rad-score can be used for discriminating small PDACs from PNETs. A fusion model incorporating radiomic, radiological, and clinical features differentiated small PDACs from PNETs well.
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Affiliation(s)
- Keren Shen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Weijie Su
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Chunmiao Liang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Dan Shi
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Jihong Sun
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
| | - Risheng Yu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.
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Hu X, Shi S, Wang Y, Yuan J, Chen M, Wei L, Deng W, Feng ST, Peng Z, Luo Y. Dual-energy CT improves differentiation of non-hypervascular pancreatic neuroendocrine neoplasms from CA 19-9-negative pancreatic ductal adenocarcinomas. LA RADIOLOGIA MEDICA 2024; 129:1-13. [PMID: 37861978 DOI: 10.1007/s11547-023-01733-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/28/2023] [Indexed: 10/21/2023]
Abstract
PURPOSE To evaluate the utility of dual-energy CT (DECT) in differentiating non-hypervascular pancreatic neuroendocrine neoplasms (PNENs) from pancreatic ductal adenocarcinomas (PDACs) with negative carbohydrate antigen 19-9 (CA 19-9). METHODS This retrospective study included 26 and 39 patients with pathologically confirmed non-hypervascular PNENs and CA 19-9-negative PDACs, respectively, who underwent contrast-enhanced DECT before treatment between June 2019 and December 2021. The clinical, conventional CT qualitative, conventional CT quantitative, and DECT quantitative parameters of the two groups were compared using univariate analysis and selected by least absolute shrinkage and selection operator regression (LASSO) analysis. Multivariate logistic regression analyses were performed to build qualitative, conventional CT quantitative, DECT quantitative, and comprehensive models. The areas under the receiver operating characteristic curve (AUCs) of the models were compared using DeLong's test. RESULTS The AUCs of the DECT quantitative (based on normalized iodine concentrations [nICs] in the arterial and portal venous phases: 0.918; 95% confidence interval [CI] 0.852-0.985) and comprehensive (based on tumour location and nICs in the arterial and portal venous phases: 0.966; 95% CI 0.889-0.995) models were higher than those of the qualitative (based on tumour location: 0.782; 95% CI 0.665-0.899) and conventional CT quantitative (based on normalized conventional CT attenuation in the arterial phase: 0.665; 95% CI 0.533-0.797; all P < 0.05) models. The DECT quantitative and comprehensive models had comparable performances (P = 0.076). CONCLUSIONS Higher nICs in the arterial and portal venous phases were associated with higher blood supply improving the identification of non-hypervascular PNENs.
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Affiliation(s)
- Xuefang Hu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, 518000, Guangdong, China
| | - Siya Shi
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China
| | - Yangdi Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China
| | - Jiaxin Yuan
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China
| | - Mingjie Chen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China
| | - Luyong Wei
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China
| | - Weiwei Deng
- Clinical and Technical Support, Philips Healthcare China, Shanghai, 200072, China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China
| | - Zhenpeng Peng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China.
| | - Yanji Luo
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, Guangdong, China.
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Park YJ, Park YS, Kim ST, Hyun SH. A Machine Learning Approach Using [ 18F]FDG PET-Based Radiomics for Prediction of Tumor Grade and Prognosis in Pancreatic Neuroendocrine Tumor. Mol Imaging Biol 2023; 25:897-910. [PMID: 37395887 DOI: 10.1007/s11307-023-01832-7] [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: 01/11/2023] [Revised: 05/30/2023] [Accepted: 06/19/2023] [Indexed: 07/04/2023]
Abstract
PURPOSE We sought to develop and validate machine learning (ML) models for predicting tumor grade and prognosis using 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography (PET)-based radiomics and clinical features in patients with pancreatic neuroendocrine tumors (PNETs). PROCEDURES A total of 58 patients with PNETs who underwent pretherapeutic [18F]FDG PET/computed tomography (CT) were retrospectively enrolled. PET-based radiomics extracted from segmented tumor and clinical features were selected to develop prediction models by the least absolute shrinkage and selection operator feature selection method. The predictive performances of ML models using neural network (NN) and random forest algorithms were compared by the areas under the receiver operating characteristic curves (AUROCs) and validated by stratified five-fold cross validation. RESULTS We developed two separate ML models for predicting high-grade tumors (Grade 3) and tumors with poor prognosis (disease progression within two years). The integrated models consisting of clinical and radiomic features with NN algorithm showed the best performances than the other models (stand-alone clinical or radiomics models). The performance metrics of the integrated model by NN algorithm were AUROC of 0.864 in the tumor grade prediction model and AUROC of 0.830 in the prognosis prediction model. In addition, AUROC of the integrated clinico-radiomics model with NN was significantly higher than that of tumor maximum standardized uptake model in predicting prognosis (P < 0.001). CONCLUSIONS Integration of clinical features and [18F]FDG PET-based radiomics using ML algorithms improved the prediction of high-grade PNET and poor prognosis in a non-invasive manner.
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Affiliation(s)
- Yong-Jin Park
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Nuclear Medicine, Ajou University Medical Center, Ajou University School of Medicine, 164, Worldcup-ro, Yeongtong-gu, Suwon, 16499, South Korea
| | - Young Suk Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea
| | - Seung Tae Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea
| | - Seung Hyup Hyun
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
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4
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Staal FC, Taghavi M, Hong EK, Tissier R, van Treijen M, Heeres BC, van der Zee D, Tesselaar ME, Beets-Tan RG, Maas M. CT-based radiomics to distinguish progressive from stable neuroendocrine liver metastases treated with somatostatin analogues: an explorative study. Acta Radiol 2023; 64:1062-1070. [PMID: 35702011 DOI: 10.1177/02841851221106598] [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] [Indexed: 11/17/2022]
Abstract
BACKGROUND Accurate response evaluation in patients with neuroendocrine liver metastases (NELM) remains a challenge. Radiomics has shown promising results regarding response assessment. PURPOSE To differentiate progressive (PD) from stable disease (SD) with radiomics in patients with NELM undergoing somatostatin analogue (SSA) treatment. MATERIAL AND METHODS A total of 46 patients with histologically confirmed gastroenteropancreatic neuroendocrine tumors (GEP-NET) with ≥1 NELM and ≥2 computed tomography (CT) scans were included. Response was assessed with Response Evaluation Criteria in Solid Tumors (RECIST1.1). Hepatic target lesions were manually delineated and analyzed with radiomics. Radiomics features were extracted from each NELM on both arterial-phase (AP) and portal-venous-phase (PVP) CT. Multiple instance learning with regularized logistic regression via LASSO penalization (with threefold cross-validation) was used to classify response. Three models were computed: (i) AP model; (ii) PVP model; and (iii) AP + PVP model for a lesion-based and patient-based outcome. Next, clinical features were added to each model. RESULTS In total, 19 (40%) patients had PD. Median follow-up was 13 months (range 1-50 months). Radiomics models could not accurately classify response (area under the curve 0.44-0.60). Adding clinical variables to the radiomics models did not significantly improve the performance of any model. CONCLUSION Radiomics features were not able to accurately classify response of NELM on surveillance CT scans during SSA treatment.
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Affiliation(s)
- Femke Cr Staal
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, 5211Maastricht University Medical Centre, Maastricht, The Netherlands
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, 1228Netherlands Cancer Institute Amsterdam/University Medical Center Utrecht, Utrecht, The Netherlands
| | - M Taghavi
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, 5211Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Eun K Hong
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, 5211Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Radiology, 26725Seoul National University Hospital, Seoul, Republic of Korea
| | - Renaud Tissier
- Biostatistics Center, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Mark van Treijen
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, 1228Netherlands Cancer Institute Amsterdam/University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Endocrine Oncology, 8124University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Birthe C Heeres
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Margot Et Tesselaar
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, 1228Netherlands Cancer Institute Amsterdam/University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Medical Oncology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina Gh Beets-Tan
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, 5211Maastricht University Medical Centre, Maastricht, The Netherlands
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Monique Maas
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
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Yuan J, Wang Y, Hu X, Shi S, Zhang N, Wang L, Deng W, Feng ST, Peng Z, Luo Y. Use of dual-layer spectral detector computed tomography in the diagnosis of pancreatic neuroendocrine neoplasms. Eur J Radiol 2023; 159:110660. [PMID: 36577182 DOI: 10.1016/j.ejrad.2022.110660] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 12/12/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE To explore the optimal energy level of dual-layer spectral detector computed tomography (DLCT) images of pancreatic neuroendocrine neoplasms (pNENs) and investigate the value in their detection. METHODS This retrospective analysis included 134 pNEN patients with 136 lesions; they underwent contrast-enhanced DLCT scanning with histopathological confirmation of pNENs. Virtual monoenergetic images (VMI) of 40-100 keV, iodine concentration map (IC map), Z-effective atomic number map (Zeff map), and conventional images were analysed. The optimal energy level was obtained by comparing the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The lesion detection rates of DLCT and conventional images were compared. Subjective image analysis was performed by two readers who assessed the image quality and lesion conspicuity on a 5-point scale. RESULTS The SNR of VMIs from 40 to 80 keV (arterial phase, P < 0.001; venous phase, P < 0.05) and CNR from 40 to 60 keV (arterial and venous phases, each P < 0.05) were higher than that of conventional images; VMI40keV showed the highest SNR and CNR. There was a good inter-reader agreement between the two reviewers (Kappa values > 0.61); the scores of Zeff and IC maps were higher than those of conventional images and VMI40keV (P < 0.05). The detection performance of DLCT images was better than conventional images. CONCLUSIONS The VMI40keV demonstrated the best CNR and SNR of pNENs compared to other VMIs. Zeff and IC maps improve objective image quality and reader preference compared to conventional images. These findings could possess important clinical implications in formulating treatment strategies.
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Affiliation(s)
- Jiaxin Yuan
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, Guangdong, China
| | - Yangdi Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, Guangdong, China
| | - Xuefang Hu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, Guangdong, China
| | - Siya Shi
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, Guangdong, China
| | - Ning Zhang
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou 510080, Guangdong, China
| | - Liqin Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, Guangdong, China
| | - Weiwei Deng
- Clinical & Technical Support, Philips Healthcare China, Shanghai 200072, China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, Guangdong, China
| | - Zhenpeng Peng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, Guangdong, China
| | - Yanji Luo
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, Guangdong, China.
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Radiomics Combined with Multiple Machine Learning Algorithms in Differentiating Pancreatic Ductal Adenocarcinoma from Pancreatic Neuroendocrine Tumor: More Hands Produce a Stronger Flame. J Clin Med 2022; 11:jcm11226789. [PMID: 36431266 PMCID: PMC9697420 DOI: 10.3390/jcm11226789] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/04/2022] [Accepted: 11/15/2022] [Indexed: 11/19/2022] Open
Abstract
The aim of this study was to assess the diagnostic ability of radiomics combined with multiple machine learning algorithms to differentiate pancreatic ductal adenocarcinoma (PDAC) from pancreatic neuroendocrine tumor (pNET). This retrospective study included a total of 238 patients diagnosed with PDAC or pNET. Using specialized software, radiologists manually mapped regions of interest (ROIs) from computed tomography images and automatically extracted radiomics features. A total of 45 discriminative models were built by five selection algorithms and nine classification algorithms. The performances of the discriminative models were assessed by sensitivity, specificity and the area under receiver operating characteristic curve (AUC) in the training and validation datasets. Using the combination of Gradient Boosting Decision Tree (GBDT) as the selection algorithm and Random Forest (RF) as the classification algorithm, the optimal diagnostic ability with the highest AUC was presented in the training and validation datasets. The sensitivity, specificity and AUC of the model were 0.804, 0.973 and 0.971 in the training dataset and 0.742, 0.934 and 0.930 in the validation dataset, respectively. The combination of radiomics and multiple machine learning algorithms showed the potential ability to discriminate PDAC from pNET. We suggest that multi-algorithm modeling should be considered for similar studies in the future rather than using a single algorithm empirically.
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7
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Update on quantitative radiomics of pancreatic tumors. Abdom Radiol (NY) 2022; 47:3118-3160. [PMID: 34292365 DOI: 10.1007/s00261-021-03216-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 02/07/2023]
Abstract
Radiomics is a newer approach for analyzing radiological images obtained from conventional imaging modalities such as computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography. Radiomics involves extracting quantitative data from the images and assessing them to identify diagnostic or prognostic features such as tumor grade, resectability, tumor response to neoadjuvant therapy, and survival. The purpose of this review is to discuss the basic principles of radiomics and provide an overview of the current clinical applications of radiomics in the field of pancreatic tumors.
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Laino ME, Ammirabile A, Lofino L, Mannelli L, Fiz F, Francone M, Chiti A, Saba L, Orlandi MA, Savevski V. Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review. Healthcare (Basel) 2022; 10:healthcare10081511. [PMID: 36011168 PMCID: PMC9408381 DOI: 10.3390/healthcare10081511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/31/2022] [Accepted: 08/08/2022] [Indexed: 12/19/2022] Open
Abstract
The diagnosis, evaluation, and treatment planning of pancreatic pathologies usually require the combined use of different imaging modalities, mainly, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Artificial intelligence (AI) has the potential to transform the clinical practice of medical imaging and has been applied to various radiological techniques for different purposes, such as segmentation, lesion detection, characterization, risk stratification, or prediction of response to treatments. The aim of the present narrative review is to assess the available literature on the role of AI applied to pancreatic imaging. Up to now, the use of computer-aided diagnosis (CAD) and radiomics in pancreatic imaging has proven to be useful for both non-oncological and oncological purposes and represents a promising tool for personalized approaches to patients. Although great developments have occurred in recent years, it is important to address the obstacles that still need to be overcome before these technologies can be implemented into our clinical routine, mainly considering the heterogeneity among studies.
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Affiliation(s)
- Maria Elena Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
- Correspondence: (M.E.L.); (A.A.)
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
- Correspondence: (M.E.L.); (A.A.)
| | - Ludovica Lofino
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | | | - Francesco Fiz
- Nuclear Medicine Unit, Department of Diagnostic Imaging, E.O. Ospedali Galliera, 56321 Genoa, Italy
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital, 72074 Tübingen, Germany
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09124 Cagliari, Italy
| | | | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
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9
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Staal FCR, Aalbersberg EA, van der Velden D, Wilthagen EA, Tesselaar MET, Beets-Tan RGH, Maas M. GEP-NET radiomics: a systematic review and radiomics quality score assessment. Eur Radiol 2022; 32:7278-7294. [PMID: 35882634 DOI: 10.1007/s00330-022-08996-w] [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: 02/21/2022] [Revised: 05/25/2022] [Accepted: 06/26/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE The number of radiomics studies in gastroenteropancreatic neuroendocrine tumours (GEP-NETs) is rapidly increasing. This systematic review aims to provide an overview of the available evidence of radiomics for clinical outcome measures in GEP-NETs, to understand which applications hold the most promise and which areas lack evidence. METHODS PubMed, Embase, and Wiley/Cochrane Library databases were searched and a forward and backward reference check of the identified studies was executed. Inclusion criteria were (1) patients with GEP-NETs and (2) radiomics analysis on CT, MRI or PET. Two reviewers independently agreed on eligibility and assessed methodological quality with the radiomics quality score (RQS) and extracted outcome data. RESULTS In total, 1364 unique studies were identified and 45 were included for analysis. Most studies focused on GEP-NET grade and differential diagnosis of GEP-NETs from other neoplasms, while only a minority analysed treatment response or long-term outcomes. Several studies were able to predict tumour grade or to differentiate GEP-NETs from other lesions with a good performance (AUCs 0.74-0.96 and AUCs 0.80-0.99, respectively). Only one study developed a model to predict recurrence in pancreas NETs (AUC 0.77). The included studies reached a mean RQS of 18%. CONCLUSION Although radiomics for GEP-NETs is still a relatively new area, some promising models have been developed. Future research should focus on developing robust models for clinically relevant aims such as prediction of response or long-term outcome in GEP-NET, since evidence for these aims is still scarce. KEY POINTS • The majority of radiomics studies in gastroenteropancreatic neuroendocrine tumours is of low quality. • Most evidence for radiomics is available for the identification of tumour grade or differentiation of gastroenteropancreatic neuroendocrine tumours from other neoplasms. • Radiomics for the prediction of response or long-term outcome in gastroenteropancreatic neuroendocrine tumours warrants further research.
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Affiliation(s)
- Femke C R Staal
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.,The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands
| | - Else A Aalbersberg
- The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands.,Department of Nuclear Medicine, The Netherlands Cancer Institute Amsterdam, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Daphne van der Velden
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Erica A Wilthagen
- Scientific Information Service, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Margot E T Tesselaar
- The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands.,Department of Medical Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.,Faculty of Health Sciences, University of Southern Denmark, J. B. Winsløws Vej 19, 3, 5000, Odense, Denmark
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
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10
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Budigi B, Oliphant M, Itri J. Pancreatic Adenocarcinoma: Diagnostic Errors, Contributing Factors and Solutions. Acad Radiol 2022; 29:967-976. [PMID: 34838452 DOI: 10.1016/j.acra.2021.10.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 10/18/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022]
Abstract
The purpose of this article is to review diagnostic errors in preoperative and post-operative imaging for pancreatic ductal adenocarcinoma (PDAC), discuss contributing factors, and provide solutions that minimize errors. Accurate radiological staging and restaging of PDAC dictates surgical management and errors can have significant negative effects on patient care, such as missed vessel involvement or metastatic disease that would preclude surgery. Familiarity with these errors and their contributing factors improves diagnostic accuracy and ultimately leads to improved patient outcomes.
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Affiliation(s)
- Bhavana Budigi
- Department of Radiology, Division of Abdominal Imaging, Wake Forest Baptist Medical Center, 1 Medical Center Boulevard, Winston-Salem, NC 27157.
| | - Michael Oliphant
- Department of Radiology, Division of Abdominal Imaging, Wake Forest Baptist Medical Center, 1 Medical Center Boulevard, Winston-Salem, NC 27157
| | - Jason Itri
- Department of Radiology, Division of Abdominal Imaging, Wake Forest Baptist Medical Center, 1 Medical Center Boulevard, Winston-Salem, NC 27157
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11
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Zeng P, Ma L, Liu J, Song Z, Liu J, Yuan H. The diagnostic value of intravoxel incoherent motion diffusion-weighted imaging for distinguishing nonhypervascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas. Eur J Radiol 2022; 150:110261. [PMID: 35316674 DOI: 10.1016/j.ejrad.2022.110261] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/19/2022] [Accepted: 03/14/2022] [Indexed: 12/12/2022]
Abstract
PURPOSE To primarily evaluate the diagnostic performance of the monoexponential and intravoxel incoherent motion (IVIM) diffusion weighted imaging (DWI) models for differentiating between nonhypervascular pancreatic neuroendocrine tumors (PNETs) and pancreatic ductal adenocarcinomas (PDACs). METHODS 63 patients with PNETs (35 nonhypervascular PNETs and 28 hypervascular PNETs) and 164 patients with PDACs were retrospectively enrolled in the study and underwent multiple b-value DWI. Intraobserver and interobserver reliabilities of DWI parameters were assessed by using the intraclass correlation coefficient (ICC). The parameters of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) of nonhypervascular PNETs were compared with PDACs and hypervascular PNETs using the independent sample t test or the Mann-Whitney U test. The diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. RESULTS All DWI parameters values showed good to excellent intra- and interobserver agreements (ICC = 0.743-0.873). Nonhypervascular PNETs had significantly lower ADC and D, but significantly higher f than PDACs (P = 0.005, P < 0.001 and P < 0.001, respectively). ADC, D and f of nonhypervascular PNETs were lower than hypervascular PNETs (P = 0.001, <0.001 and 0.093, respectively). D* of nonhypervascular PNETs showed no statistically significant differences with PDACs and hypervascular PNETs (P = 0.809 and 0.420). D showed a higher area under the curve (AUC), followed by ADC and f (AUC = 0.885, 0.665 and 0.740, respectively) in differentiating nonhypervascular PNETs from PDACs. CONCLUSION Monoexponential and IVIM diffusion models are valuable to differentiate nonhypervascular PNETs from PDACs. D showed better performance than f and ADC.
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Affiliation(s)
- Piaoe Zeng
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, Beijing, China
| | - Lu Ma
- Department of Radiology, Tsinghua University Hospital, 30 Shuangqing Road, Beijing 100084, Beijing, China
| | - Jianfang Liu
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, Beijing, China
| | - Zixiu Song
- Department of Pathology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, Beijing, China
| | - Jianyu Liu
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, Beijing, China.
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12
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Gerbaudo VH, Latif MA. A Step Forward in PET-Based Quantitation of G3 Neuroendocrine Tumor Aggressiveness - The FDZ Score…. Acad Radiol 2022; 29:1317-1319. [PMID: 35365399 DOI: 10.1016/j.acra.2022.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 03/01/2022] [Indexed: 12/01/2022]
Affiliation(s)
- Victor H Gerbaudo
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115.
| | - Muhammad A Latif
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115
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13
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Lee H, Nakamoto R, Moore SE, Pantel AR, Eads JR, Aparici CM, Pryma DA. Combined Quantification of 18F-FDG and 68Ga-DOTATATE PET/CT for Prognosis in High-Grade Gastroenteropancreatic Neuroendocrine Neoplasms. Acad Radiol 2021; 29:1308-1316. [PMID: 34836776 DOI: 10.1016/j.acra.2021.10.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 12/12/2022]
Abstract
RATIONALE AND OBJECTIVES High-grade gastroenteropancreatic neuroendocrine neoplasms (G3 GEP-NENs) are pathologically classified into well differentiated neuroendocrine tumors (G3 NETs) and poorly differentiated neuroendocrine carcinomas (G3 NECs). Using a novel parameter, we examined the prognostic value of 18F-FDG and 68Ga-DOTATATE PET/CT quantification in comparison to pathologic assessment in G3 GEP-NENs. MATERIALS AND METHODS A total of 31 patients with G3 GEP-NENs were reviewed. For each patient, the SUVmax on 18F-FDG and 68Ga-DOTATATE PET/CT were used to calculate the FDG-DOTATATE-Z (FDZ) score: a continuous parameter that increases with 68Ga-DOTATATE uptake and decreases with 18F-FDG uptake. The variation in the FDZ score with respect to pathologic variables was examined. Kaplan-Meier and Cox regression analyses were performed to evaluate the effect of FDZ score on overall survival. An external cohort of 21 patients was used for validation. RESULTS The FDZ score was significantly higher in G3 NETs compared to G3 NECs (p<0.001), and was inversely correlated with Ki67 index (R2=0.33, p<0.001). Patients in the FDZ>0.05 group showed significantly longer survival compared to those in the FDZ≤0.05 group, with median of 34.9 vs. 12.0 months (p<0.001). On univariate regression, FDZ>0.05 (p=0.005), well differentiated disease (p=0.044), and lower Ki67 index (p=0.042) were predictors of survival. On multivariate regression, only FDZ>0.05 could independently predict longer survival with HR=0.16 (p=0.018), which was reproduced in the external validation cohort. CONCLUSION Combined quantification of 18F-FDG and 68Ga-DOTATATE PET/CT into a novel parameter, the FDZ score, reflects the pathologic characteristics of G3 GEP-NENs and is a prognostic indicator of overall survival independent of differentiation.
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14
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Karmazanovsky G, Gruzdev I, Tikhonova V, Kondratyev E, Revishvili A. Computed tomography-based radiomics approach in pancreatic tumors characterization. LA RADIOLOGIA MEDICA 2021; 126:10.1007/s11547-021-01405-0. [PMID: 34386897 DOI: 10.1007/s11547-021-01405-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/27/2021] [Indexed: 12/26/2022]
Abstract
Radiomics (or texture analysis) is a new imaging analysis technique that allows calculating the distribution of texture features of pixel and voxel values depend on the type of ROI (3D or 2D), their relationships in the image. Depending on the software, up to several thousand texture elements can be obtained. Radiomics opens up wide opportunities for differential diagnosis and prognosis of pancreatic neoplasias. The aim of this review was to highlight the main diagnostic advantages of texture analysis in different pancreatic tumors. The review describes the diagnostic performance of radiomics in different pancreatic tumor types, application methods, and problems. Texture analysis in PDAC is able to predict tumor grade and associates with lymphovascular invasion and postoperative margin status. In pancreatic neuroendocrine tumors, texture features strongly correlate with differentiation grade and allows distinguishing it from the intrapancreatic accessory spleen. In pancreatic cystic lesions, radiomics is able to accurately differentiate MCN from SCN and distinguish clinically insignificant lesions from IPMNs with advanced neoplasia. In conclusion, the use of the CT radiomics approach provides a higher diagnostic performance of CT imaging in pancreatic tumors differentiation and prognosis. Future studies should be carried out to improve accuracy and facilitate radiomics workflow in pancreatic imaging.
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Affiliation(s)
- Grigory Karmazanovsky
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
- Radiology Department, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Ivan Gruzdev
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia.
| | - Valeriya Tikhonova
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
| | - Evgeny Kondratyev
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
| | - Amiran Revishvili
- Arrhythmology Department, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
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15
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Han X, Yang J, Luo J, Chen P, Zhang Z, Alu A, Xiao Y, Ma X. Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods. Front Oncol 2021; 11:606677. [PMID: 34367940 PMCID: PMC8339967 DOI: 10.3389/fonc.2021.606677] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 07/05/2021] [Indexed: 02/05/2023] Open
Abstract
Objectives The purpose of this study aimed at investigating the reliability of radiomics features extracted from contrast-enhanced CT in differentiating pancreatic cystadenomas from pancreatic neuroendocrine tumors (PNETs) using machine-learning methods. Methods In this study, a total number of 120 patients, including 66 pancreatic cystadenomas patients and 54 PNETs patients were enrolled. Forty-eight radiomic features were extracted from contrast-enhanced CT images using LIFEx software. Five feature selection methods were adopted to determine the appropriate features for classifiers. Then, nine machine learning classifiers were employed to build predictive models. The performance of the forty-five models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score in the testing group. Results The predictive models exhibited reliable ability of differentiating pancreatic cystadenomas from PNETs when combined with suitable selection methods. A combination of DC as the selection method and RF as the classifier, as well as Xgboost+RF, demonstrated the best discriminative ability, with the highest AUC of 0.997 in the testing group. Conclusions Radiomics-based machine learning methods might be a noninvasive tool to assist in differentiating pancreatic cystadenomas and PNETs.
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Affiliation(s)
- Xuejiao Han
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Yang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Melanoma and Sarcoma Medical Oncology Unit, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jingwen Luo
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Pengan Chen
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Zilong Zhang
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Aqu Alu
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yinan Xiao
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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16
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Bezzi C, Mapelli P, Presotto L, Neri I, Scifo P, Savi A, Bettinardi V, Partelli S, Gianolli L, Falconi M, Picchio M. Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance. Eur J Nucl Med Mol Imaging 2021; 48:4002-4015. [PMID: 33835220 DOI: 10.1007/s00259-021-05338-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/24/2021] [Indexed: 12/19/2022]
Abstract
PURPOSE To present the state-of-art of radiomics in the context of pancreatic neuroendocrine tumors (PanNETs), with a focus on the methodological and technical approaches used, to support the search of guidelines for optimal applications. Furthermore, an up-to-date overview of the current clinical applications of radiomics in the field of PanNETs is provided. METHODS Original articles were searched on PubMed and Science Direct with specific keywords. Evaluations of the selected studies have been focused mainly on (i) the general radiomic workflow and the assessment of radiomic features robustness/reproducibility, as well as on the major clinical applications and investigations accomplished so far with radiomics in the field of PanNETs: (ii) grade prediction, (iii) differential diagnosis from other neoplasms, (iv) assessment of tumor behavior and aggressiveness, and (v) treatment response prediction. RESULTS Thirty-one articles involving PanNETs radiomic-related objectives were selected. In regard to the grade differentiation task, yielded AUCs are currently in the range of 0.7-0.9. For differential diagnosis, the majority of studies are still focused on the preliminary identification of discriminative radiomic features. Limited information is known on the prediction of tumors aggressiveness and of treatment response. CONCLUSIONS Radiomics is recently expanding in the setting of PanNETs. From the analysis of the published data, it is emerging how, prior to clinical application, further validations are necessary and methodological implementations require optimization. Nevertheless, this new discipline might have the potential in assisting the current urgent need of improving the management strategies in PanNETs patients.
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Affiliation(s)
- C Bezzi
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy
| | - P Mapelli
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.,Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - L Presotto
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - I Neri
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - P Scifo
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - A Savi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - V Bettinardi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - S Partelli
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.,Pancreatic Surgery Unit, Pancreas Translational & Clinical Research Centre, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, 20132, Italy
| | - L Gianolli
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - M Falconi
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.,Pancreatic Surgery Unit, Pancreas Translational & Clinical Research Centre, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, 20132, Italy
| | - M Picchio
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy. .,Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy.
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