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Ursprung S, Zhang ML, Asmundo L, Hesami M, Najmi Z, Cañamaque LG, Shenoy-Bhangle AS, Pierce TT, Mojtahed A, Blake MA, Cochran R, Nikolau K, Harisinghani MG, Catalano OA. An Illustrated Review of the Recent 2019 World Health Organization Classification of Neuroendocrine Neoplasms: A Radiologic and Pathologic Correlation. J Comput Assist Tomogr 2024; 48:601-613. [PMID: 38438338 DOI: 10.1097/rct.0000000000001593] [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: 03/06/2024]
Abstract
ABSTRACT Recent advances in molecular pathology and an improved understanding of the etiology of neuroendocrine neoplasms (NENs) have given rise to an updated World Health Organization classification. Since gastroenteropancreatic NENs (GEP-NENs) are the most common forms of NENs and their incidence has been increasing constantly, they will be the focus of our attention. Here, we review the findings at the foundation of the new classification system, discuss how it impacts imaging research and radiological practice, and illustrate typical and atypical imaging and pathological findings. Gastroenteropancreatic NENs have a highly variable clinical course, which existing classification schemes based on proliferation rate were unable to fully capture. While well- and poorly differentiated NENs both express neuroendocrine markers, they are fundamentally different diseases, which may show similar proliferation rates. Genetic alterations specific to well-differentiated neuroendocrine tumors graded 1 to 3 and poorly differentiated neuroendocrine cancers of small cell and large-cell subtype have been identified. The new tumor classification places new demands and creates opportunities for radiologists to continue providing the clinically most relevant report and on researchers to design projects, which continue to be clinically applicable.
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Affiliation(s)
- Stephan Ursprung
- From the Department of Radiology, University Hospital Tuebingen, Tuebingen, Germany
| | - M Lisa Zhang
- Department of Pathology, Massachusetts General Hospital, Boston, MA
| | | | - Mina Hesami
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Zahra Najmi
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | | | | | | | | | - Michael A Blake
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Rory Cochran
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Konstantin Nikolau
- From the Department of Radiology, University Hospital Tuebingen, Tuebingen, Germany
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Modica R, Benevento E, Liccardi A, Cannavale G, Minotta R, DI Iasi G, Colao A. Recent advances and future challenges in the diagnosis of neuroendocrine neoplasms. Minerva Endocrinol (Torino) 2024; 49:158-174. [PMID: 38625065 DOI: 10.23736/s2724-6507.23.04140-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
Neuroendocrine neoplasms (NEN) are a heterogeneous group of malignancies with increasing incidence, whose diagnosis is usually delayed, negatively impacting on patients' prognosis. The latest advances in pathological classifications, biomarker identification and imaging techniques may provide early detection, leading to personalized treatment strategies. In this narrative review the recent developments in diagnosis of NEN are discussed including progresses in pathological classifications, biomarker and imaging. Furthermore, the challenges that lie ahead are investigated. By discussing the limitations of current approaches and addressing potential roadblocks, we hope to guide future research directions in this field. This article is proposed as a valuable resource for clinicians and researchers involved in the management of NEN. Update of pathological classifications and the availability of standardized templates in pathology and radiology represent a substantially improvement in diagnosis and communication among clinicians. Additional immunohistochemistry markers may now enrich pathological classifications, as well as miRNA profiling. New and multi-analytical circulating biomarkers, as liquid biopsy and NETest, are being proposed for diagnosis but their validation and availability should be improved. Radiological imaging strives for precise, non-invasive and less harmful technique to improve safety and quality of life in NEN patient. Nuclear medicine may benefit of somatostatin receptors' antagonists and membrane receptor analogues. Diagnosis in NEN still represents a challenge due to their complex biology and variable presentation. Further advancements are necessary to obtain early and minimally invasive diagnosis to improve patients' outcomes.
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Affiliation(s)
- Roberta Modica
- Unit of Endocrinology, Diabetology and Andrology, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy -
| | - Elio Benevento
- Unit of Endocrinology, Diabetology and Andrology, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Alessia Liccardi
- Unit of Endocrinology, Diabetology and Andrology, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Giuseppe Cannavale
- Unit of Endocrinology, Diabetology and Andrology, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Roberto Minotta
- Unit of Endocrinology, Diabetology and Andrology, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Gianfranco DI Iasi
- Unit of Endocrinology, Diabetology and Andrology, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Annamaria Colao
- Unit of Endocrinology, Diabetology and Andrology, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
- UNESCO Chair "Education for Health and Sustainable Development", University of Naples Federico II, Naples, Italy
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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.
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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
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Battistella A, Tacelli M, Mapelli P, Schiavo Lena M, Andreasi V, Genova L, Muffatti F, De Cobelli F, Partelli S, Falconi M. Recent developments in the diagnosis of pancreatic neuroendocrine neoplasms. Expert Rev Gastroenterol Hepatol 2024; 18:155-169. [PMID: 38647016 DOI: 10.1080/17474124.2024.2342837] [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: 10/21/2023] [Accepted: 04/10/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Pancreatic Neuroendocrine Neoplasms (PanNENs) are characterized by a highly heterogeneous clinical and biological behavior, making their diagnosis challenging. PanNENs diagnostic work-up mainly relies on biochemical markers, pathological examination, and imaging evaluation. The latter includes radiological imaging (i.e. computed tomography [CT] and magnetic resonance imaging [MRI]), functional imaging (i.e. 68Gallium [68 Ga]Ga-DOTA-peptide PET/CT and Fluorine-18 fluorodeoxyglucose [18F]FDG PET/CT), and endoscopic ultrasound (EUS) with its associated procedures. AREAS COVERED This review provides a comprehensive assessment of the recent advancements in the PanNENs diagnostic field. PubMed and Embase databases were used for the research, performed from inception to October 2023. EXPERT OPINION A deeper understanding of PanNENs biology, recent technological improvements in imaging modalities, as well as progresses achieved in molecular and cytological assays, are fundamental players for the achievement of early diagnosis and enhanced preoperative characterization of PanNENs. A multimodal diagnostic approach is required for a thorough disease assessment.
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Affiliation(s)
- Anna Battistella
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Matteo Tacelli
- Vita-Salute San Raffaele University, Milan, Italy
- Pancreato-biliary Endoscopy and EUS Division, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Mapelli
- Vita-Salute San Raffaele University, Milan, Italy
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Valentina Andreasi
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Luana Genova
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Francesca Muffatti
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesco De Cobelli
- Vita-Salute San Raffaele University, Milan, Italy
- Radiology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Stefano Partelli
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Falconi
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
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Ye JY, Fang P, Peng ZP, Huang XT, Xie JZ, Yin XY. A radiomics-based interpretable model to predict the pathological grade of pancreatic neuroendocrine tumors. Eur Radiol 2024; 34:1994-2005. [PMID: 37658884 PMCID: PMC10873440 DOI: 10.1007/s00330-023-10186-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 07/22/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVES To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to predict the pathological grade of pancreatic neuroendocrine tumors (pNETs) in a non-invasive manner. METHODS Patients with pNETs who underwent contrast-enhanced abdominal CT between 2010 and 2022 were included in this retrospective study. Radiomics features were extracted, and five radiomics-based ML models, namely logistic regression (LR), random forest (RF), support vector machine (SVM), XGBoost, and GaussianNB, were developed. The performance of these models was evaluated using a time-independent testing set, and metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) were calculated. The accuracy of the radiomics model was compared to that of needle biopsy. The Shapley Additive Explanation (SHAP) tool and the correlation between radiomics and biological features were employed to explore the interpretability of the model. RESULTS A total of 122 patients (mean age: 50 ± 14 years; 53 male) were included in the training set, whereas 100 patients (mean age: 48 ± 13 years; 50 male) were included in the testing set. The AUCs for LR, SVM, RF, XGBoost, and GaussianNB were 0.758, 0.742, 0.779, 0.744, and 0.745, respectively, with corresponding accuracies of 73.0%, 70.0%, 77.0%, 71.9%, and 72.9%. The SHAP tool identified two features of the venous phase as the most significant, which showed significant differences among the Ki-67 index or mitotic count subgroups (p < 0.001). CONCLUSIONS An interpretable radiomics-based RF model can effectively differentiate between G1 and G2/3 of pNETs, demonstrating favorable interpretability. CLINICAL RELEVANCE STATEMENT The radiomics-based interpretable model developed in this study has significant clinical relevance as it offers a non-invasive method for assessing the pathological grade of pancreatic neuroendocrine tumors and holds promise as an important complementary tool to traditional tissue biopsy. KEY POINTS • A radiomics-based interpretable model was developed to predict the pathological grade of pNETs and compared with preoperative needle biopsy in terms of accuracy. • The model, based on CT radiomics, demonstrated favorable interpretability. • The radiomics model holds potential as a valuable complementary technique to preoperative needle biopsy; however, it should not be considered a replacement for biopsy.
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Affiliation(s)
- Jing-Yuan Ye
- Department of Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, No.58 Zhongshan Er Road, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Peng Fang
- Department of Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, No.58 Zhongshan Er Road, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Zhen-Peng Peng
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, No.58 Zhongshan Er Road, Guangzhou, Guangdong, People's Republic of China
| | - Xi-Tai Huang
- Department of Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, No.58 Zhongshan Er Road, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Jin-Zhao Xie
- Department of Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, No.58 Zhongshan Er Road, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Xiao-Yu Yin
- Department of Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, No.58 Zhongshan Er Road, Guangzhou, 510080, Guangdong, People's Republic of China.
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Heo S, Park HJ, Kim HJ, Kim JH, Park SY, Kim KW, Kim SY, Choi SH, Byun JH, Kim SC, Hwang HS, Hong SM. Prognostic value of CT-based radiomics in grade 1-2 pancreatic neuroendocrine tumors. Cancer Imaging 2024; 24:28. [PMID: 38395973 PMCID: PMC10885493 DOI: 10.1186/s40644-024-00673-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 02/12/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Surgically resected grade 1-2 (G1-2) pancreatic neuroendocrine tumors (PanNETs) exhibit diverse clinical outcomes, highlighting the need for reliable prognostic biomarkers. Our study aimed to develop and validate CT-based radiomics model for predicting postsurgical outcome in patients with G1-2 PanNETs, and to compare its performance with the current clinical staging system. METHODS This multicenter retrospective study included patients who underwent dynamic CT and subsequent curative resection for G1-2 PanNETs. A radiomics-based model (R-score) for predicting recurrence-free survival (RFS) was developed from a development set (441 patients from one institution) using least absolute shrinkage and selection operator-Cox regression analysis. A clinical model (C-model) consisting of age and tumor stage according to the 8th American Joint Committee on Cancer staging system was built, and an integrative model combining the C-model and the R-score (CR-model) was developed using multivariable Cox regression analysis. Using an external test set (159 patients from another institution), the models' performance for predicting RFS and overall survival (OS) was evaluated using Harrell's C-index. The incremental value of adding the R-score to the C-model was evaluated using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS The median follow-up periods were 68.3 and 59.7 months in the development and test sets, respectively. In the development set, 58 patients (13.2%) experienced recurrence and 35 (7.9%) died. In the test set, tumors recurred in 14 patients (8.8%) and 12 (7.5%) died. In the test set, the R-score had a C-index of 0.716 for RFS and 0.674 for OS. Compared with the C-model, the CR-model showed higher C-index (RFS, 0.734 vs. 0.662, p = 0.012; OS, 0.781 vs. 0.675, p = 0.043). CR-model also showed improved classification (NRI, 0.330, p < 0.001) and discrimination (IDI, 0.071, p < 0.001) for prediction of 3-year RFS. CONCLUSIONS Our CR-model outperformed the current clinical staging system in prediction of the prognosis for G1-2 PanNETs and added incremental value for predicting postoperative recurrence. The CR-model enables precise identification of high-risk patients, guiding personalized treatment planning to improve outcomes in surgically resected grade 1-2 PanNETs.
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Affiliation(s)
- Subin Heo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, 05505, Seoul, Republic of Korea
| | - Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, 05505, Seoul, Republic of Korea
| | - Hyoung Jung Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, 05505, Seoul, Republic of Korea.
| | - Jung Hoon Kim
- Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, 110-744, Seoul, Republic of Korea
| | - Seo Young Park
- Department of Statistics and Data Science, Korea National Open University, Seoul, Republic of Korea
| | - Kyung Won Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, 05505, Seoul, Republic of Korea
| | - So Yeon Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, 05505, Seoul, Republic of Korea
| | - Sang Hyun Choi
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, 05505, Seoul, Republic of Korea
| | - Jae Ho Byun
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, 05505, Seoul, Republic of Korea
| | - Song Cheol Kim
- Division of Hepatobiliary and Pancreas Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hee Sang Hwang
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seung Mo Hong
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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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.
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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.
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Javed AA, Zhu Z, Kinny-Köster B, Habib JR, Kawamoto S, Hruban RH, Fishman EK, Wolfgang CL, He J, Chu LC. Accurate non-invasive grading of nonfunctional pancreatic neuroendocrine tumors with a CT derived radiomics signature. Diagn Interv Imaging 2024; 105:33-39. [PMID: 37598013 PMCID: PMC10873069 DOI: 10.1016/j.diii.2023.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 08/02/2023] [Accepted: 08/03/2023] [Indexed: 08/21/2023]
Abstract
PURPOSE The purpose of this study was to develop a radiomics-signature using computed tomography (CT) data for the preoperative prediction of grade of nonfunctional pancreatic neuroendocrine tumors (NF-PNETs). MATERIALS AND METHODS A retrospective study was performed on patients undergoing resection for NF-PNETs between 2010 and 2019. A total of 2436 radiomic features were extracted from arterial and venous phases of pancreas-protocol CT examinations. Radiomic features that were associated with final pathologic grade observed in the surgical specimens were subjected to joint mutual information maximization for hierarchical feature selection and the development of the radiomic-signature. Youden-index was used to identify optimal cutoff for determining tumor grade. A random forest prediction model was trained and validated internally. The performance of this tool in predicting tumor grade was compared to that of EUS-FNA sampling that was used as the standard of reference. RESULTS A total of 270 patients were included and a fusion radiomic-signature based on 10 selected features was developed using the development cohort (n = 201). There were 149 men and 121 women with a mean age of 59.4 ± 12.3 (standard deviation) years (range: 23.3-85.0 years). Upon internal validation in a new set of 69 patients, a strong discrimination was observed with an area under the curve (AUC) of 0.80 (95% confidence interval [CI]: 0.71-0.90) with corresponding sensitivity and specificity of 87.5% (95% CI: 79.7-95.3) and 73.3% (95% CI: 62.9-83.8) respectively. Of the study population, 143 patients (52.9%) underwent EUS-FNA. Biopsies were non-diagnostic in 26 patients (18.2%) and could not be graded due to insufficient sample in 42 patients (29.4%). In the cohort of 75 patients (52.4%) in whom biopsies were graded the radiomic-signature demonstrated not different AUC as compared to EUS-FNA (AUC: 0.69 vs. 0.67; P = 0.723), however greater sensitivity (i.e., ability to accurately identify G2/3 lesion was observed (80.8% vs. 42.3%; P < 0.001). CONCLUSION Non-invasive assessment of tumor grade in patients with PNETs using the proposed radiomic-signature demonstrated high accuracy. Prospective validation and optimization could overcome the commonly experienced diagnostic uncertainty in the assessment of tumor grade in patients with PNETs and could facilitate clinical decision-making.
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Affiliation(s)
- Ammar A Javed
- Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Surgery, New York University Langone Hospital, New York City, New York 10016, USA
| | - Zhuotun Zhu
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Benedict Kinny-Köster
- Department of Surgery, New York University Langone Hospital, New York City, New York 10016, USA
| | - Joseph R Habib
- Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Satomi Kawamoto
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Ralph H Hruban
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Elliot K Fishman
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Christopher L Wolfgang
- Department of Surgery, New York University Langone Hospital, New York City, New York 10016, USA
| | - Jin He
- Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Linda C Chu
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
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Liu Y, Wang J, Zhou H, Wei Z, Wang J, Wang Z, Chen X. The association between jaundice and poorly differentiated pancreatic neuroendocrine neoplasms (Ki67 index > 55.0%). BMC Gastroenterol 2023; 23:436. [PMID: 38087239 PMCID: PMC10717040 DOI: 10.1186/s12876-023-03076-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 12/05/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Jaundice occurs in some pancreatic disease. However, its occurrences and role in pancreatic neuroendocrine neoplasms (PNENs) has not been well studied. In this study we showed the association between jaundice and the risk of high grade and poorly differentiated PNENs. METHODS Ninety-three patients with head-neck PNENs were included. Poorly differentiated pancreatic neuroendocrine neoplasms were defined by a ki67 index > 55.0%. Logistic regression was used to show the association between demographic information, clinical signs and symptoms and the risk of poorly differentiated tumors. A nomogram model was developed to predict poorly differentiated tumor. RESULTS Eight of 93 PNEN patients (8.6%) had jaundice. The age and ki67 index in patients with jaundice were significantly higher than those patients without jaundice. All jaundice occurred in patients with grade 3 PNENs. Mutivariable regression analysis showed that age (odds ratio(OR) = 1.10, 95% confidence interval (CI):1.02-1.19), tumor size (OR = 1.42, 95%CI:1.01-2.00) and jaundice (OR = 14.98, 95%CI: 1.22-184.09) were associated with the risk of poorly differentiated PNENs. The age and size combination showed a good performance in predicting poorly differentiated PNENs (area under the curve (AUC) = 0.81, 95% CI: 0.71-0.90). The addition of jaundice further improved the age- and size-based model (AUC = 0.86, 95% CI: 0.78-0.91). A nomogram was developed based on age, tumor size and jaundice. CONCLUSION Our data showed that jaundice was associated with the risk of high grade PNENs and poorly differentiated PNENs.
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Affiliation(s)
- Yongkang Liu
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | - Jiangchuan Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | - Hao Zhou
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | - Zicheng Wei
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | - Jianhua Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | - Zhongqiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | - Xiao Chen
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, 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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De Muzio F, Pellegrino F, Fusco R, Tafuto S, Scaglione M, Ottaiano A, Petrillo A, Izzo F, Granata V. Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics. Diagnostics (Basel) 2023; 13:2877. [PMID: 37761243 PMCID: PMC10529975 DOI: 10.3390/diagnostics13182877] [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: 07/13/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Neuroendocrine neoplasms (NENs) are a group of lesions originating from cells of the diffuse neuroendocrine system. NENs may involve different sites, including the gastrointestinal tract (GEP-NENs). The incidence and prevalence of GEP-NENs has been constantly rising thanks to the increased diagnostic power of imaging and immuno-histochemistry. Despite the plethora of biochemical markers and imaging techniques, the prognosis and therapeutic choice in GEP-NENs still represents a challenge, mainly due to the great heterogeneity in terms of tumor lesions and clinical behavior. The concept that biomedical images contain information about tissue heterogeneity and pathological processes invisible to the human eye is now well established. From this substrate comes the idea of radiomics. Computational analysis has achieved promising results in several oncological settings, and the use of radiomics in different types of GEP-NENs is growing in the field of research, yet with conflicting results. The aim of this narrative review is to provide a comprehensive update on the role of radiomics on GEP-NEN management, focusing on the main clinical aspects analyzed by most existing reports: predicting tumor grade, distinguishing NET from other tumors, and prognosis assessment.
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Affiliation(s)
- Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | | | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy;
| | - Salvatore Tafuto
- Unit of Sarcomi e Tumori Rari, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Mariano Scaglione
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy
| | - Alessandro Ottaiano
- Unit for Innovative Therapies of Abdominal Metastastes, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
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Dong Y, Yang DH, Tian XF, Lou WH, Wang HZ, Chen S, Qiu YJ, Wang W, Dietrich CF. Pancreatic neuroendocrine tumor: prediction of tumor grades by radiomics models based on ultrasound images. Br J Radiol 2023; 96:20220783. [PMID: 37393539 PMCID: PMC10461281 DOI: 10.1259/bjr.20220783] [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: 08/17/2022] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/03/2023] Open
Abstract
OBJECTIVE We aimed to investigate whether the radiomics analysis based on B-mode ultrasound (BMUS) images could predict histopathological tumor grades in pancreatic neuroendocrine tumors (pNETs). METHODS A total of 64 patients with surgery and histopathologically confirmed pNETs were retrospectively included (34 male and 30 female, mean age 52.4 ± 12.2 years). Patients were divided into training cohort (n = 44) and validation cohort (n = 20). All pNETs were classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity according to WHO 2017 criteria. Maximum relevance minimum redundancy, least absolute shrinkage and selection operator were used for feature selection. Receiver operating characteristic curve analysis was used to evaluate the model performance. RESULTS Finally, 18 G1 pNETs, 35 G2 pNETs, and 11 G3 pNETs patients were included. The radiomic score derived from BMUS images to predict G2/G3 from G1 displayed a good performance with an area under the receiver operating characteristic curve of 0.844 in the training cohort, and 0.833 in the testing cohort. The radiomic score achieved an accuracy of 81.8% in the training cohort and 80.0% in the testing cohort, a sensitivity of 0.750 and 0.786, a specificity of 0.833 and 0.833 in the training/testing cohorts. Clinical benefit of the score also exhibited superior usefulness of the radiomic score, as shown by the decision curve analysis. CONCLUSIONS Radiomic data constructed from BMUS images have the potential for predicting histopathological tumor grades in patients with pNETs. ADVANCES IN KNOWLEDGE The radiomic model constructed from BMUS images has the potential for predicting histopathological tumor grades and Ki-67 proliferation indexes in patients with pNETs.
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Affiliation(s)
| | - Dao-Hui Yang
- Department of ultrasound, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, China
| | | | - Wen-Hui Lou
- Department of Pancreatic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Han-Zhang Wang
- Precision Health Institute, GE Healthcare China, Shanghai, China
| | | | | | - Wenping Wang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Christoph F. Dietrich
- Department General Internal Medicine, Hirslanden Clinics Beau-Site, Salem and Permancence, Bern, Switzerland
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Zhang Y, Feng W, Wu Z, Li W, Tao L, Liu X, Zhang F, Gao Y, Huang J, Guo X. Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1088. [PMID: 37374292 DOI: 10.3390/medicina59061088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 06/29/2023]
Abstract
Background and Objectives: Lung cancer remains a leading cause of cancer mortality worldwide. Accurately classifying benign pulmonary nodules and malignant ones is crucial for early diagnosis and improved patient outcomes. The purpose of this study is to explore the deep-learning model of ResNet combined with a convolutional block attention module (CBAM) for the differentiation between benign and malignant lung cancer, based on computed tomography (CT) images, morphological features, and clinical information. Methods and materials: In this study, 8241 CT slices containing pulmonary nodules were retrospectively included. A random sample comprising 20% (n = 1647) of the images was used as the test set, and the remaining data were used as the training set. ResNet combined CBAM (ResNet-CBAM) was used to establish classifiers on the basis of images, morphological features, and clinical information. Nonsubsampled dual-tree complex contourlet transform (NSDTCT) combined with SVM classifier (NSDTCT-SVM) was used as a comparative model. Results: The AUC and the accuracy of the CBAM-ResNet model were 0.940 and 0.867, respectively, in test set when there were only images as inputs. By combining the morphological features and clinical information, CBAM-ResNet shows better performance (AUC: 0.957, accuracy: 0.898). In comparison, a radiomic analysis using NSDTCT-SVM achieved AUC and accuracy values of 0.807 and 0.779, respectively. Conclusions: Our findings demonstrate that deep-learning models, combined with additional information, can enhance the classification performance of pulmonary nodules. This model can assist clinicians in accurately diagnosing pulmonary nodules in clinical practice.
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Affiliation(s)
- Yanfei Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Wei Feng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Zhiyuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Weiming Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Lixin Tao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Xiangtong Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Feng Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Yan Gao
- Department of Nuclear Medicine, Xuanwu Hospital Capital Medical University, Beijing 100053, China
| | - Jian Huang
- School of Mathematical Sciences, University College Cork, T12 YN60 Cork, Ireland
| | - Xiuhua Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
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Gu XL, Cui Y, Zhu HT, Li XT, Pei X, He XX, Yang L, Lu M, Li ZW, Sun YS. Discrimination of Liver Metastases of Digestive System Neuroendocrine Tumors From Neuroendocrine Carcinoma by Computed Tomography-Based Radiomics Analysis. J Comput Assist Tomogr 2023; 47:361-368. [PMID: 36944109 DOI: 10.1097/rct.0000000000001443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
OBJECTIVE The aim of the study is to investigate the value of computed tomography (CT) radiomics features to discriminate the liver metastases (LMs) of digestive system neuroendocrine tumors (NETs) from neuroendocrine carcinoma (NECs). METHODS Ninety-nine patients with LMs of digestive system neuroendocrine neoplasms from 2 institutions were included. Radiomics features were extracted from the portal venous phase CT images by the Pyradiomics and then selected by using the t test, Pearson correlation analysis, and least absolute shrinkage and selection operator method. The radiomics score (Rad score) for each patient was constructed by linear combination of the selected radiomics features. The radiological model was constructed by radiological features using the multivariable logistic regression. Then, the combined model was constructed by combining Rad score and the radiological model into logistic regression. The performance of all models was evaluated by the receiver operating characteristic curves with the area under curve (AUC). RESULTS In the radiological model, only the enhancement degree (odds ratio, 8.299; 95% confidence interval, 2.070-32.703; P = 0.003) was an independent predictor for discriminating the LMs of digestive system NETs from those of NECs. The combined model constructed by the Rad score in combination with the enhancement degree showed good discrimination performance, with AUCs of 0.893, 0.841, and 0.740 in the training, testing, and external validation groups, respectively. In addition, it performed better than radiological model in the training and testing groups (AUC, 0.893 vs 0.726; AUC, 0.841 vs 0.621). CONCLUSIONS The CT radiomics might be useful for discrimination LMs of digestive system NECs from NETs.
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Affiliation(s)
- Xiao-Lei Gu
- From the Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute
| | - Yong Cui
- From the Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute
| | - Hai-Tao Zhu
- From the Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute
| | - Xiao-Ting Li
- From the Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute
| | - Xiang Pei
- Department of Radiology, Beijing Shunyi District Hospital, Beijing
| | - Xiao-Xiao He
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang
| | - Li Yang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang
| | - Ming Lu
- Departments of Gastrointestinal Oncology and
| | - Zhong-Wu Li
- Pathology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Ying-Shi Sun
- From the Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute
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15
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Cao JJ, Shen L, Visser BC, Yoon L, Kamaya A, Tse JR. Growth Kinetics of Pancreatic Neuroendocrine Neoplasms by Histopathologic Grade. Pancreas 2023; 52:e135-e143. [PMID: 37523605 DOI: 10.1097/mpa.0000000000002221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
OBJECTIVES The aims of the study are to describe the growth kinetics of pathologically proven, treatment-naive pancreatic neuroendocrine neoplasms (panNENs) at imaging surveillance and to determine their association with histopathologic grade and Ki-67. METHODS This study included 100 panNENs from 95 patients who received pancreas protocol computed tomography or magnetic resonance imaging from January 2005 to July 2022. All masses were treatment-naive, had histopathologic correlation, and were imaged with at least 2 computed tomography or magnetic resonance imaging at least 90 days apart. Growth kinetics was assessed using linear and specific growth rate, stratified by grade and Ki-67. Masses were also assessed qualitatively to determine other possible imaging predictors of grade. RESULTS There were 76 grade 1 masses, 17 grade 2 masses, and 7 grade 3 masses. Median (interquartile range) linear growth rates were 0.06 cm/y (0-0.20), 0.40 cm/y (0.22-1.06), and 2.70 cm/y (0.41-3.89) for grade 1, 2, and 3 masses, respectively (P < 0.001). Linear growth rate correlated with Ki-67 with r2 of 0.623 (P < 0.001). At multivariate analyses, linear growth rate was the only imaging feature significantly associated with grade (P = 0.009). CONCLUSIONS Growth kinetics correlate with Ki-67 and grade. Grade 1 panNENs grow slowly versus grade 2-3 panNENs.
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Li Q, Li X, Liu W, Yu J, Chen Y, Zhu M, Li N, Liu F, Wang T, Fang X, Li J, Lu J, Shao C, Bian Y. Non-enhanced magnetic resonance imaging-based radiomics model for the differentiation of pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma. Front Oncol 2023; 13:1108545. [PMID: 36756153 PMCID: PMC9900003 DOI: 10.3389/fonc.2023.1108545] [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: 11/26/2022] [Accepted: 01/11/2023] [Indexed: 01/24/2023] Open
Abstract
Purpose To evaluate the diagnostic performance of radiomics model based on fully automatic segmentation of pancreatic tumors from non-enhanced magnetic resonance imaging (MRI) for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). Materials and methods In this retrospective study, patients with surgically resected histopathologically confirmed PASC and PDAC who underwent MRI scans between January 2011 and December 2020 were included in the study. Multivariable logistic regression analysis was conducted to develop a clinical and radiomics model based on non-enhanced T1-weighted and T2-weighted images. The model performances were determined based on their discrimination and clinical utility. Kaplan-Meier and log-rank tests were used for survival analysis. Results A total of 510 consecutive patients including 387 patients (age: 61 ± 9 years; range: 28-86 years; 250 males) with PDAC and 123 patients (age: 62 ± 10 years; range: 36-84 years; 78 males) with PASC were included in the study. All patients were split into training (n=382) and validation (n=128) sets according to time. The radiomics model showed good discrimination in the validation (AUC, 0.87) set and outperformed the MRI model (validation set AUC, 0.80) and the ring-enhancement (validation set AUC, 0.74). Conclusions The radiomics model based on non-enhanced MRI outperformed the MRI model and ring-enhancement to differentiate PASC from PDAC; it can, thus, provide important information for decision-making towards precise management and treatment of PASC.
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Affiliation(s)
- Qi Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China,Department of Radiology, 96601 Military Hospital of PLA, Huangshan, Anhui, China
| | - Xuezhou Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Wenbin Liu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Yukun Chen
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Mengmeng Zhu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Na Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Tiegong Wang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China,*Correspondence: Yun Bian, ; Chengwei Shao,
| | - Yun Bian
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China,*Correspondence: Yun Bian, ; Chengwei Shao,
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Xu L, Yang X, Xiang W, Hu P, Zhang X, Li Z, Li Y, Liu Y, Dai Y, Luo Y, Qiu H. Development and validation of a contrast-enhanced CT-based radiomics nomogram for preoperative diagnosis in neuroendocrine carcinoma of digestive system. Front Endocrinol (Lausanne) 2023; 14:1155307. [PMID: 37124722 PMCID: PMC10130364 DOI: 10.3389/fendo.2023.1155307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/28/2023] [Indexed: 05/02/2023] Open
Abstract
Objectives To develop and validate a contrast-enhanced CT-based radiomics nomogram for the diagnosis of neuroendocrine carcinoma of the digestive system. Methods The clinical data and contrast-enhanced CT images of 60 patients with pathologically confirmed neuroendocrine carcinoma of the digestive system and 60 patients with non-neuroendocrine carcinoma of the digestive system were retrospectively collected from August 2015 to December 2021 at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and randomly divided into a training cohort (n=84) and a validation cohort (n=36). Clinical characteristics were analyzed by logistic regression and a clinical diagnosis model was developed. Radiomics signature were established by extracting radiomic features from contrast-enhanced CT images. Based on the radiomic signature and clinical characteristics, radiomic nomogram was developed. ROC curves and Delong's test were used to evaluate the diagnostic efficacy of the three models, calibration curves and application decision curves were used to analyze the accuracy and clinical application value of nomogram. Results Logistic regression results showed that TNM stage (stage IV) (OR 6.8, 95% CI 1.320-43.164, p=0. 028) was an independent factor affecting the diagnosis for NECs of the digestive system, and a clinical model was constructed based on TNM stage (stage IV). The AUCs of the clinical model, radiomics signature, and radiomics nomogram for the diagnosis of NECs of the digestive system in the training, validation cohorts and pooled patients were 0.643, 0.893, 0.913; 0.722, 0.867, 0.932 and 0.667, 0.887, 0.917 respectively. The AUCs of radiomics signature and radiomics nomogram were higher than clinical model, with statistically significant difference (Z=4.46, 6.85, both p < 0.001); the AUC difference between radiomics signature and radiomics nomogram was not statistically significant (Z=1.63, p = 0.104). The results of the calibration curve showed favorable agreement between the predicted values of the nomogram and the pathological results, and the decision curve analysis indicated that the nomogram had favorable application in clinical practice. Conclusions The nomogram constructed based on contrast-enhanced CT radiomics and clinical characteristics was able to effectively diagnose neuroendocrine carcinoma of the digestive system.
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Affiliation(s)
- Liang Xu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xinyi Yang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenxuan Xiang
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Pengbo Hu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiuyuan Zhang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhou Li
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yiming Li
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yongqing Liu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuhong Dai
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Luo
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Hong Qiu, ; Yan Luo,
| | - Hong Qiu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Hong Qiu, ; Yan Luo,
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Mori M, Palumbo D, Muffatti F, Partelli S, Mushtaq J, Andreasi V, Prato F, Ubeira MG, Palazzo G, Falconi M, Fiorino C, De Cobelli F. Prediction of the characteristics of aggressiveness of pancreatic neuroendocrine neoplasms (PanNENs) based on CT radiomic features. Eur Radiol 2022; 33:4412-4421. [PMID: 36547673 DOI: 10.1007/s00330-022-09351-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/13/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To predict tumor grade (G1 vs. G2/3), presence of distant metastasis (M+), metastatic lymph nodes (N+), and microvascular invasion (VI) of pancreatic neuroendocrine neoplasms (PanNEN) based on preoperative CT radiomic features (RFs), by applying a machine learning approach aimed to limit overfit. METHODS This retrospective study included 101 patients who underwent surgery for PanNEN; the entire population was split into training (n = 70) and validation cohort (n = 31). Based on a previously validated methodology, after tumor segmentation on contrast-enhanced CT, RFs were extracted from unenhanced CT images. In addition, conventional radiological and clinical features were combined with RFs into multivariate logistic regression models using minimum redundancy and a bootstrap-based machine learning approach. For each endpoint, models were trained and validated including only RFs (RF_model), and both (radiomic and clinicoradiological) features (COMB_model). RESULTS Twenty-five patients had G2/G3 tumor, 37 N+, and 14 M+ and 38 were shown to have VI. From a total of 182 RFs initially extracted, few independent radiomic and clinicoradiological features were identified. For M+ and G, the resulting models showed moderate to high performances: areas under the curve (AUC) for training/validation cohorts were 0.85/0.77 (RF_model) and 0.81/0.81 (COMB_model) for M+ and 0.67/0.72 and 0.68/0.70 for G. Concerning N+ and VI, only the COMB_model could be built, with poorer performance for N+ (AUC = 0.72/0.61) compared to VI (0.82/0.75). For all endpoints, the negative predictive value was good (≥ 0.75). CONCLUSIONS Combining few radiomic and clinicoradiological features resulted in presurgical prediction of histological characteristics of PanNENs. Despite the limited risk of overfit, external validations are warranted. KEY POINTS • Histology is the only tool currently available allowing characterization of PanNEN biological characteristics important for prognostic assessment; significant limitations to this approach exist. • Based upon preoperative contrast-enhanced CT images, a machine learning approach optimized to favor models' generalizability was successfully applied to train predictive models for tumor grading (G1 vs. G2/3), microvascular invasion, metastatic lymph nodes, and distant metastatic spread. • Moderate to high discriminative models (AUC: 0.67-0.85) based on few parameters (≤ 3) showing high negative predictive value (0.75-0.98) were generated and then successfully validated.
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Prosperi D, Gentiloni Silveri G, Panzuto F, Faggiano A, Russo VM, Caruso D, Polici M, Lauri C, Filice A, Laghi A, Signore A. Nuclear Medicine and Radiological Imaging of Pancreatic Neuroendocrine Neoplasms: A Multidisciplinary Update. J Clin Med 2022; 11:jcm11226836. [PMID: 36431313 PMCID: PMC9694730 DOI: 10.3390/jcm11226836] [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: 10/03/2022] [Revised: 11/14/2022] [Accepted: 11/16/2022] [Indexed: 11/22/2022] Open
Abstract
Pancreatic neuroendocrine neoplasms (panNENs) are part of a large family of tumors arising from the neuroendocrine system. PanNENs show low-intermediate tumor grade and generally high somatostatin receptor (SSTR) expression. Therefore, panNENs benefit from functional imaging with 68Ga-somatostatin analogues (SSA) for diagnosis, staging, and treatment choice in parallel with morphological imaging. This narrative review aims to present conventional imaging techniques and new perspectives in the management of panNENs, providing the clinicians with useful insight for clinical practice. The 68Ga-SSA PET/CT is the most widely used in panNENs, not only fr diagnosis and staging purpose but also to characterize the biology of the tumor and its responsiveness to SSAs. On the contrary, the 18F-Fluordeoxiglucose (FDG) PET/CT is not employed systematically in all panNEN patients, being generally preferred in G2-G3, to predict aggressiveness and progression rate. The combination of 68Ga-SSA PET/CT and 18F-FDG PET/CT can finally suggest the best therapeutic strategy. Other radiopharmaceuticals are 68Ga-exendin-4 in case of insulinomas and 18F-dopamine (DOPA), which can be helpful in SSTR-negative tumors. New promising but still-under-investigation radiopharmaceuticals include radiolabeled SSTR antagonists and 18F-SSAs. Conventional imaging includes contrast enhanced CT and multiparametric MRI. There are now enriched by radiomics, a new non-invasive imaging approach, very promising to early predict tumor response or progression.
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Affiliation(s)
- Daniela Prosperi
- Nuclear Medicine Unit, Department of Medical-Surgical Sciences and of Translational Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, 00189 Roma, Italy
| | - Guido Gentiloni Silveri
- Nuclear Medicine Unit, Department of Medical-Surgical Sciences and of Translational Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, 00189 Roma, Italy
| | - Francesco Panzuto
- Digestive Disease Unit, Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea University Hospital, ENETS Center of Excellence, Sapienza University of Rome, 00189 Roma, Italy
| | - Antongiulio Faggiano
- Endocrinology Unit, Department of Clinical and Molecular Medicine, Sant’Andrea University Hospital, ENETS Center of Excellence, Sapienza University of Rome, 00189 Roma, Italy
| | - Vincenzo Marcello Russo
- Nuclear Medicine Unit, Department of Medical-Surgical Sciences and of Translational Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, 00189 Roma, Italy
| | - Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, 00189 Roma, Italy
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, 00189 Roma, Italy
| | - Chiara Lauri
- Nuclear Medicine Unit, Department of Medical-Surgical Sciences and of Translational Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, 00189 Roma, Italy
- Correspondence:
| | - Angelina Filice
- Nucler Medicine Unit, AUSL-IRCCS di Reggio Emilia, 42123 Reggio Emilia, Italy
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, 00189 Roma, Italy
| | - Alberto Signore
- Nuclear Medicine Unit, Department of Medical-Surgical Sciences and of Translational Medicine, Sant’Andrea University Hospital, Sapienza University of Rome, 00189 Roma, Italy
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20
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Chiti G, Grazzini G, Flammia F, Matteuzzi B, Tortoli P, Bettarini S, Pasqualini E, Granata V, Busoni S, Messserini L, Pradella S, Massi D, Miele V. Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): a radiomic model to predict tumor grade. Radiol Med 2022; 127:928-938. [DOI: 10.1007/s11547-022-01529-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/12/2022] [Indexed: 11/30/2022]
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21
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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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22
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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.
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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
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Chen HY, Pan Y, Chen JY, Liu LL, Yang YB, Li K, Yu RS, Shao GL. Quantitative analysis of enhanced CT in differentiating well-differentiated pancreatic neuroendocrine tumors and poorly differentiated neuroendocrine carcinomas. Eur Radiol 2022; 32:8317-8325. [PMID: 35759016 DOI: 10.1007/s00330-022-08891-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/02/2022] [Accepted: 05/18/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To identify quantitative CT features for distinguishing well-differentiated pancreatic neuroendocrine tumors (PNETs) from poorly differentiated pancreatic neuroendocrine carcinomas (PNECs). MATERIALS AND METHODS Seventeen patients with PNECs and 131 patients with PNETs confirmed by biopsy or surgery were retrospectively included. General demographic (sex, age) and CT quantitative parameters (arterial/portal absolute enhancement, arterial/portal relative enhancement ratio, arterial/portal enhancement ratio) were collected. Univariate and multivariate logistic regression analyses were performed to confirm independent variables for differentiating PNECs from PNETs. Receiver operating characteristic (ROC) curves for each quantitative parameter were generated to determine their diagnostic ability. RESULTS PNECs had a much lower mean arterial/portal absolute enhancement value (19.5 ± 11.0 vs. 78.8 ± 47.2; 28.1 ± 15.8 vs. 77.0 ± 39.4), arterial/portal relative enhancement ratio (0.57 ± 0.36 vs. 2.03 ± 1.31; 0.80 ± 0.52 vs. 1.99 ± 1.13), and arterial/portal enhancement ratio (0.62 ± 0.27 vs. 1.22 ± 0.49; 0.74 ± 0.19 vs. 1.21 ± 0.36) than PNETs (all p < 0.001). After multivariable analysis, arterial absolute enhancement (odds ratio [OR]: 0.96, 95% confidence interval [CI]: 0.93, 0.99) and portal absolute enhancement (OR: 0.96, 95% CI: 0.92, 0.99) were independent factors for differentiating PNECs from PNETs. For each quantitative parameter, arterial lesion enhancement yielded the highest diagnostic performance, with an area under the curve (AUC) of 0.922 (95% CI: 0.867-0.960), followed by portal absolute enhancement. CONCLUSIONS Arterial/portal absolute enhancements were independent predictors with good diagnostic accuracy for differentiating between PNETs and PNECs. Quantitative parameters of enhanced CT can distinguish PNECs from PNETs. KEY POINTS • PNECs were hypovascular and had a much lower enhanced CT attenuation in both arterial and portal phases than well-differentiated PNETs. • Quantitative parameters derived from enhanced CT can be used to distinguish PNECs from PNETs. • Arterial absolute enhancement and portal absolute enhancement were independent predictive factors for differentiating between PNETs and PNECs.
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Affiliation(s)
- Hai-Yan Chen
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, 310018, China
| | - Yao Pan
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Jiefang Road 88#, Hangzhou, 310009, China
| | - Jie-Yu Chen
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, 310018, China
| | - Lu-Lu Liu
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, 310018, China
| | - Yong-Bo Yang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, 310018, China
| | - Kai Li
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, 310018, China
| | - Ri-Sheng Yu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Jiefang Road 88#, Hangzhou, 310009, China.
| | - Guo-Liang Shao
- Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, 310018, China. .,Department of Interventional Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China. .,Clinical Research Center of Hepatobiliary and Pancreatic Diseases of Zhejiang Province, Qingchun Road 79#, Hangzhou, 310006, China.
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24
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Teng X, Zhang J, Zwanenburg A, Sun J, Huang Y, Lam S, Zhang Y, Li B, Zhou T, Xiao H, Liu C, Li W, Han X, Ma Z, Li T, Cai J. Building reliable radiomic models using image perturbation. Sci Rep 2022; 12:10035. [PMID: 35710850 PMCID: PMC9203573 DOI: 10.1038/s41598-022-14178-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 05/16/2022] [Indexed: 02/06/2023] Open
Abstract
Radiomic model reliability is a central premise for its clinical translation. Presently, it is assessed using test–retest or external data, which, unfortunately, is often scarce in reality. Therefore, we aimed to develop a novel image perturbation-based method (IPBM) for the first of its kind toward building a reliable radiomic model. We first developed a radiomic prognostic model for head-and-neck cancer patients on a training (70%) and evaluated on a testing (30%) cohort using C-index. Subsequently, we applied the IPBM to CT images of both cohorts (Perturbed-Train and Perturbed-Test cohort) to generate 60 additional samples for both cohorts. Model reliability was assessed using intra-class correlation coefficient (ICC) to quantify consistency of the C-index among the 60 samples in the Perturbed-Train and Perturbed-Test cohorts. Besides, we re-trained the radiomic model using reliable RFs exclusively (ICC > 0.75) to validate the IPBM. Results showed moderate model reliability in Perturbed-Train (ICC: 0.565, 95%CI 0.518–0.615) and Perturbed-Test (ICC: 0.596, 95%CI 0.527–0.670) cohorts. An enhanced reliability of the re-trained model was observed in Perturbed-Train (ICC: 0.782, 95%CI 0.759–0.815) and Perturbed-Test (ICC: 0.825, 95%CI 0.782–0.867) cohorts, indicating validity of the IPBM. To conclude, we demonstrated capability of the IPBM toward building reliable radiomic models, providing community with a novel model reliability assessment strategy prior to prospective evaluation.
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Affiliation(s)
- Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Alex Zwanenburg
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany.,Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Helmholtz Association / Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - Jiachen Sun
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Yuhua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Saikit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Yuanpeng Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Bing Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Xinyang Han
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Zongrui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China.
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25
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van der Velden D, Staal F, Aalbersberg E, Castagnoli F, Wilthagen E, Beets-Tan R. Prognostic value of CT characteristics in GEP-NET: a systematic review. Crit Rev Oncol Hematol 2022; 175:103713. [DOI: 10.1016/j.critrevonc.2022.103713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 05/04/2022] [Accepted: 05/16/2022] [Indexed: 11/16/2022] Open
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Abstract
The basic pancreatic lesions include location, size, shape, number, capsule, calcification/calculi, hemorrhage, cystic degeneration, fibrosis, pancreatic duct alterations, and microvessel. One or more basic lesions form a kind of pancreatic disease. As recognizing the characteristic imaging features of pancreatic basic lesions and their relationships with pathology aids in differentiating the variety of pancreatic diseases. The purpose of this study is to review the pathological and imaging features of the basic pancreatic lesions.
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Wu ZQ, Li Y, Sun NN, Xu Q, Zhou J, Su KK, Goyal H, Xu HG. Nomogram for preoperative estimation of histologic grade in gastrointestinal neuroendocrine tumors. Front Endocrinol (Lausanne) 2022; 13:991773. [PMID: 36353229 PMCID: PMC9637831 DOI: 10.3389/fendo.2022.991773] [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: 07/11/2022] [Accepted: 10/12/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The treatment strategies and prognosis for gastroenteropancreatic neuroendocrine tumors were associated with tumor grade. Preoperative predictive grading could be of great benefit in the selection of treatment options for patients. However, there is still a lack of effective non-invasive strategies to detect gastrointestinal neuroendocrine tumors (GI-NETs) grading preoperatively. METHODS The data on 147 consecutive GI-NETs patients was retrospectively collected from January 1, 2012, to December 31, 2019. Logistic regression was used to construct a predictive model of gastrointestinal neuroendocrine tumor grading using preoperative laboratory and imaging parameters.The validity of the model was assessed by area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS The factors associated with GI-NETs grading were age, tumor size, lymph nodes, neuron-specific enolase (NSE), hemoglobin (HGB) and sex, and two models were constructed by logistic regression for prediction. Combining these 6 factors, the nomogram was constructed for model 1 to distinguish between G3 and G1/2, achieving a good AUC of 0.921 (95% CI: 0.884-0.965), and the sensitivity, specificity, accuracy were 0.9167, 0.8256, 0.8630, respectively. The model 2 was to distinguish between G1 and G2/3, and the variables were age, tumor size, lymph nodes, NSE, with an AUC of 0.847 (95% CI: 0.799-0.915), and the sensitivity, specificity, accuracy were 0.7882, 0.8710, 0.8231, respectively. Two online web servers were established on the basis of the proposed nomogram to facilitate clinical use. Both models showed an excellent calibration curve through 1000 times bootstrapped dataset and the clinical usefulness were confirmed using decision curve analysis. CONCLUSION The model served as a valuable non-invasive tool for differentiating between different grades of GI-NETs, personalizing the calculation which can lead to a rational treatment choice.
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Affiliation(s)
- Zhi-Qi Wu
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Branch of National Clinical Research Center for Laboratory Medicine, Nanjing, Jiangsu, China
| | - Yan Li
- Academy for Advanced Interdisciplinary Studies, Peking University, Peking, China
| | - Na-Na Sun
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qin Xu
- Department of Laboratory Medicine, Jurong Hospital Affiliated to Jiangsu University, Jurong, China
| | - Jing Zhou
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Branch of National Clinical Research Center for Laboratory Medicine, Nanjing, Jiangsu, China
| | - Kan-Kan Su
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Branch of National Clinical Research Center for Laboratory Medicine, Nanjing, Jiangsu, China
| | - Hemant Goyal
- Department of Internal Medicine, Mercer University School of Medicine, Macon, GA, United States
| | - Hua-Guo Xu
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Branch of National Clinical Research Center for Laboratory Medicine, Nanjing, Jiangsu, China
- *Correspondence: Hua-Guo Xu,
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Casà C, Piras A, D’Aviero A, Preziosi F, Mariani S, Cusumano D, Romano A, Boskoski I, Lenkowicz J, Dinapoli N, Cellini F, Gambacorta MA, Valentini V, Mattiucci GC, Boldrini L. The impact of radiomics in diagnosis and staging of pancreatic cancer. Ther Adv Gastrointest Endosc 2022; 15:26317745221081596. [PMID: 35342883 PMCID: PMC8943316 DOI: 10.1177/26317745221081596] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 02/02/2022] [Indexed: 02/05/2023] Open
Abstract
INTRODUCTION Pancreatic cancer (PC) is one of the most aggressive tumours, and better risk stratification among patients is required to provide tailored treatment. The meaning of radiomics and texture analysis as predictive techniques are not already systematically assessed. The aim of this study is to assess the role of radiomics in PC. METHODS A PubMed/MEDLINE and Embase systematic review was conducted to assess the role of radiomics in PC. The search strategy was 'radiomics [All Fields] AND ("pancreas" [MeSH Terms] OR "pancreas" [All Fields] OR "pancreatic" [All Fields])' and only original articles referred to PC in humans in the English language were considered. RESULTS A total of 123 studies and 183 studies were obtained using the mentioned search strategy on PubMed and Embase, respectively. After the complete selection process, a total of 56 papers were considered eligible for the analysis of the results. Radiomics methods were applied in PC for assessment technical feasibility and reproducibility aspects analysis, risk stratification, biologic or genomic status prediction and treatment response prediction. DISCUSSION Radiomics seems to be a promising approach to evaluate PC from diagnosis to treatment response prediction. Further and larger studies are required to confirm the role and allowed to include radiomics parameter in a comprehensive decision support system.
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Affiliation(s)
- Calogero Casà
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | | | - Francesco Preziosi
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Silvia Mariani
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Davide Cusumano
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Angela Romano
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Ivo Boskoski
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCSS, Rome, Italy
| | - Jacopo Lenkowicz
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Nicola Dinapoli
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Francesco Cellini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Maria Antonietta Gambacorta
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Vincenzo Valentini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Gian Carlo Mattiucci
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luca Boldrini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
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Xv Y, Lv F, Guo H, Liu Z, Luo D, Liu J, Gou X, He W, Xiao M, Zheng Y. A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma. Front Oncol 2021; 11:712554. [PMID: 34926241 PMCID: PMC8677659 DOI: 10.3389/fonc.2021.712554] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 11/02/2021] [Indexed: 11/29/2022] Open
Abstract
Objective This study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs). Methods 370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). Results Using multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness. Conclusion The CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.
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Affiliation(s)
- Yingjie Xv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Haoming Guo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhaojun Liu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Di Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Liu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xin Gou
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weiyang He
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingzhao Xiao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yineng Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Wang H, Chen X, Liu H, Yu C, He L. [Computed tomography-based radiomics for differential of retroperitoneal neuroblastoma and ganglioneuroblastoma in children]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2021; 41:1569-1576. [PMID: 34755674 DOI: 10.12122/j.issn.1673-4254.2021.10.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To explore the value of CT-based radiomics in differential diagnosis of retroperitoneal neuroblastoma (NB) and ganglioneuroblastoma (GNB) in children. METHODS A total of 172 children with NB and 48 children with GNB were assigned into the training set and testing set at the ratio of 7∶3 using a random stratified sampling method. Radiomics features were extracted and selected from non-enhanced and post-enhanced CT images. Based on the subset of optimal features, a multivariate regression model was used to establish the radiomics models for each phase and the combined radiomics models. The ROC curves of the models were drawn, and the evaluation indexes such as AUC, accuracy, sensitivity and specificity of these models were calculated and compared. RESULTS A total of 1218 radiomics features were extracted from the CT images acquired in non-enhanced (NP), arterial (AP) and venous phases (VP), from which 4 features from the NP model, 3 features from the AP model, 2 features from the VP model and 5 features from the combined model were selected. The AUC of the NP model in the training set and testing set was 0.840 (95% CI: 0.778-0.902) and 0.804 (95% CI: 0.699-0.899), respectively, as compared with 0.819 (95%CI: 0.759-0.877) and 0.815 (95%CI: 0.697-0.915) for the AP model, 0.730 (95%CI: 0.649-0.803) and 0.751 (95%CI: 0.619-0.869) for the VP model, and 0.861 (95%CI: 0.809-0.910) and 0.827 (95%CI: 0.726-0.915) for the combined model. CONCLUSION Radiomics signature based on non-enhanced and post-enhanced CT images can be helpful for distinguishing retroperitoneal NB and GNB in children. Compared with the first-order histogram features, textural features can better reflect the difference of the lesions. NP, AP and VP models have similar classification efficacy in differentiating retroperitoneal NB and GNB. The efficacy of the combined model is similar to that of the NP and AP models, but superior to that of the VP model.
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Affiliation(s)
- H Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing 400014, China
| | - X Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing 400014, China
| | - H Liu
- GE Healthcare, Shanghai 201203, China
| | - C Yu
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing 400014, China
| | - L He
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing 400014, China
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31
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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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32
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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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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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Limited Clinical Application of CT-Based Prediction Model for Pathologic Grade of Pancreatic Neuroendocrine Tumor. AJR Am J Roentgenol 2021; 216:W29. [PMID: 33729879 DOI: 10.2214/ajr.20.25213] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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35
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Wang R, Liu H, Liang P, Zhao H, Li L, Gao J. Radiomics analysis of CT imaging for differentiating gastric neuroendocrine carcinomas from gastric adenocarcinomas. Eur J Radiol 2021; 138:109662. [PMID: 33774440 DOI: 10.1016/j.ejrad.2021.109662] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 01/29/2021] [Accepted: 03/16/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To develop and evaluate a CT-based radiomics nomogram for differentiating gastric neuroendocrine carcinomas (NECs) from gastric adenocarcinomas (ADCs). METHODS CT images of 63 patients with gastric NECs were collected retrospectively, and 63 patients with gastric ADCs were selected as the control group. Univariate analysis was used to identify the significant factors of clinical characteristics and CT findings for differentiating gastric NECs from ADCs. Radiomics analysis was applied to CT images of unenhanced, arterial phase and venous phase, respectively. A radiomics nomogram incorporating the radiomics signature and the subjective CT findings was developed and its diagnostic ability was evaluated. The diagnostic performances of CT findings model, radiomics signature and radiomics nomogram were compared using DeLong test. RESULTS The tumor margin and lymph node (LN) metastasis were independent predictors for differentiating gastric NECs from ADCs. The radiomics signature based on venous phase presented superior AUC of 0.798 [95 % confidence interval (CI), 0.657-0.938] in validation cohort. The nomogram incorporated the radiomics signature, tumor margin and LN metastasis showed AUCs of 0.821 (95 %CI: 0.725-0.895) in the primary cohort and 0.809 (95 %CI: 0.649-0.918) in the validation cohort. Moreover, the radiomics nomogram showed good discrimination and calibration. The diagnostic performance of CT findings model was significantly lower than that of radiomics nomogram (p = 0.001) and radiomics signature (p = 0.025). CONCLUSIONS Radiomics analysis exhibited good performance in differentiating gastric NECs from ADCs, and the radiomics nomogram may have significant clinical implications on preoperative detection of gastric malignant tumors.
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Affiliation(s)
- Rui Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Huan Liu
- Advanced Application Team, GE Healthcare, Shanghai, 201203, China
| | - Pan Liang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Huiping Zhao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Liming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
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36
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CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors. Radiol Med 2021; 126:745-760. [PMID: 33523367 DOI: 10.1007/s11547-021-01333-z] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 01/11/2021] [Indexed: 12/11/2022]
Abstract
PURPOSE To assess the ability of radiomic features (RF) extracted from contrast-enhanced CT images (ceCT) and non-contrast-enhanced (non-ceCT) in discriminating histopathologic characteristics of pancreatic neuroendocrine tumors (panNET). METHODS panNET contours were delineated on pre-surgical ceCT and non-ceCT. First- second- and higher-order RF (adjusted to eliminate redundancy) were extracted and correlated with histological panNET grade (G1 vs G2/G3), metastasis, lymph node invasion, microscopic vascular infiltration. Mann-Whitney with Bonferroni corrected p values assessed differences. Discriminative power of significant RF was calculated for each of the end-points. The performance of conventional-imaged-based-parameters was also compared to RF. RESULTS Thirty-nine patients were included (mean age 55-years-old; 24 male). Mean diameters of the lesions were 24 × 27 mm. Sixty-nine RF were considered. Sphericity could discriminate high grade tumors (AUC = 0.79, p = 0.002). Tumor volume (AUC = 0.79, p = 0.003) and several non-ceCT and ceCT RF were able to identify microscopic vascular infiltration: voxel-alignment, neighborhood intensity-difference and intensity-size-zone families (AUC ≥ 0.75, p < 0.001); voxel-alignment, intensity-size-zone and co-occurrence families (AUC ≥ 0.78, p ≤ 0.002), respectively). Non-ceCT neighborhood-intensity-difference (AUC = 0.75, p = 0.009) and ceCT intensity-size-zone (AUC = 0.73, p = 0.014) identified lymph nodal invasion; several non-ceCT and ceCT voxel-alignment family features were discriminative for metastasis (p < 0.01, AUC = 0.80-0.85). Conventional CT 'necrosis' could discriminate for microscopic vascular invasion (AUC = 0.76, p = 0.004) and 'arterial vascular invasion' for microscopic metastasis (AUC = 0.86, p = 0.001). No conventional-imaged-based-parameter was significantly associated with grade and lymph node invasion. CONCLUSIONS Radiomic features can discriminate histopathology of panNET, suggesting a role of radiomics as a non-invasive tool for tumor characterization. TRIAL REGISTRATION NUMBER NCT03967951, 30/05/2019.
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CT and MRI of pancreatic tumors: an update in the era of radiomics. Jpn J Radiol 2020; 38:1111-1124. [PMID: 33085029 DOI: 10.1007/s11604-020-01057-6] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 10/08/2020] [Indexed: 02/07/2023]
Abstract
Radiomics is a relatively new approach for image analysis. As a part of radiomics, texture analysis, which consists in extracting a great amount of quantitative data from original images, can be used to identify specific features that can help determining the actual nature of a pancreatic lesion and providing other information such as resectability, tumor grade, tumor response to neoadjuvant therapy or survival after surgery. In this review, the basic of radiomics, recent developments and the results of texture analysis using computed tomography and magnetic resonance imaging in the field of pancreatic tumors are presented. Future applications of radiomics, such as artificial intelligence, are discussed.
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