|
1
|
Chen HY, Pan Y, Chen JY, Chen J, Liu LL, Yang YB, Li K, Ma Q, Shi L, Yu RS, Shao GL. Machine Learning Methods Based on CT Features Differentiate G1/G2 From G3 Pancreatic Neuroendocrine Tumors. Acad Radiol 2024; 31:1898-1905. [PMID: 38052672 DOI: 10.1016/j.acra.2023.10.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 12/07/2023]
Abstract
RATIONALE AND OBJECTIVES To identify CT features for distinguishing grade 1 (G1)/grade 2 (G2) from grade 3 (G3) pancreatic neuroendocrine tumors (PNETs) using different machine learning (ML) methods. MATERIALS AND METHODS A total of 147 patients with 155 lesions confirmed by pathology were retrospectively included. Clinical-demographic and radiological CT features was collected. The entire cohort was separated into training and validation groups at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) algorithm and principal component analysis (PCA) were used to select features. Three ML methods, namely logistic regression (LR), support vector machine (SVM), and K-nearest neighbor (KNN) were used to build a differential model. Receiver operating characteristic (ROC) curves and precision-recall curves for each ML method were generated. The area under the curve (AUC), accuracy rate, sensitivity, and specificity were calculated. RESULTS G3 PNETs were more likely to present with invasive behaviors and lower enhancement than G1/G2 PNETs. The LR classifier yielded the highest AUC of 0.964 (95% confidence interval [CI]: 0.930, 0.972), with 95.4% accuracy rate, 95.7% sensitivity, and 92.9% specificity, followed by SVM (AUC: 0.957) and KNN (AUC: 0.893) in the training group. In the validation group, the SVM classier reached the highest AUC of 0.952 (95% CI: 0.860, 0.981), with 91.5% accuracy rate, 97.3% sensitivity, and 70% specificity, followed by LR (AUC: 0.949) and KNN (AUC: 0.923). CONCLUSIONS The LR and SVM classifiers had the best performance in the training group and validation group, respectively. ML method could be helpful in differentiating between G1/G2 and G3 PNETs.
Collapse
Affiliation(s)
- Hai-Yan Chen
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China (H.-Y.C., J.-Y.C., L.-L.L., Y.-B.Y., K.L., Q.M., L.S.)
| | - Yao Pan
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China (Y.P., R.-S.Y.)
| | - Jie-Yu Chen
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China (H.-Y.C., J.-Y.C., L.-L.L., Y.-B.Y., K.L., Q.M., L.S.)
| | - Jia Chen
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou 311121, Zhejiang Province, China (J.C.)
| | - Lu-Lu Liu
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China (H.-Y.C., J.-Y.C., L.-L.L., Y.-B.Y., K.L., Q.M., L.S.)
| | - Yong-Bo Yang
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China (H.-Y.C., J.-Y.C., L.-L.L., Y.-B.Y., K.L., Q.M., L.S.)
| | - Kai Li
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China (H.-Y.C., J.-Y.C., L.-L.L., Y.-B.Y., K.L., Q.M., L.S.)
| | - Qian Ma
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China (H.-Y.C., J.-Y.C., L.-L.L., Y.-B.Y., K.L., Q.M., L.S.)
| | - Lei Shi
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China (H.-Y.C., J.-Y.C., L.-L.L., Y.-B.Y., K.L., Q.M., L.S.)
| | - Ri-Sheng Yu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China (Y.P., R.-S.Y.)
| | - Guo-Liang Shao
- Department of Interventional Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang Province, China (G.-L.S.); Clinical Research Center of Hepatobiliary and pancreatic diseases of Zhejiang Province, Hangzhou 310006, Zhejiang Province, China (G.-L.S.).
| |
Collapse
|
|
2
|
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
|
|
3
|
Takikawa T, Kikuta K, Hamada S, Kume K, Miura S, Yoshida N, Tanaka Y, Matsumoto R, Ikeda M, Kataoka F, Sasaki A, Hayashi H, Hatta W, Ogata Y, Nakagawa K, Unno M, Masamune A. A New Preoperative Scoring System for Predicting Aggressiveness of Non-Functioning Pancreatic Neuroendocrine Neoplasms. Diagnostics (Basel) 2022; 12:diagnostics12020397. [PMID: 35204488 PMCID: PMC8870938 DOI: 10.3390/diagnostics12020397] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/01/2022] [Accepted: 02/01/2022] [Indexed: 12/04/2022] Open
Abstract
The management of non-functioning pancreatic neuroendocrine neoplasms (NF-PanNENs) is still controversial. This study aimed to develop a new scoring system for treatment decisions at initial diagnosis based on the identification of the predictive factors for aggressive NF-PanNENs. Seventy-seven patients who had been pathologically diagnosed with NF-PanNENs were enrolled. We retrospectively reviewed 13 variables that could be assessed preoperatively. Univariate and multivariate stepwise logistic regression analyses were performed to identify factors for the aggressiveness of NF-PanNENs, and a scoring system was developed by assigning weighted points proportional to their β regression coefficient. Tumor size > 20 mm on contrast-enhanced computed tomography, tumor non-vascularity, and Ki-67 labeling index ≥5% on endoscopic ultrasound-guided fine-needle aspiration specimens were identified as independent factors for predicting the aggressiveness of NF-PanNENs. The new scoring system, developed using the identified factors, had an excellent discrimination ability, with area under the curve of 0.92 (95% CI, 0.85–0.99), and good calibration (p = 0.72, Hosmer-Lemeshow test). Ten-year overall survival rates in low-risk (0 point), intermediate-risk (1 to 2 points), and high-risk (3 to 4 points) groups were 100%, 90.9%, and 24.3%, respectively. This new scoring system would be useful for treatment decisions and prognostic prediction at initial diagnosis.
Collapse
Affiliation(s)
- Tetsuya Takikawa
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
| | - Kazuhiro Kikuta
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
| | - Shin Hamada
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
| | - Kiyoshi Kume
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
| | - Shin Miura
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
| | - Naoki Yoshida
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
| | - Yu Tanaka
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
| | - Ryotaro Matsumoto
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
| | - Mio Ikeda
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
| | - Fumiya Kataoka
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
| | - Akira Sasaki
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
| | - Hidehiro Hayashi
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
| | - Waku Hatta
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
| | - Yohei Ogata
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
| | - Kei Nakagawa
- Department of Surgery, Graduate School of Medicine, Tohoku University, Sendai 980-8574, Japan; (K.N.); (M.U.)
| | - Michiaki Unno
- Department of Surgery, Graduate School of Medicine, Tohoku University, Sendai 980-8574, Japan; (K.N.); (M.U.)
| | - Atsushi Masamune
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan; (T.T.); (K.K.); (S.H.); (K.K.); (S.M.); (N.Y.); (Y.T.); (R.M.); (M.I.); (F.K.); (A.S.); (H.H.); (W.H.); (Y.O.)
- Correspondence: ; Tel.:+81-22-717-7171; Fax: +81-22-717-7177
| |
Collapse
|
|
4
|
Imaging of Pancreatic Neuroendocrine Neoplasms. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18178895. [PMID: 34501485 PMCID: PMC8430610 DOI: 10.3390/ijerph18178895] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/16/2021] [Accepted: 08/22/2021] [Indexed: 12/25/2022]
Abstract
Pancreatic neuroendocrine neoplasms (panNENs) represent the second most common pancreatic tumors. They are a heterogeneous group of neoplasms with varying clinical expression and biological behavior, from indolent to aggressive ones. PanNENs can be functioning or non-functioning in accordance with their ability or not to produce metabolically active hormones. They are histopathologically classified according to the 2017 World Health Organization (WHO) classification system. Although the final diagnosis of neuroendocrine tumor relies on histologic examination of biopsy or surgical specimens, both morphologic and functional imaging are crucial for patient care. Morphologic imaging with ultrasonography (US), computed tomography (CT) and magnetic resonance imaging (MRI) is used for initial evaluation and staging of disease, as well as surveillance and therapy monitoring. Functional imaging techniques with somatostatin receptor scintigraphy (SRS) and positron emission tomography (PET) are used for functional and metabolic assessment that is helpful for therapy management and post-therapeutic re-staging. This article reviews the morphological and functional imaging modalities now available and the imaging features of panNENs. Finally, future imaging challenges, such as radiomics analysis, are illustrated.
Collapse
|
|
5
|
Bicci E, Cozzi D, Ferrari R, Grazzini G, Pradella S, Miele V. Pancreatic neuroendocrine tumours: spectrum of imaging findings. Gland Surg 2020; 9:2215-2224. [PMID: 33447574 DOI: 10.21037/gs-20-537] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Pancreatic neuroendocrine tumours (pNETs) are rare and heterogeneous group of neoplasms presenting with a wide variety of symptoms and biological behaviour, from indolent to aggressive ones. pNETs are stratified into functional or non-functional, because of their ability to produce metabolically active hormones. pNETs can be an isolate phenomenon or a part of a hereditary syndrome like von Hippel-Lindau syndrome or neurofibromatosis-1. The incidence has increased in the last years, also because of the improvement of cross-sectional imaging. Computed tomography (CT), magnetic resonance imaging (MRI) and functional imaging are the mainstay imaging modalities used for tumour detection and disease extension assessment, due to easy availability and better contrast/spatial resolution. Radiological imaging plays a fundamental role in detection, characterization and surveillance of pNETs and is involved in almost every stage of patients' management. Moreover, with specific indications and techniques, interventional radiology can also play a role in therapeutic management. Surgery is the treatment of choice, consisting of either partial pancreatectomy or enucleation of the primary tumour. This article reviews the radiologic features of different pNETs as well as imaging mimics, in order to help radiologists to avoid potential pitfalls, to reach the correct diagnosis and to support the multidisciplinary team in establishing the right treatment.
Collapse
Affiliation(s)
- Eleonora Bicci
- Department of Emergency Radiology, Careggi University Hospital, Florence, Italy
| | - Diletta Cozzi
- Department of Emergency Radiology, Careggi University Hospital, Florence, Italy
| | - Riccardo Ferrari
- Department of Emergency Radiology, San Camillo Forlanini Hospital, Rome, Italy
| | - Giulia Grazzini
- Department of Emergency Radiology, Careggi University Hospital, Florence, Italy
| | - Silvia Pradella
- Department of Emergency Radiology, Careggi University Hospital, Florence, Italy
| | - Vittorio Miele
- Department of Emergency Radiology, Careggi University Hospital, Florence, Italy
| |
Collapse
|
|
6
|
Salahshour F, Mehrabinejad MM, Zare Dehnavi A, Alibakhshi A, Dashti H, Ataee MA, Ayoobi Yazdi N. Pancreatic neuroendocrine tumors (pNETs): the predictive value of MDCT characteristics in the differentiation of histopathological grades. Abdom Radiol (NY) 2020; 45:3155-3162. [PMID: 31897681 DOI: 10.1007/s00261-019-02372-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
PURPOSE To investigate the correlation between multiple detector computed tomography (MDCT) features of pancreatic neuroendocrine tumors (pNETs) and histopathologic grade and find valuable imaging criteria for grade prediction. MATERIAL AND METHODS MDCT of 61 patients with 65 masses, which pNETs were approved histopathologically, underwent revision retrospectively. Each MDCT was evaluated for various radiologic characteristics. Absolute and relative (R: tumor/pancreas, D: tumor-pancreas) tumor enhancements were calculated in multiple post contrast phases. RESULTS 61 patients [mean age = 50.70 ± 14.28 y/o and 30(49.2%) were male] were evaluated and classified into 2 groups histopathologically: G1: 32 (49.2%) and G2,3: 33 (50.8%). Significant relationships were observed between histopathologic tumor grade regarding age (p = 0.006), the longest tumor size (p = 0.006), presence of heterogeneity (p < 0.0001), hypodense foci in delayed phase (p = 0.004), lobulation (p = 0.002), vascular encasement (p < 0.0001), adjacent organ invasion (p = 0.01), presence (p < 0.0001) and number (0.02) of liver metastases, presence of lymphadenopathy with short axis of more than 10 mm (LAP) (p = 0.008), pathologic lymph node size (p = 0.004), relative (R and D) (p = 0.05 and 0.02, respectively), and percentage of arterial hyper-enhancing area (p = <0.0001). Tumor grades, however, had no significant relationship with gender, tumor location, tumor outline, calcification, cystic change, or pancreatic (PD) or biliary duct (BD) dilation (p = 0.21, 0.60, 0.05, 0.05 1, 0.10, and 0.51, respectively). Then, we suggested a novel imaging criteria consisting of six parameters (tumor size > 33 mm, relative (R) tumor enhancement in arterial phase ≤ 1.33, relative (D) tumor enhancement in arterial phase ≤ 16.5, percentage of arterial hyper-enhancing area ≤ 75%, vascular encasement, and lobulation), which specificity and accuracy of combination of all findings (6/6) for predicting G2,3 were 100% and 70.1%, respectively. The highest accuracy (84.21%) was seen in combinations of at least 4 of 6 findings, with 80.00% sensitivity, 87.5% specificity, 83.33% PPV, and 84.85% NPV. CONCLUSION We suggested reliable imaging criteria with high specificity and accuracy for predicting the histopathologic grade of pNETs.
Collapse
Affiliation(s)
- Faeze Salahshour
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, 1419733141, Iran
| | - Mohammad-Mehdi Mehrabinejad
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, 1419733141, Iran
- Students Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Zare Dehnavi
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, 1419733141, Iran
- Students Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Abbas Alibakhshi
- Hepatobiliary and Liver Transplantation Division, Department of General Surgery, Imam-Khomeini Hospital, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Habibollah Dashti
- Hepatobiliary and Liver Transplantation Division, Department of General Surgery, Imam-Khomeini Hospital, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mohammad-Ali Ataee
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, 1419733141, Iran
| | - Niloofar Ayoobi Yazdi
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, 1419733141, Iran.
| |
Collapse
|
|
7
|
Beleù A, Rizzo G, De Robertis R, Drudi A, Aluffi G, Longo C, Sarno A, Cingarlini S, Capelli P, Landoni L, Scarpa A, Bassi C, D’Onofrio M. Liver Tumor Burden in Pancreatic Neuroendocrine Tumors: CT Features and Texture Analysis in the Prediction of Tumor Grade and 18F-FDG Uptake. Cancers (Basel) 2020; 12:cancers12061486. [PMID: 32517291 PMCID: PMC7352332 DOI: 10.3390/cancers12061486] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 05/30/2020] [Accepted: 06/03/2020] [Indexed: 02/08/2023] Open
Abstract
Pancreatic neuroendocrine tumors (p-NETs) are a rare group of neoplasms that often present with liver metastases. Histological characteristics, metabolic behavior, and liver tumor burden (LTB) are important prognostic factors. In this study, the usefulness of texture analysis of liver metastases in evaluating the biological aggressiveness of p-NETs was assessed. Fifty-six patients with liver metastases from p-NET were retrospectively enrolled. Qualitative and quantitative CT features of LTB were evaluated. Histogram-derived parameters of liver metastases were calculated and correlated with the tumor grade (G) and 18F-fluorodeoxyglucose (18F-FDG) standardized uptake value (SUV). Arterial relative enhancement was inversely related with G (−0.37, p = 0.006). Different metastatic spread patterns of LTB were not associated with histological grade. Arterialentropy was significantly correlated to G (−0.368, p = 0.038) and to Ki67 percentage (−0.421, p = 0.018). The ROC curve for the Arterialentropy reported an area under the curve (AUC) of 0.736 (95% confidence interval 0.545–0.928, p = 0.035) in the identification of G1–2 tumors. Arterialuniformity values were correlated to G (0.346, p = 0.005) and Ki67 levels (0.383, p = 0.033). Arterialentropy values were directly correlated with the SUV (0.449, p = 0.047) which was inversely correlated with Arterialuniformity (−0.499, p = 0.025). Skewness and kurtosis reported no significant correlations. In conclusion, histogram-derived parameters may predict adverse histological features and metabolic behavior of p-NET liver metastases.
Collapse
Affiliation(s)
- Alessandro Beleù
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37134 Verona, Italy; (A.B.); (G.R.); (A.D.); (G.A.); (C.L.); (A.S.)
| | - Giulio Rizzo
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37134 Verona, Italy; (A.B.); (G.R.); (A.D.); (G.A.); (C.L.); (A.S.)
| | - Riccardo De Robertis
- Department of Radiology, Ospedale Civile Maggiore, AOUI Verona, 37134 Verona, Italy;
| | - Alessandro Drudi
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37134 Verona, Italy; (A.B.); (G.R.); (A.D.); (G.A.); (C.L.); (A.S.)
| | - Gregorio Aluffi
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37134 Verona, Italy; (A.B.); (G.R.); (A.D.); (G.A.); (C.L.); (A.S.)
| | - Chiara Longo
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37134 Verona, Italy; (A.B.); (G.R.); (A.D.); (G.A.); (C.L.); (A.S.)
| | - Alessandro Sarno
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37134 Verona, Italy; (A.B.); (G.R.); (A.D.); (G.A.); (C.L.); (A.S.)
| | - Sara Cingarlini
- Department of Oncology, G.B. Rossi Hospital, University of Verona, 37134 Verona, Italy;
| | - Paola Capelli
- Department of Pathology, G.B. Rossi Hospital, University of Verona, 37134 Verona, Italy; (P.C.); (A.S.)
| | - Luca Landoni
- Department of Surgery, G.B. Rossi Hospital, University of Verona, 37134 Verona, Italy; (L.L.); (C.B.)
| | - Aldo Scarpa
- Department of Pathology, G.B. Rossi Hospital, University of Verona, 37134 Verona, Italy; (P.C.); (A.S.)
| | - Claudio Bassi
- Department of Surgery, G.B. Rossi Hospital, University of Verona, 37134 Verona, Italy; (L.L.); (C.B.)
| | - Mirko D’Onofrio
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37134 Verona, Italy; (A.B.); (G.R.); (A.D.); (G.A.); (C.L.); (A.S.)
- Correspondence: ; Tel.: +39-045-812-4301
| |
Collapse
|
|
8
|
Grade 3 Pancreatic Neuroendocrine Tumors on MDCT: Establishing a Diagnostic Model and Comparing Survival Against Pancreatic Ductal Adenocarcinoma. AJR Am J Roentgenol 2020; 215:390-397. [PMID: 32432906 DOI: 10.2214/ajr.19.21921] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVE. The purpose of this study is to establish a diagnostic model for differentiating grade 3 (G3) pancreatic neuroendocrine tumors (PNETs) from pancreatic ductal adenocarcinomas (PDACs) and to analyze survival outcomes. MATERIALS AND METHODS. Twenty patients with G3 PNETs and 58 patients with PDACs confirmed by surgery or biopsy were retrospectively included. Demographic and radiologic information was collected. Univariate analyses and binary logistic regression analyses were performed to identify independent factors and establish a diagnostic model. An ROC curve was created to determine diagnostic ability. Kaplan-Meier survival analysis was performed. RESULTS. Patients with G3 PNETs were more likely to present with normal carbohydrate antigen (CA) 19-9 levels, normal pancreatic ducts, and round tumors with well-defined margins and higher portal enhancement ratios than were patients with PDAC (p < 0.05). After multivariate analysis, a normal CA 19-9 level (odds ratio, 0.0125; 95% CI, 0.0008-0.2036), round tumor shape (odds ratio, 0.0143; 95% CI, 0.0004-0.5461), and pancreatic duct dilation of 4 mm or less (odds ratio, 17.9804; 95% CI, 1.0098-320.1711) were independent predictors of G3 PNETs. The AUC of the ROC curve was 0.916, and sensitivity and specificity were 90.0% and 81.0%, respectively. Furthermore, patients with G3 PNETs had better overall survival than patients with PDACs. Among patients in the G3 PNET subgroup, patients with liver or lymph node metastases had worse overall survival than patients without metastases. CONCLUSION. A diagnostic model was established to differentiate G3 PNETs from PDACs. A normal CA 19-9 level, round tumor shape, and pancreatic duct dilation of 4 mm or less were factors that were strongly predictive of G3 PNET.
Collapse
|
|
9
|
Yang B, Chen HY, Zhang XY, Pan Y, Lu YF, Yu RS. The prognostic value of multidetector CT features in predicting overall survival outcomes in patients with pancreatic neuroendocrine tumors. Eur J Radiol 2020; 124:108847. [PMID: 31991300 DOI: 10.1016/j.ejrad.2020.108847] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 12/03/2019] [Accepted: 01/18/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE To assess the prognostic value of multidetector CT in predicting overall survival outcomes in patients with pancreatic neuroendocrine tumors (PNETs). METHOD Seventy-one patients pathologically diagnosed with PNETs were retrospectively included. The clinical and imaging information was evaluated by two radiologists. The difference between well-differentiated and poorly differentiated PNETs was analyzed. Cox proportional hazards models were created to determine the risk factors for overall survival. Kaplan-Meier survival analyses with log-rank tests were used among different subgroups of patients with PNETs. RESULTS In the whole cohort, the median survival was 36 months, and the 5-year survival rate was 84.8 %. Patients with poorly differentiated PNETs were more likely to present with symptoms, abnormal tumor markers, larger diameters, irregular shapes, ill-defined margins, invasion into nearby tissues, liver and lymph node metastases, and lower enhancement ratio than those with well-differentiated PNETs (P < 0.05). In the multivariate analysis, lymph node metastases (hazard ratio: 21.52, P = 0.009) and a portal enhancement ratio less than 1.02 (hazard ratio: 30.89, P = 0.024) were significant factors for overall survival. Overall survival decreased with an ill-defined margin, irregular shape, poor differentiation, grade 3 disease, nonfunctional status, abnormal tumor marker levels, invasion into nearby tissues, lymph node and liver metastases, and lower enhancement ratio (log-rank P < 0.05). CONCLUSIONS Poorly differentiated PNETs were more aggressiveness than well-differentiated PNETs. Lymph node metastases and a portal enhancement ratio < 1.02 were independent prognostic factors for worse overall survival outcomes in patients with PNETs.
Collapse
Affiliation(s)
- Bo Yang
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Department of Radiology, Zhejiang Prison Center Hospital (Zhejiang Youth Hospital), Hangzhou, China
| | - Hai-Yan Chen
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xue-Yan Zhang
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Department of Radiology, Institute of Occupational Diseases, Zhejiang Academy of Medical Sciences, Hangzhou, China
| | - Yao Pan
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuan-Fei Lu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ri-Sheng Yu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| |
Collapse
|
|
10
|
Singh A, Hines JJ, Friedman B. Multimodality Imaging of the Pancreatic Neuroendocrine Tumors. Semin Ultrasound CT MR 2019; 40:469-482. [DOI: 10.1053/j.sult.2019.04.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
|
|
11
|
Ren S, Chen X, Wang J, Zhao R, Song L, Li H, Wang Z. Differentiation of duodenal gastrointestinal stromal tumors from hypervascular pancreatic neuroendocrine tumors in the pancreatic head using contrast-enhanced computed tomography. Abdom Radiol (NY) 2019; 44:867-876. [PMID: 30293109 DOI: 10.1007/s00261-018-1803-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE To determine useful contrast-enhanced computed tomography (CE-CT) features in differentiating duodenal gastrointestinal stromal tumors (duodenal GISTs) from hypervascular pancreatic neuroendocrine tumors in the pancreatic head (pancreatic head NETs). METHODS Seventeen patients with pathologically confirmed duodenal GISTs and 25 with pancreatic NETs underwent preoperative CE-CT. CT image analysis included tumor size, morphology, and contrast enhancement. Receiver operating characteristic curves were performed, and cutoff values were calculated to determine CT findings with high sensitivity and specificity. RESULTS CT imaging showed duodenal GISTs with higher frequencies of tumor central location close to the duodenum and a predominantly solid tumor type when compared with pancreatic head NETs (p < 0.05 for both). Duodenal GISTs were larger than pancreatic head NETs (3.3 ± 0.9 cm vs. 2.5 ± 1.1 cm, p = 0.03). Duodenal GISTs had significantly lower CT attenuation values (112.9 ± 17.9HU vs. 137.4 ± 32.1HU, p < 0.01) at the arterial phase and higher CT attenuation values at the delayed phase (94.3 ± 7.9HU vs. 84.9 ± 10.4HU, p < 0.01) when compared with pancreatic head NETs. A CT attenuation value of ≤ 135 HU at the arterial phase (30 s) was 76% sensitive, 94.1% specific, and 83.3% accurate for the diagnosis of duodenal GISTs, while a CT attenuation value of ≥ 89.5 HU at the delayed phase (120 s) was 93.3% sensitive, 81.8% specific, and 76.2% accurate for the diagnosis of duodenal GISTs. CONCLUSION Tumor central location, size, texture, and contrast enhancement are valuable characteristics for the differentiation between duodenal GISTs and hypervascular pancreatic head NETs during preoperative examination.
Collapse
Affiliation(s)
- Shuai Ren
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
| | - Xiao Chen
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
| | - Jianhua Wang
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
| | - Rui Zhao
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
| | - Lina Song
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
| | - Hui Li
- Department of Pathology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu Province, China
| | - Zhongqiu Wang
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China.
| |
Collapse
|