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Barat M, Pellat A, Dohan A, Hoeffel C, Coriat R, Soyer P. CT and MRI of Gastrointestinal Stromal Tumors: New Trends and Perspectives. Can Assoc Radiol J 2024; 75:107-117. [PMID: 37386745 DOI: 10.1177/08465371231180510] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
Gastrointestinal stromal tumors (GISTs) are defined as mesenchymal tumors of the gastrointestinal tract that express positivity for CD117, which is a c-KIT proto-oncogene antigen. Expression of the c-KIT protein, a tyrosine kinase growth factor receptor, allows the distinction between GISTs and other mesenchymal tumors such as leiomyoma, leiomyosarcoma, schwannoma and neurofibroma. GISTs can develop anywhere in the gastrointestinal tract, as well as in the mesentery and omentum. Over the years, the management of GISTs has improved due to a better knowledge of their behaviors and risk or recurrence, the identification of specific mutations and the use of targeted therapies. This has resulted in a better prognosis for patients with GISTs. In parallel, imaging of GISTs has been revolutionized by tremendous progress in the field of detection, characterization, survival prediction and monitoring during therapy. Recently, a particular attention has been given to radiomics for the characterization of GISTs using analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence with the aim of better characterizing GISTs and providing a more precise assessment of tumor burden. This article sums up recent advances in computed tomography and magnetic resonance imaging of GISTs in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hopital Cochin, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
| | - Anna Pellat
- Université Paris Cité, Faculté de Médecine, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Paris, France
| | - Anthony Dohan
- Department of Radiology, Hopital Cochin, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
| | - Christine Hoeffel
- Reims Medical School, Department of Radiology, Hopital Robert Debré, CHU Reims, Université Champagne-Ardennes, Reims, France
| | - Romain Coriat
- Université Paris Cité, Faculté de Médecine, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Paris, France
| | - Philippe Soyer
- Department of Radiology, Hopital Cochin, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
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Jovanovic MM, Stefanovic AD, Sarac D, Kovac J, Jankovic A, Saponjski DJ, Tadic B, Kostadinovic M, Veselinovic M, Sljukic V, Skrobic O, Micev M, Masulovic D, Pesko P, Ebrahimi K. Possibility of Using Conventional Computed Tomography Features and Histogram Texture Analysis Parameters as Imaging Biomarkers for Preoperative Prediction of High-Risk Gastrointestinal Stromal Tumors of the Stomach. Cancers (Basel) 2023; 15:5840. [PMID: 38136387 PMCID: PMC10742259 DOI: 10.3390/cancers15245840] [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: 10/30/2023] [Revised: 12/06/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND The objective of this study is to determine the morphological computed tomography features of the tumor and texture analysis parameters, which may be a useful diagnostic tool for the preoperative prediction of high-risk gastrointestinal stromal tumors (HR GISTs). METHODS This is a prospective cohort study that was carried out in the period from 2019 to 2022. The study included 79 patients who underwent CT examination, texture analysis, surgical resection of a lesion that was suspicious for GIST as well as pathohistological and immunohistochemical analysis. RESULTS Textural analysis pointed out min norm (p = 0.032) as a histogram parameter that significantly differed between HR and LR GISTs, while min norm (p = 0.007), skewness (p = 0.035) and kurtosis (p = 0.003) showed significant differences between high-grade and low-grade tumors. Univariate regression analysis identified tumor diameter, margin appearance, growth pattern, lesion shape, structure, mucosal continuity, enlarged peri- and intra-tumoral feeding or draining vessel (EFDV) and max norm as significant predictive factors for HR GISTs. Interrupted mucosa (p < 0.001) and presence of EFDV (p < 0.001) were obtained by multivariate regression analysis as independent predictive factors of high-risk GISTs with an AUC of 0.878 (CI: 0.797-0.959), sensitivity of 94%, specificity of 77% and accuracy of 88%. CONCLUSION This result shows that morphological CT features of GIST are of great importance in the prediction of non-invasive preoperative metastatic risk. The incorporation of texture analysis into basic imaging protocols may further improve the preoperative assessment of risk stratification.
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Affiliation(s)
- Milica Mitrovic Jovanovic
- Center for Radiology and Magnetic Resonance Imaging, University Clinical Centre of Serbia, Pasterova No. 2, 11000 Belgrade, Serbia; (M.M.J.)
- Department for Radiology, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
| | - Aleksandra Djuric Stefanovic
- Center for Radiology and Magnetic Resonance Imaging, University Clinical Centre of Serbia, Pasterova No. 2, 11000 Belgrade, Serbia; (M.M.J.)
- Department for Radiology, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
| | - Dimitrije Sarac
- Center for Radiology and Magnetic Resonance Imaging, University Clinical Centre of Serbia, Pasterova No. 2, 11000 Belgrade, Serbia; (M.M.J.)
| | - Jelena Kovac
- Center for Radiology and Magnetic Resonance Imaging, University Clinical Centre of Serbia, Pasterova No. 2, 11000 Belgrade, Serbia; (M.M.J.)
- Department for Radiology, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
| | - Aleksandra Jankovic
- Center for Radiology and Magnetic Resonance Imaging, University Clinical Centre of Serbia, Pasterova No. 2, 11000 Belgrade, Serbia; (M.M.J.)
- Department for Radiology, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
| | - Dusan J. Saponjski
- Center for Radiology and Magnetic Resonance Imaging, University Clinical Centre of Serbia, Pasterova No. 2, 11000 Belgrade, Serbia; (M.M.J.)
- Department for Radiology, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
| | - Boris Tadic
- Department for HBP Surgery, Clinic for Digestive Surgery, University Clinical Centre of Serbia, Koste Todorovica Street, No. 6, 11000 Belgrade, Serbia
- Department for Surgery, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
| | - Milena Kostadinovic
- Center for Physical Medicine and Rehabilitation, University Clinical Centre of Serbia, Pasterova Street, No. 2, 11000 Beograd, Serbia
| | - Milan Veselinovic
- Department for Surgery, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
- Department of Stomach and Esophageal Surgery, Clinic for Digestive Surgery, University Clinical Centre of Serbia, Koste Todorovica Street No. 6, 11000 Belgrade, Serbia
| | - Vladimir Sljukic
- Department for Surgery, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
- Department of Stomach and Esophageal Surgery, Clinic for Digestive Surgery, University Clinical Centre of Serbia, Koste Todorovica Street No. 6, 11000 Belgrade, Serbia
| | - Ognjan Skrobic
- Department for Surgery, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
- Department of Stomach and Esophageal Surgery, Clinic for Digestive Surgery, University Clinical Centre of Serbia, Koste Todorovica Street No. 6, 11000 Belgrade, Serbia
| | - Marjan Micev
- Department for Pathology, Clinic for Digestive Surgery, University Clinical Centre of Serbia, Koste Todorovica Street, No. 6, 11000 Belgrade, Serbia
| | - Dragan Masulovic
- Center for Radiology and Magnetic Resonance Imaging, University Clinical Centre of Serbia, Pasterova No. 2, 11000 Belgrade, Serbia; (M.M.J.)
- Department for Radiology, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
| | - Predrag Pesko
- Department for Surgery, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
- Department of Stomach and Esophageal Surgery, Clinic for Digestive Surgery, University Clinical Centre of Serbia, Koste Todorovica Street No. 6, 11000 Belgrade, Serbia
| | - Keramatollah Ebrahimi
- Department for Surgery, Faculty of Medicine, University of Belgrade, Dr Subotica No. 8, 11000 Belgrade, Serbia
- Department of Stomach and Esophageal Surgery, Clinic for Digestive Surgery, University Clinical Centre of Serbia, Koste Todorovica Street No. 6, 11000 Belgrade, Serbia
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Wang S, Dai P, Si G, Zeng M, Wang M. Multi-Slice CT Features Predict Pathological Risk Classification in Gastric Stromal Tumors Larger Than 2 cm: A Retrospective Study. Diagnostics (Basel) 2023; 13:3192. [PMID: 37892014 PMCID: PMC10606329 DOI: 10.3390/diagnostics13203192] [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: 08/16/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND The Armed Forces Institute of Pathology (AFIP) had higher accuracy and reliability in prognostic assessment and treatment strategies for patients with gastric stromal tumors (GSTs). The AFIP classification is frequently used in clinical applications. But the risk classification is only available for patients who are previously untreated and received complete resection. We aimed to investigate the feasibility of multi-slice MSCT features of GSTs in predicting AFIP risk classification preoperatively. METHODS The clinical data and MSCT features of 424 patients with solitary GSTs were retrospectively reviewed. According to pathological AFIP risk criteria, 424 GSTs were divided into a low-risk group (n = 282), a moderate-risk group (n = 72), and a high-risk group (n = 70). The clinical data and MSCT features of GSTs were compared among the three groups. Those variables (p < 0.05) in the univariate analysis were included in the multivariate analysis. The nomogram was created using the rms package. RESULTS We found significant differences in the tumor location, morphology, necrosis, ulceration, growth pattern, feeding artery, vascular-like enhancement, fat-positive signs around GSTs, CT value in the venous phase, CT value increment in the venous phase, longest diameter, and maximum short diameter (all p < 0.05). Two nomogram models were successfully constructed to predict the risk of GSTs. Low- vs. high-risk group: the independent risk factors of high-risk GSTs included the location, ulceration, and longest diameter. The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.911 (95% CI: 0.872-0.951), and the sensitivity and specificity were 80.0% and 89.0%, respectively. Moderate- vs. high-risk group: the morphology, necrosis, and feeding artery were independent risk factors of a high risk of GSTs, with an AUC value of 0.826 (95% CI: 0.759-0.893), and the sensitivity and specificity were 85.7% and 70.8%, respectively. CONCLUSIONS The MSCT features of GSTs and the nomogram model have great practical value in predicting pathological AFIP risk classification between high-risk and non-high-risk groups before surgery.
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Affiliation(s)
- Sikai Wang
- Department of Radiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, No. 182 Chunhui Road, Longmatan District, Luzhou 646000, China; (S.W.); (P.D.)
| | - Ping Dai
- Department of Radiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, No. 182 Chunhui Road, Longmatan District, Luzhou 646000, China; (S.W.); (P.D.)
| | - Guangyan Si
- Department of Radiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, No. 182 Chunhui Road, Longmatan District, Luzhou 646000, China; (S.W.); (P.D.)
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China;
| | - Mingliang Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China;
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Yang L, Du D, Zheng T, Liu L, Wang Z, Du J, Yi H, Cui Y, Liu D, Fang Y. Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI. Front Oncol 2022; 12:948557. [PMID: 36505814 PMCID: PMC9727176 DOI: 10.3389/fonc.2022.948557] [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: 05/20/2022] [Accepted: 11/02/2022] [Indexed: 11/24/2022] Open
Abstract
Introduction Preoperative evaluation of the mitotic index (MI) of gastrointestinal stromal tumors (GISTs) represents the basis of individualized treatment of patients. However, the accuracy of conventional preoperative imaging methods is limited. The aim of this study was to develop a predictive model based on multiparametric MRI for preoperative MI prediction. Methods A total of 112 patients who were pathologically diagnosed with GIST were enrolled in this study. The dataset was subdivided into the development (n = 81) and test (n = 31) sets based on the time of diagnosis. With the use of T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) map, a convolutional neural network (CNN)-based classifier was developed for MI prediction, which used a hybrid approach based on 2D tumor images and radiomics features from 3D tumor shape. The trained model was tested on an internal test set. Then, the hybrid model was comprehensively tested and compared with the conventional ResNet, shape radiomics classifier, and age plus diameter classifier. Results The hybrid model showed good MI prediction ability at the image level; the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and accuracy in the test set were 0.947 (95% confidence interval [CI]: 0.927-0.968), 0.964 (95% CI: 0.930-0.978), and 90.8 (95% CI: 88.0-93.0), respectively. With the average probabilities from multiple samples per patient, good performance was also achieved at the patient level, with AUROC, AUPRC, and accuracy of 0.930 (95% CI: 0.828-1.000), 0.941 (95% CI: 0.792-1.000), and 93.6% (95% CI: 79.3-98.2) in the test set, respectively. Discussion The deep learning-based hybrid model demonstrated the potential to be a good tool for the operative and non-invasive prediction of MI in GIST patients.
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Affiliation(s)
- Linsha Yang
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Dan Du
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Tao Zheng
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Lanxiang Liu
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Zhanqiu Wang
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Juan Du
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Huiling Yi
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Yujie Cui
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China
| | - Defeng Liu
- Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, China,*Correspondence: Defeng Liu, ; Yuan Fang,
| | - Yuan Fang
- Medical Imaging Center, Chongqing Yubei District People’s Hospital, Chongqing, China,*Correspondence: Defeng Liu, ; Yuan Fang,
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The Utility of Conventional CT, CT Perfusion and Quantitative Diffusion-Weighted Imaging in Predicting the Risk Level of Gastrointestinal Stromal Tumors of the Stomach: A Prospective Comparison of Classical CT Features, CT Perfusion Values, Apparent Diffusion Coefficient and Intravoxel Incoherent Motion-Derived Parameters. Diagnostics (Basel) 2022; 12:diagnostics12112841. [PMID: 36428901 PMCID: PMC9689886 DOI: 10.3390/diagnostics12112841] [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: 09/28/2022] [Revised: 11/01/2022] [Accepted: 11/09/2022] [Indexed: 11/19/2022] Open
Abstract
Background: The role of advanced functional imaging techniques in prediction of pathological risk categories of gastrointestinal stromal tumors (GIST) is still unknown. The purpose of this study was to evaluate classical CT features, CT-perfusion and magnetic-resonance-diffusion-weighted-imaging (MR-DWI)-related parameters in predicting the metastatic risk of gastric GIST. Patients and methods: Sixty-two patients with histologically proven GIST who underwent CT perfusion and MR-DWI using multiple b-values were prospectively included. Morphological CT characteristics and CT-perfusion parameters of tumor were comparatively analyzed in the high-risk (HR) and low-risk (LR) GIST groups. Apparent diffusion coefficient (ADC) and intravoxel-incoherent-motion (IVIM)-related parameters were also analyzed in 45 and 34 patients, respectively. Results: Binary logistic regression analysis revealed that greater tumor diameter (p < 0.001), cystic structure (p < 0.001), irregular margins (p = 0.007), irregular shape (p < 0.001), disrupted mucosa (p < 0.001) and visible EFDV (p < 0.001), as well as less ADC value (p = 0.001) and shorter time-to-peak (p = 0.006), were significant predictors of HR GIST. Multivariate analysis extracted irregular shape (p = 0.006) and enlarged feeding or draining vessels (EFDV) (p = 0.017) as independent predictors of HR GIST (area under curve (AUC) of predicting model 0.869). Conclusion: Although certain classical CT imaging features remain most valuable, some functional imaging parameters may add the diagnostic value in preoperative prediction of HR gastric GIST.
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Webb EM, Mongan J. Gastrointestinal Stromal Tumors: Radiomics may Increase the Role of Imaging in Malignant Risk Assessment. Acad Radiol 2022; 29:817-818. [PMID: 35248459 DOI: 10.1016/j.acra.2022.01.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 01/31/2022] [Indexed: 11/20/2022]
Affiliation(s)
- Emily M Webb
- University of California, San Francisco Department of Radiology and Biomedical Imaging, 505 Parnassus Ave., San Francisco, California 94143-0628.
| | - John Mongan
- University of California, San Francisco Department of Radiology and Biomedical Imaging, 505 Parnassus Ave., San Francisco, California 94143-0628
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Li S, Lin D, Tang M, Liu D, Lyu Q, Zhang J. Value of 18F-FDG PET/CT for differentiating diagnosis between malignant and benign primary gastric gastrointestinal mesenchymal tumors: a single-center retrospective study. J Gastrointest Oncol 2022; 13:637-646. [PMID: 35557562 PMCID: PMC9086061 DOI: 10.21037/jgo-22-287] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 04/13/2022] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Malignant primary gastric gastrointestinal stromal tumors (gGISTs) without treatment with imatinib are prone to bleeding and peritoneum implantation during operation. Therefore, preoperative assessment of the malignant potential of gGIST is essential. The use of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) combined with computed tomography (PET/CT) as a non-invasive tool for diagnosis, staging and prognosis evaluation in oncology, may also be useful for gGISTs. In the present study, we analyzed the value of 18F-FDG PET-CT in assessing the malignant potential of gGISTs before treatment. METHODS Patients who were diagnosed with gGIST by pathology and underwent 18F-FDG PET/CT at the same time were collected. The clinicopathological features of 26 patients with gGISTs were retrospectively analyzed at last. The gGIST risk classification was graded according to the US National Institutes of Health (NIH) GIST risk classification criteria [2008]. Lesions were classified as malignant group (moderate- or high-risk category) and benign group (low- or very low-risk category) according to pathology. The relationship between the maximal standard uptake value (SUVmax) and GIST risk category, tumor diameter, Ki-67 index, and mitotic count was analyzed. The cut-off level of SUVmax for the diagnosis of malignant gGIST with the highest sensitivity was calculated based on the receiver-operating characteristic (ROC) curve. RESULTS The SUVmax, tumor diameter, Ki-67 index, and mitotic count of the 26 gGIST patients were 5.90±4.49, 7.40±4.92 cm, 7.62%±11.76%, (5.96±3.19)/50 high-power field (HPF), respectively. SUVmax was significantly correlated with GIST risk category, Ki-67 index, and mitotic count (r=0.855, 0.860, and 0.690, all P<0.01) but not with tumor diameter (r=0.383, P=0.054). The SUVmax of gGIST was 7.00±4.57 in the malignant group (moderate or high NIH risk category in 20 patients), which was significantly different from that (2.25±0.77) in the benign group (low or extremely low NIH risk category in 6 patients) (t=4.566, P<0.01). ROC curve analysis showed that a SUVmax cut-off of 2.60 was most sensitive for predicting malignant gGIST. When the area under the curve was 0.967, the sensitivity was 100% and the specificity was 83.3%. CONCLUSIONS SUVmax may be used as a complementary indicator for predicting the malignant potential of gGISTs before treatment.
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Affiliation(s)
- Shengxu Li
- Department of Nuclear Medicine, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, Fuzhou, China
| | - Duanyu Lin
- Department of Nuclear Medicine, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, Fuzhou, China
| | - Mingdeng Tang
- Department of Nuclear Medicine, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, Fuzhou, China
| | - Daojia Liu
- Department of Nuclear Medicine, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, Fuzhou, China
| | - Qinghu Lyu
- Department of Nuclear Medicine, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, Fuzhou, China
| | - Jieping Zhang
- Department of Nuclear Medicine, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, Fuzhou, China
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Palatresi D, Fedeli F, Danti G, Pasqualini E, Castiglione F, Messerini L, Massi D, Bettarini S, Tortoli P, Busoni S, Pradella S, Miele V. Correlation of CT radiomic features for GISTs with pathological classification and molecular subtypes: preliminary and monocentric experience. Radiol Med 2022; 127:117-128. [PMID: 35022956 DOI: 10.1007/s11547-021-01446-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/30/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE Our primary purpose was to search for computed tomography (CT) radiomic features of gastrointestinal stromal tumors (GISTs) that could potentially correlate with the risk class according to the Miettinen classification. Subsequently, assess the existence of features with possible predictive value in differentiating responder from non-responder patients to first-line therapy with Imatinib. METHODS A retrospective study design was carried out using data from June 2009 to December 2020. We analyzed all the preoperative CTs of patients undergoing surgery for GISTs. We segmented non-contrast-enhanced CT (NCECT) and contrast-enhanced venous CT (CECT) images obtained either on three different CT scans (heterogeneous cohort) or on a single CT scan (homogeneous cohort). We then divided the patients into two groups according to Miettinen classification criteria and based on the predictive value of response to first-line therapy with Imatinib. RESULTS We examined 54 patients with pathological confirmation of GISTs. For the heterogeneous cohort, we found a statistically significant relationship between 57 radiomic features for NCECT and 56 radiomic features for CECT using the Miettinen risk classification. In the homogeneous cohort, we found the same relationship between 8 features for the NCECT and 5 features for CECT, all included in the heterogeneous cohort. The various radiomic features are distributed with different values in the two risk stratification groups according to the Miettinen classification. We also found some features for groups predictive of response to first-line therapy with Imatinib. CONCLUSIONS We found radiomic features that correlate with statistical significance for both the Miettinen risk classification and the molecular subtypes of response. All features found in the homogeneous study cohort were also found in the heterogeneous cohort. CT radiomic features may be useful in assessing the risk class and prognosis of GISTs.
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Affiliation(s)
- Daniele Palatresi
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Filippo Fedeli
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
| | - Elisa Pasqualini
- Pathology Unit, Department of Health Sciences, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Francesca Castiglione
- Histopathology and Molecular Diagnostics Unit, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Luca Messerini
- Department of Experimental and Clinical Medicine, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Daniela Massi
- Pathology Unit, Department of Health Sciences, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Silvia Bettarini
- Medical Physics Department, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Paolo Tortoli
- Medical Physics Department, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Simone Busoni
- Medical Physics Department, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
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Danti G, Flammia F, Matteuzzi B, Cozzi D, Berti V, Grazzini G, Pradella S, Recchia L, Brunese L, Miele V. Gastrointestinal neuroendocrine neoplasms (GI-NENs): hot topics in morphological, functional, and prognostic imaging. Radiol Med 2021; 126:1497-1507. [PMID: 34427861 PMCID: PMC8702509 DOI: 10.1007/s11547-021-01408-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 07/30/2021] [Indexed: 12/17/2022]
Abstract
Neuroendocrine neoplasms (NENs) are heterogeneous tumours with a common phenotype descended from the diffuse endocrine system. NENs are found nearly anywhere in the body but the most frequent location is the gastrointestinal tract. Gastrointestinal neuroendocrine neoplasms (GI-NENs) are rather uncommon, representing around 2% of all gastrointestinal tumours and 20–30% of all primary neoplasms of the small bowel. GI-NENs have various clinical manifestations due to the different substances they can produce; some of these tumours appear to be associated with familial syndromes, such as multiple endocrine neoplasm and neurofibromatosis type 1. The current WHO classification (2019) divides NENs into three major categories: well-differentiated NENs, poorly differentiated NENs, and mixed neuroendocrine-non-neuroendocrine neoplasms. The diagnosis, localization, and staging of GI-NENs include morphology and functional imaging, above all contrast-enhanced computed tomography (CECT), and in the field of nuclear medicine imaging, a key role is played by 68Ga-labelled-somatostatin analogues (68Ga-DOTA-peptides) positron emission tomography/computed tomography (PET/TC). In this review of recent literature, we described the objectives of morphological/functional imaging and potential future possibilities of prognostic imaging in the assessment of GI-NENs.
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Affiliation(s)
- Ginevra Danti
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy.
| | - Federica Flammia
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Benedetta Matteuzzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Diletta Cozzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Valentina Berti
- Department of Nuclear Medicine, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Silvia Pradella
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
| | - Laura Recchia
- Department of Medicine and Health Sciences, University of Molise, 86100, Campobasso, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences, University of Molise, 86100, Campobasso, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134, Florence, Italy
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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.
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