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Cheng M, Zhang H, Huang W, Li F, Gao J. Deep Learning Radiomics Analysis of CT Imaging for Differentiating Between Crohn's Disease and Intestinal Tuberculosis. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1516-1528. [PMID: 38424279 PMCID: PMC11300798 DOI: 10.1007/s10278-024-01059-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/17/2024] [Accepted: 02/21/2024] [Indexed: 03/02/2024]
Abstract
This study aimed to develop and evaluate a CT-based deep learning radiomics model for differentiating between Crohn's disease (CD) and intestinal tuberculosis (ITB). A total of 330 patients with pathologically confirmed as CD or ITB from the First Affiliated Hospital of Zhengzhou University were divided into the validation dataset one (CD: 167; ITB: 57) and validation dataset two (CD: 78; ITB: 28). Based on the validation dataset one, the synthetic minority oversampling technique (SMOTE) was adopted to create balanced dataset as training data for feature selection and model construction. The handcrafted and deep learning (DL) radiomics features were extracted from the arterial and venous phases images, respectively. The interobserver consistency analysis, Spearman's correlation, univariate analysis, and the least absolute shrinkage and selection operator (LASSO) regression were used to select features. Based on extracted multi-phase radiomics features, six logistic regression models were finally constructed. The diagnostic performances of different models were compared using ROC analysis and Delong test. The arterial-venous combined deep learning radiomics model for differentiating between CD and ITB showed a high prediction quality with AUCs of 0.885, 0.877, and 0.800 in SMOTE dataset, validation dataset one, and validation dataset two, respectively. Moreover, the deep learning radiomics model outperformed the handcrafted radiomics model in same phase images. In validation dataset one, the Delong test results indicated that there was a significant difference in the AUC of the arterial models (p = 0.037), while not in venous and arterial-venous combined models (p = 0.398 and p = 0.265) as comparing deep learning radiomics models and handcrafted radiomics models. In our study, the arterial-venous combined model based on deep learning radiomics analysis exhibited good performance in differentiating between CD and ITB.
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Affiliation(s)
- Ming Cheng
- Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
| | - Hanyue Zhang
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, 100034, China
| | - Fei Li
- School of Cyber Science and Engineering, Wuhan University, Wuhan, 430072, China
| | - Jianbo Gao
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
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Mo S, Wang Y, Huang C, Wu W, Qin S. A novel endoscopic ultrasomics-based machine learning model and nomogram to predict the pathological grading of pancreatic neuroendocrine tumors. Heliyon 2024; 10:e34344. [PMID: 39130461 PMCID: PMC11315146 DOI: 10.1016/j.heliyon.2024.e34344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 07/08/2024] [Accepted: 07/08/2024] [Indexed: 08/13/2024] Open
Abstract
Objectives This research aimed to retrospectively construct and authenticate ultrasomics models using endoscopic ultrasonography (EUS) images for forecasting the pathological grading of pancreatic neuroendocrine tumors (PNETs). Methods After confirmation through pathological examination, a retrospective analysis of 79 patients was conducted, including 49 with grade 1 PNETs and 30 with grade 2/3 PNETs. These patients were randomized to the training or test cohort in a 6:4 proportion. The least absolute shrinkage and selection operator (LASSO) algorithm was used to reduce the dimensionality of ultrasomics features derived from standard EUS images. These nonzero coefficient features were retained and applied to construct prediction models via eight machine-learning algorithms. The optimum ulstrasomics model was determined, followed by creating and evaluating a nomogram. Results Ultrasomics features of 107 were extracted, and only those with coefficients greater than zero were retained. The XGboost ultrasomics model performed exceptionally well, achieving AUCs of 0.987 and 0.781 in the training and test cohorts, respectively. Furthermore, an effective nomogram was developed and visually represented. Finally, the calibration curves, decision curve analysis (DCA) plots, and clinical impact curve (CIC) displayed in the ulstrasomics model and nomogram demonstrated high accuracy. They provided significant net benefits for clinical decision-making. Conclusions A novel ulstrasomics model and nomogram were created and certified to predict the pathological grading of PNETs using EUS images. This study has the potential to provide valuable insights that improve the clinical applicability and efficacy of EUS in predicting the grading of PNETs.
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Affiliation(s)
- Shuangyang Mo
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Gastroenterology Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Yingwei Wang
- Gastroenterology Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Cheng Huang
- Oncology Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Wenhong Wu
- Gastroenterology Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Shanyu Qin
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Geng W, Zhu J, Li M, Pi B, Wang X, Xing J, Xu H, Yang H. Radiomics Based on Multimodal magnetic resonance imaging for the Differential Diagnosis of Benign and Malignant Vertebral Compression Fractures. Orthop Surg 2024. [PMID: 38982652 DOI: 10.1111/os.14148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/06/2024] [Accepted: 06/09/2024] [Indexed: 07/11/2024] Open
Abstract
OBJECTIVES Recent studies have indicated that radiomics may have excellent performance and clinical application prospects in the differential diagnosis of benign and malignant vertebral compression fractures (VCFs). However, multimodal magnetic resonance imaging (MRI)-based radiomics model is rarely used in the differential diagnosis of benign and malignant VCFs, and is limited to lumbar. Herein, this study intends to develop and validate MRI radiomics models for differential diagnoses of benign and malignant VCFs in patients. METHODS This cross-sectional study involved 151 adult patients diagnosed with VCF in The First Affiliated Hospital of Soochow University in 2016-2021. The study was conducted in three steps: (i) the original MRI images were segmented, and the region of interest (ROI) was marked out; (ii) among the extracted features, those features with Pearson's correlation coefficient lower than 0.9 and the top 15 with the highest variance and Lasso regression coefficient less than and more than 0 were selected; (iii) MRI images and combined data were studied by logistic regression, decision tree, random forest and extreme gradient boosting (XGBoost) models in training set and the test set (ratio of 8:2), respectively; and the models were further verified and evaluated for the differential diagnosis performance. The evaluated indexes included area under receiver (AUC) of operating characteristic curve, accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and 95% confidence intervals (CIs). The AUCs were used to assess the predictive performance of different machine learning modes for benign and malignant VCFs. RESULTS A total of 1144 radiomics features, and 14 clinical features were extracted. Finally, 12 radiomics features were included in the radiomics model, and 12 radiomics features with 14 clinical features were included in the combined model. In the radiomics model, the differential diagnosis performance in the logistic regression model with the AUC of 0.905 ± 0.026, accuracy of 0.817 ± 0.057, sensitivity of 0.831 ± 0.065, and negative predictive value of 0.813 ± 0.042, was superior to the other three. In the combined model, XGBoost model had the superior differential diagnosis performance with specificity (0.979 ± 0.026) and positive predictive value (0.971 ± 0.035). CONCLUSION The multimodal MRI-based radiomics model performed well in the differential diagnosis of benign and malignant VCFs, which may provide a tool for clinicians to differentially diagnose VCFs.
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Affiliation(s)
- Wei Geng
- Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jingfen Zhu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Mao Li
- Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Bin Pi
- Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiantao Wang
- Department of Orthopedics, Ruihua Affiliated of Soochow University, Suzhou, China
| | - Junhui Xing
- Department of Orthopedics, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, China
| | - Haibo Xu
- Department of Orthopedics, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, China
| | - Huilin Yang
- Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou, China
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Mapelli P, Bezzi C, Muffatti F, Ghezzo S, Canevari C, Magnani P, Schiavo Lena M, Battistella A, Scifo P, Andreasi V, Partelli S, Chiti A, Falconi M, Picchio M. Preoperative assessment of lymph nodal metastases with [ 68Ga]Ga-DOTATOC PET radiomics for improved surgical planning in well-differentiated pancreatic neuroendocrine tumours. Eur J Nucl Med Mol Imaging 2024; 51:2774-2783. [PMID: 38696129 DOI: 10.1007/s00259-024-06730-w] [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: 01/19/2024] [Accepted: 04/23/2024] [Indexed: 07/05/2024]
Abstract
PURPOSE Accurate identification of lymph node (LN) metastases is pivotal for surgical planning of pancreatic neuroendocrine tumours (PanNETs); however, current imaging techniques have sub-optimal diagnostic sensitivity. Aim of this study is to investigate whether [68Ga]Ga-DOTATOC PET radiomics might improve the identification of LN metastases in patients with non-functioning PanNET (NF-PanNET) referred to surgical intervention. METHODS Seventy-two patients who performed preoperative [68Ga]Ga-DOTATOC PET between December 2017 and March 2022 for NF-PanNET. [68Ga]Ga-DOTATOC PET qualitative assessment of LN metastases was measured using diagnostic balanced accuracy (bACC), sensitivity (SN), specificity (SP), positive and negative predictive values (PPV, NPV). SUVmax, SUVmean, Somatostatin receptor density (SRD), total lesion SRD (TLSRD) and IBSI-compliant radiomic features (RFs) were obtained from the primary tumours. To predict LN involvement, these parameters were engineered, selected and used to train different machine learning models. Models were validated using tenfold repeated cross-validation and control models were developed. Models' bACC, SN, SP, PPV and NPV were collected and compared (Kruskal-Wallis, Mann-Whitney). RESULTS LN metastases were detected in 29/72 patients at histology. [68Ga]Ga-DOTATOC PET qualitative examination of LN involvement provided bACC = 60%, SN = 24%, SP = 95%, PPV = 78% and NPV = 65%. The best-performing radiomic model provided a bACC = 70%, SN = 77%, SP = 61%, PPV = 60% and NPV = 83% (outperforming the control model, p < 0.05*). CONCLUSION In this study, [68Ga]Ga-DOTATOC PET radiomics allowed to increase diagnostic sensitivity in detecting LN metastases from 24 to 77% in NF-PanNET patients candidate to surgery. Especially in case of micrometastatic involvement, this approach might assist clinicians in a better patients' stratification.
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Affiliation(s)
- Paola Mapelli
- Vita-Salute San Raffaele University, Milan, Italy
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Carolina Bezzi
- Vita-Salute San Raffaele University, Milan, Italy
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesca Muffatti
- Pancreatic Surgery Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Samuele Ghezzo
- Vita-Salute San Raffaele University, Milan, Italy
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Carla Canevari
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Patrizia Magnani
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Anna Battistella
- Vita-Salute San Raffaele University, Milan, Italy
- Pancreatic Surgery Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Scifo
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Valentina Andreasi
- Vita-Salute San Raffaele University, Milan, Italy
- Pancreatic Surgery Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Stefano Partelli
- Vita-Salute San Raffaele University, Milan, Italy
- Pancreatic Surgery Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Arturo Chiti
- Vita-Salute San Raffaele University, Milan, Italy
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Falconi
- Vita-Salute San Raffaele University, Milan, Italy
- Pancreatic Surgery Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria Picchio
- Vita-Salute San Raffaele University, Milan, Italy.
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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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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Wu L, Cen C, Yue X, Chen L, Wu H, Yang M, Lu Y, Ma L, Li X, Wu H, Zheng C, Han P. A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma. Cancer Imaging 2024; 24:55. [PMID: 38725034 PMCID: PMC11080083 DOI: 10.1186/s40644-024-00700-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND This study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC). METHODS A total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA). RESULTS The PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit. CONCLUSIONS The radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.
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Affiliation(s)
- Linxia Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Chunyuan Cen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Xiaofei Yue
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Lei Chen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Hongying Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Ming Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Yuting Lu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Ling Ma
- Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan Province, 610041, The People's Republic of China
| | - Xin Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China
| | - Heshui Wu
- Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China.
| | - Ping Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, 430022, The People's Republic of China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, The People's Republic of China.
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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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9
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Yang J, Cai H, Liu N, Huang J, Pan Y, Zhang B, Tong M, Zhang Z. Application of radiomics in ischemic stroke. J Int Med Res 2024; 52:3000605241238141. [PMID: 38565321 PMCID: PMC10993685 DOI: 10.1177/03000605241238141] [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: 08/30/2023] [Accepted: 02/20/2024] [Indexed: 04/04/2024] Open
Abstract
In recent years, radiomics has emerged as a novel research methodology that plays a crucial role in the diagnosis and treatment of ischemic stroke. By integrating multimodal medical imaging techniques such as computed tomography and magnetic resonance imaging, radiomics offers in-depth insights into aspects such as the extent of brain tissue damage and hemodynamics. These data help physicians to accurately assess patient condition, select optimal treatment strategies, and predict recovery trajectories and long-term prognoses, thereby enhancing treatment efficacy and reducing the risk of complications. With the anticipated further advancements in radiomic technology, this methodology has great potential for expanded applications in the early detection, treatment, and prognosis of ischemic stroke. The present narrative review explores the burgeoning field of radiomics and its transformative impact on ischemic stroke.
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Affiliation(s)
- Jie Yang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Huabo Cai
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ning Liu
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiajie Huang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yun Pan
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Bo Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minfeng Tong
- Department of Neurosurgery, Department of Neuro Intensive Care Unit, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
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10
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Ingwersen EW, Rijssenbeek PMW, Marquering HA, Kazemier G, Daams F. Radiomics for the prediction of a postoperative pancreatic fistula following a pancreatoduodenectomy: A systematic review and radiomic score quality assessment. Pancreatology 2024; 24:306-313. [PMID: 38238193 DOI: 10.1016/j.pan.2023.12.007] [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: 06/28/2023] [Revised: 12/13/2023] [Accepted: 12/18/2023] [Indexed: 03/02/2024]
Abstract
BACKGROUND Postoperative pancreatic fistula (POPF) is a severe complication following a pancreatoduodenectomy. An accurate prediction of POPF could assist the surgeon in offering tailor-made treatment decisions. The use of radiomic features has been introduced to predict POPF. A systematic review was conducted to evaluate the performance of models predicting POPF using radiomic features and to systematically evaluate the methodological quality. METHODS Studies with patients undergoing a pancreatoduodenectomy and radiomics analysis on computed tomography or magnetic resonance imaging were included. Methodological quality was assessed using the Radiomics Quality Score (RQS) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement. RESULTS Seven studies were included in this systematic review, comprising 1300 patients, of whom 364 patients (28 %) developed POPF. The area under the curve (AUC) of the included studies ranged from 0.76 to 0.95. Only one study externally validated the model, showing an AUC of 0.89 on this dataset. Overall adherence to the RQS (31 %) and TRIPOD guidelines (54 %) was poor. CONCLUSION This systematic review showed that high predictive power was reported of studies using radiomic features to predict POPF. However, the quality of most studies was poor. Future studies need to standardize the methodology. REGISTRATION not registered.
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Affiliation(s)
- Erik W Ingwersen
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Surgery, Amsterdam, the Netherlands; Cancer Center Amsterdam, the Netherlands; Amsterdam Gastroenterology Endocrinology and Metabolism, the Netherlands
| | - Pieter M W Rijssenbeek
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Surgery, Amsterdam, the Netherlands
| | - Henk A Marquering
- Amsterdam UMC, Location University of Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, the Netherlands; Amsterdam UMC, Location University of Amsterdam, Department of Biomedical Engineering and Physics Department, the Netherlands
| | - Geert Kazemier
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Surgery, Amsterdam, the Netherlands; Cancer Center Amsterdam, the Netherlands
| | - Freek Daams
- Amsterdam UMC, Location Vrije Universiteit Amsterdam, Department of Surgery, Amsterdam, the Netherlands; Cancer Center Amsterdam, the Netherlands.
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11
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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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12
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Qin S, Yang Y, Zhang J, Yin Y, Liu W, Zhang H, Fan X, Yang M, Yu F. Effective Treatment of SSTR2-Positive Small Cell Lung Cancer Using 211At-Containing Targeted α-Particle Therapy Agent Which Promotes Endogenous Antitumor Immune Response. Mol Pharm 2023; 20:5543-5553. [PMID: 37788300 PMCID: PMC10630944 DOI: 10.1021/acs.molpharmaceut.3c00427] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 09/15/2023] [Accepted: 09/15/2023] [Indexed: 10/05/2023]
Abstract
Small cell lung cancer (SCLC) is a neuroendocrine tumor with a high degree of malignancy. Due to limited treatment options, patients with SCLC have a poor prognosis. We have found, however, that intravenously administered octreotide (Oct) armed with astatine-211 ([211At]SAB-Oct) is effective against a somatostatin receptor 2 (SSTR2)-positive SCLC tumor in SCLC tumor-bearing BALB/c nude mice. In biodistribution analysis, [211At]SAB-Oct achieved the highest concentration in the SCLC tumors up to 3 h after injection as time proceeded. A single intravenous injection of [211At]SAB-Oct (370 kBq) was sufficient to suppress SSTR2-positive SCLC tumor growth in treated mice by inducing DNA double-strand breaks. Additionally, a multitreatment course (370 kBq followed by twice doses of 370 kBq for a total of 1110 kBq) inhibited the growth of the tumor compared to the untreated control group without significant off-target toxicity. Surprisingly, we found that [211At]SAB-Oct could up-regulate the expressions of calreticulin and major histocompatibility complex I (MHC-I) on the tumor cell membrane surface, suggesting that α-particle internal irradiation may activate an endogenous antitumor immune response through the regulation of immune cells in the tumor microenvironment, which could synergically enhance the efficacy of immunotherapy. We conclude that [211At]SAB-Oct is a potential new therapeutic option for SSTR2-positive SCLC.
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Affiliation(s)
- Shanshan Qin
- Department
of Nuclear Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, No. 301 Yan-chang-zhong Road, Shanghai 200072, People’s Republic of China
- Institute
of Nuclear Medicine, Tongji University School
of Medicine, No. 301
Yan-chang-zhong Road, Shanghai 200072, People’s Republic
of China
| | - Yuanyou Yang
- Key
Laboratory of Radiation Physics and Technology, Ministry of Education,
Institute of Nuclear Science and Technology, Sichuan University, Chengdu 610064, People’s
Republic of China
| | - Jiajia Zhang
- Department
of Nuclear Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, No. 301 Yan-chang-zhong Road, Shanghai 200072, People’s Republic of China
- Institute
of Nuclear Medicine, Tongji University School
of Medicine, No. 301
Yan-chang-zhong Road, Shanghai 200072, People’s Republic
of China
| | - Yuzhen Yin
- Department
of Nuclear Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, No. 301 Yan-chang-zhong Road, Shanghai 200072, People’s Republic of China
- Institute
of Nuclear Medicine, Tongji University School
of Medicine, No. 301
Yan-chang-zhong Road, Shanghai 200072, People’s Republic
of China
| | - Weihao Liu
- Key
Laboratory of Radiation Physics and Technology, Ministry of Education,
Institute of Nuclear Science and Technology, Sichuan University, Chengdu 610064, People’s
Republic of China
| | - Han Zhang
- Department
of Nuclear Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, No. 301 Yan-chang-zhong Road, Shanghai 200072, People’s Republic of China
- Institute
of Nuclear Medicine, Tongji University School
of Medicine, No. 301
Yan-chang-zhong Road, Shanghai 200072, People’s Republic
of China
| | - Xin Fan
- Department
of Nuclear Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, No. 301 Yan-chang-zhong Road, Shanghai 200072, People’s Republic of China
- Institute
of Nuclear Medicine, Tongji University School
of Medicine, No. 301
Yan-chang-zhong Road, Shanghai 200072, People’s Republic
of China
| | - Mengdie Yang
- Department
of Nuclear Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, No. 301 Yan-chang-zhong Road, Shanghai 200072, People’s Republic of China
- Institute
of Nuclear Medicine, Tongji University School
of Medicine, No. 301
Yan-chang-zhong Road, Shanghai 200072, People’s Republic
of China
| | - Fei Yu
- Department
of Nuclear Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, No. 301 Yan-chang-zhong Road, Shanghai 200072, People’s Republic of China
- Institute
of Nuclear Medicine, Tongji University School
of Medicine, No. 301
Yan-chang-zhong Road, Shanghai 200072, People’s Republic
of China
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Coppola A, Gatta T, Pini GM, Scordi G, Fontana F, Piacentino F, Minici R, Laganà D, Basile A, Dehò F, Carcano G, Franzi F, Uccella S, Sessa F, Venturini M. Neuroendocrine Carcinoma of the Urinary Bladder: CT Findings and Radiomics Signature. J Clin Med 2023; 12:6510. [PMID: 37892647 PMCID: PMC10607129 DOI: 10.3390/jcm12206510] [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: 09/30/2023] [Revised: 10/07/2023] [Accepted: 10/12/2023] [Indexed: 10/29/2023] Open
Abstract
Background: We present a case series of Neuroendocrine Carcinoma of the Urinary Bladder (NECB) to analyse their radiologic appearance on CT, find a "Radiomic signature", and review the current literature. Methods: 14 CT cases of NECB were reviewed and compared with a control group of 42 patients with high-grade non-neuroendocrine bladder neoplasm for the following parameters: ring enhancement; implantation site; dimensions; density; margins; central necrosis; calcifications; number of lesions; wall thickness; depth of invasion in the soft tissue; invasion of fat tissue; invasion of adjacent organs; lymph-node involvement; abdominal organ metastasis. To extract radiomic features, volumes of interest of bladder lesions were manually delineated on the portal-venous phase. The radiomic features of the two groups were identified and compared. Results: Statistical differences among NECB and control group were found in the prevalence of male sex (100% vs. 69.0%), hydronephrosis (71.4% vs. 33.3%), mean density of the mass (51.01 ± 15.48 vs. 76.27 ± 22.26 HU); product of the maximum diameters on the axial plane (38.1 ± 59.3 vs. 14.44 ± 12.98 cm2) in the control group, trigonal region involvement (78.57% vs. 19.05%). About the radiomic features, Student's t-test showed significant correlation for the variables: "DependenceNonUniformity" (p: 0.048), "JointAverage" (p: 0.013), "LargeAreaLowGrayLevelEmphasis" (p: 0.014), "Maximum2DDiameterColumn" (p: 0.04), "Maximum 2DDiameterSlice" (p: 0.007), "MeanAbsoluteDeviation" (p: 0.021), "BoundingBoxA" (p: 0.022) and "CenterOfMassB" (p: 0.007). Conclusions: There is a typical pattern (male patient, large mass, trigonal area involvement) of NECB presentation on contrast-enhanced CT. Certain morphological characteristics and encouraging results about Radiomic features can help define the diagnosis.
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Affiliation(s)
- Andrea Coppola
- Diagnostic and Interventional Radiology Unit, Circolo Hospital, ASST Sette Laghi, 21100 Varese, Italy; (T.G.); (F.F.); (F.P.); (M.V.)
- Department of Medicine and Technological Innovation, Insubria University, 21100 Varese, Italy; (F.D.); (G.C.); (F.F.); (F.S.)
| | - Tonia Gatta
- Diagnostic and Interventional Radiology Unit, Circolo Hospital, ASST Sette Laghi, 21100 Varese, Italy; (T.G.); (F.F.); (F.P.); (M.V.)
- Department of Medicine and Technological Innovation, Insubria University, 21100 Varese, Italy; (F.D.); (G.C.); (F.F.); (F.S.)
| | - Giacomo Maria Pini
- Department of Pathology, Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo, 15121 Alessandria, Italy;
| | - Giorgia Scordi
- Postgraduate School of Radiology Technician, Insubria University, 21100 Varese, Italy;
| | - Federico Fontana
- Diagnostic and Interventional Radiology Unit, Circolo Hospital, ASST Sette Laghi, 21100 Varese, Italy; (T.G.); (F.F.); (F.P.); (M.V.)
- Department of Medicine and Technological Innovation, Insubria University, 21100 Varese, Italy; (F.D.); (G.C.); (F.F.); (F.S.)
| | - Filippo Piacentino
- Diagnostic and Interventional Radiology Unit, Circolo Hospital, ASST Sette Laghi, 21100 Varese, Italy; (T.G.); (F.F.); (F.P.); (M.V.)
- Department of Medicine and Technological Innovation, Insubria University, 21100 Varese, Italy; (F.D.); (G.C.); (F.F.); (F.S.)
| | - Roberto Minici
- Radiology Unit, Department of Experimental and Clinical Medicine, University Hospital Mater Domini, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy; (R.M.); (D.L.)
| | - Domenico Laganà
- Radiology Unit, Department of Experimental and Clinical Medicine, University Hospital Mater Domini, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy; (R.M.); (D.L.)
| | - Antonio Basile
- Radiodiagnostic and Radiotherapy Unit, Department of Medical and Surgical Sciences and Advanced Technologies, University Hospital “Policlinico-Vittorio Emanuele”, 95123 Catania, Italy;
| | - Federico Dehò
- Department of Medicine and Technological Innovation, Insubria University, 21100 Varese, Italy; (F.D.); (G.C.); (F.F.); (F.S.)
- Urology Unit, CircoloHospital, ASST Sette Laghi, 21100 Varese, Italy
| | - Giulio Carcano
- Department of Medicine and Technological Innovation, Insubria University, 21100 Varese, Italy; (F.D.); (G.C.); (F.F.); (F.S.)
- General, Emergency and Transplant Surgery Unit, Circolo Hospital, ASST Sette Laghi, 21100 Varese, Italy
| | - Francesca Franzi
- Department of Medicine and Technological Innovation, Insubria University, 21100 Varese, Italy; (F.D.); (G.C.); (F.F.); (F.S.)
- Patology Unit, Circolo Hospital, ASST Sette Laghi, 21100 Varese, Italy
| | - Silvia Uccella
- Pathology Unit, Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy;
| | - Fausto Sessa
- Department of Medicine and Technological Innovation, Insubria University, 21100 Varese, Italy; (F.D.); (G.C.); (F.F.); (F.S.)
- Patology Unit, Circolo Hospital, ASST Sette Laghi, 21100 Varese, Italy
| | - Massimo Venturini
- Diagnostic and Interventional Radiology Unit, Circolo Hospital, ASST Sette Laghi, 21100 Varese, Italy; (T.G.); (F.F.); (F.P.); (M.V.)
- Department of Medicine and Technological Innovation, Insubria University, 21100 Varese, Italy; (F.D.); (G.C.); (F.F.); (F.S.)
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14
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Liu L, Liu W, Jia Z, Li Y, Wu H, Qu S, Zhu J, Liu X, Xu C. Application of machine learning algorithms to predict lymph node metastasis in gastric neuroendocrine neoplasms. Heliyon 2023; 9:e20928. [PMID: 37928390 PMCID: PMC10622622 DOI: 10.1016/j.heliyon.2023.e20928] [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: 02/23/2023] [Revised: 10/09/2023] [Accepted: 10/11/2023] [Indexed: 11/07/2023] Open
Abstract
Background Neuroendocrine neoplasms (NENs) are tumors that originate from secretory cells of the diffuse endocrine system and typically produce bioactive amines or peptide hormones. This paper describes the development and validation of a predictive model of the risk of lymph node metastasis among gastric NEN patients based on machine learning platform. Methods In this investigation, data from 1256 patients were used, of whom 119 patients from the First Affiliated Hospital of Soochow University in China and 1137 cases from the surveillance epidemiology and end results (SEER) database were combined. Six machine learning algorithms, including the logistic regression model (LR), random forest (RF), decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), were used to build the predictive model. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results Among the 1256 patients with gastric NENs, 276 patients (21.97 %) developed lymph node metastasis. T stage, tumor size, degree of differentiation, and sex were predictive factors of lymph node metastasis. The RF model achieved the best predictive performance among the six machine learning models, with an AUC, accuracy, sensitivity, and specificity of 0.81, 0.78, 0.76, and 0.82, respectively. Conclusion The RF model provided the best prediction and can help physicians determine the lymph node metastasis risk of gastric NEN patients to formulate individualized medical strategies.
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Affiliation(s)
- Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Wen Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhenyu Jia
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yao Li
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Hongyu Wu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Shuting Qu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaolin Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Chunfang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
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15
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Yu C, Li T, Yang X, Xin L, Zhao Z, Yang Z, Zhang R. The maximal contrast-enhanced range of CT for differentiating the WHO pathological subtypes and risk subgroups of thymic epithelial tumors. Br J Radiol 2023; 96:20221076. [PMID: 37486626 PMCID: PMC10546431 DOI: 10.1259/bjr.20221076] [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: 11/17/2022] [Revised: 07/01/2023] [Accepted: 07/11/2023] [Indexed: 07/25/2023] Open
Abstract
OBJECTIVE To explore the value of maximal contrast-enhanced (CEmax) range using contrast-enhanced CT (CECT) imaging in differentiating the pathological subtypes and risk subgroups of thymic epithelial tumors (TETs). METHODS The pre-treatment-CECT images of 319 TET patients from May 2012 to November 2021 were analyzed retrospectively. The CEmax was defined as the maximum difference between the CT value of the solid tumor on pre-contrast and contrast-enhanced images. The mean CEmax value was calculated at three different tumor levels. RESULTS There was a significant difference in the CEmax among the eight main pathological subtypes [types A, AB, B1, B2, and B3 thymoma, thymic carcinoma (TC), low-grade neuroendocrine tumor (NET) and high-grade NET] (p < 0.001). Among the eight subtypes, the CEmax values of types A, AB, and low-risk NET were higher than those of the other subtypes (all p < 0.001), and there was no difference among types B1-B3 and high-risk NET (all p > 0.05). There was no difference for CEmax values between NET and TC (p = 0.491). For the risk subgroups, the CEmax of TC (including NET) was 35.35 ± 11.41 HU, which was lower than that of low-risk thymoma (A and AB) (57.73±21.24 HU) (P < 0.001) and was higher than that of high-risk thymoma (B1-B3) (27.37±8.27 HU) (P < 0.001). The CEmax cut-off values were 38.5 HU and 30.5 HU respectively (AUC: 0.829 and 0.712; accuracy, 72.4% and 67.7%). CONCLUSION The tumor CEmax on CECT helps differentiate the pathological subtypes and risk subgroups of TETs. ADVANCES IN KNOWLEDGE In this study, an improved simplified risk grouping method was proposed based on the traditional (2004 edition) simplified risk grouping method for TETs. If Type B1 thymoma is classified as high-risk, radiologists using this improved method may improve the accuracy in differentiating risk level of TETs compared with the traditional method.
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Affiliation(s)
- Chunhai Yu
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Ting Li
- Department of Nephrology, Taiyuan People's Hospital, Taiyuan, China
| | - Xiaotang Yang
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Lei Xin
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Zhikai Zhao
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Zhao Yang
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Ruiping Zhang
- First Hospital of Shanxi Medical University, Taiyuan, China
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Ni H, Zhou G, Chen X, Ren J, Yang M, Zhang Y, Zhang Q, Zhang L, Mao C, Li X. Predicting Recurrence in Pancreatic Ductal Adenocarcinoma after Radical Surgery Using an AX-Unet Pancreas Segmentation Model and Dynamic Nomogram. Bioengineering (Basel) 2023; 10:828. [PMID: 37508855 PMCID: PMC10376503 DOI: 10.3390/bioengineering10070828] [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: 06/01/2023] [Revised: 07/01/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
This study aims to investigate the reliability of radiomic features extracted from contrast-enhanced computer tomography (CT) by AX-Unet, a pancreas segmentation model, to analyse the recurrence of pancreatic ductal adenocarcinoma (PDAC) after radical surgery. In this study, we trained an AX-Unet model to extract the radiomic features from preoperative contrast-enhanced CT images on a training set of 205 PDAC patients. Then we evaluated the segmentation ability of AX-Unet and the relationship between radiomic features and clinical characteristics on an independent testing set of 64 patients with clear prognoses. The lasso regression analysis was used to screen for variables of interest affecting patients' post-operative recurrence, and the Cox proportional risk model regression analysis was used to screen for risk factors and create a nomogram prediction model. The proposed model achieved an accuracy of 85.9% for pancreas segmentation, meeting the requirements of most clinical applications. Radiomic features were found to be significantly correlated with clinical characteristics such as lymph node metastasis, resectability status, and abnormally elevated serum carbohydrate antigen 19-9 (CA 19-9) levels. Specifically, variance and entropy were associated with the recurrence rate (p < 0.05). The AUC for the nomogram predicting whether the patient recurred after surgery was 0.92 (95% CI: 0.78-0.99) and the C index was 0.62 (95% CI: 0.48-0.78). The AX-Unet pancreas segmentation model shows promise in analysing recurrence risk factors after radical surgery for PDAC. Additionally, our findings suggest that a dynamic nomogram model based on AX-Unet can provide pancreatic oncologists with more accurate prognostic assessments for their patients.
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Affiliation(s)
- Haixu Ni
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
- Department of General Surgery, First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Gonghai Zhou
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Xinlong Chen
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
| | - Jing Ren
- The Reproductive Medicine Hospital of the First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Minqiang Yang
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Yuhong Zhang
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Qiyu Zhang
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
- Department of General Surgery, First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Lei Zhang
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
- Department of General Surgery, First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Chengsheng Mao
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Xun Li
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
- Department of General Surgery, First Hospital of Lanzhou University, Lanzhou 730000, China
- Key Laboratory of Biotherapy and Regenerative Medicine of Gansu Province, Lanzhou 730000, China
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17
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van Beek DJ, Verschuur AVD, Brosens LAA, Valk GD, Pieterman CRC, Vriens MR. Status of Surveillance and Nonsurgical Therapy for Small Nonfunctioning Pancreatic Neuroendocrine Tumors. Surg Oncol Clin N Am 2023; 32:343-371. [PMID: 36925190 DOI: 10.1016/j.soc.2022.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Pancreatic neuroendocrine tumors (PNETs) occur in < 1/100,000 patients and most are nonfunctioning (NF). Approximately 5% occur as part of multiple endocrine neoplasia type 1. Anatomic and molecular imaging have a pivotal role in the diagnosis, staging and active surveillance. Surgery is generally recommended for nonfunctional pancreatic neuroendocrine tumors (NF-PNETs) >2 cm to prevent metastases. For tumors ≤2 cm, active surveillance is a viable alternative. Tumor size and grade are important factors to guide management. Assessment of death domain-associated protein 6/alpha-thalassemia/mental retardation X-linked and alternative lengthening of telomeres are promising novel prognostic markers. This review summarizes the status of surveillance and nonsurgical management for small NF-PNETs, including factors that can guide management.
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Affiliation(s)
- Dirk-Jan van Beek
- Department of Endocrine Surgical Oncology, University Medical Center Utrecht, Internal Mail Number G.04.228, PO Box 85500, Utrecht 3508 GA, the Netherlands
| | - Anna Vera D Verschuur
- Department of Pathology, University Medical Center Utrecht, Internal Mail Number G02.5.26, PO Box 85500, Utrecht 3508 GA, the Netherlands. https://twitter.com/annaveraverschu
| | - Lodewijk A A Brosens
- Department of Pathology, University Medical Center Utrecht, Internal Mail Number G4.02.06, PO Box 85500, Utrecht 3508 GA, the Netherlands
| | - Gerlof D Valk
- Department of Endocrine Oncology, University Medical Center Utrecht, Internal Mail Number Q.05.4.300, PO Box 85500, Utrecht 3508 GA, the Netherlands
| | - Carolina R C Pieterman
- Department of Endocrine Oncology, University Medical Center Utrecht, Internal Mail Number Q.05.4.300, PO Box 85500, Utrecht 3508 GA, the Netherlands.
| | - Menno R Vriens
- Department of Endocrine Surgical Oncology, University Medical Center Utrecht, Internal Mail Number G.04.228, PO Box 85500, Utrecht 3508 GA, the Netherlands
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18
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Gu Q, He M, He Y, Dai A, Liu J, Chen X, Liu P. CT-measured body composition radiomics predict lymph node metastasis in localized pancreatic ductal adenocarcinoma. Discov Oncol 2023; 14:16. [PMID: 36735166 PMCID: PMC9898483 DOI: 10.1007/s12672-023-00624-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 01/31/2023] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND To explored the value of CT-measured body composition radiomics in preoperative evaluation of lymph node metastasis (LNM) in localized pancreatic ductal adenocarcinoma (LPDAC). METHODS We retrospectively collected patients with LPDAC who underwent surgical resection from January 2016 to June 2022. According to whether there was LNM after operation, the patients were divided into LNM group and non-LNM group in both male and female patients. The patient's body composition was measured by CT images at the level of the L3 vertebral body before surgery, and the radiomics features of adipose tissue and muscle were extracted. Multivariate logistic regression (forward LR) analyses were used to determine the predictors of LNM from male and female patient, respectively. Sexual dimorphism prediction signature using adipose tissue radiomics features, muscle tissue radiomics features and combined signature of both were developed and compared. The model performance is evaluated on discrimination and validated through a leave-one-out cross-validation method. RESULTS A total of 196 patients (mean age, 60 years ± 9 [SD]; 117 men) were enrolled, including 59 LNM in male and 36 LNM in female. Both male and female CT-measured body composition radiomics signatures have a certain predictive power on LNM of LPDAC. Among them, the female adipose tissue signature showed the highest performance (area under the ROC curve (AUC), 0.895), and leave one out cross validation (LOOCV) indicated that the signature could accurately classify 83.5% of cases; The prediction efficiency of the signature can be further improved after adding the muscle radiomics features (AUC, 0.924, and the accuracy of the LOOCV was 87.3%); The abilities of male adipose tissue and muscle tissue radiomics signatures in predicting LNM of LPDAC was similar, AUC was 0.735 and 0.773, respectively, and the accuracy of LOOCV was 62.4% and 68.4%, respectively. CONCLUSIONS CT-measured body composition Radiomics strategy showed good performance for predicting LNM in LPDAC, and has sexual dimorphism. It may provide a reference for individual treatment of LPDAC and related research about body composition in the future.
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Affiliation(s)
- Qianbiao Gu
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Mengqing He
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Yaqiong He
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Anqi Dai
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Jianbin Liu
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Xiang Chen
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
| | - Peng Liu
- Department of Radiology, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005 China
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19
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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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20
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Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 15:cancers15010063. [PMID: 36612061 PMCID: PMC9817513 DOI: 10.3390/cancers15010063] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.
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Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Correspondence:
| | - Shin Mei Chan
- Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06510, USA
| | - Omar Mustafa Fathy Omar
- Center for Vascular Biology, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - Shams I. Iqbal
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael S. Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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21
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Value of T2-weighted-based radiomics model in distinguishing Warthin tumor from pleomorphic adenoma of the parotid. Eur Radiol 2022; 33:4453-4463. [PMID: 36502461 DOI: 10.1007/s00330-022-09295-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 11/01/2022] [Accepted: 11/09/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVES The differentiation of Warthin tumor and pleomorphic adenoma before treatment is crucial for clinical strategies. The aim of this study was to develop and test a T2-weighted-based radiomics model for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland. METHODS A total of 117 patients, including 61 cases of Warthin tumor and 56 cases of pleomorphic adenoma, were retrospectively enrolled from two centers between January 2010 and June 2022. The training set included 82 cases, and the validation set included 35 cases. From T2-weighted images, 971 radiomics features were extracted. Seven radiomics features remained after a two-step selection process. We used the seven radiomics features and clinical factors through multivariable logistic regression to build radiomics and clinical models, respectively. A radiomics-clinical model was also built that combined the independent clinical predictors with the radiomics features. Through ROC curves, the three models were evaluated and compared. RESULTS In the radiomics model, AUCs were 0.826 and 0.796 in training and validation sets, respectively. In the clinical model, the AUCs were 0.923 and 0.926 in the training and validation sets, respectively. Decision curve analysis revealed that the radiomics-clinical model had the best diagnostic performance for distinguishing Warthin tumor from pleomorphic adenoma of the parotid gland (AUC = 0.962 and 0.934 for the training and validation sets, respectively). CONCLUSION The radiomics-clinical model performed well in differentiating pleomorphic adenoma from Warthin tumor of the parotid gland. KEY POINTS • The clinical model outperformed the radiomics model in distinguishing pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics features extracted from T2-weighted images could help differentiate pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics-clinical model was superior to the radiomics and the clinical models for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland.
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22
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Chen H, Li Z, Hu Y, Xu X, Ye Z, Lou X, Zhang W, Gao H, Qin Y, Zhang Y, Chen X, Chen J, Tang W, Yu X, Ji S. Maximum Value on Arterial Phase Computed Tomography Predicts Prognosis and Treatment Efficacy of Sunitinib for Pancreatic Neuroendocrine Tumours. Ann Surg Oncol 2022; 30:2988-2998. [PMID: 36310316 DOI: 10.1245/s10434-022-12693-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 10/06/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE This study was designed to assess the computed tomography maximum (CTmax) value on pretherapeutic arterial phase computed tomography (APCT) images to predict pancreatic neuroendocrine tumours (pNETs) recurrence and clarify its role in predicting the outcome of tumour therapy. METHODS This retrospective study enrolled 250 surgical patients and 24 nonsurgical patients with sunitinib-based treatment in our hospital from 2008 to 2019. CT images were assessed, the maximum value was defined as "CTmax," and recurrence-free survival (RFS) or progression-free survival (PFS) was compared between a high-CTmax group and a low-CTmax group among patients who underwent surgical resection or nonsurgical, sunitinib-based treatment according to the CTmax cutoff value. RESULTS In ROC curve analysis, a CTmax of 108 Hounsfield units, as the cutoff value, achieved an AUC of 0.796 in predicting recurrence. Compared with the low-CTmax group, the high-CTmax group had a longer RFS (p < 0.001). Low CTmax was identified as an independent factor for RFS (p < 0.001) in multivariate analysis; these results were confirmed using the internal validation set. The CTmax value was significantly correlated with the microvascular density (MVD) value (p < 0.001) and the vascular endothelial growth factor receptor 2 (VEGFR2) score (p < 0.001). Furthermore, the high-CTmax group had a better PFS than the low-CTmax group among the sunitinib treatment group (p = 0.007). CONCLUSIONS The tumour CTmax on APCT might be a potential and independent indicator for predicting recurrence in patients who have undergone surgical resection and assessing the efficacy of sunitinib for patients with advanced metastatic pNETs.
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Affiliation(s)
- Haidi Chen
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Zheng Li
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Yuheng Hu
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Xiaowu Xu
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Zeng Ye
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Xin Lou
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Wuhu Zhang
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Heli Gao
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Yi Qin
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Yue Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The Third Affiliated Hospital of Suzhou University, The First People's Hospital of Changzhou, Changzhou, China
| | - Xuemin Chen
- Department of Hepatobiliary and Pancreatic Surgery, The Third Affiliated Hospital of Suzhou University, The First People's Hospital of Changzhou, Changzhou, China
| | - Jie Chen
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Wei Tang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Xianjun Yu
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
- Shanghai Pancreatic Cancer Institute, Shanghai, China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, China.
| | - Shunrong Ji
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
- Shanghai Pancreatic Cancer Institute, Shanghai, China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, China.
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23
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Wang S, Lin C, Kolomaya A, Ostdiek-Wille GP, Wong J, Cheng X, Lei Y, Liu C. Compute Tomography Radiomics Analysis on Whole Pancreas Between Healthy Individual and Pancreatic Ductal Adenocarcinoma Patients: Uncertainty Analysis and Predictive Modeling. Technol Cancer Res Treat 2022; 21:15330338221126869. [PMID: 36184987 PMCID: PMC9530578 DOI: 10.1177/15330338221126869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Radiomics is a rapidly growing field that quantitatively extracts image features
in a high-throughput manner from medical imaging. In this study, we analyzed the
radiomics features of the whole pancreas between healthy individuals and
pancreatic cancer patients, and we established a predictive model that can
distinguish cancer patients from healthy individuals based on these radiomics
features. Methods: We retrospectively collected venous-phase scans
of contrast-enhanced computed tomography (CT) images from 181 control subjects
and 85 cancer case subjects for radiomics analysis and predictive modeling. An
attending radiation oncologist delineated the pancreas for all the subjects in
the Varian Eclipse system, and we extracted 924 radiomics features using
PyRadiomics. We established a feature selection pipeline to exclude redundant or
unstable features. We randomly selected 189 cases (60 cancer and 129 control) as
the training set. The remaining 77 subjects (25 cancer and 52 control) as a test
set. We trained a Random Forest model utilizing the stable features to
distinguish the cancer patients from the healthy individuals on the training
dataset. We analyzed the performance of our best model by running 5-fold
cross-validations on the training dataset and applied our best model to the test
set. Results: We identified that 91 radiomics features are stable
against various uncertainty sources, including bin width, resampling, image
transformation, image noise, and segmentation uncertainty. Eight of the 91
features are nonredundant. Our final predictive model, using these 8 features,
has achieved a mean area under the receiver operating characteristic curve (AUC)
of 0.99 ± 0.01 on the training dataset (189 subjects) by cross-validation. The
model achieved an AUC of 0.910 on the independent test set (77 subjects) and an
accuracy of 0.935. Conclusion: CT-based radiomics analysis based on
the whole pancreas can distinguish cancer patients from healthy individuals, and
it could potentially become an early detection tool for pancreatic cancer.
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Affiliation(s)
- Shuo Wang
- Department of Radiation Oncology, University of Nebraska Medical
Center, Omaha, NE, USA,Shuo Wang, PhD, Department of Radiation
Oncology, University of Nebraska Medical Center, 986861 Nebraska Medical Center,
Omaha, NE 68198, USA. Chi Lin, MD, PhD,
Department of Radiation Oncology, University of Nebraska Medical Center, 986861
Nebraska Medical Center, Omaha, NE 68198, USA
| | - Chi Lin
- Department of Radiation Oncology, University of Nebraska Medical
Center, Omaha, NE, USA
| | - Alexander Kolomaya
- College of Medicine, University of Nebraska Medical
Center, Omaha, NE, USA
| | | | - Jeffrey Wong
- Department of Radiation Oncology, University of Nebraska Medical
Center, Omaha, NE, USA
| | - Xiaoyue Cheng
- Department of Mathematics, University of Nebraska
Omaha, Omaha, NE, USA
| | - Yu Lei
- Department of Radiation Oncology, Barrow Neurological
Institute, Phoenix, AZ, USA
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24
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Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2022; 47:2986-3002. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 01/18/2023]
Abstract
Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.
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Affiliation(s)
- Kyle J Lafata
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA. .,Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA. .,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.
| | - Yuqi Wang
- Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA
| | - Brandon Konkel
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Medicine, Gastroenterology, Duke University School of Medicine, Durham, NC, USA
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25
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A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability. Insights Imaging 2022; 13:139. [PMID: 35986798 PMCID: PMC9391628 DOI: 10.1186/s13244-022-01279-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/26/2022] [Indexed: 12/16/2022] Open
Abstract
Background Multiple tools have been applied to radiomics evaluation, while evidence rating tools for this field are still lacking. This study aims to assess the quality of pancreatitis radiomics research and test the feasibility of the evidence level rating tool. Results Thirty studies were included after a systematic search of pancreatitis radiomics studies until February 28, 2022, via five databases. Twenty-four studies employed radiomics for diagnostic purposes. The mean ± standard deviation of the adherence rate was 38.3 ± 13.3%, 61.3 ± 11.9%, and 37.1 ± 27.2% for the Radiomics Quality Score (RQS), the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guideline for preprocessing steps, respectively. The median (range) of RQS was 7.0 (− 3.0 to 18.0). The risk of bias and application concerns were mainly related to the index test according to the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The meta-analysis on differential diagnosis of autoimmune pancreatitis versus pancreatic cancer by CT and mass-forming pancreatitis versus pancreatic cancer by MRI showed diagnostic odds ratios (95% confidence intervals) of, respectively, 189.63 (79.65–451.48) and 135.70 (36.17–509.13), both rated as weak evidence mainly due to the insufficient sample size. Conclusions More research on prognosis of acute pancreatitis is encouraged. The current pancreatitis radiomics studies have insufficient quality and share common scientific disadvantages. The evidence level rating is feasible and necessary for bringing the field of radiomics from preclinical research area to clinical stage. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01279-4.
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Battistella A, Partelli S, Andreasi V, Marinoni I, Palumbo D, Tacelli M, Lena MS, Muffatti F, Mushtaq J, Capurso G, Arcidiacono PG, De Cobelli F, Doglioni C, Perren A, Falconi M. Preoperative assessment of microvessel density in nonfunctioning pancreatic neuroendocrine tumors (NF-PanNETs). Surgery 2022; 172:1236-1244. [PMID: 35953308 DOI: 10.1016/j.surg.2022.06.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/26/2022] [Accepted: 06/13/2022] [Indexed: 10/15/2022]
Abstract
BACKGROUND Hypervascularization is a typical feature of pancreatic neuroendocrine tumors, and it frequently allows their recognition at imaging studies. However, the density of microvessels in pancreatic neuroendocrine tumors changes according to their biological behavior, and a low microvessel density is associated with higher disease aggressiveness. The primary aim was to investigate the relationship between microvessel density and aggressiveness of nonfunctioning pancreatic neuroendocrine tumors. The secondary aim was to evaluate the ability of contrast-enhanced computed tomography and contrast-enhanced endoscopic ultrasound in predicting tumor microvessel density. METHODS The patients who underwent surgery for nonfunctioning pancreatic neuroendocrine tumors (n = 66) with an available preoperative contrast-enhanced computed tomography (n = 39) and/or contrast-enhanced endoscopic ultrasound (n = 37) performed at San Raffaele Hospital (2016-2020) were included. The tumor vascularization was assessed by CD-34 staining, contrast-enhanced computed tomography, and contrast-enhanced endoscopic ultrasound. Median microvessel density (165 microvessels/mm2) was chosen as the cutoff to define low microvessel density and high microvessel density. RESULTS The patients with a low microvessel density showed a significantly higher frequency of nodal metastases (P = .026), G2-G3 tumors (P = .022), and death domain-associated protein/α-thalassemia/mental retardation syndrome X-linked loss (P = .011) compared to patients with high microvessel density. The contrast-enhanced computed tomography tumor density in the arterial phase was significantly higher in patients with high microvessel density compared to those with low microvessel density (P = .016). The patients with a low microvessel density showed a significantly higher frequency of contrast-enhanced endoscopic ultrasound arterial hypoenhancement (P = .042) and late washout (P = .034). Contrast-enhanced computed tomography arterial hypoenhancement (P = .007) and contrast-enhanced endoscopic ultrasound late washout (P = .048) independently predicted a low microvessel density in the patients who underwent contrast-enhanced computed tomography and contrast-enhanced endoscopic ultrasound, respectively. CONCLUSION A low microvessel density represents a marker of aggressiveness in the patients with nonfunctioning pancreatic neuroendocrine tumors. Contrast-enhanced computed tomography and contrast-enhanced endoscopic ultrasound are reliable and easily available tools for preoperative assessment of microvessel density.
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Affiliation(s)
- Anna Battistella
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy. http://www.twitter.com/annabattistell
| | - Stefano Partelli
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy. http://www.twitter.com/spartelli
| | - Valentina Andreasi
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy. http://www.twitter.com/valentinandreas
| | - Ilaria Marinoni
- Institute of Pathology, University of Bern, Bern, Switzerland. http://www.twitter.com/ilamarinoni
| | - Diego Palumbo
- Radiology Unit, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy. http://www.twitter.com/DiegoPalumbo89
| | - Matteo Tacelli
- Pancreato-biliary Endoscopy and EUS Division, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy. http://www.twitter.com/TacelliMatteo
| | - Marco Schiavo Lena
- Pathology Unit, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Francesca Muffatti
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Junaid Mushtaq
- Radiology Unit, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Gabriele Capurso
- Pancreato-biliary Endoscopy and EUS Division, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy. http://www.twitter.com/lelecapurso
| | - Paolo Giorgio Arcidiacono
- Pancreato-biliary Endoscopy and EUS Division, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Francesco De Cobelli
- Radiology Unit, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy. http://www.twitter.com/FDeCobelli
| | - Claudio Doglioni
- Pathology Unit, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Aurel Perren
- Institute of Pathology, University of Bern, Bern, Switzerland. http://www.twitter.com/AurelPerren
| | - Massimo Falconi
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.
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Chiapponi C, Bruns CJ. [Modern molecular and imaging diagnostics in pancreatic neuroendocrine neoplasms]. CHIRURGIE (HEIDELBERG, GERMANY) 2022; 93:731-738. [PMID: 35913626 DOI: 10.1007/s00104-022-01645-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/20/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE New molecular diagnostic and radiologic imaging techniques can be used to assess the extent, risk of recurrence, prognosis and response to treatment of pancreatic neuroendocrine neoplasms (pNENs). They therefore represent a decisive help in setting the indications for surgical treatment, especially in advanced stages. METHODS This article presents a narrative assessment of the options and evidence for modern molecular and radiologic imaging diagnostics of pNENs based on the current literature. RESULTS While circulating DNA, circulating tumor cells and microRNAs have not yet become established in everyday clinical practice, the current literature suggests a promising role for the so-called NETest. Recent studies demonstrated its possible importance for the surgical management of pNENs. Besides [68Ga]Ga-DOTA-SSA-PET and [18]FDG-PET, which remain the gold standards for imaging NENs, radiomics represent an exciting alternative to biopsies and will possibly play an increasingly important role in the future. DISCUSSION There are new promising alternatives to chromogranin A, which has been clinically widespread since the 1970s despite several drawbacks, to map the extent, risk of recurrence, prognosis and response to treatment of pancreatic pNENs. In terms of personalized medicine, modern molecular and radiological diagnostics should play an increasing role for indicating and planning surgical treatment and for follow-up in the future.
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Affiliation(s)
- Costanza Chiapponi
- Klinik für Allgemein‑, Viszeral‑, Tumor- und Transplantationschirurgie, Uniklinik Köln, Kerpenerstr. 62, 50937, Köln, Deutschland.
| | - Christiane J Bruns
- Klinik für Allgemein‑, Viszeral‑, Tumor- und Transplantationschirurgie, Uniklinik Köln, Kerpenerstr. 62, 50937, Köln, Deutschland
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Pantelis AG, Panagopoulou PA, Lapatsanis DP. Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms—A Scoping Review. Diagnostics (Basel) 2022; 12:diagnostics12040874. [PMID: 35453922 PMCID: PMC9027316 DOI: 10.3390/diagnostics12040874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 03/27/2022] [Accepted: 03/29/2022] [Indexed: 12/21/2022] Open
Abstract
Neuroendocrine neoplasms (NENs) and tumors (NETs) are rare neoplasms that may affect any part of the gastrointestinal system. In this scoping review, we attempt to map existing evidence on the role of artificial intelligence, machine learning and deep learning in the diagnosis and management of NENs of the gastrointestinal system. After implementation of inclusion and exclusion criteria, we retrieved 44 studies with 53 outcome analyses. We then classified the papers according to the type of studied NET (26 Pan-NETs, 59.1%; 3 metastatic liver NETs (6.8%), 2 small intestinal NETs, 4.5%; colorectal, rectal, non-specified gastroenteropancreatic and non-specified gastrointestinal NETs had from 1 study each, 2.3%). The most frequently used AI algorithms were Supporting Vector Classification/Machine (14 analyses, 29.8%), Convolutional Neural Network and Random Forest (10 analyses each, 21.3%), Random Forest (9 analyses, 19.1%), Logistic Regression (8 analyses, 17.0%), and Decision Tree (6 analyses, 12.8%). There was high heterogeneity on the description of the prediction model, structure of datasets, and performance metrics, whereas the majority of studies did not report any external validation set. Future studies should aim at incorporating a uniform structure in accordance with existing guidelines for purposes of reproducibility and research quality, which are prerequisites for integration into clinical practice.
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Affiliation(s)
- Athanasios G. Pantelis
- 4th Department of Surgery, Evaggelismos General Hospital of Athens, 10676 Athens, Greece;
- Correspondence:
| | | | - Dimitris P. Lapatsanis
- 4th Department of Surgery, Evaggelismos General Hospital of Athens, 10676 Athens, Greece;
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Mapelli P, Bezzi C, Palumbo D, Canevari C, Ghezzo S, Samanes Gajate AM, Catalfamo B, Messina A, Presotto L, Guarnaccia A, Bettinardi V, Muffatti F, Andreasi V, Schiavo Lena M, Gianolli L, Partelli S, Falconi M, Scifo P, De Cobelli F, Picchio M. 68Ga-DOTATOC PET/MR imaging and radiomic parameters in predicting histopathological prognostic factors in patients with pancreatic neuroendocrine well-differentiated tumours. Eur J Nucl Med Mol Imaging 2022; 49:2352-2363. [DOI: 10.1007/s00259-022-05677-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/31/2021] [Indexed: 12/17/2022]
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Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms. JOURNAL OF ONCOLOGY 2021; 2021:8615450. [PMID: 34671399 PMCID: PMC8523238 DOI: 10.1155/2021/8615450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/13/2021] [Accepted: 09/20/2021] [Indexed: 12/24/2022]
Abstract
Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.
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Atkinson C, Ganeshan B, Endozo R, Wan S, Aldridge MD, Groves AM, Bomanji JB, Gaze MN. Radiomics-Based Texture Analysis of 68Ga-DOTATATE Positron Emission Tomography and Computed Tomography Images as a Prognostic Biomarker in Adults With Neuroendocrine Cancers Treated With 177Lu-DOTATATE. Front Oncol 2021; 11:686235. [PMID: 34408979 PMCID: PMC8366561 DOI: 10.3389/fonc.2021.686235] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 07/12/2021] [Indexed: 12/25/2022] Open
Abstract
Purpose Neuroendocrine tumors (NET) are rare cancers with variable behavior. A better understanding of prognosis would aid individualized management. The aim of this hypothesis-generating pilot study was to investigate the prognostic potential of tumor heterogeneity and tracer avidity in NET using texture analysis (TA) of 68Ga-DOTATATE positron emission tomography (PET) and non-enhanced computed tomography (CT) performed at baseline in patients treated with 177Lu-DOTATATE. It aims to justify a larger-scale study to evaluate its clinical value. Methods The pretherapy 68Ga-DOTATATE PET-CT scans of 44 patients with metastatic NET (carcinoid, pancreatic, thyroid, head and neck, catecholamine-secreting, and unknown primary NET) treated with 177Lu-DOTATATE were analyzed retrospectively using commercially available texture analysis research software. Image filtration extracted and enhanced objects of different sizes (fine, medium, coarse), then quantified heterogeneity by statistical and histogram-based parameters (mean intensity, standard deviation, entropy, mean of positive pixels, skewness, and kurtosis). Regions of interest were manually drawn around up to five of the most 68Ga-DOTATATE avid lesions for each patient. 68Gallium uptake on PET was quantified as SUVmax and SUVmean. Associations between imaging and clinical markers with progression-free (PFS) and overall survival (OS) were assessed using univariate Kaplan-Meier analysis. Independence of the significant univariate markers of survival was tested using multivariate Cox regression analysis. Results Measures of heterogeneity (higher kurtosis, higher entropy, and lower skewness) on coarse-texture scale CT and unfiltered PET images predicted shorter PFS (CT coarse kurtosis: p=0.05, PET entropy: p=0.01, PET skewness: p=0.03) and shorter OS (CT coarse kurtosis: p=0.05, PET entropy: p=0.01, PET skewness p=0.02). Conventional PET parameters such as SUVmax and SUVmean showed trends towards predicting outcome but were not statistically significant. Multivariate analysis identified that CT-TA (coarse kurtosis: HR=2.57, 95% CI=1.22–5.38, p=0.013) independently predicted PFS, and PET-TA (unfiltered skewness: HR=9.05, 95% CI=1.19–68.91, p=0.033) independently predicted OS. Conclusion These preliminary data generate a hypothesis that radiomic analysis of neuroendocrine cancer on 68Ga-DOTATATE PET-CT may be of prognostic value and a valuable addition to the assessment of patients.
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Affiliation(s)
- Charlotte Atkinson
- Departments of Oncology and Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London, London, United Kingdom
| | - Raymond Endozo
- Institute of Nuclear Medicine, University College London, London, United Kingdom
| | - Simon Wan
- Departments of Oncology and Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Matthew D Aldridge
- Departments of Oncology and Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Ashley M Groves
- Institute of Nuclear Medicine, University College London, London, United Kingdom
| | - Jamshed B Bomanji
- Departments of Oncology and Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Mark N Gaze
- Departments of Oncology and Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, United Kingdom
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