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Zheng D, Grandgenett PM, Zhang Q, Baine M, Shi Y, Du Q, Liang X, Wong J, Iqbal S, Preuss K, Kamal A, Yu H, Du H, Hollingsworth MA, Zhang C. radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer. Sci Rep 2024; 14:12316. [PMID: 38811597 PMCID: PMC11137018 DOI: 10.1038/s41598-024-62741-5] [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: 11/17/2023] [Accepted: 05/21/2024] [Indexed: 05/31/2024] Open
Abstract
Addressing the significant level of variability exhibited by pancreatic cancer necessitates the adoption of a systems biology approach that integrates molecular data, biological properties of the tumors, medical images, and clinical features of the patients. In this study, a comprehensive multi-omics methodology was employed to examine a distinctive collection of patient dataset containing rapid autopsy tumor and normal tissue samples as well as longitudinal imaging with a focus on pancreatic cancer. By performing a whole exome sequencing analysis on tumor and normal tissues to identify somatic gene variants and a radiomic feature analysis to tumor CT images, the genome-wide association approach established a connection between pancreatic cancer driver genes and relevant radiomic features, enabling a thorough and quantitative assessment of the heterogeneity of pancreatic tumors. The significant association between sets of genes and radiomic features revealed the involvement of genes in shaping tumor morphological heterogeneity. Some results of the association established a connection between the molecular level mechanism and their outcomes at the level of tumor structural heterogeneity. Because tumor structure and tumor structural heterogeneity are related to the patients' overall survival, patients who had pancreatic cancer driver gene mutations with an association to a certain radiomic feature have been observed to experience worse survival rates than cases without these somatic mutations. Furthermore, the association analysis has revealed potential gene mutations and radiomic feature candidates that warrant further investigation in future research endeavors.
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Affiliation(s)
- Dandan Zheng
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA.
| | - Paul M Grandgenett
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA
| | - Qi Zhang
- Department of Mathematics and Statistics, University of New Hampshire, Durham, NH, USA
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Yu Shi
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA
| | - Qian Du
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL, USA
| | - Jeffrey Wong
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Subhan Iqbal
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA
| | - Kiersten Preuss
- Department of Nutrition and Health Sciences, University of Nebraska, Lincoln, NE, USA
| | - Ahsan Kamal
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Hongfeng Yu
- School of Computing, University of Nebraska, Lincoln, NE, USA
| | - Huijing Du
- Department of Mathematics, University of Nebraska, Lincoln, NE, USA
| | - Michael A Hollingsworth
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA.
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA.
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Choi W, Jia Y, Kwak J, Werner-Wasik M, Dicker AP, Simone NL, Storozynsky E, Jain V, Vinogradskiy Y. Novel Functional Radiomics for Prediction of Cardiac Positron Emission Tomography Avidity in Lung Cancer Radiotherapy. JCO Clin Cancer Inform 2024; 8:e2300241. [PMID: 38452302 PMCID: PMC10939651 DOI: 10.1200/cci.23.00241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/22/2023] [Accepted: 01/26/2024] [Indexed: 03/09/2024] Open
Abstract
PURPOSE Traditional methods of evaluating cardiotoxicity focus on radiation doses to the heart. Functional imaging has the potential to provide improved prediction for cardiotoxicity for patients with lung cancer. Fluorine-18 (18F) fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) imaging is routinely obtained in a standard cancer staging workup. This work aimed to develop a radiomics model predicting clinical cardiac assessment using 18F-FDG PET/CT scans before thoracic radiation therapy. METHODS Pretreatment 18F-FDG PET/CT scans from three study populations (N = 100, N = 39, N = 70) were used, comprising two single-institutional protocols and one publicly available data set. A clinician (V.J.) classified the PET/CT scans per clinical cardiac guidelines as no uptake, diffuse uptake, or focal uptake. The heart was delineated, and 210 novel functional radiomics features were selected to classify cardiac FDG uptake patterns. Training data were divided into training (80%)/validation (20%) sets. Feature reduction was performed using the Wilcoxon test, hierarchical clustering, and recursive feature elimination. Ten-fold cross-validation was carried out for training, and the accuracy of the models to predict clinical cardiac assessment was reported. RESULTS From 202 of 209 scans, cardiac FDG uptake was scored as no uptake (39.6%), diffuse uptake (25.3%), and focal uptake (35.1%), respectively. Sixty-two independent radiomics features were reduced to nine clinically pertinent features. The best model showed 93% predictive accuracy in the training data set and 80% and 92% predictive accuracy in two external validation data sets. CONCLUSION This work used an extensive patient data set to develop a functional cardiac radiomic model from standard-of-care 18F-FDG PET/CT scans, showing good predictive accuracy. The radiomics model has the potential to provide an automated method to predict existing cardiac conditions and provide an early functional biomarker to identify patients at risk of developing cardiac complications after radiotherapy.
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Affiliation(s)
- Wookjin Choi
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Yingcui Jia
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Jennifer Kwak
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO
| | - Maria Werner-Wasik
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Adam P. Dicker
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Nicole L. Simone
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Eugene Storozynsky
- Department of Cardiology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Varsha Jain
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
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Lee JH, Shin J, Min JH, Jeong WK, Kim H, Choi SY, Lee J, Hong S, Kim K. Preoperative prediction of early recurrence in resectable pancreatic cancer integrating clinical, radiologic, and CT radiomics features. Cancer Imaging 2024; 24:6. [PMID: 38191489 PMCID: PMC10775464 DOI: 10.1186/s40644-024-00653-3] [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: 07/26/2023] [Accepted: 12/29/2023] [Indexed: 01/10/2024] Open
Abstract
OBJECTIVES To use clinical, radiographic, and CT radiomics features to develop and validate a preoperative prediction model for the early recurrence of pancreatic cancer. METHODS We retrospectively analyzed 190 patients (150 and 40 in the development and test cohort from different centers) with pancreatic cancer who underwent pancreatectomy between January 2018 and June 2021. Radiomics, clinical-radiologic (CR), and clinical-radiologic-radiomics (CRR) models were developed for the prediction of recurrence within 12 months after surgery. Performance was evaluated using the area under the curve (AUC), Brier score, sensitivity, and specificity. RESULTS Early recurrence occurred in 36.7% and 42.5% of the development and test cohorts, respectively (P = 0.62). The features for the CR model included carbohydrate antigen 19-9 > 500 U/mL (odds ratio [OR], 3.60; P = 0.01), abutment to the portal and/or superior mesenteric vein (OR, 2.54; P = 0.054), and adjacent organ invasion (OR, 2.91; P = 0.03). The CRR model demonstrated significantly higher AUCs than the radiomics model in the internal (0.77 vs. 0.73; P = 0.048) and external (0.83 vs. 0.69; P = 0.038) validations. Although we found no significant difference between AUCs of the CR and CRR models (0.83 vs. 0.76; P = 0.17), CRR models showed more balanced sensitivity and specificity (0.65 and 0.87) than CR model (0.41 and 0.91) in the test cohort. CONCLUSIONS The CRR model outperformed the radiomics and CR models in predicting the early recurrence of pancreatic cancer, providing valuable information for risk stratification and treatment guidance.
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Affiliation(s)
- Jeong Hyun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Jaeseung Shin
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Ji Hye Min
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Woo Kyoung Jeong
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Honsoul Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Seo-Youn Choi
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro Gangnam-gu, Seoul, 06351, Republic of Korea
- Department of Radiology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Republic of Korea
| | - Jisun Lee
- Department of Radiology, College of Medicine, Chungbuk National University, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - Sungjun Hong
- Department of Digital Health, Samsung Advanced Institute of Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Kyunga Kim
- Department of Digital Health, Samsung Advanced Institute of Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
- Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
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Zhang Z, Tian H, Xu Z, Bian Y, Wu J. Application of a pyramid pooling Unet model with integrated attention mechanism and Inception module in pancreatic tumor segmentation. J Appl Clin Med Phys 2023; 24:e14204. [PMID: 37937804 PMCID: PMC10691628 DOI: 10.1002/acm2.14204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/26/2023] [Accepted: 10/28/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND The segmentation and recognition of pancreatic tumors are crucial tasks in the diagnosis and treatment of pancreatic diseases. However, due to the relatively small proportion of the pancreas in the abdomen and significant shape and size variations, pancreatic tumor segmentation poses considerable challenges. PURPOSE To construct a network model that combines a pyramid pooling module with Inception architecture and SE attention mechanism (PIS-Unet), and observe its effectiveness in pancreatic tumor images segmentation, thereby providing supportive recommendations for clinical practitioners. MATERIALS AND METHODS A total of 303 patients with histologically confirmed pancreatic cystic neoplasm (PCN), including serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN), from Shanghai Changhai Hospital between March 2011 and November 2021 were included. A total of 1792 T2-weighted imaging (T2WI) slices were used to build a CNN model. The model employed a pyramid pooling Inception module with a fused attention mechanism. The attention mechanism enhanced the network's focus on local features, while the Inception module and pyramid pooling allowed the network to extract features at different scales and improve the utilization efficiency of global information, thereby effectively enhancing network performance. RESULTS Using three-fold cross-validation, the model constructed by us achieved a dice score of 85.49 ± 2.02 for SCN images segmentation, and a dice score of 87.90 ± 4.19 for MCN images segmentation. CONCLUSION This study demonstrates that using deep learning networks for the segmentation of PCNs yields favorable results. Applying this network as an aid to physicians in PCN diagnosis shows potential for clinical applications.
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Affiliation(s)
- Zhiwei Zhang
- School of Health Science and EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
| | - Hui Tian
- School of Health Science and EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
| | - Zhenshun Xu
- School of Health Science and EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
| | - Yun Bian
- Department of RadiologyChanghai HospitalThe Navy Military Medical UniversityShanghaiChina
| | - Jie Wu
- School of Health Science and EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
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Zheng D, Grandgenett PM, Zhang Q, Baine M, Shi Y, Du Q, Liang X, Wong J, Iqbal S, Preuss K, Kamal A, Yu H, Du H, Hollingsworth MA, Zhang C. radioGWAS: link radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.02.23297995. [PMID: 37961101 PMCID: PMC10635263 DOI: 10.1101/2023.11.02.23297995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Addressing the significant level of variability exhibited by pancreatic cancer necessitates the adoption of a systems biology approach that integrates molecular data, biological properties of the tumors, and clinical features of the patients. In this study, a comprehensive multi-omics methodology was employed to examine a distinctive collection patient dataset containing rapid autopsy tumor and normal tissue samples as well as longitudinal imaging with a focus on pancreatic cancer. By performing a whole exome sequencing analysis on tumor and normal tissues to identify somatic gene variants and a radiomics feature analysis to tumor CT images, the genome-wide association approach established a connection between pancreatic cancer driver genes and relevant radiomics features, enabling a thorough and quantitative assessment of the heterogeneity of pancreatic tumors. The significant association between sets of genes and radiomics features revealed the involvement of genes in shaping tumor morphological heterogeneity. Some results of the association established a connection between the molecular level mechanism and their outcomes at the level of tumor structural heterogeneity. Because tumor structure and tumor structural heterogeneity are related to the patients' overall survival, patients who had pancreatic cancer driver gene mutations with an association to a certain radiomics feature have been observed to experience worse survival rates than cases without these somatic mutations. Furthermore, the outcome of the association analysis has revealed potential gene mutations and radiomics feature candidates that warrant further investigation in future research endeavors.
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6
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De Muzio F, Pellegrino F, Fusco R, Tafuto S, Scaglione M, Ottaiano A, Petrillo A, Izzo F, Granata V. Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics. Diagnostics (Basel) 2023; 13:2877. [PMID: 37761243 PMCID: PMC10529975 DOI: 10.3390/diagnostics13182877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Neuroendocrine neoplasms (NENs) are a group of lesions originating from cells of the diffuse neuroendocrine system. NENs may involve different sites, including the gastrointestinal tract (GEP-NENs). The incidence and prevalence of GEP-NENs has been constantly rising thanks to the increased diagnostic power of imaging and immuno-histochemistry. Despite the plethora of biochemical markers and imaging techniques, the prognosis and therapeutic choice in GEP-NENs still represents a challenge, mainly due to the great heterogeneity in terms of tumor lesions and clinical behavior. The concept that biomedical images contain information about tissue heterogeneity and pathological processes invisible to the human eye is now well established. From this substrate comes the idea of radiomics. Computational analysis has achieved promising results in several oncological settings, and the use of radiomics in different types of GEP-NENs is growing in the field of research, yet with conflicting results. The aim of this narrative review is to provide a comprehensive update on the role of radiomics on GEP-NEN management, focusing on the main clinical aspects analyzed by most existing reports: predicting tumor grade, distinguishing NET from other tumors, and prognosis assessment.
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Affiliation(s)
- Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | | | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy;
| | - Salvatore Tafuto
- Unit of Sarcomi e Tumori Rari, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Mariano Scaglione
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy
| | - Alessandro Ottaiano
- Unit for Innovative Therapies of Abdominal Metastastes, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
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Yao L, Zhang Z, Keles E, Yazici C, Tirkes T, Bagci U. A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging. Curr Opin Gastroenterol 2023; 39:436-447. [PMID: 37523001 PMCID: PMC10403281 DOI: 10.1097/mog.0000000000000966] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
PURPOSE OF REVIEW Early and accurate diagnosis of pancreatic cancer is crucial for improving patient outcomes, and artificial intelligence (AI) algorithms have the potential to play a vital role in computer-aided diagnosis of pancreatic cancer. In this review, we aim to provide the latest and relevant advances in AI, specifically deep learning (DL) and radiomics approaches, for pancreatic cancer diagnosis using cross-sectional imaging examinations such as computed tomography (CT) and magnetic resonance imaging (MRI). RECENT FINDINGS This review highlights the recent developments in DL techniques applied to medical imaging, including convolutional neural networks (CNNs), transformer-based models, and novel deep learning architectures that focus on multitype pancreatic lesions, multiorgan and multitumor segmentation, as well as incorporating auxiliary information. We also discuss advancements in radiomics, such as improved imaging feature extraction, optimized machine learning classifiers and integration with clinical data. Furthermore, we explore implementing AI-based clinical decision support systems for pancreatic cancer diagnosis using medical imaging in practical settings. SUMMARY Deep learning and radiomics with medical imaging have demonstrated strong potential to improve diagnostic accuracy of pancreatic cancer, facilitate personalized treatment planning, and identify prognostic and predictive biomarkers. However, challenges remain in translating research findings into clinical practice. More studies are required focusing on refining these methods, addressing significant limitations, and developing integrative approaches for data analysis to further advance the field of pancreatic cancer diagnosis.
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Affiliation(s)
- Lanhong Yao
- Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University
| | - Zheyuan Zhang
- Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University
| | - Elif Keles
- Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University
| | - Cemal Yazici
- Division of Gastroentrrology and Hepatology, University of Illinois Chicago, Chicago, Illinois
| | - Temel Tirkes
- Department of Radiology & Imaging Sciences, Medicine and Urology, Indiana University School of Medicine, Indianapolis, Indianapolis, USA
| | - Ulas Bagci
- Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University
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Tan D, Mohamad Salleh SA, Manan HA, Yahya N. Delta-radiomics-based models for toxicity prediction in radiotherapy: A systematic review and meta-analysis. J Med Imaging Radiat Oncol 2023; 67:564-579. [PMID: 37309680 DOI: 10.1111/1754-9485.13546] [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: 01/10/2023] [Accepted: 05/28/2023] [Indexed: 06/14/2023]
Abstract
INTRODUCTION Delta-radiomics models are potentially able to improve the treatment assessment than single-time point features. The purpose of this study is to systematically synthesize the performance of delta-radiomics-based models for radiotherapy (RT)-induced toxicity. METHODS A literature search was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases in October 2022. Retrospective and prospective studies on the delta-radiomics model for RT-induced toxicity were included based on predefined PICOS criteria. A random-effect meta-analysis of AUC was performed on the performance of delta-radiomics models, and a comparison with non-delta radiomics models was included. RESULTS Of the 563 articles retrieved, 13 selected studies of RT-treated patients on different types of cancer (HNC = 571, NPC = 186, NSCLC = 165, oesophagus = 106, prostate = 33, OPC = 21) were eligible for inclusion in the systematic review. Included studies show that morphological and dosimetric features may improve the predictive model performance for the selected toxicity. Four studies that reported both delta and non-delta radiomics features with AUC were included in the meta-analysis. The AUC random effects estimate for delta and non-delta radiomics models were 0.80 and 0.78 with heterogeneity, I2 of 73% and 27% respectively. CONCLUSION Delta-radiomics-based models were found to be promising predictors of predefined end points. Future studies should consider using standardized methods and radiomics features and external validation to the reviewed delta-radiomics model.
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Affiliation(s)
- Daryl Tan
- Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | | | - Hanani Abdul Manan
- Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
| | - Noorazrul Yahya
- Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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Xie N, Fan X, Chen D, Chen J, Yu H, He M, Liu H, Yin X, Li B, Wang H. Peritumoral and Intratumoral Texture Features Based on Multiparametric MRI and Multiple Machine Learning Methods to Preoperatively Evaluate the Pathological Outcomes of Pancreatic Cancer. J Magn Reson Imaging 2023; 58:379-391. [PMID: 36426965 DOI: 10.1002/jmri.28538] [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: 09/19/2022] [Revised: 11/13/2022] [Accepted: 11/14/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Radiomics-based preoperative evaluation of lymph node metastasis (LNM) and histological grade (HG) might facilitate the decision-making for pancreatic cancer and further efforts are needed to develop effective models. PURPOSE To develop multiparametric MRI (MP-MRI)-based radiomics models to evaluate LNM and HG. STUDY TYPE Retrospective. POPULATION The pancreatic cancer patients from the main center (n = 126) were assigned to the training and validation sets at a 4:1 ratio. The patients from the other center (n = 40) served as external test sets. FIELD STRENGTH/SEQUENCE A 3.0 T and 1.5 T/T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast enhancement T1-weighted imaging. ASSESSMENT A total of 10,686 peritumoral and intratumoral radiomics features were extracted which contained first-order, shape-based, and texture features. The following three-step method was applied to reduce the feature dimensionality: SelectKBest (a function from scikit-learn package), least absolute shrinkage and selection operator (LASSO), and recursive feature elimination based on random forest (RFE-RF). Six classifiers (random forest, logistic regression, support vector machine, K-nearest neighbor, decision tree, and XGBOOST) were trained and selected based on their performance to construct the clinical, radiomics, and combination models. STATISTICAL TESTS Delong's test was used to compare the models' performance. P value less than 0.05 was considered significant. RESULTS Twelve significant features for LNM and 11 features for HG were obtained. Random forest and logistic regression performed better than the other classifiers in evaluating LNM and HG, respectively, according to the surgical pathological results. The best performance was obtained with the models that combined peritumoral and intratumoral features with area under curve (AUC) values of 0.944 and 0.892 in the validation and external test sets for HG and 0.924 and 0.875 for LNM. DATA CONCLUSION Radiomics holds the potential to evaluate LNM and HG of pancreatic cancer. The combination of peritumoral and intratumoral features will make models more accurate. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Ni Xie
- Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Pancreatic Diseases, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xuhui Fan
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- R & D Center of Medical Artificial Intelligence and Medical Engineering, Shanghai General Hospital, Shanghai, China
- National Center for Translational Medicine (Shanghai), Shanghai, China
| | - Desheng Chen
- Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingwen Chen
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- R & D Center of Medical Artificial Intelligence and Medical Engineering, Shanghai General Hospital, Shanghai, China
- National Center for Translational Medicine (Shanghai), Shanghai, China
| | - Hongwei Yu
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- R & D Center of Medical Artificial Intelligence and Medical Engineering, Shanghai General Hospital, Shanghai, China
- National Center for Translational Medicine (Shanghai), Shanghai, China
| | - Meijuan He
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- R & D Center of Medical Artificial Intelligence and Medical Engineering, Shanghai General Hospital, Shanghai, China
- National Center for Translational Medicine (Shanghai), Shanghai, China
| | - Hao Liu
- Yizhun Medical AI Technology Co. Ltd., Beijing, China
| | - Xiaorui Yin
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- R & D Center of Medical Artificial Intelligence and Medical Engineering, Shanghai General Hospital, Shanghai, China
| | - Baiwen Li
- Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Pancreatic Diseases, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Han Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- R & D Center of Medical Artificial Intelligence and Medical Engineering, Shanghai General Hospital, Shanghai, China
- National Center for Translational Medicine (Shanghai), Shanghai, China
- Jiading Branch of Shanghai General Hospital, Shanghai, China
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10
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Yin M, Liu L, Gao J, Lin J, Qu S, Xu W, Liu X, Xu C, Zhu J. Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review. Int J Med Inform 2023; 174:105044. [PMID: 36948061 DOI: 10.1016/j.ijmedinf.2023.105044] [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: 09/30/2022] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 03/19/2023]
Abstract
BACKGROUND AND AIMS Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic review is to evaluate the role of DL algorithms in assisting EUS diagnosis of pancreatic diseases. METHODS Literature search were conducted in PubMed and Semantic Scholar databases. Studies that developed DL models for pancreatic diseases based on EUS were eligible for inclusion. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and quality assessment of the included studies was performed according to the IJMEDI checklist. RESULTS A total of 23 studies were enrolled into this systematic review, which could be categorized into three groups according to computer vision tasks: classification, detection and segmentation. Seventeen studies focused on the classification task, among which five studies developed simple neural network (NN) models while twelve studies constructed convolutional NN (CNN) models. Three studies were concerned the detection task and five studies were the segmentation task, all based on CNN architectures. All models presented in the studies performed well based on EUS images, videos or voice. According to the IJMEDI checklist, six studies were recognized as high-grade quality, with scores beyond 35 points. CONCLUSIONS DL algorithms show great potential in EUS images/videos/voice for pancreatic diseases. However, there is room for improvement such as sample sizes, multi-center cooperation, data preprocessing, model interpretability, and code sharing.
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Affiliation(s)
- Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Shuting Qu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Wei Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Xiaolin Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Chunfang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China.
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China.
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Bian Y, Zhou J, Zhu M, Yu J, Zhao H, Fang X, Liu F, Wang T, Li J, Wang L, Lu J, Shao C. Replacing secretin-enhanced MRCP with MRI radiomics model based on a fully automated pancreas segmentation for assessing pancreatic exocrine function in chronic pancreatitis. Eur Radiol 2023; 33:3580-3591. [PMID: 36884086 DOI: 10.1007/s00330-023-09448-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 01/06/2023] [Accepted: 01/18/2023] [Indexed: 03/09/2023]
Abstract
OBJECTIVES To develop and validate a radiomics nomogram based on a fully automated pancreas segmentation to assess pancreatic exocrine function. Furthermore, we aimed to compare the performance of the radiomics nomogram with the pancreatic flow output rate (PFR) and conclude on the replacement of secretin-enhanced magnetic resonance cholangiopancreatography (S-MRCP) by the radiomics nomogram for pancreatic exocrine function assessment. METHODS All participants underwent S-MRCP between April 2011 and December 2014 in this retrospective study. PFR was quantified using S-MRCP. Participants were divided into normal and pancreatic exocrine insufficiency (PEI) groups using the cut-off of 200 µg/L of fecal elastase-1. Two prediction models were developed including the clinical and non-enhanced T1-weighted imaging radiomics model. A multivariate logistic regression analysis was conducted to develop the prediction models. The models' performances were determined based on their discrimination, calibration, and clinical utility. RESULTS A total of 159 participants (mean age [Formula: see text] standard deviation, 45 years [Formula: see text] 14;119 men) included 85 normal and 74 PEI. All the participants were divided into a training set comprising 119 consecutive patients and an independent validation set comprising 40 consecutive patients. The radiomics score was an independent risk factor for PEI (odds ratio = 11.69; p < 0.001). In the validation set, the radiomics nomogram exhibited the highest performance (AUC, 0.92) in PEI prediction, whereas the clinical nomogram and PFR had AUCs of 0.79 and 0.78, respectively. CONCLUSION The radiomics nomogram accurately predicted pancreatic exocrine function and outperformed pancreatic flow output rate on S-MRCP in patients with chronic pancreatitis. KEY POINTS • The clinical nomogram exhibited moderate performance in diagnosing pancreatic exocrine insufficiency. • The radiomics score was an independent risk factor for pancreatic exocrine insufficiency, and every point rise in the rad-score was associated with an 11.69-fold increase in pancreatic exocrine insufficiency risk. • The radiomics nomogram accurately predicted pancreatic exocrine function and outperformed the clinical model and pancreatic flow output rate quantified by secretin-enhanced magnetic resonance cholangiopancreatography on MRI in patients with chronic pancreatitis.
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Affiliation(s)
- Yun Bian
- Department of Radiology, Changhai Hospital, Navy Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Jian Zhou
- Department of Radiology, Changhai Hospital, Navy Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Mengmeng Zhu
- Department of Radiology, Changhai Hospital, Navy Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Navy Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Haiyan Zhao
- Department of Radiology, Changhai Hospital, Navy Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Navy Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Navy Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Tiegong Wang
- Department of Radiology, Changhai Hospital, Navy Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Li Wang
- Department of Radiology, Changhai Hospital, Navy Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Navy Medical University, Changhai Road 168, Shanghai, 200434, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Navy Medical University, Changhai Road 168, Shanghai, 200434, China.
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Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation. Diagnostics (Basel) 2023; 13:diagnostics13050849. [PMID: 36899993 PMCID: PMC10001394 DOI: 10.3390/diagnostics13050849] [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: 01/29/2023] [Revised: 02/13/2023] [Accepted: 02/20/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND Radiomics refers to a recent area of knowledge that studies features extracted from different imaging techniques and subsequently transformed into high-dimensional data that can be associated with biological events. Diffuse midline gliomas (DMG) are one of the most devastating types of cancer, with a median survival of approximately 11 months after diagnosis and 4-5 months after radiological and clinical progression. METHODS A retrospective study. From a database of 91 patients with DMG, only 12 had the H3.3K27M mutation and brain MRI DICOM files available. Radiomic features were extracted from MRI T1 and T2 sequences using LIFEx software. Statistical analysis included normal distribution tests and the Mann-Whitney U test, ROC analysis, and calculation of cut-off values. RESULTS A total of 5760 radiomic values were included in the analyses. AUROC demonstrated 13 radiomics with statistical significance for progression-free survival (PFS) and overall survival (OS). Diagnostic performance tests showed nine radiomics with specificity for PFS above 90% and one with a sensitivity of 97.2%. For OS, 3 out of 4 radiomics demonstrated between 80 and 90% sensitivity. CONCLUSIONS Several radiomic features demonstrated statistical significance and have the potential to further aid DMG diagnostic assessment non-invasively. The most significant radiomics were first- and second-order features with GLCM texture profile, GLZLM_GLNU, and NGLDM_Contrast.
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13
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The Role of Molecular Imaging in Personalized Medicine. J Pers Med 2023; 13:jpm13020369. [PMID: 36836603 PMCID: PMC9959741 DOI: 10.3390/jpm13020369] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
The concept of personalized medicine refers to the tailoring of medical treatment to each patient's unique characteristics. Scientific advancements have led to a better understanding of how a person's unique molecular and genetic profile makes them susceptible to certain diseases. It provides individualized medical treatments that will be safe and effective for each patient. Molecular imaging modalities play an essential role in this aspect. They are used widely in screening, detection and diagnosis, treatment, assessing disease heterogeneity and progression planning, molecular characteristics, and long-term follow-up. In contrast to conventional imaging approaches, molecular imaging techniques approach images as the knowledge that can be processed, allowing for the collection of relevant knowledge in addition to the evaluation of enormous patient groups. This review presents the fundamental role of molecular imaging modalities in personalized medicine.
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14
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Liao J, Li X, Gan Y, Han S, Rong P, Wang W, Li W, Zhou L. Artificial intelligence assists precision medicine in cancer treatment. Front Oncol 2023; 12:998222. [PMID: 36686757 PMCID: PMC9846804 DOI: 10.3389/fonc.2022.998222] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/22/2022] [Indexed: 01/06/2023] Open
Abstract
Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.
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Affiliation(s)
- Jinzhuang Liao
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiaoying Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yu Gan
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Shuangze Han
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Pengfei Rong
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Wei Wang
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Wei Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Li Zhou
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,Department of Pathology, The Xiangya Hospital of Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
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15
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Li J, Yin W, Wang Y. CT classification model of pancreatic serous cystic neoplasm and mucinous cystic neoplasm based on deep transfer learning. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:167-180. [PMID: 36404568 DOI: 10.3233/xst-221281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
BACKGROUND Pancreatic cancer is a highly lethal disease. The preoperative distinction between pancreatic serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN) remains a clinical challenge. OBJECTIVE The goal of this study is to provide clinicians with supportive advice and avoid overtreatment by constructing a convolutional neural network (CNN) classifier to automatically identify pancreatic cancer using computed tomography (CT) images. METHODS We construct a CNN model using a dataset of 6,173 CT images obtained from 107 pathologically confirmed pancreatic cancer patients at Shanghai Changhai Hospital from January 2017 to February 2022. We divide CT slices into three categories namely, SCN, MCN, and no tumor, to train the DenseNet201-based CNN model with multi-head spatial attention mechanism (MSAM-DenseNet201). The attention module enhances the network's attention to local features and effectively improves the network performance. The trained model is applied to process all CT image slices and finally realize the two categories classification of MCN and SCN patients through a joint voting strategy. RESULTS Using a 10-fold cross validation method, this new MSAM-DenseNet201 model achieves a classification accuracy of 92.52%, a precision of 92.16%, a sensitivity of 92.16%, and a specificity of 92.86%, respectively. CONCLUSIONS This study demonstrates the feasibility of using a deep learning network or classification model to help diagnose MCN and SCN cases. This, the new method has great potential for developing new computer-aided diagnosis systems and applying in future clinical practice.
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Affiliation(s)
- Jin Li
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Wei Yin
- Department of Radiology, Changhai Hospital, The Naval Military Medical University, Shanghai, China
| | - Yuanjun Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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16
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Jan Z, El Assadi F, Abd-alrazaq A, Jithesh PV. Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review (Preprint).. [DOI: 10.2196/preprints.44248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
BACKGROUND
Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.
OBJECTIVE
This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.
METHODS
A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.
RESULTS
Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.
CONCLUSIONS
This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.
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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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Preoperative Extrapancreatic Extension Prediction in Patients with Pancreatic Cancer Using Multiparameter MRI and Machine Learning-Based Radiomics Model. Acad Radiol 2022:S1076-6332(22)00508-6. [DOI: 10.1016/j.acra.2022.09.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/12/2022] [Accepted: 09/17/2022] [Indexed: 11/21/2022]
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19
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Role of CA 19.9 in the Management of Resectable Pancreatic Cancer: State of the Art and Future Perspectives. Biomedicines 2022; 10:biomedicines10092091. [PMID: 36140192 PMCID: PMC9495897 DOI: 10.3390/biomedicines10092091] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/14/2022] [Accepted: 08/23/2022] [Indexed: 12/28/2022] Open
Abstract
Background: Surgery still represents the gold standard of treatment for resectable pancreatic ductal adenocarcinoma (PDAC). Neoadjuvant treatments (NAT), currently proposed for borderline and locally advanced PDACs, are gaining momentum even in resectable tumors due to the recent interesting concept of “biological resectability”. In this scenario, CA 19.9 is having increasing importance in preoperative staging and in the choice of therapeutic strategies. We aimed to assess the state of the art and to highlight the future perspectives of CA 19.9 use in the management of patients with resectable pancreatic cancer. Methods: A PubMed database search of articles published up to December 2021 has been carried out. Results: Elevated pre-operative levels of CA 19.9 have been associated with reduced overall survival, nodal involvement, and margin status positivity after surgery. These abilities of CA 19.9 increase when combined with radiological or different biological criteria. Unfortunately, due to strong limitations of previously published articles, CA 19.9 alone cannot be yet considered as a key player in resectable pancreatic cancer patient management. Conclusion: The potential of CA 19.9 must be fully explored in order to standardize its role in the “biological staging” of patients with resectable pancreatic cancer.
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Agbley BLY, Li J, Hossin MA, Nneji GU, Jackson J, Monday HN, James EC. Federated Learning-Based Detection of Invasive Carcinoma of No Special Type with Histopathological Images. Diagnostics (Basel) 2022; 12:diagnostics12071669. [PMID: 35885573 PMCID: PMC9323034 DOI: 10.3390/diagnostics12071669] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/03/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
Invasive carcinoma of no special type (IC-NST) is known to be one of the most prevalent kinds of breast cancer, hence the growing research interest in studying automated systems that can detect the presence of breast tumors and appropriately classify them into subtypes. Machine learning (ML) and, more specifically, deep learning (DL) techniques have been used to approach this problem. However, such techniques usually require massive amounts of data to obtain competitive results. This requirement makes their application in specific areas such as health problematic as privacy concerns regarding the release of patients’ data publicly result in a limited number of publicly available datasets for the research community. This paper proposes an approach that leverages federated learning (FL) to securely train mathematical models over multiple clients with local IC-NST images partitioned from the breast histopathology image (BHI) dataset to obtain a global model. First, we used residual neural networks for automatic feature extraction. Then, we proposed a second network consisting of Gabor kernels to extract another set of features from the IC-NST dataset. After that, we performed a late fusion of the two sets of features and passed the output through a custom classifier. Experiments were conducted for the federated learning (FL) and centralized learning (CL) scenarios, and the results were compared. Competitive results were obtained, indicating the positive prospects of adopting FL for IC-NST detection. Additionally, fusing the Gabor features with the residual neural network features resulted in the best performance in terms of accuracy, F1 score, and area under the receiver operation curve (AUC-ROC). The models show good generalization by performing well on another domain dataset, the breast cancer histopathological (BreakHis) image dataset. Our method also outperformed other methods from the literature.
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Affiliation(s)
- Bless Lord Y. Agbley
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (B.L.Y.A.); (H.N.M.)
| | - Jianping Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (B.L.Y.A.); (H.N.M.)
- Correspondence:
| | - Md Altab Hossin
- School of Innovation and Entrepreneurship, Chengdu University, Chengdu 610106, China;
| | - Grace Ugochi Nneji
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (E.C.J.)
| | - Jehoiada Jackson
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (E.C.J.)
| | - Happy Nkanta Monday
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (B.L.Y.A.); (H.N.M.)
| | - Edidiong Christopher James
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (E.C.J.)
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Chen Q, Li J, Shen P, Yuan H, Yin J, Ge W, Wang W, Chen G, Yang T, Xiao B, Miao Y, Lu Z, Wu P, Jiang K. Biological functions, mechanisms, and clinical significance of circular RNA in pancreatic cancer: a promising rising star. Cell Biosci 2022; 12:97. [PMID: 35729650 PMCID: PMC9210669 DOI: 10.1186/s13578-022-00833-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 06/12/2022] [Indexed: 02/08/2023] Open
Abstract
Pancreatic cancer (PC) is a highly malignant solid tumor with insidious onset and easy early metastasis. Despite tremendous efforts devoted to research in this field, the mechanisms underlying PC tumorigenesis and progression remain unclear. Additionally, robust biomarkers and satisfactory therapeutic strategies for clinical use in PC patients are still lacking. Circular RNAs (circRNAs) are a new type of non-coding RNA originating from precursor messenger RNAs, with a covalent continuous closed-loop structure, strong stability and high specificity. Accumulating evidence suggests that circRNAs may participate in PC development and progression. Abnormal expression of circRNAs in PC is considered a vital factor that affects tumor cell proliferation, migration, invasion, apoptosis, angiogenesis and drug resistance. In this review of relevant articles published in recent years, we describe the basic knowledge concerning circRNAs, including their classification, biogenesis, functions and research approaches. Moreover, the biological roles and clinical significance of circRNAs related to PC are discussed. Finally, we note the questions remaining from recent studies and anticipate that further investigations will address these gaps in knowledge in this field. In conclusion, we expect to provide insights into circRNAs as potential targets for specific PC diagnosis and treatment in the future.
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Affiliation(s)
- Qun Chen
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiajia Li
- Affiliated Hospital of Yangzhou University, Yangzhou, China
| | - Peng Shen
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hao Yuan
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jie Yin
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wanli Ge
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wujun Wang
- Nanjing Hospital of Chinese Medicine, Affiliated to Nanjing University of Chinese Medicine, Nanjing, China
| | - Guangbin Chen
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Taoyue Yang
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Bin Xiao
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yi Miao
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zipeng Lu
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Pengfei Wu
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - Kuirong Jiang
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
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