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Mao KZ, Ma C, Song B. Radiomics advances in the evaluation of pancreatic cystic neoplasms. Heliyon 2024; 10:e25535. [PMID: 38333791 PMCID: PMC10850586 DOI: 10.1016/j.heliyon.2024.e25535] [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: 09/06/2023] [Revised: 01/23/2024] [Accepted: 01/29/2024] [Indexed: 02/10/2024] Open
Abstract
With the development of medical imaging, the detection rate of pancreatic cystic neoplasms (PCNs) has increased greatly. Serous cystic neoplasm, solid pseudopapillary neoplasm, intraductal papillary mucinous neoplasm and mucinous cystic neoplasm are the main subtypes of PCN, and their treatment options vary greatly due to the different biological behaviours of the tumours. Different from conventional qualitative imaging evaluation, radiomics is a promising noninvasive approach for the diagnosis, classification, and risk stratification of diseases involving high-throughput extraction of medical image features. We present a review of radiomics in the diagnosis of serous cystic neoplasm and mucinous cystic neoplasm, risk classification of intraductal papillary mucinous neoplasm and prediction of solid pseudopapillary neoplasm invasiveness compared to conventional imaging diagnosis. Radiomics is a promising tool in the field of medical imaging, providing a noninvasive, high-performance model for preoperative diagnosis and risk stratification of PCNs and improving prospects regarding management of these diseases. Further studies are warranted to investigate MRI image radiomics in connection with PCNs to improve the diagnosis and treatment strategies in the management of PCN patients.
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Affiliation(s)
- Kuan-Zheng Mao
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
- Department of Pancreatic Surgery, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
- College of Electronic and Information Engineering, Tongji University, Shanghai, 201804, China
| | - Bin Song
- Department of Pancreatic Surgery, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
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2
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Balduzzi A, Janssen BV, De Pastena M, Pollini T, Marchegiani G, Marquering H, Stoker J, Verpalen I, Bassi C, Besselink MG, Salvia R. Artificial intelligence-based models to assess the risk of malignancy on radiological imaging in patients with intraductal papillary mucinous neoplasm of the pancreas: scoping review. Br J Surg 2023; 110:1623-1627. [PMID: 37402951 PMCID: PMC10638536 DOI: 10.1093/bjs/znad201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/13/2023] [Accepted: 06/13/2023] [Indexed: 07/06/2023]
Affiliation(s)
- Alberto Balduzzi
- Department of Surgery and Oncology, Unit of General and Pancreatic Surgery, University of Verona Hospital Trust, Verona, Italy
| | - Boris V Janssen
- Department of Surgery, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
- Department of Pathology, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
- Cancer Centre Amsterdam, Amsterdam, the Netherlands
| | - Matteo De Pastena
- Department of Surgery and Oncology, Unit of General and Pancreatic Surgery, University of Verona Hospital Trust, Verona, Italy
| | - Tommaso Pollini
- Department of Surgery and Oncology, Unit of General and Pancreatic Surgery, University of Verona Hospital Trust, Verona, Italy
| | - Giovanni Marchegiani
- Department of Surgery and Oncology, Unit of General and Pancreatic Surgery, University of Verona Hospital Trust, Verona, Italy
| | - Henk Marquering
- Cancer Centre Amsterdam, Amsterdam, the Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location University of Amsterdam, Amsterdam, the Netherlands
| | - Jaap Stoker
- Cancer Centre Amsterdam, Amsterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location University of Amsterdam, Amsterdam, the Netherlands
| | - Inez Verpalen
- Cancer Centre Amsterdam, Amsterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location University of Amsterdam, Amsterdam, the Netherlands
| | - Claudio Bassi
- Department of Surgery and Oncology, Unit of General and Pancreatic Surgery, University of Verona Hospital Trust, Verona, Italy
| | - Marc G Besselink
- Department of Surgery, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
- Cancer Centre Amsterdam, Amsterdam, the Netherlands
| | - Roberto Salvia
- Department of Surgery and Oncology, Unit of General and Pancreatic Surgery, University of Verona Hospital Trust, Verona, Italy
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Preuss K, Thach N, Liang X, Baine M, Chen J, Zhang C, Du H, Yu H, Lin C, Hollingsworth MA, Zheng D. Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications. Cancers (Basel) 2022; 14:cancers14071654. [PMID: 35406426 PMCID: PMC8997008 DOI: 10.3390/cancers14071654] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/16/2022] [Accepted: 03/18/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary With a five-year survival rate of only 3% for the majority of patients, pancreatic cancer is a global healthcare challenge. Radiomics and deep learning, two novel quantitative imaging methods that treat medical images as minable data instead of just pictures, have shown promise in advancing personalized management of pancreatic cancer through diagnosing precursor diseases, early detection, accurate diagnosis, and treatment personalization. Radiomics and deep learning methods aim to collect hidden information in medical images that is missed by conventional radiology practices through expanding the data search and comparing information across different patients. Both methods have been studied and applied in pancreatic cancer. In this review, we focus on the current progress of these two methods in pancreatic cancer and provide a comprehensive narrative review on the topic. With better regulation, enhanced workflow, and larger prospective patient datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through personalized precision medicine. Abstract As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.
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Affiliation(s)
- Kiersten Preuss
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Nutrition and Health Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA
| | - Nate Thach
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Justin Chen
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Naperville North High School, Naperville, IL 60563, USA
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Huijing Du
- Department of Mathematics, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Hongfeng Yu
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Chi Lin
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Michael A. Hollingsworth
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Dandan Zheng
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Radiation Oncology, University of Rochester, Rochester, NY 14626, USA
- Correspondence: ; Tel.: +1-(585)-276-3255
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Permuth JB, Vyas S, Li J, Chen DT, Jeong D, Choi JW. Comparison of Radiomic Features in a Diverse Cohort of Patients With Pancreatic Ductal Adenocarcinomas. Front Oncol 2021; 11:712950. [PMID: 34367997 PMCID: PMC8339963 DOI: 10.3389/fonc.2021.712950] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/05/2021] [Indexed: 12/20/2022] Open
Abstract
Background Significant racial disparities in pancreatic cancer incidence and mortality rates exist, with the highest rates in African Americans compared to Non-Hispanic Whites and Hispanic/Latinx populations. Computer-derived quantitative imaging or “radiomic” features may serve as non-invasive surrogates for underlying biological factors and heterogeneity that characterize pancreatic tumors from African Americans, yet studies are lacking in this area. The objective of this pilot study was to determine if the radiomic tumor profile extracted from pretreatment computed tomography (CT) images differs between African Americans, Non-Hispanic Whites, and Hispanic/Latinx with pancreatic cancer. Methods We evaluated a retrospective cohort of 71 pancreatic cancer cases (23 African American, 33 Non-Hispanic White, and 15 Hispanic/Latinx) who underwent pretreatment CT imaging at Moffitt Cancer Center and Research Institute. Whole lesion semi-automated segmentation was performed on each slice of the lesion on all pretreatment venous phase CT exams using Healthmyne Software (Healthmyne, Madison, WI, USA) to generate a volume of interest. To reduce feature dimensionality, 135 highly relevant non-texture and texture features were extracted from each segmented lesion and analyzed for each volume of interest. Results Thirty features were identified and significantly associated with race/ethnicity based on Kruskal-Wallis test. Ten of the radiomic features were highly associated with race/ethnicity independent of tumor grade, including sphericity, volumetric mean Hounsfield units (HU), minimum HU, coefficient of variation HU, four gray level texture features, and two wavelet texture features. A radiomic signature summarized by the first principal component partially differentiated African American from non-African American tumors (area underneath the curve = 0.80). Poorer survival among African Americans compared to Non-African Americans was observed for tumors with lower volumetric mean CT [HR: 3.90 (95% CI:1.19–12.78), p=0.024], lower GLCM Avg Column Mean [HR:4.75 (95% CI: 1.44,15.37), p=0.010], and higher GLCM Cluster Tendency [HR:3.36 (95% CI: 1.06–10.68), p=0.040], and associations persisted in volumetric mean CT and GLCM Avg Column after adjustment for key clinicopathologic factors. Conclusions This pilot study identified several textural radiomics features associated with poor overall survival among African Americans with PDAC, independent of other prognostic factors such as grade. Our findings suggest that CT radiomic features may serve as surrogates for underlying biological factors and add value in predicting clinical outcomes when integrated with other parameters in ongoing and future studies of cancer health disparities.
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Affiliation(s)
- Jennifer B Permuth
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States.,Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Shraddha Vyas
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Jiannong Li
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Dung-Tsa Chen
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Daniel Jeong
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States.,Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Jung W Choi
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
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Tobaly D, Santinha J, Sartoris R, Dioguardi Burgio M, Matos C, Cros J, Couvelard A, Rebours V, Sauvanet A, Ronot M, Papanikolaou N, Vilgrain V. CT-Based Radiomics Analysis to Predict Malignancy in Patients with Intraductal Papillary Mucinous Neoplasm (IPMN) of the Pancreas. Cancers (Basel) 2020; 12:cancers12113089. [PMID: 33114028 PMCID: PMC7690711 DOI: 10.3390/cancers12113089] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 10/17/2020] [Accepted: 10/21/2020] [Indexed: 02/07/2023] Open
Abstract
To assess the performance of CT-based radiomics analysis in differentiating benign from malignant intraductal papillary mucinous neoplasms of the pancreas (IPMN), preoperative scans of 408 resected patients with IPMN were retrospectively analyzed. IPMNs were classified as benign (low-grade dysplasia, n = 181), or malignant (high grade, n = 128, and invasive, n = 99). Clinicobiological data were reported. Patients were divided into a training cohort (TC) of 296 patients and an external validation cohort (EVC) of 112 patients. After semi-automatic tumor segmentation, PyRadiomics was used to extract radiomics features. A multivariate model was developed using a logistic regression approach. In the training cohort, 85/107 radiomics features were significantly different between patients with benign and malignant IPMNs. Unsupervised clustering analysis revealed four distinct clusters of patients with similar radiomics features patterns with malignancy as the most significant association. The multivariate model differentiated benign from malignant tumors in TC with an area under the ROC curve (AUC) of 0.84, sensitivity (Se) of 0.82, specificity (Spe) of 0.74, and in EVC with an AUC of 0.71, Se of 0.69, Spe of 0.57. This large study confirms the high diagnostic performance of preoperative CT-based radiomics analysis to differentiate between benign from malignant IPMNs.
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Affiliation(s)
- David Tobaly
- Service De Radiologie, Assistance Publique-Hôpitaux De Paris, APHP. Nord, Hôpital Beaujon, 92110 Clichy, France; (R.S.); (M.D.B.); (M.R.)
- Correspondence: (D.T.); (V.V.)
| | - Joao Santinha
- Computational Clinical Imaging Group, Champalimaud Research, Champalimaud Foundation, Avenida Brasília, 1400-038 Lisbon, Portugal;
- Instituto de Telecomunicações, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal
| | - Riccardo Sartoris
- Service De Radiologie, Assistance Publique-Hôpitaux De Paris, APHP. Nord, Hôpital Beaujon, 92110 Clichy, France; (R.S.); (M.D.B.); (M.R.)
- Centre De Recherche De L’inflammation (Cri), Inserm U1149, Université De Paris, 75018 Paris, France
| | - Marco Dioguardi Burgio
- Service De Radiologie, Assistance Publique-Hôpitaux De Paris, APHP. Nord, Hôpital Beaujon, 92110 Clichy, France; (R.S.); (M.D.B.); (M.R.)
- Centre De Recherche De L’inflammation (Cri), Inserm U1149, Université De Paris, 75018 Paris, France
| | - Celso Matos
- Radiology Department, Champalimaud Foundation, Avenida Brasília, 1400-038 Lisbon, Portugal;
- Champalimaud Research, Champalimaud Foundation, Avenida Brasília, 1400-038 Lisbon, Portugal
| | - Jérôme Cros
- Service D’Anatomopathologie, Assistance Publique-Hôpitaux De Paris, APHP.Nord, Hôpital Beaujon, 92110 Clichy, France;
| | - Anne Couvelard
- Service D’Anatomopathologie, Assistance Publique-Hôpitaux De Paris, APHP.Nord, Hôpital Bichat, 75018 Paris, France;
| | - Vinciane Rebours
- Service De Pancréatologie, Assistance Publique-Hôpitaux De Paris, APHP.Nord, Hôpital Beaujon, 92110 Clichy, France;
| | - Alain Sauvanet
- Service De Chirurgie HPB, Assistance Publique-Hôpitaux De Paris, APHP.Nord, Hôpital Beaujon, 92110 Clichy, France;
| | - Maxime Ronot
- Service De Radiologie, Assistance Publique-Hôpitaux De Paris, APHP. Nord, Hôpital Beaujon, 92110 Clichy, France; (R.S.); (M.D.B.); (M.R.)
- Centre De Recherche De L’inflammation (Cri), Inserm U1149, Université De Paris, 75018 Paris, France
| | - Nikolaos Papanikolaou
- Computational Clinical Imaging Group, Champalimaud Research, Champalimaud Foundation, 1400-038 Lisbon, Portugal;
| | - Valérie Vilgrain
- Service De Radiologie, Assistance Publique-Hôpitaux De Paris, APHP. Nord, Hôpital Beaujon, 92110 Clichy, France; (R.S.); (M.D.B.); (M.R.)
- Centre De Recherche De L’inflammation (Cri), Inserm U1149, Université De Paris, 75018 Paris, France
- Correspondence: (D.T.); (V.V.)
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CT and MRI of pancreatic tumors: an update in the era of radiomics. Jpn J Radiol 2020; 38:1111-1124. [PMID: 33085029 DOI: 10.1007/s11604-020-01057-6] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 10/08/2020] [Indexed: 02/07/2023]
Abstract
Radiomics is a relatively new approach for image analysis. As a part of radiomics, texture analysis, which consists in extracting a great amount of quantitative data from original images, can be used to identify specific features that can help determining the actual nature of a pancreatic lesion and providing other information such as resectability, tumor grade, tumor response to neoadjuvant therapy or survival after surgery. In this review, the basic of radiomics, recent developments and the results of texture analysis using computed tomography and magnetic resonance imaging in the field of pancreatic tumors are presented. Future applications of radiomics, such as artificial intelligence, are discussed.
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Machicado JD, Koay EJ, Krishna SG. Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions. Diagnostics (Basel) 2020; 10:diagnostics10070505. [PMID: 32708348 PMCID: PMC7399814 DOI: 10.3390/diagnostics10070505] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/20/2020] [Accepted: 07/20/2020] [Indexed: 12/12/2022] Open
Abstract
Radiomics, also known as quantitative imaging or texture analysis, involves extracting a large number of features traditionally unmeasured in conventional radiological cross-sectional images and converting them into mathematical models. This review describes this approach and its use in the evaluation of pancreatic cystic lesions (PCLs). This discipline has the potential of more accurately assessing, classifying, risk stratifying, and guiding the management of PCLs. Existing studies have provided important insight into the role of radiomics in managing PCLs. Although these studies are limited by the use of retrospective design, single center data, and small sample sizes, radiomic features in combination with clinical data appear to be superior to the current standard of care in differentiating cyst type and in identifying mucinous PCLs with high-grade dysplasia. Combining radiomic features with other novel endoscopic diagnostics, including cyst fluid molecular analysis and confocal endomicroscopy, can potentially optimize the predictive accuracy of these models. There is a need for multicenter prospective studies to elucidate the role of radiomics in the management of PCLs.
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Affiliation(s)
- Jorge D. Machicado
- Division of Gastroenterology and Hepatology, Mayo Clinic Heath System, Eau Claire, WI 54703, USA;
| | - Eugene J. Koay
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Somashekar G. Krishna
- Division of Gastroenterology, Hepatology and Nutrition, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
- Correspondence:
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