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Corallo C, Al-Adhami AS, Jamieson N, Valle J, Radhakrishna G, Moir J, Albazaz R. An update on pancreatic cancer imaging, staging, and use of the PACT-UK radiology template pre- and post-neoadjuvant treatment. Br J Radiol 2025; 98:13-26. [PMID: 39460945 DOI: 10.1093/bjr/tqae217] [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: 12/12/2023] [Revised: 10/01/2024] [Accepted: 10/22/2024] [Indexed: 10/28/2024] Open
Abstract
Pancreatic ductal adenocarcinoma continues to have a poor prognosis, although recent advances in neoadjuvant treatments (NATs) have provided some hope. Imaging assessment of suspected tumours can be challenging and requires a specific approach, with pancreas protocol CT being the primary imaging modality for staging with other modalities used as problem-solving tools to facilitate appropriate management. Imaging assessment post NAT can be particularly difficult due to a current lack of robust radiological criteria to predict response and differentiate treatment induced fibrosis/inflammation from residual tumour. This review aims to provide an update of pancreatic ductal adenocarcinoma with particular focus on three points: tumour staging pre- and post-NAT including vascular assessment, structured reporting with introduction of the PAncreatic Cancer reporting Template-UK (PACT-UK) radiology template, and the potential future role of artificial intelligence in the diagnosis and staging of pancreatic cancer.
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Affiliation(s)
- Carmelo Corallo
- Department of Radiology, St James's University Hospital, Leeds LS9 7TF, United Kingdom
| | - Abdullah S Al-Adhami
- Department of Radiology, Glasgow Royal Infirmary, Glasgow G31 2ER, United Kingdom
| | - Nigel Jamieson
- HPB Unit, Glasgow Royal Infirmary, Glasgow G31 2ER, United Kingdom
| | - Juan Valle
- Division of Cancer Sciences, University of Manchester, Manchester M20 4GJ, United Kingdom
- Department of Medical Oncology, The Christie NHS Foundation Trust, Manchester M20 4 BX, United Kingdom
| | | | - John Moir
- HPB Unit, Freeman Hospital, Newcastle Upon Tyne NE7 7DN, United Kingdom
| | - Raneem Albazaz
- Department of Radiology, St James's University Hospital, Leeds LS9 7TF, United Kingdom
- University of Leeds, Leeds LS2 9JT, United Kingdom
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2
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Rai HM, Yoo J, Razaque A. Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques. EXPERT SYSTEMS WITH APPLICATIONS 2024; 255:124838. [DOI: 10.1016/j.eswa.2024.124838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
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3
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Maletz S, Balagurunathan Y, Murphy K, Folio L, Chima R, Zaheer A, Vadvala H. AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis. Abdom Radiol (NY) 2024:10.1007/s00261-024-04512-4. [PMID: 39133362 DOI: 10.1007/s00261-024-04512-4] [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: 06/04/2024] [Revised: 07/16/2024] [Accepted: 07/29/2024] [Indexed: 08/13/2024]
Abstract
Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pancreatitis and its complications. We provide an overview of advancements in diagnostic imaging and quantitative imaging methods along with the evolution of artificial intelligence (AI). In this article, we review the current and future states of methodology and limitations of AI in improving clinical support in the context of early detection and management of pancreatitis.
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Affiliation(s)
- Sebastian Maletz
- University of South Florida Morsani College of Medicine, Tampa, USA
| | | | - Kade Murphy
- University of South Florida Morsani College of Medicine, Tampa, USA
| | - Les Folio
- University of South Florida Morsani College of Medicine, Tampa, USA
- Moffitt Cancer Center, Tampa, USA
| | - Ranjit Chima
- University of South Florida Morsani College of Medicine, Tampa, USA
- Moffitt Cancer Center, Tampa, USA
| | | | - Harshna Vadvala
- University of South Florida Morsani College of Medicine, Tampa, USA.
- Moffitt Cancer Center, Tampa, USA.
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Datta D, Selvakumar B, Goel AD, Chhibber S, Varshney VK, Kumar R. Diagnostic performance of F-18 FDG PET/CT in differentiating autoimmune pancreatitis from pancreatic cancer: a systemic review and meta-analysis. Ann Nucl Med 2024; 38:619-629. [PMID: 38750330 DOI: 10.1007/s12149-024-01934-4] [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: 01/28/2024] [Accepted: 04/18/2024] [Indexed: 06/14/2024]
Abstract
OBJECTIVES This study aims to evaluate the utility of F-18 FDG PET/CT in the non-invasive diagnosis of autoimmune pancreatitis (AIP) and differentiating it from pancreatic cancer (CaP) based on the amount and pattern of FDG uptake, as well as involvement of extra-pancreatic sites. METHODS A systematic search was conducted using PubMed, Scopus, Cochrane Library and Google Scholar. Only those studies that compared the findings of F-18 FDG PET/CT in terms of SUVmax, pattern of FDG uptake and presence of FDG-avid extra-pancreatic sites in both AIP and CaP were included. Studies were qualitatively assessed for risk of bias and publication bias. The diagnostic performance of parameters on PET/CT was examined through pooled sensitivity, specificity, diagnostic odd's ratio (DOR) and summary receiver operator characteristic (SROC) curve analysis. RESULTS Six studies were included with a total of 580 patients. 178 patients had AIP (Age 18-90 years, male, M: female, F ratio-8.4:1) and 402 patients had CaP (Age 22-88 years, M:F ratio-1.5:1). Type of AIP was reported in only 3 studies, with the included cases predominantly being type 1 AIP. All studies were retrospective with heterogeneity and a risk on patient selection and index test. The FDG uptake, expressed as SUVmax, was lower in AIP with a weighted mean difference of -3.11 (95% confidence interval, CI: -5.28 to -0.94). To diagnose AIP, the pooled sensitivity, specificity and DOR of diffuse pattern of FDG uptake were 0.59 (95% CI: 0.51-0.66), 0.89 (95% CI: 0.86-0.92) and 21.07 (95% CI: 5.07-88.32), respectively, with an area under curve (AUC) of 0.717 on SROC analysis. The pooled sensitivity, specificity and DOR of FDG-avid extra pancreatic sites were 0.55 (95% CI: 0.45-0.65), 0.58 (95% CI: 0.52-0.64) and 2.33 (95% CI: 1.40-3.89), respectively, with an AUC of 0.632. CONCLUSION On F-18 FDG PET/CT, a pancreatic lesion of AIP has a lower SUVmax value than CaP. A diffuse pattern of FDG uptake and presence of an extra-pancreatic FDG-avid site are nearly 21 times and twice more likely in AIP than CaP, respectively.
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Affiliation(s)
- Deepanksha Datta
- Department of Nuclear Medicine, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | - B Selvakumar
- Department of Surgical Gastroenterology, All India Institute of Medical Sciences, Basni Industrial Area Phase 2, Jodhpur, Rajasthan, 342005, India.
| | - Akhil Dhanesh Goel
- Department of Community and Family Medicine, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | | | - Vaibhav Kumar Varshney
- Department of Surgical Gastroenterology, All India Institute of Medical Sciences, Basni Industrial Area Phase 2, Jodhpur, Rajasthan, 342005, India
| | - Rajesh Kumar
- Department of Nuclear Medicine, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
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5
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Bette S, Canalini L, Feitelson LM, Woźnicki P, Risch F, Huber A, Decker JA, Tehlan K, Becker J, Wollny C, Scheurig-Münkler C, Wendler T, Schwarz F, Kroencke T. Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography. Diagnostics (Basel) 2024; 14:718. [PMID: 38611632 PMCID: PMC11011980 DOI: 10.3390/diagnostics14070718] [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: 02/02/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CT is to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomics of medical images has rapidly increased during the past decade, and the main focus has been on oncological imaging and tumor classification. Previous studies assessed the value of radiomics for differentiating between malignancies and inflammatory diseases of the pancreas as well as for prediction of AP severity. The aim of our study was to evaluate an automatic machine learning model for AP detection using radiomics analysis. Patients with abdominal pain and contrast-enhanced CT of the abdomen in an emergency setting were retrospectively included in this single-center study. The pancreas was automatically segmented using TotalSegmentator and radiomics features were extracted using PyRadiomics. We performed unsupervised hierarchical clustering and applied the random-forest based Boruta model to select the most important radiomics features. Important features and lipase levels were included in a logistic regression model with AP as the dependent variable. The model was established in a training cohort using fivefold cross-validation and applied to the test cohort (80/20 split). From a total of 1012 patients, 137 patients with AP and 138 patients without AP were included in the final study cohort. Feature selection confirmed 28 important features (mainly shape and first-order features) for the differentiation between AP and controls. The logistic regression model showed excellent diagnostic accuracy of radiomics features for the detection of AP, with an area under the curve (AUC) of 0.932. Using lipase levels only, an AUC of 0.946 was observed. Using both radiomics features and lipase levels, we showed an excellent AUC of 0.933 for the detection of AP. Automated segmentation of the pancreas and consecutive radiomics analysis almost achieved the high diagnostic accuracy of lipase levels, a well-established predictor of AP, and might be considered an additional diagnostic tool in unclear cases. This study provides scientific evidence that automated image analysis of the pancreas achieves comparable diagnostic accuracy to lipase levels and might therefore be used in the future in the rapidly growing era of AI-based image analysis.
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Affiliation(s)
- Stefanie Bette
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Luca Canalini
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Laura-Marie Feitelson
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Piotr Woźnicki
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, University of Würzburg, 97080 Würzburg, Germany;
| | - Franka Risch
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Adrian Huber
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Josua A. Decker
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Kartikay Tehlan
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Judith Becker
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Claudia Wollny
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Christian Scheurig-Münkler
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
| | - Thomas Wendler
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
- Institute of Digital Health, University Hospital Augsburg, Faculty of Medicine, University of Augsburg, 86356 Neusaess, Germany
- Computer-Aided Medical Procedures and Augmented Reality, School of Computation, Information and Technology, Technical University of Munich, 85748 Garching bei Muenchen, Germany
| | - Florian Schwarz
- Centre for Diagnostic Imaging and Interventional Therapy, Donau-Isar-Klinikum, 94469 Deggendorf, Germany;
| | - Thomas Kroencke
- Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
- Centre for Advanced Analytics and Predictive Sciences (CAAPS), University of Augsburg, 86159 Augsburg, Germany
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Rai HM, Yoo J, Atif Moqurrab S, Dashkevych S. Advancements in traditional machine learning techniques for detection and diagnosis of fatal cancer types: Comprehensive review of biomedical imaging datasets. MEASUREMENT 2024; 225:114059. [DOI: 10.1016/j.measurement.2023.114059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
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Rawlani P, Ghosh NK, Kumar A. Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis. Artif Intell Gastroenterol 2023; 4:48-63. [DOI: 10.35712/aig.v4.i3.48] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/11/2023] [Accepted: 10/08/2023] [Indexed: 12/07/2023] Open
Abstract
Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.
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Affiliation(s)
- Palash Rawlani
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Nalini Kanta Ghosh
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Ashok Kumar
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
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Li Y, Song H, Meng X, Li R, Leung PSC, Gershwin ME, Zhang S, Sun S, Song J. Autoimmune pancreatitis type 2 (idiopathic duct-centric pancreatitis): A comprehensive review. J Autoimmun 2023; 140:103121. [PMID: 37826920 DOI: 10.1016/j.jaut.2023.103121] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 09/20/2023] [Accepted: 10/02/2023] [Indexed: 10/14/2023]
Abstract
Autoimmune pancreatitis (AIP) is an uncommon fibro-inflammatory disorder precipitated by autoimmune/inflammatory reactions. Currently, there are two clinical subtypes of AIP (type 1 [AIP-1] and type 2 [AIP-2]) that correspond to two histologic descriptors (lymphoplasmacytic sclerosing pancreatitis and idiopathic duct-centric pancreatitis, respectively). While our understanding of AIP-1 has evolved considerably over the years, little is known about AIP-2 due to its rarity, often leading to misdiagnosis, delayed treatment, and even unnecessary surgical resection. Compared to AIP-1, AIP-2 exhibits distinct clinical and histologic features. Because AIP-2 is a pancreas-restricted disease without a specific serum marker, the evaluation of histologic features (e.g., granulocytic epithelial lesions) is essential for an accurate diagnosis. Patients with AIP-2 respond well to glucocorticoids, with anti-tumor necrosis factor-alpha antibodies as a promising alternative therapy. The prognosis of AIP-2 is generally favorable and relapse is uncommon. Here, we provide an overview of our current knowledge on the clinical features, diagnosis, therapeutic regimens, prognosis, and putative mechanisms underlying AIP-2. Notably, the diagnostic differentiation between AIP-2, especially the mass-forming/focal type, and pancreatic cancer is important, but challenging. In this regard, endoscopic ultrasound-guided core biopsy has a key role, but novel diagnostic markers and modalities are clearly needed.
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Affiliation(s)
- Yang Li
- Department of Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, PR China
| | - Hanyi Song
- Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, PR China
| | - Xiangzhen Meng
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, PR China
| | - Runzhuo Li
- Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, PR China
| | - Patrick S C Leung
- Division of Rheumatology/Allergy and Clinical Immunology, School of Medicine, University of California, Davis, CA 95616 USA
| | - M Eric Gershwin
- Division of Rheumatology/Allergy and Clinical Immunology, School of Medicine, University of California, Davis, CA 95616 USA
| | - Shucheng Zhang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, PR China.
| | - Siyu Sun
- Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, PR China.
| | - Junmin Song
- Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning Province, PR China.
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Li C, Chen H, Zhang B, Fang Y, Sun W, Wu D, Su Z, Shen L, Wei Q. Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Cancers (Basel) 2023; 15:5134. [PMID: 37958309 PMCID: PMC10648149 DOI: 10.3390/cancers15215134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/15/2023] [Accepted: 10/19/2023] [Indexed: 11/15/2023] Open
Abstract
The objective of this study was to evaluate the discriminative capabilities of radiomics signatures derived from three distinct machine learning algorithms and to identify a robust radiomics signature capable of predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients diagnosed with locally advanced rectal cancer (LARC). In a retrospective study, 211 LARC patients were consecutively enrolled and divided into a training cohort (n = 148) and a validation cohort (n = 63). From pretreatment contrast-enhanced planning CT images, a total of 851 radiomics features were extracted. Feature selection and radiomics score (Radscore) construction were performed using three different machine learning methods: least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM). The SVM-derived Radscore demonstrated a strong correlation with the pCR status, yielding area under the receiver operating characteristic curves (AUCs) of 0.880 and 0.830 in the training and validation cohorts, respectively, outperforming the RF and LASSO methods. Based on this, a nomogram was developed by combining the SVM-based Radscore with clinical indicators to predict pCR after neoadjuvant chemoradiotherapy. The nomogram exhibited superior predictive power, achieving AUCs of 0.910 and 0.866 in the training and validation cohorts, respectively. Calibration curves and decision curve analyses confirmed its appropriateness. The SVM-based Radscore demonstrated promising performance in predicting pCR for LARC patients. The machine learning-driven nomogram, which integrates the Radscore and clinical indicators, represents a valuable tool for predicting pCR in LARC patients.
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Affiliation(s)
- Chao Li
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Haiyan Chen
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Bicheng Zhang
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Yimin Fang
- Department of Colorectal Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China;
| | - Wenzheng Sun
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Dang Wu
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Zhuo Su
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Li Shen
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
| | - Qichun Wei
- Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; (C.L.); (H.C.); (B.Z.); (W.S.); (D.W.); (Z.S.)
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10
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Wang F, Cheng C, Cao W, Wu Z, Wang H, Wei W, Yan Z, Liu Z. MFCNet: A multi-modal fusion and calibration networks for 3D pancreas tumor segmentation on PET-CT images. Comput Biol Med 2023; 155:106657. [PMID: 36791551 DOI: 10.1016/j.compbiomed.2023.106657] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 01/29/2023] [Accepted: 02/09/2023] [Indexed: 02/12/2023]
Abstract
In clinical diagnosis, positron emission tomography and computed tomography (PET-CT) images containing complementary information are fused. Tumor segmentation based on multi-modal PET-CT images is an important part of clinical diagnosis and treatment. However, the existing current PET-CT tumor segmentation methods mainly focus on positron emission tomography (PET) and computed tomography (CT) feature fusion, which weakens the specificity of the modality. In addition, the information interaction between different modal images is usually completed by simple addition or concatenation operations, but this has the disadvantage of introducing irrelevant information during the multi-modal semantic feature fusion, so effective features cannot be highlighted. To overcome this problem, this paper propose a novel Multi-modal Fusion and Calibration Networks (MFCNet) for tumor segmentation based on three-dimensional PET-CT images. First, a Multi-modal Fusion Down-sampling Block (MFDB) with a residual structure is developed. The proposed MFDB can fuse complementary features of multi-modal images while retaining the unique features of different modal images. Second, a Multi-modal Mutual Calibration Block (MMCB) based on the inception structure is designed. The MMCB can guide the network to focus on a tumor region by combining different branch decoding features using the attention mechanism and extracting multi-scale pathological features using a convolution kernel of different sizes. The proposed MFCNet is verified on both the public dataset (Head and Neck cancer) and the in-house dataset (pancreas cancer). The experimental results indicate that on the public and in-house datasets, the average Dice values of the proposed multi-modal segmentation network are 74.14% and 76.20%, while the average Hausdorff distances are 6.41 and 6.84, respectively. In addition, the experimental results show that the proposed MFCNet outperforms the state-of-the-art methods on the two datasets.
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Affiliation(s)
- Fei Wang
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China; Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Chao Cheng
- Department of Nuclear Medicine, The First Affiliated Hospital of Naval Medical University(Changhai Hospital), Shanghai, 200433, China
| | - Weiwei Cao
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Zhongyi Wu
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Heng Wang
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China
| | - Wenting Wei
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China
| | - Zhuangzhi Yan
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China.
| | - Zhaobang Liu
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
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Lu J, Jiang N, Zhang Y, Li D. A CT based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma. Front Oncol 2023; 13:979437. [PMID: 36937433 PMCID: PMC10014827 DOI: 10.3389/fonc.2023.979437] [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: 06/27/2022] [Accepted: 02/20/2023] [Indexed: 03/05/2023] Open
Abstract
Objectives The purpose of this study was to develop and validate an CT-based radiomics nomogram for the preoperative differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma. Methods 96 patients with focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma have been enrolled in the study (32 and 64 cases respectively). All cases have been confirmed by imaging, clinical follow-up and/or pathology. The imaging data were considered as: 70% training cohort and 30% test cohort. Pancreatic lesions have been manually delineated by two radiologists and image segmentation was performed to extract radiomic features from the CT images. Independent-sample T tests and LASSO regression were used for feature selection. The training cohort was classified using a variety of machine learning-based classifiers, and 5-fold cross-validation has been performed. The classification performance was evaluated using the test cohort. Multivariate logistic regression analysis was then used to develop a radiomics nomogram model, containing the CT findings and Rad-Score. Calibration curves have been plotted showing the agreement between the predicted and actual probabilities of the radiomics nomogram model. Different patients have been selected to test and evaluate the model prediction process. Finally, receiver operating characteristic curves and decision curves were plotted, and the radiomics nomogram model was compared with a single model to visually assess its diagnostic ability. Results A total of 158 radiomics features were extracted from each image. 7 features were selected to construct the radiomics model, then a variety of classifiers were used for classification and multinomial logistic regression (MLR) was selected to be the optimal classifier. Combining CT findings with radiomics model, a prediction model based on CT findings and radiomics was finally obtained. The nomogram model showed a good sensitivity and specificity with AUCs of 0.87 and 0.83 in training and test cohorts, respectively. The areas under the curve and decision curve analysis showed that the radiomics nomogram model may provide better diagnostic performance than the single model and achieve greater clinical net benefits than the CT finding model and radiomics signature model individually. Conclusions The CT image-based radiomics nomogram model can accurately distinguish between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma patients and provide additional clinical benefits.
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Affiliation(s)
- Jia Lu
- Department of Radiology, The People’s Hospital of China Medical University and The People’s Hospital of Liaoning Province, Shenyang, China
| | - Nannan Jiang
- Department of Radiology, The People’s Hospital of Liaoning Province, Shenyang, China
| | - Yuqing Zhang
- Department of Radiology, The People’s Hospital of China Medical University and The People’s Hospital of Liaoning Province, Shenyang, China
| | - Daowei Li
- Department of Radiology, The People’s Hospital of China Medical University and The People’s Hospital of Liaoning Province, Shenyang, China
- *Correspondence: Daowei Li,
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12
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Lovinfosse P, Ferreira M, Withofs N, Jadoul A, Derwael C, Frix AN, Guiot J, Bernard C, Diep AN, Donneau AF, Lejeune M, Bonnet C, Vos W, Meyer PE, Hustinx R. Distinction of Lymphoma from Sarcoidosis on 18F-FDG PET/CT: Evaluation of Radiomics-Feature-Guided Machine Learning Versus Human Reader Performance. J Nucl Med 2022; 63:1933-1940. [PMID: 35589406 PMCID: PMC9730930 DOI: 10.2967/jnumed.121.263598] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 05/10/2022] [Indexed: 01/11/2023] Open
Abstract
Sarcoidosis and lymphoma often share common features on 18F-FDG PET/CT, such as intense hypermetabolic lesions in lymph nodes and multiple organs. We aimed at developing and validating radiomics signatures to differentiate sarcoidosis from Hodgkin lymphoma (HL) and diffuse large B-cell lymphoma (DLBCL). Methods: We retrospectively collected 420 patients (169 sarcoidosis, 140 HL, and 111 DLBCL) who underwent pretreatment 18F-FDG PET/CT at the University Hospital of Liege. The studies were randomly distributed to 4 physicians, who gave their diagnostic suggestion among the 3 diseases. The individual and pooled performance of the physicians was then calculated. Interobserver variability was evaluated using a sample of 34 studies interpreted by all physicians. Volumes of interest were delineated over the lesions and the liver using MIM software, and 215 radiomics features were extracted using the RadiomiX Toolbox. Models were developed combining clinical data (age, sex, and weight) and radiomics (original and tumor-to-liver TLR radiomics), with 7 different feature selection approaches and 4 different machine-learning (ML) classifiers, to differentiate sarcoidosis and lymphomas on both lesion-based and patient-based approaches. Results: For identifying lymphoma versus sarcoidosis, physicians' pooled sensitivity, specificity, area under the receiver-operating-characteristic curve (AUC), and accuracy were 0.99 (95% CI, 0.97-1.00), 0.75 (95% CI, 0.68-0.81), 0.87 (95% CI, 0.84-0.90), and 89.3%, respectively, whereas for identifying HL in the tumor population, it was 0.58 (95% CI, 0.49-0.66), 0.82 (95% CI, 0.74-0.89), 0.70 (95% CI, 0.64-0.75) and 68.5%, respectively. Moderate agreement was found among observers for the diagnosis of lymphoma versus sarcoidosis and HL versus DLBCL, with Fleiss κ-values of 0.66 (95% CI, 0.45-0.87) and 0.69 (95% CI, 0.45-0.93), respectively. The best ML models for identifying lymphoma versus sarcoidosis showed an AUC of 0.94 (95% CI, 0.93-0.95) and 0.85 (95% CI, 0.82-0.88) in lesion- and patient-based approaches, respectively, using TLR radiomics (plus age for the second). To differentiate HL from DLBCL, we obtained an AUC of 0.95 (95% CI, 0.93-0.96) in the lesion-based approach using TLR radiomics and 0.86 (95% CI, 0.80-0.91) in the patient-based approach using original radiomics and age. Conclusion: Characterization of sarcoidosis and lymphoma lesions is feasible using ML and radiomics, with very good to excellent performance, equivalent to or better than that of physicians, who showed significant interobserver variability in their assessment.
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Affiliation(s)
- Pierre Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, CHU of Liège, Liège, Belgium
| | - Marta Ferreira
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
| | - Nadia Withofs
- Division of Nuclear Medicine and Oncological Imaging, CHU of Liège, Liège, Belgium
| | - Alexandre Jadoul
- Division of Nuclear Medicine and Oncological Imaging, CHU of Liège, Liège, Belgium
| | - Céline Derwael
- Division of Nuclear Medicine and Oncological Imaging, CHU of Liège, Liège, Belgium
| | - Anne-Noelle Frix
- Department of Respiratory Medicine, CHU of Liège, Liège, Belgium
| | - Julien Guiot
- Department of Respiratory Medicine, CHU of Liège, Liège, Belgium
| | - Claire Bernard
- Division of Nuclear Medicine and Oncological Imaging, CHU of Liège, Liège, Belgium
| | - Anh Nguyet Diep
- Biostatistics Unit, Department of Public Health, University of Liège, Liège, Belgium
| | | | - Marie Lejeune
- Department of Hematology, CHU of Liège, Liège, Belgium
| | | | - Wim Vos
- Radiomics SA, Liège, Belgium; and
| | - Patrick E. Meyer
- Bioinformatics and Systems Biology Lab, University of Liège, Liège, Belgium
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, CHU of Liège, Liège, Belgium
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Wei W, Jia G, Wu Z, Wang T, Wang H, Wei K, Cheng C, Liu Z, Zuo C. A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on 18F-FDG PET/CT images. Jpn J Radiol 2022; 41:417-427. [PMID: 36409398 PMCID: PMC9676903 DOI: 10.1007/s11604-022-01363-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 11/09/2022] [Indexed: 11/22/2022]
Abstract
PURPOSE To explore a multidomain fusion model of radiomics and deep learning features based on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images to distinguish pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP), which could effectively improve the accuracy of diseases diagnosis. MATERIALS AND METHODS This retrospective study included 48 patients with AIP (mean age, 65 ± 12.0 years; range, 37-90 years) and 64 patients with PDAC patients (mean age, 66 ± 11.3 years; range, 32-88 years). Three different methods were discussed to identify PDAC and AIP based on 18F-FDG PET/CT images, including the radiomics model (RAD_model), the deep learning model (DL_model), and the multidomain fusion model (MF_model). We also compared the classification results of PET/CT, PET, and CT images in these three models. In addition, we explored the attributes of deep learning abstract features by analyzing the correlation between radiomics and deep learning features. Five-fold cross-validation was used to calculate receiver operating characteristic (ROC), area under the roc curve (AUC), accuracy (Acc), sensitivity (Sen), and specificity (Spe) to quantitatively evaluate the performance of different classification models. RESULTS The experimental results showed that the multidomain fusion model had the best comprehensive performance compared with radiomics and deep learning models, and the AUC, accuracy, sensitivity, specificity were 96.4% (95% CI 95.4-97.3%), 90.1% (95% CI 88.7-91.5%), 87.5% (95% CI 84.3-90.6%), and 93.0% (95% CI 90.3-95.6%), respectively. And our study proved that the multimodal features of PET/CT were superior to using either PET or CT features alone. First-order features of radiomics provided valuable complementary information for the deep learning model. CONCLUSION The preliminary results of this paper demonstrated that our proposed multidomain fusion model fully exploits the value of radiomics and deep learning features based on 18F-FDG PET/CT images, which provided competitive accuracy for the discrimination of PDAC and AIP.
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Affiliation(s)
- Wenting Wei
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022 China ,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163 China
| | - Guorong Jia
- Department of Nuclear Medicine, The First Affiliated Hospital of Naval Medical University (Changhai Hospital), 168 Changhai Road, Shanghai, 200433 China
| | - Zhongyi Wu
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163 China
| | - Tao Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Naval Medical University (Changhai Hospital), 168 Changhai Road, Shanghai, 200433 China
| | - Heng Wang
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022 China ,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163 China
| | - Kezhen Wei
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022 China ,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163 China
| | - Chao Cheng
- Department of Nuclear Medicine, The First Affiliated Hospital of Naval Medical University (Changhai Hospital), 168 Changhai Road, Shanghai, 200433 China
| | - Zhaobang Liu
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163 China
| | - Changjing Zuo
- Department of Nuclear Medicine, The First Affiliated Hospital of Naval Medical University (Changhai Hospital), 168 Changhai Road, Shanghai, 200433 China
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Mao Q, Zhou MT, Zhao ZP, Liu N, Yang L, Zhang XM. Role of radiomics in the diagnosis and treatment of gastrointestinal cancer. World J Gastroenterol 2022; 28:6002-6016. [PMID: 36405385 PMCID: PMC9669820 DOI: 10.3748/wjg.v28.i42.6002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/24/2022] [Accepted: 10/27/2022] [Indexed: 11/10/2022] Open
Abstract
Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.
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Affiliation(s)
- Qi Mao
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Mao-Ting Zhou
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Zhang-Ping Zhao
- Department of Radiology, Panzhihua Central Hospital, Panzhihua 617000, Sichuan Province, China
| | - Ning Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Lin Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Ming Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
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15
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Liao H, Yang J, Li Y, Liang H, Ye J, Liu Y. One 3D VOI-based deep learning radiomics strategy, clinical model and radiologists for predicting lymph node metastases in pancreatic ductal adenocarcinoma based on multiphasic contrast-enhanced computer tomography. Front Oncol 2022; 12:990156. [PMID: 36158647 PMCID: PMC9500296 DOI: 10.3389/fonc.2022.990156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 08/09/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose We designed to construct one 3D VOI-based deep learning radiomics strategy for identifying lymph node metastases (LNM) in pancreatic ductal adenocarcinoma on the basis of multiphasic contrast-enhanced computer tomography and to assist clinical decision-making. Methods This retrospective research enrolled 139 PDAC patients undergoing pre-operative arterial phase and venous phase scanning examination between 2015 and 2021. A primary group (training group and validation group) and an independent test group were divided. The DLR strategy included three sections. (1) Residual network three dimensional-18 (Resnet 3D-18) architecture was constructed for deep learning feature extraction. (2) Least absolute shrinkage and selection operator model was used for feature selection. (3) Fully connected network served as the classifier. The DLR strategy was applied for constructing different 3D CNN models using 5-fold cross-validation. Radiomics scores (Rad score) were calculated for distinguishing the statistical difference between negative and positive lymph nodes. A clinical model was constructed by combining significantly different clinical variables using univariate and multivariable logistic regression. The manifestation of two radiologists was detected for comparing with computer-developed models. Receiver operating characteristic curves, the area under the curve, accuracy, precision, recall, and F1 score were used for evaluating model performance. Results A total of 45, 49, and 59 deep learning features were selected via LASSO model. No matter in which 3D CNN model, Rad score demonstrated the deep learning features were significantly different between non-LNM and LNM groups. The AP+VP DLR model yielded the best performance in predicting status of lymph node in PDAC with an AUC of 0.995 (95% CI:0.989-1.000) in training group; an AUC of 0.940 (95% CI:0.910-0.971) in validation group; and an AUC of 0.949 (95% CI:0.914-0.984) in test group. The clinical model enrolled the histological grade, CA19-9 level and CT-reported tumor size. The AP+VP DLR model outperformed AP DLR model, VP DLR model, clinical model, and two radiologists. Conclusions The AP+VP DLR model based on Resnet 3D-18 demonstrated excellent ability for identifying LNM in PDAC, which could act as a non-invasive and accurate guide for clinical therapeutic strategies. This 3D CNN model combined with 3D tumor segmentation technology is labor-saving, promising, and effective.
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Affiliation(s)
- Hongfan Liao
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Junjun Yang
- Key Laboratory of Optoelectronic Technology and Systems of the Ministry of Education, Chongqing University, Chongqing, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongwei Liang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Junyong Ye
- Key Laboratory of Optoelectronic Technology and Systems of the Ministry of Education, Chongqing University, Chongqing, China
| | - Yanbing Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
- *Correspondence: Yanbing Liu,
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16
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Feng L, Yang X, Lu X, Kan Y, Wang C, Sun D, Zhang H, Wang W, Yang J. 18F-FDG PET/CT-based radiomics nomogram could predict bone marrow involvement in pediatric neuroblastoma. Insights Imaging 2022; 13:144. [PMID: 36057694 PMCID: PMC9440965 DOI: 10.1186/s13244-022-01283-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 08/07/2022] [Indexed: 11/10/2022] Open
Abstract
Objective To develop and validate an 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics nomogram for non-invasively prediction of bone marrow involvement (BMI) in pediatric neuroblastoma. Methods A total of 133 patients with neuroblastoma were retrospectively included and randomized into the training set (n = 93) and test set (n = 40). Radiomics features were extracted from both CT and PET images. The radiomics signature was developed. Independent clinical risk factors were identified using the univariate and multivariate logistic regression analyses to construct the clinical model. The clinical-radiomics model, which integrated the radiomics signature and the independent clinical risk factors, was constructed using multivariate logistic regression analysis and finally presented as a radiomics nomogram. The predictive performance of the clinical-radiomics model was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis (DCA). Results Twenty-five radiomics features were selected to construct the radiomics signature. Age at diagnosis, neuron-specific enolase and vanillylmandelic acid were identified as independent predictors to establish the clinical model. In the training set, the clinical-radiomics model outperformed the radiomics model or clinical model (AUC: 0.924 vs. 0.900, 0.875) in predicting the BMI, which was then confirmed in the test set (AUC: 0.925 vs. 0.893, 0.910). The calibration curve and DCA demonstrated that the radiomics nomogram had a good consistency and clinical utility. Conclusion The 18F-FDG PET/CT-based radiomics nomogram which incorporates radiomics signature and independent clinical risk factors could non-invasively predict BMI in pediatric neuroblastoma. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01283-8.
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Affiliation(s)
- Lijuan Feng
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Xu Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Xia Lu
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Ying Kan
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Chao Wang
- Sinounion Medical Technology (Beijing) Co., Ltd., Beijing, 100192, China
| | - Dehui Sun
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Hui Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Wei Wang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China.
| | - Jigang Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China.
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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: 10] [Impact Index Per Article: 3.3] [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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Laino ME, Ammirabile A, Lofino L, Mannelli L, Fiz F, Francone M, Chiti A, Saba L, Orlandi MA, Savevski V. Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review. Healthcare (Basel) 2022; 10:healthcare10081511. [PMID: 36011168 PMCID: PMC9408381 DOI: 10.3390/healthcare10081511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/31/2022] [Accepted: 08/08/2022] [Indexed: 12/19/2022] Open
Abstract
The diagnosis, evaluation, and treatment planning of pancreatic pathologies usually require the combined use of different imaging modalities, mainly, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Artificial intelligence (AI) has the potential to transform the clinical practice of medical imaging and has been applied to various radiological techniques for different purposes, such as segmentation, lesion detection, characterization, risk stratification, or prediction of response to treatments. The aim of the present narrative review is to assess the available literature on the role of AI applied to pancreatic imaging. Up to now, the use of computer-aided diagnosis (CAD) and radiomics in pancreatic imaging has proven to be useful for both non-oncological and oncological purposes and represents a promising tool for personalized approaches to patients. Although great developments have occurred in recent years, it is important to address the obstacles that still need to be overcome before these technologies can be implemented into our clinical routine, mainly considering the heterogeneity among studies.
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Affiliation(s)
- Maria Elena Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
- Correspondence: (M.E.L.); (A.A.)
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
- Correspondence: (M.E.L.); (A.A.)
| | - Ludovica Lofino
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | | | - Francesco Fiz
- Nuclear Medicine Unit, Department of Diagnostic Imaging, E.O. Ospedali Galliera, 56321 Genoa, Italy
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital, 72074 Tübingen, Germany
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09124 Cagliari, Italy
| | | | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
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Schuurmans M, Alves N, Vendittelli P, Huisman H, Hermans J. Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. Cancers (Basel) 2022; 14:cancers14143498. [PMID: 35884559 PMCID: PMC9316850 DOI: 10.3390/cancers14143498] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/07/2022] [Accepted: 07/15/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, associated with a 98% loss of life expectancy and a 30% increase in disability-adjusted life years. Image-based artificial intelligence (AI) can help improve outcomes for PDAC given that current clinical guidelines are non-uniform and lack evidence-based consensus. However, research on image-based AI for PDAC is too scattered and lacking in sufficient quality to be incorporated into clinical workflows. In this review, an international, multi-disciplinary team of the world’s leading experts in pancreatic cancer breaks down the patient pathway and pinpoints the current clinical touchpoints in each stage. The available PDAC imaging AI literature addressing each pathway stage is then rigorously analyzed, and current performance and pitfalls are identified in a comprehensive overview. Finally, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed. Abstract Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment response assessment, and follow-up are non-uniform and lack evidence-based consensus. Artificial Intelligence (AI) can leverage multimodal data to improve patient outcomes, but PDAC AI research is too scattered and lacking in quality to be incorporated into clinical workflows. This review describes the patient pathway and derives touchpoints for image-based AI research in collaboration with a multi-disciplinary, multi-institutional expert panel. The literature exploring AI to address these touchpoints is thoroughly retrieved and analysed to identify the existing trends and knowledge gaps. The results show absence of multi-institutional, well-curated datasets, an essential building block for robust AI applications. Furthermore, most research is unimodal, does not use state-of-the-art AI techniques, and lacks reliable ground truth. Based on this, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.
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Affiliation(s)
- Megan Schuurmans
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Natália Alves
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Pierpaolo Vendittelli
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - Henkjan Huisman
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - John Hermans
- Department of Medical Imaging, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands;
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20
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Yan G, Yan G, Li H, Liang H, Peng C, Bhetuwal A, McClure MA, Li Y, Yang G, Li Y, Zhao L, Fan X. Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review. Front Med (Lausanne) 2022; 9:922299. [PMID: 35814756 PMCID: PMC9259974 DOI: 10.3389/fmed.2022.922299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 05/31/2022] [Indexed: 11/25/2022] Open
Abstract
Radiomics involves high-throughput extraction and analysis of quantitative information from medical images. Since it was proposed in 2012, there are some publications on the application of radiomics for (1) predicting recurrent acute pancreatitis (RAP), clinical severity of acute pancreatitis (AP), and extrapancreatic necrosis in AP; (2) differentiating mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC), focal autoimmune pancreatitis (AIP) from PDAC, and functional abdominal pain (functional gastrointestinal diseases) from RAP and chronic pancreatitis (CP); and (3) identifying CP and normal pancreas, and CP risk factors and complications. In this review, we aim to systematically summarize the applications and progress of radiomics in pancreatitis and it associated situations, so as to provide reference for related research.
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Affiliation(s)
- Gaowu Yan
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Gaowen Yan
- Department of Radiology, The First Hospital of Suining, Suining, China
| | - Hongwei Li
- Department of Radiology, The Third Hospital of Mianyang and Sichuan Mental Health Center, Mianyang, China
| | - Hongwei Liang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chen Peng
- Department of Gastroenterology, The First Hospital of Suining, Suining, China
| | - Anup Bhetuwal
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Morgan A. McClure
- Department of Radiology and Imaging, Institute of Rehabilitation and Development of Brain Function, The Second Clinical Medical College of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Yongmei Li
| | - Guoqing Yang
- Department of Radiology, Suining Central Hospital, Suining, China
- Guoqing Yang
| | - Yong Li
- Department of Radiology, Suining Central Hospital, Suining, China
- Yong Li
| | - Linwei Zhao
- Department of Radiology, Suining Central Hospital, Suining, China
| | - Xiaoping Fan
- Department of Radiology, Suining Central Hospital, Suining, China
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21
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061330. [PMID: 35741139 PMCID: PMC9222024 DOI: 10.3390/diagnostics12061330] [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: 05/04/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 12/04/2022] Open
Abstract
The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.
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Affiliation(s)
- David Morland
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
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22
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Chen K, Yin G, Xu W. Predictive Value of 18F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer. Diagnostics (Basel) 2022; 12:diagnostics12040997. [PMID: 35454045 PMCID: PMC9030613 DOI: 10.3390/diagnostics12040997] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/08/2022] [Accepted: 04/13/2022] [Indexed: 01/26/2023] Open
Abstract
Background: To develop and validate a radiomics model based on 18F-FDG PET/CT images to preoperatively predict occult axillary lymph node (ALN) metastases in patients with invasive ductal breast cancer (IDC) with clinically node-negative (cN0); Methods: A total of 180 patients (mean age, 55 years; range, 31–82 years) with pathologically proven IDC and a preoperative 18F-FDG PET/CT scan from January 2013 to January 2021 were included in this retrospective study. According to the intraoperative pathological results of ALN, we divided patients into the true-negative group and ALN occult metastasis group. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, t-tests, and LASSO were used to screen the feature, and the random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and k-nearest neighbor (KNN) were used to build the prediction models. The best-performing model was further tested by the permutation test; Results: Among the four models, RF had the best prediction results, the AUC range of RF was 0.661–0.929 (mean AUC, 0.817), and the accuracy range was 65.3–93.9% (mean accuracy, 81.2%). The p-values of the permutation tests for the RF model with maximum and minimum accuracy were less than 0.01; Conclusions: The developed RF model was able to predict occult ALN metastases in IDC patients based on preoperative 18F-FDG PET/CT radiomic features.
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Affiliation(s)
- Kun Chen
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi Distinct, Tianjin 300060, China; (K.C.); (G.Y.)
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for China, Tianjin 300060, China
| | - Guotao Yin
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi Distinct, Tianjin 300060, China; (K.C.); (G.Y.)
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for China, Tianjin 300060, China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi Distinct, Tianjin 300060, China; (K.C.); (G.Y.)
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for China, Tianjin 300060, China
- Correspondence: or
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23
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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: 24] [Impact Index Per Article: 8.0] [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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24
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Dynamic whole-body FDG-PET imaging for oncology studies. Clin Transl Imaging 2022. [DOI: 10.1007/s40336-022-00479-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Abstract
Introduction
Recent PET/CT systems have improved sensitivity and spatial resolution by smaller PET detectors and improved reconstruction software. In addition, continuous-bed-motion mode is now available in some PET systems for whole-body PET imaging. In this review, we describe the advantages of dynamic whole-body FDG-PET in oncology studies.
Methods
PET–CT imaging was obtained at 60 min after FDG administration. Dynamic whole-body imaging with continuous bed motion in 3 min each with flow motion was obtained over 400 oncology cases. For routine image analysis, these dynamic phases (usually four phases) were summed as early FDG imaging. The image quality of each serial dynamic imaging was visually evaluated. In addition, changes in FDG uptake were analyzed in consecutive dynamic imaging and also in early delayed (90 min after FDG administration) time point imaging (dual-time-point imaging; DTPI). Image interpretation was performed by consensus of two nuclear medicine physicians.
Result
All consecutive dynamic whole-body PET images of 3 min duration had acceptable image quality. Many of the areas with physiologically high FDG uptake had altered uptake on serial images. On the other hand, most of the benign and malignant lesions did not show visual changes on serial images. In the study of 60 patients with suspected colorectal cancer, unchanged uptake was noted in almost all regions with pathologically proved FDG uptake, indicating high sensitivity with high negative predictive value on both serial dynamic imaging and on DTPI. We proposed another application of serial dynamic imaging for minimizing motion artifacts for patients who may be likely to move during PET studies.
Discussion
Dynamic whole-body imaging has several advantages over the static imaging. Serial assessment of changes in FDG uptake over a short period of time is useful for distinguishing pathological from physiological uptake, especially in the abdominal regions. These dynamic PET studies may minimize the need for DPTI. In addition, continuous dynamic imaging has the potential to reduce motion artifacts in patients who are likely to move during PET imaging. Furthermore, kinetic analysis of the FDG distribution in tumor areas has a potential for precise tissue characterization.
Conclusion
Dynamic whole-body FDG-PET imaging permits assessment of serial FDG uptake change which is particularly useful for differentiation of pathological uptake from physiological uptake with high diagnostic accuracy. This imaging can be applied for minimizing motion artifacts. Wide clinical applications of such serial, dynamic whole-body PET imaging is expected in oncological studies in the near future.
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25
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Xie T, Wang X, Zhang Z, Zhou Z. CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas. Front Oncol 2021; 11:621520. [PMID: 34178619 PMCID: PMC8231011 DOI: 10.3389/fonc.2021.621520] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 05/12/2021] [Indexed: 12/25/2022] Open
Abstract
Objectives To investigate the value of CT-based radiomics analysis in preoperatively discriminating pancreatic mucinous cystic neoplasms (MCN) and atypical serous cystadenomas (ASCN). Methods A total of 103 MCN and 113 ASCN patients who underwent surgery were retrospectively enrolled. A total of 764 radiomics features were extracted from preoperative CT images. The optimal features were selected by Mann-Whitney U test and minimum redundancy and maximum relevance method. The radiomics score (Rad-score) was then built using random forest algorithm. Radiological/clinical features were also assessed for each patient. Multivariable logistic regression was used to construct a radiological model. The performance of the Rad-score and the radiological model was evaluated using 10-fold cross-validation for area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy. Results Ten screened optimal features were identified and the Rad-score was then built based on them. The radiological model was built based on four radiological/clinical factors. In the 10-fold cross-validation, the Rad-score was proved to be robust and reliable (average AUC: 0.784, sensitivity: 0.847, specificity: 0.745, PPV: 0.767, NPV: 0.849, accuracy: 0.793). The radiological model performed slightly less well in classification (average AUC: average AUC: 0.734 sensitivity: 0.748, specificity: 0.705, PPV: 0.732, NPV: 0.798, accuracy: 0.728. Conclusions The CT-based radiomics analysis provided promising performance for preoperatively discriminating MCN from ASCN and showed good potential in improving diagnostic power, which may serve as a novel tool for guiding clinical decision-making for these patients.
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Affiliation(s)
- Tiansong Xie
- Department of Radiology, Shanghai Cancer Center, Fudan University, Shanghai, China.,Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China
| | - Xuanyi Wang
- Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China.,Department of Radiation Oncology, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zehua Zhang
- Department of Radiology, Shanghai Cancer Center, Fudan University, Shanghai, China.,Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China
| | - Zhengrong Zhou
- Department of Radiology, Shanghai Cancer Center, Fudan University, Shanghai, China.,Minhang Branch, Shanghai Cancer Center, Fudan University, Shanghai, China
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26
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Li Q, Dong F, Jiang B, Zhang M. Exploring MRI Characteristics of Brain Diffuse Midline Gliomas With the H3 K27M Mutation Using Radiomics. Front Oncol 2021; 11:646267. [PMID: 34109112 PMCID: PMC8182051 DOI: 10.3389/fonc.2021.646267] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 04/26/2021] [Indexed: 01/01/2023] Open
Abstract
Objectives To explore the magnetic resonance imaging (MRI) characteristics of brain diffuse midline gliomas with the H3 K27M mutation (DMG-M) using radiomics. Materials and Methods Thirty patients with diffuse midline gliomas, including 16 with the H3 K27M mutant and 14 with wild type tumors, were retrospectively included in this study. A total of 272 radiomic features were initially extracted from MR images of each tumor. Principal component analysis, univariate analysis, and three other feature selection methods, including variance thresholding, recursive feature elimination, and the elastic net, were used to analyze the radiomic features. Based on the results, related visually accessible features of the tumors were further evaluated. Results Patients with DMG-M were younger than those with diffuse midline gliomas with H3 K27M wild (DMG-W) (median, 25.5 and 48 years old, respectively; p=0.005). Principal component analysis showed that there were obvious overlaps in the first two principal components for both DMG-M and DMG-W tumors. The feature selection results showed that few features from T2-weighted images (T2WI) were useful for differentiating DMG-M and DMG-W tumors. Thereafter, four visually accessible features related to T2WI were further extracted and analyzed. Among these features, only cystic formation showed a significant difference between the two types of tumors (OR=7.800, 95% CI 1.476-41.214, p=0.024). Conclusions DMGs with and without the H3 K27M mutation shared similar MRI characteristics. T2W sequences may be valuable, and cystic formation a useful MRI biomarker, for diagnosing brain DMG-M.
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Affiliation(s)
- Qian Li
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fei Dong
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Biao Jiang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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