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Battistella A, Tacelli M, Mapelli P, Schiavo Lena M, Andreasi V, Genova L, Muffatti F, De Cobelli F, Partelli S, Falconi M. Recent developments in the diagnosis of pancreatic neuroendocrine neoplasms. Expert Rev Gastroenterol Hepatol 2024; 18:155-169. [PMID: 38647016 DOI: 10.1080/17474124.2024.2342837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 04/10/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Pancreatic Neuroendocrine Neoplasms (PanNENs) are characterized by a highly heterogeneous clinical and biological behavior, making their diagnosis challenging. PanNENs diagnostic work-up mainly relies on biochemical markers, pathological examination, and imaging evaluation. The latter includes radiological imaging (i.e. computed tomography [CT] and magnetic resonance imaging [MRI]), functional imaging (i.e. 68Gallium [68 Ga]Ga-DOTA-peptide PET/CT and Fluorine-18 fluorodeoxyglucose [18F]FDG PET/CT), and endoscopic ultrasound (EUS) with its associated procedures. AREAS COVERED This review provides a comprehensive assessment of the recent advancements in the PanNENs diagnostic field. PubMed and Embase databases were used for the research, performed from inception to October 2023. EXPERT OPINION A deeper understanding of PanNENs biology, recent technological improvements in imaging modalities, as well as progresses achieved in molecular and cytological assays, are fundamental players for the achievement of early diagnosis and enhanced preoperative characterization of PanNENs. A multimodal diagnostic approach is required for a thorough disease assessment.
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Affiliation(s)
- Anna Battistella
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Matteo Tacelli
- Vita-Salute San Raffaele University, Milan, Italy
- Pancreato-biliary Endoscopy and EUS Division, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Mapelli
- Vita-Salute San Raffaele University, Milan, Italy
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Valentina Andreasi
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Luana Genova
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Francesca Muffatti
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesco De Cobelli
- Vita-Salute San Raffaele University, Milan, Italy
- Radiology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Stefano Partelli
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Falconi
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
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Wang J, Fu K, Wang Z, Wang N, Wang X, Xu T, Li H, Han X, Wu Y. MRI-based clinical-radiomics nomogram to predict early neurological deterioration in isolated acute pontine infarction: a two-center study in Northeast China. BMC Neurol 2024; 24:39. [PMID: 38263044 PMCID: PMC10804506 DOI: 10.1186/s12883-024-03533-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 01/08/2024] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVE To predict the appearance of early neurological deterioration (END) among patients with isolated acute pontine infarction (API) based on magnetic resonance imaging (MRI)-derived radiomics of the infarct site. METHODS 544 patients with isolated API were recruited from two centers and divided into the training set (n = 344) and the verification set (n = 200). In total, 1702 radiomics characteristics were extracted from each patient. A support vector machine algorithm was used to construct a radiomics signature (rad-score). Subsequently, univariate and multivariate logistic regression (LR) analysis was adopted to filter clinical indicators and establish clinical models. Then, based on the LR algorithm, the rad-score and clinical indicators were integrated to construct the clinical-radiomics model, which was compared with other models. RESULTS A clinical-radiomics model was established, including the 5 indicators rad-score, age, initial systolic blood pressure, initial National Institute of Health Stroke Scale, and triglyceride. A nomogram was then made based on the model. The nomogram had good predictive accuracy, with an area under the curve (AUC) of 0.966 (95% confidence interval [CI] 0.947-0.985) and 0.920 (95% [CI] 0.873-0.967) in the training and verification sets, respectively. According to the decision curve analysis, the clinical-radiomics model showed better clinical value than the other models. In addition, the calibration curves also showed that the model has excellent consistency. CONCLUSION The clinical-radiomics model combined MRI-derived radiomics and clinical metrics and may serve as a scoring tool for early prediction of END among patients with isolated API.
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Affiliation(s)
- Jia Wang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, No.148. Baojian Road, NanGangDistrict, Heilongjiang, Heilongjiang prov, China
| | - Kuang Fu
- Department of MR Diagnosis, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang, China
| | - Zhenqi Wang
- Department of Neurology, The Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, Heilongjiang, China
| | - Ning Wang
- Department of MR Diagnosis, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang, China
| | - Xiaokun Wang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, No.148. Baojian Road, NanGangDistrict, Heilongjiang, Heilongjiang prov, China
| | - Tianquan Xu
- Department of MR Diagnosis, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang, China
| | - Haoran Li
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, No.148. Baojian Road, NanGangDistrict, Heilongjiang, Heilongjiang prov, China
| | - Xv Han
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, No.148. Baojian Road, NanGangDistrict, Heilongjiang, Heilongjiang prov, China
| | - Yun Wu
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, No.148. Baojian Road, NanGangDistrict, Heilongjiang, Heilongjiang prov, China.
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A X, Liu M, Chen T, Chen F, Qian G, Zhang Y, Chen Y. Non-Contrast Cine Cardiac Magnetic Resonance Derived-Radiomics for the Prediction of Left Ventricular Adverse Remodeling in Patients With ST-Segment Elevation Myocardial Infarction. Korean J Radiol 2023; 24:827-837. [PMID: 37634638 PMCID: PMC10462896 DOI: 10.3348/kjr.2023.0061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/26/2023] [Accepted: 06/15/2023] [Indexed: 08/29/2023] Open
Abstract
OBJECTIVE To investigate the predictive value of radiomics features based on cardiac magnetic resonance (CMR) cine images for left ventricular adverse remodeling (LVAR) after acute ST-segment elevation myocardial infarction (STEMI). MATERIALS AND METHODS We conducted a retrospective, single-center, cohort study involving 244 patients (random-split into 170 and 74 for training and testing, respectively) having an acute STEMI (88.5% males, 57.0 ± 10.3 years of age) who underwent CMR examination at one week and six months after percutaneous coronary intervention. LVAR was defined as a 20% increase in left ventricular end-diastolic volume 6 months after acute STEMI. Radiomics features were extracted from the one-week CMR cine images using the least absolute shrinkage and selection operator regression (LASSO) analysis. The predictive performance of the selected features was evaluated using receiver operating characteristic curve analysis and the area under the curve (AUC). RESULTS Nine radiomics features with non-zero coefficients were included in the LASSO regression of the radiomics score (RAD score). Infarct size (odds ratio [OR]: 1.04 (1.00-1.07); P = 0.031) and RAD score (OR: 3.43 (2.34-5.28); P < 0.001) were independent predictors of LVAR. The RAD score predicted LVAR, with an AUC (95% confidence interval [CI]) of 0.82 (0.75-0.89) in the training set and 0.75 (0.62-0.89) in the testing set. Combining the RAD score with infarct size yielded favorable performance in predicting LVAR, with an AUC of 0.84 (0.72-0.95). Moreover, the addition of the RAD score to the left ventricular ejection fraction (LVEF) significantly increased the AUC from 0.68 (0.52-0.84) to 0.82 (0.70-0.93) (P = 0.018), which was also comparable to the prediction provided by the combined microvascular obstruction, infarct size, and LVEF with an AUC of 0.79 (0.65-0.94) (P = 0.727). CONCLUSION Radiomics analysis using non-contrast cine CMR can predict LVAR after STEMI independently and incrementally to LVEF and may provide an alternative to traditional CMR parameters.
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Affiliation(s)
- Xin A
- Chinese People's Liberation Army General Hospital, Chinese People's Liberation Army Medical School, Beijing, China
- The Senior Department of Cardiology, the Sixth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Mingliang Liu
- Nankai University, School of Medicine, Tianjin, Nankai, China
| | - Tong Chen
- Chinese People's Liberation Army General Hospital, Chinese People's Liberation Army Medical School, Beijing, China
- The Senior Department of Cardiology, the Sixth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Feng Chen
- Department of Computer Science, the University of Adelaide, Adelaide, Australia
| | - Geng Qian
- Chinese People's Liberation Army General Hospital, Chinese People's Liberation Army Medical School, Beijing, China
- The Senior Department of Cardiology, the Sixth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Ying Zhang
- The Senior Department of Cardiology, the Sixth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yundai Chen
- The Senior Department of Cardiology, the Sixth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China.
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Li N, Hu Z, Liu Y, Ding J, Han P, Jing X, Kan Y. Dynamic contrast-enhanced ultrasound characteristics of renal tumors: VueBox™ quantitative analysis. Clin Hemorheol Microcirc 2023; 85:341-354. [PMID: 37742629 DOI: 10.3233/ch-231750] [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] [Indexed: 09/26/2023]
Abstract
BACKGROUND VueBoxtrademark has been used for contrast analysis in DCE-US-based quantitative research. OBJECTIVE Aim of this study was to use the enhancement-mode and VueBoxtrademark parameters to further evaluate the differential diagnostic value of DCE-US for renal tumors. METHODS 24 patients with renal tumors, including 7 benign and 17 malignant, were retrospectively analyzed.The DCE-US enhancement-mode and VueBoxtrademark parameters correlated with the histological analyses of tumors were obtained and analyzed. RESULTS The benign and malignant renal tumors showed significant differences in enhancement degree (P = 0.017) and presence of a pseudocapsule (P = 0.009) and in the VueBoxtrademark parameters FT (P = 0.045) and RT (P = 0.039). Receiver operating characteristic analysis for differential diagnosis of benign and malignant renal tumors showed that AUC for a combination of enhancement degree and presence of a pseudocapsule was greater (AUC = 0.815) than the values for either parameter of enhancement mode alone. Similarly, the AUC for a combination of RT and FT was greater (AUC = 0.798) than the values for RT or FT alone. A comprehensive index obtaining by combining the enhancement-mode and VueBoxtrademark parameters showed the largest AUC (AUC = 0.916) with relatively high accuracy (87.50%), sensitivity (76.50%), and specificity (85.70%). CONCLUSIONS DCE-US with enhancement mode and quantitative analysis can facilitate preoperative differential diagnosis of benign and malignant renal tumors.
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Affiliation(s)
- Ning Li
- Department of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, China
| | - Ziyue Hu
- Department of Ultrasound, The Third Central Hospital of Tianjin, Tianjin, China
- Tianjin Institute of Hepatobiliary Disease, Tianjin, China
| | - Yang Liu
- Department of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, China
| | - Jianmin Ding
- Department of Ultrasound, The Third Central Hospital of Tianjin, Tianjin, China
- Tianjin Institute of Hepatobiliary Disease, Tianjin, China
| | - Pengcheng Han
- College of Stomatology, Tianjin Medical University, Tianjin, China
| | - Xiang Jing
- Department of Ultrasound, The Third Central Hospital of Tianjin, Tianjin, China
- Tianjin Institute of Hepatobiliary Disease, Tianjin, China
| | - Yanmin Kan
- Department of Ultrasound, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, China
- Department of Ultrasound, The Third Central Hospital of Tianjin, Tianjin, China
- Tianjin Institute of Hepatobiliary Disease, Tianjin, China
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Boeken T, Feydy J, Lecler A, Soyer P, Feydy A, Barat M, Duron L. Artificial intelligence in diagnostic and interventional radiology: Where are we now? Diagn Interv Imaging 2023; 104:1-5. [PMID: 36494290 DOI: 10.1016/j.diii.2022.11.004] [Citation(s) in RCA: 57] [Impact Index Per Article: 57.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022]
Abstract
The emergence of massively parallel yet affordable computing devices has been a game changer for research in the field of artificial intelligence (AI). In addition, dramatic investment from the web giants has fostered the development of a high-quality software stack. Going forward, the combination of faster computers with dedicated software libraries and the widespread availability of data has opened the door to more flexibility in the design of AI models. Radiomics is a process used to discover new imaging biomarkers that has multiple applications in radiology and can be used in conjunction with AI. AI can be used throughout the various processes of diagnostic imaging, including data acquisition, reconstruction, analysis and reporting. Today, the concept of "AI-augmented" radiologists is preferred to the theory of the replacement of radiologists by AI in many indications. Current evidence bolsters the assumption that AI-assisted radiologists work better and faster. Interventional radiology becomes a data-rich specialty where the entire procedure is fully recorded in a standardized DICOM format and accessible via standard picture archiving and communication systems. No other interventional specialty can bolster such readiness. In this setting, interventional radiology could lead the development of AI-powered applications in the broader interventional community. This article provides an update on the current status of radiomics and AI research, analyzes upcoming challenges and also discusses the main applications in AI in interventional radiology to help radiologists better understand and criticize articles reporting AI in medical imaging.
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Affiliation(s)
- Tom Boeken
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Vascular and Oncological Interventional Radiology, Hôpital Européen Georges Pompidou, APHP, Paris 75015, France; HeKA team, INRIA, Paris 75012 , France.
| | | | - Augustin Lecler
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Rothschild Foundation Hospital, Paris 75019, France
| | - Philippe Soyer
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Hôpital Cochin, APHP, Paris 75014, France
| | - Antoine Feydy
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Hôpital Cochin, APHP, Paris 75014, France
| | - Maxime Barat
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Hôpital Cochin, APHP, Paris 75014, France
| | - Loïc Duron
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Radiology, Rothschild Foundation Hospital, Paris 75019, France
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Chamroukhi F, Brivet S, Savadjiev P, Coates M, Forghani R. DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation. Diagnostics (Basel) 2022; 12:diagnostics12123072. [PMID: 36553079 PMCID: PMC9776609 DOI: 10.3390/diagnostics12123072] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/24/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
Dual-energy computed tomography (DECT) is an advanced CT computed tomography scanning technique enabling material characterization not possible with conventional CT scans. It allows the reconstruction of energy decay curves at each 3D image voxel, representing varied image attenuation at different effective scanning energy levels. In this paper, we develop novel unsupervised learning techniques based on mixture models and functional data analysis models to the clustering of DECT images. We design functional mixture models that integrate spatial image context in mixture weights, with mixture component densities being constructed upon the DECT energy decay curves as functional observations. We develop dedicated expectation-maximization algorithms for the maximum likelihood estimation of the model parameters. To our knowledge, this is the first article to develop statistical functional data analysis and model-based clustering techniques to take advantage of the full spectral information provided by DECT. We evaluate the application of DECT to head and neck squamous cell carcinoma. Current image-based evaluation of these tumors in clinical practice is largely qualitative, based on a visual assessment of tumor anatomic extent and basic one- or two-dimensional tumor size measurements. We evaluate our methods on 91 head and neck cancer DECT scans and compare our unsupervised clustering results to tumor contours traced manually by radiologists, as well as to several baseline algorithms. Given the inter-rater variability even among experts at delineating head and neck tumors, and given the potential importance of tissue reactions surrounding the tumor itself, our proposed methodology has the potential to add value in downstream machine learning applications for clinical outcome prediction based on DECT data in head and neck cancer.
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Affiliation(s)
- Faicel Chamroukhi
- IRT SystemX, 2 Boulevard Thomas Gobert, 91120 Palaiseau, France
- Correspondence:
| | - Segolene Brivet
- Electrical and Computer Engineering Department, McGill University, Montreal, QC H3A 0G4, Canada
| | - Peter Savadjiev
- Augmented Intelligence and Precision Health Laboratory (AIPHL), Department of Radiology, McGill University, Montreal, QC H3G 1A4, Canada
| | - Mark Coates
- Electrical and Computer Engineering Department, McGill University, Montreal, QC H3A 0G4, Canada
| | - Reza Forghani
- Augmented Intelligence and Precision Health Laboratory (AIPHL), Department of Radiology, McGill University, Montreal, QC H3G 1A4, Canada
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL 32610, USA
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Dana J, Venkatasamy A, Saviano A, Lupberger J, Hoshida Y, Vilgrain V, Nahon P, Reinhold C, Gallix B, Baumert TF. Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease. Hepatol Int 2022; 16:509-522. [PMID: 35138551 PMCID: PMC9177703 DOI: 10.1007/s12072-022-10303-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/17/2022] [Indexed: 12/14/2022]
Abstract
Chronic liver diseases, resulting from chronic injuries of various causes, lead to cirrhosis with life-threatening complications including liver failure, portal hypertension, hepatocellular carcinoma. A key unmet medical need is robust non-invasive biomarkers to predict patient outcome, stratify patients for risk of disease progression and monitor response to emerging therapies. Quantitative imaging biomarkers have already been developed, for instance, liver elastography for staging fibrosis or proton density fat fraction on magnetic resonance imaging for liver steatosis. Yet, major improvements, in the field of image acquisition and analysis, are still required to be able to accurately characterize the liver parenchyma, monitor its changes and predict any pejorative evolution across disease progression. Artificial intelligence has the potential to augment the exploitation of massive multi-parametric data to extract valuable information and achieve precision medicine. Machine learning algorithms have been developed to assess non-invasively certain histological characteristics of chronic liver diseases, including fibrosis and steatosis. Although still at an early stage of development, artificial intelligence-based imaging biomarkers provide novel opportunities to predict the risk of progression from early-stage chronic liver diseases toward cirrhosis-related complications, with the ultimate perspective of precision medicine. This review provides an overview of emerging quantitative imaging techniques and the application of artificial intelligence for biomarker discovery in chronic liver disease.
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Affiliation(s)
- Jérémy Dana
- Institut de Recherche sur les Maladies Virales et Hépatiques, Institut National de la Santé et de la Recherche Médicale (Inserm), U1110, 3 Rue Koeberlé, 67000, Strasbourg, France.
- Institut Hospitalo-Universitaire (IHU), Strasbourg, France.
- Université de Strasbourg, Strasbourg, France.
- Department of Diagnostic Radiology, McGill University, Montreal, Canada.
| | - Aïna Venkatasamy
- Institut Hospitalo-Universitaire (IHU), Strasbourg, France
- Streinth Lab (Stress Response and Innovative Therapies), Inserm UMR_S 1113 IRFAC, Interface Recherche Fondamentale et Appliquée à la Cancérologie, 3 Avenue Moliere, Strasbourg, France
- Department of Radiology Medical Physics, Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Killianstrasse 5a, 79106, Freiburg, Germany
| | - Antonio Saviano
- Institut de Recherche sur les Maladies Virales et Hépatiques, Institut National de la Santé et de la Recherche Médicale (Inserm), U1110, 3 Rue Koeberlé, 67000, Strasbourg, France
- Université de Strasbourg, Strasbourg, France
- Pôle Hépato-Digestif, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Joachim Lupberger
- Institut de Recherche sur les Maladies Virales et Hépatiques, Institut National de la Santé et de la Recherche Médicale (Inserm), U1110, 3 Rue Koeberlé, 67000, Strasbourg, France
- Université de Strasbourg, Strasbourg, France
| | - Yujin Hoshida
- Liver Tumor Translational Research Program, Division of Digestive and Liver Diseases, Department of Internal Medicine, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, USA
| | - Valérie Vilgrain
- Radiology Department, Hôpital Beaujon, Université de Paris, CRI, INSERM 1149, APHP. Nord, Paris, France
| | - Pierre Nahon
- Liver Unit, Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Paris Seine Saint-Denis, Bobigny, France
- Université Sorbonne Paris Nord, 93000, Bobigny, France
- Inserm, UMR-1138 "Functional Genomics of Solid Tumors", Paris, France
| | - Caroline Reinhold
- Department of Diagnostic Radiology, McGill University, Montreal, Canada
- Augmented Intelligence and Precision Health Laboratory, Research Institute of McGill University Health Centre, Montreal, Canada
- Montreal Imaging Experts Inc., Montreal, Canada
| | - Benoit Gallix
- Institut Hospitalo-Universitaire (IHU), Strasbourg, France
- Université de Strasbourg, Strasbourg, France
- Department of Diagnostic Radiology, McGill University, Montreal, Canada
| | - Thomas F Baumert
- Institut de Recherche sur les Maladies Virales et Hépatiques, Institut National de la Santé et de la Recherche Médicale (Inserm), U1110, 3 Rue Koeberlé, 67000, Strasbourg, France.
- Université de Strasbourg, Strasbourg, France.
- Pôle Hépato-Digestif, Hôpitaux Universitaires de Strasbourg, Strasbourg, France.
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SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans. Appl Bionics Biomech 2022; 2022:1139587. [PMID: 35607427 PMCID: PMC9124150 DOI: 10.1155/2022/1139587] [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: 04/07/2022] [Accepted: 05/02/2022] [Indexed: 11/17/2022] Open
Abstract
Accurate lung tumor identification is crucial for radiation treatment planning. Due to the low contrast of the lung tumor in computed tomography (CT) images, segmentation of the tumor in CT images is challenging. This paper effectively integrates the U-Net with the channel attention module (CAM) to segment the malignant lung area from the surrounding chest region. The SegChaNet method encodes CT slices of the input lung into feature maps utilizing the trail of encoders. Finally, we explicitly developed a multiscale, dense-feature extraction module to extract multiscale features from the collection of encoded feature maps. We have identified the segmentation map of the lungs by employing the decoders and compared SegChaNet with the state-of-the-art. The model has learned the dense-feature extraction in lung abnormalities, while iterative downsampling followed by iterative upsampling causes the network to remain invariant to the size of the dense abnormality. Experimental results show that the proposed method is accurate and efficient and directly provides explicit lung regions in complex circumstances without postprocessing.
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Pang Y, Wang H, Li H. Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy. Front Oncol 2022; 11:764665. [PMID: 35111666 PMCID: PMC8801459 DOI: 10.3389/fonc.2021.764665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 11/29/2021] [Indexed: 12/22/2022] Open
Abstract
Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.
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Affiliation(s)
- Yaru Pang
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Hui Wang
- Department of Chemical Engineering, University College London, London, United Kingdom
| | - He Li
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
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Dana J, Lefebvre TL, Savadjiev P, Bodard S, Gauvin S, Bhatnagar SR, Forghani R, Hélénon O, Reinhold C. Malignancy risk stratification of cystic renal lesions based on a contrast-enhanced CT-based machine learning model and a clinical decision algorithm. Eur Radiol 2022; 32:4116-4127. [DOI: 10.1007/s00330-021-08449-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/17/2021] [Accepted: 10/29/2021] [Indexed: 01/06/2023]
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Golchi S, Fu J, Liu X, Yu E, Forghani R, Bhatnagar S. Sparse Bayesian Predictive Modelling of Tumor Response Using Radiomic Features. Stat (Int Stat Inst) 2022. [DOI: 10.1002/sta4.450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Shirin Golchi
- Department of Epidemiology and Biostatistics McGill University QC Canada
| | - Jingyan Fu
- Department of Epidemiology and Biostatistics McGill University QC Canada
| | - Xiaoyang Liu
- Princess Margaret Hospital University of Toronto ON Canada
- Department of Radiology, Brigham and Women’s Hospital Harvard University MA USA
- Department of Medical Imaging University of Toronto ON Canada
| | - Eugene Yu
- Princess Margaret Hospital University of Toronto ON Canada
- Department of Medical Imaging University of Toronto ON Canada
| | - Reza Forghani
- Department of Diagnostic Radiology McGill University QC Canada
| | - Sahir Bhatnagar
- Department of Epidemiology and Biostatistics McGill University QC Canada
- Department of Diagnostic Radiology McGill University QC Canada
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12
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Zhou Y, Gu HL, Zhang XL, Tian ZF, Xu XQ, Tang WW. Multiparametric magnetic resonance imaging-derived radiomics for the prediction of disease-free survival in early-stage squamous cervical cancer. Eur Radiol 2021; 32:2540-2551. [PMID: 34642807 DOI: 10.1007/s00330-021-08326-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 08/10/2021] [Accepted: 09/09/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVE To conduct multiparametric magnetic resonance imaging (MRI)-derived radiomics based on multi-scale tumor region for predicting disease-free survival (DFS) in early-stage squamous cervical cancer (ESSCC). METHODS A total of 191 ESSCC patients (training cohort, n = 135; validation cohort, n = 56) from March 2016 to September 2019 were retrospectively recruited. Radiomics features were derived from the T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CET1WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) map for each patient. DFS-related radiomics features were selected in 3 target tumor volumes (VOIentire, VOI+5 mm, and VOI-5 mm) to build 3 rad-scores using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Logistic regression was applied to build combined model incorporating rad-scores with clinical risk factors and compared with clinical model alone. Kaplan-Meier analysis was used to further validate prognostic value of selected clinical and radiomics characteristics. RESULTS Three radiomics scores all showed favorable performances in DFS prediction. Rad-score (VOI+5 mm) performed best with a C-index of 0.750 in the training set and 0.839 in the validation set. Combined model was constructed by incorporating age categorized by 55, Federation of Gynecology and Obstetrics (Figo) stage, and lymphovascular space invasion with rad-score (VOI+5 mm). Combined model performed better than clinical model in DFS prediction in both the training set (C-index 0.815 vs 0.709; p = 0.024) and the validation set (C-index 0.866 vs 0.719; p = 0.001). CONCLUSION Multiparametric MRI-derived radiomics based on multi-scale tumor region can aid in the prediction of DFS for ESSCC patients, thereby facilitating clinical decision-making. KEY POINTS • Three radiomics scores based on multi-scale tumor region all showed favorable performances in DFS prediction. Rad-score (VOI+5 mm) performed best with favorable C-index values. • Combined model incorporating multiparametric MRI-based radiomics with clinical risk factors performed significantly better in DFS prediction than the clinical model. • Combined model presented as a nomogram can be easily used to predict survival, thereby facilitating clinical decision-making.
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Affiliation(s)
- Yan Zhou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Gulou District, No. 300, Guangzhou Rd, Nanjing, 210029, People's Republic of China
| | - Hai-Lei Gu
- Department of Radiology, Women's Hospital of Nanjing Medical University, No. 123, Mochou Rd, Qinhuai District, Nanjing, 210029, People's Republic of China
| | - Xin-Lu Zhang
- Department of Radiology, Women's Hospital of Nanjing Medical University, No. 123, Mochou Rd, Qinhuai District, Nanjing, 210029, People's Republic of China
| | - Zhong-Fu Tian
- Department of Radiology, Women's Hospital of Nanjing Medical University, No. 123, Mochou Rd, Qinhuai District, Nanjing, 210029, People's Republic of China
| | - Xiao-Quan Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Gulou District, No. 300, Guangzhou Rd, Nanjing, 210029, People's Republic of China.
| | - Wen-Wei Tang
- Department of Radiology, Women's Hospital of Nanjing Medical University, No. 123, Mochou Rd, Qinhuai District, Nanjing, 210029, People's Republic of China.
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13
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Scherer J, Nolden M, Kleesiek J, Metzger J, Kades K, Schneider V, Bach M, Sedlaczek O, Bucher AM, Vogl TJ, Grünwald F, Kühn JP, Hoffmann RT, Kotzerke J, Bethge O, Schimmöller L, Antoch G, Müller HW, Daul A, Nikolaou K, la Fougère C, Kunz WG, Ingrisch M, Schachtner B, Ricke J, Bartenstein P, Nensa F, Radbruch A, Umutlu L, Forsting M, Seifert R, Herrmann K, Mayer P, Kauczor HU, Penzkofer T, Hamm B, Brenner W, Kloeckner R, Düber C, Schreckenberger M, Braren R, Kaissis G, Makowski M, Eiber M, Gafita A, Trager R, Weber WA, Neubauer J, Reisert M, Bock M, Bamberg F, Hennig J, Meyer PT, Ruf J, Haberkorn U, Schoenberg SO, Kuder T, Neher P, Floca R, Schlemmer HP, Maier-Hein K. Joint Imaging Platform for Federated Clinical Data Analytics. JCO Clin Cancer Inform 2021; 4:1027-1038. [PMID: 33166197 PMCID: PMC7713526 DOI: 10.1200/cci.20.00045] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
PURPOSE Image analysis is one of the most promising applications of artificial intelligence (AI) in health care, potentially improving prediction, diagnosis, and treatment of diseases. Although scientific advances in this area critically depend on the accessibility of large-volume and high-quality data, sharing data between institutions faces various ethical and legal constraints as well as organizational and technical obstacles. METHODS The Joint Imaging Platform (JIP) of the German Cancer Consortium (DKTK) addresses these issues by providing federated data analysis technology in a secure and compliant way. Using the JIP, medical image data remain in the originator institutions, but analysis and AI algorithms are shared and jointly used. Common standards and interfaces to local systems ensure permanent data sovereignty of participating institutions. RESULTS The JIP is established in the radiology and nuclear medicine departments of 10 university hospitals in Germany (DKTK partner sites). In multiple complementary use cases, we show that the platform fulfills all relevant requirements to serve as a foundation for multicenter medical imaging trials and research on large cohorts, including the harmonization and integration of data, interactive analysis, automatic analysis, federated machine learning, and extensibility and maintenance processes, which are elementary for the sustainability of such a platform. CONCLUSION The results demonstrate the feasibility of using the JIP as a federated data analytics platform in heterogeneous clinical information technology and software landscapes, solving an important bottleneck for the application of AI to large-scale clinical imaging data.
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Affiliation(s)
- Jonas Scherer
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.,German Cancer Consortium, Heidelberg, Germany
| | - Marco Nolden
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.,German Cancer Consortium, Heidelberg, Germany.,Pattern Analysis and Learning Group, Radio-oncology and Clinical Radiotherapy, Heidelberg University Hospital, Heidelberg, Germany
| | - Jens Kleesiek
- German Cancer Consortium, Heidelberg, Germany.,Division of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Jasmin Metzger
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.,German Cancer Consortium, Heidelberg, Germany
| | - Klaus Kades
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.,German Cancer Consortium, Heidelberg, Germany
| | - Verena Schneider
- German Cancer Consortium, Heidelberg, Germany.,Division of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Michael Bach
- German Cancer Consortium, Heidelberg, Germany.,Division of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Oliver Sedlaczek
- German Cancer Consortium, Heidelberg, Germany.,Division of Radiology, German Cancer Research Center, Heidelberg, Germany.,Klinik Diagnostische und Interventionelle Radiologie der Universität Heidelberg, Heidelberg, Germany
| | - Andreas M Bucher
- German Cancer Consortium, Heidelberg, Germany.,Institut für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Frankfurt, Frankfurt, Germany
| | - Thomas J Vogl
- German Cancer Consortium, Heidelberg, Germany.,Institut für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Frankfurt, Frankfurt, Germany
| | - Frank Grünwald
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Nuklearmedizin, Universitätsklinikum Frankfurt, Frankfurt, Germany
| | - Jens-Peter Kühn
- German Cancer Consortium, Heidelberg, Germany.,Institut und Poliklinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Carl Gustav Carus Dresden, Dresden, Germany
| | - Ralf-Thorsten Hoffmann
- German Cancer Consortium, Heidelberg, Germany.,Institut und Poliklinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Carl Gustav Carus Dresden, Dresden, Germany
| | - Jörg Kotzerke
- German Cancer Consortium, Heidelberg, Germany.,Klinik und Poliklinik für Nuklearmedizin, Universitätsklinikum Carl Gustav Carus Dresden, Dresden, Germany
| | - Oliver Bethge
- German Cancer Consortium, Heidelberg, Germany.,Medical Faculty, Department of Diagnostic and Interventional Radiology, University Düsseldorf, Düsseldorf, Germany
| | - Lars Schimmöller
- German Cancer Consortium, Heidelberg, Germany.,Medical Faculty, Department of Diagnostic and Interventional Radiology, University Düsseldorf, Düsseldorf, Germany
| | - Gerald Antoch
- German Cancer Consortium, Heidelberg, Germany.,Medical Faculty, Department of Diagnostic and Interventional Radiology, University Düsseldorf, Düsseldorf, Germany
| | - Hans-Wilhelm Müller
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Nuklearmedizin, Universitätsklinikum Düsseldorf, Düsseldorf, Germany
| | - Andreas Daul
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Tübingen, Tübingen, Germany
| | - Konstantin Nikolaou
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Tübingen, Tübingen, Germany
| | - Christian la Fougère
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Nuklearmedizin und Klinische Molekulare Bildgebung, Universitätsklinikum Tübingen, Tübingen, Germany
| | - Wolfgang G Kunz
- German Cancer Consortium, Heidelberg, Germany.,Department of Radiology, University Hospital, Ludwig Maximilian University Munich, Munich, Germany
| | - Michael Ingrisch
- German Cancer Consortium, Heidelberg, Germany.,Department of Radiology, University Hospital, Ludwig Maximilian University Munich, Munich, Germany
| | - Balthasar Schachtner
- German Cancer Consortium, Heidelberg, Germany.,Department of Radiology, University Hospital, Ludwig Maximilian University Munich, Munich, Germany.,German Center of Lung Research, Giessen, Germany
| | - Jens Ricke
- German Cancer Consortium, Heidelberg, Germany.,Department of Radiology, University Hospital, Ludwig Maximilian University Munich, Munich, Germany
| | - Peter Bartenstein
- German Cancer Consortium, Heidelberg, Germany.,Klinik und Poliklinik für Nuklearmedizin, Klinikum der Universität München, München, Germany
| | - Felix Nensa
- German Cancer Consortium, Heidelberg, Germany.,Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen AöR, Essen, Germany
| | - Alexander Radbruch
- German Cancer Consortium, Heidelberg, Germany.,Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen AöR, Essen, Germany
| | - Lale Umutlu
- German Cancer Consortium, Heidelberg, Germany.,Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen AöR, Essen, Germany
| | - Michael Forsting
- German Cancer Consortium, Heidelberg, Germany.,Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen AöR, Essen, Germany
| | - Robert Seifert
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Nuklearmedizin, Universitätsklinikum Essen AöR, Essen, Germany
| | - Ken Herrmann
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Nuklearmedizin, Universitätsklinikum Essen AöR, Essen, Germany
| | - Philipp Mayer
- German Cancer Consortium, Heidelberg, Germany.,Klinik Diagnostische und Interventionelle Radiologie der Universität Heidelberg, Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- German Cancer Consortium, Heidelberg, Germany.,Klinik Diagnostische und Interventionelle Radiologie der Universität Heidelberg, Heidelberg, Germany.,German Center of Lung Research, Giessen, Germany
| | - Tobias Penzkofer
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Radiologie (mit dem Bereich Kinderradiologie), Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Bernd Hamm
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Radiologie (mit dem Bereich Kinderradiologie), Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Winfried Brenner
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Nuklearmedizin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Roman Kloeckner
- German Cancer Consortium, Heidelberg, Germany.,Klinik und Poliklinik für Diagnostische und Interventionelle Radiologie, Universitätsmedizin Mainz, Mainz, Germany
| | - Christoph Düber
- German Cancer Consortium, Heidelberg, Germany.,Klinik und Poliklinik für Diagnostische und Interventionelle Radiologie, Universitätsmedizin Mainz, Mainz, Germany
| | - Mathias Schreckenberger
- German Cancer Consortium, Heidelberg, Germany.,Klinik und Poliklinik für Nuklearmedizin, Universitätsmedizin Mainz, Mainz, Germany
| | - Rickmer Braren
- German Cancer Consortium, Heidelberg, Germany.,Institut für Diagnostische und Interventionelle Radiologie, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Georgios Kaissis
- German Cancer Consortium, Heidelberg, Germany.,Pattern Analysis and Learning Group, Radio-oncology and Clinical Radiotherapy, Heidelberg University Hospital, Heidelberg, Germany.,Institut für Diagnostische und Interventionelle Radiologie, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.,Department of Computing, Imperial College London, London, United Kingdom
| | - Marcus Makowski
- German Cancer Consortium, Heidelberg, Germany.,Institut für Diagnostische und Interventionelle Radiologie, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Matthias Eiber
- German Cancer Consortium, Heidelberg, Germany.,Klinik und Poliklinik für Nuklearmedizin, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Andrei Gafita
- German Cancer Consortium, Heidelberg, Germany.,Klinik und Poliklinik für Nuklearmedizin, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rupert Trager
- German Cancer Consortium, Heidelberg, Germany.,Klinik und Poliklinik für Nuklearmedizin, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Wolfgang A Weber
- German Cancer Consortium, Heidelberg, Germany.,Klinik und Poliklinik für Nuklearmedizin, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jakob Neubauer
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Freiburg, Germany
| | - Marco Reisert
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Freiburg, Germany
| | - Michael Bock
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Freiburg, Germany
| | - Fabian Bamberg
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Freiburg, Germany
| | - Jürgen Hennig
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Freiburg, Freiburg, Germany
| | - Philipp Tobias Meyer
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Nuklearmedizin, Universitätsklinikum Freiburg, Freiburg, Germany
| | - Juri Ruf
- German Cancer Consortium, Heidelberg, Germany.,Klinik für Nuklearmedizin, Universitätsklinikum Freiburg, Freiburg, Germany
| | - Uwe Haberkorn
- German Cancer Consortium, Heidelberg, Germany.,Klinische Kooperationseinheit Nuklearmedizin, Deutsches Krebsforschungszentrum Heidelberg, Heidelberg, Germany
| | - Stefan O Schoenberg
- German Cancer Consortium, Heidelberg, Germany.,Universitätsmedizin Mannheim, Medizinische Fakultät Mannheim der Universität Heidelberg, Heidelberg, Germany
| | - Tristan Kuder
- German Cancer Consortium, Heidelberg, Germany.,Medizinische Physik in der Radiologie, Deutsches Krebsforschungszentrum Heidelberg, Heidelberg, Germany
| | - Peter Neher
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.,German Cancer Consortium, Heidelberg, Germany
| | - Ralf Floca
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.,German Cancer Consortium, Heidelberg, Germany.,Pattern Analysis and Learning Group, Radio-oncology and Clinical Radiotherapy, Heidelberg University Hospital, Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, Germany.,German Cancer Consortium, Heidelberg, Germany.,Division of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.,Pattern Analysis and Learning Group, Radio-oncology and Clinical Radiotherapy, Heidelberg University Hospital, Heidelberg, Germany
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14
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Santiago R, Ortiz Jimenez J, Forghani R, Muthukrishnan N, Del Corpo O, Karthigesu S, Haider MY, Reinhold C, Assouline S. CT-based radiomics model with machine learning for predicting primary treatment failure in diffuse large B-cell Lymphoma. Transl Oncol 2021; 14:101188. [PMID: 34343854 PMCID: PMC8348197 DOI: 10.1016/j.tranon.2021.101188] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/28/2021] [Accepted: 07/23/2021] [Indexed: 12/29/2022] Open
Abstract
CT-based radiomics with machine learning classifier is able to accurately predict primary refractory Diffuse Large B Cell Lymphomas (DLBCL). The radiomics model exhibits a better discrimination for refractory DLBCL identification compared to available standard clinical criteria.
Biomarkers which can identify Diffuse Large B-Cell Lymphoma (DLBCL) likely to be refractory to first-line therapy are essential for selecting this population prior to therapy initiation to offer alternate therapeutic options that can improve prognosis. We tested the ability of a CT-based radiomics approach with machine learning to predict Primary Treatment Failure (PTF)-DLBCL from initial imaging evaluation. Twenty-six refractory patients were matched to 26 non-refractory patients, yielding 180 lymph nodes for analysis. Manual 3D delineation of the total node volume was performed by two independent readers to test the reproducibility. Then, 1218 hand-crafted radiomic features were extracted. The Random Forests machine learning approach was used as a classifier for constructing the prediction models. Seventy percent of the nodes were randomly assigned to a training set and the remaining 30% were assigned to an independent test set. The final model was tested on the dataset from the 2 readers, showing a mean accuracy, sensitivity and specificity of 73%, 62% and 82%, respectively, for distinguishing between refractory and non-refractory patients. The area under the receiver operating characteristic curve (AUC) was 0.83 and 0.79 for the two readers. We conclude that machine learning CT-based radiomics analysis is able to identify a priori PTF-DLBCL with a good accuracy.
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Affiliation(s)
- Raoul Santiago
- Jewish General Hospital - McGill University, Canada; Segal Cancer Centre and Lady Davis Institute for Medical Research, Canada
| | | | - Reza Forghani
- Segal Cancer Centre and Lady Davis Institute for Medical Research, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL) of the Department of Radiology and the Research Institute of McGill University Health Centre, Canada; Gerald Bronfman Department of Oncology, Canada; McGill University, Canada.
| | - Nikesh Muthukrishnan
- Segal Cancer Centre and Lady Davis Institute for Medical Research, Canada; Augmented Intelligence & Precision Health Laboratory (AIPHL) of the Department of Radiology and the Research Institute of McGill University Health Centre, Canada
| | | | | | | | - Caroline Reinhold
- Augmented Intelligence & Precision Health Laboratory (AIPHL) of the Department of Radiology and the Research Institute of McGill University Health Centre, Canada; McGill University, Canada
| | - Sarit Assouline
- Jewish General Hospital - McGill University, Canada; Segal Cancer Centre and Lady Davis Institute for Medical Research, Canada
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15
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Gowin E, Jończyk-Potoczna K, Sosnowska-Sienkiewicz P, Belen Larque A, Kurzawa P, Januszkiewicz-Lewandowska D. Semi-Automatic Volumetric and Standard Three-Dimensional Measurements for Primary Tumor Evaluation and Response to Treatment Assessment in Pediatric Rhabdomyosarcoma. J Pers Med 2021; 11:jpm11080717. [PMID: 34442361 PMCID: PMC8399942 DOI: 10.3390/jpm11080717] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/11/2021] [Accepted: 07/23/2021] [Indexed: 02/06/2023] Open
Abstract
Current prognostic classification of rhabdomyosarcoma in children requires precise measurements of the tumor. The purpose of the study was to compare the standard three-dimensional (3D) measurements with semi-automatic tumor volume measurement method concerning assessment of the primary tumor size and the degree of response to treatment for rhabdomyosarcoma in children. Magnetic Resonance Imaging data on 31 children with treated rhabdomyosarcoma based on the Cooperative Weichteilsarkom Studiengruppe (CWS) guidance was evaluated. Tumor sizes were measured by two methods: 3D standard measurements and semi-automatic tumor volume measurement (VOI) at diagnosis, and after 9 and 17/18 weeks of the induction chemotherapy. Response to treatment and prediction values were assessed. The tumor volume medians calculated using VOI were significantly higher in comparison with those calculated using the 3D method both during the diagnosis as well as after 9 weeks of the chemotherapy and during the 17-18th week of the treatment. The volume measurements based on the generalized estimating equations on the VOI method were significantly better than the 3D method (p = 0.037). The volumetric measurements alone can hardly be considered an unequivocal marker used to make decisions on modification of the therapy in patients with rhabdomyosarcoma.
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Affiliation(s)
- Ewelina Gowin
- Department of Health Promotion, Poznan University of Medical Sciences, Święcickiego 6 Street, 60-781 Poznan, Poland;
| | - Katarzyna Jończyk-Potoczna
- Department of Pediatric Radiology, Poznan University of Medical Sciences, Szpitalna Street 27/33, 60-572 Poznan, Poland;
| | - Patrycja Sosnowska-Sienkiewicz
- Department of Pediatric Surgery, Traumatology and Urology, Poznan University of Medical Sciences, Szpitalna Street 27/33, 60-572 Poznan, Poland
- Correspondence: or ; Tel.: +48-61-849-15-78; Fax: +48-61-849-52-28
| | - Anna Belen Larque
- Department of Pathology, Hospital Clínic, Villarroel, 170, 08036 Barcelona, Spain;
| | - Paweł Kurzawa
- Department of Pathology, Hospital of Lord’s Transfiguration, University of Medical Sciences, Długa Street 1/2, 61-848 Poznan, Poland;
| | - Danuta Januszkiewicz-Lewandowska
- Department of Pediatric Oncology, Hematology and Transplantology, Poznan University of Medical Sciences, Szpitalna 27/33, 60-572 Poznan, Poland;
- Department of Medical Diagnostics, Dobra 38a, 60-595 Poznan, Poland
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16
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Lecointre L, Dana J, Lodi M, Akladios C, Gallix B. Artificial intelligence-based radiomics models in endometrial cancer: A systematic review. Eur J Surg Oncol 2021; 47:2734-2741. [PMID: 34183201 DOI: 10.1016/j.ejso.2021.06.023] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/03/2021] [Accepted: 06/20/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Radiological preoperative assessment of endometrial cancer (EC) is in some cases not precise enough and its performances improvement could lead to a clinical benefit. Radiomics is a recent field of application of artificial intelligence (AI) in radiology. AIMS To investigate the contribution of radiomics on the radiological preoperative assessment of patients with EC; and to establish a simple and reproducible AI Quality Score applicable to Machine Learning and Deep Learning studies. METHODS We conducted a systematic review of current literature including original articles that studied EC through imaging-based AI techniques. Then, we developed a novel Simplified and Reproducible AI Quality score (SRQS) based on 10 items which ranged to 0 to 20 points in total which focused on clinical relevance, data collection, model design and statistical analysis. SRQS cut-off was defined at 10/20. RESULTS We included 17 articles which studied different radiological parameters such as deep myometrial invasion, lympho-vascular space invasion, lymph nodes involvement, etc. One article was prospective, and the others were retrospective. The predominant technique was magnetic resonance imaging. Two studies developed Deep Learning models, while the others machine learning ones. We evaluated each article with SRQS by 2 independent readers. Finally, we kept only 7 high-quality articles with clinical impact. SRQS was highly reproducible (Kappa = 0.95 IC 95% [0.907-0.988]). CONCLUSION There is currently insufficient evidence on the benefit of radiomics in EC. Nevertheless, this field is promising for future clinical practice. Quality should be a priority when developing these new technologies.
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Affiliation(s)
- Lise Lecointre
- Department of Gynecologic Surgery, Hôpitaux Universitaires de Strasbourg, Strasbourg, France; I-Cube UMR 7357 - Laboratoire des Sciences de L'ingénieur, de L'informatique et de L'imagerie, Université de Strasbourg, Strasbourg, France; Institut Hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, Strasbourg, France.
| | - Jérémy Dana
- Institut Hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, Strasbourg, France; Inserm U1110, Institut de Recherche sur Les Maladies Virales et Hépatiques, Strasbourg, France
| | - Massimo Lodi
- Department of Gynecologic Surgery, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Chérif Akladios
- Department of Gynecologic Surgery, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Benoît Gallix
- I-Cube UMR 7357 - Laboratoire des Sciences de L'ingénieur, de L'informatique et de L'imagerie, Université de Strasbourg, Strasbourg, France; Institut Hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, Strasbourg, France; Department of Diagnostic Radiology, McGill University, Montreal, Canada
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17
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Beer L, Martin-Gonzalez P, Delgado-Ortet M, Reinius M, Rundo L, Woitek R, Ursprung S, Escudero L, Sahin H, Funingana IG, Ang JE, Jimenez-Linan M, Lawton T, Phadke G, Davey S, Nguyen NQ, Markowetz F, Brenton JD, Crispin-Ortuzar M, Addley H, Sala E. Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: technical development and initial experience in metastatic ovarian cancer. Eur Radiol 2021; 31:3765-3772. [PMID: 33315123 PMCID: PMC8128813 DOI: 10.1007/s00330-020-07560-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 09/29/2020] [Accepted: 11/23/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE To develop a precision tissue sampling technique that uses computed tomography (CT)-based radiomic tumour habitats for ultrasound (US)-guided targeted biopsies that can be integrated in the clinical workflow of patients with high-grade serous ovarian cancer (HGSOC). METHODS Six patients with suspected HGSOC scheduled for US-guided biopsy before starting neoadjuvant chemotherapy were included in this prospective study from September 2019 to February 2020. The tumour segmentation was performed manually on the pre-biopsy contrast-enhanced CT scan. Spatial radiomic maps were used to identify tumour areas with similar or distinct radiomic patterns, and tumour habitats were identified using the Gaussian mixture modelling. CT images with superimposed habitat maps were co-registered with US images by means of a landmark-based rigid registration method for US-guided targeted biopsies. The dice similarity coefficient (DSC) was used to assess the tumour-specific CT/US fusion accuracy. RESULTS We successfully co-registered CT-based radiomic tumour habitats with US images in all patients. The median time between CT scan and biopsy was 21 days (range 7-30 days). The median DSC for tumour-specific CT/US fusion accuracy was 0.53 (range 0.79 to 0.37). The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76-0.79) while it was lower for the smaller omental metastases (DSC: 0.37-0.53). CONCLUSION We developed a precision tissue sampling technique that uses radiomic habitats to guide in vivo biopsies using CT/US fusion and that can be seamlessly integrated in the clinical routine for patients with HGSOC. KEY POINTS • We developed a prevision tissue sampling technique that co-registers CT-based radiomics-based tumour habitats with US images. • The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76-0.79) while it was lower for the smaller omental metastases (DSC: 0.37-0.53).
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Affiliation(s)
- Lucian Beer
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Paula Martin-Gonzalez
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Maria Delgado-Ortet
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Marika Reinius
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, 1090, Vienna, Austria
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Lorena Escudero
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Hilal Sahin
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Ionut-Gabriel Funingana
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Joo-Ern Ang
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | | | | | | | | | - Nghia Q Nguyen
- Information Engineering Division, Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK
| | - Florian Markowetz
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - James D Brenton
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Helen Addley
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK.
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK.
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18
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Pinto Dos Santos D, Dietzel M, Baessler B. A decade of radiomics research: are images really data or just patterns in the noise? Eur Radiol 2021; 31:1-4. [PMID: 32767103 PMCID: PMC7755615 DOI: 10.1007/s00330-020-07108-w] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 07/07/2020] [Accepted: 07/23/2020] [Indexed: 12/24/2022]
Abstract
KEY POINTS • Although radiomics is potentially a promising approach to analyze medical image data, many pitfalls need to be considered to avoid a reproducibility crisis.• There is a translation gap in radiomics research, with many studies being published but so far little to no translation into clinical practice.• Going forward, more studies with higher levels of evidence are needed, ideally also focusing on prospective studies with relevant clinical impact.
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Affiliation(s)
- Daniel Pinto Dos Santos
- Institute of Diagnostic and Interventional Radiology, University of Cologne, Medical Faculty and University Hospital Cologne, Kerpener Str. 62, 50937, Köln, Germany.
| | - Matthias Dietzel
- Institute of Radiology, University Hospital Erlangen, Maximiliansplatz 3, Erlangen, 91054, Germany
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
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19
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Hain E, Barat M, Da Costa C, Dautry R, Baillard C, Bonnet S, Dousset B, Soyer P, Dohan A, Fuks D, Gaujoux S. Preoperative assessment of patient comorbidities before left colectomy: Comparison between ASA performance status scale and a new computed tomography physical status score. Diagn Interv Imaging 2020; 102:313-319. [PMID: 33257202 DOI: 10.1016/j.diii.2020.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 11/02/2020] [Accepted: 11/03/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE To compare a newly developed preoperative computed tomography physical status (CT-PS) score with the American Society of Anesthesiology performance status (ASA-PS) scale in the assessment of patient preoperative health status and stratification of perioperative risk before left colectomy. MATERIALS AND METHODS Preoperative chest-abdomen-pelvis CT examinations of patients who were scheduled to undergo elective laparoscopic left colonic resection for cancer in two centers were reviewed by two radiologists blinded to clinical data for the presence of several key imaging features in order to assess general, cardiac, pulmonary, abdominal, renal, vascular and musculoskeletal status. CT examinations of patients from center 1 were used to build a CT-PS score to predict ASA-PS≥III. CT-PS score was further validated using an external cohort of patients from center 2. RESULTS During a 2-year period, 117 consecutive patients (63 men, 54 women; mean age, 65±13 [SD] years; age range: 53-90 years) who underwent laparoscopic left colectomy for cancer in center 1 (66 patients, building cohort) and center 2 (51 patients, validation cohort) were retrospectively included. Ninety-one percent of patients were ASA-PS 1-2. Overall postoperative morbidity was 23% and severe morbidity 12%. The area under the receiver operating characteristic curve of CT-PS score was 0.968 (95% CI: 0.901-1.000) in the building cohort and 0.828 (95% CI: 0.693-0.963) in the validation cohort. The optimal thresholds yielded 87% (95% CI: 83-91%) sensitivity and 100% (95% CI: 91-100%) specificity in the building cohort and 75% (95% CI: 69-81%) sensitivity and 83% (95% CI: 77-88%) specificity in the validation cohort for the prediction of ASA-PS. CONCLUSION Preoperative chest-abdomen-pelvis CT thoroughly and wisely read is highly accurate to differentiate patients with ASA-PS I/II from those with ASA-PS III/IV before left colectomy.
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Affiliation(s)
- Elisabeth Hain
- Department of Digestive, Hepato-biliary and Endocrine Surgery, Cochin Hospital, Assistance Publique-Hôpitaux de Paris, 75014 Paris, France; Université de Paris, Descartes-Paris 5, 75006 Paris, France
| | - Maxime Barat
- Université de Paris, Descartes-Paris 5, 75006 Paris, France; Department of Radiology, Cochin hospital, Assistance Publique-Hôpitaux de Paris, 75014 Paris, France; Inserm U1016, CNRS UMR 8104, Institut Cochin, 75014 Paris, France.
| | - Carla Da Costa
- Department of Radiology, Cochin hospital, Assistance Publique-Hôpitaux de Paris, 75014 Paris, France
| | - Raphael Dautry
- Department of Radiology, Cochin hospital, Assistance Publique-Hôpitaux de Paris, 75014 Paris, France
| | - Christophe Baillard
- Université de Paris, Descartes-Paris 5, 75006 Paris, France; Department of Anesthesiology, Cochin Hospital, Assistance Publique-Hôpitaux de Paris, 75014 Paris, France
| | - Stéphane Bonnet
- Université de Paris, Descartes-Paris 5, 75006 Paris, France; Department of Digestive, Oncologic and Metabolic Surgery, Institut Mutualiste Monsouris, 75013 Paris, France
| | - Bertrand Dousset
- Department of Digestive, Hepato-biliary and Endocrine Surgery, Cochin Hospital, Assistance Publique-Hôpitaux de Paris, 75014 Paris, France; Université de Paris, Descartes-Paris 5, 75006 Paris, France
| | - Philippe Soyer
- Université de Paris, Descartes-Paris 5, 75006 Paris, France; Department of Radiology, Cochin hospital, Assistance Publique-Hôpitaux de Paris, 75014 Paris, France
| | - Anthony Dohan
- Université de Paris, Descartes-Paris 5, 75006 Paris, France; Department of Radiology, Cochin hospital, Assistance Publique-Hôpitaux de Paris, 75014 Paris, France
| | - David Fuks
- Université de Paris, Descartes-Paris 5, 75006 Paris, France; Department of Digestive, Oncologic and Metabolic Surgery, Institut Mutualiste Monsouris, 75013 Paris, France
| | - Sébastien Gaujoux
- Department of Digestive, Hepato-biliary and Endocrine Surgery, Cochin Hospital, Assistance Publique-Hôpitaux de Paris, 75014 Paris, France; Université de Paris, Descartes-Paris 5, 75006 Paris, France
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20
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Dana J, Agnus V, Ouhmich F, Gallix B. Multimodality Imaging and Artificial Intelligence for Tumor Characterization: Current Status and Future Perspective. Semin Nucl Med 2020; 50:541-548. [PMID: 33059823 DOI: 10.1053/j.semnuclmed.2020.07.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Research in medical imaging has yet to do to achieve precision oncology. Over the past 30 years, only the simplest imaging biomarkers (RECIST, SUV,…) have become widespread clinical tools. This may be due to our inability to accurately characterize tumors and monitor intratumoral changes in imaging. Artificial intelligence, through machine learning and deep learning, opens a new path in medical research because it can bring together a large amount of heterogeneous data into the same analysis to reach a single outcome. Supervised or unsupervised learning may lead to new paradigms by identifying unrevealed structural patterns across data. Deep learning will provide human-free, undefined upstream, reproducible, and automated quantitative imaging biomarkers. Since tumor phenotype is driven by its genotype and thus indirectly defines tumoral progression, tumor characterization using machine learning and deep learning algorithms will allow us to monitor molecular expression noninvasively, anticipate therapeutic failure, and lead therapeutic management. To follow this path, quality standards have to be set: standardization of imaging acquisition as it has been done in the field of biology, transparency of the model development as it should be reproducible by different institutions, validation, and testing through a high-quality process using large and complex open databases and better interpretability of these algorithms.
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Affiliation(s)
- Jérémy Dana
- IHU of Strasbourg, Strasbourg, France; Inserm & University of Strasbourg UMR-S1110, Strasbourg, France; Faculty of Medicine, University of Paris, Paris, France
| | - Vincent Agnus
- IHU of Strasbourg, Strasbourg, France; Icube Laboratory, University of Strasbourg, Strasbourg, France
| | - Farid Ouhmich
- IHU of Strasbourg, Strasbourg, France; Icube Laboratory, University of Strasbourg, Strasbourg, France
| | - Benoit Gallix
- IHU of Strasbourg, Strasbourg, France; Icube Laboratory, University of Strasbourg, Strasbourg, France; Faculty of Medicine, University of Strasbourg, Strasbourg, France; Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
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21
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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: 38] [Impact Index Per Article: 9.5] [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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22
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Shukla M, Forghani R, Agarwal M. Patient-Centric Head and Neck Cancer Radiation Therapy: Role of Advanced Imaging. Neuroimaging Clin N Am 2020; 30:341-357. [PMID: 32600635 DOI: 10.1016/j.nic.2020.04.005] [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] [Indexed: 12/24/2022]
Abstract
The traditional 'one-size-fits-all' approach to H&N cancer therapy is archaic. Advanced imaging can identify radioresistant areas by using biomarkers that detect tumor hypoxia, hypercellularity etc. Highly conformal radiotherapy can target resistant areas with precision. The critical information that can be gleaned about tumor biology from these advanced imaging modalities facilitates individualized radiotherapy. The tumor imaging world is pushing its boundaries. Molecular imaging can now detect protein expression and genotypic variations across tumors that can be exploited for tailoring treatment. The exploding field of radiomics and radiogenomics extracts quantitative, biologic and genetic information and further expands the scope of personalized therapy.
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Affiliation(s)
- Monica Shukla
- Department of Radiation Oncology, Froedtert and Medical College of Wisconsin, 9200 W. Wisconsin Avenue, Milwaukee, WI 53226, USA
| | - Reza Forghani
- Augmented Intelligence & Precision Health Laboratory, Department of Radiology, Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada
| | - Mohit Agarwal
- Department of Radiology, Section of Neuroradiology, Froedtert and Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA.
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23
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Chen BB. Artificial intelligence in pancreatic disease. Artif Intell Med Imaging 2020; 1:19-30. [DOI: 10.35711/aimi.v1.i1.19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 06/18/2020] [Accepted: 06/19/2020] [Indexed: 02/06/2023] Open
Abstract
In recent years, the application of artificial intelligence (AI) in radiology has been growing rapidly, fueled by the availability of large datasets, advances in computing power, and newly developed algorithms. Progress in AI applied to medical imaging analyses has transformed these images into quantitative data, termed radiomics. When combined with patients’ clinical data, these models, when developed by machine learning, have the potential to improve diagnostic, prognostic, and predictive accuracy. Currently, limited literature is available on the use of radiomics for pancreatic disease. Here, we will review recent studies in the application of AI in a variety of pancreatic diseases, mainly involving lesion detection, tumor characterization, tumor grading, response, and prognosis evaluation. Finally, we will also discuss the challenges and prospects in the field of radiomics for pancreatic disease.
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Affiliation(s)
- Bang-Bin Chen
- Department of Medical Imaging, National Taiwan University Hospital, Taipei 10016, Taiwan
- Department of Radiology, College of Medicine, National Taiwan University, Taipei 10016, Taiwan
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24
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Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective? Eur Radiol 2020; 30:5510-5524. [PMID: 32377810 PMCID: PMC7476980 DOI: 10.1007/s00330-020-06874-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 03/18/2020] [Accepted: 04/08/2020] [Indexed: 12/18/2022]
Abstract
Digitization of medicine requires systematic handling of the increasing amount of health data to improve medical diagnosis. In this context, the integration of the versatile diagnostic information, e.g., from anamnesis, imaging, histopathology, and clinical chemistry, and its comprehensive analysis by artificial intelligence (AI)–based tools is expected to improve diagnostic precision and the therapeutic conduct. However, the complex medical environment poses a major obstacle to the translation of integrated diagnostics into clinical research and routine. There is a high need to address aspects like data privacy, data integration, interoperability standards, appropriate IT infrastructure, and education of staff. Besides this, a plethora of technical, political, and ethical challenges exists. This is complicated by the high diversity of approaches across Europe. Thus, we here provide insights into current international activities on the way to digital comprehensive diagnostics. This includes a technical view on challenges and solutions for comprehensive diagnostics in terms of data integration and analysis. Current data communications standards and common IT solutions that are in place in hospitals are reported. Furthermore, the international hospital digitalization scoring and the European funding situation were analyzed. In addition, the regional activities in radiomics and the related publication trends are discussed. Our findings show that prerequisites for comprehensive diagnostics have not yet been sufficiently established throughout Europe. The manifold activities are characterized by a heterogeneous digitization progress and they are driven by national efforts. This emphasizes the importance of clear governance, concerted investments, and cooperation at various levels in the health systems. Key Points • Europe is characterized by heterogeneity in its digitization progress with predominantly national efforts. Infrastructural prerequisites for comprehensive diagnostics are not given and not sufficiently funded throughout Europe, which is particularly true for data integration. • The clinical establishment of comprehensive diagnostics demands for a clear governance, significant investments, and cooperation at various levels in the healthcare systems. • While comprehensive diagnostics is on its way, concerted efforts should be taken in Europe to get consensus concerning interoperability and standards, security, and privacy as well as ethical and legal concerns.
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25
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Shen L, Li Y, Li N, Zhao Y, Zhou Q, Li Z. Clinical utility of contrast-enhanced ultrasonography in the diagnosis of benign and malignant small renal masses among Asian population. Cancer Med 2019; 8:7532-7541. [PMID: 31642189 PMCID: PMC6912038 DOI: 10.1002/cam4.2635] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 10/09/2019] [Accepted: 10/10/2019] [Indexed: 12/29/2022] Open
Abstract
We assessed the clinical utility of contrast-enhanced ultrasonography (CEUS) in the diagnosis of benign and malignant small renal masses using a meta-analysis of diagnostic test. We performed a comprehensive online search in the following database including PubMed, Embase, Web of Science, Wanfang, and Chinese National Knowledge Infrastructure from the inception to August 25, 2019. the following index were calculated for assessing the diagnostic ability, including sensitivity, specificity, diagnostic odds ratio (DOR), negative likelihood ratio (NLR), positive likelihood ratio (PLR), area under the curve (AUC) with 95% confidence intervals (CIs). Seventeen studies were included in the qualitative and quantitative analyses. The overall sensitivity was 0.93 with 95% CI of 0.88-0.95. The specificity was 0.71 and the 95% CI was 0.60-0.80. The pooled AUC was 0.91 (95% CI: 0.88-0.93). The diagnostic odds ratio was 31 (95% CI: 21-45). The NLR and PLR were 0.10 (95% CI: 0.07-0.15) and 3.2(95% CI: 2.3-4.4), respectively. There is a slight heterogeneity within studies. The subgroup analysis was also performed. For retrospective and perspective, the sensitivity and specificity were 0.93, 0.92 and 0.71, 0.73; For different diameter lesions, the sensitivity and specificity were 0.93, 0.94 and 0.64, 0.74; For sample size (≤median vs. >median), the sensitivity and specificity were 0.94, 0.93 and 0.67, 0.77. The Deek's funnel plot asymmetry test in indicated no publication bias. The CEUS have a high diagnostic ability in differentiating benign and malignant small renal masses among Asian population.
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Affiliation(s)
- Lin Shen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yanyan Li
- Department of Nursing, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Na Li
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yajie Zhao
- Department of Nuclear Medicine, Central South University, Changsha, Hunan, China
| | - Qin Zhou
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhanzhan Li
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, China
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26
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Dalal V, Carmicheal J, Dhaliwal A, Jain M, Kaur S, Batra SK. Radiomics in stratification of pancreatic cystic lesions: Machine learning in action. Cancer Lett 2019; 469:228-237. [PMID: 31629933 DOI: 10.1016/j.canlet.2019.10.023] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 10/03/2019] [Accepted: 10/15/2019] [Indexed: 12/15/2022]
Abstract
Pancreatic cystic lesions (PCLs) are well-known precursors of pancreatic cancer. Their diagnosis can be challenging as their behavior varies from benign to malignant disease. Precise and timely management of malignant pancreatic cysts might prevent transformation to pancreatic cancer. However, the current consensus guidelines, which rely on standard imaging features to predict cyst malignancy potential, are conflicting and unclear. This has led to an increased interest in radiomics, a high-throughput extraction of comprehensible data from standard of care images. Radiomics can be used as a diagnostic and prognostic tool in personalized medicine. It utilizes quantitative image analysis to extract features in conjunction with machine learning and artificial intelligence (AI) methods like support vector machines, random forest, and convolutional neural network for feature selection and classification. Selected features can then serve as imaging biomarkers to predict high-risk PCLs. Radiomics studies conducted heretofore on PCLs have shown promising results. This cost-effective approach would help us to differentiate benign PCLs from malignant ones and potentially guide clinical decision-making leading to better utilization of healthcare resources. In this review, we discuss the process of radiomics, its myriad applications such as diagnosis, prognosis, and prediction of therapy response. We also discuss the outcomes of studies involving radiomic analysis of PCLs and pancreatic cancer, and challenges associated with this novel field along with possible solutions. Although these studies highlight the potential benefit of radiomics in the prevention and optimal treatment of pancreatic cancer, further studies are warranted before incorporating radiomics into the clinical decision support system.
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Affiliation(s)
- Vipin Dalal
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Joseph Carmicheal
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Amaninder Dhaliwal
- Department of Gastroenterology and Hepatology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Maneesh Jain
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA; Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA; The Fred and Pamela Buffet Cancer Center, University of Nebraska Medical Center, Omaha, NE, USA
| | - Sukhwinder Kaur
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Surinder K Batra
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA; Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA; The Fred and Pamela Buffet Cancer Center, University of Nebraska Medical Center, Omaha, NE, USA.
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27
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Abstract
Radiomics is an emerging field which extracts quantitative radiology data from medical images and explores their correlation with clinical outcomes in a non-invasive manner. This review aims to assess whether radiomics is a useful and reproducible method for clinical management of hepatocellular carcinoma (HCC) by reviewing the strengths and weaknesses of current radiomics literature pertaining specifically to HCC. From an initial set of 48 articles recovered through database searches, 23 articles were retained to be included in this review after full screening. Among these 23 studies, 7 used a radiomics approach in magnetic resonance imaging (MRI). Only two studies applied radiomics to positron emission tomography-computed tomography (PET-CT). In the remaining 14 articles, a radiomics analysis was performed on computed tomography (CT). Eight studies dealt with the relationship between biological signatures and imaging findings, and can be classified as radiogenomic studies. For each study included in our review, we computed a Radiomics Quality Score (RQS) as proposed by Lambin et al. We found that the RQS (mean ± standard deviation) was 8.35 ± 5.38 (out of a possible maximum value of 36). Although these scores are fairly low, and radiomics has not yet reached clinical utility in HCC, it is important to underscore the fact that these early studies pave the way for the radiomics field with a focus on HCC. Radiomics is still a very young field, and is far from being mature, but it remains a very promising technology for the future for developing adequate personalized treatment as a non-invasive approach, for complementing or replacing tumor biopsies, as well as for developing novel prognostic biomarkers in HCC patients.
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