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Alyami AS, Madkhali Y, Majrashi NA, Alwadani B, Elbashir M, Ali S, Ageeli W, El-Bahkiry HS, Althobity AA, Refaee T. The role of molecular imaging in detecting fibrosis in Crohn's disease. Ann Med 2024; 56:2313676. [PMID: 38346385 PMCID: PMC10863520 DOI: 10.1080/07853890.2024.2313676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
Fibrosis is a pathological process that occurs due to chronic inflammation, leading to the proliferation of fibroblasts and the excessive deposition of extracellular matrix (ECM). The process of long-term fibrosis initiates with tissue hypofunction and progressively culminates in the ultimate manifestation of organ failure. Intestinal fibrosis is a significant complication of Crohn's disease (CD) that can result in persistent luminal narrowing and strictures, which are difficult to reverse. In recent years, there have been significant advances in our understanding of the cellular and molecular mechanisms underlying intestinal fibrosis in inflammatory bowel disease (IBD). Significant progress has been achieved in the fields of pathogenesis, diagnosis, and management of intestinal fibrosis in the last few years. A significant amount of research has also been conducted in the field of biomarkers for the prediction or detection of intestinal fibrosis, including novel cross-sectional imaging modalities such as positron emission tomography (PET) and single photon emission computed tomography (SPECT). Molecular imaging represents a promising biomedical approach that enables the non-invasive visualization of cellular and subcellular processes. Molecular imaging has the potential to be employed for early detection, disease staging, and prognostication in addition to assessing disease activity and treatment response in IBD. Molecular imaging methods also have a potential role to enabling minimally invasive assessment of intestinal fibrosis. This review discusses the role of molecular imaging in combination of AI in detecting CD fibrosis.
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Affiliation(s)
- Ali S. Alyami
- Department of Diagnostic Radiography Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Yahia Madkhali
- Department of Diagnostic Radiography Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Naif A. Majrashi
- Department of Diagnostic Radiography Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Bandar Alwadani
- Department of Diagnostic Radiography Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Meaad Elbashir
- Department of Diagnostic Radiography Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Sarra Ali
- Department of Diagnostic Radiography Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Wael Ageeli
- Department of Diagnostic Radiography Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Hesham S. El-Bahkiry
- Department of Diagnostic Radiography Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Abdullah A. Althobity
- Department of Radiological Sciences and Medical Imaging, College of Applied Medical Sciences, Majmaah University, Majmaah, Saudi Arabia
| | - Turkey Refaee
- Department of Diagnostic Radiography Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
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Ibrahim A, Vaidyanathan A, Primakov S, Belmans F, Bottari F, Refaee T, Lovinfosse P, Jadoul A, Derwael C, Hertel F, Woodruff HC, Zacho HD, Walsh S, Vos W, Occhipinti M, Hanin FX, Lambin P, Mottaghy FM, Hustinx R. Deep learning based identification of bone scintigraphies containing metastatic bone disease foci. Cancer Imaging 2023; 23:12. [PMID: 36698217 PMCID: PMC9875407 DOI: 10.1186/s40644-023-00524-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 01/05/2023] [Indexed: 01/26/2023] Open
Abstract
PURPOSE Metastatic bone disease (MBD) is the most common form of metastases, most frequently deriving from prostate cancer. MBD is screened with bone scintigraphy (BS), which have high sensitivity but low specificity for the diagnosis of MBD, often requiring further investigations. Deep learning (DL) - a machine learning technique designed to mimic human neuronal interactions- has shown promise in the field of medical imaging analysis for different purposes, including segmentation and classification of lesions. In this study, we aim to develop a DL algorithm that can classify areas of increased uptake on bone scintigraphy scans. METHODS We collected 2365 BS from three European medical centres. The model was trained and validated on 1203 and 164 BS scans respectively. Furthermore we evaluated its performance on an external testing set composed of 998 BS scans. We further aimed to enhance the explainability of our developed algorithm, using activation maps. We compared the performance of our algorithm to that of 6 nuclear medicine physicians. RESULTS The developed DL based algorithm is able to detect MBD on BSs, with high specificity and sensitivity (0.80 and 0.82 respectively on the external test set), in a shorter time compared to the nuclear medicine physicians (2.5 min for AI and 30 min for nuclear medicine physicians to classify 134 BSs). Further prospective validation is required before the algorithm can be used in the clinic.
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Affiliation(s)
- Abdalla Ibrahim
- grid.5012.60000 0001 0481 6099The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ,grid.239585.00000 0001 2285 2675Department of Radiology and Nuclear Medicine, Columbia University Irving Medical Center, New York, United States ,grid.411374.40000 0000 8607 6858Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liege, Liege, Belgium ,grid.412301.50000 0000 8653 1507Department of Nuclear Medicine and Comprehensive diagnostic centre Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - Akshayaa Vaidyanathan
- grid.5012.60000 0001 0481 6099The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ,Radiomics (Oncoradiomics SA), Liege, Belgium
| | - Sergey Primakov
- grid.5012.60000 0001 0481 6099The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ,grid.239585.00000 0001 2285 2675Department of Radiology and Nuclear Medicine, Columbia University Irving Medical Center, New York, United States
| | | | | | - Turkey Refaee
- grid.5012.60000 0001 0481 6099The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ,grid.411831.e0000 0004 0398 1027Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Pierre Lovinfosse
- grid.411374.40000 0000 8607 6858Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liege, Liege, Belgium
| | - Alexandre Jadoul
- grid.411374.40000 0000 8607 6858Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liege, Liege, Belgium
| | - Celine Derwael
- grid.411374.40000 0000 8607 6858Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liege, Liege, Belgium
| | - Fabian Hertel
- grid.412301.50000 0000 8653 1507Department of Nuclear Medicine and Comprehensive diagnostic centre Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - Henry C. Woodruff
- grid.5012.60000 0001 0481 6099The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ,grid.239585.00000 0001 2285 2675Department of Radiology and Nuclear Medicine, Columbia University Irving Medical Center, New York, United States
| | - Helle D. Zacho
- grid.27530.330000 0004 0646 7349Department of Nuclear Medicine, Clinical Cancer Research Centre, Aalborg University Hospital, Aalborg, Denmark ,grid.5117.20000 0001 0742 471XDepartment of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liege, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liege, Belgium
| | | | - François-Xavier Hanin
- grid.7942.80000 0001 2294 713XDepartment of Nuclear Medicine, Universite´CatholiqueUniversite´Catholique de Louvain, CHU-UCL-Namur, Ottignies-Louvain-la-Neuve, Belgium
| | - Philippe Lambin
- grid.5012.60000 0001 0481 6099The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ,grid.239585.00000 0001 2285 2675Department of Radiology and Nuclear Medicine, Columbia University Irving Medical Center, New York, United States
| | - Felix M. Mottaghy
- grid.239585.00000 0001 2285 2675Department of Radiology and Nuclear Medicine, Columbia University Irving Medical Center, New York, United States ,grid.412301.50000 0000 8653 1507Department of Nuclear Medicine and Comprehensive diagnostic centre Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - Roland Hustinx
- grid.411374.40000 0000 8607 6858Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liege, Liege, Belgium
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Refaee T, Salahuddin Z, Frix AN, Yan C, Wu G, Woodruff HC, Gietema H, Meunier P, Louis R, Guiot J, Lambin P. Diagnosis of Idiopathic Pulmonary Fibrosis in High-Resolution Computed Tomography Scans Using a Combination of Handcrafted Radiomics and Deep Learning. Front Med (Lausanne) 2022; 9:915243. [PMID: 35814761 PMCID: PMC9259876 DOI: 10.3389/fmed.2022.915243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 06/07/2022] [Indexed: 11/25/2022] Open
Abstract
Purpose To develop handcrafted radiomics (HCR) and deep learning (DL) based automated diagnostic tools that can differentiate between idiopathic pulmonary fibrosis (IPF) and non-IPF interstitial lung diseases (ILDs) in patients using high-resolution computed tomography (HRCT) scans. Material and Methods In this retrospective study, 474 HRCT scans were included (mean age, 64.10 years ± 9.57 [SD]). Five-fold cross-validation was performed on 365 HRCT scans. Furthermore, an external dataset comprising 109 patients was used as a test set. An HCR model, a DL model, and an ensemble of HCR and DL model were developed. A virtual in-silico trial was conducted with two radiologists and one pulmonologist on the same external test set for performance comparison. The performance was compared using DeLong method and McNemar test. Shapley Additive exPlanations (SHAP) plots and Grad-CAM heatmaps were used for the post-hoc interpretability of HCR and DL models, respectively. Results In five-fold cross-validation, the HCR model, DL model, and the ensemble of HCR and DL models achieved accuracies of 76.2 ± 6.8, 77.9 ± 4.6, and 85.2 ± 2.7%, respectively. For the diagnosis of IPF and non-IPF ILDs on the external test set, the HCR, DL, and the ensemble of HCR and DL models achieved accuracies of 76.1, 77.9, and 85.3%, respectively. The ensemble model outperformed the diagnostic performance of clinicians who achieved a mean accuracy of 66.3 ± 6.7% (p < 0.05) during the in-silico trial. The area under the receiver operating characteristic curve (AUC) for the ensemble model on the test set was 0.917 which was significantly higher than the HCR model (0.817, p = 0.02) and the DL model (0.823, p = 0.005). The agreement between HCR and DL models was 61.4%, and the accuracy and specificity for the predictions when both the models agree were 93 and 97%, respectively. SHAP analysis showed the texture features as the most important features for IPF diagnosis and Grad-CAM showed that the model focused on the clinically relevant part of the image. Conclusion Deep learning and HCR models can complement each other and serve as useful clinical aids for the diagnosis of IPF and non-IPF ILDs.
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Affiliation(s)
- Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
- Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
- *Correspondence: Turkey Refaee,
| | - Zohaib Salahuddin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
| | - Anne-Noelle Frix
- Department of Respiratory Medicine, University Hospital of Liège, Liège, Belgium
| | - Chenggong Yan
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Guangyao Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
- Department of Radiology and Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Hester Gietema
- Department of Radiology and Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Paul Meunier
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Renaud Louis
- Department of Respiratory Medicine, University Hospital of Liège, Liège, Belgium
| | - Julien Guiot
- Department of Respiratory Medicine, University Hospital of Liège, Liège, Belgium
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
- Department of Radiology and Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Center, Maastricht, Netherlands
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Ibrahim A, Barufaldi B, Refaee T, Silva Filho TM, Acciavatti RJ, Salahuddin Z, Hustinx R, Mottaghy FM, Maidment ADA, Lambin P. MaasPenn Radiomics Reproducibility Score: A Novel Quantitative Measure for Evaluating the Reproducibility of CT-Based Handcrafted Radiomic Features. Cancers (Basel) 2022; 14:cancers14071599. [PMID: 35406372 PMCID: PMC8997100 DOI: 10.3390/cancers14071599] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/16/2022] [Accepted: 03/18/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary The reproducibility of handcrafted radiomic features (HRFs) has been reported to be affected by variations in imaging acquisition and reconstruction parameters. However, to date, these effects have not been understood or quantified. In this study, we analyzed a significantly large number of scenarios in an effort to quantify the effects of variations on the reproducibility of HRFs. In addition, we assessed the performance of ComBat harmonization in each of the 31,375 investigated scenarios. We developed a novel score that can be considered the first attempt to objectively assess the number of reproducible HRFs in different scenario. Following further validation, the score could be used to decide on the inclusion of data acquired differently, as well as the assessment of the generalizability of developed radiomic signatures. Abstract The reproducibility of handcrafted radiomic features (HRFs) has been reported to be affected by variations in imaging parameters, which significantly affect the generalizability of developed signatures and translation to clinical practice. However, the collective effect of the variations in imaging parameters on the reproducibility of HRFs remains unclear, with no objective measure to assess it in the absence of reproducibility analysis. We assessed these effects of variations in a large number of scenarios and developed the first quantitative score to assess the reproducibility of CT-based HRFs without the need for phantom or reproducibility studies. We further assessed the potential of image resampling and ComBat harmonization for removing these effects. Our findings suggest a need for radiomics-specific harmonization methods. Our developed score should be considered as a first attempt to introduce comprehensive metrics to quantify the reproducibility of CT-based handcrafted radiomic features. More research is warranted to demonstrate its validity in clinical contexts and to further improve it, possibly by the incorporation of more realistic situations, which better reflect real patients’ situations.
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Affiliation(s)
- Abdalla Ibrahim
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands; (T.R.); (Z.S.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands;
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU de Liege, CRC In Vivo Imaging, University of Liège, 4000 Liege, Belgium;
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
- Correspondence:
| | - Bruno Barufaldi
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (B.B.); (R.J.A.); (A.D.A.M.)
| | - Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands; (T.R.); (Z.S.); (P.L.)
- Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia
| | - Telmo M. Silva Filho
- Department of Statistics, Federal University of Paraíba, João Pessoa 58051-900, Brazil;
| | - Raymond J. Acciavatti
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (B.B.); (R.J.A.); (A.D.A.M.)
| | - Zohaib Salahuddin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands; (T.R.); (Z.S.); (P.L.)
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, CHU de Liege, CRC In Vivo Imaging, University of Liège, 4000 Liege, Belgium;
| | - Felix M. Mottaghy
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands;
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
| | - Andrew D. A. Maidment
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (B.B.); (R.J.A.); (A.D.A.M.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands; (T.R.); (Z.S.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands;
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Refaee T, Bondue B, Van Simaeys G, Wu G, Yan C, Woodruff HC, Goldman S, Lambin P. A Handcrafted Radiomics-Based Model for the Diagnosis of Usual Interstitial Pneumonia in Patients with Idiopathic Pulmonary Fibrosis. J Pers Med 2022; 12:jpm12030373. [PMID: 35330373 PMCID: PMC8948773 DOI: 10.3390/jpm12030373] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 02/23/2022] [Accepted: 02/26/2022] [Indexed: 02/05/2023] Open
Abstract
The most common idiopathic interstitial lung disease (ILD) is idiopathic pulmonary fibrosis (IPF). It can be identified by the presence of usual interstitial pneumonia (UIP) via high-resolution computed tomography (HRCT) or with the use of a lung biopsy. We hypothesized that a CT-based approach using handcrafted radiomics might be able to identify IPF patients with a radiological or histological UIP pattern from those with an ILD or normal lungs. A total of 328 patients from one center and two databases participated in this study. Each participant had their lungs automatically contoured and sectorized. The best radiomic features were selected for the random forest classifier and performance was assessed using the area under the receiver operator characteristics curve (AUC). A significant difference in the volume of the trachea was seen between a normal state, IPF, and non-IPF ILD. Between normal and fibrotic lungs, the AUC of the classification model was 1.0 in validation. When classifying between IPF with a typical HRCT UIP pattern and non-IPF ILD the AUC was 0.96 in validation. When classifying between IPF with UIP (radiological or biopsy-proved) and non-IPF ILD, an AUC of 0.66 was achieved in the testing dataset. Classification between normal, IPF/UIP, and other ILDs using radiomics could help discriminate between different types of ILDs via HRCT, which are hardly recognizable with visual assessments. Radiomic features could become a valuable tool for computer-aided decision-making in imaging, and reduce the need for unnecessary biopsies.
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Affiliation(s)
- Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (T.R.); (C.Y.); (H.C.W.)
- Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia
| | - Benjamin Bondue
- Department of Pneumology, Erasme University Hospital, Université libre de Bruxelles, 1070 Brussels, Belgium;
| | - Gaetan Van Simaeys
- Department of Nuclear Medicine, Erasme University Hospital, Université libre de Bruxelles, 1070 Brussels, Belgium; (G.V.S.); (S.G.)
| | - Guangyao Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430074, China;
| | - Chenggong Yan
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (T.R.); (C.Y.); (H.C.W.)
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (T.R.); (C.Y.); (H.C.W.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 MD Maastricht, The Netherlands
| | - Serge Goldman
- Department of Nuclear Medicine, Erasme University Hospital, Université libre de Bruxelles, 1070 Brussels, Belgium; (G.V.S.); (S.G.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (T.R.); (C.Y.); (H.C.W.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 MD Maastricht, The Netherlands
- Correspondence:
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Ibrahim A, Widaatalla Y, Refaee T, Primakov S, Miclea RL, Öcal O, Fabritius MP, Ingrisch M, Ricke J, Hustinx R, Mottaghy FM, Woodruff HC, Seidensticker M, Lambin P. Reproducibility of CT-Based Hepatocellular Carcinoma Radiomic Features across Different Contrast Imaging Phases: A Proof of Concept on SORAMIC Trial Data. Cancers (Basel) 2021; 13:cancers13184638. [PMID: 34572870 PMCID: PMC8468150 DOI: 10.3390/cancers13184638] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/11/2021] [Accepted: 09/13/2021] [Indexed: 12/17/2022] Open
Abstract
Simple Summary Radiomics has been reported to have potential for correlating with clinical outcomes. However, handcrafted radiomic features (HRFs)—the quantitative features extracted from medical images—are limited by their sensitivity to variations in scanning parameters. Furthermore, radiomics analyses require big data with good quality to achieve desirable performances. In this study, we investigated the reproducibility of HRFs between scans acquired with the same scanning parameters except for the imaging phase (arterial and portal venous phases) to assess the possibilities of merging scans from different phases or replacing missing scans from a phase with other phases to increase data entries. Additionally, we assessed the potential of ComBat harmonization to remove batch effects attributed to this variation. Our results show that the majority of HRFs were not reproducible between the arterial and portal venous phases before or after ComBat harmonization. We provide a guide for analyzing scans of different imaging phases. Abstract Handcrafted radiomic features (HRFs) are quantitative imaging features extracted from regions of interest on medical images which can be correlated with clinical outcomes and biologic characteristics. While HRFs have been used to train predictive and prognostic models, their reproducibility has been reported to be affected by variations in scan acquisition and reconstruction parameters, even within the same imaging vendor. In this work, we evaluated the reproducibility of HRFs across the arterial and portal venous phases of contrast-enhanced computed tomography images depicting hepatocellular carcinomas, as well as the potential of ComBat harmonization to correct for this difference. ComBat harmonization is a method based on Bayesian estimates that was developed for gene expression arrays, and has been investigated as a potential method for harmonizing HRFs. Our results show that the majority of HRFs are not reproducible between the arterial and portal venous imaging phases, yet a number of HRFs could be used interchangeably between those phases. Furthermore, ComBat harmonization increased the number of reproducible HRFs across both phases by 1%. Our results guide the pooling of arterial and venous phases from different patients in an effort to increase cohort size, as well as joint analysis of the phases.
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Affiliation(s)
- Abdalla Ibrahim
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (S.P.); (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 MD Maastricht, The Netherlands; (R.L.M.); (F.M.M.)
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liege and GIGA CRC-In Vivo Imaging, University of Liege, 4000 Liege, Belgium;
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
- Correspondence: (A.I.); (T.R.)
| | - Yousif Widaatalla
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (S.P.); (H.C.W.); (P.L.)
| | - Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (S.P.); (H.C.W.); (P.L.)
- Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia
- Correspondence: (A.I.); (T.R.)
| | - Sergey Primakov
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (S.P.); (H.C.W.); (P.L.)
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
| | - Razvan L. Miclea
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 MD Maastricht, The Netherlands; (R.L.M.); (F.M.M.)
| | - Osman Öcal
- Department of Radiology, University Hospital, LMU Munich, 80336 Munich, Germany; (O.Ö.); (M.P.F.); (M.I.); (J.R.); (M.S.)
| | - Matthias P. Fabritius
- Department of Radiology, University Hospital, LMU Munich, 80336 Munich, Germany; (O.Ö.); (M.P.F.); (M.I.); (J.R.); (M.S.)
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, 80336 Munich, Germany; (O.Ö.); (M.P.F.); (M.I.); (J.R.); (M.S.)
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, 80336 Munich, Germany; (O.Ö.); (M.P.F.); (M.I.); (J.R.); (M.S.)
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liege and GIGA CRC-In Vivo Imaging, University of Liege, 4000 Liege, Belgium;
| | - Felix M. Mottaghy
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 MD Maastricht, The Netherlands; (R.L.M.); (F.M.M.)
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (S.P.); (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 MD Maastricht, The Netherlands; (R.L.M.); (F.M.M.)
| | - Max Seidensticker
- Department of Radiology, University Hospital, LMU Munich, 80336 Munich, Germany; (O.Ö.); (M.P.F.); (M.I.); (J.R.); (M.S.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (S.P.); (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 MD Maastricht, The Netherlands; (R.L.M.); (F.M.M.)
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Ibrahim A, Refaee T, Primakov S, Barufaldi B, Acciavatti RJ, Granzier RWY, Hustinx R, Mottaghy FM, Woodruff HC, Wildberger JE, Lambin P, Maidment ADA. Reply to Orlhac, F.; Buvat, I. Comment on "Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features' Stability with and without ComBat Harmonization. Cancers 2021, 13, 1848". Cancers (Basel) 2021; 13:cancers13123080. [PMID: 34205490 PMCID: PMC8235557 DOI: 10.3390/cancers13123080] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 06/16/2021] [Indexed: 11/25/2022] Open
Affiliation(s)
- Abdalla Ibrahim
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 Maastricht, The Netherlands; (T.R.); (S.P.); (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands; (F.M.M.); (J.E.W.)
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liège and GIGA CRC-In Vivo Imaging, University of Liège, 4000 Liege, Belgium;
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
- Correspondence:
| | - Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 Maastricht, The Netherlands; (T.R.); (S.P.); (H.C.W.); (P.L.)
- Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia
| | - Sergey Primakov
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 Maastricht, The Netherlands; (T.R.); (S.P.); (H.C.W.); (P.L.)
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
| | - Bruno Barufaldi
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (B.B.); (R.J.A.); (A.D.A.M.)
| | - Raymond J. Acciavatti
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (B.B.); (R.J.A.); (A.D.A.M.)
| | - Renée W. Y. Granzier
- Department of Surgery, GROW—School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands;
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liège and GIGA CRC-In Vivo Imaging, University of Liège, 4000 Liege, Belgium;
| | - Felix M. Mottaghy
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands; (F.M.M.); (J.E.W.)
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 Maastricht, The Netherlands; (T.R.); (S.P.); (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands; (F.M.M.); (J.E.W.)
| | - Joachim E. Wildberger
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands; (F.M.M.); (J.E.W.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 Maastricht, The Netherlands; (T.R.); (S.P.); (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands; (F.M.M.); (J.E.W.)
| | - Andrew D. A. Maidment
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (B.B.); (R.J.A.); (A.D.A.M.)
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Ibrahim A, Refaee T, Leijenaar RTH, Primakov S, Hustinx R, Mottaghy FM, Woodruff HC, Maidment ADA, Lambin P. The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset. PLoS One 2021; 16:e0251147. [PMID: 33961646 PMCID: PMC8104396 DOI: 10.1371/journal.pone.0251147] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 04/20/2021] [Indexed: 12/22/2022] Open
Abstract
Radiomics–the high throughput extraction of quantitative features from medical images and their correlation with clinical and biological endpoints- is the subject of active and extensive research. Although the field shows promise, the generalizability of radiomic signatures is affected significantly by differences in scan acquisition and reconstruction settings. Previous studies reported on the sensitivity of radiomic features (RFs) to test-retest variability, inter-observer segmentation variability, and intra-scanner variability. A framework involving robust radiomics analysis and the application of a post-reconstruction feature harmonization method using ComBat was recently proposed to address these challenges. In this study, we investigated the reproducibility of RFs across different scanners and scanning parameters using this framework. We analysed thirteen scans of a ten-layer phantom that were acquired differently. Each layer was subdivided into sixteen regions of interest (ROIs), and the scans were compared in a pairwise manner, resulting in seventy-eight different scenarios. Ninety-one RFs were extracted from each ROI. As hypothesized, we demonstrate that the reproducibility of a given RF is not a constant but is dependent on the heterogeneity found in the data under analysis. The number (%) of reproducible RFs varied across the pairwise scenarios investigated, having a wide range between 8 (8.8%) and 78 (85.7%) RFs. Furthermore, in contrast to what has been previously reported, and as hypothesized in the robust radiomics analysis framework, our results demonstrate that ComBat cannot be applied to all RFs but rather on a percentage of those–the “ComBatable” RFs–which differed depending on the data being harmonized. The number (%) of reproducible RFs following ComBat harmonization varied across the pairwise scenarios investigated, ranging from 14 (15.4%) to 80 (87.9%) RFs, and was found to depend on the heterogeneity in the data. We conclude that the standardization of image acquisition protocols remains the cornerstone for improving the reproducibility of RFs, and the generalizability of the signatures developed. Our proposed approach helps identify the reproducible RFs across different datasets.
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Affiliation(s)
- Abdalla Ibrahim
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liège and GIGA CRC-in vivo imaging, University of Liège, Liege, Belgium
- Department of Nuclear Medicine and Comprehensive Diagnostic Centre Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
- * E-mail:
| | - Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University, Maastricht, The Netherlands
- Faculty of Applied Medical Sciences, Department of Diagnostic Radiology, Jazan University, Jazan, Saudi Arabia
| | | | - Sergey Primakov
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University, Maastricht, The Netherlands
- Department of Nuclear Medicine and Comprehensive Diagnostic Centre Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liège and GIGA CRC-in vivo imaging, University of Liège, Liege, Belgium
| | - Felix M. Mottaghy
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Department of Nuclear Medicine and Comprehensive Diagnostic Centre Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Andrew D. A. Maidment
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
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Ibrahim A, Refaee T, Primakov S, Barufaldi B, Acciavatti RJ, Granzier RWY, Hustinx R, Mottaghy FM, Woodruff HC, Wildberger JE, Lambin P, Maidment ADA. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features' Stability with and without ComBat Harmonization. Cancers (Basel) 2021; 13:cancers13081848. [PMID: 33924382 PMCID: PMC8103509 DOI: 10.3390/cancers13081848] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/07/2021] [Accepted: 04/08/2021] [Indexed: 02/06/2023] Open
Abstract
Simple Summary Handcrafted radiomic features (HRFs) are quantitative features extracted from medical images, and they are mined for associations with different clinical endpoints. While many studies reported on the potential of HRFs to unravel clinical endpoints, the sensitivity of HRFs to variations in scanning parameters is affecting the inclusion of radiomic signatures in clinical decision-making. In this study, we investigated the effects of variations in the in-plane resolution of scans, while all other scanning parameters were fixed. Moreover, we investigated the effects of ten different image resampling methods and ComBat harmonization on the reproducibility of HRFs. Our results show that the majority of HRFs are significantly and variably affected by the differences in in-plane resolution. The majority of image resampling methods resulted in a higher number of reproducible HRFs compared to ComBat harmonization. Our developed framework guides identifying the reproducible and harmonizable HRFs in different scenarios. Abstract While handcrafted radiomic features (HRFs) have shown promise in the field of personalized medicine, many hurdles hinder its incorporation into clinical practice, including but not limited to their sensitivity to differences in acquisition and reconstruction parameters. In this study, we evaluated the effects of differences in in-plane spatial resolution (IPR) on HRFs, using a phantom dataset (n = 14) acquired on two scanner models. Furthermore, we assessed the effects of interpolation methods (IMs), the choice of a new unified in-plane resolution (NUIR), and ComBat harmonization on the reproducibility of HRFs. The reproducibility of HRFs was significantly affected by variations in IPR, with pairwise concordant HRFs, as measured by the concordance correlation coefficient (CCC), ranging from 42% to 95%. The number of concordant HRFs (CCC > 0.9) after resampling varied depending on (i) the scanner model, (ii) the IM, and (iii) the NUIR. The number of concordant HRFs after ComBat harmonization depended on the variations between the batches harmonized. The majority of IMs resulted in a higher number of concordant HRFs compared to ComBat harmonization, and the combination of IMs and ComBat harmonization did not yield a significant benefit. Our developed framework can be used to assess the reproducibility and harmonizability of RFs.
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Affiliation(s)
- Abdalla Ibrahim
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 Maastricht, The Netherlands; (T.R.); (S.P.); (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands; (F.M.M.); (J.E.W.)
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liège and GIGA CRC-In Vivo Imaging, University of Liège, 4000 Liege, Belgium;
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
- Correspondence:
| | - Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 Maastricht, The Netherlands; (T.R.); (S.P.); (H.C.W.); (P.L.)
- Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia
| | - Sergey Primakov
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 Maastricht, The Netherlands; (T.R.); (S.P.); (H.C.W.); (P.L.)
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
| | - Bruno Barufaldi
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (B.B.); (R.J.A.); (A.D.A.M.)
| | - Raymond J. Acciavatti
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (B.B.); (R.J.A.); (A.D.A.M.)
| | - Renée W. Y. Granzier
- Department of Surgery, GROW—School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands;
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liège and GIGA CRC-In Vivo Imaging, University of Liège, 4000 Liege, Belgium;
| | - Felix M. Mottaghy
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands; (F.M.M.); (J.E.W.)
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 Maastricht, The Netherlands; (T.R.); (S.P.); (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands; (F.M.M.); (J.E.W.)
| | - Joachim E. Wildberger
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands; (F.M.M.); (J.E.W.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, 6200 Maastricht, The Netherlands; (T.R.); (S.P.); (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands; (F.M.M.); (J.E.W.)
| | - Andrew D. A. Maidment
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (B.B.); (R.J.A.); (A.D.A.M.)
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Wu G, Jochems A, Refaee T, Ibrahim A, Yan C, Sanduleanu S, Woodruff HC, Lambin P. Structural and functional radiomics for lung cancer. Eur J Nucl Med Mol Imaging 2021; 48:3961-3974. [PMID: 33693966 PMCID: PMC8484174 DOI: 10.1007/s00259-021-05242-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 02/03/2021] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. METHODS Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. CONCLUSION The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form "Medomics."
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Affiliation(s)
- Guangyao Wu
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands. .,Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. .,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
| | - Arthur Jochems
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands
| | - Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Abdalla Ibrahim
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium.,Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - Chenggong Yan
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Sebastian Sanduleanu
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands
| | - Henry C Woodruff
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, 6229, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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Wu G, Woodruff HC, Shen J, Refaee T, Sanduleanu S, Ibrahim A, Leijenaar RTH, Wang R, Xiong J, Bian J, Wu J, Lambin P. Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study. Radiology 2020; 297:E282. [PMID: 33074784 DOI: 10.1148/radiol.2020209019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Wu G, Woodruff HC, Shen J, Refaee T, Sanduleanu S, Ibrahim A, Leijenaar RTH, Wang R, Xiong J, Bian J, Wu J, Lambin P. Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study. Radiology 2020; 297:451-458. [PMID: 32840472 DOI: 10.1148/radiol.2020192431] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Background Solid components of part-solid nodules (PSNs) at CT are reflective of invasive adenocarcinoma, but studies describing radiomic features of PSNs and the perinodular region are lacking. Purpose To develop and to validate radiomic signatures diagnosing invasive lung adenocarcinoma in PSNs compared with the Brock, clinical-semantic features, and volumetric models. Materials and Methods This retrospective multicenter study (https://ClinicalTrials.gov, NCT03872362) included 291 patients (median age, 60 years; interquartile range, 55-65 years; 191 women) from January 2013 to October 2017 with 297 PSN lung adenocarcinomas split into training (n = 229) and test (n = 68) data sets. Radiomic features were extracted from the different regions (gross tumor volume [GTV], solid, ground-glass, and perinodular). Random-forest models were trained using clinical-semantic, volumetric, and radiomic features, and an online nodule calculator was used to compute the Brock model. Performances of models were evaluated using standard metrics such as area under the curve (AUC), accuracy, and calibration. The integrated discrimination improvement was applied to assess model performance changes after the addition of perinodular features. Results The radiomics model based on ground-glass and solid features yielded an AUC of 0.98 (95% confidence interval [CI]: 0.96, 1.00) on the test data set, which was significantly higher than the Brock (AUC, 0.83 [95% CI: 0.72, 0.94]; P = .007), clinical-semantic (AUC, 0.90 [95% CI: 0.83, 0.98]; P = .03), volumetric GTV (AUC, 0.87 [95% CI: 0.78, 0.96]; P = .008), and radiomics GTV (AUC, 0.88 [95% CI: 0.80, 0.96]; P = .01) models. It also achieved the best accuracy (93% [95% CI: 84%, 98%]). Both this model and the model with added perinodular features showed good calibration, whereas adding perinodular features did not improve the performance (integrated discrimination improvement, -0.02; P = .56). Conclusion Separating ground-glass and solid CT radiomic features of part-solid nodules was useful in diagnosing the invasiveness of lung adenocarcinoma, yielding a better predictive performance than the Brock, clinical-semantic, volumetric, and radiomics gross tumor volume models. Online supplemental material is available for this article. See also the editorial by Nishino in this issue. Published under a CC BY 4.0 license.
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Affiliation(s)
- Guangyao Wu
- From the Departments of Precision Medicine (G.W., H.C.W., T.R., S.S., I.A., R.T.H.L., P.L.) and Radiology and Nuclear Medicine (H.C.W., I.A., P.L.), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, People's Republic of China (G.W., J.S., J.W.); Department of Radiology, The Fifth Hospital of Dalian, Dalian, People's Republic of China (R.W.); and Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China (J.X., J.B.)
| | - Henry C Woodruff
- From the Departments of Precision Medicine (G.W., H.C.W., T.R., S.S., I.A., R.T.H.L., P.L.) and Radiology and Nuclear Medicine (H.C.W., I.A., P.L.), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, People's Republic of China (G.W., J.S., J.W.); Department of Radiology, The Fifth Hospital of Dalian, Dalian, People's Republic of China (R.W.); and Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China (J.X., J.B.)
| | - Jing Shen
- From the Departments of Precision Medicine (G.W., H.C.W., T.R., S.S., I.A., R.T.H.L., P.L.) and Radiology and Nuclear Medicine (H.C.W., I.A., P.L.), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, People's Republic of China (G.W., J.S., J.W.); Department of Radiology, The Fifth Hospital of Dalian, Dalian, People's Republic of China (R.W.); and Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China (J.X., J.B.)
| | - Turkey Refaee
- From the Departments of Precision Medicine (G.W., H.C.W., T.R., S.S., I.A., R.T.H.L., P.L.) and Radiology and Nuclear Medicine (H.C.W., I.A., P.L.), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, People's Republic of China (G.W., J.S., J.W.); Department of Radiology, The Fifth Hospital of Dalian, Dalian, People's Republic of China (R.W.); and Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China (J.X., J.B.)
| | - Sebastian Sanduleanu
- From the Departments of Precision Medicine (G.W., H.C.W., T.R., S.S., I.A., R.T.H.L., P.L.) and Radiology and Nuclear Medicine (H.C.W., I.A., P.L.), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, People's Republic of China (G.W., J.S., J.W.); Department of Radiology, The Fifth Hospital of Dalian, Dalian, People's Republic of China (R.W.); and Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China (J.X., J.B.)
| | - Abdalla Ibrahim
- From the Departments of Precision Medicine (G.W., H.C.W., T.R., S.S., I.A., R.T.H.L., P.L.) and Radiology and Nuclear Medicine (H.C.W., I.A., P.L.), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, People's Republic of China (G.W., J.S., J.W.); Department of Radiology, The Fifth Hospital of Dalian, Dalian, People's Republic of China (R.W.); and Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China (J.X., J.B.)
| | - Ralph T H Leijenaar
- From the Departments of Precision Medicine (G.W., H.C.W., T.R., S.S., I.A., R.T.H.L., P.L.) and Radiology and Nuclear Medicine (H.C.W., I.A., P.L.), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, People's Republic of China (G.W., J.S., J.W.); Department of Radiology, The Fifth Hospital of Dalian, Dalian, People's Republic of China (R.W.); and Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China (J.X., J.B.)
| | - Rui Wang
- From the Departments of Precision Medicine (G.W., H.C.W., T.R., S.S., I.A., R.T.H.L., P.L.) and Radiology and Nuclear Medicine (H.C.W., I.A., P.L.), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, People's Republic of China (G.W., J.S., J.W.); Department of Radiology, The Fifth Hospital of Dalian, Dalian, People's Republic of China (R.W.); and Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China (J.X., J.B.)
| | - Jingtong Xiong
- From the Departments of Precision Medicine (G.W., H.C.W., T.R., S.S., I.A., R.T.H.L., P.L.) and Radiology and Nuclear Medicine (H.C.W., I.A., P.L.), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, People's Republic of China (G.W., J.S., J.W.); Department of Radiology, The Fifth Hospital of Dalian, Dalian, People's Republic of China (R.W.); and Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China (J.X., J.B.)
| | - Jie Bian
- From the Departments of Precision Medicine (G.W., H.C.W., T.R., S.S., I.A., R.T.H.L., P.L.) and Radiology and Nuclear Medicine (H.C.W., I.A., P.L.), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, People's Republic of China (G.W., J.S., J.W.); Department of Radiology, The Fifth Hospital of Dalian, Dalian, People's Republic of China (R.W.); and Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China (J.X., J.B.)
| | - Jianlin Wu
- From the Departments of Precision Medicine (G.W., H.C.W., T.R., S.S., I.A., R.T.H.L., P.L.) and Radiology and Nuclear Medicine (H.C.W., I.A., P.L.), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, People's Republic of China (G.W., J.S., J.W.); Department of Radiology, The Fifth Hospital of Dalian, Dalian, People's Republic of China (R.W.); and Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China (J.X., J.B.)
| | - Philippe Lambin
- From the Departments of Precision Medicine (G.W., H.C.W., T.R., S.S., I.A., R.T.H.L., P.L.) and Radiology and Nuclear Medicine (H.C.W., I.A., P.L.), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, People's Republic of China (G.W., J.S., J.W.); Department of Radiology, The Fifth Hospital of Dalian, Dalian, People's Republic of China (R.W.); and Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China (J.X., J.B.)
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13
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Walsh S, de Jong EEC, van Timmeren JE, Ibrahim A, Compter I, Peerlings J, Sanduleanu S, Refaee T, Keek S, Larue RTHM, van Wijk Y, Even AJG, Jochems A, Barakat MS, Leijenaar RTH, Lambin P. Decision Support Systems in Oncology. JCO Clin Cancer Inform 2020; 3:1-9. [PMID: 30730766 PMCID: PMC6873918 DOI: 10.1200/cci.18.00001] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708. As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restriction of human decision making, current methods are untenable. A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available data—clinical, imaging, biologic, genetic, cost—to produce validated predictive models. DSSs compare the personalized probable outcomes—toxicity, tumor control, quality of life, cost effectiveness—of various care pathway decisions to ensure optimal efficacy and economy. DSSs can be integrated into the workflows both strategically (at the multidisciplinary tumor board level to support treatment choice, eg, surgery or radiotherapy) and tactically (at the specialist level to support treatment technique, eg, prostate spacer or not). In some countries, the reimbursement of certain treatments, such as proton therapy, is already conditional on the basis that a DSS is used. DSSs have many stakeholders—clinicians, medical directors, medical insurers, patient advocacy groups—and are a natural consequence of big data in health care. Here, we provide an overview of DSSs, their challenges, opportunities, and capacity to improve clinical decision making, with an emphasis on the utility in oncology.
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Affiliation(s)
- Seán Walsh
- Maastricht University, Maastricht, the Netherlands
| | | | | | | | - Inge Compter
- Maastricht University, Maastricht, the Netherlands
| | | | | | | | - Simon Keek
- Maastricht University, Maastricht, the Netherlands
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14
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Ibrahim A, Primakov S, Beuque M, Woodruff HC, Halilaj I, Wu G, Refaee T, Granzier R, Widaatalla Y, Hustinx R, Mottaghy FM, Lambin P. Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Methods 2020; 188:20-29. [PMID: 32504782 DOI: 10.1016/j.ymeth.2020.05.022] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 05/20/2020] [Accepted: 05/26/2020] [Indexed: 12/13/2022] Open
Abstract
The advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for the development of clinical decision support systems (cDSS). Radiomics, a method for the high throughput extraction of hand-crafted features from medical images, and deep learning -the data driven modeling techniques based on the principles of simplified brain neuron interactions, are the most researched quantitative imaging techniques. Many studies reported on the potential of such techniques in the context of cDSS. Such techniques could be highly appealing due to the reuse of existing data, automation of clinical workflows, minimal invasiveness, three-dimensional volumetric characterization, and the promise of high accuracy and reproducibility of results and cost-effectiveness. Nevertheless, there are several challenges that quantitative imaging techniques face, and need to be addressed before the translation to clinical use. These challenges include, but are not limited to, the explainability of the models, the reproducibility of the quantitative imaging features, and their sensitivity to variations in image acquisition and reconstruction parameters. In this narrative review, we report on the status of quantitative medical image analysis using radiomics and deep learning, the challenges the field is facing, propose a framework for robust radiomics analysis, and discuss future prospects.
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Affiliation(s)
- A Ibrahim
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands; Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium; Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany.
| | - S Primakov
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands; Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - M Beuque
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - H C Woodruff
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - I Halilaj
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - G Wu
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - T Refaee
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands; Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - R Granzier
- Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands; Department of Surgery, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Y Widaatalla
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - R Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium
| | - F M Mottaghy
- Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands; Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - P Lambin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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15
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Affiliation(s)
- Pishtiwan H S Kalmet
- Maastricht University Medical Center+, Department of Trauma Surgery, Maastricht;; ,Correspondence:
| | - Sebastian Sanduleanu
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht;;
| | - Sergey Primakov
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht;;
| | - Guangyao Wu
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht;;
| | - Arthur Jochems
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht;;
| | - Turkey Refaee
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht;;
| | | | - Luca v. Hulst
- Maastricht University Medical Center+, Department of Trauma Surgery, Maastricht;;
| | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht;; ,Department of Radiology and Nuclear Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands;;
| | - Martijn Poeze
- Maastricht University Medical Center+, Department of Trauma Surgery, Maastricht;; ,Nutrim School for Nutrition, Toxicology and Metabolism, Maastricht University, Maastricht, The Netherlands
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16
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Abstract
Artificial intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI, particularly deep learning, has recently made substantial strides in perception tasks allowing machines to better represent and interpret complex data. Deep learning is a subset of AI represented by the combination of artificial neuron layers. In the last years, deep learning has gained great momentum. In the field of orthopaedics and traumatology, some studies have been done using deep learning to detect fractures in radiographs. Deep learning studies to detect and classify fractures on computed tomography (CT) scans are even more limited. In this narrative review, we provide a brief overview of deep learning technology: we (1) describe the ways in which deep learning until now has been applied to fracture detection on radiographs and CT examinations; (2) discuss what value deep learning offers to this field; and finally (3) comment on future directions of this technology.
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Affiliation(s)
| | - Sebastian Sanduleanu
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
| | - Sergey Primakov
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
| | - Guangyao Wu
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
| | - Arthur Jochems
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
| | - Turkey Refaee
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
| | - Abdalla Ibrahim
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
| | - Luca v. Hulst
- Maastricht University Medical Center+, Department of Trauma Surgery, Maastricht
| | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht
| | - Martijn Poeze
- Maastricht University Medical Center+, Department of Trauma Surgery, Maastricht
- Nutrim School for Nutrition, Toxicology and Metabolism, Maastricht University, Maastricht, The Netherlands
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17
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Wu G, Woodruff HC, Sanduleanu S, Refaee T, Jochems A, Leijenaar R, Gietema H, Shen J, Wang R, Xiong J, Bian J, Wu J, Lambin P. Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study. Eur Radiol 2020; 30:2680-2691. [PMID: 32006165 PMCID: PMC7160197 DOI: 10.1007/s00330-019-06597-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 11/07/2019] [Accepted: 11/18/2019] [Indexed: 12/19/2022]
Abstract
Objectives Develop a CT-based radiomics model and combine it with frozen section (FS) and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM). Methods This multicenter study cohort of 623 lung adenocarcinomas was split into training (n = 331), testing (n = 143), and external validation dataset (n = 149). Random forest models were built using selected radiomics features, results from FS, lesion volume, clinical and semantic features, and combinations thereof. The area under the receiver operator characteristic curves (AUC) was used to evaluate model performances. The diagnosis accuracy, calibration, and decision curves of models were tested. Results The radiomics-based model shows good predictive performance and diagnostic accuracy for distinguishing IA from PM, with AUCs of 0.89, 0.89, and 0.88, in the training, testing, and validation datasets, respectively, and with corresponding accuracies of 0.82, 0.79, and 0.85. Adding lesion volume and FS significantly increases the performance of the model with AUCs of 0.96, 0.97, and 0.96, and with accuracies of 0.91, 0.94, and 0.93 in the three datasets. There is no significant difference in AUC between the FS model enriched with radiomics and volume against an FS model enriched with volume alone, while the former has higher accuracy. The model combining all available information shows minor non-significant improvements in AUC and accuracy compared with an FS model enriched with radiomics and volume. Conclusions Radiomics signatures are potential biomarkers for the risk of IA, especially in combination with FS, and could help guide surgical strategy for pulmonary nodules patients. Key Points • A CT-based radiomics model may be a valuable tool for preoperative prediction of invasive adenocarcinoma for patients with pulmonary nodules. • Radiomics combined with frozen sections could help in guiding surgery strategy for patients with pulmonary nodules. Electronic supplementary material The online version of this article (10.1007/s00330-019-06597-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Guangyao Wu
- The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian, 116001, People's Republic of China.
| | - Henry C Woodruff
- The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Sebastian Sanduleanu
- The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Turkey Refaee
- The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Arthur Jochems
- The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Ralph Leijenaar
- The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Hester Gietema
- Department of Radiology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Jing Shen
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian, 116001, People's Republic of China
| | - Rui Wang
- Department of Radiology, The Fifth Hospital of Dalian, Dalian, People's Republic of China
| | - Jingtong Xiong
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China
| | - Jie Bian
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, People's Republic of China
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian, 116001, People's Republic of China.
| | - Philippe Lambin
- The D-Lab: Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
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18
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Refaee T, Wu G, Ibrahim A, Halilaj I, Leijenaar RTH, Rogers W, Gietema HA, Hendriks LEL, Lambin P, Woodruff HC. The Emerging Role of Radiomics in COPD and Lung Cancer. Respiration 2020; 99:99-107. [PMID: 31991420 DOI: 10.1159/000505429] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 12/12/2019] [Indexed: 12/24/2022] Open
Abstract
Medical imaging plays a key role in evaluating and monitoring lung diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. The application of artificial intelligence in medical imaging has transformed medical images into mineable data, by extracting and correlating quantitative imaging features with patients' outcomes and tumor phenotype - a process termed radiomics. While this process has already been widely researched in lung oncology, the evaluation of COPD in this fashion remains in its infancy. Here we outline the main applications of radiomics in lung cancer and briefly review the workflow from image acquisition to the evaluation of model performance. Finally, we discuss the current assessments of COPD and the potential application of radiomics in COPD.
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Affiliation(s)
- Turkey Refaee
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands, .,Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia,
| | - Guangyao Wu
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Abdallah Ibrahim
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Centre Hospitalier Universitaire de Liège, Liège, Belgium.,Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - Iva Halilaj
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Ralph T H Leijenaar
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - William Rogers
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Thoracic Oncology, IRCCS Foundation National Cancer Institute, Milan, Italy
| | - Hester A Gietema
- Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lizza E L Hendriks
- Department of Pulmonary Diseases, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Henry C Woodruff
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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19
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Ibrahim A, Vallières M, Woodruff H, Primakov S, Beheshti M, Keek S, Refaee T, Sanduleanu S, Walsh S, Morin O, Lambin P, Hustinx R, Mottaghy FM. Radiomics Analysis for Clinical Decision Support in Nuclear Medicine. Semin Nucl Med 2019; 49:438-449. [DOI: 10.1053/j.semnuclmed.2019.06.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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20
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De Jong E, Van Elmpt W, Rizzo S, Leijenaar R, Refaee T, Hendriks L, Reymen B, Dingemans A, Lambin P. EP-1380: Can radiomic features describe lung semantic features in NSCLC patients? Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)31689-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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