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Trojani V, Bassi MC, Verzellesi L, Bertolini M. Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review. Cancers (Basel) 2024; 16:2668. [PMID: 39123396 PMCID: PMC11311340 DOI: 10.3390/cancers16152668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 07/16/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Lately, radiomic studies featuring the development of a signature to use in prediction models in diagnosis or prognosis outcomes have been increasingly published. While the results are shown to be promising, these studies still have many pitfalls and limitations. One of the main issues of these studies is that radiomic features depend on how the images are preprocessed before their computation. Since, in widely known and used software for radiomic features calculation, it is possible to set these preprocessing parameters before the calculation of the radiomic feature, there are ongoing studies assessing the stability and repeatability of radiomic features to find the most suitable preprocessing parameters for every used imaging modality. MATERIALS AND METHODS We performed a comprehensive literature search using four electronic databases: PubMed, Cochrane Library, Embase, and Scopus. Mesh terms and free text were modeled in search strategies for databases. The inclusion criteria were studies where preprocessing parameters' influence on feature values and model predictions was addressed. Records lacking information on image acquisition parameters were excluded, and any eligible studies with full-text versions were included in the review process, while conference proceedings and monographs were disregarded. We used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to investigate the risk of bias. We synthesized our data in a table divided by the imaging modalities subgroups. RESULTS After applying the inclusion and exclusion criteria, we selected 43 works. This review examines the impact of preprocessing parameters on the reproducibility and reliability of radiomic features extracted from multimodality imaging (CT, MRI, CBCT, and PET/CT). Standardized preprocessing is crucial for consistent radiomic feature extraction. Key preprocessing steps include voxel resampling, normalization, and discretization, which influence feature robustness and reproducibility. In total, 44% of the included works studied the effects of an isotropic voxel resampling, and most studies opted to employ a discretization strategy. From 2021, several studies started selecting the best set of preprocessing parameters based on models' best performance. As for comparison metrics, ICC was the most used in MRI studies in 58% of the screened works. CONCLUSIONS From our work, we highlighted the need to harmonize the use of preprocessing parameters and their values, especially in light of future studies of prospective studies, which are still lacking in the current literature.
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Affiliation(s)
- Valeria Trojani
- Medical Physics, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy; (L.V.); (M.B.)
| | | | - Laura Verzellesi
- Medical Physics, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy; (L.V.); (M.B.)
| | - Marco Bertolini
- Medical Physics, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy; (L.V.); (M.B.)
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2
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Horng H, Scott C, Winham S, Jensen M, Pantalone L, Mankowski W, Kerlikowske K, Vachon CM, Kontos D, Shinohara RT. Multivariate testing and effect size measures for batch effect evaluation in radiomic features. Sci Rep 2024; 14:13923. [PMID: 38886407 PMCID: PMC11183083 DOI: 10.1038/s41598-024-64208-z] [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: 01/28/2024] [Accepted: 06/06/2024] [Indexed: 06/20/2024] Open
Abstract
While precision medicine applications of radiomics analysis are promising, differences in image acquisition can cause "batch effects" that reduce reproducibility and affect downstream predictive analyses. Harmonization methods such as ComBat have been developed to correct these effects, but evaluation methods for quantifying batch effects are inconsistent. In this study, we propose the use of the multivariate statistical test PERMANOVA and the Robust Effect Size Index (RESI) to better quantify and characterize batch effects in radiomics data. We evaluate these methods in both simulated and real radiomics features extracted from full-field digital mammography (FFDM) data. PERMANOVA demonstrated higher power than standard univariate statistical testing, and RESI was able to interpretably quantify the effect size of site at extremely large sample sizes. These methods show promise as more powerful and interpretable methods for the detection and quantification of batch effects in radiomics studies.
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Affiliation(s)
- Hannah Horng
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Penn Statistics in Imaging Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | | | | | | | - Lauren Pantalone
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Walter Mankowski
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | | | - Despina Kontos
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), Columbia University, New York, NY, 10027, USA
| | - Russell T Shinohara
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Statistics in Imaging Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
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3
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Zhang X, Iqbal Bin Saripan M, Wu Y, Wang Z, Wen D, Cao Z, Wang B, Xu S, Liu Y, Marhaban MH, Dong X. The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers. BMC Med Imaging 2024; 24:137. [PMID: 38844854 PMCID: PMC11157873 DOI: 10.1186/s12880-024-01306-4] [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: 01/02/2023] [Accepted: 05/23/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND This study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning models. MATERIALS AND METHODS 135 CT images of Credence Cartridge Radiomic phantoms were collected and screened from three scanners manufactured by Siemens, Philips, and GE. 100 radiomic features were extracted and 20 radiomic features were screened according to the Lasso regression method. The radiomic features extracted from the rubber and resin-filled regions in the cartridges were labeled into different categories for evaluating the performance of the machine learning model. Radiomics features were divided into three groups based on the different scanner manufacturers. The radiomic features were randomly divided into training and test sets with a ratio of 8:2. Five machine learning models (lasso, logistic regression, random forest, support vector machine, neural network) were employed to evaluate the impact of Combat on radiomic features. The variability among radiomic features were assessed using analysis of variance (ANOVA) and principal component analysis (PCA). Accuracy, precision, recall, and area under the receiver curve (AUC) were used as evaluation metrics for model classification. RESULTS The principal component and ANOVA analysis results show that the variability of different scanner manufacturers in radiomic features was removed (P˃0.05). After harmonization with the Combat algorithm, the distributions of radiomic features were aligned in terms of location and scale. The performance of machine learning models for classification improved, with the Random Forest model showing the most significant enhancement. The AUC value increased from 0.88 to 0.92. CONCLUSIONS The Combat algorithm has reduced variability in radiomic features from different scanners. In the phantom CT dataset, it appears that the machine learning model's classification performance may have improved after Combat harmonization. However, further investigation and validation are required to fully comprehend Combat's impact on radiomic features in medical imaging.
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Affiliation(s)
- Xiaolei Zhang
- Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia.
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.
- Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.
| | | | - Yanjun Wu
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei Province, China
| | - Zhongxiao Wang
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei Province, China
| | - Dong Wen
- Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
| | - Zhendong Cao
- Department of Radiology, the Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China
| | - Bingzhen Wang
- Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei Province, China
| | - Shiqi Xu
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei Province, China
| | - Yanli Liu
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei Province, China
| | | | - Xianling Dong
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.
- Hebei Provincial Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde City, Hebei Province, China.
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4
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Salimi Y, Mansouri Z, Hajianfar G, Sanaat A, Shiri I, Zaidi H. Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning. Med Phys 2024; 51:4095-4104. [PMID: 38629779 DOI: 10.1002/mp.17076] [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: 12/28/2023] [Revised: 03/19/2024] [Accepted: 04/02/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and language. This is a barrier to effective use of available CECT images in clinical research. PURPOSE The aim of this study is to detect contrast media injection phase from CT images by means of organ segmentation and machine learning algorithms. METHODS A total number of 2509 CT images split into four subsets of non-contrast (class #0), arterial (class #1), venous (class #2), and delayed (class #3) after contrast media injection were collected from two CT scanners. Seven organs including the liver, spleen, heart, kidneys, lungs, urinary bladder, and aorta along with body contour masks were generated by pre-trained deep learning algorithms. Subsequently, five first-order statistical features including average, standard deviation, 10, 50, and 90 percentiles extracted from the above-mentioned masks were fed to machine learning models after feature selection and reduction to classify the CT images in one of four above mentioned classes. A 10-fold data split strategy was followed. The performance of our methodology was evaluated in terms of classification accuracy metrics. RESULTS The best performance was achieved by Boruta feature selection and RF model with average area under the curve of more than 0.999 and accuracy of 0.9936 averaged over four classes and 10 folds. Boruta feature selection selected all predictor features. The lowest classification was observed for class #2 (0.9888), which is already an excellent result. In the 10-fold strategy, only 33 cases from 2509 cases (∼1.4%) were misclassified. The performance over all folds was consistent. CONCLUSIONS We developed a fast, accurate, reliable, and explainable methodology to classify contrast media phases which may be useful in data curation and annotation in big online datasets or local datasets with non-standard or no series description. Our model containing two steps of deep learning and machine learning may help to exploit available datasets more effectively.
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Affiliation(s)
- Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Amirhossein Sanaat
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
- University Research and Innovation Center, Óbuda University, Budapest, Hungary
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5
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Caldarella C, De Risi M, Massaccesi M, Miccichè F, Bussu F, Galli J, Rufini V, Leccisotti L. Role of 18F-FDG PET/CT in Head and Neck Squamous Cell Carcinoma: Current Evidence and Innovative Applications. Cancers (Basel) 2024; 16:1905. [PMID: 38791983 PMCID: PMC11119768 DOI: 10.3390/cancers16101905] [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: 04/05/2024] [Revised: 05/08/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
This article provides an overview of the use of 18F-FDG PET/CT in various clinical scenarios of head-neck squamous cell carcinoma, ranging from initial staging to treatment-response assessment, and post-therapy follow-up, with a focus on the current evidence, debated issues, and innovative applications. Methodological aspects and the most frequent pitfalls in head-neck imaging interpretation are described. In the initial work-up, 18F-FDG PET/CT is recommended in patients with metastatic cervical lymphadenectomy and occult primary tumor; moreover, it is a well-established imaging tool for detecting cervical nodal involvement, distant metastases, and synchronous primary tumors. Various 18F-FDG pre-treatment parameters show prognostic value in terms of disease progression and overall survival. In this scenario, an emerging role is played by radiomics and machine learning. For radiation-treatment planning, 18F-FDG PET/CT provides an accurate delineation of target volumes and treatment adaptation. Due to its high negative predictive value, 18F-FDG PET/CT, performed at least 12 weeks after the completion of chemoradiotherapy, can prevent unnecessary neck dissections. In addition to radiomics and machine learning, emerging applications include PET/MRI, which combines the high soft-tissue contrast of MRI with the metabolic information of PET, and the use of PET radiopharmaceuticals other than 18F-FDG, which can answer specific clinical needs.
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Affiliation(s)
- Carmelo Caldarella
- Nuclear Medicine Unit, Department of Radiology and Oncologic Radiotherapy, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (C.C.); (M.D.R.); (L.L.)
| | - Marina De Risi
- Nuclear Medicine Unit, Department of Radiology and Oncologic Radiotherapy, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (C.C.); (M.D.R.); (L.L.)
| | - Mariangela Massaccesi
- Radiation Oncology Unit, Department of Radiology and Oncologic Radiotherapy, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Francesco Miccichè
- Radiation Oncology Unit, Ospedale Isola Tiberina—Gemelli Isola, 00186 Rome, Italy;
| | - Francesco Bussu
- Otorhinolaryngology Operative Unit, Azienda Ospedaliero Universitaria Sassari, 07100 Sassari, Italy;
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Jacopo Galli
- Otorhinolaryngology Unit, Department of Neurosciences, Sensory Organs and Thorax, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
- Section of Otolaryngology, Department of Head-Neck and Sensory Organs, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Vittoria Rufini
- Nuclear Medicine Unit, Department of Radiology and Oncologic Radiotherapy, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (C.C.); (M.D.R.); (L.L.)
- Section of Nuclear Medicine, Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Lucia Leccisotti
- Nuclear Medicine Unit, Department of Radiology and Oncologic Radiotherapy, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (C.C.); (M.D.R.); (L.L.)
- Section of Nuclear Medicine, Department of Radiological Sciences and Hematology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
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6
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Louis T, Lucia F, Cousin F, Mievis C, Jansen N, Duysinx B, Le Pennec R, Visvikis D, Nebbache M, Rehn M, Hamya M, Geier M, Salaun PY, Schick U, Hatt M, Coucke P, Lovinfosse P, Hustinx R. Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence. Sci Rep 2024; 14:9028. [PMID: 38641673 PMCID: PMC11031577 DOI: 10.1038/s41598-024-58551-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 04/01/2024] [Indexed: 04/21/2024] Open
Abstract
The primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography of early-stage non-small cell lung cancer patients, which remain unaffected by variations in segmentation quality and in computed tomography image acquisition protocol. The robustness of these features to segmentation variations was assessed by analyzing the correlation of feature values extracted from lesion volumes delineated by two annotators. The robustness to variations in acquisition protocol was evaluated by examining the correlation of features extracted from high-dose and low-dose computed tomography scans, both of which were acquired for each patient as part of the stereotactic body radiotherapy planning process. Among 106 radiomic features considered, 21 were identified as robust. An analysis including univariate and multivariate assessments was subsequently conducted to estimate the predictive performance of these robust features on the outcome of early-stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. The univariate predictive analysis revealed that robust features demonstrated superior predictive potential compared to non-robust features. The multivariate analysis indicated that linear regression models built with robust features displayed greater generalization capabilities by outperforming other models in predicting the outcomes of an external validation dataset.
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Affiliation(s)
- Thomas Louis
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium.
| | - François Lucia
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium.
- Radiation Oncology Department, University Hospital of Brest, Brest, France.
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France.
| | - François Cousin
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - Carole Mievis
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Nicolas Jansen
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Bernard Duysinx
- Division of Pulmonology, University Hospital of Liège, Liège, Belgium
| | - Romain Le Pennec
- Nuclear Medicine Department, University Hospital of Brest, Brest, France
- GETBO INSERM UMR 1304, University of Brest, UBO, Brest, France
| | | | - Malik Nebbache
- Radiation Oncology Department, University Hospital of Brest, Brest, France
| | - Martin Rehn
- Radiation Oncology Department, University Hospital of Brest, Brest, France
| | - Mohamed Hamya
- Radiation Oncology Department, University Hospital of Brest, Brest, France
| | - Margaux Geier
- Medical Oncology Department, University Hospital of Brest, Brest, France
| | - Pierre-Yves Salaun
- Nuclear Medicine Department, University Hospital of Brest, Brest, France
- GETBO INSERM UMR 1304, University of Brest, UBO, Brest, France
| | - Ulrike Schick
- Radiation Oncology Department, University Hospital of Brest, Brest, France
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - Philippe Coucke
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
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7
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Zhan W, Yang Q, Chen S, Liu S, Liu Y, Li H, Li S, Gong Q, Liu L, Chen H. Semi-automatic fine delineation scheme for pancreatic cancer. Med Phys 2024; 51:1860-1871. [PMID: 37665772 DOI: 10.1002/mp.16718] [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: 06/04/2023] [Revised: 07/18/2023] [Accepted: 08/19/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Pancreatic cancer fine delineation in medical images by physicians is a major challenge due to the vast volume of medical images and the variability of patients. PURPOSE A semi-automatic fine delineation scheme was designed to assist doctors in accurately and quickly delineating the cancer target region to improve the delineation accuracy of pancreatic cancer in computed tomography (CT) images and effectively reduce the workload of doctors. METHODS A target delineation scheme in image blocks was also designed to provide more information for the deep learning delineation model. The start and end slices of the image block were manually delineated by physicians, and the cancer in the middle slices were accurately segmented using a three-dimensional Res U-Net model. Specifically, the input of the network is the CT image of the image block and the delineation of the cancer in the start and end slices, while the output of the network is the cancer area in the middle slices of the image block. Meanwhile, the model performance of pancreatic cancer delineation and the workload of doctors in different image block sizes were studied. RESULTS We used 37 3D CT volumes for training, 11 volumes for validating and 11 volumes for testing. The influence of different image block sizes on doctors' workload was compared quantitatively. Experimental results showed that the physician's workload was minimal when the image block size was 5, and all cancer could be accurately delineated. The Dice similarity coefficient was 0.894 ± 0.029, the 95% Hausdorff distance was 3.465 ± 0.710 mm, the normalized surface Dice was 0.969 ± 0.019. By completing the accurate delineation of all the CT images, the speed of the new method is 2.16 times faster than that of manual sketching. CONCLUSION Our proposed 3D semi-automatic delineative method based on the idea of block prediction could accurately delineate CT images of pancreatic cancer and effectively deal with the challenges of class imbalance, background distractions, and non-rigid geometrical features. This study had a significant advantage in reducing doctors' workload, and was expected to help doctors improve their work efficiency in clinical application.
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Affiliation(s)
- Weizong Zhan
- School of Life & Environmental Science, Guilin University of Electronic Technology, Guilin, China
| | - Qiuxia Yang
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Shuchao Chen
- School of Life & Environmental Science, Guilin University of Electronic Technology, Guilin, China
| | - Shanshan Liu
- School of Life & Environmental Science, Guilin University of Electronic Technology, Guilin, China
| | - Yifei Liu
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Haojiang Li
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Shuqi Li
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Qiong Gong
- School of Life & Environmental Science, Guilin University of Electronic Technology, Guilin, China
- Guangxi Human Physiological Information NonInvasive Detection Engineering Technology Research Center, Guilin, China
- Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin, China
| | - Lizhi Liu
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Hongbo Chen
- School of Life & Environmental Science, Guilin University of Electronic Technology, Guilin, China
- Guangxi Human Physiological Information NonInvasive Detection Engineering Technology Research Center, Guilin, China
- Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin, China
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8
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Palomino-Fernández D, Seiffert AP, Gómez-Grande A, Jiménez López-Guarch C, Moreno G, Bueno H, Gómez EJ, Sánchez-González P. Robustness of [ 18F]FDG PET/CT radiomic analysis in the setting of drug-induced cardiotoxicity. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107981. [PMID: 38154326 DOI: 10.1016/j.cmpb.2023.107981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 11/01/2023] [Accepted: 12/12/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND AND OBJECTIVES Standardization of radiomic data acquisition protocols is still at a very early stage, revealing a strong need to work towards the definition of uniform image processing methodologies The aim of this study is to identify sources of variability in radiomic data derived from image discretization and resampling methodologies prior to image feature extraction. Furthermore, to identify robust potential image-based biomarkers for the early detection of cardiotoxicity. METHODS Image post-acquisition processing, interpolation, and volume of interest (VOI) segmentation were performed. Four experiments were conducted to assess the reliability in terms of the intraclass correlation coefficient (ICC) of the radiomic features and the effects of the variation of voxel size and gray level discretization. Statistical analysis was performed separating the patients according to cardiotoxicity diagnosis. Differences of texture features were studied with Mann-Whitney U test. P-values <0.05 after multiple testing correction were considered statistically significant. Additionally, a non-supervised k-Means clustering algorithm was evaluated. RESULTS The effect of the variation in the voxel size demonstrated a non-dependency relationship with the values of the radiomic features, regardless of the chosen discretization method. The median ICC values were 0.306 and 0.872 for absolute agreement and consistency, respectively, when varying the discretization bin number. The median ICC values were 0.678 and 0.878 for absolute agreement and consistency, respectively, when varying the discretization bin size. A total of 16 first order, 6 Gray Level Co-occurrence Matrix (GLCM), 4 Gray Level Dependence Matrix (GLDM) and 4 Gray Level Run Length Matrix (GLRLM) features demonstrated statistically significant differences between the diagnosis groups for interim scans (P<0.05) for the fixed bin size (FBS) discretization methodology. However, no statistically significant differences between diagnostic groups were found for the fixed bin number (FBN) discretization methodology. Two clusters based on the radiomic features were identified. CONCLUSIONS Gray level discretization has a major impact on the repeatability of the radiomic features. The selection of the optimal processing methodology has led to the identification of texture-based patterns for the differentiation of early cardiac damage profiles.
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Affiliation(s)
- David Palomino-Fernández
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain.
| | - Alexander P Seiffert
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain
| | - Adolfo Gómez-Grande
- Department of Nuclear Medicine, Hospital Universitario 12 de Octubre, Spain; Facultad de Medicina, Universidad Complutense de Madrid, Spain
| | - Carmen Jiménez López-Guarch
- Facultad de Medicina, Universidad Complutense de Madrid, Spain; Cardiology Department and Instituto de Investigación Sanitaria (imas12), Hospital Universitario 12 de Octubre, Spain; Centro de Investigación Biomédica en Red de enfermedades Cardiovasculares (CIBERCV), Spain
| | - Guillermo Moreno
- Cardiology Department and Instituto de Investigación Sanitaria (imas12), Hospital Universitario 12 de Octubre, Spain; Facultad de Enfermería, Fisioterapia y Podología, Universidad Complutense de Madrid, Spain
| | - Héctor Bueno
- Facultad de Medicina, Universidad Complutense de Madrid, Spain; Cardiology Department and Instituto de Investigación Sanitaria (imas12), Hospital Universitario 12 de Octubre, Spain; Centro de Investigación Biomédica en Red de enfermedades Cardiovasculares (CIBERCV), Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Spain
| | - Enrique J Gómez
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Spain
| | - Patricia Sánchez-González
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Spain.
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9
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Cherezov D, Viswanathan VS, Fu P, Gupta A, Madabhushi A. Rank acquisition impact on radiomics estimation (AсquIRE) in chest CT imaging: A retrospective multi-site, multi-use-case study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107990. [PMID: 38194767 PMCID: PMC10872259 DOI: 10.1016/j.cmpb.2023.107990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/12/2023] [Accepted: 12/19/2023] [Indexed: 01/11/2024]
Abstract
BACKGROUND Radiomics is a method within medical image analysis that involves the extraction of quantitative data from radiologic scans, often in conjunction with machine learning algorithms to phenotype disease appearance, prognosticate disease outcome, and predict treatment response. However, variance in CT scanner acquisition parameters, such as convolution kernels or pixel spacing, can impact radiomics texture feature values. PURPOSE The extent to which the parameters influence radiomics features continues to be an active area of investigation. In this study, we describe a novel approach, Acquisition Impact on Radiomics Estimation (AcquIRE), to rank the impact of CT acquisition parameters on radiomic texture features. METHODS In this work, we used three chest CT imaging datasets (n = 749 patients) from nine sites comprising: i) lung granulomas and adenocarcinomas (D1) (10 and 52 patients, respectively); ii) minimal and frank invasive adenocarcinoma (D2) (74 and 145 patients); and iii) early-stage NSCLC patients (D3) (315 patients). Datasets D2 and D3 were collected from four sites each, and D1 from a single site. For each patient, 744 texture features and nine acquisition parameters were extracted and utilized to evaluate which parameters impact radiomic features the most. The AcquIRE method establishes a relative assessment between acquisition parameters and radiomic texture featuresa through the creation of a classification model, which is then utilized to assess the rank of the acquisition parameters. RESULTS Across the use cases, CT software version and convolution kernel parameters were found to have the most variance. In D1, it was observed that the Haralick texture feature family was the least affected by variations in acquisition parameters, while the Gabor feature family was the most impacted. However, in datasets D2 and D3, the Gabor features were found to be the least affected. Our findings suggest that the impact on radiomic parameters is as much a function of the problem in question as it is acquisition parameters. CONCLUSIONS The software version and convolution kernel parameters impacted the radiomics feature the most.
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Affiliation(s)
- Dmitry Cherezov
- Department of Biomedical Engineering, Emory University, Atlanta, GA, USA
| | | | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Radiology, Case Western Reserve University, Cleveland, OH, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University, Atlanta, GA, USA; Atlanta Veterans Administration Medical Center, Atlanta, GA, USA.
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10
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Ho WLJ, Fetisov N, Hall LO, Goldgof D, Schabath MB. Evaluating clinical and radiomic features for predicting lung cancer recurrence pre- and post-tumor resection. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2024; 12926:1292623. [PMID: 38993353 PMCID: PMC11238903 DOI: 10.1117/12.3006091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
Among patients with early-stage non-small cell lung cancer (NSCLC) undergoing surgical resection, identifying who is at high-risk of recurrence can inform clinical guidelines with respect to more aggressive follow-up and/or adjuvant therapy. While predicting recurrence based on pre-surgical resection data is ideal, clinically important pathological features are only evaluated postoperatively. Therefore, we developed two supervised classification models to assess the importance of pre- and post-surgical features for predicting 5-year recurrence. An integrated dataset was generated by combining clinical covariates and radiomic features calculated from pre-surgical computed tomography images. After removing correlated radiomic features, the SHapley Additive exPlanations (SHAP) method was used to measure feature importance and select relevant features. Binary classification was performed using a Support Vector Machine, followed by a feature ablation study assessing the impact of radiomic and clinical features. We demonstrate that the post-surgical model significantly outperforms the pre-surgical model in predicting lung cancer recurrence, with tumor pathological features and peritumoral radiomic features contributing significantly to the model's performance.
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Affiliation(s)
- Wai Lone J Ho
- University of South Florida, Morsani College of Medicine, 560 Channelside Dr, Tampa, FL, USA 33602
| | - Nikolai Fetisov
- Dept. of Computer Science and Engineering, University of South Florida, Tampa, FL, USA 33620
| | - Lawrence O Hall
- Dept. of Computer Science and Engineering, University of South Florida, Tampa, FL, USA 33620
| | - Dmitry Goldgof
- Dept. of Computer Science and Engineering, University of South Florida, Tampa, FL, USA 33620
| | - Matthew B Schabath
- H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Dr, Tampa, FL, USA 33612
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11
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Tong X, Wang S, Zhang J, Fan Y, Liu Y, Wei W. Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images. Bioengineering (Basel) 2024; 11:50. [PMID: 38247927 PMCID: PMC10813496 DOI: 10.3390/bioengineering11010050] [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: 11/28/2023] [Revised: 12/21/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
OBJECTIVE Develop two fully automatic osteoporosis screening systems using deep learning (DL) and radiomics (Rad) techniques based on low-dose chest CT (LDCT) images and evaluate their diagnostic effectiveness. METHODS In total, 434 patients who underwent LDCT and bone mineral density (BMD) examination were retrospectively enrolled and divided into the development set (n = 333) and temporal validation set (n = 101). An automatic thoracic vertebra cancellous bone (TVCB) segmentation model was developed. The Dice similarity coefficient (DSC) was used to evaluate the segmentation performance. Furthermore, the three-class Rad and DL models were developed to distinguish osteoporosis, osteopenia, and normal bone mass. The diagnostic performance of these models was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). RESULTS The automatic segmentation model achieved excellent segmentation performance, with a mean DSC of 0.96 ± 0.02 in the temporal validation set. The Rad model was used to identify osteoporosis, osteopenia, and normal BMD in the temporal validation set, with respective area under the receiver operating characteristic curve (AUC) values of 0.943, 0.801, and 0.932. The DL model achieved higher AUC values of 0.983, 0.906, and 0.969 for the same categories in the same validation set. The Delong test affirmed that both models performed similarly in BMD assessment. However, the accuracy of the DL model is 81.2%, which is better than the 73.3% accuracy of the Rad model in the temporal validation set. Additionally, DCA indicated that the DL model provided a greater net benefit compared to the Rad model across the majority of the reasonable threshold probabilities Conclusions: The automated segmentation framework we developed can accurately segment cancellous bone on low-dose chest CT images. These predictive models, which are based on deep learning and radiomics, provided comparable diagnostic performance in automatic BMD assessment. Nevertheless, it is important to highlight that the DL model demonstrates higher accuracy and precision than the Rad model.
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Affiliation(s)
| | | | | | | | | | - Wei Wei
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116014, China (S.W.); (Y.F.)
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12
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Tomassini S, Falcionelli N, Bruschi G, Sbrollini A, Marini N, Sernani P, Morettini M, Müller H, Dragoni AF, Burattini L. On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans. Comput Med Imaging Graph 2023; 110:102310. [PMID: 37979340 DOI: 10.1016/j.compmedimag.2023.102310] [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: 07/18/2023] [Revised: 10/25/2023] [Accepted: 11/03/2023] [Indexed: 11/20/2023]
Abstract
Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decision-support system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-Radiomics-Genomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visually-understandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information.
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Affiliation(s)
- Selene Tomassini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Nicola Falcionelli
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Giulia Bruschi
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Agnese Sbrollini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Niccolò Marini
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
| | - Paolo Sernani
- Department of Law, University of Macerata (UNIMC), Macerata, Italy
| | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
| | - Aldo Franco Dragoni
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy.
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13
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Selvam M, Chandrasekharan A, Sadanandan A, Anand VK, Murali A, Krishnamurthi G. Radiomics as a non-invasive adjunct to Chest CT in distinguishing benign and malignant lung nodules. Sci Rep 2023; 13:19062. [PMID: 37925565 PMCID: PMC10625576 DOI: 10.1038/s41598-023-46391-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 10/31/2023] [Indexed: 11/06/2023] Open
Abstract
In an observational study conducted from 2016 to 2021, we assessed the utility of radiomics in differentiating between benign and malignant lung nodules detected on computed tomography (CT) scans. Patients in whom a final diagnosis regarding the lung nodules was available according to histopathology and/or 2017 Fleischner Society guidelines were included. The radiomics workflow included lesion segmentation, region of interest (ROI) definition, pre-processing, and feature extraction. Employing random forest feature selection, we identified ten important radiomic features for distinguishing between benign and malignant nodules. Among the classifiers tested, the Decision Tree model demonstrated superior performance, achieving 79% accuracy, 75% sensitivity, 85% specificity, 82% precision, and 90% F1 score. The implementation of the XGBoost algorithm further enhanced these results, yielding 89% accuracy, 89% sensitivity, 89% precision, and an F1 score of 89%, alongside a specificity of 85%. Our findings highlight tumor texture as the primary predictor of malignancy, emphasizing the importance of texture-based features in computational oncology. Thus, our study establishes radiomics as a powerful, non-invasive adjunct to CT scans in the differentiation of lung nodules, with significant implications for clinical decision-making, especially for indeterminate nodules, and the enhancement of diagnostic and predictive accuracy in this clinical context.
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Affiliation(s)
- Minmini Selvam
- Department of Radiology and Imaging Sciences, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, 600 116, India.
| | - Anupama Chandrasekharan
- Department of Radiology and Imaging Sciences, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, 600 116, India
| | - Abjasree Sadanandan
- Department of Engineering Design, Indian Institute of Technology-Madras, Chennai, 600 036, India
| | - Vikas Kumar Anand
- Department of Engineering Design, Indian Institute of Technology-Madras, Chennai, 600 036, India
| | - Arunan Murali
- Department of Radiology and Imaging Sciences, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, 600 116, India
| | - Ganapathy Krishnamurthi
- Department of Engineering Design, Indian Institute of Technology-Madras, Chennai, 600 036, India
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14
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Prosper AE, Kammer MN, Maldonado F, Aberle DR, Hsu W. Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization. Radiology 2023; 309:e222904. [PMID: 37815447 PMCID: PMC10623199 DOI: 10.1148/radiol.222904] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/23/2023] [Accepted: 03/30/2023] [Indexed: 10/11/2023]
Abstract
The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United States, increasing the opportunity for early lung cancer diagnosis. Yet, realization of the full potential of these opportunities is dependent on the ability to accurately analyze image data for purposes of nodule classification and early lung cancer characterization. This review presents an overview of traditional image analysis approaches in chest CT using semantic characterization as well as more recent advances in the technology and application of machine learning models using CT-derived radiomic features and deep learning architectures to characterize lung nodules and early cancers. Methodological challenges currently faced in translating these decision aids to clinical practice, as well as the technical obstacles of heterogeneous imaging parameters, optimal feature selection, choice of model, and the need for well-annotated image data sets for the purposes of training and validation, will be reviewed, with a view toward the ultimate incorporation of these potentially powerful decision aids into routine clinical practice.
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Affiliation(s)
- Ashley Elizabeth Prosper
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Michael N. Kammer
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Fabien Maldonado
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Denise R. Aberle
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - William Hsu
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
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15
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Altinok O, Guvenis A. Interpretable radiomics method for predicting human papillomavirus status in oropharyngeal cancer using Bayesian networks. Phys Med 2023; 114:102671. [PMID: 37708571 DOI: 10.1016/j.ejmp.2023.102671] [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: 03/01/2023] [Revised: 06/14/2023] [Accepted: 09/06/2023] [Indexed: 09/16/2023] Open
Abstract
OBJECTIVES To develop a simple interpretable Bayesian Network (BN) to classify HPV status in patients with oropharyngeal cancer. METHODS Two hundred forty-six patients, 216 of whom were HPV positive, were used in this study. We extracted 851 radiomics markers from patients' contrast-enhanced Computed Tomography (CT) images. Mens eX Machina (MXM) approach selected two most relevant predictors: sphericity and max2DDiameterRow. The area under the curve (AUC) demonstrated BN model performance in 30% of the data reserved for testing. A Support Vector Machine (SVM) based method was also implemented for comparison purposes. RESULTS The Mens eX Machina (MXM) approach selected two most relevant predictors: sphericity and max2DDiameterRow. Areas under the Curves (AUC) were found 0.78 and 0.72 on the training and test data, respectively. When using support vector machine (SVM) and 25 features, the AUC was found 0.83 on the test data. CONCLUSIONS The straightforward structure and power of interpretability of our BN model will help clinicians make treatment decisions and enable the non-invasive detection of HPV status from contrast-enhanced CT images. Higher accuracy can be obtained using more complex structures at the expense of lower interpretability. ADVANCES IN KNOWLEDGE Radiomics is being studied lately as a simple imaging data based HPV status detection technique which can be an alternative to laboratory approaches. However, it generally lacks interpretability. This work demonstrated the feasibility of using Bayesian networks based radiomics for predicting HPV positivity in an interpretable way.
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Affiliation(s)
- Oya Altinok
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey; Biomedical Engineering, Namik Kemal University, Tekirdağ, Turkey.
| | - Albert Guvenis
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
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16
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Saborido-Moral JD, Fernández-Patón M, Tejedor-Aguilar N, Cristian-Marín A, Torres-Espallardo I, Campayo-Esteban JM, Pérez-Calatayud J, Baltas D, Martí-Bonmatí L, Carles M. Free automatic software for quality assurance of computed tomography calibration, edges and radiomics metrics reproducibility. Phys Med 2023; 114:103153. [PMID: 37778209 DOI: 10.1016/j.ejmp.2023.103153] [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: 06/20/2023] [Revised: 09/16/2023] [Accepted: 09/22/2023] [Indexed: 10/03/2023] Open
Abstract
PURPOSE To develop a QA procedure, easy to use, reproducible and based on open-source code, to automatically evaluate the stability of different metrics extracted from CT images: Hounsfield Unit (HU) calibration, edge characterization metrics (contrast and drop range) and radiomic features. METHODS The QA protocol was based on electron density phantom imaging. Home-made open-source Python code was developed for the automatic computation of the metrics and their reproducibility analysis. The impact on reproducibility was evaluated for different radiation therapy protocols, and phantom positions within the field of view and systems, in terms of variability (Shapiro-Wilk test for 15 repeated measurements carried out over three days) and comparability (Bland-Altman analysis and Wilcoxon Rank Sum Test or Kendall Rank Correlation Coefficient). RESULTS Regarding intrinsic variability, most metrics followed a normal distribution (88% of HU, 63% of edge parameters and 82% of radiomic features). Regarding comparability, HU and contrast were comparable in all conditions, and drop range only in the same CT scanner and phantom position. The percentages of comparable radiomic features independent of protocol, position and system were 59%, 78% and 54%, respectively. The non-significantly differences in HU calibration curves obtained for two different institutions (7%) translated in comparable Gamma Index G (1 mm, 1%, >99%). CONCLUSIONS An automated software to assess the reproducibility of different CT metrics was successfully created and validated. A QA routine proposal is suggested.
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Affiliation(s)
- Juan D Saborido-Moral
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain.
| | - Matías Fernández-Patón
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain
| | - Natalia Tejedor-Aguilar
- Department of Radiation Oncology, La Fe Polytechnic and University Hospital, Valencia, Spain
| | - Andrei Cristian-Marín
- Department of Radiation Protection, La Fe Polytechnic and University Hospital, Valencia, Spain
| | | | - Juan M Campayo-Esteban
- Department of Radiation Protection, La Fe Polytechnic and University Hospital, Valencia, Spain
| | - José Pérez-Calatayud
- Department of Radiation Oncology, La Fe Polytechnic and University Hospital, Valencia, Spain
| | - Dimos Baltas
- Division of Medical Physics, Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg, Heidelberg, Germany
| | - Luis Martí-Bonmatí
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain
| | - Montserrat Carles
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain
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17
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Bartholomeus GA, van Amsterdam WAC, Harder AMD, Willemink MJ, van Hamersvelt RW, de Jong PA, Leiner T. Robustness of pulmonary nodule radiomic features on computed tomography as a function of varying radiation dose levels-a multi-dose in vivo patient study. Eur Radiol 2023; 33:7044-7055. [PMID: 37074424 PMCID: PMC10511375 DOI: 10.1007/s00330-023-09643-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 03/16/2023] [Accepted: 03/28/2023] [Indexed: 04/20/2023]
Abstract
OBJECTIVE Analysis of textural features of pulmonary nodules in chest CT, also known as radiomics, has several potential clinical applications, such as diagnosis, prognostication, and treatment response monitoring. For clinical use, it is essential that these features provide robust measurements. Studies with phantoms and simulated lower dose levels have demonstrated that radiomic features can vary with different radiation dose levels. This study presents an in vivo stability analysis of radiomic features for pulmonary nodules against varying radiation dose levels. METHODS Nineteen patients with a total of thirty-five pulmonary nodules underwent four chest CT scans at different radiation dose levels (60, 33, 24, and 15 mAs) in a single session. The nodules were manually delineated. To assess the robustness of features, we calculated the intra-class correlation coefficient (ICC). To visualize the effect of milliampere-second variation on groups of features, a linear model was fitted to each feature. We calculated bias and calculated the R2 value as a measure of goodness of fit. RESULTS A small minority of 15/100 (15%) radiomic features were considered stable (ICC > 0.9). Bias increased and R2 decreased at lower dose, but shape features seemed to be more robust to milliampere-second variations than other feature classes. CONCLUSION A large majority of pulmonary nodule radiomic features were not inherently robust to radiation dose level variations. For a subset of features, it was possible to correct this variability by a simple linear model. However, the correction became increasingly less accurate at lower radiation dose levels. CLINICAL RELEVANCE STATEMENT Radiomic features provide a quantitative description of a tumor based on medical imaging such as computed tomography (CT). These features are potentially useful in several clinical tasks such as diagnosis, prognosis prediction, treatment effect monitoring, and treatment effect estimation. KEY POINTS • The vast majority of commonly used radiomic features are strongly influenced by variations in radiation dose level. • A small minority of radiomic features, notably the shape feature class, are robust against dose-level variations according to ICC calculations. • A large subset of radiomic features can be corrected by a linear model taking into account only the radiation dose level.
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Affiliation(s)
| | | | | | - Martin J Willemink
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Pim A de Jong
- University Medical Center Utrecht, Utrecht, the Netherlands
| | - Tim Leiner
- University Medical Center Utrecht, Utrecht, the Netherlands
- Mayo Clinic, Rochester, MN, USA
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18
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Chen C, Du X, Yang L, Liu H, Li Z, Gou Z, Qi J. Research on application of radiomics in glioma: a bibliometric and visual analysis. Front Oncol 2023; 13:1083080. [PMID: 37771434 PMCID: PMC10523166 DOI: 10.3389/fonc.2023.1083080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 08/16/2023] [Indexed: 09/30/2023] Open
Abstract
Background With the continuous development of medical imaging informatics technology, radiomics has become a new and evolving field in medical applications. Radiomics aims to be an aid to support clinical decision making by extracting quantitative features from medical images and has a very wide range of applications. The purpose of this study was to perform a bibliometric and visual analysis of scientific results and research trends in the research application of radiomics in glioma. Methods We searched the Web of Science Core Collection (WOScc) for publications related to glioma radiomics. A bibliometric and visual analysis of online publications in this field related to countries/regions, authors, journals, references and keywords was performed using CiteSpace and R software. Results A total of 587 relevant literature published from 2012 to September 2022 were retrieved in WOScc, and finally a total of 484 publications were obtained according to the filtering criteria, including 393 (81.20%) articles and 91 (18.80%) reviews. The number of relevant publications increases year by year. The highest number of publications was from the USA (171 articles, 35.33%) and China (170 articles, 35.12%). The research institution with the highest number of publications was Chinese Acad Sci (24), followed by Univ Penn (22) and Fudan Univ (21). WANG Y (27) had the most publications, followed by LI Y (22), and WANG J (20). Among the 555 co-cited authors, LOUIS DN (207) and KICKINGEREDER P (207) were the most cited authors. FRONTIERS IN ONCOLOGY (42) was the most published journal and NEURO-ONCOLOGY (412) was the most co-cited journal. The most frequent keywords in all publications included glioblastoma (187), survival (136), classification (131), magnetic resonance imaging (113), machine learning (100), tumor (82), and feature (79), central nervous system (66), IDH (57), and radiomics (55). Cluster analysis was performed on the basis of keyword co-occurrence, and a total of 16 clusters were formed, indicating that these directions are the current hotspots of radiomics research applications in glioma and may be the future directions of continuous development. Conclusion In the past decade, radiomics has received much attention in the medical field and has been widely used in clinical research applications. Cooperation and communication between countries/regions need to be enhanced in future research to promote the development of radiomics in the field of medicine. In addition, the application of radiomics has improved the accuracy of pre-treatment diagnosis, efficacy prediction and prognosis assessment of glioma and helped to promote the development into precision medicine, the future still faces many challenges.
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Affiliation(s)
- Chunbao Chen
- Department of Neurosurgery, Afiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xue Du
- Department of Oncology, The People's Hospital of Hechuan, Chongqing, China
- Department of Oncology, North Sichuan Medical College, Nanchong, China
| | - Lu Yang
- Department of Oncology, Suining Central Hospital, Suining, China
| | - Hongjun Liu
- Department of Neurosurgery, Afiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Zhou Li
- Department of Neurosurgery, Nanchong Central Hospital, The Afiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, China
| | - Zhangyang Gou
- Department of Neurosurgery, Afiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jian Qi
- Department of Neurosurgery, Afiliated Hospital of North Sichuan Medical College, Nanchong, China
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19
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Kalantar R, Hindocha S, Hunter B, Sharma B, Khan N, Koh DM, Ahmed M, Aboagye EO, Lee RW, Blackledge MD. Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19. Sci Rep 2023; 13:10568. [PMID: 37386097 PMCID: PMC10310777 DOI: 10.1038/s41598-023-36712-1] [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: 09/15/2022] [Accepted: 06/07/2023] [Indexed: 07/01/2023] Open
Abstract
Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomography (CT) imaging-based artificial intelligence models for COVID-19 research. However, contrast heterogeneity from real-world datasets may impair model performance. Contrast-homogenous datasets present a potential solution. We developed a 3D patch-based cycle-consistent generative adversarial network (cycle-GAN) to synthesize non-contrast images from contrast CTs, as a data homogenization tool. We used a multi-centre dataset of 2078 scans from 1,650 patients with COVID-19. Few studies have previously evaluated GAN-generated images with handcrafted radiomics, DL and human assessment tasks. We evaluated the performance of our cycle-GAN with these three approaches. In a modified Turing-test, human experts identified synthetic vs acquired images, with a false positive rate of 67% and Fleiss' Kappa 0.06, attesting to the photorealism of the synthetic images. However, on testing performance of machine learning classifiers with radiomic features, performance decreased with use of synthetic images. Marked percentage difference was noted in feature values between pre- and post-GAN non-contrast images. With DL classification, deterioration in performance was observed with synthetic images. Our results show that whilst GANs can produce images sufficient to pass human assessment, caution is advised before GAN-synthesized images are used in medical imaging applications.
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Affiliation(s)
- Reza Kalantar
- Division of Radiotherapy and Imaging, the Institute of Cancer, London, SM2 5NG, UK
| | - Sumeet Hindocha
- Division of Radiotherapy and Imaging, the Institute of Cancer, London, SM2 5NG, UK
- AI for Healthcare Centre for Doctoral Training, Imperial College London, Exhibition Road, London, SW7 2BX, UK
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Early Diagnosis and Detection Team, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Benjamin Hunter
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Early Diagnosis and Detection Team, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Bhupinder Sharma
- Division of Radiotherapy and Imaging, the Institute of Cancer, London, SM2 5NG, UK
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Sutton, SM2 5PT, UK
| | - Nasir Khan
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Sutton, SM2 5PT, UK
| | - Dow-Mu Koh
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Sutton, SM2 5PT, UK
| | - Merina Ahmed
- Lung Unit, The Royal Marsden NHS Foundation Trust, Sutton, SM2 5PT, UK
| | - Eric O Aboagye
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Richard W Lee
- Early Diagnosis and Detection Team, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Matthew D Blackledge
- Division of Radiotherapy and Imaging, the Institute of Cancer, London, SM2 5NG, UK.
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20
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Reginelli A, Giacobbe G, Del Canto MT, Alessandrella M, Balestrucci G, Urraro F, Russo GM, Gallo L, Danti G, Frittoli B, Stoppino L, Schettini D, Iafrate F, Cappabianca S, Laghi A, Grassi R, Brunese L, Barile A, Miele V. Peritoneal Carcinosis: What the Radiologist Needs to Know. Diagnostics (Basel) 2023; 13:diagnostics13111974. [PMID: 37296826 DOI: 10.3390/diagnostics13111974] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/17/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
Peritoneal carcinosis is a condition characterized by the spread of cancer cells to the peritoneum, which is the thin membrane that lines the abdominal cavity. It is a serious condition that can result from many different types of cancer, including ovarian, colon, stomach, pancreatic, and appendix cancer. The diagnosis and quantification of lesions in peritoneal carcinosis are critical in the management of patients with the condition, and imaging plays a central role in this process. Radiologists play a vital role in the multidisciplinary management of patients with peritoneal carcinosis. They need to have a thorough understanding of the pathophysiology of the condition, the underlying neoplasms, and the typical imaging findings. In addition, they need to be aware of the differential diagnoses and the advantages and disadvantages of the various imaging methods available. Imaging plays a central role in the diagnosis and quantification of lesions, and radiologists play a critical role in this process. Ultrasound, computed tomography, magnetic resonance, and PET/CT scans are used to diagnose peritoneal carcinosis. Each imaging procedure has advantages and disadvantages, and particular imaging techniques are recommended based on patient conditions. Our aim is to provide knowledge to radiologists regarding appropriate techniques, imaging findings, differential diagnoses, and treatment options. With the advent of AI in oncology, the future of precision medicine appears promising, and the interconnection between structured reporting and AI is likely to improve diagnostic accuracy and treatment outcomes for patients with peritoneal carcinosis.
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Affiliation(s)
- Alfonso Reginelli
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Giuliana Giacobbe
- General and Emergency Radiology Department, "Antonio Cardarelli" Hospital, 80131 Naples, Italy
| | - Maria Teresa Del Canto
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Marina Alessandrella
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Giovanni Balestrucci
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Fabrizio Urraro
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Gaetano Maria Russo
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Luigi Gallo
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Ginevra Danti
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Barbara Frittoli
- Department of Radiology, Spedali Civili Hospital, 25123 Brescia, Italy
| | - Luca Stoppino
- Department of Radiology, University Hospital of Foggia, 71122 Foggia, Italy
| | - Daria Schettini
- Department of Radiology, Villa Scassi Hospital, Corso Scassi 1, 16121 Genova, Italy
| | - Franco Iafrate
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Andrea Laghi
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza-University of Rome, Radiology Unit-Sant'Andrea University Hospital, 00189 Rome, Italy
| | - Roberto Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, 86100 Campobasso, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L'Aquila, 67100 L'Aquila, Italy
| | - Vittorio Miele
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, 56126 Pisa, Italy
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21
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Le VH, Kha QH, Minh TNT, Nguyen VH, Le VL, Le NQK. Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer. J Digit Imaging 2023; 36:911-922. [PMID: 36717518 PMCID: PMC10287593 DOI: 10.1007/s10278-023-00778-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 02/01/2023] Open
Abstract
The malignant tumors in nature share some common morphological characteristics. Radiomics is not only images but also data; we think that a probability exists in a set of radiomics signatures extracted from CT scan images of one cancer tumor in one specific organ also be utilized for overall survival prediction in different types of cancers in different organs. The retrospective study enrolled four data sets of cancer patients in three different organs (420, 157, 137, and 191 patients for lung 1 training, lung 2 testing, and two external validation set: kidney and head and neck, respectively). In the training set, radiomics features were obtained from CT scan images, and essential features were chosen by LASSO algorithm. Univariable and multivariable analyses were then conducted to find a radiomics signature via Cox proportional hazard regression. The Kaplan-Meier curve was performed based on the risk score. The integrated time-dependent area under the ROC curve (iAUC) was calculated for each predictive model. In the training set, Kaplan-Meier curve classified patients as high or low-risk groups (p-value < 0.001; log-rank test). The risk score of radiomics signature was locked and independently evaluated in the testing set, and two external validation sets showed significant differences (p-value < 0.05; log-rank test). A combined model (radiomics + clinical) showed improved iAUC in lung 1, lung 2, head and neck, and kidney data set are 0.621 (95% CI 0.588, 0.654), 0.736 (95% CI 0.654, 0.819), 0.732 (95% CI 0.655, 0.809), and 0.834 (95% CI 0.722, 0.946), respectively. We believe that CT-based radiomics signatures for predicting overall survival in various cancer sites may exist.
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Affiliation(s)
- Viet Huan Le
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
- Department of Thoracic Surgery, Khanh Hoa General Hospital, Nha Trang, 65000, Vietnam
| | - Quang Hien Kha
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
| | - Tran Nguyen Tuan Minh
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
| | - Van Hiep Nguyen
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
- Oncology Center, Bai Chay Hospital, Quang Ninh, 20000, Vietnam
| | - Van Long Le
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
- Department of Anesthesiology and Critical Care, Hue University of Medicine and Pharmacy, Hue University, Hue City, 52000, Vietnam
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 106, Taiwan.
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, 106, Taiwan.
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, 110, Taiwan.
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22
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Ninomiya K, Arimura H, Tanaka K, Chan WY, Kabata Y, Mizuno S, Gowdh NFM, Yaakup NA, Liam CK, Chai CS, Ng KH. Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107544. [PMID: 37148668 DOI: 10.1016/j.cmpb.2023.107544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 02/16/2023] [Accepted: 04/07/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVES To elucidate a novel radiogenomics approach using three-dimensional (3D) topologically invariant Betti numbers (BNs) for topological characterization of epidermal growth factor receptor (EGFR) Del19 and L858R mutation subtypes. METHODS In total, 154 patients (wild-type EGFR, 72 patients; Del19 mutation, 45 patients; and L858R mutation, 37 patients) were retrospectively enrolled and randomly divided into 92 training and 62 test cases. Two support vector machine (SVM) models to distinguish between wild-type and mutant EGFR (mutation [M] classification) as well as between the Del19 and L858R subtypes (subtype [S] classification) were trained using 3DBN features. These features were computed from 3DBN maps by using histogram and texture analyses. The 3DBN maps were generated using computed tomography (CT) images based on the Čech complex constructed on sets of points in the images. These points were defined by coordinates of voxels with CT values higher than several threshold values. The M classification model was built using image features and demographic parameters of sex and smoking status. The SVM models were evaluated by determining their classification accuracies. The feasibility of the 3DBN model was compared with those of conventional radiomic models based on pseudo-3D BN (p3DBN), two-dimensional BN (2DBN), and CT and wavelet-decomposition (WD) images. The validation of the model was repeated with 100 times random sampling. RESULTS The mean test accuracies for M classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.810, 0.733, 0.838, 0.782, and 0.799, respectively. The mean test accuracies for S classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.773, 0.694, 0.657, 0.581, and 0.696, respectively. CONCLUSION 3DBN features, which showed a radiogenomic association with the characteristics of the EGFR Del19/L858R mutation subtypes, yielded higher accuracy for subtype classifications in comparison with conventional features.
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Affiliation(s)
- Kenta Ninomiya
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Sanford Burnham Prebys Medical Discovery Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA.
| | - Hidetaka Arimura
- Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kentaro Tanaka
- Department of Respiratory Medicine, Kyushu University Hospital, Fukuoka, Japan; Department of Respiratory Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia; Radiology Department, Gleneagles Hospital Kuala Lumpur, Jalan Ampang, 50450 Kuala Lumpur, Malaysia
| | - Yutaro Kabata
- School of Information and Data Sciences, Nagasaki University, Nagasaki, Japan
| | - Shinichi Mizuno
- Division of Medical Sciences and Technology, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | | | - Nur Adura Yaakup
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Chong-Kin Liam
- Department of Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Chee-Shee Chai
- Department of Medicine, Faculty of Medicine and Health Science, University Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia; Faculty of Medicine and Health Sciences, UCSI University, Springhill, Negeri Sembilan, Malaysia
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23
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Kawazoe Y, Shiinoki T, Fujimoto K, Yuasa Y, Hirano T, Matsunaga K, Tanaka H. Investigation of the combination of intratumoral and peritumoral radiomic signatures for predicting epidermal growth factor receptor mutation in lung adenocarcinoma. J Appl Clin Med Phys 2023:e13980. [DOI: 10.1002/acm2.13980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/15/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023] Open
Affiliation(s)
- Yusuke Kawazoe
- Department of Radiation Oncology Graduate School of Medicine Yamaguchi University Ube Japan
| | - Takehiro Shiinoki
- Department of Radiation Oncology Graduate School of Medicine Yamaguchi University Ube Japan
| | - Koya Fujimoto
- Department of Radiation Oncology Graduate School of Medicine Yamaguchi University Ube Japan
| | - Yuki Yuasa
- Department of Radiation Oncology Graduate School of Medicine Yamaguchi University Ube Japan
| | - Tsunahiko Hirano
- Department of Respiratory Medicine and Infectious Disease Graduate School of Medicine Yamaguchi University Ube Japan
| | - Kazuto Matsunaga
- Department of Respiratory Medicine and Infectious Disease Graduate School of Medicine Yamaguchi University Ube Japan
| | - Hidekazu Tanaka
- Department of Radiation Oncology Graduate School of Medicine Yamaguchi University Ube Japan
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24
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Bang C, Bernard G, Le WT, Lalonde A, Kadoury S, Bahig H. Artificial intelligence to predict outcomes of head and neck radiotherapy. Clin Transl Radiat Oncol 2023; 39:100590. [PMID: 36935854 PMCID: PMC10014342 DOI: 10.1016/j.ctro.2023.100590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 01/28/2023] [Accepted: 01/28/2023] [Indexed: 02/01/2023] Open
Abstract
Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy. Clinical implementation of these models could lead to individualized risk-based therapeutic decision making, but the reliability of the current studies is limited. Understanding, validating and expanding these models to larger multi-institutional data sets and testing them in the context of clinical trials is needed to ensure safe clinical implementation. This review summarizes the current state of the art of machine learning models for prediction of head and neck cancer radiotherapy outcomes.
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Key Words
- ADASYN, adaptive synthetic sampling
- AI, artificial intelligence
- ANN, artificial neural network
- AUC, Area Under the ROC Curve
- Artificial intelligence
- BMI, body mass index
- C-Index, concordance index
- CART, Classification and Regression Tree
- CBCT, cone-beam computed tomography
- CIFE, conditional informax feature extraction
- CNN, convolutional neural network
- CRT, chemoradiation
- CT, computed tomography
- Cancer outcomes
- DL, deep learning
- DM, distant metastasis
- DSC, Dice Similarity Coefficient
- DSS, clinical decision support systems
- DT, Decision Tree
- DVH, Dose-volume histogram
- GANs, Generative Adversarial Networks
- GB, Gradient boosting
- GPU, graphical process units
- HNC, head and neck cancer
- HPV, human papillomavirus
- HR, hazard ratio
- Head and neck cancer
- IAMB, incremental association Markov blanket
- IBDM, image based data mining
- IBMs, image biomarkers
- IMRT, intensity-modulated RT
- KNN, k nearest neighbor
- LLR, Local linear forest
- LR, logistic regression
- LRR, loco-regional recurrence
- MIFS, mutual information based feature selection
- ML, machine learning
- MRI, Magnetic resonance imaging
- MRMR, Minimum redundancy feature selection
- Machine learning
- N-MLTR, Neural Multi-Task Logistic Regression
- NPC, nasopharynx
- NTCP, Normal Tissue Complication Probability
- OPC, oropharyngeal cancer
- ORN, osteoradionecrosis
- OS, overall survival
- PCA, Principal component analysis
- PET, Positron emission tomography
- PG, parotid glands
- PLR, Positive likelihood ratio
- PM, pharyngeal mucosa
- PTV, Planning target volumes
- PreSANet, deep preprocessor module and self-attention
- Predictive modeling
- QUANTEC, Quantitative Analyses of Normal Tissue Effects in the Clinic
- RF, random forest
- RFC, random forest classifier
- RFS, recurrence free survival
- RLR, Rigid logistic regression
- RRF, Regularized random forest
- RSF, random survival forest
- RT, radiotherapy
- RTLI, radiation-induced temporal lobe injury
- Radiomic
- SDM, shared decision making
- SMG, submandibular glands
- SMOTE, synthetic minority over-sampling technique
- STIC, sticky saliva
- SVC, support vector classifier
- SVM, support vector machine
- XGBoost, extreme gradient boosting
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Affiliation(s)
- Chulmin Bang
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
| | - Galaad Bernard
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
| | - William T. Le
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Polytechnique Montréal, Montreal, QC, Canada
| | - Arthur Lalonde
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Université de Montréal, Montreal, QC, Canada
| | - Samuel Kadoury
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Polytechnique Montréal, Montreal, QC, Canada
| | - Houda Bahig
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
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25
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Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT. Cancers (Basel) 2023; 15:cancers15030932. [PMID: 36765889 PMCID: PMC9913076 DOI: 10.3390/cancers15030932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
PURPOSE This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on 18F-FDG PET/CT images. METHODS 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. RESULTS There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. CONCLUSION Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.
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Zhou B, Wang J, Yang X, Henry S, Lin JY, Torres MA, Liu T. Ultrasound Histogram Assessment of Acute Breast Toxicity After Breast Cancer Radiation Therapy: A Prospective Longitudinal Study. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:309-317. [PMID: 36441032 DOI: 10.1016/j.ultrasmedbio.2022.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/19/2022] [Accepted: 09/04/2022] [Indexed: 06/16/2023]
Abstract
Accurate assessment of radiation-induced breast toxicity is crucial for the management of breast radiation therapy (RT). Standard assessment of breast toxicity based on clinicians' visual inspection and palpation has considerable inter- and intra-observer variability. To overcome this challenge, we present an ultrasound histogram method that objectively evaluates radiation-induced breast toxicity longitudinally. In a prospective study, patients enrolled (n = 67) received ultrasound scans at four time points: prior to RT, last day of RT, 3-4 wk post-RT and 9-12-wk post-RT. Ultrasound scans were acquired at five locations (tumor bed and 3, 6, 9 and 12 o'clock) on both breasts. Two hundred sixty-four ultrasound scans and 2640 B-mode images were analyzed. The histogram differences between irradiated and contralateral breasts were calculated to evaluate radiation-induced breast changes. On the basis of the B-mode images, the severity of breast toxicity was graded as absent, mild, moderate or severe. The performance of the histogram method was assessed with the receiver operating characteristic (ROC) curve. The areas under the ROC curve ranged from 0.78 to 0.9 (sensitivity: 0.88-0.96, specificity: 0.53-0.83) at the lower quadrant for differentiating absent/mild from moderate/severe toxicity at various time points. This study provides preliminary evidence that ultrasound histogram differences can serve as an imaging biomarker to longitudinally assess radiation-induced acute toxicity.
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Affiliation(s)
- Boran Zhou
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
| | - Jing Wang
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
| | - Simone Henry
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
| | - Jolinta Y Lin
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
| | - Mylin A Torres
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
| | - Tian Liu
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA.
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27
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Tomassini S, Falcionelli N, Sernani P, Burattini L, Dragoni AF. Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey. Comput Biol Med 2022; 146:105691. [PMID: 35691714 DOI: 10.1016/j.compbiomed.2022.105691] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 05/26/2022] [Accepted: 05/31/2022] [Indexed: 11/30/2022]
Abstract
Lung cancer is among the deadliest cancers. Besides lung nodule classification and diagnosis, developing non-invasive systems to classify lung cancer histological types/subtypes may help clinicians to make targeted treatment decisions timely, having a positive impact on patients' comfort and survival rate. As convolutional neural networks have proven to be responsible for the significant improvement of the accuracy in lung cancer diagnosis, with this survey we intend to: show the contribution of convolutional neural networks not only in identifying malignant lung nodules but also in classifying lung cancer histological types/subtypes directly from computed tomography data; point out the strengths and weaknesses of slice-based and scan-based approaches employing convolutional neural networks; and highlight the challenges and prospective solutions to successfully apply convolutional neural networks for such classification tasks. To this aim, we conducted a comprehensive analysis of relevant Scopus-indexed studies involved in lung nodule diagnosis and cancer histology classification up to January 2022, dividing the investigation in convolutional neural network-based approaches fed with planar or volumetric computed tomography data. Despite the application of convolutional neural networks in lung nodule diagnosis and cancer histology classification is a valid strategy, some challenges raised, mainly including the lack of publicly-accessible annotated data, together with the lack of reproducibility and clinical interpretability. We believe that this survey will be helpful for future studies involved in lung nodule diagnosis and cancer histology classification prior to lung biopsy by means of convolutional neural networks.
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Affiliation(s)
- Selene Tomassini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Nicola Falcionelli
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Paolo Sernani
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Laura Burattini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Aldo Franco Dragoni
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
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Teng X, Zhang J, Zwanenburg A, Sun J, Huang Y, Lam S, Zhang Y, Li B, Zhou T, Xiao H, Liu C, Li W, Han X, Ma Z, Li T, Cai J. Building reliable radiomic models using image perturbation. Sci Rep 2022; 12:10035. [PMID: 35710850 PMCID: PMC9203573 DOI: 10.1038/s41598-022-14178-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 05/16/2022] [Indexed: 02/06/2023] Open
Abstract
Radiomic model reliability is a central premise for its clinical translation. Presently, it is assessed using test–retest or external data, which, unfortunately, is often scarce in reality. Therefore, we aimed to develop a novel image perturbation-based method (IPBM) for the first of its kind toward building a reliable radiomic model. We first developed a radiomic prognostic model for head-and-neck cancer patients on a training (70%) and evaluated on a testing (30%) cohort using C-index. Subsequently, we applied the IPBM to CT images of both cohorts (Perturbed-Train and Perturbed-Test cohort) to generate 60 additional samples for both cohorts. Model reliability was assessed using intra-class correlation coefficient (ICC) to quantify consistency of the C-index among the 60 samples in the Perturbed-Train and Perturbed-Test cohorts. Besides, we re-trained the radiomic model using reliable RFs exclusively (ICC > 0.75) to validate the IPBM. Results showed moderate model reliability in Perturbed-Train (ICC: 0.565, 95%CI 0.518–0.615) and Perturbed-Test (ICC: 0.596, 95%CI 0.527–0.670) cohorts. An enhanced reliability of the re-trained model was observed in Perturbed-Train (ICC: 0.782, 95%CI 0.759–0.815) and Perturbed-Test (ICC: 0.825, 95%CI 0.782–0.867) cohorts, indicating validity of the IPBM. To conclude, we demonstrated capability of the IPBM toward building reliable radiomic models, providing community with a novel model reliability assessment strategy prior to prospective evaluation.
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Affiliation(s)
- Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Alex Zwanenburg
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany.,Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Helmholtz Association / Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - Jiachen Sun
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Yuhua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Saikit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Yuanpeng Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Bing Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Xinyang Han
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Zongrui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China.
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CT Reconstruction Kernels and the Effect of Pre- and Post-Processing on the Reproducibility of Handcrafted Radiomic Features. J Pers Med 2022; 12:jpm12040553. [PMID: 35455668 PMCID: PMC9030848 DOI: 10.3390/jpm12040553] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/27/2022] [Accepted: 03/28/2022] [Indexed: 02/06/2023] Open
Abstract
Handcrafted radiomics features (HRFs) are quantitative features extracted from medical images to decode biological information to improve clinical decision making. Despite the potential of the field, limitations have been identified. The most important identified limitation, currently, is the sensitivity of HRF to variations in image acquisition and reconstruction parameters. In this study, we investigated the use of Reconstruction Kernel Normalization (RKN) and ComBat harmonization to improve the reproducibility of HRFs across scans acquired with different reconstruction kernels. A set of phantom scans (n = 28) acquired on five different scanner models was analyzed. HRFs were extracted from the original scans, and scans were harmonized using the RKN method. ComBat harmonization was applied on both sets of HRFs. The reproducibility of HRFs was assessed using the concordance correlation coefficient. The difference in the number of reproducible HRFs in each scenario was assessed using McNemar’s test. The majority of HRFs were found to be sensitive to variations in the reconstruction kernels, and only six HRFs were found to be robust with respect to variations in reconstruction kernels. The use of RKN resulted in a significant increment in the number of reproducible HRFs in 19 out of the 67 investigated scenarios (28.4%), while the ComBat technique resulted in a significant increment in 36 (53.7%) scenarios. The combination of methods resulted in a significant increment in 53 (79.1%) scenarios compared to the HRFs extracted from original images. Since the benefit of applying the harmonization methods depended on the data being harmonized, reproducibility analysis is recommended before performing radiomics analysis. For future radiomics studies incorporating images acquired with similar image acquisition and reconstruction parameters, except for the reconstruction kernels, we recommend the systematic use of the pre- and post-processing approaches (respectively, RKN and ComBat).
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Lin FY, Chang YC, Huang HY, Li CC, Chen YC, Chen CM. A radiomics approach for lung nodule detection in thoracic CT images based on the dynamic patterns of morphological variation. Eur Radiol 2022; 32:3767-3777. [PMID: 35020016 DOI: 10.1007/s00330-021-08456-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 09/20/2021] [Accepted: 11/02/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To propose and evaluate a set of radiomic features, called morphological dynamics features, for pulmonary nodule detection, which were rooted in the dynamic patterns of morphological variation and needless precise lesion segmentation. MATERIALS AND METHODS Two datasets were involved, namely, university hospital (UH) and LIDC datasets, comprising 72 CT scans (360 nodules) and 888 CT scans (2230 nodules), respectively. Each nodule was annotated by multiple radiologists. Denoted the category of nodules identified by at least k radiologists as ALk. A nodule detection algorithm, called CAD-MD algorithm, was proposed based on the morphological dynamics radiomic features, characterizing a lesion by ten sets of the same features with different values extracted from ten different thresholding results. Each nodule candidate was classified by a two-level classifier, including ten decision trees and a random forest, respectively. The CAD-MD algorithm was compared with a deep learning approach, the N-Net, using the UH dataset. RESULTS On the AL1 and AL2 of the UH dataset, the AUC of the AFROC curves were 0.777 and 0.851 for the CAD-MD algorithm and 0.478 and 0.472 for the N-Net, respectively. The CAD-MD algorithm achieved the sensitivities of 84.4% and 91.4% with 2.98 and 3.69 FPs/scan and the N-Net 74.4% and 80.7% with 3.90 and 4.49 FPs/scan, respectively. On the LIDC dataset, the CAD-MD algorithm attained the sensitivities of 87.6%, 89.2%, 92.2%, and 95.0% with 4 FPs/scan for AL1-AL4, respectively. CONCLUSION The morphological dynamics radiomic features might serve as an effective set of radiomic features for lung nodule detection. KEY POINTS • Texture features varied with such CT system settings as reconstruction kernels of CT images, CT scanner models, and parameter settings, and so on. • Shape and first-order statistics were shown to be the most robust features against variation in CT imaging parameters. • The morphological dynamics radiomic features, which mainly characterized the dynamic patterns of morphological variation, were shown to be effective for lung nodule detection.
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Affiliation(s)
- Fan-Ya Lin
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei, 100, Taiwan
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | | | - Chia-Chen Li
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei, 100, Taiwan
| | - Yi-Chang Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei, 100, Taiwan.,Department of Medical Imaging, Cardinal Tien Hospital, New Taipei City, Taiwan
| | - Chung-Ming Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei, 100, Taiwan.
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31
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Janse van Rensburg HJ, Spiliopoulou P, Siu LL. OUP accepted manuscript. Oncologist 2022; 27:352-362. [PMID: 35285488 PMCID: PMC9074993 DOI: 10.1093/oncolo/oyac047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 01/31/2022] [Indexed: 12/02/2022] Open
Abstract
Circulating biomarkers have emerged as valuable surrogates for evaluating disease states in solid malignancies. Their relative ease of access and rapid turnover has bolstered clinical applications in monitoring treatment efficacy and cancer progression. In this review, the roles of various circulating biomarkers in monitoring treatment response are described. Non-specific markers of disease burden, tumor markers (eg CA 125, CEA, PSA, etc.), circulating tumor cells, nucleic acids, exosomes, and metabolomic arrays are highlighted. Specifically, the discovery of each of these markers is reviewed, with examples illustrating their use in influencing treatment decisions, and barriers to their application noted where these exist. Finally, opportunities for future work using these circulating biomarkers are discussed.
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Affiliation(s)
| | | | - Lillian L Siu
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Corresponding author: Lillian L. Siu, Princess Margaret Cancer Centre, 700 University Avenue, Toronto, ON, Canada M5G 1Z5. Tel: +1 416 946 2911;
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32
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Wang X, Wang S, Yin X, Zheng Y. MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma. Comput Biol Med 2021; 141:105058. [PMID: 34836622 DOI: 10.1016/j.compbiomed.2021.105058] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 11/19/2021] [Accepted: 11/20/2021] [Indexed: 02/07/2023]
Abstract
OBJECT To distinguish combined hepatocellular cholangiocarcinoma (cHCC-CC), hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC) before operation using MRI radiomics. METHOD This study retrospectively analyzed 196 liver cancers: 33 cHCC-CC, 88 HCC and 75 CC. They had confirmed by pathological analysis in the Affiliated Hospital of Hebei University. MRI lesions were manually segmented by a radiologist.1316 features were extracted from MRI lesions by Pyradiomics. Useful features were retained through two-level feature selection to establish a classification model. Receiver operating characteristic (ROC), area under curve (AUC) and F1-score were used to evaluate the performance of the model. RESULTS Compared with low-order image features, the performance of the model based on high-order features was improved by about 10%. The model showed better performance in identifying HCC tumors during the delay phase (AUC = 0.91, sensitivity = 0.88, specificity = 0.89, accuracy = 0.89, F1-Score = 0.88). CONCLUSION The classification ability of cHCC-CC, HCC and CC can be further improved by extracting MRI high-order features and using a two-level feature selection method.
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Affiliation(s)
- Xuehu Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China; Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China; Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China.
| | - Shuping Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China; Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China; Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China
| | - Xiaoping Yin
- Affiliated Hospital of Hebei University, Baoding, 071000, China
| | - Yongchang Zheng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, 100010, China
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Mahmood U, Shrestha R, Bates DDB, Mannelli L, Corrias G, Erdi YE, Kanan C. Detecting Spurious Correlations With Sanity Tests for Artificial Intelligence Guided Radiology Systems. Front Digit Health 2021; 3:671015. [PMID: 34713144 PMCID: PMC8521929 DOI: 10.3389/fdgth.2021.671015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 06/29/2021] [Indexed: 11/23/2022] Open
Abstract
Artificial intelligence (AI) has been successful at solving numerous problems in machine perception. In radiology, AI systems are rapidly evolving and show progress in guiding treatment decisions, diagnosing, localizing disease on medical images, and improving radiologists' efficiency. A critical component to deploying AI in radiology is to gain confidence in a developed system's efficacy and safety. The current gold standard approach is to conduct an analytical validation of performance on a generalization dataset from one or more institutions, followed by a clinical validation study of the system's efficacy during deployment. Clinical validation studies are time-consuming, and best practices dictate limited re-use of analytical validation data, so it is ideal to know ahead of time if a system is likely to fail analytical or clinical validation. In this paper, we describe a series of sanity tests to identify when a system performs well on development data for the wrong reasons. We illustrate the sanity tests' value by designing a deep learning system to classify pancreatic cancer seen in computed tomography scans.
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Affiliation(s)
- Usman Mahmood
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Robik Shrestha
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, United States
| | - David D. B. Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Lorenzo Mannelli
- Institute of Research and Medical Care (IRCCS) SDN, Institute of Diagnostic and Nuclear Research, Naples, Italy
| | - Giuseppe Corrias
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Yusuf Emre Erdi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Christopher Kanan
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, United States
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Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) Using Radiomics: The Impact of the Variation of CT Scanner. Cancers (Basel) 2021; 13:cancers13092269. [PMID: 34066857 PMCID: PMC8125906 DOI: 10.3390/cancers13092269] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 04/29/2021] [Accepted: 05/06/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Recent studies exploring the application of radiomics features in medicine have shown promising results. However, variation in imaging parameters may impact the robustness of these features. Feature robustness may then in turn affect the prediction performance of the machine learning models built upon these features. While numerous studies have tested feature robustness against a variety of imaging parameters, the extent to which feature robustness affects predictions remains unclear. A particularly notable application of radiomics in clinical oncology is the prediction of Human Papillomavirus (HPV) association in Oropharyngeal cancer. In this study we explore how CT scanner type affects the performance of radiomics features for HPV association prediction and highlight the need to implement precautionary approaches so as to minimize this effect. Abstract Studies have shown that radiomic features are sensitive to the variability of imaging parameters (e.g., scanner models), and one of the major challenges in these studies lies in improving the robustness of quantitative features against the variations in imaging datasets from multi-center studies. Here, we assess the impact of scanner choice on computed tomography (CT)-derived radiomic features to predict the association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV). This experiment was performed on CT image datasets acquired from two different scanner manufacturers. We demonstrate strong scanner dependency by developing a machine learning model to classify HPV status from radiological images. These experiments reveal the effect of scanner manufacturer on the robustness of radiomic features, and the extent of this dependency is reflected in the performance of HPV prediction models. The results of this study highlight the importance of implementing an appropriate approach to reducing the impact of imaging parameters on radiomic features and consequently on the machine learning models, without removing features which are deemed non-robust but may contain learning information.
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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] [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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Romanov A, Bach M, Yang S, Franzeck FC, Sommer G, Anastasopoulos C, Bremerich J, Stieltjes B, Weikert T, Sauter AW. Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds. Diagnostics (Basel) 2021; 11:diagnostics11050738. [PMID: 33919094 PMCID: PMC8143124 DOI: 10.3390/diagnostics11050738] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/16/2021] [Accepted: 04/17/2021] [Indexed: 02/06/2023] Open
Abstract
CT patterns of viral pneumonia are usually only qualitatively described in radiology reports. Artificial intelligence enables automated and reliable segmentation of lungs with chest CT. Based on this, the purpose of this study was to derive meaningful imaging biomarkers reflecting CT patterns of viral pneumonia and assess their potential to discriminate between healthy lungs and lungs with viral pneumonia. This study used non-enhanced and CT pulmonary angiograms (CTPAs) of healthy lungs and viral pneumonia (SARS-CoV-2, influenza A/B) identified by radiology reports and RT-PCR results. After deep learning segmentation of the lungs, histogram-based and threshold-based analyses of lung attenuation were performed and compared. The derived imaging biomarkers were correlated with parameters of clinical and biochemical severity (modified WHO severity scale; c-reactive protein). For non-enhanced CTs (n = 526), all imaging biomarkers significantly differed between healthy lungs and lungs with viral pneumonia (all p < 0.001), a finding that was not reproduced for CTPAs (n = 504). Standard deviation (histogram-derived) and relative high attenuation area [600-0 HU] (HU-thresholding) differed most. The strongest correlation with disease severity was found for absolute high attenuation area [600-0 HU] (r = 0.56, 95% CI = 0.46-0.64). Deep-learning segmentation-based histogram and HU threshold analysis could be deployed in chest CT evaluation for the differentiating of healthy lungs from AP lungs.
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Affiliation(s)
- Andrej Romanov
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland; (A.R.); (G.S.); (J.B.); (T.W.); (A.W.S.)
| | - Michael Bach
- Department of Research & Analytic Services, University Hospital Basel, University of Basel, Spitalstrasse 8, 4031 Basel, Switzerland; (M.B.); (S.Y.); (F.C.F.); (B.S.)
| | - Shan Yang
- Department of Research & Analytic Services, University Hospital Basel, University of Basel, Spitalstrasse 8, 4031 Basel, Switzerland; (M.B.); (S.Y.); (F.C.F.); (B.S.)
| | - Fabian C. Franzeck
- Department of Research & Analytic Services, University Hospital Basel, University of Basel, Spitalstrasse 8, 4031 Basel, Switzerland; (M.B.); (S.Y.); (F.C.F.); (B.S.)
| | - Gregor Sommer
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland; (A.R.); (G.S.); (J.B.); (T.W.); (A.W.S.)
| | - Constantin Anastasopoulos
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland; (A.R.); (G.S.); (J.B.); (T.W.); (A.W.S.)
- Correspondence:
| | - Jens Bremerich
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland; (A.R.); (G.S.); (J.B.); (T.W.); (A.W.S.)
| | - Bram Stieltjes
- Department of Research & Analytic Services, University Hospital Basel, University of Basel, Spitalstrasse 8, 4031 Basel, Switzerland; (M.B.); (S.Y.); (F.C.F.); (B.S.)
| | - Thomas Weikert
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland; (A.R.); (G.S.); (J.B.); (T.W.); (A.W.S.)
- Department of Research & Analytic Services, University Hospital Basel, University of Basel, Spitalstrasse 8, 4031 Basel, Switzerland; (M.B.); (S.Y.); (F.C.F.); (B.S.)
| | - Alexander Walter Sauter
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland; (A.R.); (G.S.); (J.B.); (T.W.); (A.W.S.)
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