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Wagner S, Ewald C, Freitag D, Herrmann KH, Koch A, Bauer J, Vogl TJ, Kemmling A, Gufler H. Radiomics and visual analysis for predicting success of transplantation of heterotopic glioblastoma in mice with MRI. J Neurooncol 2024:10.1007/s11060-024-04725-z. [PMID: 38960965 DOI: 10.1007/s11060-024-04725-z] [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: 03/31/2024] [Accepted: 05/25/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND Quantifying tumor growth and treatment response noninvasively poses a challenge to all experimental tumor models. The aim of our study was, to assess the value of quantitative and visual examination and radiomic feature analysis of high-resolution MR images of heterotopic glioblastoma xenografts in mice to determine tumor cell proliferation (TCP). METHODS Human glioblastoma cells were injected subcutaneously into both flanks of immunodeficient mice and followed up on a 3 T MR scanner. Volumes and signal intensities were calculated. Visual assessment of the internal tumor structure was based on a scoring system. Radiomic feature analysis was performed using MaZda software. The results were correlated with histopathology and immunochemistry. RESULTS 21 tumors in 14 animals were analyzed. The volumes of xenografts with high TCP (H-TCP) increased, whereas those with low TCP (L-TCP) or no TCP (N-TCP) continued to decrease over time (p < 0.05). A low intensity rim (rim sign) on unenhanced T1-weighted images provided the highest diagnostic accuracy at visual analysis for assessing H-TCP (p < 0.05). Applying radiomic feature analysis, wavelet transform parameters were best for distinguishing between H-TCP and L-TCP / N-TCP (p < 0.05). CONCLUSION Visual and radiomic feature analysis of the internal structure of heterotopically implanted glioblastomas provide reproducible and quantifiable results to predict the success of transplantation.
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Affiliation(s)
- Sabine Wagner
- Department of Neuroradiology, Marburg University Hospital - Philipps University, 35043, Marburg, Germany.
- Department of Neuroradiology, Institute for Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University, 07747, Jena, Germany.
| | - Christian Ewald
- Department of Neurosurgery, Brandenburg Medical School, Theodor Fontane, University Hospital Brandenburg/Havel, 14770, Brandenburg/Havel, Germany
| | - Diana Freitag
- Department of Neurosurgery, Section of Experimental Neurooncology, Jena University Hospital - Friedrich Schiller University, 07747, Jena, Germany
| | - Karl-Heinz Herrmann
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University, 07743, Jena, Germany
| | - Arend Koch
- Department of Neuropathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, and Berlin Institute of Health, Charité University Medicine, 10117, Berlin, Germany
| | - Johannes Bauer
- Department of Neurosurgery, Brandenburg Medical School, Theodor Fontane, University Hospital Brandenburg/Havel, 14770, Brandenburg/Havel, Germany
| | - Thomas J Vogl
- Department of Diagnostic and Interventional Radiology, Goethe University Hospital Frankfurt, 60590, Frankfurt Am Main, Germany
| | - André Kemmling
- Department of Neuroradiology, Marburg University Hospital - Philipps University, 35043, Marburg, Germany
| | - Hubert Gufler
- Department of Diagnostic and Interventional Radiology, Goethe University Hospital Frankfurt, 60590, Frankfurt Am Main, Germany
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2
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Lee J, Kim H, Yook S, Kang TY. Identification of document paper using hybrid feature extraction. J Forensic Sci 2023; 68:1808-1815. [PMID: 37420317 DOI: 10.1111/1556-4029.15330] [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: 04/06/2023] [Revised: 06/19/2023] [Accepted: 06/22/2023] [Indexed: 07/09/2023]
Abstract
Document forgery is a significant issue in Korea, with around ten thousand cases reported every year. Analyzing paper plays a crucial role in examining questionable documents such as marketable securities and contracts, which can aid in solving criminal cases of document forgery. Paper analysis can also provide essential insights in other types of criminal cases, serving as an important clue for solving cases such as the source of a blackmail letter. The papermaking process generates distinct forming fabric marks and formations, which are critical features for paper classification. These characteristics are observable under transmitted light and are created by the forming fabric pattern and the distribution of pulp fibers, respectively. In this study, we propose a novel approach for paper identification based on hybrid features. This method combines texture features extracted from images converted using the gray-level co-occurrence matrix (GLCM) approach and a convolutional neural network (CNN), with another set of features extracted by the CNN using the same images as input. We applied the proposed method to classification tasks for seven major paper brands available in the Korean market, achieving an accuracy of 97.66%. The results confirm the applicability of this method for visually inspecting paper products and demonstrate its potential for assisting in solving criminal cases involving document forgery.
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Affiliation(s)
- Joong Lee
- Institute of AI and Big Data in Medicine, Yonsei University Wonju College of Medicine, Wonju-si, South Korea
| | - Hongseok Kim
- Digital Analysis Division, National Forensic Service, Wonju-si, South Korea
| | - Simyub Yook
- Digital Analysis Division, National Forensic Service, Wonju-si, South Korea
| | - Tae-Yi Kang
- Digital Analysis Division, National Forensic Service, Wonju-si, South Korea
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3
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Saeed H, Lu YC, Andescavage N, Kapse K, Andersen NR, Lopez C, Quistorff J, Barnett S, Henderson D, Bulas D, Limperopoulos C. Influence of maternal psychological distress during COVID-19 pandemic on placental morphometry and texture. Sci Rep 2023; 13:7374. [PMID: 37164993 PMCID: PMC10172401 DOI: 10.1038/s41598-023-33343-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 04/12/2023] [Indexed: 05/12/2023] Open
Abstract
The Coronavirus Disease 2019 (COVID-19) pandemic has been accompanied by increased prenatal maternal distress (PMD). PMD is associated with adverse pregnancy outcomes which may be mediated by the placenta. However, the potential impact of the pandemic on in vivo placental development remains unknown. To examine the impact of the pandemic and PMD on in vivo structural placental development using advanced magnetic resonance imaging (MRI), acquired anatomic images of the placenta from 63 pregnant women without known COVID-19 exposure during the pandemic and 165 pre-pandemic controls. Measures of placental morphometry and texture were extracted. PMD was determined from validated questionnaires. Generalized estimating equations were utilized to compare differences in PMD placental features between COVID-era and pre-pandemic cohorts. Maternal stress and depression scores were significantly higher in the pandemic cohort. Placental volume, thickness, gray level kurtosis, skewness and run length non-uniformity were increased in the pandemic cohort, while placental elongation, mean gray level and long run emphasis were decreased. PMD was a mediator of the association between pandemic status and placental features. Altered in vivo placental structure during the pandemic suggests an underappreciated link between disturbances in maternal environment and perturbed placental development. The long-term impact on offspring is currently under investigation.
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Affiliation(s)
- Haleema Saeed
- Department of Obstetrics & Gynecology, MedStar Washington Hospital Center, Washington, DC, 20010, USA
| | - Yuan-Chiao Lu
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Nickie Andescavage
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
- Division of Neonatology, Children's National Hospital, Washington, DC, 20010, USA
| | - Kushal Kapse
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Nicole R Andersen
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Catherine Lopez
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Jessica Quistorff
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Scott Barnett
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Diedtra Henderson
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA
| | - Dorothy Bulas
- Division of Radiology, Children's National Hospital, Washington, DC, 20010, USA
| | - Catherine Limperopoulos
- Developing Brain Institute, Children's National Hospital, 111 Michigan Ave. NW, Washington, DC, 20010, USA.
- Division of Radiology, Children's National Hospital, Washington, DC, 20010, USA.
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4
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Panjaitan NSD, Lestari CSW, Maha MS. Thoughtful Response on "MRI-based Texture Analysis for Preoperative Prediction of BRAF V600E Mutation in Papillary Thyroid Carcinoma" [Letter]. J Multidiscip Healthc 2023; 16:275-276. [PMID: 36726481 PMCID: PMC9885877 DOI: 10.2147/jmdh.s405040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023] Open
Affiliation(s)
- Novaria Sari Dewi Panjaitan
- Center for Biomedical Research, Research Organization for Health, National Research and Innovation Agency (BRIN), Cibinong Science Center, Cibinong, West Java, Indonesia,Correspondence: Novaria Sari Dewi Panjaitan, Center for Biomedical Research, Research Organization for Health, National Research and Innovation Agency (BRIN), Cibinong Science Center, Jl. Raya Bogor No. 490, Cibinong – Bogor Km. 46, Cibinong, West Java, Indonesia, Email
| | - Christina Safira Whinie Lestari
- Center for Biomedical Research, Research Organization for Health, National Research and Innovation Agency (BRIN), Cibinong Science Center, Cibinong, West Java, Indonesia
| | - Masri Sembiring Maha
- Center for Biomedical Research, Research Organization for Health, National Research and Innovation Agency (BRIN), Cibinong Science Center, Cibinong, West Java, Indonesia
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Identification of texture MRI brain abnormalities on first-episode psychosis and clinical high-risk subjects using explainable artificial intelligence. Transl Psychiatry 2022; 12:481. [PMID: 36385133 PMCID: PMC9668814 DOI: 10.1038/s41398-022-02242-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 10/21/2022] [Accepted: 10/27/2022] [Indexed: 11/17/2022] Open
Abstract
Structural MRI studies in first-episode psychosis and the clinical high-risk state have consistently shown volumetric abnormalities. Aim of the present study was to introduce radiomics texture features in identification of psychosis. Radiomics texture features describe the interrelationship between voxel intensities across multiple spatial scales capturing the hidden information of underlying disease dynamics in addition to volumetric changes. Structural MR images were acquired from 77 first-episode psychosis (FEP) patients, 58 clinical high-risk subjects with no later transition to psychosis (CHR_NT), 15 clinical high-risk subjects with later transition (CHR_T), and 44 healthy controls (HC). Radiomics texture features were extracted from non-segmented images, and two-classification schemas were performed for the identification of FEP vs. HC and FEP vs. CHR_NT. The group of CHR_T was used as external validation in both schemas. The classification of a subject's clinical status was predicted by importing separately (a) the difference of entropy feature map and (b) the contrast feature map, resulting in classification balanced accuracy above 72% in both analyses. The proposed framework enhances the classification decision for FEP, CHR_NT, and HC subjects, verifies diagnosis-relevant features and may potentially contribute to identification of structural biomarkers for psychosis, beyond and above volumetric brain changes.
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Hsieh HP, Wu DY, Hung KC, Lim SW, Chen TY, Fan-Chiang Y, Ko CC. Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features. J Pers Med 2022; 12:jpm12040522. [PMID: 35455638 PMCID: PMC9032338 DOI: 10.3390/jpm12040522] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/09/2022] [Accepted: 03/22/2022] [Indexed: 01/04/2023] Open
Abstract
A subset of parasagittal and parafalcine (PSPF) meningiomas may show early progression/recurrence (P/R) after surgery. This study applied machine learning using combined clinical and texture features to predict P/R in PSPF meningiomas. A total of 57 consecutive patients with pathologically confirmed (WHO grade I) PSPF meningiomas treated in our institution between January 2007 to January 2019 were included. All included patients had complete preoperative magnetic resonance imaging (MRI) and more than one year MRI follow-up after surgery. Preoperative contrast-enhanced T1WI, T2WI, T1WI, and T2 fluid-attenuated inversion recovery (FLAIR) were analyzed retrospectively. The most significant 12 clinical features (extracted by LightGBM) and 73 texture features (extracted by SVM) were combined in random forest to predict P/R, and personalized radiomic scores were calculated. Thirteen patients (13/57, 22.8%) had P/R after surgery. The radiomic score was a high-risk factor for P/R with hazard ratio of 15.73 (p < 0.05) in multivariate hazards analysis. In receiver operating characteristic (ROC) analysis, an AUC of 0.91 with cut-off value of 0.269 was observed in radiomic scores for predicting P/R. Subtotal resection, low apparent diffusion coefficient (ADC) values, and high radiomic scores were associated with shorter progression-free survival (p < 0.05). Among different data input, machine learning using combined clinical and texture features showed the best predictive performance, with an accuracy of 91%, precision of 85%, and AUC of 0.88. Machine learning using combined clinical and texture features may have the potential to predict recurrence in PSPF meningiomas.
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Affiliation(s)
- Hsun-Ping Hsieh
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan; (H.-P.H.); (D.-Y.W.); (Y.F.-C.)
| | - Ding-You Wu
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan; (H.-P.H.); (D.-Y.W.); (Y.F.-C.)
| | - Kuo-Chuan Hung
- Department of Anesthesiology, Chi Mei Medical Center, Tainan City 71004, Taiwan;
- Department of Hospital and Health Care Administration, College of Recreation and Health Management, Chia Nan University of Pharmacy and Science, Tainan 71710, Taiwan
| | - Sher-Wei Lim
- Department of Neurosurgery, Chi Mei Medical Center, Chiali, Tainan 722, Taiwan;
- Department of Nursing, Min-Hwei College of Health Care Management, Tainan 73658, Taiwan
| | - Tai-Yuan Chen
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 71004, Taiwan;
- Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan 71101, Taiwan
| | - Yang Fan-Chiang
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan; (H.-P.H.); (D.-Y.W.); (Y.F.-C.)
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 71004, Taiwan;
- Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan 71710, Taiwan
- Institute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan
- Correspondence:
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Kalasauskas D, Kosterhon M, Keric N, Korczynski O, Kronfeld A, Ringel F, Othman A, Brockmann MA. Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors. Cancers (Basel) 2022; 14:cancers14030836. [PMID: 35159103 PMCID: PMC8834271 DOI: 10.3390/cancers14030836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/30/2022] [Accepted: 02/04/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Tumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors. Abstract The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.
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Affiliation(s)
- Darius Kalasauskas
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Michael Kosterhon
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Naureen Keric
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Oliver Korczynski
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Andrea Kronfeld
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Florian Ringel
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Ahmed Othman
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Marc A. Brockmann
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
- Correspondence:
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8
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Zhou G, Lu B, Hu X, Ni T. Sparse Representation-Based Discriminative Metric Learning for Brain MRI Image Retrieval. Front Neurosci 2022; 15:829040. [PMID: 35095411 PMCID: PMC8795867 DOI: 10.3389/fnins.2021.829040] [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: 12/04/2021] [Accepted: 12/27/2021] [Indexed: 12/20/2022] Open
Abstract
Magnetic resonance imaging (MRI) can have a good diagnostic function for important organs and parts of the body. MRI technology has become a common and important disease detection technology. At the same time, medical imaging data is increasing at an explosive rate. Retrieving similar medical images from a huge database is of great significance to doctors’ auxiliary diagnosis and treatment. In this paper, combining the advantages of sparse representation and metric learning, a sparse representation-based discriminative metric learning (SRDML) approach is proposed for medical image retrieval of brain MRI. The SRDML approach uses a sparse representation framework to learn robust feature representation of brain MRI, and uses metric learning to project new features into the metric space with matching discrimination. In such a metric space, the optimal similarity measure is obtained by using the local constraints of atoms and the pairwise constraints of coding coefficients, so that the distance between similar images is less than the given threshold, and the distance between dissimilar images is greater than another given threshold. The experiments are designed and tested on the brain MRI dataset created by Chang. Experimental results show that the SRDML approach can obtain satisfactory retrieval performance and achieve accurate brain MRI image retrieval.
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Affiliation(s)
- Guohua Zhou
- School of Information Engineering, Changzhou Institute of Industry Technology, Changzhou, China
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
- College of Information Engineering, Yangzhou University, Yangzhou, China
| | - Bing Lu
- School of Information Engineering, Changzhou Institute of Industry Technology, Changzhou, China
| | - Xuelong Hu
- College of Information Engineering, Yangzhou University, Yangzhou, China
| | - Tongguang Ni
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
- *Correspondence: Tongguang Ni,
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Park CJ, Park YW, Ahn SS, Kim D, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK. Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation. Korean J Radiol 2022; 23:77-88. [PMID: 34983096 PMCID: PMC8743155 DOI: 10.3348/kjr.2021.0421] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/05/2021] [Accepted: 08/05/2021] [Indexed: 12/14/2022] Open
Abstract
Objective Our study aimed to evaluate the quality of radiomics studies on brain metastases based on the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guidelines. Materials and Methods PubMed MEDLINE, and EMBASE were searched for articles on radiomics for evaluating brain metastases, published until February 2021. Of the 572 articles, 29 relevant original research articles were included and evaluated according to the RQS, TRIPOD checklist, and IBSI guidelines. Results External validation was performed in only three studies (10.3%). The median RQS was 3.0 (range, -6 to 12), with a low basic adherence rate of 50.0%. The adherence rate was low in comparison to the “gold standard” (10.3%), stating the potential clinical utility (10.3%), performing the cut-off analysis (3.4%), reporting calibration statistics (6.9%), and providing open science and data (3.4%). None of the studies involved test-retest or phantom studies, prospective studies, or cost-effectiveness analyses. The overall rate of adherence to the TRIPOD checklist was 60.3% and low for reporting title (3.4%), blind assessment of outcome (0%), description of the handling of missing data (0%), and presentation of the full prediction model (0%). The majority of studies lacked pre-processing steps, with bias-field correction, isovoxel resampling, skull stripping, and gray-level discretization performed in only six (20.7%), nine (31.0%), four (3.8%), and four (13.8%) studies, respectively. Conclusion The overall scientific and reporting quality of radiomics studies on brain metastases published during the study period was insufficient. Radiomics studies should adhere to the RQS, TRIPOD, and IBSI guidelines to facilitate the translation of radiomics into the clinical field.
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Affiliation(s)
- Chae Jung Park
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Dain Kim
- Department of Psychology, Yonsei University, Seoul, Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
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Ramesh S, Chokkara S, Shen T, Major A, Volchenboum SL, Mayampurath A, Applebaum MA. Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review. JCO Clin Cancer Inform 2021; 5:1208-1219. [PMID: 34910588 DOI: 10.1200/cci.21.00102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
PURPOSE There is a need for an improved understanding of clinical and biologic risk factors in pediatric cancer to improve patient outcomes. Machine learning (ML) represents the application of computational inference from advanced statistical methods that can be applied to increasing amount of data available for study in pediatric oncology. The goal of this systematic review was to systematically characterize the state of ML in pediatric oncology and highlight advances and opportunities in the field. METHODS We conducted a systematic review of the Embase, Scopus, and MEDLINE databases for applications of ML in pediatric oncology. Query results from all three databases were aggregated and duplicate studies were removed. RESULTS A total of 42 unique articles that examined the applications of ML in pediatric oncology met inclusion criteria for review. We identified 20 studies of CNS tumors, 13 of solid tumors, and nine of leukemia. ML tasks included classification, prediction of treatment response, and dose optimization with a variety of methods being used including neural network, k-nearest neighbor, random forest, naive Bayes, and support vector machines. Strengths of the identified studies included matching or outperforming physician comparators via automated analysis and predicting therapeutic response. Common limitations included significant heterogeneity in reporting standards, clinical applicability, small sample sizes, and missing external validation cohorts. CONCLUSION We identified areas where ML can enhance clinical care in ways that may not otherwise be achievable. Although ML promises enormous potential in improving diagnostics, decision making, and monitoring for children with cancer, the field remains in early stages and future work will be aided by standards and guidelines to ensure rigorous methodologic design and maximizing clinical utility.
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Affiliation(s)
- Siddhi Ramesh
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Sukarn Chokkara
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Timothy Shen
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Ajay Major
- Department of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Samuel L Volchenboum
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Anoop Mayampurath
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Mark A Applebaum
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
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Gu X, Shen Z, Xue J, Fan Y, Ni T. Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint. Front Neurosci 2021; 15:679847. [PMID: 34122001 PMCID: PMC8193950 DOI: 10.3389/fnins.2021.679847] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 04/09/2021] [Indexed: 11/30/2022] Open
Abstract
Brain tumor image classification is an important part of medical image processing. It assists doctors to make accurate diagnosis and treatment plans. Magnetic resonance (MR) imaging is one of the main imaging tools to study brain tissue. In this article, we propose a brain tumor MR image classification method using convolutional dictionary learning with local constraint (CDLLC). Our method integrates the multi-layer dictionary learning into a convolutional neural network (CNN) structure to explore the discriminative information. Encoding a vector on a dictionary can be considered as multiple projections into new spaces, and the obtained coding vector is sparse. Meanwhile, in order to preserve the geometric structure of data and utilize the supervised information, we construct the local constraint of atoms through a supervised k-nearest neighbor graph, so that the discrimination of the obtained dictionary is strong. To solve the proposed problem, an efficient iterative optimization scheme is designed. In the experiment, two clinically relevant multi-class classification tasks on the Cheng and REMBRANDT datasets are designed. The evaluation results demonstrate that our method is effective for brain tumor MR image classification, and it could outperform other comparisons.
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Affiliation(s)
- Xiaoqing Gu
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Zongxuan Shen
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Jing Xue
- Department of Nephrology, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Yiqing Fan
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Tongguang Ni
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
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Roura E, Maclair G, Andorrà M, Juanals F, Pulido-Valdeolivas I, Saiz A, Blanco Y, Sepulveda M, Llufriu S, Martínez-Heras E, Solana E, Martinez-Lapiscina EH, Villoslada P. Cortical fractal dimension predicts disability worsening in Multiple Sclerosis patients. Neuroimage Clin 2021; 30:102653. [PMID: 33838548 PMCID: PMC8045041 DOI: 10.1016/j.nicl.2021.102653] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 03/14/2021] [Accepted: 03/26/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND Fractal geometry measures the morphology of the brain and detects CNS damage. We aimed to assess the longitudinal changes on brain's fractal geometry and its predictive value for disease worsening in patients with Multiple Sclerosis (MS). METHODS We prospectively analyzed 146 consecutive patients with relapsing-remitting MS with up to 5 years of clinical and brain MRI (3 T) assessments. The fractal dimension and lacunarity were calculated for brain regions using box-counting methods. Longitudinal changes were analyzed in mixed-effect models and the risk of disability accumulation were assessed using Cox Proportional Hazard regression analysis. RESULTS There was a significant decrease in the fractal dimension and increases of lacunarity in different brain regions over the 5-year follow-up. Lower cortical fractal dimension increased the risk of disability accumulation for the Expanded Disability Status Scale [HR 0.9734, CI 0.8420-0.9125; Harrell C 0.59; Wald p 0.038], 9-hole peg test [HR 0.9734, CI 0.8420-0.9125; Harrell C 0.59; Wald p 0.0083], 2.5% low contrast vision [HR 0.4311, CI 0.2035-0.9133; Harrell C 0.58; Wald p 0.0403], symbol digit modality test [HR 2.215, CI 1.043-4.705; Harrell C 0.65; Wald p 0.0384] and MS Functional Composite-4 [HR 0.55, CI 0.317-0.955; Harrell C 0.59; Wald p 0.0029]. CONCLUSIONS Fractal geometry analysis of brain MRI identified patients at risk of increasing their disability in the next five years.
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Affiliation(s)
| | | | - Magí Andorrà
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | | | - Irene Pulido-Valdeolivas
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Albert Saiz
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Yolanda Blanco
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Maria Sepulveda
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Sara Llufriu
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Eloy Martínez-Heras
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Elisabeth Solana
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Elena H Martinez-Lapiscina
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Pablo Villoslada
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain; Stanford University, Stanford, CA, USA.
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13
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Kunimatsu A, Yasaka K, Akai H, Sugawara H, Kunimatsu N, Abe O. Texture Analysis in Brain Tumor MR Imaging. Magn Reson Med Sci 2021; 21:95-109. [PMID: 33692222 PMCID: PMC9199980 DOI: 10.2463/mrms.rev.2020-0159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brain tumors using MR-based texture analysis (MRTA) to determine the correlation of various clinical measures with MRTA features. Promising results in cerebral gliomas have been shown in the previous MRTA studies in terms of the correlation with the World Health Organization grades, risk stratification in gliomas, and the differentiation of gliomas from other brain tumors. Multiple MRTA studies in gliomas have repeatedly shown high performance of entropy, a measure of the randomness in image intensity values, of either histogram- or gray-level co-occurrence matrix parameters. Similarly, researchers have applied MRTA to other brain tumors, including meningiomas and pediatric posterior fossa tumors. However, the value of MRTA in the clinical use remains undetermined, probably because previous studies have shown only limited reproducibility of the result in the real world. The low-to-modest generalizability may be attributed to variations in MRTA methods, sampling bias that originates from single-institution studies, and overfitting problems to a limited number of samples. To enhance the reliability and reproducibility of MRTA studies, researchers have realized the importance of standardizing methods in the field of radiomics. Another advancement is the recent development of a comprehensive assessment system to ensure the quality of a radiomics study. These two-way approaches will secure the validity of upcoming MRTA studies. The clinical use of texture analysis in brain MRI will be accelerated by these continuous efforts.
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Affiliation(s)
- Akira Kunimatsu
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Koichiro Yasaka
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Hiroyuki Akai
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Haruto Sugawara
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Natsuko Kunimatsu
- Department of Radiology, International University of Health and Welfare, Mita Hospital
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
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14
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Sonn GA, Fan RE, Kunder CA, Gold GE, James KM, Parker ID, Carlson JM, Cannizzaro SM, James TW. MR method for measuring microscopic histologic soft tissue textures. Magn Reson Med 2021; 86:308-319. [PMID: 33608954 DOI: 10.1002/mrm.28731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 01/22/2021] [Accepted: 01/25/2021] [Indexed: 11/09/2022]
Abstract
PURPOSE Provide a direct, non-invasive diagnostic measure of microscopic tissue texture in the size scale between tens of microns and the much larger scale measurable by clinical imaging. This paper presents a method and data demonstrating the ability to measure these microscopic pathologic tissue textures (histology) in the presence of subject motion in an MR scanner. This size range is vital to diagnosing a wide range of diseases. THEORY/METHODS MR micro-Texture (MRµT) resolves these textures by a combination of measuring a targeted set of k-values to characterize texture-as in diffraction analysis of materials, performing a selective internal excitation to isolate a volume of interest (VOI), applying a high k-value phase encode to the excited spins in the VOI, and acquiring each individual k-value data point in a single excitation-providing motion immunity and extended acquisition time for maximizing signal-to-noise ratio. Additional k-value measurements from the same tissue can be made to characterize the tissue texture in the VOI-there is no need for these additional measurements to be spatially coherent as there is no image to be reconstructed. This method was applied to phantoms and tissue specimens including human prostate tissue. RESULTS Data demonstrating resolution <50 µm, motion immunity, and clearly differentiating between normal and cancerous tissue textures are presented. CONCLUSION The data reveal textural differences not resolvable by standard MR imaging. As MRµT is a pulse sequence, it is directly translatable to MRI scanners currently in clinical practice to meet the need for further improvement in cancer imaging.
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Affiliation(s)
- Geoffrey A Sonn
- Department of Urology, Stanford School of Medicine, Stanford, California, USA.,Department of Radiology, Stanford School of Medicine, Stanford, California, USA
| | - Richard E Fan
- Department of Urology, Stanford School of Medicine, Stanford, California, USA
| | - Christian A Kunder
- Department of Pathology, Stanford School of Medicine, Stanford, California, USA
| | - Garry E Gold
- Department of Radiology, Stanford School of Medicine, Stanford, California, USA
| | | | - Ian D Parker
- Formerly at BioProtonics, now at Samsung Research America, Mountain View, California, USA
| | - Jean M Carlson
- Department of Physics, University of California, Santa Barbara, California, USA
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15
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Wady SH, Yousif RZ, Hasan HR. A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction. BIOMED RESEARCH INTERNATIONAL 2020; 2020:8125392. [PMID: 32733955 PMCID: PMC7369660 DOI: 10.1155/2020/8125392] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 04/10/2020] [Accepted: 06/08/2020] [Indexed: 12/28/2022]
Abstract
Discrete wavelet transform (DWT) is often implemented by an iterative filter bank; hence, a lake of optimization of a discrete time basis is observed with respect to time localization for a constant number of zero moments. This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor. The MR images in the neutrosophic domain are defined using three membership sets, true (T), false (F), and indeterminate (I); then, SLT was applied to each membership set. Three statistical measurement-based methods are used to extract texture features from images of brain MRI. One-way ANOVA has been applied as a method of reducing the number of extracted features for the classifiers; then, the extracted features are subsequently provided to the four neural network classification techniques, Support Vector Machine Neural Network (SVM-NN), Decision Tree Neural Network (DT-NN), K-Nearest Neighbor Neural Network (KNN-NN), and Naive Bayes Neural Networks (NB-NN), to predict the type of the brain tumor. Meanwhile, the performance of the proposed model is assessed by calculating average accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate that the proposed approach is quite accurate and efficient for diagnosing brain tumors when the Gray Level Run Length Matrix (GLRLM) features derived from the composite NS-SLT technique is used.
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Affiliation(s)
- Shakhawan H. Wady
- Applied Computer, College of Medicals and Applied Sciences, Charmo University, Chamchamal, Sulaimani, KRG, Iraq
- Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, KRG, Iraq
- Department of Information Technology, University College of Goizha, Sulaimani, KRG, Iraq
| | - Raghad Z. Yousif
- Department of Physics, College of Science, Salahaddin University, Erbil, KRG, Iraq
- Department of IT, College of Information Technology, Catholic University in Erbil, KRG, Iraq
| | - Harith R. Hasan
- Department of Computer Science, Kurdistan Technical Institute, Sulaimani, KRG, Iraq
- Computer Science Institute, Sulaimani Polytechnic University, Sulaimani, KRG, Iraq
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