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Jiao C, Lao Y, Zhang W, Braunstein S, Salans M, Villanueva-Meyer JE, Hervey-Jumper SL, Yang B, Morin O, Valdes G, Fan Z, Shiroishi M, Zada G, Sheng K, Yang W. Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction . Phys Med Biol 2024; 69:10.1088/1361-6560/ad64b8. [PMID: 39019073 PMCID: PMC11308744 DOI: 10.1088/1361-6560/ad64b8] [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: 02/04/2024] [Accepted: 07/17/2024] [Indexed: 07/19/2024]
Abstract
Objective.We aim to develop a Multi-modal Fusion and Feature Enhancement U-Net (MFFE U-Net) coupling with stem cell niche proximity estimation to improve voxel-wise Glioblastoma (GBM) recurrence prediction.Approach.57 patients with pre- and post-surgery magnetic resonance (MR) scans were retrospectively solicited from 4 databases. Post-surgery MR scans included two months before the clinical diagnosis of recurrence and the day of the radiologicaly confirmed recurrence. The recurrences were manually annotated on the T1ce. The high-risk recurrence region was first determined. Then, a sparse multi-modal feature fusion U-Net was developed. The 50 patients from 3 databases were divided into 70% training, 10% validation, and 20% testing. 7 patients from the 4th institution were used as external testing with transfer learning. Model performance was evaluated by recall, precision, F1-score, and Hausdorff Distance at the 95% percentile (HD95). The proposed MFFE U-Net was compared to the support vector machine (SVM) model and two state-of-the-art neural networks. An ablation study was performed.Main results.The MFFE U-Net achieved a precision of 0.79 ± 0.08, a recall of 0.85 ± 0.11, and an F1-score of 0.82 ± 0.09. Statistically significant improvement was observed when comparing MFFE U-Net with proximity estimation couple SVM (SVMPE), mU-Net, and Deeplabv3. The HD95 was 2.75 ± 0.44 mm and 3.91 ± 0.83 mm for the 10 patients used in the model construction and 7 patients used for external testing, respectively. The ablation test showed that all five MR sequences contributed to the performance of the final model, with T1ce contributing the most. Convergence analysis, time efficiency analysis, and visualization of the intermediate results further discovered the characteristics of the proposed method.Significance. We present an advanced MFFE learning framework, MFFE U-Net, for effective voxel-wise GBM recurrence prediction. MFFE U-Net performs significantly better than the state-of-the-art networks and can potentially guide early RT intervention of the disease recurrence.
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Affiliation(s)
- Changzhe Jiao
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143
| | - Yi Lao
- Department of Radiation Oncology, UC Los Angeles, Los Angeles, CA 90095
| | - Wenwen Zhang
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143
| | - Steve Braunstein
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143
| | - Mia Salans
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143
| | | | | | - Bo Yang
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143
| | - Olivier Morin
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143
| | - Gilmer Valdes
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143
| | - Zhaoyang Fan
- Department of Radiology, University of Southern California, Los Angeles, CA 90033
| | - Mark Shiroishi
- Department of Radiology, University of Southern California, Los Angeles, CA 90033
| | - Gabriel Zada
- Department of Neurosurgery, University of Southern California, Los Angeles, CA 90033
| | - Ke Sheng
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143
| | - Wensha Yang
- Department of Radiation Oncology, UC San Francisco, San Francisco, CA 94143
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Lv Y, Liu J, Tian X, Yang P, Pan Y. CFINet: Cross-Modality MRI Feature Interaction Network for Pseudoprogression Prediction of Glioblastoma. J Comput Biol 2024. [PMID: 38975725 DOI: 10.1089/cmb.2024.0518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2024] Open
Abstract
Pseudoprogression (PSP) is a related reaction of glioblastoma treatment, and misdiagnosis can lead to unnecessary intervention. Magnetic resonance imaging (MRI) provides cross-modality images for PSP prediction studies. However, how to effectively use the complementary information between the cross-modality MRI to improve PSP prediction is still a challenging task. To address this challenge, we propose a cross-modality feature interaction network for PSP prediction. Firstly, we propose a triple-branch multi-scale module to extract low-order feature representations and a skip-connection multi-scale module to extract high-order feature representations. Then, a cross-modality interaction module based on attention mechanism is designed to make the complementary information between cross-modality MRI fully interact. Finally, the high-order cross-modality interaction information is fed into a multi-layer perceptron to achieve the PSP prediction task. We evaluate the proposed network on a private dataset with 52 subjects from Hunan Cancer Hospital and validate it on a private dataset with 30 subjects from Xiangya Hospital. The accuracy of our proposed network on the datasets is 0.954 and 0.929, respectively, which is better than most typical convolutional neural network and interaction methods.
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Affiliation(s)
- Ya Lv
- Xinjiang Engineering Research Center of Big Data and Intelligent Software, School of Software, Xinjiang University, Wulumuqi, China
| | - Jin Liu
- Xinjiang Engineering Research Center of Big Data and Intelligent Software, School of Software, Xinjiang University, Wulumuqi, China
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
| | - Xu Tian
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
| | - Pei Yang
- Radiation Oncology Department, Hunan Cancer Hospital, Changsha, China
| | - Yi Pan
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Bhattacharya K, Rastogi S, Mahajan A. Post-treatment imaging of gliomas: challenging the existing dogmas. Clin Radiol 2024; 79:e376-e392. [PMID: 38123395 DOI: 10.1016/j.crad.2023.11.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 10/23/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023]
Abstract
Gliomas are the commonest malignant central nervous system tumours in adults and imaging is the cornerstone of diagnosis, treatment, and post-treatment follow-up of these patients. With the ever-evolving treatment strategies post-treatment imaging and interpretation in glioma remains challenging, more so with the advent of anti-angiogenic drugs and immunotherapy, which can significantly alter the appearance in this setting, thus making interpretation of routine imaging findings such as contrast enhancement, oedema, and mass effect difficult to interpret. This review details the various methods of management of glioma including the upcoming novel therapies and their impact on imaging findings, with a comprehensive description of the imaging findings in conventional and advanced imaging techniques. A systematic appraisal for the existing and emerging techniques of imaging in these settings and their clinical application including various response assessment guidelines and artificial intelligence based response assessment will also be discussed.
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Affiliation(s)
- K Bhattacharya
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - S Rastogi
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - A Mahajan
- Department of imaging, The Clatterbridge Cancer Centre, NHS Foundation Trust, Pembroke Place, Liverpool L7 8YA, UK; University of Liverpool, Liverpool L69 3BX, UK.
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4
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Wamelink IJHG, Azizova A, Booth TC, Mutsaerts HJMM, Ogunleye A, Mankad K, Petr J, Barkhof F, Keil VC. Brain Tumor Imaging without Gadolinium-based Contrast Agents: Feasible or Fantasy? Radiology 2024; 310:e230793. [PMID: 38319162 PMCID: PMC10902600 DOI: 10.1148/radiol.230793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 08/07/2023] [Accepted: 08/14/2023] [Indexed: 02/07/2024]
Abstract
Gadolinium-based contrast agents (GBCAs) form the cornerstone of current primary brain tumor MRI protocols at all stages of the patient journey. Though an imperfect measure of tumor grade, GBCAs are repeatedly used for diagnosis and monitoring. In practice, however, radiologists will encounter situations where GBCA injection is not needed or of doubtful benefit. Reducing GBCA administration could improve the patient burden of (repeated) imaging (especially in vulnerable patient groups, such as children), minimize risks of putative side effects, and benefit costs, logistics, and the environmental footprint. On the basis of the current literature, imaging strategies to reduce GBCA exposure for pediatric and adult patients with primary brain tumors will be reviewed. Early postoperative MRI and fixed-interval imaging of gliomas are examples of GBCA exposure with uncertain survival benefits. Half-dose GBCAs for gliomas and T2-weighted imaging alone for meningiomas are among options to reduce GBCA use. While most imaging guidelines recommend using GBCAs at all stages of diagnosis and treatment, non-contrast-enhanced sequences, such as the arterial spin labeling, have shown a great potential. Artificial intelligence methods to generate synthetic postcontrast images from decreased-dose or non-GBCA scans have shown promise to replace GBCA-dependent approaches. This review is focused on pediatric and adult gliomas and meningiomas. Special attention is paid to the quality and real-life applicability of the reviewed literature.
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Affiliation(s)
- Ivar J. H. G. Wamelink
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Aynur Azizova
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Thomas C. Booth
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Henk J. M. M. Mutsaerts
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Afolabi Ogunleye
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Kshitij Mankad
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Jan Petr
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Frederik Barkhof
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
| | - Vera C. Keil
- From the Department of Radiology and Nuclear Medicine, Amsterdam
University Medical Center, VUMC Site, De Boelelaan 1117, Amsterdam 1081 HV, the
Netherlands (I.J.H.G.W., A.A., H.J.M.M.M., J.P., F.B., V.C.K.); Department of
Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, the Netherlands
(I.J.H.G.W., A.A., H.J.M.M.M., V.C.K.); School of Biomedical Engineering and
Imaging Sciences, King’s College London, London, United Kingdom (T.C.B.);
Department of Neuroradiology, King’s College Hospital, NHS Foundation
Trust, London, UK (T.C.B.); Department of Brain Imaging, Amsterdam Neuroscience,
Amsterdam, the Netherlands (H.J.M.M.M., F.B., V.C.K.); Department of Radiology,
Lagos State University Teaching Hospital, Ikeja, Nigeria Radiology (A.O.);
Department of Radiology, Great Ormond Street Hospital for Children, NHS
Foundation Trust, London, United Kingdom (K.M.); Institute of
Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf,
Dresden, Germany (J.P.); and Queen Square Institute of Neurology and Centre for
Medical Image Computing, University College London, London, United Kingdom
(F.B.)
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Pal S, Singh RP, Kumar A. Analysis of Hybrid Feature Optimization Techniques Based on the Classification Accuracy of Brain Tumor Regions Using Machine Learning and Further Evaluation Based on the Institute Test Data. J Med Phys 2024; 49:22-32. [PMID: 38828069 PMCID: PMC11141750 DOI: 10.4103/jmp.jmp_77_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 02/23/2024] [Accepted: 02/23/2024] [Indexed: 06/05/2024] Open
Abstract
Aim The goal of this study was to get optimal brain tumor features from magnetic resonance imaging (MRI) images and classify them based on the three groups of the tumor region: Peritumoral edema, enhancing-core, and necrotic tumor core, using machine learning classification models. Materials and Methods This study's dataset was obtained from the multimodal brain tumor segmentation challenge. A total of 599 brain MRI studies were employed, all in neuroimaging informatics technology initiative format. The dataset was divided into training, validation, and testing subsets online test dataset (OTD). The dataset includes four types of MRI series, which were combined together and processed for intensity normalization using contrast limited adaptive histogram equalization methodology. To extract radiomics features, a python-based library called pyRadiomics was employed. Particle-swarm optimization (PSO) with varying inertia weights was used for feature optimization. Inertia weight with a linearly decreasing strategy (W1), inertia weight with a nonlinear coefficient decreasing strategy (W2), and inertia weight with a logarithmic strategy (W3) were different strategies used to vary the inertia weight for feature optimization in PSO. These selected features were further optimized using the principal component analysis (PCA) method to further reducing the dimensionality and removing the noise and improve the performance and efficiency of subsequent algorithms. Support vector machine (SVM), light gradient boosting (LGB), and extreme gradient boosting (XGB) machine learning classification algorithms were utilized for the classification of images into different tumor regions using optimized features. The proposed method was also tested on institute test data (ITD) for a total of 30 patient images. Results For OTD test dataset, the classification accuracy of SVM was 0.989, for the LGB model (LGBM) was 0.992, and for the XGB model (XGBM) was 0.994, using the varying inertia weight-PSO optimization method and the classification accuracy of SVM was 0.996 for the LGBM was 0.998, and for the XGBM was 0.994, using PSO and PCA-a hybrid optimization technique. For ITD test dataset, the classification accuracy of SVM was 0.994 for the LGBM was 0.993, and for the XGBM was 0.997, using the hybrid optimization technique. Conclusion The results suggest that the proposed method can be used to classify a brain tumor as used in this study to classify the tumor region into three groups: Peritumoral edema, enhancing-core, and necrotic tumor core. This was done by extracting the different features of the tumor, such as its shape, grey level, gray-level co-occurrence matrix, etc., and then choosing the best features using hybrid optimal feature selection techniques. This was done without much human expertise and in much less time than it would take a person.
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Affiliation(s)
- Soniya Pal
- Department of Physics, GLA University, Mathura, Uttar Pradesh, India
- Batra Hospital and Medical Research Center, New Delhi, India
| | - Raj Pal Singh
- Department of Physics, GLA University, Mathura, Uttar Pradesh, India
| | - Anuj Kumar
- Department of Radiotherapy, S. N. Medical College, Agra, Uttar Pradesh, India
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Liu Y, Wu M. Deep learning in precision medicine and focus on glioma. Bioeng Transl Med 2023; 8:e10553. [PMID: 37693051 PMCID: PMC10486341 DOI: 10.1002/btm2.10553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 04/13/2023] [Accepted: 05/08/2023] [Indexed: 09/12/2023] Open
Abstract
Deep learning (DL) has been successfully applied to different fields for a range of tasks. In medicine, DL methods have been also used to improve the efficiency of disease diagnosis. In this review, we first summarize the history of the development of artificial intelligence models, demonstrate the features of the subtypes of machine learning and different DL networks, and then explore their application in the different fields of precision medicine, such as cardiology, gastroenterology, ophthalmology, dermatology, and oncology. By digging more information and extracting multilevel features from medical data, we found that DL helps doctors assess diseases automatically and monitor patients' physical health. In gliomas, research regarding application prospect of DL was mainly shown through magnetic resonance imaging and then by pathological slides. However, multi-omics data, such as whole exome sequence, RNA sequence, proteomics, and epigenomics, have not been covered thus far. In general, the quality and quantity of DL datasets still need further improvements, and more fruitful multi-omics characteristics will bring more comprehensive and accurate diagnosis in precision medicine and glioma.
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Affiliation(s)
- Yihao Liu
- Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunanChina
- NHC Key Laboratory of Carcinogenesis, Xiangya HospitalCentral South UniversityChangshaHunanChina
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research InstituteCentral South UniversityChangshaHunanChina
| | - Minghua Wu
- Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunanChina
- NHC Key Laboratory of Carcinogenesis, Xiangya HospitalCentral South UniversityChangshaHunanChina
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research InstituteCentral South UniversityChangshaHunanChina
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Ranjbarzadeh R, Caputo A, Tirkolaee EB, Jafarzadeh Ghoushchi S, Bendechache M. Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools. Comput Biol Med 2023; 152:106405. [PMID: 36512875 DOI: 10.1016/j.compbiomed.2022.106405] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 11/06/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients' lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients survival rates. However, detecting brain tumors in their initial stages is a demanding task and an unmet need. METHODS The present study presents a comprehensive review of the recent Artificial Intelligence (AI) methods of diagnosing brain tumors using MRI images. These AI techniques can be divided into Supervised, Unsupervised, and Deep Learning (DL) methods. RESULTS Diagnosing and segmenting brain tumors usually begin with Magnetic Resonance Imaging (MRI) on the brain since MRI is a noninvasive imaging technique. Another existing challenge is that the growth of technology is faster than the rate of increase in the number of medical staff who can employ these technologies. It has resulted in an increased risk of diagnostic misinterpretation. Therefore, developing robust automated brain tumor detection techniques has been studied widely over the past years. CONCLUSION The current review provides an analysis of the performance of modern methods in this area. Moreover, various image segmentation methods in addition to the recent efforts of researchers are summarized. Finally, the paper discusses open questions and suggests directions for future research.
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Affiliation(s)
- Ramin Ranjbarzadeh
- School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland.
| | - Annalina Caputo
- School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland.
| | | | | | - Malika Bendechache
- Lero & ADAPT Research Centres, School of Computer Science, University of Galway, Ireland.
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8
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Radiomics-Based Machine Learning to Predict Recurrence in Glioma Patients Using Magnetic Resonance Imaging. J Comput Assist Tomogr 2023; 47:129-135. [PMID: 36194851 DOI: 10.1097/rct.0000000000001386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Recurrence is a major factor in the poor prognosis of patients with glioma. The aim of this study was to predict glioma recurrence using machine learning based on radiomic features. METHODS We recruited 77 glioma patients, consisting of 57 newly diagnosed patients and 20 patients with recurrence. After extracting the radiomic features from T2-weighted images, the data set was randomly divided into training (58 patients) and testing (19 patients) cohorts. An automated machine learning method (the Tree-based Pipeline Optimization Tool) was applied to generate 10 independent recurrence prediction models. The final model was determined based on the area under the curve (AUC) and average specificity. Moreover, an independent validation set of 20 patients with glioma was used to verify the model performance. RESULTS Recurrence in glioma patients was successfully predicting by machine learning using radiomic features. Among the 10 recurrence prediction models, the best model achieved an accuracy of 0.81, an AUC value of 0.85, and a specificity of 0.69 in the testing cohort, but an accuracy of 0.75 and an AUC value of 0.87 in the independent validation set. CONCLUSIONS Our algorithm that is generated by machine learning exhibits promising power and may predict recurrence noninvasively, thereby offering potential value for the early development of interventions to delay or prevent recurrence in glioma patients.
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9
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Li AY, Iv M. Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging. FRONTIERS IN RADIOLOGY 2022; 2:883293. [PMID: 37492665 PMCID: PMC10365131 DOI: 10.3389/fradi.2022.883293] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/06/2022] [Indexed: 07/27/2023]
Abstract
Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.
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Affiliation(s)
- Anna Y. Li
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
| | - Michael Iv
- Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
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10
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Abstract
Abstract
Purpose
Gliomas, the most common primary brain tumours, have recently been re-classified incorporating molecular aspects with important clinical, prognostic, and predictive implications. Concurrently, the reprogramming of metabolism, altering intracellular and extracellular metabolites affecting gene expression, differentiation, and the tumour microenvironment, is increasingly being studied, and alterations in metabolic pathways are becoming hallmarks of cancer. Magnetic resonance spectroscopy (MRS) is a complementary, non-invasive technique capable of quantifying multiple metabolites. The aim of this review focuses on the methodology and analysis techniques in proton MRS (1H MRS), including a brief look at X-nuclei MRS, and on its perspectives for diagnostic and prognostic biomarkers in gliomas in both clinical practice and preclinical research.
Methods
PubMed literature research was performed cross-linking the following key words: glioma, MRS, brain, in-vivo, human, animal model, clinical, pre-clinical, techniques, sequences, 1H, X-nuclei, Artificial Intelligence (AI), hyperpolarization.
Results
We selected clinical works (n = 51), preclinical studies (n = 35) and AI MRS application papers (n = 15) published within the last two decades. The methodological papers (n = 62) were taken into account since the technique first description.
Conclusions
Given the development of treatments targeting specific cancer metabolic pathways, MRS could play a key role in allowing non-invasive assessment for patient diagnosis and stratification, predicting and monitoring treatment responses and prognosis. The characterization of gliomas through MRS will benefit of a wide synergy among scientists and clinicians of different specialties within the context of new translational competences. Head coils, MRI hardware and post-processing analysis progress, advances in research, experts’ consensus recommendations and specific professionalizing programs will make the technique increasingly trustworthy, responsive, accessible.
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11
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Bhandari A, Marwah R, Smith J, Nguyen D, Bhatti A, Lim CP, Lasocki A. Machine learning imaging applications in the differentiation of true tumour progression from
treatment‐related
effects in brain tumours: A systematic review and
meta‐analysis. J Med Imaging Radiat Oncol 2022; 66:781-797. [PMID: 35599360 PMCID: PMC9545346 DOI: 10.1111/1754-9485.13436] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 05/04/2022] [Indexed: 12/21/2022]
Abstract
Introduction Chemotherapy and radiotherapy can produce treatment‐related effects, which may mimic tumour progression. Advances in Artificial Intelligence (AI) offer the potential to provide a more consistent approach of diagnosis with improved accuracy. The aim of this study was to determine the efficacy of machine learning models to differentiate treatment‐related effects (TRE), consisting of pseudoprogression (PsP) and radiation necrosis (RN), and true tumour progression (TTP). Methods The systematic review was conducted in accordance with PRISMA‐DTA guidelines. Searches were performed on PubMed, Scopus, Embase, Medline (Ovid) and ProQuest databases. Quality was assessed according to the PROBAST and CLAIM criteria. There were 25 original full‐text journal articles eligible for inclusion. Results For gliomas: PsP versus TTP (16 studies, highest AUC = 0.98), RN versus TTP (4 studies, highest AUC = 0.9988) and TRE versus TTP (3 studies, highest AUC = 0.94). For metastasis: RN vs. TTP (2 studies, highest AUC = 0.81). A meta‐analysis was performed on 9 studies in the gliomas PsP versus TTP group using STATA. The meta‐analysis reported a high sensitivity of 95.2% (95%CI: 86.6–98.4%) and specificity of 82.4% (95%CI: 67.0–91.6%). Conclusion TRE can be distinguished from TTP with good performance using machine learning‐based imaging models. There remain issues with the quality of articles and the integration of models into clinical practice. Future studies should focus on the external validation of models and utilize standardized criteria such as CLAIM to allow for consistency in reporting.
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Affiliation(s)
- Abhishta Bhandari
- Townsville University Hospital Townsville Queensland Australia
- College of Medicine and Dentistry James Cook University Townsville Queensland Australia
| | - Ravi Marwah
- Townsville University Hospital Townsville Queensland Australia
| | - Justin Smith
- Townsville University Hospital Townsville Queensland Australia
- College of Medicine and Dentistry James Cook University Townsville Queensland Australia
| | - Duy Nguyen
- Institute for Intelligent Systems Research and Innovation Deakin University Melbourne Victoria Australia
| | - Asim Bhatti
- Department of Cancer Imaging Peter MacCallum Cancer Centre Melbourne Victoria Australia
| | - Chee Peng Lim
- Institute for Intelligent Systems Research and Innovation Deakin University Melbourne Victoria Australia
| | - Arian Lasocki
- Department of Cancer Imaging Peter MacCallum Cancer Centre Melbourne Victoria Australia
- Sir Peter MacCallum Department of Oncology The University of Melbourne Melbourne Victoria Australia
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Das S, Nayak GK, Saba L, Kalra M, Suri JS, Saxena S. An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review. Comput Biol Med 2022; 143:105273. [PMID: 35228172 DOI: 10.1016/j.compbiomed.2022.105273] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 01/15/2022] [Accepted: 01/24/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND Artificial intelligence (AI) has become a prominent technique for medical diagnosis and represents an essential role in detecting brain tumors. Although AI-based models are widely used in brain lesion segmentation (BLS), understanding their effectiveness is challenging due to their complexity and diversity. Several reviews on brain tumor segmentation are available, but none of them describe a link between the threats due to risk-of-bias (RoB) in AI and its architectures. In our review, we focused on linking RoB and different AI-based architectural Cluster in popular DL framework. Further, due to variance in these designs and input data types in medical imaging, it is necessary to present a narrative review considering all facets of BLS. APPROACH The proposed study uses a PRISMA strategy based on 75 relevant studies found by searching PubMed, Scopus, and Google Scholar. Based on the architectural evolution, DL studies were subsequently categorized into four classes: convolutional neural network (CNN)-based, encoder-decoder (ED)-based, transfer learning (TL)-based, and hybrid DL (HDL)-based architectures. These studies were then analyzed considering 32 AI attributes, with clusters including AI architecture, imaging modalities, hyper-parameters, performance evaluation metrics, and clinical evaluation. Then, after these studies were scored for all attributes, a composite score was computed, normalized, and ranked. Thereafter, a bias cutoff (AP(ai)Bias 1.0, AtheroPoint, Roseville, CA, USA) was established to detect low-, moderate- and high-bias studies. CONCLUSION The four classes of architectures, from best-to worst-performing, are TL > ED > CNN > HDL. ED-based models had the lowest AI bias for BLS. This study presents a set of three primary and six secondary recommendations for lowering the RoB.
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Affiliation(s)
- Suchismita Das
- CSE Department, International Institute of Information Technology, Bhubaneswar, Odisha, India; CSE Department, KIIT Deemed to be University, Bhubaneswar, Odisha, India
| | - G K Nayak
- CSE Department, International Institute of Information Technology, Bhubaneswar, Odisha, India
| | - Luca Saba
- Department of Radiology, AOU, University of Cagliari, Cagliari, Italy
| | - Mannudeep Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA
| | - Jasjit S Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™ LLC, Roseville, CA, USA.
| | - Sanjay Saxena
- CSE Department, International Institute of Information Technology, Bhubaneswar, Odisha, India
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Henriksen OM, del Mar Álvarez-Torres M, Figueiredo P, Hangel G, Keil VC, Nechifor RE, Riemer F, Schmainda KM, Warnert EAH, Wiegers EC, Booth TC. High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques. Front Oncol 2022; 12:810263. [PMID: 35359414 PMCID: PMC8961422 DOI: 10.3389/fonc.2022.810263] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 01/05/2022] [Indexed: 01/16/2023] Open
Abstract
Objective Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments. Methods Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics). Results High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies. Conclusion Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.
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Affiliation(s)
- Otto M. Henriksen
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | | | - Patricia Figueiredo
- Department of Bioengineering and Institute for Systems and Robotics-Lisboa, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Gilbert Hangel
- Department of Neurosurgery, Medical University, Vienna, Austria
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University, Vienna, Austria
| | - Vera C. Keil
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Ruben E. Nechifor
- International Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Department of Clinical Psychology and Psychotherapy, Babes-Bolyai University, Cluj-Napoca, Romania
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Kathleen M. Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | | | - Evita C. Wiegers
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Thomas C. Booth
-
School of Biomedical Engineering and Imaging Sciences, St. Thomas’ Hospital, King’s College London, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, London, United Kingdom
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14
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Booth TC, Grzeda M, Chelliah A, Roman A, Al Busaidi A, Dragos C, Shuaib H, Luis A, Mirchandani A, Alparslan B, Mansoor N, Lavrador J, Vergani F, Ashkan K, Modat M, Ourselin S. Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies. Front Oncol 2022; 12:799662. [PMID: 35174084 PMCID: PMC8842649 DOI: 10.3389/fonc.2022.799662] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/03/2022] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVE Monitoring biomarkers using machine learning (ML) may determine glioblastoma treatment response. We systematically reviewed quality and performance accuracy of recently published studies. METHODS Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy, we extracted articles from MEDLINE, EMBASE and Cochrane Register between 09/2018-01/2021. Included study participants were adults with glioblastoma having undergone standard treatment (maximal resection, radiotherapy with concomitant and adjuvant temozolomide), and follow-up imaging to determine treatment response status (specifically, distinguishing progression/recurrence from progression/recurrence mimics, the target condition). Using Quality Assessment of Diagnostic Accuracy Studies Two/Checklist for Artificial Intelligence in Medical Imaging, we assessed bias risk and applicability concerns. We determined test set performance accuracy (sensitivity, specificity, precision, F1-score, balanced accuracy). We used a bivariate random-effect model to determine pooled sensitivity, specificity, area-under the receiver operator characteristic curve (ROC-AUC). Pooled measures of balanced accuracy, positive/negative likelihood ratios (PLR/NLR) and diagnostic odds ratio (DOR) were calculated. PROSPERO registered (CRD42021261965). RESULTS Eighteen studies were included (1335/384 patients for training/testing respectively). Small patient numbers, high bias risk, applicability concerns (particularly confounding in reference standard and patient selection) and low level of evidence, allow limited conclusions from studies. Ten studies (10/18, 56%) included in meta-analysis gave 0.769 (0.649-0.858) sensitivity [pooled (95% CI)]; 0.648 (0.749-0.532) specificity; 0.706 (0.623-0.779) balanced accuracy; 2.220 (1.560-3.140) PLR; 0.366 (0.213-0.572) NLR; 6.670 (2.800-13.500) DOR; 0.765 ROC-AUC. CONCLUSION ML models using MRI features to distinguish between progression and mimics appear to demonstrate good diagnostic performance. However, study quality and design require improvement.
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Affiliation(s)
- Thomas C. Booth
- School of Biomedical Engineering & Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Mariusz Grzeda
- School of Biomedical Engineering & Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Andrei Roman
- Department of Radiology, Guy’s & St. Thomas’ National Health Service Foundation Trust, London, United Kingdom
- Department of Radiology, The Oncology Institute “Prof. Dr. Ion Chiricuţă” Cluj-Napoca, Cluj-Napoca, Romania
| | - Ayisha Al Busaidi
- Department of Neuroradiology, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Carmen Dragos
- Department of Radiology, Buckinghamshire Healthcare National Health Service Trust, Amersham, United Kingdom
| | - Haris Shuaib
- Department of Medical Physics, Guy’s & St. Thomas’ National Health Service Foundation Trust, London, United Kingdom
- Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Aysha Luis
- Department of Neuroradiology, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Ayesha Mirchandani
- Department of Radiology, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, United Kingdom
| | - Burcu Alparslan
- Department of Neuroradiology, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
- Department of Radiology, Kocaeli University, İzmit, Turkey
| | - Nina Mansoor
- Department of Neuroradiology, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Jose Lavrador
- Department of Neurosurgery, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Francesco Vergani
- Department of Neurosurgery, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Keyoumars Ashkan
- Department of Neurosurgery, King’s College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
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15
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Guo P, Unberath M, Heo HY, Eberhart CG, Lim M, Blakeley JO, Jiang S. Learning-based analysis of amide proton transfer-weighted MRI to identify true progression in glioma patients. NEUROIMAGE: CLINICAL 2022; 35:103121. [PMID: 35905666 PMCID: PMC9421489 DOI: 10.1016/j.nicl.2022.103121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 07/13/2022] [Accepted: 07/14/2022] [Indexed: 11/29/2022] Open
Affiliation(s)
- Pengfei Guo
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Mathias Unberath
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Hye-Young Heo
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | | | - Michael Lim
- Department of Neurosurgery, Johns Hopkins University, Baltimore, MD, USA
| | | | - Shanshan Jiang
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA.
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16
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Li Y, Ma Y, Wu Z, Xie R, Zeng F, Cai H, Lui S, Song B, Chen L, Wu M. Advanced Imaging Techniques for Differentiating Pseudoprogression and Tumor Recurrence After Immunotherapy for Glioblastoma. Front Immunol 2021; 12:790674. [PMID: 34899760 PMCID: PMC8656432 DOI: 10.3389/fimmu.2021.790674] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 11/08/2021] [Indexed: 02/05/2023] Open
Abstract
Glioblastoma (GBM) is the most common malignant tumor of the central nervous system with poor prognosis. Although the field of immunotherapy in glioma is developing rapidly, glioblastoma is still prone to recurrence under strong immune intervention. The major challenges in the process of immunotherapy are evaluating the curative effect, accurately distinguishing between treatment-related reactions and tumor recurrence, and providing guidance for clinical decision-making. Since the conventional magnetic resonance imaging (MRI) is usually difficult to distinguish between pseudoprogression and the true tumor progression, many studies have used various advanced imaging techniques to evaluate treatment-related responses. Meanwhile, criteria for efficacy evaluation of immunotherapy are constantly updated and improved. A standard imaging scheme to evaluate immunotherapeutic response will benefit patients finally. This review mainly summarizes the application status and future trend of several advanced imaging techniques in evaluating the efficacy of GBM immunotherapy.
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Affiliation(s)
- Yan Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Yiqi Ma
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Zijun Wu
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Ruoxi Xie
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Fanxin Zeng
- Department of Clinic Medical Center, Dazhou Central Hospital, Dazhou, China
| | - Huawei Cai
- Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Bin Song
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
| | - Lei Chen
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China.,Department of Clinical Research Management, West China Hospital, Sichuan University, Chengdu, China
| | - Min Wu
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China.,Department of Clinic Medical Center, Dazhou Central Hospital, Dazhou, China
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Stumpo V, Kernbach JM, van Niftrik CHB, Sebök M, Fierstra J, Regli L, Serra C, Staartjes VE. Machine Learning Algorithms in Neuroimaging: An Overview. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:125-138. [PMID: 34862537 DOI: 10.1007/978-3-030-85292-4_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Machine learning (ML) and artificial intelligence (AI) applications in the field of neuroimaging have been on the rise in recent years, and their clinical adoption is increasing worldwide. Deep learning (DL) is a field of ML that can be defined as a set of algorithms enabling a computer to be fed with raw data and progressively discover-through multiple layers of representation-more complex and abstract patterns in large data sets. The combination of ML and radiomics, namely the extraction of features from medical images, has proven valuable, too: Radiomic information can be used for enhanced image characterization and prognosis or outcome prediction. This chapter summarizes the basic concepts underlying ML application for neuroimaging and discusses technical aspects of the most promising algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), in order to provide the readership with the fundamental theoretical tools to better understand ML in neuroimaging. Applications are highlighted from a practical standpoint in the last section of the chapter, including: image reconstruction and restoration, image synthesis and super-resolution, registration, segmentation, classification, and outcome prediction.
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Affiliation(s)
- Vittorio Stumpo
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Julius M Kernbach
- Neurosurgical Artificial Intelligence Lab Aachen (NAILA), Department of Neurosurgery, RWTH University Hospital, Aachen, Germany
- Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Christiaan H B van Niftrik
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martina Sebök
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Jorn Fierstra
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Luca Regli
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Carlo Serra
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Victor E Staartjes
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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18
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Zegers C, Posch J, Traverso A, Eekers D, Postma A, Backes W, Dekker A, van Elmpt W. Current applications of deep-learning in neuro-oncological MRI. Phys Med 2021; 83:161-173. [DOI: 10.1016/j.ejmp.2021.03.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 02/01/2021] [Accepted: 03/02/2021] [Indexed: 12/18/2022] Open
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Abstract
With the ongoing advances in imaging techniques, increasing volumes of anatomical and functional data are being generated as part of the routine clinical workflow. This surge of available imaging data coincides with increasing research in quantitative imaging, particularly in the domain of imaging features. An important and novel approach is radiomics, where high-dimensional image properties are extracted from routine medical images. The fundamental principle of radiomics is the hypothesis that biomedical images contain predictive information, not discernible to the human eye, that can be mined through quantitative image analysis. In this review, a general outline of radiomics and artificial intelligence (AI) will be provided, along with prominent use cases in immunotherapy (e.g. response and adverse event prediction) and targeted therapy (i.e. radiogenomics). While the increased use and development of radiomics and AI in immuno-oncology is highly promising, the technology is still in its early stages, and different challenges still need to be overcome. Nevertheless, novel AI algorithms are being constructed with an ever-increasing scope of applications.
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Affiliation(s)
- Z. Bodalal
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - I. Wamelink
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Technical Medicine, University of Twente, Enschede, The Netherlands
| | - S. Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - R.G.H. Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
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Li J, Wang W, Liao L, Liu X. Analysis of the nonperfused volume ratio of adenomyosis from MRI images based on fewshot learning. Phys Med Biol 2021; 66:045019. [PMID: 33361557 DOI: 10.1088/1361-6560/abd66b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
The nonperfused volume (NPV) ratio is the key to the success of high intensity focused ultrasound (HIFU) ablation treatment of adenomyosis. However, there are no qualitative interpretation standards for predicting the NPV ratio of adenomyosis using magnetic resonance imaging (MRI) before HIFU ablation treatment, which leading to inter-reader variability. Convolutional neural networks (CNNs) have achieved state-of-the-art performance in the automatic disease diagnosis of MRI. Since the use of HIFU to treat adenomyosis is a novel treatment, there is not enough MRI data to support CNNs. We proposed a novel few-shot learning framework that extends CNNs to predict NPV ratio of HIFU ablation treatment for adenomyosis. We collected a dataset from 208 patients with adenomyosis who underwent MRI examination before and after HIFU treatment. Our proposed method was trained and evaluated by fourfold cross validation. This framework obtained sensitivity of 85.6%, 89.6% and 92.8% at 0.799, 0.980 and 1.180 FPs per patient. In the receiver operating characteristics analysis for NPV ratio of adenomyosis, our proposed method received the area under the curve of 0.8233, 0.8289, 0.8412, 0.8319, 0.7010, 0.7637, 0.8375, 0.8219, 0.8207, 0.9812 for the classifications of NPV ratio interval [0%-10%), [10%-20%), …, [90%-100%], respectively. The present study demonstrated that few-shot learning on NPV ratio prediction of HIFU ablation treatment for adenomyosis may contribute to the selection of eligible patients and the pre-judgment of clinical efficacy.
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Affiliation(s)
- Jiaqi Li
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Wei Wang
- Department of Ultrasound, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Lejian Liao
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Xin Liu
- Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
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Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning. Sci Rep 2020; 10:20331. [PMID: 33230285 PMCID: PMC7683728 DOI: 10.1038/s41598-020-77389-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 10/30/2020] [Indexed: 12/23/2022] Open
Abstract
Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurrence. In this study, we proposed to use a multiparametric MRI data as a sequence input for the convolutional neural network with the recurrent neural network based deep learning structure to discriminate between pseudoprogression and true tumor progression. In this study, 43 biopsy-proven patient data identified as diffuse infiltrating glioma patients whose disease progressed/recurred were used. The dataset consists of five original MRI sequences; pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, FLAIR, and ADC images as well as two engineered sequences; T1post-T1pre and T2-FLAIR. Next, we used three CNN-LSTM models with a different set of sequences as input sequences to pass through CNN-LSTM layers. We performed threefold cross-validation in the training dataset and generated the boxplot, accuracy, and ROC curve, AUC from each trained model with the test dataset to evaluate models. The mean accuracy for VGG16 models ranged from 0.44 to 0.60 and the mean AUC ranged from 0.47 to 0.59. For CNN-LSTM model, the mean accuracy ranged from 0.62 to 0.75 and the mean AUC ranged from 0.64 to 0.81. The performance of the proposed CNN-LSTM with multiparametric sequence data was found to outperform the popular convolutional CNN with a single MRI sequence. In conclusion, incorporating all available MRI sequences into a sequence input for a CNN-LSTM model improved diagnostic performance for discriminating between pseudoprogression and true tumor progression.
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Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlenther Onkol 2020; 196:856-867. [PMID: 32394100 PMCID: PMC7498494 DOI: 10.1007/s00066-020-01626-8] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
Abstract
Background Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors. Methods This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases. Results Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80–90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods. Conclusion Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.
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