1
|
De Sutter S, Wuts J, Geens W, Vanbinst AM, Duerinck J, Vandemeulebroucke J. Modality redundancy for MRI-based glioblastoma segmentation. Int J Comput Assist Radiol Surg 2024:10.1007/s11548-024-03238-4. [PMID: 39093499 DOI: 10.1007/s11548-024-03238-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 07/11/2024] [Indexed: 08/04/2024]
Abstract
PURPOSE Automated glioblastoma segmentation from magnetic resonance imaging is generally performed on a four-modality input, including T1, contrast T1, T2 and FLAIR. We hypothesize that information redundancy is present within these image combinations, which can possibly reduce a model's performance. Moreover, for clinical applications, the risk of encountering missing data rises as the number of required input modalities increases. Therefore, this study aimed to explore the relevance and influence of the different modalities used for MRI-based glioblastoma segmentation. METHODS After the training of multiple segmentation models based on nnU-Net and SwinUNETR architectures, differing only in their amount and combinations of input modalities, each model was evaluated with regard to segmentation accuracy and epistemic uncertainty. RESULTS Results show that T1CE-based segmentation (for enhanced tumor and tumor core) and T1CE-FLAIR-based segmentation (for whole tumor and overall segmentation) can reach segmentation accuracies comparable to the full-input version. Notably, the highest segmentation accuracy for nnU-Net was found for a three-input configuration of T1CE-FLAIR-T1, suggesting the confounding effect of redundant input modalities. The SwinUNETR architecture appears to suffer less from this, where said three-input and the full-input model yielded statistically equal results. CONCLUSION The T1CE-FLAIR-based model can therefore be considered as a minimal-input alternative to the full-input configuration. Addition of modalities beyond this does not statistically improve and can even deteriorate accuracy, but does lower the segmentation uncertainty.
Collapse
Affiliation(s)
- Selene De Sutter
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium.
| | - Joris Wuts
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium
- Department of Radiology and Medical Imaging, Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium
| | - Wietse Geens
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Anne-Marie Vanbinst
- Department of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Johnny Duerinck
- Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Jef Vandemeulebroucke
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium
- Department of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), Vrije Universiteit Brussel (VUB), Brussels, Belgium
- imec, Leuven, Belgium
| |
Collapse
|
2
|
Yoshimura H, Kawahara D, Saito A, Ozawa S, Nagata Y. Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN. Phys Eng Sci Med 2024:10.1007/s13246-024-01443-8. [PMID: 38884673 DOI: 10.1007/s13246-024-01443-8] [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: 05/24/2023] [Accepted: 05/14/2024] [Indexed: 06/18/2024]
Abstract
To propose a style transfer model for multi-contrast magnetic resonance imaging (MRI) images with a cycle-consistent generative adversarial network (CycleGAN) and evaluate the image quality and prognosis prediction performance for glioblastoma (GBM) patients from the extracted radiomics features. Style transfer models of T1 weighted MRI image (T1w) to T2 weighted MRI image (T2w) and T2w to T1w with CycleGAN were constructed using the BraTS dataset. The style transfer model was validated with the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset. Moreover, imaging features were extracted from real and synthesized images. These features were transformed to rad-scores by the least absolute shrinkage and selection operator (LASSO)-Cox regression. The prognosis performance was estimated by the Kaplan-Meier method. For the accuracy of the image quality of the real and synthesized MRI images, the MI, RMSE, PSNR, and SSIM were 0.991 ± 2.10 × 10 - 4 , 2.79 ± 0.16, 40.16 ± 0.38, and 0.995 ± 2.11 × 10 - 4 , for T2w, and .992 ± 2.63 × 10 - 4 , 2.49 ± 6.89 × 10 - 2 , 40.51 ± 0.22, and 0.993 ± 3.40 × 10 - 4 for T1w, respectively. The survival time had a significant difference between good and poor prognosis groups for both real and synthesized T2w (p < 0.05). However, the survival time had no significant difference between good and poor prognosis groups for both real and synthesized T1w. On the other hand, there was no significant difference between the real and synthesized T2w in both good and poor prognoses. The results of T1w were similar in the point that there was no significant difference between the real and synthesized T1w. It was found that the synthesized image could be used for prognosis prediction. The proposed prognostic model using CycleGAN could reduce the cost and time of image scanning, leading to a promotion to build the patient's outcome prediction with multi-contrast images.
Collapse
Affiliation(s)
- Hisanori Yoshimura
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
- Department of Radiology, National Hospital Organization Kure Medical Center, Hiroshima, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan.
| | - Akito Saito
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
| | - Shuichi Ozawa
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, 732-0057, Japan
| | - Yasushi Nagata
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, 732-0057, Japan
| |
Collapse
|
3
|
Chen J, Yang R, Yu H, Wu H, Wu N, Wang S, Yin X, Shi X, Wang H. Ultrasmall iron oxide nanoparticles with MRgFUS for enhanced magnetic resonance imaging of orthotopic glioblastoma. J Mater Chem B 2024; 12:4833-4842. [PMID: 38647018 DOI: 10.1039/d3tb02966b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Ultrasmall iron oxide nanoparticles (USIO NPs) are expected to become the next generation T1 contrast agents; however, their diagnostic and therapeutic potential for primary brain tumors (such as glioblastoma multiforme, GBM) is yet to be explored. At present, the main challenge is the effective hindering of biological barriers, including the blood-brain barrier (BBB) and the blood-brain tumor barrier (BBTB). Herein, we aimed to investigate whether the USIO NPs, in combination with MR-guided focused ultrasound (MRgFUS), could intensify MR imaging of GBM. In this study, we presented zwitterionic USIO NPs for enhanced MR imaging of both xenografted and orthotopic GBM mouse models. We first synthesized citric-stabilized USIO NPs with a size of 3.19 ± 0.76 nm, modified with ethylenediamine, and decorated with 1,3-propanesultone (1,3-PS) to form USIO NPs-1,3-PS. The obtained USIO NPs-1,3-PS exhibited good cytocompatibility and cellular uptake efficiency. MRgFUS, in combination with microbubbles, provided a non-invasive and safe technique for BBB opening, which, in turn, promoted the delivery of USIO NPs-1,3-PS in orthotopic GBM. This developed USIO NP nanoplatform may improve the precision imaging of solid tumors and therapeutic efficacy in the central nervous system.
Collapse
Affiliation(s)
- Jingwen Chen
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China.
| | - Rui Yang
- Shanghai Engineering Research Center of Nano-Biomaterials and Regenerative Medicine, College of Biological Science and Medical Engineering, Donghua University, Shanghai 201620, China
| | - Hongwei Yu
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China.
| | - Hao Wu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China
| | - Nan Wu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China
| | - Suhe Wang
- Department of Internal Medicine, Michigan Nanotechnology Institute for Medicine and Biological Sciences, University of Michigan, 109 Zina Pitcher Place, Ann Arbor, MI 48109, USA
| | - Xiaorui Yin
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China.
| | - Xiangyang Shi
- Shanghai Engineering Research Center of Nano-Biomaterials and Regenerative Medicine, College of Biological Science and Medical Engineering, Donghua University, Shanghai 201620, China
| | - Han Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China.
- R & D Center of Medical Artificial Intelligence and Medical Engineering, Haining Rd.100, Shanghai General Hospital, Shanghai 200080, China
- National Center for Translational Medicine (Shanghai), New Songjiang Rd.650, Shanghai 201620, China
- Jiading Branch of Shanghai General Hospital, Huangjia Garden Rd. 800, Shanghai 201803, China
| |
Collapse
|
4
|
Chen P, Wang P, Gao B. The application value of deep learning in the background of precision medicine in glioblastoma. Sci Prog 2024; 107:368504231223353. [PMID: 38262933 PMCID: PMC10807326 DOI: 10.1177/00368504231223353] [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] [Indexed: 01/25/2024]
Abstract
Introduction: Glioblastoma is a highly malignant central nervous system tumor, World Health Organization Ⅳ, glioblastoma is the most common primary malignancy, due to its own specificity and complexity, different patients often benefit from the current conventional treatment regimen because of different molecular subtypes, in the context of precision medicine, the application of deep learning to identify the salient features of tumors on brain imaging, prognostic predictive assessment combined with clinical data to maximize the benefits of each patient from the treatment regimen is a non-invasive and feasible regimen. Methods: We conducted a comprehensive review of the existing literature on the role of deep learning in glioblastomas, covering molecular classification and diagnosis, prognosis assessment. Results: Data based on a variety of magnetic resonance imaging sequences, genetic information, and clinical combinations enable noninvasive predictive tumor diagnosis of glioblastoma and assess overall survival and treatment response accuracy. For images, standardized image acquisition and data extraction techniques can be effectively translated into learning models for clinical practice. However, it must be recognized that interventions in the treatment of glioblastoma using deep learning are still in their infancy, and the robustness of the model is challenged, as the current total number of glioblastoma samples is insufficient for large-scale experimental methods, which is directly related to the difficulty of application of the model. Conclusion: Compared to radiomics and shallow machine learning, deep learning can be a more robust, non-invasive, and effective approach, providing more valuable information as clinicians develop personalized medical protocols for glioblastoma patients.
Collapse
Affiliation(s)
- Pengyu Chen
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guizhou, China
| | - Ping Wang
- Key Laboratory of Brain Imaging, Guizhou Medical University, Guiyang, China
| | - Bo Gao
- Department of Neurology, The Affiliated Hospital of Guizhou Medical University, Guizhou, China
| |
Collapse
|
5
|
Ahamed MF, Hossain MM, Nahiduzzaman M, Islam MR, Islam MR, Ahsan M, Haider J. A review on brain tumor segmentation based on deep learning methods with federated learning techniques. Comput Med Imaging Graph 2023; 110:102313. [PMID: 38011781 DOI: 10.1016/j.compmedimag.2023.102313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 11/13/2023] [Accepted: 11/13/2023] [Indexed: 11/29/2023]
Abstract
Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists segment the tumor manually, which is time-consuming, error-prone, and expensive. In recent years, automated segmentation based on deep learning has demonstrated promising results in solving computer vision problems such as image classification and segmentation. Brain tumor segmentation has recently become a prevalent task in medical imaging to determine the tumor location, size, and shape using automated methods. Many researchers have worked on various machine and deep learning approaches to determine the most optimal solution using the convolutional methodology. In this review paper, we discuss the most effective segmentation techniques based on the datasets that are widely used and publicly available. We also proposed a survey of federated learning methodologies to enhance global segmentation performance and ensure privacy. A comprehensive literature review is suggested after studying more than 100 papers to generalize the most recent techniques in segmentation and multi-modality information. Finally, we concentrated on unsolved problems in brain tumor segmentation and a client-based federated model training strategy. Based on this review, future researchers will understand the optimal solution path to solve these issues.
Collapse
Affiliation(s)
- Md Faysal Ahamed
- Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Munawar Hossain
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Nahiduzzaman
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Rabiul Islam
- Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Robiul Islam
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Mominul Ahsan
- Department of Computer Science, University of York, Deramore Lane, Heslington, York YO10 5GH, UK
| | - Julfikar Haider
- Department of Engineering, Manchester Metropolitan University, Chester St, Manchester M1 5GD, UK.
| |
Collapse
|
6
|
Lee M. Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature. BIOLOGY 2023; 12:893. [PMID: 37508326 PMCID: PMC10376033 DOI: 10.3390/biology12070893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023]
Abstract
Deep learning has brought about a significant transformation in machine learning, leading to an array of novel methodologies and consequently broadening its influence. The application of deep learning in various sectors, especially biomedical data analysis, has initiated a period filled with noteworthy scientific developments. This trend has majorly influenced cancer prognosis, where the interpretation of genomic data for survival analysis has become a central research focus. The capacity of deep learning to decode intricate patterns embedded within high-dimensional genomic data has provoked a paradigm shift in our understanding of cancer survival. Given the swift progression in this field, there is an urgent need for a comprehensive review that focuses on the most influential studies from 2021 to 2023. This review, through its careful selection and thorough exploration of dominant trends and methodologies, strives to fulfill this need. The paper aims to enhance our existing understanding of applications of deep learning in cancer survival analysis, while also highlighting promising directions for future research. This paper undertakes aims to enrich our existing grasp of the application of deep learning in cancer survival analysis, while concurrently shedding light on promising directions for future research in this vibrant and rapidly proliferating field.
Collapse
Affiliation(s)
- Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| |
Collapse
|
7
|
Hussain S, Haider S, Maqsood S, Damaševičius R, Maskeliūnas R, Khan M. ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction. Diagnostics (Basel) 2023; 13:diagnostics13081456. [PMID: 37189556 DOI: 10.3390/diagnostics13081456] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 03/30/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
Technology-assisted diagnosis is increasingly important in healthcare systems. Brain tumors are a leading cause of death worldwide, and treatment plans rely heavily on accurate survival predictions. Gliomas, a type of brain tumor, have particularly high mortality rates and can be further classified as low- or high-grade, making survival prediction challenging. Existing literature provides several survival prediction models that use different parameters, such as patient age, gross total resection status, tumor size, or tumor grade. However, accuracy is often lacking in these models. The use of tumor volume instead of size may improve the accuracy of survival prediction. In response to this need, we propose a novel model, the enhanced brain tumor identification and survival time prediction (ETISTP), which computes tumor volume, classifies it into low- or high-grade glioma, and predicts survival time with greater accuracy. The ETISTP model integrates four parameters: patient age, survival days, gross total resection (GTR) status, and tumor volume. Notably, ETISTP is the first model to employ tumor volume for prediction. Furthermore, our model minimizes the computation time by allowing for parallel execution of tumor volume computation and classification. The simulation results demonstrate that ETISTP outperforms prominent survival prediction models.
Collapse
Affiliation(s)
- Shah Hussain
- Department of Computer Science, City University of Science and Information Technology, Peshawar 25000, Pakistan
| | - Shahab Haider
- Department of Computer Science, City University of Science and Information Technology, Peshawar 25000, Pakistan
| | - Sarmad Maqsood
- Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania
| | - Robertas Damaševičius
- Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania
| | - Rytis Maskeliūnas
- Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Muzammil Khan
- Department of Computer & Software Technology, University of Swat, Swat 19200, Pakistan
| |
Collapse
|
8
|
Zhou T, Ruan S, Hu H. A literature survey of MR-based brain tumor segmentation with missing modalities. Comput Med Imaging Graph 2023; 104:102167. [PMID: 36584536 DOI: 10.1016/j.compmedimag.2022.102167] [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: 07/25/2022] [Revised: 11/01/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022]
Abstract
Multimodal MR brain tumor segmentation is one of the hottest issues in the community of medical image processing. However, acquiring the complete set of MR modalities is not always possible in clinical practice, due to the acquisition protocols, image corruption, scanner availability, scanning cost or allergies to certain contrast materials. The missing information can cause some restraints to brain tumor diagnosis, monitoring, treatment planning and prognosis. Thus, it is highly desirable to develop brain tumor segmentation methods to address the missing modalities problem. Based on the recent advancements, in this review, we provide a detailed analysis of the missing modality issue in MR-based brain tumor segmentation. First, we briefly introduce the biomedical background concerning brain tumor, MR imaging techniques, and the current challenges in brain tumor segmentation. Then, we provide a taxonomy of the state-of-the-art methods with five categories, namely, image synthesis-based method, latent feature space-based model, multi-source correlation-based method, knowledge distillation-based method, and domain adaptation-based method. In addition, the principles, architectures, benefits and limitations are elaborated in each method. Following that, the corresponding datasets and widely used evaluation metrics are described. Finally, we analyze the current challenges and provide a prospect for future development trends. This review aims to provide readers with a thorough knowledge of the recent contributions in the field of brain tumor segmentation with missing modalities and suggest potential future directions.
Collapse
Affiliation(s)
- Tongxue Zhou
- School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
| | - Su Ruan
- Université de Rouen Normandie, LITIS - QuantIF, Rouen 76183, France
| | - Haigen Hu
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; Key Laboratory of Visual Media Intelligent Processing Technology of Zhejiang Province, Hangzhou 310023, China.
| |
Collapse
|
9
|
He XM, Zhao JX, He DL, Ren JL, Zhao LP, Huang G. Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma. Eur J Radiol Open 2023; 10:100476. [PMID: 36793772 PMCID: PMC9922923 DOI: 10.1016/j.ejro.2023.100476] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 01/30/2023] Open
Abstract
Purpose To develop models based on radiomics and genomics for predicting the histopathologic nuclear grade with localized clear cell renal cell carcinoma (ccRCC) and to assess whether macro-radiomics models can predict the microscopic pathological changes. Method In this multi-institutional retrospective study, a computerized tomography (CT) radiomic model for nuclear grade prediction was developed. Utilizing a genomics analysis cohort, nuclear grade-associated gene modules were identified, and a gene model was constructed based on top 30 hub mRNA to predict the nuclear grade. Using a radiogenomic development cohort, biological pathways were enriched by hub genes and a radiogenomic map was created. Results The four-features-based SVM model predicted nuclear grade with an area under the curve (AUC) score of 0.94 in validation sets, while a five-gene-based model predicted nuclear grade with an AUC of 0.73 in the genomics analysis cohort. A total of five gene modules were identified to be associated with the nuclear grade. Radiomic features were only associated with 271 out of 603 genes in five gene modules and eight top 30 hub genes. Differences existed in the enrichment pathway between associated and un-associated with radiomic features, which were associated with two genes of five-gene signatures in the mRNA model. Conclusion The CT radiomics models exhibited higher predictive performance than mRNA models. The association between radiomic features and mRNA related to nuclear grade is not universal.
Collapse
Key Words
- Computer Applications
- FDR, False discovery rate
- GLRLM, Gray level run length matrix
- GLSZM, Gray level size matrix
- KEGG, KOBAS-Kyoto Encyclopedia of Genes and Genomes
- Kidney
- NGTDM, Neighborhood gray tone difference matrix
- Neoplasms-Primary
- PPI, Protein-Protein Interaction Networks
- Pathological nuclear grade
- Radiogenomics
- Radiomics
- TCGA, The cancer genome atlas
- TCIA, The cancer imaging archive
- WGCNA, Weighted gene co-expression network
- WHO/ISUP, World Health Organization and International Society of Urological Pathology
- ccRCC, Clear cell renal cell carcinoma
Collapse
Affiliation(s)
- Xuan-ming He
- The First Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou, China
| | - Jian-xin Zhao
- The First Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou, China
| | - Di-liang He
- The First Clinical Medical College, Gansu University of Chinese Medicine, Lanzhou, China
| | | | - Lian-ping Zhao
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Gang Huang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China,Corresponding author.
| |
Collapse
|
10
|
Moya‐Sáez E, Navarro‐González R, Cepeda S, Pérez‐Núñez Á, de Luis‐García R, Aja‐Fernández S, Alberola‐López C. Synthetic MRI improves radiomics-based glioblastoma survival prediction. NMR IN BIOMEDICINE 2022; 35:e4754. [PMID: 35485596 PMCID: PMC9542221 DOI: 10.1002/nbm.4754] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/26/2022] [Accepted: 04/26/2022] [Indexed: 06/14/2023]
Abstract
Glioblastoma is an aggressive and fast-growing brain tumor with poor prognosis. Predicting the expected survival of patients with glioblastoma is a key task for efficient treatment and surgery planning. Survival predictions could be enhanced by means of a radiomic system. However, these systems demand high numbers of multicontrast images, the acquisitions of which are time consuming, giving rise to patient discomfort and low healthcare system efficiency. Synthetic MRI could favor deployment of radiomic systems in the clinic by allowing practitioners not only to reduce acquisition time, but also to retrospectively complete databases or to replace artifacted images. In this work we analyze the replacement of an actually acquired MR weighted image by a synthesized version to predict survival of glioblastoma patients with a radiomic system. Each synthesized version was realistically generated from two acquired images with a deep learning synthetic MRI approach based on a convolutional neural network. Specifically, two weighted images were considered for the replacement one at a time, a T2w and a FLAIR, which were synthesized from the pairs T1w and FLAIR, and T1w and T2w, respectively. Furthermore, a radiomic system for survival prediction, which can classify patients into two groups (survival >480 days and ≤ 480 days), was built. Results show that the radiomic system fed with the synthesized image achieves similar performance compared with using the acquired one, and better performance than a model that does not include this image. Hence, our results confirm that synthetic MRI does add to glioblastoma survival prediction within a radiomics-based approach.
Collapse
Affiliation(s)
- Elisa Moya‐Sáez
- Laboratorio de Procesado de ImagenUniversidad de ValladolidValladolid
| | | | - Santiago Cepeda
- Departamento de NeurocirugíaHospital Universitario Río HortegaValladolidSpain
| | - Ángel Pérez‐Núñez
- Departamento de NeurocirugíaHospital Universitario 12 de OctubreMadridSpain
| | | | | | | |
Collapse
|
11
|
Yu X, Wu Y, Bai Y, Han H, Chen L, Gao H, Wei H, Wang M. A lightweight 3D UNet model for glioma grading. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7d33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/29/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. Glioma is one of the most fatal cancers in the world which has been divided into low grade glioma (LGG) and high grade glioma (HGG), and its image grading has become a hot topic of contemporary research. Magnetic resonance imaging (MRI) is a vital diagnostic tool for brain tumor detection, analysis, and surgical planning. Accurate and automatic glioma grading is crucial for speeding up diagnosis and treatment planning. Aiming at the problems of (1) large number of parameters, (2) complex calculation, and (3) poor speed of the current glioma grading algorithms based on deep learning, this paper proposes a lightweight 3D UNet deep learning framework, which can improve classification accuracy in comparison with the existing methods. Approach. To improve efficiency while maintaining accuracy, existing 3D UNet has been excluded, and depthwise separable convolution has been applied to 3D convolution to reduce the number of network parameters. The weight of parameters on the basis of space and channel compression & excitation module has been strengthened to improve the model in the feature map, reduce the weight of redundant parameters, and strengthen the performance of the model. Main results. A total of 560 patients with glioma were retrospectively reviewed. All patients underwent MRI before surgery. The experiments were carried out on T1w, T2w, fluid attenuated inversion recovery, and CET1w images. Additionally, a way of marking tumor area by cube bounding box is presented which has no significant difference in model performance with the manually drawn ground truth. Evaluated on test datasets using the proposed model has shown good results (with accuracy of 89.29%). Significance. This work serves to achieve LGG/HGG grading by simple, effective, and non-invasive diagnostic approaches to provide diagnostic suggestions for clinical usage, thereby facilitating hasten treatment decisions.
Collapse
|
12
|
Jian A, Liu S, Di Ieva A. Artificial Intelligence for Survival Prediction in Brain Tumors on Neuroimaging. Neurosurgery 2022; 91:8-26. [PMID: 35348129 DOI: 10.1227/neu.0000000000001938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/08/2022] [Indexed: 12/30/2022] Open
Abstract
Survival prediction of patients affected by brain tumors provides essential information to guide surgical planning, adjuvant treatment selection, and patient counseling. Current reliance on clinical factors, such as Karnofsky Performance Status Scale, and simplistic radiological characteristics are, however, inadequate for survival prediction in tumors such as glioma that demonstrate molecular and clinical heterogeneity with variable survival outcomes. Advances in the domain of artificial intelligence have afforded powerful tools to capture a large number of hidden high-dimensional imaging features that reflect abundant information about tumor structure and physiology. Here, we provide an overview of current literature that apply computational analysis tools such as radiomics and machine learning methods to the pipeline of image preprocessing, tumor segmentation, feature extraction, and construction of classifiers to establish survival prediction models based on neuroimaging. We also discuss challenges relating to the development and evaluation of such models and explore ethical issues surrounding the future use of machine learning predictions.
Collapse
Affiliation(s)
- Anne Jian
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
- Royal Melbourne Hospital, Melbourne, Australia
| | - Sidong Liu
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
- Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| |
Collapse
|
13
|
Rangaraj S, Islam M, Vs V, Wijethilake N, Uppal U, See AAQ, Chan J, James ML, King NKK, Ren H. Identifying risk factors of intracerebral hemorrhage stability using explainable attention model. Med Biol Eng Comput 2021; 60:337-348. [PMID: 34859369 DOI: 10.1007/s11517-021-02459-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 09/29/2021] [Indexed: 10/19/2022]
Abstract
Segmentation of intracerebral hemorrhage (ICH) helps improve the quality of diagnosis, draft the desired treatment methods, and clinically observe the variations with healthy patients. The clinical utilization of various ICH progression scoring systems has limitations due to the systems' modest predictive value. This paper proposes a single pipeline of a multi-task model for end-to-end hemorrhage segmentation and risk estimation. We introduce a 3D spatial attention unit and integrate it into the state-of-the-art segmentation architecture, UNet, to enhance the accuracy by bootstrapping the global spatial representation. We further extract the geometric features from the segmented hemorrhage volume and fuse them with clinical features such as CT angiography (CTA) spot, Glasgow Coma Scale (GCS), and age to predict the ICH stability. Several state-of-the-art machine learning techniques such as multilayer perceptron (MLP), support vector machine (SVM), gradient boosting, and random forests are applied to train stability estimation and to compare the performances. To align clinical intuition with model learning, we determine the shapely values (SHAP) and explain the most significant features for the ICH risk scoring system. A total of 79 patients are included, of which 20 are found in critical condition. Our proposed single pipeline model achieves a segmentation accuracy of 86.3%, stability prediction accuracy of 78.3%, and precision of 82.9%; the mean square error of exact expansion rate regression is observed to be 0.46. The SHAP analysis reveals that CTA spot sign, age, solidity, location, and length of the first axis of the ICH volume are the most critical characteristics that help define the stability of the stroke lesion. We also show that integrating significant geometric features with clinical features can improve the ICH progression scoring by predicting long-term outcomes. Graphical abstract Overview of our proposed method comprising of spatial attention and feature extraction mechanisms. The architecture is trained on the input CT images, and the first step output is the predicted segmentation of the hemorrhagic region. The output is fed into a geometric feature extractor and is fused with clinical features to estimate ICH stability using a multilayer perceptron (MLP).
Collapse
Affiliation(s)
- Seshasayi Rangaraj
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.,Department of ECE, National Institute of Technology, Tiruchirappalli, India
| | - Mobarakol Islam
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.,NUS Graduate School for Integrative Sciences and Engineering (NGS), National University of Singapore, Singapore, Singapore
| | - Vibashan Vs
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.,Department of ECE, National Institute of Technology, Tiruchirappalli, India
| | - Navodini Wijethilake
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.,Department of ENTC, University of Moratuwa, Moratuwa, Sri Lanka
| | - Utkarsh Uppal
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.,Department of Electrical Engineering, Punjab Engineering College, Chandigarh, India
| | - Angela An Qi See
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Jasmine Chan
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | | | - Nicolas Kon Kam King
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore.,Neuro Asia Care, Mount Elizabeth Hospital, Singapore, Singapore
| | - Hongliang Ren
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore. .,Department of Electronic Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong (CUHK), Hong Kong, Hong Kong.
| |
Collapse
|