1
|
Lyu Q, Parreno-Centeno M, Papa JP, Öztürk-Isik E, Booth TC, Costen F. SurvNet: A low-complexity convolutional neural network for survival time classification of patients with glioblastoma. Heliyon 2024; 10:e32870. [PMID: 38988550 PMCID: PMC11234028 DOI: 10.1016/j.heliyon.2024.e32870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 07/12/2024] Open
Abstract
Background and objective Malignant primary brain tumors cause the greatest number of years of life lost than any other cancer. Grade 4 glioma is particularly devastating: The median survival without any treatment is less than six months and with standard-of-care treatment is only 14.6 months. Accurate identification of the overall survival time of patients with brain tumors is of profound importance in many clinical applications. Automated image analytics with magnetic resonance imaging (MRI) can provide insights into the prognosis of patients with brain tumors. Methods In this paper, We propose SurvNet, a low-complexity deep learning architecture based on the convolutional neural network to classify the overall survival time of patients with brain tumors into long-time and short-time survival cohorts. Through the incorporation of diverse MRI modalities as inputs, we facilitate deep feature extraction at various anatomical sites, thereby augmenting the precision of predictive modeling. We compare SurvNet with the Inception V3, VGG 16 and ensemble CNN models on pre-operative magnetic resonance image datasets. We also analyzed the effect of segmented brain tumors and training data on the system performance. Results Several measures, such as accuracy, precision, and recall, are calculated to examine the perfor-mance of SurvNet on three-fold cross-validation. SurvNet with T1 MRI modality achieved a 62.7 % accuracy, compared with 52.9 % accuracy of the Inception V3 model, 58.5 % accuracy of the VGG 16 model, and 54.9 % of the ensemble CNN model. By increasing the MRI input modalities, SurvNet becomes more accurate and achieves 76.5 % accuracy with four MRI modalities. Combining the segmented data, SurvNet achieved the highest accuracy of 82.4 %. Conclusions The research results show that SurvNet achieves higher metrics such as accuracy and f1-score than the comparisons. Our research also proves that by using multiparametric MRI modalities, SurvNet is able to learn more image features and performs a better classification accuracy. We can conclude that SurvNet with the complete scenario, i.e., segmented data and four MRI modalities, achieved the best accuracy, showing the validity of segmentation information during the survival time prediction process.
Collapse
Affiliation(s)
- Qiyuan Lyu
- Electrical and Electronic Engineering Department, The University of Manchester, Oxford Rd, Manchester M13 9PL, United Kingdom
| | | | | | - Esin Öztürk-Isik
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
| | - Thomas C. Booth
- 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
| | - Fumie Costen
- Electrical and Electronic Engineering Department, The University of Manchester, Oxford Rd, Manchester M13 9PL, United Kingdom
- Image Processing Research Team, Centre for Advanced Photonics, RIKEN, Saitama, Japan
| |
Collapse
|
2
|
Herr J, Stoyanova R, Mellon EA. Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review. Crit Rev Oncog 2024; 29:33-65. [PMID: 38683153 DOI: 10.1615/critrevoncog.2023050852] [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: 05/01/2024]
Abstract
Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.
Collapse
Affiliation(s)
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, Fl 33136, USA
| | - Eric Albert Mellon
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, Fl 33136, USA
| |
Collapse
|
3
|
Ortega-Martorell S, Olier I, Hernandez O, Restrepo-Galvis PD, Bellfield RAA, Candiota AP. Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks. Cancers (Basel) 2023; 15:4002. [PMID: 37568818 PMCID: PMC10417313 DOI: 10.3390/cancers15154002] [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: 06/07/2023] [Revised: 07/26/2023] [Accepted: 08/05/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Glioblastoma (GB) is a malignant brain tumour that is challenging to treat, often relapsing even after aggressive therapy. Evaluating therapy response relies on magnetic resonance imaging (MRI) following the Response Assessment in Neuro-Oncology (RANO) criteria. However, early assessment is hindered by phenomena such as pseudoprogression and pseudoresponse. Magnetic resonance spectroscopy (MRS/MRSI) provides metabolomics information but is underutilised due to a lack of familiarity and standardisation. METHODS This study explores the potential of spectroscopic imaging (MRSI) in combination with several machine learning approaches, including one-dimensional convolutional neural networks (1D-CNNs), to improve therapy response assessment. Preclinical GB (GL261-bearing mice) were studied for method optimisation and validation. RESULTS The proposed 1D-CNN models successfully identify different regions of tumours sampled by MRSI, i.e., normal brain (N), control/unresponsive tumour (T), and tumour responding to treatment (R). Class activation maps using Grad-CAM enabled the study of the key areas relevant to the models, providing model explainability. The generated colour-coded maps showing the N, T and R regions were highly accurate (according to Dice scores) when compared against ground truth and outperformed our previous method. CONCLUSIONS The proposed methodology may provide new and better opportunities for therapy response assessment, potentially providing earlier hints of tumour relapsing stages.
Collapse
Affiliation(s)
- Sandra Ortega-Martorell
- Data Science Research Centre, Liverpool John Moores University, Liverpool L3 3AF, UK; (I.O.); (R.A.A.B.)
| | - Ivan Olier
- Data Science Research Centre, Liverpool John Moores University, Liverpool L3 3AF, UK; (I.O.); (R.A.A.B.)
| | - Orlando Hernandez
- Escuela Colombiana de Ingeniería Julio Garavito, Bogota 111166, Colombia; (O.H.); (P.D.R.-G.)
| | | | - Ryan A. A. Bellfield
- Data Science Research Centre, Liverpool John Moores University, Liverpool L3 3AF, UK; (I.O.); (R.A.A.B.)
| | - Ana Paula Candiota
- Centro de Investigación Biomédica en Red: Bioingeniería, Biomateriales y Nanomedicina, 08193 Cerdanyola del Vallès, Spain
- Departament de Bioquímica i Biologia Molecular, Facultat de Biociències, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain
| |
Collapse
|
4
|
Zhang H, Xi Q, Zhang F, Li Q, Jiao Z, Ni X. Application of Deep Learning in Cancer Prognosis Prediction Model. Technol Cancer Res Treat 2023; 22:15330338231199287. [PMID: 37709267 PMCID: PMC10503281 DOI: 10.1177/15330338231199287] [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: 09/16/2023] Open
Abstract
As an important branch of artificial intelligence and machine learning, deep learning (DL) has been widely used in various aspects of cancer auxiliary diagnosis, among which cancer prognosis is the most important part. High-accuracy cancer prognosis is beneficial to the clinical management of patients with cancer. Compared with other methods, DL models can significantly improve the accuracy of prediction. Therefore, this article is a systematic review of the latest research on DL in cancer prognosis prediction. First, the data type, construction process, and performance evaluation index of the DL model are introduced in detail. Then, the current mainstream baseline DL cancer prognosis prediction models, namely, deep neural networks, convolutional neural networks, deep belief networks, deep residual networks, and vision transformers, including network architectures, the latest application in cancer prognosis, and their respective characteristics, are discussed. Next, some key factors that affect the predictive performance of the model and common performance enhancement techniques are listed. Finally, the limitations of the DL cancer prognosis prediction model in clinical practice are summarized, and the future research direction is prospected. This article could provide relevant researchers with a comprehensive understanding of DL cancer prognostic models and is expected to promote the research progress of cancer prognosis prediction.
Collapse
Affiliation(s)
- Heng Zhang
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
| | - Qianyi Xi
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Fan Zhang
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Qixuan Li
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Zhuqing Jiao
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Xinye Ni
- Department of Radiotherapy Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Medical Physics Research Center, Nanjing Medical University, Changzhou, China
- Key Laboratory of Medical Physics in Changzhou, Changzhou, China
| |
Collapse
|
5
|
García-García S, García-Galindo M, Arrese I, Sarabia R, Cepeda S. Current Evidence, Limitations and Future Challenges of Survival Prediction for Glioblastoma Based on Advanced Noninvasive Methods: A Narrative Review. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121746. [PMID: 36556948 PMCID: PMC9786785 DOI: 10.3390/medicina58121746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/16/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022]
Abstract
Background and Objectives: Survival estimation for patients diagnosed with Glioblastoma (GBM) is an important information to consider in patient management and communication. Despite some known risk factors, survival estimation remains a major challenge. Novel non-invasive technologies such as radiomics and artificial intelligence (AI) have been implemented to increase the accuracy of these predictions. In this article, we reviewed and discussed the most significant available research on survival estimation for GBM through advanced non-invasive methods. Materials and Methods: PubMed database was queried for articles reporting on survival prognosis for GBM through advanced image and data management methods. Articles including in their title or abstract the following terms were initially screened: ((glioma) AND (survival)) AND ((artificial intelligence) OR (radiomics)). Exclusively English full-text articles, reporting on humans, published as of 1 September 2022 were considered. Articles not reporting on overall survival, evaluating the effects of new therapies or including other tumors were excluded. Research with a radiomics-based methodology were evaluated using the radiomics quality score (RQS). Results: 382 articles were identified. After applying the inclusion criteria, 46 articles remained for further analysis. These articles were thoroughly assessed, summarized and discussed. The results of the RQS revealed some of the limitations of current radiomics investigation on this field. Limitations of analyzed studies included data availability, patient selection and heterogeneity of methodologies. Future challenges on this field are increasing data availability, improving the general understanding of how AI handles data and establishing solid correlations between image features and tumor's biology. Conclusions: Radiomics and AI methods of data processing offer a new paradigm of possibilities to tackle the question of survival prognosis in GBM.
Collapse
Affiliation(s)
- Sergio García-García
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
- Correspondence:
| | - Manuel García-Galindo
- Faculty of Medicine, University of Valladolid, Avenida Ramón y Cajal 7, 47003 Valladolid, Spain
| | - Ignacio Arrese
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
| | - Rosario Sarabia
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
| | - Santiago Cepeda
- Department of Neurosurgery, University Hospital Río Hortega, Dulzaina 2, 47012 Valladolid, Spain
| |
Collapse
|
6
|
Dong J, Zhang Y, Meng Y, Yang T, Ma W, Wu H. Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks. Stem Cells Int 2022; 2022:8619690. [PMID: 36299467 PMCID: PMC9592238 DOI: 10.1155/2022/8619690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 08/22/2022] [Accepted: 09/26/2022] [Indexed: 11/18/2022] Open
Abstract
This work focused on the application value of magnetic resonance imaging (MRI) image segmentation algorithm based on fully convolutional DenseNet neural network (FCDNN) in glioma diagnosis. In this work, based on the fully convolutional DenseNet algorithm, a new MRI image automatic semantic segmentation method cerebral gliomas semantic segmentation network (CGSSNet) was established and was applied to glioma MRI image segmentation by using the BraTS public dataset as research data. Under the same conditions, compare the differences of dice similarity coefficient (DSC), sensitivity, and Hausdroff distance (HD) between this algorithm and other algorithms in MRI image processing. The results showed that the CGSSNet network segmentation algorithm significantly improved the segmentation accuracy of glioma MRI images. In addition, its DSC, sensitivity, and HD values for glioma MRI images were 0.937, 0.811, and 1.201, respectively. Under different iteration times, the DSC, sensitivity, and HD values of the CGSSNet network segmentation algorithm are significantly better than other algorithms. It showed that the CGSSNet model based on the DenseNet can improve the segmentation accuracy of glioma MRI images, and has potential application value in clinical practice.
Collapse
Affiliation(s)
- Jie Dong
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
| | - Yueying Zhang
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
| | - Yun Meng
- Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Tingxiao Yang
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
| | - Wei Ma
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
| | - Huixin Wu
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
| |
Collapse
|