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Magoulianitis V, Yang J, Yang Y, Xue J, Kaneko M, Cacciamani G, Abreu A, Duddalwar V, Kuo CCJ, Gill IS, Nikias C. PCa-RadHop: A transparent and lightweight feed-forward method for clinically significant prostate cancer segmentation. Comput Med Imaging Graph 2024; 116:102408. [PMID: 38908295 DOI: 10.1016/j.compmedimag.2024.102408] [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: 01/26/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/24/2024]
Abstract
Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as "black-boxes" in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity.
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Affiliation(s)
- Vasileios Magoulianitis
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA.
| | - Jiaxin Yang
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Yijing Yang
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Jintang Xue
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Masatomo Kaneko
- Department of Urology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA
| | - Giovanni Cacciamani
- Department of Urology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA
| | - Andre Abreu
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Vinay Duddalwar
- Department of Urology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA; Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA
| | - C-C Jay Kuo
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Inderbir S Gill
- Department of Urology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA
| | - Chrysostomos Nikias
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
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Kushol R, Luk CC, Dey A, Benatar M, Briemberg H, Dionne A, Dupré N, Frayne R, Genge A, Gibson S, Graham SJ, Korngut L, Seres P, Welsh RC, Wilman AH, Zinman L, Kalra S, Yang YH. SF2Former: Amyotrophic lateral sclerosis identification from multi-center MRI data using spatial and frequency fusion transformer. Comput Med Imaging Graph 2023; 108:102279. [PMID: 37573646 DOI: 10.1016/j.compmedimag.2023.102279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 07/17/2023] [Accepted: 07/22/2023] [Indexed: 08/15/2023]
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disorder characterized by motor neuron degeneration. Significant research has begun to establish brain magnetic resonance imaging (MRI) as a potential biomarker to diagnose and monitor the state of the disease. Deep learning has emerged as a prominent class of machine learning algorithms in computer vision and has shown successful applications in various medical image analysis tasks. However, deep learning methods applied to neuroimaging have not achieved superior performance in classifying ALS patients from healthy controls due to insignificant structural changes correlated with pathological features. Thus, a critical challenge in deep models is to identify discriminative features from limited training data. To address this challenge, this study introduces a framework called SF2Former, which leverages the power of the vision transformer architecture to distinguish ALS subjects from the control group by exploiting the long-range relationships among image features. Additionally, spatial and frequency domain information is combined to enhance the network's performance, as MRI scans are initially captured in the frequency domain and then converted to the spatial domain. The proposed framework is trained using a series of consecutive coronal slices and utilizes pre-trained weights from ImageNet through transfer learning. Finally, a majority voting scheme is employed on the coronal slices of each subject to generate the final classification decision. The proposed architecture is extensively evaluated with multi-modal neuroimaging data (i.e., T1-weighted, R2*, FLAIR) using two well-organized versions of the Canadian ALS Neuroimaging Consortium (CALSNIC) multi-center datasets. The experimental results demonstrate the superiority of the proposed strategy in terms of classification accuracy compared to several popular deep learning-based techniques.
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Affiliation(s)
- Rafsanjany Kushol
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
| | - Collin C Luk
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, AB, Canada; Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Avyarthana Dey
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Michael Benatar
- Department of Neurology, University of Miami, Miller School of Medicine, Miami, FL, USA
| | - Hannah Briemberg
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Annie Dionne
- Axe Neurosciences, CHU de Québec, Université Laval, Québec, QC, Canada; Department of Medicine, Faculty of Medicine, Université Laval, Quebec City, QC, Canada
| | - Nicolas Dupré
- Axe Neurosciences, CHU de Québec, Université Laval, Québec, QC, Canada; Department of Medicine, Faculty of Medicine, Université Laval, Quebec City, QC, Canada
| | - Richard Frayne
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Angela Genge
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Summer Gibson
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Simon J Graham
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Lawrence Korngut
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Peter Seres
- Departments of Biomedical Engineering and Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, Canada
| | - Robert C Welsh
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Alan H Wilman
- Departments of Biomedical Engineering and Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, Canada
| | - Lorne Zinman
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada; Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Sanjay Kalra
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada; Division of Neurology, Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Yee-Hong Yang
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
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Das T, Kaur H, Gour P, Prasad K, Lynn AM, Prakash A, Kumar V. Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review. Brief Bioinform 2022; 23:6780269. [PMID: 36411673 DOI: 10.1093/bib/bbac442] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/12/2022] [Accepted: 09/13/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques. OBJECTIVE This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways. METHODS The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria. RESULTS We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.
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Affiliation(s)
- Trishala Das
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Harbinder Kaur
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Pratibha Gour
- Dept. of Plant Molecular Biology, University of Delhi, South Campus, New Delhi-110021, India
| | - Kartikay Prasad
- Amity Institute of Neuropsychology & Neurosciences (AINN), Amity University, Noida, UP-201303, India
| | - Andrew M Lynn
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Amresh Prakash
- Amity Institute of Integrative Sciences and Health, Amity University Haryana, Gurgaon-122413, India
| | - Vijay Kumar
- Amity Institute of Neuropsychology & Neurosciences (AINN), Amity University, Noida, UP-201303, India
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