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Yuan M, Ding H, Guo B, Yang M, Yang Y, Xu XS. Image-Based Subtype Classification for Glioblastoma Using Deep Learning: Prognostic Significance and Biologic Relevance. JCO Clin Cancer Inform 2024; 8:e2300154. [PMID: 38231003 DOI: 10.1200/cci.23.00154] [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: 08/20/2023] [Revised: 11/03/2023] [Accepted: 11/21/2023] [Indexed: 01/18/2024] Open
Abstract
PURPOSE To apply deep learning algorithms to histopathology images, construct image-based subtypes independent of known clinical and molecular classifications for glioblastoma, and produce novel insights into molecular and immune characteristics of the glioblastoma tumor microenvironment. MATERIALS AND METHODS Using whole-slide hematoxylin and eosin images from 214 patients with glioblastoma in The Cancer Genome Atlas (TCGA), a fine-tuned convolutional neural network model extracted deep learning features. Biclustering was used to identify subtypes and image feature modules. Prognostic value of image subtypes was assessed via Cox regression on survival outcomes and validated with 189 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set. Morphological, molecular, and immune characteristics of glioblastoma image subtypes were analyzed. RESULTS Four distinct subtypes and modules (imClust1-4) were identified for the TCGA patients with glioblastoma on the basis of the image feature data. The glioblastoma image subtypes were significantly associated with overall survival (OS; P = .028) and progression-free survival (P = .003). Apparent association was also observed for disease-specific survival (P = .096). imClust2 had the best prognosis for all three survival end points (eg, after 25 months, imClust2 had >7% surviving patients than the other subtypes). Examination of OS in the external validation using the unseen CPTAC data set showed consistent patterns. Multivariable Cox analyses confirmed that the image subtypes carry unique prognostic information independent of known clinical and molecular predictors. Molecular and immune profiling revealed distinct immune compositions of the tumor microenvironment in different image subtypes and may provide biologic explanations for the patterns in patients' outcomes. CONCLUSION Our image-based subtype classification on the basis of deep learning models is a novel tool to refine risk stratification in cancers. The image subtypes detected for glioblastoma represent a promising prognostic biomarker with distinct molecular and immune characteristics and may facilitate developing novel, individualized immunotherapies for glioblastoma.
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Affiliation(s)
- Min Yuan
- Department of Health Data Science, Anhui Medical University, Hefei, China
| | - Haolun Ding
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, China
| | - Bangwei Guo
- School of Data Science, University of Science and Technology of China, Hefei, China
| | - Miaomiao Yang
- Clinical Pathology Center, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yaning Yang
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, China
| | - Xu Steven Xu
- Clinical Pharmacology and Quantitative Science, Genmab Inc, Princeton, NJ
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Coudray N, Juarez MC, Criscito MC, Quiros AC, Wilken R, Cullison SRJ, Stevenson ML, Doudican NA, Yuan K, Aquino JD, Klufas DM, North JP, Yu SS, Murad F, Ruiz E, Schmults CD, Tsirigos A, Carucci JA. Self-supervised artificial intelligence predicts recurrence, metastasis and disease specific death from primary cutaneous squamous cell carcinoma at diagnosis. RESEARCH SQUARE 2023:rs.3.rs-3607399. [PMID: 38168253 PMCID: PMC10760225 DOI: 10.21203/rs.3.rs-3607399/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome (PO) including recurrence, metastasis and disease specific death (DSD) at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. In this multi-institutional study, we developed a state-of-the-art self-supervised deep-learning approach with interpretability power and demonstrated its ability to predict poor outcomes of cSCCs at the time of initial biopsy. By highlighting histomorphological phenotypes, our approach demonstrates that poor differentiation and deep invasion correlate with poor prognosis. Our approach is particularly efficient at defining poor outcome risk in Brigham and Women's Hospital (BWH) T2a and American Joint Committee on Cancer (AJCC) T2 cSCCs. This bridges a significant gap in our ability to assess risk among T2a/T2 cSCCs and may be useful in defining patients at highest risk of poor outcome at the time of diagnosis. Early identification of highest-risk patients could signal implementation of more stringent surveillance, rigorous diagnostic work up and identify patients who might best respond to early postoperative adjunctive treatment.
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Affiliation(s)
- Nicolas Coudray
- Applied Bioinformatics Laboratories, New York University School of Medicine, New York, NY, USA
- Department of Cell Biology, New York University School of Medicine, New York, NY, USA
| | - Michelle C. Juarez
- The Ronald O. Perelman Department of Dermatology, New York University Grossman School of Medicine, New York, NY, USA
| | - Maressa C. Criscito
- The Ronald O. Perelman Department of Dermatology, New York University Grossman School of Medicine, New York, NY, USA
| | | | - Reason Wilken
- Department of Dermatology, Northwell Health, New York, NY, USA
| | | | - Mary L. Stevenson
- The Ronald O. Perelman Department of Dermatology, New York University Grossman School of Medicine, New York, NY, USA
| | - Nicole A. Doudican
- The Ronald O. Perelman Department of Dermatology, New York University Grossman School of Medicine, New York, NY, USA
| | - Ke Yuan
- School of Computing Science, University of Glasgow, Glasgow, Scotland, UK (Ke Yuan)
- School of Cancer Sciences, University of Glasgow, Glasgow, Scotland, UK (Ke Yuan)
- Cancer Research UK Beatson Institute, Glasgow, Scotland, UK (Ke Yuan)
| | - Jamie D. Aquino
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA
| | - Daniel M. Klufas
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA
| | - Jeffrey P. North
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA
| | - Siegrid S. Yu
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA
| | - Fadi Murad
- Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Emily Ruiz
- Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Chrysalyne D. Schmults
- Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Aristotelis Tsirigos
- Applied Bioinformatics Laboratories, New York University School of Medicine, New York, NY, USA
- Department of Pathology, New York University School of Medicine, New York, NY, USA
| | - John A. Carucci
- The Ronald O. Perelman Department of Dermatology, New York University Grossman School of Medicine, New York, NY, USA
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Wang X, Zeng H, Lin L, Huang Y, Lin H, Que Y. Deep learning-empowered crop breeding: intelligent, efficient and promising. FRONTIERS IN PLANT SCIENCE 2023; 14:1260089. [PMID: 37860239 PMCID: PMC10583549 DOI: 10.3389/fpls.2023.1260089] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 09/13/2023] [Indexed: 10/21/2023]
Abstract
Crop breeding is one of the main approaches to increase crop yield and improve crop quality. However, the breeding process faces challenges such as complex data, difficulties in data acquisition, and low prediction accuracy, resulting in low breeding efficiency and long cycle. Deep learning-based crop breeding is a strategy that applies deep learning techniques to improve and optimize the breeding process, leading to accelerated crop improvement, enhanced breeding efficiency, and the development of higher-yielding, more adaptive, and disease-resistant varieties for agricultural production. This perspective briefly discusses the mechanisms, key applications, and impact of deep learning in crop breeding. We also highlight the current challenges associated with this topic and provide insights into its future application prospects.
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Affiliation(s)
- Xiaoding Wang
- Fujian Provincial Key Lab of Network Security & Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China
| | - Haitao Zeng
- Fujian Provincial Key Lab of Network Security & Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China
| | - Limei Lin
- Fujian Provincial Key Lab of Network Security & Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China
| | - Yanze Huang
- School of Computer Science and Mathematics, Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
| | - Hui Lin
- Fujian Provincial Key Lab of Network Security & Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China
| | - Youxiong Que
- Key Laboratory of Sugarcane Biology and Genetic Breeding, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
- National Key Laboratory for Tropical Crop Breeding, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Hainan, China
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Zhao Y, Zhang J, Hu D, Qu H, Tian Y, Cui X. Application of Deep Learning in Histopathology Images of Breast Cancer: A Review. MICROMACHINES 2022; 13:2197. [PMID: 36557496 PMCID: PMC9781697 DOI: 10.3390/mi13122197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/04/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios.
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Affiliation(s)
- Yue Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110169, China
- Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang 110169, China
| | - Jie Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
| | - Dayu Hu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
| | - Hui Qu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
| | - Ye Tian
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
| | - Xiaoyu Cui
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110169, China
- Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang 110169, China
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Khafaga DS, Ibrahim A, El-Kenawy ESM, Abdelhamid AA, Karim FK, Mirjalili S, Khodadadi N, Lim WH, Eid MM, Ghoneim ME. An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease. Diagnostics (Basel) 2022; 12:diagnostics12112892. [PMID: 36428952 PMCID: PMC9689640 DOI: 10.3390/diagnostics12112892] [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: 10/18/2022] [Revised: 11/04/2022] [Accepted: 11/18/2022] [Indexed: 11/23/2022] Open
Abstract
Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing monkeypox. Such conditions often carry less obvious but no less devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due to the low visual resolution of monkeypox disease images, medical specialists with high-level tools are typically required for a proper diagnosis. The manual diagnosis of monkeypox disease is subjective, time-consuming, and labor-intensive. Therefore, it is necessary to create a computer-aided approach for the automated diagnosis of monkeypox disease. Most research articles on monkeypox disease relied on convolutional neural networks (CNNs) and using classical loss functions, allowing them to pick up discriminative elements in monkeypox images. To enhance this, a novel framework using Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) is proposed to fine-tune the deep CNN layers for classifying monkeypox disease from images. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space and learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework's efficacy, as it outperforms numerous examples of prior research on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models.
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Affiliation(s)
- Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
- Correspondence: (D.S.K.); (E.-S.M.E.-K.); (A.A.A.); (F.K.K.)
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - El-Sayed M. El-Kenawy
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
- Correspondence: (D.S.K.); (E.-S.M.E.-K.); (A.A.A.); (F.K.K.)
| | - Abdelaziz A. Abdelhamid
- Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
- Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
- Correspondence: (D.S.K.); (E.-S.M.E.-K.); (A.A.A.); (F.K.K.)
| | - Faten Khalid Karim
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
- Correspondence: (D.S.K.); (E.-S.M.E.-K.); (A.A.A.); (F.K.K.)
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, QLD 4006, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul 03722, Republic of Korea
| | - Nima Khodadadi
- Department of Civil and Environmental Engineering, Florida International University, Miami, FL 33199, USA
| | - Wei Hong Lim
- Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia
| | - Marwa M. Eid
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
| | - Mohamed E. Ghoneim
- Department of Mathematical Sciences, Faculty of Applied Science, Umm Al-Qura University, Makkah 21955, Saudi Arabia
- Faculty of Computers and Artificial Intelligence, Damietta University, Damietta 34511, Egypt
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