1
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Lorenzo G, Ahmed SR, Hormuth DA, Vaughn B, Kalpathy-Cramer J, Solorio L, Yankeelov TE, Gomez H. Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data. Annu Rev Biomed Eng 2024; 26:529-560. [PMID: 38594947 DOI: 10.1146/annurev-bioeng-081623-025834] [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: 04/11/2024]
Abstract
Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires the integration of patient-specific information with an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous yet practical mathematical theory of tumor initiation, development, invasion, and response to therapy. We begin this review with an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on big data and artificial intelligence. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.
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Affiliation(s)
- Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Syed Rakin Ahmed
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
- Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard University, Cambridge, Massachusetts, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - David A Hormuth
- Livestrong Cancer Institutes, University of Texas, Austin, Texas, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
| | - Brenna Vaughn
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | | | - Luis Solorio
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | - Thomas E Yankeelov
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, Texas, USA
- Department of Biomedical Engineering, Department of Oncology, and Department of Diagnostic Medicine, University of Texas, Austin, Texas, USA
- Livestrong Cancer Institutes, University of Texas, Austin, Texas, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
| | - Hector Gomez
- School of Mechanical Engineering and Purdue Center for Cancer Research, Purdue University, West Lafayette, Indiana, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
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2
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Longo LHDC, Roberto GF, Tosta TAA, de Faria PR, Loyola AM, Cardoso SV, Silva AB, do Nascimento MZ, Neves LA. Classification of Multiple H&E Images via an Ensemble Computational Scheme. ENTROPY (BASEL, SWITZERLAND) 2023; 26:34. [PMID: 38248160 PMCID: PMC10814107 DOI: 10.3390/e26010034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/23/2023] [Accepted: 12/25/2023] [Indexed: 01/23/2024]
Abstract
In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19). The descriptors were investigated through different associations. The most relevant combinations, defined through a ranking algorithm, were analyzed via a heterogeneous ensemble of classifiers with the support vector machine, naive Bayes, random forest and K-nearest neighbors algorithms. The proposed scheme was applied to histological samples representative of breast cancer, colorectal cancer, oral dysplasia and liver tissue. The best results were accuracy rates of 94.83% to 100%, with the identification of pattern ensembles for classifying multiple histological images. The computational scheme indicated solutions exploring a reduced number of features (a maximum of 25 descriptors) and with better performance values than those observed in the literature. The presented information in this study is useful to complement and improve the development of computer-aided diagnosis focused on histological images.
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Affiliation(s)
- Leonardo H. da Costa Longo
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, São José do Rio Preto 15054-000, SP, Brazil
| | - Guilherme F. Roberto
- Department of Informatics Engineering, Faculty of Engineering, University of Porto, Dr. Roberto Frias, sn, 4200-465 Porto, Portugal;
| | - Thaína A. A. Tosta
- Science and Technology Institute, Federal University of São Paulo (UNIFESP), Avenida Cesare Mansueto Giulio Lattes, 1201, São José dos Campos 12247-014, SP, Brazil;
| | - Paulo R. de Faria
- Department of Histology and Morphology, Institute of Biomedical Science, Federal University of Uberlândia (UFU), Av. Amazonas, S/N, Uberlândia 38405-320, MG, Brazil;
| | - Adriano M. Loyola
- Area of Oral Pathology, School of Dentistry, Federal University of Uberlândia (UFU), R. Ceará—Umuarama, Uberlândia 38402-018, MG, Brazil; (A.M.L.)
| | - Sérgio V. Cardoso
- Area of Oral Pathology, School of Dentistry, Federal University of Uberlândia (UFU), R. Ceará—Umuarama, Uberlândia 38402-018, MG, Brazil; (A.M.L.)
| | - Adriano B. Silva
- Faculty of Computer Science (FACOM), Federal University of Uberlândia (UFU), Avenida João Naves de Ávila 2121, Bl.B, Uberlândia 38400-902, MG, Brazil
| | - Marcelo Z. do Nascimento
- Faculty of Computer Science (FACOM), Federal University of Uberlândia (UFU), Avenida João Naves de Ávila 2121, Bl.B, Uberlândia 38400-902, MG, Brazil
| | - Leandro A. Neves
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, São José do Rio Preto 15054-000, SP, Brazil
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3
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Thomas L, Sheeja MK. Fourier ptychographic and deep learning using breast cancer histopathological image classification. JOURNAL OF BIOPHOTONICS 2023; 16:e202300194. [PMID: 37296518 DOI: 10.1002/jbio.202300194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 06/12/2023]
Abstract
Automated, as well as accurate classification with breast cancer histological images, was crucial for medical applications because of detecting malignant tumors via histopathological images. In this work create a Fourier ptychographic (FP) and deep learning using breast cancer histopathological image classification. Here the FP method used in the process begins with such a random guess that builds a high-resolution complex hologram, subsequently uses iterative retrieval using FP constraints to stitch around each other low-resolution multi-view means of production owned from either the hologram's high-resolution hologram's elemental images captured via integral imaging. Next, the feature extraction process includes entropy, geometrical features, and textural features. The entropy-based normalization is used to optimize the features. Finally, it attains the classification process of the proposed ENDNN classifies the breast cancer images into normal or abnormal. The experimental outcomes demonstrate that our presented technique overtakes the traditional techniques.
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Affiliation(s)
- Leena Thomas
- Department of Electronics & Communication Engineering, Sree Chitra Thirunal College of Engineering, Thiruvananthapuram, Kerala, India
- APJ Abdul Kalam Technological University, Kerala, India
- College of Engineering Kallooppara, Pathanamthitta, Kerala, India
| | - M K Sheeja
- Department of Electronics & Communication Engineering, Sree Chitra Thirunal College of Engineering, Thiruvananthapuram, Kerala, India
- APJ Abdul Kalam Technological University, Kerala, India
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4
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Jing Y, Li C, Du T, Jiang T, Sun H, Yang J, Shi L, Gao M, Grzegorzek M, Li X. A comprehensive survey of intestine histopathological image analysis using machine vision approaches. Comput Biol Med 2023; 165:107388. [PMID: 37696178 DOI: 10.1016/j.compbiomed.2023.107388] [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: 06/08/2023] [Revised: 08/06/2023] [Accepted: 08/25/2023] [Indexed: 09/13/2023]
Abstract
Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.
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Affiliation(s)
- Yujie Jing
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.
| | - Tianming Du
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Tao Jiang
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China; International Joint Institute of Robotics and Intelligent Systems, Chengdu University of Information Technology, Chengdu, China
| | - Hongzan Sun
- Shengjing Hospital of China Medical University, Shenyang, China
| | - Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Liyu Shi
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Minghe Gao
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Marcin Grzegorzek
- Institute for Medical Informatics, University of Luebeck, Luebeck, Germany; Department of Knowledge Engineering, University of Economics in Katowice, Katowice, Poland
| | - Xiaoyan Li
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China.
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5
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Kanadath A, Jothi JAA, Urolagin S. Multilevel Colonoscopy Histopathology Image Segmentation Using Particle Swarm Optimization Techniques. SN COMPUTER SCIENCE 2023; 4:427. [PMID: 37304839 PMCID: PMC10245360 DOI: 10.1007/s42979-023-01915-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/16/2023] [Indexed: 06/13/2023]
Abstract
Histopathology image segmentation is a challenging task in medical image processing. This work aims to segment lesion regions from colonoscopy histopathology images. Initially, the images are preprocessed and then segmented using the multilevel image thresholding technique. Multilevel thresholding is considered an optimization problem. Particle swarm optimization (PSO) and its variants, darwinian particle swarm optimization (DPSO), and fractional order darwinian particle swarm optimization (FODPSO) are used to solve the optimization problem and they generate the threshold values. The threshold values obtained are used to segment the lesion regions from the images of the colonoscopy tissue data set. Segmented images containing the lesion regions are then postprocessed to remove unnecessary regions. Experimental results reveal that the FODPSO algorithm with Otsu's discriminant criterion as the objective function achieves the best accuracy, Dice and Jaccard values of 0.89, 0.68 and 0.52, respectively, for the colonoscopy data set. The FODPSO algorithm also outperforms other optimization methods such as artificial bee colony and the firefly algorithms in terms of the accuracy, Dice and Jaccard values.
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Affiliation(s)
- Anusree Kanadath
- Department of Computer Science, Birla Institute of Science and Technology Pilani, Dubai Campus, Dubai International Academic City, Dubai, 345055 United Arab Emirates
| | - J Angel Arul Jothi
- Department of Computer Science, Birla Institute of Science and Technology Pilani, Dubai Campus, Dubai International Academic City, Dubai, 345055 United Arab Emirates
| | - Siddhaling Urolagin
- Department of Computer Science, Birla Institute of Science and Technology Pilani, Dubai Campus, Dubai International Academic City, Dubai, 345055 United Arab Emirates
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6
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Kumar D, Bhattacharjee RM. Application of wrapper based hybrid system for classification of risk tolerance in the Indian mining industry. Sci Rep 2023; 13:6181. [PMID: 37061559 PMCID: PMC10105760 DOI: 10.1038/s41598-023-32693-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 03/31/2023] [Indexed: 04/17/2023] Open
Abstract
The degree to which an individual is willing to take risks i.e., risk tolerance is often cited as a significant causal element in the majority of workplace accidents. It is essential to determine the risk tolerance level of miners and utilise their risk profiles to design improved training modules, safety, recruitment, and deployment policies. This paper aims to identify the most critical factors (or features) influencing miners' risk tolerance in the Indian coal industry and develop a robust prediction model to learn their risk tolerance levels. To do end, we first conducted a questionnaire survey representing the complete feature set (with 36 features) among 360 miners and divided their responses into five classes of risk tolerance. Next, we propose a wrapper based hybrid system that combines particle swarm optimization (PSO) and random forest (RF) to train a multi-class classifier with a subset of features. In general, the proposed system selects the best feature subset by iteratively generating different feature combinations using the PSO and training an RF classifier model to assess the effectiveness of the generated feature subsets for the F1-score. At last, we compared the PSO-RF with four traditional classification methods to evaluate its effectiveness in terms of precision, recall, F1-score, accuracy, goodness-of-fit, and area under the curve.
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Affiliation(s)
- Deepak Kumar
- Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India.
- Bharat Coking Coal Limited, Dhanbad, 826004, India.
| | - Ram Madhab Bhattacharjee
- Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India
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7
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Kanadath A, Angel Arul Jothi J, Urolagin S. Multilevel Multiobjective Particle Swarm Optimization Guided Superpixel Algorithm for Histopathology Image Detection and Segmentation. J Imaging 2023; 9:jimaging9040078. [PMID: 37103229 PMCID: PMC10145642 DOI: 10.3390/jimaging9040078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 03/31/2023] Open
Abstract
Histopathology image analysis is considered as a gold standard for the early diagnosis of serious diseases such as cancer. The advancements in the field of computer-aided diagnosis (CAD) have led to the development of several algorithms for accurately segmenting histopathology images. However, the application of swarm intelligence for segmenting histopathology images is less explored. In this study, we introduce a Multilevel Multiobjective Particle Swarm Optimization guided Superpixel algorithm (MMPSO-S) for the effective detection and segmentation of various regions of interest (ROIs) from Hematoxylin and Eosin (H&E)-stained histopathology images. Several experiments are conducted on four different datasets such as TNBC, MoNuSeg, MoNuSAC, and LD to ascertain the performance of the proposed algorithm. For the TNBC dataset, the algorithm achieves a Jaccard coefficient of 0.49, a Dice coefficient of 0.65, and an F-measure of 0.65. For the MoNuSeg dataset, the algorithm achieves a Jaccard coefficient of 0.56, a Dice coefficient of 0.72, and an F-measure of 0.72. Finally, for the LD dataset, the algorithm achieves a precision of 0.96, a recall of 0.99, and an F-measure of 0.98. The comparative results demonstrate the superiority of the proposed method over the simple Particle Swarm Optimization (PSO) algorithm, its variants (Darwinian particle swarm optimization (DPSO), fractional order Darwinian particle swarm optimization (FODPSO)), Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), non-dominated sorting genetic algorithm 2 (NSGA2), and other state-of-the-art traditional image processing methods.
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8
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Nasir ES, Parvaiz A, Fraz MM. Nuclei and glands instance segmentation in histology images: a narrative review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10372-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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9
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Alzoubi I, Bao G, Zhang R, Loh C, Zheng Y, Cherepanoff S, Gracie G, Lee M, Kuligowski M, Alexander KL, Buckland ME, Wang X, Graeber MB. An Open-Source AI Framework for the Analysis of Single Cells in Whole-Slide Images with a Note on CD276 in Glioblastoma. Cancers (Basel) 2022; 14:3441. [PMID: 35884502 PMCID: PMC9316952 DOI: 10.3390/cancers14143441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/10/2022] [Accepted: 07/13/2022] [Indexed: 02/04/2023] Open
Abstract
Routine examination of entire histological slides at cellular resolution poses a significant if not insurmountable challenge to human observers. However, high-resolution data such as the cellular distribution of proteins in tissues, e.g., those obtained following immunochemical staining, are highly desirable. Our present study extends the applicability of the PathoFusion framework to the cellular level. We illustrate our approach using the detection of CD276 immunoreactive cells in glioblastoma as an example. Following automatic identification by means of PathoFusion's bifocal convolutional neural network (BCNN) model, individual cells are automatically profiled and counted. Only discriminable cells selected through data filtering and thresholding were segmented for cell-level analysis. Subsequently, we converted the detection signals into the corresponding heatmaps visualizing the distribution of the detected cells in entire whole-slide images of adjacent H&E-stained sections using the Discrete Wavelet Transform (DWT). Our results demonstrate that PathoFusion is capable of autonomously detecting and counting individual immunochemically labelled cells with a high prediction performance of 0.992 AUC and 97.7% accuracy. The data can be used for whole-slide cross-modality analyses, e.g., relationships between immunochemical signals and anaplastic histological features. PathoFusion has the potential to be applied to additional problems that seek to correlate heterogeneous data streams and to serve as a clinically applicable, weakly supervised system for histological image analyses in (neuro)pathology.
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Affiliation(s)
- Islam Alzoubi
- School of Computer Science, The University of Sydney, J12/1 Cleveland St, Sydney, NSW 2008, Australia; (I.A.); (G.B.); (R.Z.)
| | - Guoqing Bao
- School of Computer Science, The University of Sydney, J12/1 Cleveland St, Sydney, NSW 2008, Australia; (I.A.); (G.B.); (R.Z.)
| | - Rong Zhang
- School of Computer Science, The University of Sydney, J12/1 Cleveland St, Sydney, NSW 2008, Australia; (I.A.); (G.B.); (R.Z.)
| | - Christina Loh
- Ken Parker Brain Tumour Research Laboratories, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia; (C.L.); (Y.Z.)
| | - Yuqi Zheng
- Ken Parker Brain Tumour Research Laboratories, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia; (C.L.); (Y.Z.)
| | - Svetlana Cherepanoff
- St Vincent’s Hospital, Victoria Street, Darlinghurst, NSW 2010, Australia; (S.C.); (G.G.)
| | - Gary Gracie
- St Vincent’s Hospital, Victoria Street, Darlinghurst, NSW 2010, Australia; (S.C.); (G.G.)
| | - Maggie Lee
- Department of Neuropathology, RPA Hospital and Brain and Mind Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; (M.L.); (K.L.A.); (M.E.B.)
| | - Michael Kuligowski
- Sydney Microscopy and Microanalysis, The University of Sydney, Sydney, NSW 2006, Australia;
| | - Kimberley L. Alexander
- Department of Neuropathology, RPA Hospital and Brain and Mind Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; (M.L.); (K.L.A.); (M.E.B.)
- Neurosurgery Department, Chris O’Brien Lifehouse, Camperdown, NSW 2050, Australia
- School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia
| | - Michael E. Buckland
- Department of Neuropathology, RPA Hospital and Brain and Mind Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; (M.L.); (K.L.A.); (M.E.B.)
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, J12/1 Cleveland St, Sydney, NSW 2008, Australia; (I.A.); (G.B.); (R.Z.)
| | - Manuel B. Graeber
- Ken Parker Brain Tumour Research Laboratories, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia; (C.L.); (Y.Z.)
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10
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New bag-of-feature for histopathology image classification using reinforced cat swarm algorithm and weighted Gaussian mixture modelling. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00726-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
AbstractThe progress in digital histopathology for computer-aided diagnosis leads to advancement in automated histopathological image classification system. However, heterogeneity and complexity in structural background make it a challenging process. Therefore, this paper introduces robust and reliable new bag-of-feature framework. The optimal visual words are obtained by applying proposed reinforcement cat swarm optimization algorithm. Moreover, the frequency of occurrence of each visual words is depicted through histogram using new weighted Gaussian mixture modelling method. Reinforcement cat swarm optimization algorithm is evaluated on the IEEE CEC 2017 benchmark function problems and compared with other state-of-the-art algorithms. Moreover, statistical test analysis is done on acquired mean and the best fitness values from benchmark functions. The proposed classification model effectively identifies and classifies the different categories of histopathological images. Furthermore, the comparative experimental result analysis of proposed reinforcement cat swarm optimization-based bag-of-feature is performed on standard quality metrics measures. The observation states that reinforcement cat swarm optimization-based bag-of-feature outperforms the other methods and provides promising results.
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11
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Wang X, Wang R, Yang S, Zhang J, Wang M, Zhong D, Zhang J, Han X. Combining Radiology and Pathology for Automatic Glioma Classification. Front Bioeng Biotechnol 2022; 10:841958. [PMID: 35387307 PMCID: PMC8977526 DOI: 10.3389/fbioe.2022.841958] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 02/23/2022] [Indexed: 11/17/2022] Open
Abstract
Subtype classification is critical in the treatment of gliomas because different subtypes lead to different treatment options and postoperative care. Although many radiological- or histological-based glioma classification algorithms have been developed, most of them focus on single-modality data. In this paper, we propose an innovative two-stage model to classify gliomas into three subtypes (i.e., glioblastoma, oligodendroglioma, and astrocytoma) based on radiology and histology data. In the first stage, our model classifies each image as having glioblastoma or not. Based on the obtained non-glioblastoma images, the second stage aims to accurately distinguish astrocytoma and oligodendroglioma. The radiological images and histological images pass through the two-stage design with 3D and 2D models, respectively. Then, an ensemble classification network is designed to automatically integrate the features of the two modalities. We have verified our method by participating in the MICCAI 2020 CPM-RadPath Challenge and won 1st place. Our proposed model achieves high performance on the validation set with a balanced accuracy of 0.889, Cohen’s Kappa of 0.903, and an F1-score of 0.943. Our model could advance multimodal-based glioma research and provide assistance to pathologists and neurologists in diagnosing glioma subtypes. The code has been publicly available online at https://github.com/Xiyue-Wang/1st-in-MICCAI2020-CPM.
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Affiliation(s)
- Xiyue Wang
- College of Biomedical Engineering, Sichuan University, Chengdu, China.,College of Computer Science, Sichuan University, Chengdu, China
| | - Ruijie Wang
- School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, China
| | | | | | - Minghui Wang
- College of Biomedical Engineering, Sichuan University, Chengdu, China.,College of Computer Science, Sichuan University, Chengdu, China
| | - Dexing Zhong
- School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, China.,Pazhou Lab, Guangzhou, China.,State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
| | - Jing Zhang
- College of Biomedical Engineering, Sichuan University, Chengdu, China
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12
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Tomas RC, Sayat AJ, Atienza AN, Danganan JL, Ramos MR, Fellizar A, Notarte KI, Angeles LM, Bangaoil R, Santillan A, Albano PM. Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks. PLoS One 2022; 17:e0262489. [PMID: 35081148 PMCID: PMC8791515 DOI: 10.1371/journal.pone.0262489] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 12/27/2021] [Indexed: 12/27/2022] Open
Abstract
In this study, three (3) neural networks (NN) were designed to discriminate between malignant (n = 78) and benign (n = 88) breast tumors using their respective attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral data. A proposed NN-based sensitivity analysis was performed to determine the most significant IR regions that distinguished benign from malignant samples. The result of the NN-based sensitivity analysis was compared to the obtained results from FTIR visual peak identification. In training each NN models, a 10-fold cross validation was performed and the performance metrics-area under the curve (AUC), accuracy, positive predictive value (PPV), specificity rate (SR), negative predictive value (NPV), and recall rate (RR)-were averaged for comparison. The NN models were compared to six (6) machine learning models-logistic regression (LR), Naïve Bayes (NB), decision trees (DT), random forest (RF), support vector machine (SVM) and linear discriminant analysis (LDA)-for benchmarking. The NN models were able to outperform the LR, NB, DT, RF, and LDA for all metrics; while only surpassing the SVM in accuracy, NPV and SR. The best performance metric among the NN models was 90.48% ± 10.30% for AUC, 96.06% ± 7.07% for ACC, 92.18 ± 11.88% for PPV, 94.19 ± 10.57% for NPV, 89.04% ± 16.75% for SR, and 94.34% ± 10.54% for RR. Results from the proposed sensitivity analysis were consistent with the visual peak identification. However, unlike the FTIR visual peak identification method, the NN-based method identified the IR region associated with C-OH C-OH group carbohydrates as significant. IR regions associated with amino acids and amide proteins were also determined as possible sources of variability. In conclusion, results show that ATR-FTIR via NN is a potential diagnostic tool. This study also suggests a possible more specific method in determining relevant regions within a sample's spectrum using NN.
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Affiliation(s)
- Rock Christian Tomas
- Department of Electrical Engineering, University of the Philippines Los Baños, Los Baños, Laguna, Philippines
| | - Anthony Jay Sayat
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
| | - Andrea Nicole Atienza
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
| | - Jannah Lianne Danganan
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
| | - Ma. Rollene Ramos
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
| | - Allan Fellizar
- Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines
- The Graduate School, University of Santo Tomas, Manila, Philippines
- Mariano Marcos Memorial Hospital and Medical Center, Batac, Ilocos Norte, Philippines
| | - Kin Israel Notarte
- Faculty of Medicine and Surgery, University of Santo Tomas, Manila, Philippines
| | - Lara Mae Angeles
- Faculty of Medicine and Surgery, University of Santo Tomas, Manila, Philippines
- University of Santo Tomas Hospital, Manila, Philippines
| | - Ruth Bangaoil
- Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines
- The Graduate School, University of Santo Tomas, Manila, Philippines
| | - Abegail Santillan
- Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines
- The Graduate School, University of Santo Tomas, Manila, Philippines
| | - Pia Marie Albano
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
- Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines
- The Graduate School, University of Santo Tomas, Manila, Philippines
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Brodie A, Dai N, Teoh JYC, Decaestecker K, Dasgupta P, Vasdev N. Artificial intelligence in urological oncology: An update and future applications. Urol Oncol 2021; 39:379-399. [PMID: 34024704 DOI: 10.1016/j.urolonc.2021.03.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 12/20/2020] [Accepted: 03/21/2021] [Indexed: 01/16/2023]
Abstract
There continues to be rapid developments and research in the field of Artificial Intelligence (AI) in Urological Oncology worldwide. In this review we discuss the basics of AI, application of AI per tumour group (Renal, Prostate and Bladder Cancer) and application of AI in Robotic Urological Surgery. We also discuss future applications of AI being developed with the benefits to patients with Urological Oncology.
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Affiliation(s)
- Andrew Brodie
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Nick Dai
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Jeremy Yuen-Chun Teoh
- S.H. Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Prokar Dasgupta
- Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Nikhil Vasdev
- Hertfordshire and Bedfordshire Urological Cancer Centre, Department of Urology, Lister Hospital, Stevenage, United Kingdom; School of Medicine and Life Sciences, University of Hertfordshire, Hatfield, United Kingdom.
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15
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da Silva LG, da Silva Monteiro WRS, de Aguiar Moreira TM, Rabelo MAE, de Assis EACP, de Souza GT. Fractal dimension analysis as an easy computational approach to improve breast cancer histopathological diagnosis. Appl Microsc 2021; 51:6. [PMID: 33929635 PMCID: PMC8087740 DOI: 10.1186/s42649-021-00055-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 04/20/2021] [Indexed: 12/31/2022] Open
Abstract
Histopathology is a well-established standard diagnosis employed for the majority of malignancies, including breast cancer. Nevertheless, despite training and standardization, it is considered operator-dependent and errors are still a concern. Fractal dimension analysis is a computational image processing technique that allows assessing the degree of complexity in patterns. We aimed here at providing a robust and easily attainable method for introducing computer-assisted techniques to histopathology laboratories. Slides from two databases were used: A) Breast Cancer Histopathological; and B) Grand Challenge on Breast Cancer Histology. Set A contained 2480 images from 24 patients with benign alterations, and 5429 images from 58 patients with breast cancer. Set B comprised 100 images of each type: normal tissue, benign alterations, in situ carcinoma, and invasive carcinoma. All images were analyzed with the FracLac algorithm in the ImageJ computational environment to yield the box count fractal dimension (Db) results. Images on set A on 40x magnification were statistically different (p = 0.0003), whereas images on 400x did not present differences in their means. On set B, the mean Db values presented promissing statistical differences when comparing. Normal and/or benign images to in situ and/or invasive carcinoma (all p < 0.0001). Interestingly, there was no difference when comparing normal tissue to benign alterations. These data corroborate with previous work in which fractal analysis allowed differentiating malignancies. Computer-aided diagnosis algorithms may beneficiate from using Db data; specific Db cut-off values may yield ~ 99% specificity in diagnosing breast cancer. Furthermore, the fact that it allows assessing tissue complexity, this tool may be used to understand the progression of the histological alterations in cancer.
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Affiliation(s)
- Lucas Glaucio da Silva
- Faculty of Medical and Health Sciences of Juiz de Fora, Alameda Salvaterra, Juiz de Fora, Minas Gerais, 200 - 36033-003, Brazil
| | | | - Tiago Medeiros de Aguiar Moreira
- Department of Biology - Genetics - Federal University of Juiz de Fora, Rua José Lourenço Kelmer, s/n, Juiz de Fora, Minas Gerais, 36036-900, Brazil
| | - Maria Aparecida Esteves Rabelo
- Faculty of Medical and Health Sciences of Juiz de Fora, Alameda Salvaterra, Juiz de Fora, Minas Gerais, 200 - 36033-003, Brazil
| | - Emílio Augusto Campos Pereira de Assis
- Faculty of Medical and Health Sciences of Juiz de Fora, Alameda Salvaterra, Juiz de Fora, Minas Gerais, 200 - 36033-003, Brazil
- Animal Reproduction Laboratory - Brazilian Agricultural Research Corporation - Dairy Cattle, Laboratory of Animal Reproduction, Av. Eugênio do Nascimento, Juiz de Fora, Minas Gerais, 610 - 36038-330, Brazil
| | - Gustavo Torres de Souza
- Department of Biology - Genetics - Federal University of Juiz de Fora, Rua José Lourenço Kelmer, s/n, Juiz de Fora, Minas Gerais, 36036-900, Brazil.
- Center for Investigation and Diagnosis of Pathological Anatomy, Avenida Itamar Franco, Juiz de Fora, Minas Gerais, 4001 - 36033-318, Brazil.
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Ayyad SM, Shehata M, Shalaby A, Abou El-Ghar M, Ghazal M, El-Melegy M, Abdel-Hamid NB, Labib LM, Ali HA, El-Baz A. Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey. SENSORS (BASEL, SWITZERLAND) 2021; 21:2586. [PMID: 33917035 PMCID: PMC8067693 DOI: 10.3390/s21082586] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/29/2021] [Accepted: 04/04/2021] [Indexed: 02/07/2023]
Abstract
Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.
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Affiliation(s)
- Sarah M. Ayyad
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - Mohamed Shehata
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.S.)
| | - Ahmed Shalaby
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.S.)
| | - Mohamed Abou El-Ghar
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt;
| | - Mohammed Ghazal
- Department of Electrical and Computer Engineering, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Moumen El-Melegy
- Department of Electrical Engineering, Assiut University, Assiut 71511, Egypt;
| | - Nahla B. Abdel-Hamid
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - Labib M. Labib
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - H. Arafat Ali
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - Ayman El-Baz
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.S.)
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17
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Ray P, Reddy SS, Banerjee T. Various dimension reduction techniques for high dimensional data analysis: a review. Artif Intell Rev 2021. [DOI: 10.1007/s10462-020-09928-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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18
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Kaur S, Bawa S, Kumar R. A survey of mono- and multi-lingual character recognition using deep and shallow architectures: indic and non-indic scripts. Artif Intell Rev 2020. [DOI: 10.1007/s10462-019-09720-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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20
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Abstract
Artificial intelligence (AI) - the ability of a machine to perform cognitive tasks to achieve a particular goal based on provided data - is revolutionizing and reshaping our health-care systems. The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Accessibility to 'big data' enables the 'cognitive' computer to scan billions of bits of unstructured information, extract the relevant information and recognize complex patterns with increasing confidence. Computer-based decision-support systems based on machine learning (ML) have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.
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21
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Su KY, Lee WL. Fourier Transform Infrared Spectroscopy as a Cancer Screening and Diagnostic Tool: A Review and Prospects. Cancers (Basel) 2020; 12:E115. [PMID: 31906324 PMCID: PMC7017192 DOI: 10.3390/cancers12010115] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 12/21/2019] [Accepted: 12/24/2019] [Indexed: 02/07/2023] Open
Abstract
Infrared spectroscopy has long been used to characterize chemical compounds, but the applicability of this technique to the analysis of biological materials containing highly complex chemical components is arguable. However, recent advances in the development of infrared spectroscopy have significantly enhanced the capacity of this technique in analyzing various types of biological specimens. Consequently, there is an increased number of studies investigating the application of infrared spectroscopy in screening and diagnosis of various diseases. The lack of highly sensitive and specific methods for early detection of cancer has warranted the search for novel approaches. Being more simple, rapid, accurate, inexpensive, non-destructive and suitable for automation compared to existing screening, diagnosis, management and monitoring methods, Fourier transform infrared spectroscopy can potentially improve clinical decision-making and patient outcomes by detecting biochemical changes in cancer patients at the molecular level. Besides the commonly analyzed blood and tissue samples, extracellular vesicle-based method has been gaining popularity as a non-invasive approach. Therefore, infrared spectroscopic analysis of extracellular vesicles could be a useful technique in the future for biomedical applications. In this review, we discuss the potential clinical applications of Fourier transform infrared spectroscopic analysis using various types of biological materials for cancer. Additionally, the rationale and advantages of using extracellular vesicles in the spectroscopic analysis for cancer diagnostics are discussed. Furthermore, we highlight the challenges and future directions of clinical translation of the technique for cancer.
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Affiliation(s)
| | - Wai-Leng Lee
- School of Science, Monash University Malaysia, Subang Jaya 47500, Malaysia
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22
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Vicente-Munuera P, Burgos-Panadero R, Noguera I, Navarro S, Noguera R, Escudero LM. The topology of vitronectin: A complementary feature for neuroblastoma risk classification based on computer-aided detection. Int J Cancer 2019; 146:553-565. [PMID: 31173338 PMCID: PMC6899647 DOI: 10.1002/ijc.32495] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 04/30/2019] [Accepted: 05/27/2019] [Indexed: 12/13/2022]
Abstract
Tumors are complex networks of constantly interacting elements: tumor cells, stromal cells, immune and stem cells, blood/lympathic vessels, nerve fibers and extracellular matrix components. These elements can influence their microenvironment through mechanical and physical signals to promote tumor cell growth. To get a better understanding of tumor biology, cooperation between multidisciplinary fields is needed. Diverse mathematic computations and algorithms have been designed to find prognostic targets and enhance diagnostic assessment. In this work, we use computational digital tools to study the topology of vitronectin, a glycoprotein of the extracellular matrix. Vitronectin is linked to angiogenesis and migration, two processes closely related to tumor cell spread. Here, we investigate whether the distribution of this molecule in the tumor stroma may confer mechanical properties affecting neuroblastoma aggressiveness. Combining image analysis and graph theory, we analyze different topological features that capture the organizational cues of vitronectin in histopathological images taken from human samples. We find that the Euler number and the branching of territorial vitronectin, two topological features, could allow for a more precise pretreatment risk stratification to guide treatment strategies in neuroblastoma patients. A large amount of recently synthesized VN would create migration tracks, pinpointed by both topological features, for malignant neuroblasts, so that dramatic change in the extracellular matrix would increase tumor aggressiveness and worsen patient outcomes. What's new? The tumor microenvironment has a strong influence on cancer malignancy. Here, the authors investigate whether the organization of the extracellular matrix glycoprotein vitronectin in the tumor stroma may confer mechanical properties affecting neuroblastoma aggressiveness. Combining image analysis and graph theory, they identify two topological features of vitronectin that could potentially be used to improve patient pre‐treatment risk stratification. The data also point to the creation of vitronectin migration tracks for malignant neuroblasts, so that dramatic changes in the extracellular matrix would increase tumor stiffness and aggressiveness and worsen patient outcomes.
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Affiliation(s)
- Pablo Vicente-Munuera
- Departamento de Biología Celular, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Roció/CSIC/Universidad de Sevilla, Seville, Spain.,Biomedical Network Research Centre on Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Rebeca Burgos-Panadero
- Department of Pathology, Medical School, University of Valencia/INCLIVA, Valencia, Spain.,Biomedical Network Research Centre on Oncology (CIBERONC), Madrid, Spain
| | - Inmaculada Noguera
- Central Support Service for Experimental Research (SCSIE), University of Valencia, Valencia, Spain
| | - Samuel Navarro
- Department of Pathology, Medical School, University of Valencia/INCLIVA, Valencia, Spain.,Biomedical Network Research Centre on Oncology (CIBERONC), Madrid, Spain
| | - Rosa Noguera
- Department of Pathology, Medical School, University of Valencia/INCLIVA, Valencia, Spain.,Biomedical Network Research Centre on Oncology (CIBERONC), Madrid, Spain
| | - Luis M Escudero
- Departamento de Biología Celular, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Roció/CSIC/Universidad de Sevilla, Seville, Spain.,Biomedical Network Research Centre on Neurodegenerative Diseases (CIBERNED), Madrid, Spain
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Nir G, Hor S, Karimi D, Fazli L, Skinnider BF, Tavassoli P, Turbin D, Villamil CF, Wang G, Wilson RS, Iczkowski KA, Lucia MS, Black PC, Abolmaesumi P, Goldenberg SL, Salcudean SE. Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts. Med Image Anal 2018; 50:167-180. [DOI: 10.1016/j.media.2018.09.005] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 07/11/2018] [Accepted: 09/21/2018] [Indexed: 01/17/2023]
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25
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Park YW, Oh J, You SC, Han K, Ahn SS, Choi YS, Chang JH, Kim SH, Lee SK. Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. Eur Radiol 2018; 29:4068-4076. [DOI: 10.1007/s00330-018-5830-3] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 09/19/2018] [Accepted: 10/12/2018] [Indexed: 11/27/2022]
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26
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A survey for the applications of content-based microscopic image analysis in microorganism classification domains. Artif Intell Rev 2017. [DOI: 10.1007/s10462-017-9572-4] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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27
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Wang K, Gou C, Zheng N, Rehg JM, Wang FY. Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives. Artif Intell Rev 2017. [DOI: 10.1007/s10462-017-9569-z] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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28
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Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships. Med Biol Eng Comput 2017; 55:1829-1848. [DOI: 10.1007/s11517-017-1630-1] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 02/13/2017] [Indexed: 01/12/2023]
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