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A Heuristic Machine Learning-Based Optimization Technique to Predict Lung Cancer Patient Survival. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:4506488. [PMID: 36776617 PMCID: PMC9911240 DOI: 10.1155/2023/4506488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/26/2022] [Accepted: 11/24/2022] [Indexed: 02/05/2023]
Abstract
Cancer has been a significant threat to human health and well-being, posing the biggest obstacle in the history of human sickness. The high death rate in cancer patients is primarily due to the complexity of the disease and the wide range of clinical outcomes. Increasing the accuracy of the prediction is equally crucial as predicting the survival rate of cancer patients, which has become a key issue of cancer research. Many models have been suggested at the moment. However, most of them simply use single genetic data or clinical data to construct prediction models for cancer survival. There is a lot of emphasis in present survival studies on determining whether or not a patient will survive five years. The personal issue of how long a lung cancer patient will survive remains unanswered. The proposed technique Naive Bayes and SSA is estimating the overall survival time with lung cancer. Two machine learning challenges are derived from a single customized query. To begin with, determining whether a patient will survive for more than five years is a simple binary question. The second step is to develop a five-year survival model using regression analysis. When asked to forecast how long a lung cancer patient would survive within five years, the mean absolute error (MAE) of this technique's predictions is accurate within a month. Several biomarker genes have been associated with lung cancers. The accuracy, recall, and precision achieved from this algorithm are 98.78%, 98.4%, and 98.6%, respectively.
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Brancato V, Garbino N, Salvatore M, Cavaliere C. MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma. Diagnostics (Basel) 2022; 12:diagnostics12051085. [PMID: 35626241 PMCID: PMC9139902 DOI: 10.3390/diagnostics12051085] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 04/06/2022] [Accepted: 04/23/2022] [Indexed: 02/04/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common form of liver cancer. Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) in the management of HCC. The purpose of our study is to develop an MRI-based radiomics approach to preoperatively detect HCC and predict its histological grade. Thirty-eight HCC patients at staging who underwent axial T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) were considered. Three-dimensional volumes of interest (VOIs) were manually placed on HCC lesions and normal hepatic tissue (HT) on arterial phase post-contrast images. Radiomic features from T2 images and arterial, portal and tardive post-contrast images from DCE-MRI were extracted by using Pyradiomics. Feature selection was performed using correlation filter, Wilcoxon-rank sum test and mutual information. Predictive models were constructed for HCC differentiation with respect to HT and HCC histopathologic grading used at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. Promising results were obtained from radiomic prediction models, with best AUCs ranging from 71% to 96%. Radiomics MRI based on T2 and DCE-MRI revealed promising results concerning both HCC detection and grading. It may be a suitable tool for personalized treatment of HCC patients and could also be used to develop new prognostic biomarkers useful for HCC assessment without the need for invasive procedures.
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Diao S, Tian Y, Hu W, Hou J, Lambo R, Zhang Z, Xie Y, Nie X, Zhang F, Racoceanu D, Qin W. Weakly Supervised Framework for Cancer Region Detection of Hepatocellular Carcinoma in Whole-Slide Pathologic Images Based on Multiscale Attention Convolutional Neural Network. THE AMERICAN JOURNAL OF PATHOLOGY 2022; 192:553-563. [PMID: 34896390 DOI: 10.1016/j.ajpath.2021.11.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 11/10/2021] [Accepted: 11/17/2021] [Indexed: 06/14/2023]
Abstract
Visual inspection of hepatocellular carcinoma cancer regions by experienced pathologists in whole-slide images (WSIs) is a challenging, labor-intensive, and time-consuming task because of the large scale and high resolution of WSIs. Therefore, a weakly supervised framework based on a multiscale attention convolutional neural network (MSAN-CNN) was introduced into this process. Herein, patch-based images with image-level normal/tumor annotation (rather than images with pixel-level annotation) were fed into a classification neural network. To further improve the performances of cancer region detection, multiscale attention was introduced into the classification neural network. A total of 100 cases were obtained from The Cancer Genome Atlas and divided into 70 training and 30 testing data sets that were fed into the MSAN-CNN framework. The experimental results showed that this framework significantly outperforms the single-scale detection method according to the area under the curve and accuracy, sensitivity, and specificity metrics. When compared with the diagnoses made by three pathologists, MSAN-CNN performed better than a junior- and an intermediate-level pathologist, and slightly worse than a senior pathologist. Furthermore, MSAN-CNN provided a very fast detection time compared with the pathologists. Therefore, a weakly supervised framework based on MSAN-CNN has great potential to assist pathologists in the fast and accurate detection of cancer regions of hepatocellular carcinoma on WSIs.
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Affiliation(s)
- Songhui Diao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Science, Shenzhen, China
| | - Yinli Tian
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
| | - Wanming Hu
- Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jiaxin Hou
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Science, Shenzhen, China
| | - Ricardo Lambo
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhicheng Zhang
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Yaoqin Xie
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Science, Shenzhen, China
| | - Xiu Nie
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fa Zhang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Daniel Racoceanu
- Sorbonne Université, Paris Brain Institute-Institut du Cerveau-ICM, Institut National de Santé et en Recherche Médicale, Centre National de Recherche Scientifique, Assistance Publique Hôpitaux de Paris, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Wenjian Qin
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Science, Shenzhen, China.
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Singh I, Lele TP. Nuclear Morphological Abnormalities in Cancer: A Search for Unifying Mechanisms. Results Probl Cell Differ 2022; 70:443-467. [PMID: 36348118 PMCID: PMC9722227 DOI: 10.1007/978-3-031-06573-6_16] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Irregularities in nuclear shape and/or alterations to nuclear size are a hallmark of malignancy in a broad range of cancer types. Though these abnormalities are commonly used for diagnostic purposes and are often used to assess cancer progression in the clinic, the mechanisms through which they occur are not well understood. Nuclear size alterations in cancer could potentially arise from aneuploidy, changes in osmotic coupling with the cytoplasm, and perturbations to nucleocytoplasmic transport. Nuclear shape changes may occur due to alterations to cell-generated mechanical stresses and/or alterations to nuclear structural components, which balance those stresses, such as the nuclear lamina and chromatin. A better understanding of the mechanisms underlying abnormal nuclear morphology and size may allow the development of new therapeutics to target nuclear aberrations in cancer.
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Affiliation(s)
- Ishita Singh
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | - Tanmay P. Lele
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA,Department of Chemical Engineering, University of Florida, Gainesville, FL, USA,Department of Translational Medical Sciences, Texas A&M University, Houston, TX, USA
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SHIMAZAKI T, DESHPANDE A, HAJRA A, THOMAS T, MUTA K, YAMADA N, YASUI Y, SHODA T. Deep learning-based image-analysis algorithm for classification and quantification of multiple histopathological lesions in rat liver. J Toxicol Pathol 2021; 35:135-147. [PMID: 35516841 PMCID: PMC9018404 DOI: 10.1293/tox.2021-0053] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 11/08/2021] [Indexed: 12/02/2022] Open
Abstract
Artificial intelligence (AI)-based image analysis is increasingly being used for
preclinical safety-assessment studies in the pharmaceutical industry. In this paper, we
present an AI-based solution for preclinical toxicology studies. We trained a set of
algorithms to learn and quantify multiple typical histopathological findings in whole
slide images (WSIs) of the livers of young Sprague Dawley rats by using a U-Net-based deep
learning network. The trained algorithms were validated using 255 liver WSIs to detect,
classify, and quantify seven types of histopathological findings (including vacuolation,
bile duct hyperplasia, and single-cell necrosis) in the liver. The algorithms showed
consistently good performance in detecting abnormal areas. Approximately 75% of all
specimens could be classified as true positive or true negative. In general, findings with
clear boundaries with the surrounding normal structures, such as vacuolation and
single-cell necrosis, were accurately detected with high statistical scores. The results
of quantitative analyses and classification of the diagnosis based on the threshold values
between “no findings” and “abnormal findings” correlated well with diagnoses made by
professional pathologists. However, the scores for findings ambiguous boundaries, such as
hepatocellular hypertrophy, were poor. These results suggest that deep learning-based
algorithms can detect, classify, and quantify multiple findings simultaneously on rat
liver WSIs. Thus, it can be a useful supportive tool for a histopathological evaluation,
especially for primary screening in rat toxicity studies.
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Affiliation(s)
- Taishi SHIMAZAKI
- Toxicology Research Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-13-2 Fukuura, Kanazawa-ku, Yokohama, Kanagawa 236-0004, Japan
| | - Ameya DESHPANDE
- AIRA Matrix Private Limited, Dosti Pinnacle, 801, Rd Number 22, Wagle Industrial Estate, Thane, Maharashtra 400604, India
| | - Anindya HAJRA
- AIRA Matrix Private Limited, Dosti Pinnacle, 801, Rd Number 22, Wagle Industrial Estate, Thane, Maharashtra 400604, India
| | - Tijo THOMAS
- AIRA Matrix Private Limited, Dosti Pinnacle, 801, Rd Number 22, Wagle Industrial Estate, Thane, Maharashtra 400604, India
| | - Kyotaka MUTA
- Toxicology Research Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-13-2 Fukuura, Kanazawa-ku, Yokohama, Kanagawa 236-0004, Japan
| | - Naohito YAMADA
- Toxicology Research Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-13-2 Fukuura, Kanazawa-ku, Yokohama, Kanagawa 236-0004, Japan
| | - Yuzo YASUI
- Toxicology Research Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-13-2 Fukuura, Kanazawa-ku, Yokohama, Kanagawa 236-0004, Japan
| | - Toshiyuki SHODA
- Toxicology Research Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-13-2 Fukuura, Kanazawa-ku, Yokohama, Kanagawa 236-0004, Japan
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6
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Melo RCN, Raas MWD, Palazzi C, Neves VH, Malta KK, Silva TP. Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders. Front Med (Lausanne) 2020; 6:310. [PMID: 31970160 PMCID: PMC6960181 DOI: 10.3389/fmed.2019.00310] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Accepted: 12/09/2019] [Indexed: 12/11/2022] Open
Abstract
Histological analysis of hepatic tissue specimens is essential for evaluating the pathology of several liver disorders such as chronic liver diseases, hepatocellular carcinomas, liver steatosis, and infectious liver diseases. Manual examination of histological slides on the microscope is a classically used method to study these disorders. However, it is considered time-consuming, limited, and associated with intra- and inter-observer variability. Emerging technologies such as whole slide imaging (WSI), also termed virtual microscopy, have increasingly been used to improve the assessment of histological features with applications in both clinical and research laboratories. WSI enables the acquisition of the tissue morphology/pathology from glass slides and translates it into a digital form comparable to a conventional microscope, but with several advantages such as easy image accessibility and storage, portability, sharing, annotation, qualitative and quantitative image analysis, and use for educational purposes. WSI-generated images simultaneously provide high resolution and a wide field of observation that can cover the entire section, extending any single field of view. In this review, we summarize current knowledge on the application of WSI to histopathological analyses of liver disorders as well as to understand liver biology. We address how WSI may improve the assessment and quantification of multiple histological parameters in the liver, and help diagnose several hepatic conditions with important clinical implications. The WSI technical limitations are also discussed.
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Affiliation(s)
- Rossana C N Melo
- Laboratory of Cellular Biology, Department of Biology, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | - Maximilian W D Raas
- Laboratory of Cellular Biology, Department of Biology, Federal University of Juiz de Fora, Juiz de Fora, Brazil.,Faculty of Medical Sciences, Radboud University, Nijmegen, Netherlands
| | - Cinthia Palazzi
- Laboratory of Cellular Biology, Department of Biology, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | - Vitor H Neves
- Laboratory of Cellular Biology, Department of Biology, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | - Kássia K Malta
- Laboratory of Cellular Biology, Department of Biology, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | - Thiago P Silva
- Laboratory of Cellular Biology, Department of Biology, Federal University of Juiz de Fora, Juiz de Fora, Brazil
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Chen QF, Huang T, Si-Tu QJ, Wu P, Shen L, Li W, Huang Z. Analysis of competing endogenous RNA network identifies a poorly differentiated cancer-specific RNA signature for hepatocellular carcinoma. J Cell Biochem 2019; 121:2303-2317. [PMID: 31642123 DOI: 10.1002/jcb.29454] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 10/08/2019] [Indexed: 12/12/2022]
Abstract
Plenty of evidence has suggested that long noncoding RNAs (lncRNAs) play a vital role in competing endogenous RNA (ceRNA) networks. Poorly differentiated hepatocellular carcinoma (PDHCC) is a malignant phenotype. This paper aimed to explore the effect and the underlying regulatory mechanism of lncRNAs on PDHCC as a kind of ceRNA. Additionally, prognosis prediction was assessed. A total of 943 messenger RNAs (mRNAs), 86 miRNAs, and 468 lncRNAs that were differentially expressed between 137 PDHCCs and 235 well-differentiated HCCs were identified. Thereafter, a ceRNA network related to the dysregulated lncRNAs was established according to bioinformatic analysis and included 29 lncRNAs, 9 miRNAs, and 96 mRNAs. RNA-related overall survival (OS) curves were determined using the Kaplan-Meier method. The lncRNA ARHGEF7-AS2 was markedly correlated with OS in HCC (P = .041). Moreover, Cox regression analysis revealed that patients with low ARHGEF7-AS2 expression were associated with notably shorter survival time (P = .038). In addition, the area under the curve values of the lncRNA signature for 1-, 3-, and 5-year survival were 0.806, 0.741, and 0.701, respectively. Furthermore, a lncRNA nomogram was established, and the C-index of the internal validation was 0.717. In vitro experiments were performed to demonstrate that silencing ARHGEF7-AS2 expression significantly promoted HCC cell proliferation and migration. Taken together, our findings shed more light on the ceRNA network related to lncRNAs in PDHCC, and ARHGEF7-AS2 may be used as an independent biomarker to predict the prognosis of HCC.
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Affiliation(s)
- Qi-Feng Chen
- Department of Medical Imaging and Interventional Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China.,Department of Medical Imaging and Interventional Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Tao Huang
- Department of Medical Imaging and Interventional Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Qi-Jiao Si-Tu
- Department of Medical Imaging and Interventional Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Peihong Wu
- Department of Medical Imaging and Interventional Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Lujun Shen
- Department of Medical Imaging and Interventional Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Wang Li
- Department of Medical Imaging and Interventional Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Zilin Huang
- Department of Medical Imaging and Interventional Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
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8
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Ishikawa M, Okamoto C, Shinoda K, Komagata H, Iwamoto C, Ohuchida K, Hashizume M, Shimizu A, Kobayashi N. Detection of pancreatic tumor cell nuclei via a hyperspectral analysis of pathological slides based on stain spectra. BIOMEDICAL OPTICS EXPRESS 2019; 10:4568-4588. [PMID: 31565510 PMCID: PMC6757471 DOI: 10.1364/boe.10.004568] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 07/30/2019] [Accepted: 07/31/2019] [Indexed: 05/07/2023]
Abstract
Hyperspectral imaging (HSI) provides more detailed information than red-green-blue (RGB) imaging, and therefore has potential applications in computer-aided pathological diagnosis. This study aimed to develop a pattern recognition method based on HSI, called hyperspectral analysis of pathological slides based on stain spectrum (HAPSS), to detect cancers in hematoxylin and eosin-stained pathological slides of pancreatic tumors. The samples, comprising hyperspectral cubes of 420-750 nm, were harvested for HSI and tissue microarray (TMA) analysis. As a result of conducting HAPSS experiments with a support vector machine (SVM) classifier, we obtained maximal accuracy of 94%, a 14% improvement over the widely used RGB images. Thus, HAPSS is a suitable method to automatically detect tumors in pathological slides of the pancreas.
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Affiliation(s)
- Masahiro Ishikawa
- Saitama Medical University, Faculty of Health & Medical Care, Yamane-1397-1, Hidaka-shi, 350-1241, Japan
| | - Chisato Okamoto
- Saitama Medical University, Faculty of Health & Medical Care, Yamane-1397-1, Hidaka-shi, 350-1241, Japan
| | - Kazuma Shinoda
- Saitama Medical University, Faculty of Health & Medical Care, Yamane-1397-1, Hidaka-shi, 350-1241, Japan
- Graduate School of Engineering, Utsunomiya University, 7-1-2 Yoto, Utsunomiya, Tochigi 321-8585, Japan
| | - Hideki Komagata
- Saitama Medical University, Faculty of Health & Medical Care, Yamane-1397-1, Hidaka-shi, 350-1241, Japan
| | - Chika Iwamoto
- Department of Advanced Medical Initiatives, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kenoki Ohuchida
- Department of Advanced Medical Initiatives, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Makoto Hashizume
- Department of Advanced Medical Initiatives, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Akinobu Shimizu
- Tokyo University of Agriculture and Technology, Nakacho 2-24-16, Koganei, Tokyo 184-8588, Japan
| | - Naoki Kobayashi
- Saitama Medical University, Faculty of Health & Medical Care, Yamane-1397-1, Hidaka-shi, 350-1241, Japan
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9
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Horai Y, Mizukawa M, Nishina H, Nishikawa S, Ono Y, Takemoto K, Baba N. Quantification of histopathological findings using a novel image analysis platform. J Toxicol Pathol 2019; 32:319-327. [PMID: 31719761 PMCID: PMC6831494 DOI: 10.1293/tox.2019-0022] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 05/20/2019] [Indexed: 01/07/2023] Open
Abstract
Digital pathology, including image analysis and automatic diagnosis of pathological
tissue, has been developed remarkably. HALO is an image analysis platform specialized for
the study of pathological tissues, which enables tissue segmentation by using artificial
intelligence. In this study, we used HALO to quantify various histopathological changes
and findings that were difficult to analyze using conventional image processing software.
Using the tissue classifier module, the morphological features of degeneration/necrosis of
the hepatocytes and muscle fibers, bile duct in the liver, basophilic tubules and hyaline
casts in the kidney, cortex in the thymus, and red pulp, white pulp, and marginal zone in
the spleen were learned and separated, and areas of interest were quantified. Furthermore,
using the cytonuclear module and vacuole module in combination with the tissue classifier
module, the number of erythroblasts in the red pulp of the spleen and each area of acinar
cells in the parotid gland were quantified. The results of quantitative analysis were
correlated with the histopathological grades evaluated by pathologists. By using
artificial intelligence and other functions of HALO, we recognized morphological features,
analyzed histopathological changes, and quantified the histopathological grades of various
findings. The analysis of histopathological changes using HALO is expected to support
pathology evaluations.
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Affiliation(s)
- Yasushi Horai
- Sohyaku Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 2-2-50 Kawagishi, Toda-shi, Saitama 335-8505, Japan
| | - Mao Mizukawa
- Sohyaku Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 2-2-50 Kawagishi, Toda-shi, Saitama 335-8505, Japan
| | - Hironobu Nishina
- Sohyaku Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 2-2-50 Kawagishi, Toda-shi, Saitama 335-8505, Japan
| | - Satomi Nishikawa
- Sohyaku Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 2-2-50 Kawagishi, Toda-shi, Saitama 335-8505, Japan
| | - Yuko Ono
- Sohyaku Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 2-2-50 Kawagishi, Toda-shi, Saitama 335-8505, Japan
| | - Kana Takemoto
- Sohyaku Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 2-2-50 Kawagishi, Toda-shi, Saitama 335-8505, Japan
| | - Nobuyuki Baba
- Sohyaku Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 2-2-50 Kawagishi, Toda-shi, Saitama 335-8505, Japan
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10
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Liu C, Huang Y, Ozolek JA, Hanna MG, Singh R, Rohde GK. SetSVM: An Approach to Set Classification in Nuclei-Based Cancer Detection. IEEE J Biomed Health Inform 2018; 23:351-361. [PMID: 29994380 DOI: 10.1109/jbhi.2018.2803793] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Due to the importance of nuclear structure in cancer diagnosis, several predictive models have been described for diagnosing a wide variety of cancers based on nuclear morphology. In many computer-aided diagnosis (CAD) systems, cancer detection tasks can be generally formulated as set classification problems, which can not be directly solved by classifying single instances. In this paper, we propose a novel set classification approach SetSVM to build a predictive model by considering any nuclei set as a whole without specific assumptions. SetSVM features highly discriminative power in cancer detection challenges in the sense that it not only optimizes the classifier decision boundary but also transfers discriminative information to set representation learning. During model training, these two processes are unified in the support vector machine (SVM) maximum separation margin problem. Experiment results show that SetSVM provides significant improvements compared with five commonly used approaches in cancer detection tasks utilizing 260 patients in total across three different cancer types, namely, thyroid cancer, liver cancer, and melanoma. In addition, we show that SetSVM enables visual interpretation of discriminative nuclear characteristics representing the nuclei set. These features make SetSVM a potentially practical tool in building accurate and interpretable CAD systems for cancer detection.
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11
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Saco A, Diaz A, Hernandez M, Martinez D, Montironi C, Castillo P, Rakislova N, Del Pino M, Martinez A, Ordi J. Validation of whole-slide imaging in the primary diagnosis of liver biopsies in a University Hospital. Dig Liver Dis 2017; 49:1240-1246. [PMID: 28780052 DOI: 10.1016/j.dld.2017.07.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 06/11/2017] [Accepted: 07/11/2017] [Indexed: 12/11/2022]
Abstract
BACKGROUND Experience in the use of whole slide imaging (WSI) for primary diagnosis is limited and there are no comprehensive reports evaluating this technology in liver biopsy specimens. AIMS To determine the accuracy of interpretation of WSI compared with conventional light microscopy (CLM) in the diagnosis of needle liver biopsies. METHODS Two experienced liver pathologists blindly analyzed 176 consecutive biopsies from the Pathology Department at the Hospital Clinic of Barcelona. One of the observers performed the initial evaluation with CLM, and the second evaluation with WSI, whereas the second observer performed the first evaluation with WSI and the second with CLM. All slides were digitized in a Ventana iScan HT at 400× and evaluated with the Virtuoso viewer (Roche diagnostics). We used kappa statistics (κ) for two observations. RESULTS Intra-observer agreement between WSI and CLM evaluations was almost perfect (96.6%, κ=0.9; 95% confidence interval [95% CI]: 0.9-1 for observer 1, and 90.3%, κ=0.9; 95%CI: 0.8-0.9 for observer 2). Both native and transplantation biopsies showed an almost perfect concordance in the diagnosis. CONCLUSION Diagnosis of needle liver biopsy specimens using WSI is accurate. This technology can reliably be introduced in routine diagnosis.
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Affiliation(s)
- Adela Saco
- Department of Pathology, Hospital Clínic, Barcelona, Spain
| | - Alba Diaz
- Department of Pathology, Hospital Clínic, Barcelona, Spain
| | | | | | | | - Paola Castillo
- Department of Pathology, Hospital Clínic, Barcelona, Spain; ISGlobal, Barcelona Ctr. Int. Health Res. (CRESIB), Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
| | | | - Marta Del Pino
- ISGlobal, Barcelona Ctr. Int. Health Res. (CRESIB), Hospital Clínic - Universitat de Barcelona, Barcelona, Spain; Institute of Gynecology, Obstetrics and Neonatology, Hospital Clínic - Institut d́Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Faculty of Medicine, University of Barcelona, Spain
| | - Antonio Martinez
- Department of Pathology, Hospital Clínic, Barcelona, Spain; ISGlobal, Barcelona Ctr. Int. Health Res. (CRESIB), Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
| | - Jaume Ordi
- Department of Pathology, Hospital Clínic, Barcelona, Spain; ISGlobal, Barcelona Ctr. Int. Health Res. (CRESIB), Hospital Clínic - Universitat de Barcelona, Barcelona, Spain; University of Barcelona, School of Medicine, Barcelona, Spain.
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12
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Shao P, Sun D, Wang L, Fan R, Gao Z. Deep sequencing and comprehensive expression analysis identifies several molecules potentially related to human poorly differentiated hepatocellular carcinoma. FEBS Open Bio 2017; 7:1696-1706. [PMID: 29123978 PMCID: PMC5666400 DOI: 10.1002/2211-5463.12310] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2017] [Revised: 07/27/2017] [Accepted: 08/23/2017] [Indexed: 11/26/2022] Open
Abstract
Hepatocellular carcinoma (HCC) that is graded histologically as poorly differentiated has a high recurrence, metastasis and poor prognosis. We sought to determine the regulatory mechanisms of HCC tumorigenesis and to identify molecules closely related to poorly differentiated HCC. High‐throughput sequencing was used to construct microRNA (miRNA) and mRNA expression profiles for poorly differentiated HCC tissues and adjacent tissues. Network analysis was carried out to study miRNA–target interactions. Integrating the miRNA and mRNA data of HCC with four tumor grades from The Cancer Genome Atlas (TCGA) portal enabled the identification of potential closely related molecules for early diagnosis of poorly differentiated HCC. Electronic validation of RNA‐seq data and survival analysis was also performed. In total, 1051 differentially expressed genes and 165 differentially expressed miRNAs were identified between HCC tumor and paired non‐tumorous tissue. Based on 3718 miRNA–target interactions, we established an miRNA–target interaction network; the target genes were mainly involved in bile acid biosynthesis and bile secretion. Integrating expression data of HCC from TCGA indicated that two proteins, TM4SF1 and ANXA2, are convincing indicators for initial diagnosis of poorly differentiated HCC. According to the survival analysis, three proteins, ANXA2, C8orf33 and IGF2BP3, were identified as being associated with the survival time of HCC patients. Moreover, we suggest that hsa‐miR‐1180 may be an effective biomarker for poorly differentiated HCC. Three molecules, TM4SF1, ANXA2 and C8orf33, are potential biomarkers for distinguishing poorly differentiated from well‐differentiated HCC.
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Affiliation(s)
- Ping Shao
- Department of Hepatobiliary and Pancreatic Surgery The Second Hospital of Dalian Medical University Dalian City China
| | - Deguang Sun
- Department of Hepatobiliary and Pancreatic Surgery The Second Hospital of Dalian Medical University Dalian City China
| | - Liming Wang
- Department of Hepatobiliary and Pancreatic Surgery The Second Hospital of Dalian Medical University Dalian City China
| | - Rong Fan
- Department of Medical Practice The Second Hospital of Dalian Medical University Dalian City China
| | - Zhenming Gao
- Department of Hepatobiliary and Pancreatic Surgery The Second Hospital of Dalian Medical University Dalian City China
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13
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Horai Y, Kakimoto T, Takemoto K, Tanaka M. Quantitative analysis of histopathological findings using image processing software. J Toxicol Pathol 2017; 30:351-358. [PMID: 29097847 PMCID: PMC5660959 DOI: 10.1293/tox.2017-0031] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 06/16/2017] [Indexed: 12/18/2022] Open
Abstract
In evaluating pathological changes in drug efficacy and toxicity studies, morphometric analysis can be quite robust. In this experiment, we examined whether morphometric changes of major pathological findings in various tissue specimens stained with hematoxylin and eosin could be recognized and quantified using image processing software. Using Tissue Studio, hypertrophy of hepatocytes and adrenocortical cells could be quantified based on the method of a previous report, but the regions of red pulp, white pulp, and marginal zones in the spleen could not be recognized when using one setting condition. Using Image-Pro Plus, lipid-derived vacuoles in the liver and mucin-derived vacuoles in the intestinal mucosa could be quantified using two criteria (area and/or roundness). Vacuoles derived from phospholipid could not be quantified when small lipid deposition coexisted in the liver and adrenal cortex. Mononuclear inflammatory cell infiltration in the liver could be quantified to some extent, except for specimens with many clustered infiltrating cells. Adipocyte size and the mean linear intercept could be quantified easily and efficiently using morphological processing and the macro tool equipped in Image-Pro Plus. These methodologies are expected to form a base system that can recognize morphometric features and analyze quantitatively pathological findings through the use of information technology.
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Affiliation(s)
- Yasushi Horai
- Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 2-2-50 Kawagishi, Toda-shi, Saitama 335-8505, Japan
| | - Tetsuhiro Kakimoto
- Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 2-2-50 Kawagishi, Toda-shi, Saitama 335-8505, Japan
| | - Kana Takemoto
- Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 2-2-50 Kawagishi, Toda-shi, Saitama 335-8505, Japan
| | - Masaharu Tanaka
- Sohyaku. Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, 2-2-50 Kawagishi, Toda-shi, Saitama 335-8505, Japan
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14
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Yi F, Huang J, Yang L, Xie Y, Xiao G. Automatic extraction of cell nuclei from H&E-stained histopathological images. J Med Imaging (Bellingham) 2017; 4:027502. [PMID: 28653017 PMCID: PMC5478972 DOI: 10.1117/1.jmi.4.2.027502] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 05/31/2017] [Indexed: 12/15/2022] Open
Abstract
Extraction of cell nuclei from hematoxylin and eosin (H&E)-stained histopathological images is an essential preprocessing step in computerized image analysis for disease detection, diagnosis, and prognosis. We present an automated cell nuclei segmentation approach that works with H&E-stained images. A color deconvolution algorithm was first applied to the image to get the hematoxylin channel. Using a morphological operation and thresholding technique on the hematoxylin channel image, candidate target nuclei and background regions were detected, which were then used as markers for a marker-controlled watershed transform segmentation algorithm. Moreover, postprocessing was conducted to split the touching nuclei. For each segmented region from the previous steps, the regional maximum value positions were identified as potential nuclei centers. These maximum values were further grouped into [Formula: see text]-clusters, and the locations within each cluster were connected with the minimum spanning tree technique. Then, these connected positions were utilized as new markers for a watershed segmentation approach. The final number of nuclei at each region was determined by minimizing an objective function that iterated all of the possible [Formula: see text]-values. The proposed method was applied to the pathological images of the tumor tissues from The Cancer Genome Atlas study. Experimental results show that the proposed method can lead to promising results in terms of segmentation accuracy and separation of touching nuclei.
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Affiliation(s)
- Faliu Yi
- University of Texas Southwestern Medical Center, Quantitative Biomedical Research Center, Department of Clinical Science, Dallas, Texas, United States
| | - Junzhou Huang
- University of Texas at Arlington, Department of Computer Science and Engineering, Arlington, Texas, United States
| | - Lin Yang
- University of Texas Southwestern Medical Center, Quantitative Biomedical Research Center, Department of Clinical Science, Dallas, Texas, United States
- Chinese Academy of Medical Science and Peking Union Medical College, National Cancer Center/Cancer Hospital, Department of Pathology, Chaoyang District, Beijing, China
| | - Yang Xie
- University of Texas Southwestern Medical Center, Quantitative Biomedical Research Center, Department of Clinical Science, Dallas, Texas, United States
- University of Texas Southwestern Medical Center, Department of Bioinformatics, Dallas, Texas, United States
- University of Texas Southwestern Medical Center, Harold C. Simmons Comprehensive Cancer Center, Dallas, Texas, United States
| | - Guanghua Xiao
- University of Texas Southwestern Medical Center, Quantitative Biomedical Research Center, Department of Clinical Science, Dallas, Texas, United States
- University of Texas Southwestern Medical Center, Department of Bioinformatics, Dallas, Texas, United States
- University of Texas Southwestern Medical Center, Harold C. Simmons Comprehensive Cancer Center, Dallas, Texas, United States
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