1
|
Yasuda Y, Tokunaga K, Koga T, Sakamoto C, Goldberg IG, Saitoh N, Nakao M. Computational analysis of morphological and molecular features in gastric cancer tissues. Cancer Med 2020; 9:2223-2234. [PMID: 32012497 PMCID: PMC7064096 DOI: 10.1002/cam4.2885] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 11/13/2019] [Accepted: 01/14/2020] [Indexed: 02/06/2023] Open
Abstract
Biological morphologies of cells and tissues represent their physiological and pathological conditions. The importance of quantitative assessment of morphological information has been highly recognized in clinical diagnosis and therapeutic strategies. In this study, we used a supervised machine learning algorithm wndchrm to classify hematoxylin and eosin (H&E)‐stained images of human gastric cancer tissues. This analysis distinguished between noncancer and cancer tissues with different histological grades. We then classified the H&E‐stained images by expression levels of cancer‐associated nuclear ATF7IP/MCAF1 and membranous PD‐L1 proteins using immunohistochemistry of serial sections. Interestingly, classes with low and high expressions of each protein exhibited significant morphological dissimilarity in H&E images. These results indicated that morphological features in cancer tissues are correlated with expression of specific cancer‐associated proteins, suggesting the usefulness of biomolecular‐based morphological classification.
Collapse
Affiliation(s)
- Yoko Yasuda
- Department of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan.,Department of Health Science, Faculty of Medical Science, Kyushu University, Fukuoka, Japan
| | - Kazuaki Tokunaga
- Department of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan
| | - Tomoaki Koga
- Department of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan
| | - Chiyomi Sakamoto
- Department of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan
| | - Ilya G Goldberg
- Image Informatics and Computational Biology Unit, Laboratory of Genetics, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | | | - Mitsuyoshi Nakao
- Department of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan
| |
Collapse
|
2
|
Nuclear morphometric analysis in tissue as an objective tool with potential use to improve melanoma staging. Melanoma Res 2019; 29:474-482. [PMID: 30839356 DOI: 10.1097/cmr.0000000000000594] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Alterations in nuclear size and shape are commonly observed in cancers, and its objective evaluation may provide valuable clinical information about the outcome of the disease. Here, we applied the nuclear morphometric analysis in tissues in hematoxylin and eosin-digitized slides of nevi and melanoma, to objectively contribute to the prognostic evaluation of these tumors. To this, we analyzed the nuclear morphometry of 34 melanomas classified according to the TNM stage. Eight cases of melanocytic nevi were used as non-neoplastic tissues to set the non-neoplastic parameters of nuclear morphology. Our samples were set as G1 (control, nevi), G2 (T1T2N0M0), G3 (T3T4N0M0), G4 (T1T2N1M1), and G5 (T3T4N1M1). Image-Pro Plus 6.0 software was used to acquire measurements related to nuclear size (variable: Area) and shape (variables: Aspect, AreaBox, Roundness, and RadiusRatio, which were used to generate the Nuclear Irregularity Index). From these primary variables, a set of secondary variables were generated. All the seven primary and secondary variables related to the nuclear area were different among groups (Pillai's trace P<0.001), whereas Nuclear Irregularity Index, which is the variable related to nuclear shape, did not differ among groups. The secondary variable 'Average Area of Large Nuclei' was able to differ all pairwise comparisons, including thin nonmetastatic from thin metastatic tumors. In conclusion, the objective quantification of nuclear area in hematoxylin and eosin slides may provide objective information about the risk stratification of these tumors and has the potential to be used as an additional method in clinical decision making.
Collapse
|
3
|
Orlov NV, Makrogiannis S, Ferrucci L, Goldberg IG. Differential Aging Signals in Abdominal CT Scans. Acad Radiol 2017; 24:1535-1543. [PMID: 28927581 DOI: 10.1016/j.acra.2017.07.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Revised: 05/30/2017] [Accepted: 07/10/2017] [Indexed: 11/25/2022]
Abstract
RATIONALE AND OBJECTIVES Changes in the composition of body tissues are major aging phenotypes, but they have been difficult to study in depth. Here we describe age-related change in abdominal tissues observable in computed tomography (CT) scans. We used pattern recognition and machine learning to detect and quantify these changes in a model-agnostic fashion. MATERIALS AND METHODS CT scans of abdominal L4 sections were obtained from Baltimore Longitudinal Study of Aging (BLSA) participants. Age-related change in the constituent tissues were determined by training machine classifiers to differentiate age groups within male and female strata ("Younger" at 50-70 years old vs "Older" at 80-99 years old). The accuracy achieved by the classifiers in differentiating the age cohorts was used as a surrogate measure of the aging signal in the different tissues. RESULTS The highest accuracy for discriminating age differences was 0.76 and 0.72 for males and females, respectively. The classification accuracy was 0.79 and 0.71 for adipose tissue, 0.70 and 0.68 for soft tissue, and 0.65 and 0.64 for bone. CONCLUSIONS Using image data from a large sample of well-characterized pool of participants dispersed over a wide age range, we explored age-related differences in gross morphology and texture of abdominal tissues. This technology is advantageous for tracking effects of biological aging and predicting adverse outcomes when compared to the traditional use of specific molecular biomarkers. Application of pattern recognition and machine learning as a tool for analyzing medical images may provide much needed insight into tissue changes occurring with aging and, further, connect these changes with their metabolic and functional consequences.
Collapse
|
4
|
Macedo ND, Buzin AR, de Araujo IBBA, Nogueira BV, de Andrade TU, Endringer DC, Lenz D. Objective detection of apoptosis in rat renal tissue sections using light microscopy and free image analysis software with subsequent machine learning: Detection of apoptosis in renal tissue. Tissue Cell 2016; 49:22-27. [PMID: 28073590 DOI: 10.1016/j.tice.2016.12.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Revised: 12/20/2016] [Accepted: 12/20/2016] [Indexed: 01/13/2023]
Abstract
OBJECTIVE The current study proposes an automated machine learning approach for the quantification of cells in cell death pathways according to DNA fragmentation. METHODS A total of 17 images of kidney histological slide samples from male Wistar rats were used. The slides were photographed using an Axio Zeiss Vert.A1 microscope with a 40x objective lens coupled with an Axio Cam MRC Zeiss camera and Zen 2012 software. The images were analyzed using CellProfiler (version 2.1.1) and CellProfiler Analyst open-source software. RESULTS Out of the 10,378 objects, 4970 (47,9%) were identified as TUNEL positive, and 5408 (52,1%) were identified as TUNEL negative. On average, the sensitivity and specificity values of the machine learning approach were 0.80 and 0.77, respectively. CONCLUSION Image cytometry provides a quantitative analytical alternative to the more traditional qualitative methods more commonly used in studies.
Collapse
Affiliation(s)
- Nayana Damiani Macedo
- Masters Program in Pharmaceutical Sciences, University Vila Velha, Vila Velha, ES, Brazil
| | - Aline Rodrigues Buzin
- Masters Program in Pharmaceutical Sciences, University Vila Velha, Vila Velha, ES, Brazil
| | - Isabela Bastos Binotti Abreu de Araujo
- Department of Morphology, Federal University of Espírito Santo, Vitória, ES, Brazil; Faculty of Medicine Carl Gustav Curav-Technical University Dresden, Dresden, Germany
| | | | | | | | - Dominik Lenz
- Masters Program in Pharmaceutical Sciences, University Vila Velha, Vila Velha, ES, Brazil.
| |
Collapse
|
5
|
Petrolis R, Ramonaitė R, Jančiauskas D, Kupčinskas J, Pečiulis R, Kupčinskas L, Kriščiukaitis A. Digital imaging of colon tissue: method for evaluation of inflammation severity by spatial frequency features of the histological images. Diagn Pathol 2015; 10:159. [PMID: 26370784 PMCID: PMC4570696 DOI: 10.1186/s13000-015-0389-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Accepted: 08/28/2015] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND The efficacy of histological analysis of colon sections used for evaluation of inflammation severity can be improved by means of digital imaging giving quantitative estimates of main diagnostic features. The aim of this study was to reveal most valuable diagnostic features reflecting inflammation severity in colon and elaborate the evaluation method for computer-aided diagnostics. METHODS Tissue specimens from 24 BALB/c mice and 15 patients were included in the study. Chronic and acute colon inflammation in mice was induced by oral administration of dextran sulphate sodium (DSS) solution, while mice in the control group did not get DSS. Human samples of inflamed colon tissue were obtained from patients with ulcerative colitis (n = 6). Non-inflamed colon tissue of control subjects (n = 9) was obtained from patients with irritable bowel syndrome or functional obstipation. Analysis of morphological changes in mice and human colon mucosa was performed using 4-μm haematoxylin-eosin (HE) sections. The features reflecting morphological changes in the images of colon mucosa were calculated by convolution of Gabor filter bank and array of pixel values. All features were generalized by calculating mean, histogram skewness and entropy of every image response. Principal component analysis was used to construct optimal representation of morphological changes. RESULTS First principal component (PC1) was representing the major part of features variation (97 % in mice and 71 % in human specimens) and was selected as a measure of inflammation severity. Validation of new measure was performed by means of custom-made software realizing double blind comparison of differences in PC1 with expert's opinion about inflammation severity presented in two compared pictures. Overall accuracy of 80 % for mice and 67 % for human was reached. CONCLUSION Principal component analysis of spatial frequency features of histological images may provide continuous scale estimation of inflammation severity of colon tissue.
Collapse
Affiliation(s)
- Robertas Petrolis
- Neuroscience Institute, Lithuanian University of Health Sciences, Eiveniu str. 2, LT 50009, Kaunas, Lithuania.
| | - Rima Ramonaitė
- Institute for Digestive Research, Lithuanian University of Health Sciences, Kaunas, LT 44307, Lithuania
| | - Dainius Jančiauskas
- Clinic of Pathology, Lithuanian University of Health Sciences, Kaunas, LT 50009, Lithuania
| | - Juozas Kupčinskas
- Institute for Digestive Research, Lithuanian University of Health Sciences, Kaunas, LT 44307, Lithuania
- Department of Gastroenterology, Lithuanian University of Health Sciences, Kaunas, LT 50161, Lithuania
| | - Rokas Pečiulis
- Lithuanian University of Health Sciences, Kaunas, LT 50009, Lithuania
| | - Limas Kupčinskas
- Institute for Digestive Research, Lithuanian University of Health Sciences, Kaunas, LT 44307, Lithuania
- Department of Gastroenterology, Lithuanian University of Health Sciences, Kaunas, LT 50161, Lithuania
| | - Algimantas Kriščiukaitis
- Neuroscience Institute, Lithuanian University of Health Sciences, Eiveniu str. 2, LT 50009, Kaunas, Lithuania
- Department of Physics, Mathematics and Biophysics, Lithuanian University of Health Sciences, Kaunas, LT 50009, Lithuania
| |
Collapse
|
6
|
Abstract
Melanoma is a malignant tumor of melanocytes. Although extensive investigations have been done to study metabolic changes in primary melanoma in vivo and in vitro, little effort has been devoted to metabolic profiling of metastatic tumors in organs other than lymph nodes. In this work, NMR-based metabolomics combined with multivariate data analysis is used to study metastatic B16-F10 melanoma in C57BL/6J mouse spleen. Principal Component Analysis (PCA), an unsupervised multivariate data analysis method, is used to detect possible outliers, while Orthogonal Projection to Latent Structure (OPLS), a supervised multivariate data analysis method, is employed to find important metabolites responsible for discriminating the control and the melanoma groups. Two different strategies, i.e. spectral binning and spectral deconvolution, are used to reduce the original spectral data before statistical analysis. Spectral deconvolution is found to be superior for identifying a set of discriminatory metabolites between the control and the melanoma groups, especially when the sample size is small. OPLS results show that the melanoma group can be well separated from its control group. It is found that taurine, glutamate, aspartate, O-Phosphoethanolamine, niacinamide,ATP, lipids and glycerol derivatives are decreased statistically and significantly while alanine, malate, xanthine, histamine, dCTP, GTP, thymidine, 2'-Deoxyguanosine are statistically and significantly elevated. These significantly changed metabolites are associated with multiple biological pathways and may be potential biomarkers for metastatic melanoma in spleen.
Collapse
Affiliation(s)
- Xuan Wang
- Pacific Northwest National Laboratory, Richland, WA 99352, USA
- Wuhan Institute of Physics and Mathematics, the Chinese Academy of Sciences, Wuhan, 430071, PR China
| | - Mary Hu
- Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Ju Feng
- Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Maili Liu
- Wuhan Institute of Physics and Mathematics, the Chinese Academy of Sciences, Wuhan, 430071, PR China
| | - Jian Zhi Hu
- Pacific Northwest National Laboratory, Richland, WA 99352, USA
- To whom correspondence should be addressed: Jian Zhi Hu; ; Phone: (509) 371-6544; Fax: (509) 371-6546
| |
Collapse
|
7
|
Wang X, Hu M, Liu M, Hu JZ. Metastatic Melanoma Induced Metabolic Changes in C57BL/6J Mouse Stomach Measured by 1H NMR Spectroscopy. METABOLOMICS : OPEN ACCESS 2014; 4:1000135. [PMID: 26246958 PMCID: PMC4523238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Melanoma is a malignant tumor of melanocytes with high capability of invasion and rapid metastasis to other organs. Malignant melanoma is the most common metastatic malignancy found in Gastrointestinal Tract (GI). In this work, the 1H NMR-based metabolomics approach is used to investigate the metabolite profile differences of stomach tissue extracts of metastatic B16-F10 melanoma and control groups in C57BL/6J mouse and to search for specific metabolite biomarker candidates. Principal Component Analysis (PCA), an unsupervised multivariate data analysis method, is used to detect possible outliers, while Orthogonal Projection to Latent Structure (OPLS), a supervised multivariate data analysis method, is employed to evaluate important metabolites responsible for discriminating the control and the melanoma groups. Both PCA and OPLS results reveal that the melanoma group can be well separated from its control group. Among the 50 identified metabolites, it is found that the concentrations of 19 metabolites are significantly changed with the levels of O-phosphocholine and hypoxanthine down-regulated while the levels of isoleucine, leucine, valine, isobutyrate, threonine, cadaverine, alanine, glutamate, glutamine, methionine, citrate, asparagine, tryptophan, glycine, serine, uracil, and formate up-regulated in the melanoma group. These significantly changed metabolites are associated with multiple biological pathways and may be potential biomarkers for metastatic melanoma in stomach.
Collapse
Affiliation(s)
- X Wang
- Pacific Northwest National Laboratory, Richland, WA 99352, USA
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, the Chinese Academy of Sciences, Wuhan, 430071, PR China
| | - M Hu
- Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - M Liu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, the Chinese Academy of Sciences, Wuhan, 430071, PR China
| | - JZ Hu
- Pacific Northwest National Laboratory, Richland, WA 99352, USA
| |
Collapse
|