1
|
Radulović M, Li X, Djuričić GJ, Milovanović J, Todorović Raković N, Vujasinović T, Banovac D, Kanjer K. Bridging Histopathology and Radiomics Toward Prognosis of Metastasis in Early Breast Cancer. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2024:ozae057. [PMID: 38973606 DOI: 10.1093/mam/ozae057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/14/2024] [Accepted: 06/09/2024] [Indexed: 07/09/2024]
Abstract
Tumor histomorphology is crucial for the prognostication of breast cancer outcomes because it contains histological, cellular, and molecular tumor heterogeneity related to metastatic potential. To enhance breast cancer prognosis, we aimed to apply radiomics analysis-traditionally used in 3D scans-to 2D histopathology slides. This study tested radiomics analysis in a cohort of 92 breast tumor specimens for outcome prognosis, addressing -omics dimensionality by comparing models with moderate and high feature counts, using least absolute shrinkage and selection operator for feature selection and machine learning for prognostic modeling. In the test folds, models with radiomics features [area under the curves (AUCs) range 0.799-0.823] significantly outperformed the benchmark model, which only included clinicopathological (CP) parameters (AUC = 0.584). The moderate-dimensionality model with 11 CP + 93 radiomics features matched the performance of the highly dimensional models with 1,208 radiomics or 11 CP + 1,208 radiomics features, showing average AUCs of 0.823, 0.799, and 0.807 and accuracies of 79.8, 79.3, and 76.6%, respectively. In conclusion, our application of deep texture radiomics analysis to 2D histopathology showed strong prognostic performance with a moderate-dimensionality model, surpassing a benchmark based on standard CP parameters, indicating that this deep texture histomics approach could potentially become a valuable prognostic tool.
Collapse
Affiliation(s)
- Marko Radulović
- Department of Experimental Oncology, Institute of Oncology and Radiology of Serbia, Pasterova 14, Belgrade 11000, Serbia
| | - Xingyu Li
- Electrical & Computer Engineering Department, Faculty of Engineering, University of Alberta, 9211 116 Street NW, AB, Edmonton T6G 1H9, Canada
| | - Goran J Djuričić
- Department of Diagnostic Imaging, University Children's Hospital, University of Belgrade, Tiršova 10, Belgrade 11000, Serbia
| | - Jelena Milovanović
- Department of Experimental Oncology, Institute of Oncology and Radiology of Serbia, Pasterova 14, Belgrade 11000, Serbia
| | - Nataša Todorović Raković
- Department of Experimental Oncology, Institute of Oncology and Radiology of Serbia, Pasterova 14, Belgrade 11000, Serbia
| | - Tijana Vujasinović
- Department of Experimental Oncology, Institute of Oncology and Radiology of Serbia, Pasterova 14, Belgrade 11000, Serbia
| | - Dušan Banovac
- Department of Diagnostic Imaging, University Children's Hospital, University of Belgrade, Tiršova 10, Belgrade 11000, Serbia
| | - Ksenija Kanjer
- Department of Experimental Oncology, Institute of Oncology and Radiology of Serbia, Pasterova 14, Belgrade 11000, Serbia
| |
Collapse
|
2
|
Lu HJ, Shen CY, Chiu YW, Lin WL, Peng CY, Tseng HC, Hsin CH, Chuang CY, Chen CC, Wu MF, Huang WS, Shen WC. Radiomic biomarkers for platinum-refractory head and neck cancer in the era of immunotherapy. Oral Dis 2024. [PMID: 38178608 DOI: 10.1111/odi.14854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 11/17/2023] [Accepted: 12/20/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVE Immune checkpoint inhibitors (ICI) are recommended as the first-line therapy for platinum-refractory head and neck squamous cell carcinoma (HNSCC), a disease with a poor prognosis. However, biomarkers in this situation are rare. The objective was to identify radiomic features-associated biomarkers to guide the prognosis and treatment opinions in the era of ICI. METHODS A total of 31 platinum-refractory HNSCC patients were retrospectively enrolled. Of these, 65.5% (20/31) received ICI-based therapy and 35.5% (11/31) did not. Radiomic features of the primary site at the onset of recurrent metastatic (R/M) status were extracted. Prognostic and predictive radiomic biomarkers were analysed. RESULTS The median overall survival from R/M status (R/M OS) was 9.6 months. Grey-level co-occurrence matrix-associated texture features were the most important in identifying the patients with or without 9-month R/M death. A radiomic risk-stratification model was established and equally separated the patients into high-, intermittent- and lower-risk groups (1-year R/M death rate, 100.0% vs. 70.8% vs. 27.1%, p = 0.001). Short-run high grey-level emphasis (SRHGE) was more suitable than programmed death ligand 1 (PD-L1) expression in selecting whether patients received ICI-based therapy. CONCLUSIONS Radiomic features were effective prognostic and predictive biomarkers. Future studies are warranted.
Collapse
Affiliation(s)
- Hsueh-Ju Lu
- Division of Hematology and Oncology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Chao-Yu Shen
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Yu-Wei Chiu
- Department of Stomatology, Chung Shan Medical University Hospital, Taichung, Taiwan
- College of Oral Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Wea-Lung Lin
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Pathology, Chung Shan Medical University and Hospital, Taichung, Taiwan
| | - Chih-Yu Peng
- Department of Stomatology, Chung Shan Medical University Hospital, Taichung, Taiwan
- College of Oral Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Hsien-Chun Tseng
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Radiation Oncology, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Chung-Han Hsin
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Otolaryngology, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Chun-Yi Chuang
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Otolaryngology, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Chun-Chia Chen
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Division of Plastic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Ming-Fang Wu
- Division of Hematology and Oncology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Wei-Shiou Huang
- Division of Hematology and Oncology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan
- College of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Wei-Chih Shen
- Department of Medical Informatics, Chung Shan Medical University, Taichung, Taiwan
- Artificial Intelligence Center, Chung Shan Medical University Hospital, Taichung, Taiwan
| |
Collapse
|
3
|
Nussi AD, de Castro Lopes SLP, De Rosa CS, Gomes JPP, Ogawa CM, Braz-Silva PH, Costa ALF. In vivo study of cone beam computed tomography texture analysis of mandibular condyle and its correlation with gender and age. Oral Radiol 2023; 39:191-197. [PMID: 35585223 DOI: 10.1007/s11282-022-00620-3] [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: 03/19/2022] [Accepted: 04/24/2022] [Indexed: 01/07/2023]
Abstract
OBJECTIVE Texture analysis is an image processing method that aims to assess the distribution of gray-level intensity and spatial organization of the pixels in the image. The purpose of this study was to investigate whether the texture analysis applied to cone beam computed tomography (CBCT) images could detect variation in the condyle trabecular bone of individuals from different age groups and genders. METHODS The sample consisted of imaging exams from 63 individuals divided into three groups according to age groups of 03-13, 14-24 and 25-34. For texture analysis, the MaZda® software was used to extract the following parameters: second angular momentum, contrast, correlation, sum of squares, inverse difference moment, sum entropy and entropy. Statistical analysis was performed using Mann-Whitney test for gender and Kruskal-Wallis test for age (P = 5%). RESULTS No statistically significant differences were found between age groups for any of the parameters. Males had lower values for the parameter correlation than those of females (P < 0.05). CONCLUSION Texture analysis proved to be useful to discriminate mandibular condyle trabecular bone between genders.
Collapse
Affiliation(s)
- Amanda Drumstas Nussi
- Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), Rua Galvão Bueno, 868, Liberdade, São Paulo, SP, 01506-000, Brazil
| | - Sérgio Lucio Pereira de Castro Lopes
- Department of Diagnosis and Surgery, Science and Technology Institute, São Paulo State University (UNESP), São José dos Campos, São Paulo, Brazil
| | - Catharina Simioni De Rosa
- Division of General Pathology, Department of Stomatology, School of Dentistry, University of São Paulo (USP), São Paulo, SP, Brazil
| | - João Pedro Perez Gomes
- Division of General Pathology, Department of Stomatology, School of Dentistry, University of São Paulo (USP), São Paulo, SP, Brazil
| | - Celso Massahiro Ogawa
- Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), Rua Galvão Bueno, 868, Liberdade, São Paulo, SP, 01506-000, Brazil
| | - Paulo Henrique Braz-Silva
- Division of General Pathology, Department of Stomatology, School of Dentistry, University of São Paulo (USP), São Paulo, SP, Brazil
- Laboratory of Virology, Institute of Tropical Medicine of São Paulo, School of Medicine, University of São Paulo, São Paulo, SP, Brazil
| | - Andre Luiz Ferreira Costa
- Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), Rua Galvão Bueno, 868, Liberdade, São Paulo, SP, 01506-000, Brazil.
| |
Collapse
|
4
|
Development and Validation of a CT-Based Signature for the Prediction of Distant Metastasis Before Treatment of Non-Small Cell Lung Cancer. Acad Radiol 2022; 29 Suppl 2:S62-S72. [PMID: 33402298 DOI: 10.1016/j.acra.2020.12.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/11/2020] [Accepted: 12/11/2020] [Indexed: 01/06/2023]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a radiomics model, a clinical-semantic model and a combined model by using standard methods for the pretreatment prediction of distant metastasis (DM) in patients with non-small-cell lung cancer (NSCLC) and to explore whether the combined model provides added value compared to the individual models. MATERIALS AND METHODS This retrospective study involved 356 patients with NSCLC. According to the image biomarker standardization initiative reference manual, we standardized the image processing and feature extraction using in-house software. Finally, 6692 radiomics features were extracted from each lesion based on contrast-enhanced chest CT images. The least absolute shrinkage selection operator and the recursive feature elimination algorithm were used to select features. The logistic regression classifier was used to build the model. Three models (radiomics model, clinical-semantic model and combined model) were constructed to predict DM in NSCLC. Area under the receiver operating characteristic curves were used to validate the ability of the three models to predict DM. A visual nomogram based on the combined model was developed for DM risk assessment in each patient. RESULTS The receiver operating characteristic curve showed predictive performance for DM of the radiomics model (area under the curve [AUC] values for training and validation were 0.76 [95% CI, 0.704 - 0.820] and 0.76 [95% CI, 0.653 - 0.858], respectively). The combined model had AUCs of 0.78 (95% CI, 0.723 - 0.835) and 0.77 (95% CI, 0.673 - 0.870) in the training and validation cohorts, respectively. Both the radiomics model and combined model performed better than the clinical-semantic model (0.70 [95% CI, 0.634 - 0.760] and 0.67 [95% CI, 0.554 - 0.787] in the training and validation cohorts, respectively). CONCLUSION The radiomics model and combined model may be useful for the prediction of DM in patients with NSCLC.
Collapse
|
5
|
Fujima N, Shimizu Y, Yoshida D, Kano S, Mizumachi T, Homma A, Yasuda K, Onimaru R, Sakai O, Kudo K, Shirato H. Multiparametric Analysis of Tumor Morphological and Functional MR Parameters Potentially Predicts Local Failure in Pharynx Squamous Cell Carcinoma Patients. THE JOURNAL OF MEDICAL INVESTIGATION 2021; 68:354-361. [PMID: 34759158 DOI: 10.2152/jmi.68.354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Purpose : To predict local control / failure by a multiparametric approach using magnetic resonance (MR)-derived tumor morphological and functional parameters in pharynx squamous cell carcinoma (SCC) patients. Materials and Methods : Twenty-eight patients with oropharyngeal and hypopharyngeal SCCs were included in this study. Quantitative morphological parameters and intratumoral characteristics on T2-weighted images, tumor blood flow from pseudo-continuous arterial spin labeling, and tumor diffusion parameters of three diffusion models from multi-b-value diffusion-weighted imaging as well as patients' characteristics were analyzed. The patients were divided into local control / failure groups. Univariate and multiparametric analysis were performed for the patient group division. Results : The value of morphological parameter of 'sphericity' and intratumoral characteristic of 'homogeneity' was revealed respectively significant for the prediction of the local control status in univariate analysis. Higher diagnostic performance was obtained with the sensitivity of 0.8, specificity of 0.75, positive predictive value of 0.89, negative predictive value of 0.6 and accuracy of 0.79 by multiparametric diagnostic model compared to results in the univariate analysis. Conclusion : A multiparametric analysis with MR-derived quantitative parameters may be useful to predict local control in pharynx SCC patients. J. Med. Invest. 68 : 354-361, August, 2021.
Collapse
Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan.,The Global Station for Quantum Medical Science and Engineering, Global Institution for collaborative research and education, Sapporo, Japan
| | - Yukie Shimizu
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Daisuke Yoshida
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Satoshi Kano
- Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Takatsugu Mizumachi
- Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Akihiro Homma
- Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Koichi Yasuda
- The Global Station for Quantum Medical Science and Engineering, Global Institution for collaborative research and education, Sapporo, Japan.,Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Rikiya Onimaru
- Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Osamu Sakai
- Departments of Radiology, Otolaryngology-Head and Neck Surgery, and Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Kohsuke Kudo
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan.,The Global Station for Quantum Medical Science and Engineering, Global Institution for collaborative research and education, Sapporo, Japan
| | - Hiroki Shirato
- The Global Station for Quantum Medical Science and Engineering, Global Institution for collaborative research and education, Sapporo, Japan.,Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| |
Collapse
|
6
|
Zaletel I, Milutinović K, Bajčetić M, Nowakowski RS. Differentiation of Amyloid Plaques Between Alzheimer's Disease and Non-Alzheimer's Disease Individuals Based on Gray-Level Co-occurrence Matrix Texture Analysis. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2021; 27:1146-1153. [PMID: 35105417 DOI: 10.1017/s1431927621012095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Amyloid plaques, one of the main hallmarks of Alzheimer's disease (AD), are classified into diffuse (associated with cognitive impairment) and dense-core types (a common finding in brains of people without Alzheimer's disease (non-AD) and without impaired cognitive function) based on their morphology. We tried to determine the usability of gray-level co-occurrence matrix (GLCM) texture parameters of homogeneity and heterogeneity for the differentiation of amyloid plaque images obtained from AD and non-AD individuals. Images of amyloid-β (Aβ) immunostained brain tissue samples were obtained from the Aging, Dementia and Traumatic Brain Injury Project. A total of 1,039 plaques were isolated from different brain regions of 69 AD and non-AD individuals and used for further GLCM analysis. Images of Aβ stained plaques show higher values of heterogeneity parameters and lower values of homogeneity parameters in AD patients, and vice versa in non-AD patients. Additionally, GLCM analysis shows differences in Aβ plaque texture between different brain regions in non-AD patients and correlates with variables that characterize patient's dementia status. The present study shows that GLCM texture analysis is an efficient method to discriminate between different types of amyloid plaques based on their morphology and thus can prove as a valuable tool in the neuropathological investigation of dementia.
Collapse
Affiliation(s)
- Ivan Zaletel
- Faculty of Medicine, Institute of Histology and Embryology "Aleksandar Đ. Kostić", University of Belgrade, Belgrade11000, Republic of Serbia
| | - Katarina Milutinović
- Faculty of Medicine, Institute of Histology and Embryology "Aleksandar Đ. Kostić", University of Belgrade, Belgrade11000, Republic of Serbia
| | - Miloš Bajčetić
- Faculty of Medicine, Institute of Histology and Embryology "Aleksandar Đ. Kostić", University of Belgrade, Belgrade11000, Republic of Serbia
| | - Richard S Nowakowski
- Department of Biomedical Sciences, Florida State University College of Medicine, Tallahassee, 32306-4300, FL, USA
| |
Collapse
|
7
|
Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients. Breast Cancer Res Treat 2021; 186:379-389. [PMID: 33486639 DOI: 10.1007/s10549-020-06093-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 12/31/2020] [Indexed: 10/22/2022]
Abstract
PURPOSE Neoadjuvant chemotherapy (NAC) is used to treat patients with high-risk breast cancer. The tumor response to NAC can be classified as either a pathological partial response (pPR) or pathological complete response (pCR), defined as complete eradication of invasive tumor cells, with a pCR conferring a significantly lower risk of recurrence. Predicting the response to NAC, however, remains a significant clinical challenge. The objective of this study was to determine if analysis of nuclear features on core biopsies using artificial intelligence (AI) can predict response to NAC. METHODS Fifty-eight HER2-positive or triple-negative breast cancer patients were included in this study (pCR n = 37, pPR n = 21). Multiple deep convolutional neural networks were developed to automate tumor detection and nuclear segmentation. Nuclear count, area, and circularity, as well as image-based first- and second-order features including mean pixel intensity and correlation of the gray-level co-occurrence matrix (GLCM-COR) were determined. RESULTS In univariate analysis, the pCR group had fewer multifocal/multicentric tumors, higher nuclear intensity, and lower GLCM-COR compared to the pPR group. In multivariate binary logistic regression, tumor multifocality/multicentricity (OR = 0.14, p = 0.012), nuclear intensity (OR = 1.23, p = 0.018), and GLCM-COR (OR = 0.96, p = 0.043) were each independently associated with likelihood of achieving a pCR, and the model was able to successful classify 79% of cases (62% for pPR and 89% for pCR). CONCLUSION Analysis of tumor nuclear features using digital pathology/AI can significantly improve models to predict pathological response to NAC.
Collapse
|
8
|
Dif N, Elberrichi Z. Efficient Regularization Framework for Histopathological Image Classification Using Convolutional Neural Networks. INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2020. [DOI: 10.4018/ijcini.2020100104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Deep learning methods are characterized by their capacity to learn data representation compared to the traditional machine learning algorithms. However, these methods are prone to overfitting on small volumes of data. The objective of this research is to overcome this limitation by improving the generalization in the proposed deep learning framework based on various techniques: data augmentation, small models, optimizer selection, and ensemble learning. For ensembling, the authors used selected models from different checkpoints and both voting and unweighted average methods for combination. The experimental study on the lymphomas histopathological dataset highlights the efficiency of the MobileNet2 network combined with the stochastic gradient descent (SGD) optimizer in terms of generalization. The best results have been achieved by the combination of the best three checkpoint models (98.67% of accuracy). These findings provide important insights into the efficiency of the checkpoint ensemble learning method for histopathological image classification.
Collapse
Affiliation(s)
- Nassima Dif
- EEDIS Laboratory ,Djillali Liabes University, Sidi Bel Abbes, Algeria
| | | |
Collapse
|
9
|
Ameen M, Alrahmawy M, AbouEleneen A, Tolba A. Neighborhood preserving perceptual fidelity aware MSE for visual inspection of industrial flat surface products. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-192071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Mustafa Ameen
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Egypt
| | - Mohammed Alrahmawy
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Egypt
| | - Amal AbouEleneen
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Egypt
| | - Ahmad Tolba
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Egypt
| |
Collapse
|
10
|
Analysis of Spatial Distribution and Prognostic Value of Different Pan Cytokeratin Immunostaining Intensities in Breast Tumor Tissue Sections. Int J Mol Sci 2020; 21:ijms21124434. [PMID: 32580421 PMCID: PMC7352516 DOI: 10.3390/ijms21124434] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 06/14/2020] [Accepted: 06/18/2020] [Indexed: 01/19/2023] Open
Abstract
Cancer risk prognosis could improve patient survival through early personalized treatment decisions. This is the first systematic analysis of the spatial and prognostic distribution of different pan cytokeratin immunostaining intensities in breast tumors. The prognostic model included 102 breast carcinoma patients, with distant metastasis occurrence as the endpoint. We segmented the full intensity range (0–255) of pan cytokeratin digitized immunostaining into seven discrete narrow grey level ranges: 0–130, 130–160, 160–180, 180–200, 200–220, 220–240, and 240–255. These images were subsequently examined by 33 major (GLCM), fractal and first-order statistics computational analysis features. Interestingly, while moderate intensities were strongly associated with metastasis outcome, high intensities of pan cytokeratin immunostaining provided no prognostic value even after an exhaustive computational analysis. The intense pan cytokeratin immunostaining was also relatively rare, suggesting the low differentiation state of epithelial cells. The observed variability in immunostaining intensities highlighted the intratumoral heterogeneity of the malignant cells and its association with a poor disease outcome. The prognostic importance of the moderate intensity range established by complex computational morphology analyses was supported by simple measurements of its immunostaining area which was associated with favorable disease outcome. This study reveals intratumoral heterogeneity of the pan cytokeratin immunostaining together with the prognostic evaluation and spatial distribution of its discrete intensities.
Collapse
|
11
|
Qi Y, Cui X, Han M, Li R, Zhang T, Geng B, Xiu J, Liu J, Liu Z, Han M. Radiomics analysis of lung CT image for the early detection of metastases in patients with breast cancer: preliminary findings from a retrospective cohort study. Eur Radiol 2020; 30:4545-4556. [PMID: 32166487 DOI: 10.1007/s00330-020-06745-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 01/16/2020] [Accepted: 02/12/2020] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To investigate whether subtle changes in radiomics features are present in lung CT images prior to the development of CT-detectable lung metastases in patients with breast cancer. METHODS Thirty-three radiomics features were measured in the metastasis region (MR) and in matched contralateral tissues (non-metastasis region, NMR) of 29 breast cancer patients at the last CT scan, as well as in the corresponding regions of the patients' pre-metastasis scan (pre-MR and pre-NMR). We also compared them with normal lung tissues (control group, CG) from 29 healthy volunteers. Then, 8 patients from the 29 patients with lung metastases and 8 patients who did not develop lung metastases were chosen for further study of the correlation between radiomics parameters and tumor growth. RESULTS In the MR vs. NMR and MR vs. CG groups, almost all radiomics features were significantly different. Twenty-six parameters showed significant differences between the pre-MRs and pre-NMRs. Linear fitting demonstrated a significant correlation between 5 features and tumor growth in the metastasis group, but not in the non-metastasis group. Among them, run percentage was the most representative feature. The calculated area under curves (AUCs), based on run percentage for the classification of metastasis and pre-metastasis, were 0.954 and 0.852, respectively. CONCLUSIONS Radiomics features may allow early detection of lung metastases before they become visually detectable, and the feature run percentage may be a promising image surrogate marker for the microinvasion of tumor cells into the lung tissue. KEY POINTS • The significant differences in radiomics features between pre-MR and pre-NMR are critical for the early detection of lung metastases. • Five radiomics features show a correlation with tumor growth. • The radiomics feature run percentage may be a potential imaging biomarker for the early detection of lung metastases.
Collapse
Affiliation(s)
- Yana Qi
- Cancer Therapy and Research Center, Shandong Provincial Hospital affiliated to Shandong University, Shandong University, Jinan, People's Republic of China
| | - Xiaoxiao Cui
- School of Information Science and Engineering, Shandong University, Jinan, People's Republic of China
| | - Meng Han
- School of Basic Medical Sciences, Shandong First Medical University, Jinan, People's Republic of China
| | - Ranran Li
- Cancer Therapy and Research Center, Shandong Provincial Hospital affiliated to Shandong University, Shandong University, Jinan, People's Republic of China
| | - Tiehong Zhang
- Cancer Therapy and Research Center, Shandong Provincial Hospital affiliated to Shandong University, Shandong University, Jinan, People's Republic of China
| | - Baocheng Geng
- Cancer Therapy and Research Center, Shandong Provincial Hospital affiliated to Shandong University, Shandong University, Jinan, People's Republic of China
| | - Jianjun Xiu
- Cancer Therapy and Research Center, Shandong Provincial Hospital affiliated to Shandong University, Shandong University, Jinan, People's Republic of China
| | - Jing Liu
- School of Public Health, Shandong University, Jinan, People's Republic of China
| | - Zhi Liu
- School of Information Science and Engineering, Shandong University, Jinan, People's Republic of China.
| | - Mingyong Han
- Cancer Therapy and Research Center, Shandong Provincial Hospital affiliated to Shandong University, Shandong University, Jinan, People's Republic of China.
| |
Collapse
|
12
|
Size and Shape Filtering of Malignant Cell Clusters within Breast Tumors Identifies Scattered Individual Epithelial Cells as the Most Valuable Histomorphological Clue in the Prognosis of Distant Metastasis Risk. Cancers (Basel) 2019; 11:cancers11101615. [PMID: 31652628 PMCID: PMC6826383 DOI: 10.3390/cancers11101615] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Revised: 10/08/2019] [Accepted: 10/18/2019] [Indexed: 12/13/2022] Open
Abstract
Survival and life quality of breast cancer patients could be improved by more aggressive chemotherapy for those at high metastasis risk and less intense treatments for low-risk patients. Such personalized treatment cannot be currently achieved due to the insufficient reliability of metastasis risk prognosis. The purpose of this study was therefore, to identify novel histopathological prognostic markers of metastasis risk through exhaustive computational image analysis of 80 size and shape subsets of epithelial clusters in breast tumors. The group of 102 patients had a follow-up median of 12.3 years, without lymph node spread and systemic treatments. Epithelial cells were stained by the AE1/AE3 pan-cytokeratin antibody cocktail. The size and shape subsets of the stained epithelial cell clusters were defined in each image by use of the circularity and size filters and analyzed for prognostic performance. Epithelial areas with the optimal prognostic performance were uniformly small and round and could be recognized as individual epithelial cells scattered in tumor stroma. Their count achieved an area under the receiver operating characteristic curve (AUC) of 0.82, total area (AUC = 0.77), average size (AUC = 0.63), and circularity (AUC = 0.62). In conclusion, by use of computational image analysis as a hypothesis-free discovery tool, this study reveals the histomorphological marker with a high prognostic value that is simple and therefore easy to quantify by visual microscopy.
Collapse
|
13
|
Li J, Chen C, Fu R, Zhang Y, Fan Y, Xu J, Cen Y. Texture Analysis of T1-Weighted Contrast-Enhanced Magnetic Resonance Imaging Potentially Predicts Outcomes of Patients with Non-Wingless-Type/Non-Sonic Hedgehog Medulloblastoma. World Neurosurg 2019; 137:e27-e33. [PMID: 31589984 DOI: 10.1016/j.wneu.2019.09.142] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 09/25/2019] [Accepted: 09/26/2019] [Indexed: 02/05/2023]
Abstract
OBJECTIVE To investigate whether tumor texture features derived from preoperative T1-weighted magnetic resonance imaging (MRI) are associated with overall survival (OS) of patients with non-wingless-type (WNT)/non-sonic hedgehog (SHH) medulloblastoma. METHODS We retrospectively reviewed 38 patients with non-WNT/non-SHH (encompassing group 3 and group 4) medulloblastoma treated with surgery in our institution from 2013 to 2016. All patients were followed-up for at least 2 years or until death. Primary tumor traditional parameters were evaluated, and texture features were extracted from preoperative T1-weighted MRI, including 4 features from the histogram matrix and 6 textures from the gray-level co-occurrence matrix (GLCM). Texture features were dichotomized into 2 subgroups based on their optimal cutoff values obtained from receiver operating characteristics curve analysis. Two-year OS was compared between the dichotomized subgroups using the Kaplan-Meier analysis and log-rank test. Multivariate Cox regression analysis was performed to determine independent prognostic factors. RESULTS The therapy regimen was the only basic characteristic significantly related to 2-year OS (P = 0.015). Two features of the GLCM were shown to be significantly associated with 24-month OS. Multivariate Cox regression analysis revealed that GLCM homogeneity (adjusted hazard ratio, 0.145; P = 0.013) was an independent prognostic predictor for patients. CONCLUSIONS Texture analysis on T1-weighted contrast-enhanced MRI potentially serves as a prognostic predictor of survival for patients with non-WNT/non-SHH medulloblastoma.
Collapse
Affiliation(s)
- Jiaqi Li
- Department of Plastic and Burn Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Chaoyue Chen
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Rao Fu
- Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Zhang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yimeng Fan
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, China
| | - Jianguo Xu
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Ying Cen
- Department of Plastic and Burn Surgery, West China Hospital, Sichuan University, Chengdu, China.
| |
Collapse
|
14
|
Korolj A, Wu HT, Radisic M. A healthy dose of chaos: Using fractal frameworks for engineering higher-fidelity biomedical systems. Biomaterials 2019; 219:119363. [PMID: 31376747 PMCID: PMC6759375 DOI: 10.1016/j.biomaterials.2019.119363] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Revised: 07/09/2019] [Accepted: 07/14/2019] [Indexed: 12/18/2022]
Abstract
Optimal levels of chaos and fractality are distinctly associated with physiological health and function in natural systems. Chaos is a type of nonlinear dynamics that tends to exhibit seemingly random structures, whereas fractality is a measure of the extent of organization underlying such structures. Growing bodies of work are demonstrating both the importance of chaotic dynamics for proper function of natural systems, as well as the suitability of fractal mathematics for characterizing these systems. Here, we review how measures of fractality that quantify the dose of chaos may reflect the state of health across various biological systems, including: brain, skeletal muscle, eyes and vision, lungs, kidneys, tumours, cell regulation, skin and wound repair, bone, vasculature, and the heart. We compare how reports of either too little or too much chaos and fractal complexity can be damaging to normal biological function, and suggest that aiming for the healthy dose of chaos may be an effective strategy for various biomedical applications. We also discuss rising examples of the implementation of fractal theory in designing novel materials, biomedical devices, diagnostics, and clinical therapies. Finally, we explain important mathematical concepts of fractals and chaos, such as fractal dimension, criticality, bifurcation, and iteration, and how they are related to biology. Overall, we promote the effectiveness of fractals in characterizing natural systems, and suggest moving towards using fractal frameworks as a basis for the research and development of better tools for the future of biomedical engineering.
Collapse
Affiliation(s)
- Anastasia Korolj
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada
| | - Hau-Tieng Wu
- Department of Statistical Science, Duke University, Durham, NC, USA; Department of Mathematics, Duke University, Durham, NC, USA; Mathematics Division, National Center for Theoretical Sciences, Taipei, Taiwan
| | - Milica Radisic
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada; Toronto General Research Institute, University Health Network, Toronto, Canada; The Heart and Stroke/Richard Lewar Center of Excellence, Toronto, Canada.
| |
Collapse
|
15
|
Petrujkić K, Milošević N, Rajković N, Stanisavljević D, Gavrilović S, Dželebdžić D, Ilić R, Di Ieva A, Maksimović R. Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis. Eur J Radiol 2019; 119:108634. [PMID: 31473463 DOI: 10.1016/j.ejrad.2019.08.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 07/28/2019] [Accepted: 08/05/2019] [Indexed: 01/31/2023]
Abstract
PURPOSE Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis. METHOD In a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods. RESULTS All five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (SCOR), contrary to SWI images which showed different values of two parameters (angular second moment-SASM and contrast-SCON). Only three fractal features (binary box dimension-Dbin, normalized box dimension-Dnorm and lacunarity-λ) measured on T2W and Dnorm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (SIDM) on T2W and SIDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images. CONCLUSIONS Computational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.
Collapse
Affiliation(s)
- Katarina Petrujkić
- Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia.
| | - Nebojša Milošević
- Department of Biophysics, School of Medicine, University of Belgrade, Višegradska 26/2, Belgrade 11000, Serbia
| | - Nemanja Rajković
- Department of Biophysics, School of Medicine, University of Belgrade, Višegradska 26/2, Belgrade 11000, Serbia
| | - Dejana Stanisavljević
- Department for Medical Statistics, School of Medicine, University of Belgrade, Dr Subotića 8, Belgrade 11000, Serbia
| | - Svetlana Gavrilović
- Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia
| | - Dragana Dželebdžić
- Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia
| | - Rosanda Ilić
- Department of Neurosurgery, School of Medicine, University of Belgrade, Dr Subotića 8, Belgrade 11000, Serbia; Clinical Centre of Serbia, Clinical for Neurosurgery, Dr Koste Todorovića 54, 11000 Belgrade, Serbia
| | - Antonio Di Ieva
- Department of Clinical Medicine, Faculty of Medicine and Health Science, Neurosurgery Unit, Macquarie University, 2 Technology Place, Macquarie University, Sydney, NSW 2109, Australia
| | - Ružica Maksimović
- Clinical Centre of Serbia, Centre for Radiology and Magnetic Resonance, Pasterova 2, Belgrade 11000, Serbia; Department of Radiology, School of Medicine, University of Belgrade, Dr Subotića 8, Belgrade 11000, Serbia
| |
Collapse
|
16
|
Fujima N, Shimizu Y, Yoshida D, Kano S, Mizumachi T, Homma A, Yasuda K, Onimaru R, Sakai O, Kudo K, Shirato H. Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study. Cancers (Basel) 2019; 11:cancers11060800. [PMID: 31185611 PMCID: PMC6627127 DOI: 10.3390/cancers11060800] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 06/02/2019] [Accepted: 06/06/2019] [Indexed: 02/06/2023] Open
Abstract
The purpose of this study was to determine the predictive power for treatment outcome of a machine-learning algorithm combining magnetic resonance imaging (MRI)-derived data in patients with sinonasal squamous cell carcinomas (SCCs). Thirty-six primary lesions in 36 patients were evaluated. Quantitative morphological parameters and intratumoral characteristics from T2-weighted images, tumor perfusion parameters from arterial spin labeling (ASL) and tumor diffusion parameters of five diffusion models from multi-b-value diffusion-weighted imaging (DWI) were obtained. Machine learning by a non-linear support vector machine (SVM) was used to construct the best diagnostic algorithm for the prediction of local control and failure. The diagnostic accuracy was evaluated using a 9-fold cross-validation scheme, dividing patients into training and validation sets. Classification criteria for the division of local control and failure in nine training sets could be constructed with a mean sensitivity of 0.98, specificity of 0.91, positive predictive value (PPV) of 0.94, negative predictive value (NPV) of 0.97, and accuracy of 0.96. The nine validation data sets showed a mean sensitivity of 1.0, specificity of 0.82, PPV of 0.86, NPV of 1.0, and accuracy of 0.92. In conclusion, a machine-learning algorithm using various MR imaging-derived data can be helpful for the prediction of treatment outcomes in patients with sinonasal SCCs.
Collapse
Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo 060-8638, Hokkaido, Japan.
| | - Yukie Shimizu
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo 060-8638, Hokkaido, Japan.
| | - Daisuke Yoshida
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo 060-8638, Hokkaido, Japan.
| | - Satoshi Kano
- Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, Sapporo 060-8638, Hokkaido, Japan.
| | - Takatsugu Mizumachi
- Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, Sapporo 060-8638, Hokkaido, Japan.
| | - Akihiro Homma
- Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, Sapporo 060-8638, Hokkaido, Japan.
| | - Koichi Yasuda
- Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo 060-8638, Hokkaido, Japan.
| | - Rikiya Onimaru
- Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo 060-8638, Hokkaido, Japan.
| | - Osamu Sakai
- Departments of Radiology, Otolaryngology-Head and Neck Surgery, and Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA 02118, USA.
| | - Kohsuke Kudo
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo 060-8638, Hokkaido, Japan.
| | - Hiroki Shirato
- Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo 060-8638, Hokkaido, Japan.
- The Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education, Sapporo 060-0808, Hokkaido, Japan.
| |
Collapse
|
17
|
|
18
|
Fujima N, Homma A, Harada T, Shimizu Y, Tha KK, Kano S, Mizumachi T, Li R, Kudo K, Shirato H. The utility of MRI histogram and texture analysis for the prediction of histological diagnosis in head and neck malignancies. Cancer Imaging 2019; 19:5. [PMID: 30717792 PMCID: PMC6360729 DOI: 10.1186/s40644-019-0193-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 01/30/2019] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND To assess the utility of histogram and texture analysis of magnetic resonance (MR) fat-suppressed T2-weighted imaging (Fs-T2WI) for the prediction of histological diagnosis of head and neck squamous cell carcinoma (SCC) and malignant lymphoma (ML). METHODS The cases of 57 patients with SCC (45 well/moderately and 12 poorly differentiated SCC) and 10 patients with ML were retrospectively analyzed. Quantitative parameters with histogram features (relative mean signal, coefficient of variation, kurtosis and skewness) and gray-level co-occurrence matrix (GLCM) features (contrast, correlation, energy and homogeneity) were calculated using Fs-T2WI data with a manual tumor region of interest (ROI). RESULTS The following significantly different values were obtained for the total SCC versus ML groups: relative mean signal (3.65 ± 0.86 vs. 2.61 ± 0.49), contrast (72.9 ± 16.2 vs. 49.3 ± 8.7) and homogeneity (2.22 ± 0.25 × 10- 1 vs. 2.53 ± 0.12 × 10- 1). In the comparison of the SCC histological grades, the relative mean signal and contrast were significantly lower in the poorly differentiated SCC (2.89 ± 0.63, 56.2 ± 12.9) compared to the well/moderately SCC (3.85 ± 0.81, 77.5 ± 13.9). The homogeneity in poorly differentiated SCC (2.56 ± 0.15 × 10- 1) was higher than that of the well/moderately SCC (2.1 ± 0.18 × 10- 1). CONCLUSIONS Parameters obtained by histogram and texture analysis of Fs-T2WI may be useful for noninvasive prediction of histological type and grade in head and neck malignancy.
Collapse
Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, 0608638, Japan.
| | - Akihiro Homma
- Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, N15 W7, Kita-Ku, Sapporo, 0608638, Japan
| | - Taisuke Harada
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, 0608638, Japan
| | - Yukie Shimizu
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, 0608638, Japan
| | - Khin Khin Tha
- Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, N15 W7, Kita-Ku, Sapporo, 0608638, Japan.,The Global Station for Quantum Medical Science and Engineering, Global Institution for collaborative research and education, N15 W8, Kita-Ku, Sapporo, 0608638, Japan
| | - Satoshi Kano
- Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, N15 W7, Kita-Ku, Sapporo, 0608638, Japan
| | - Takatsugu Mizumachi
- Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, N15 W7, Kita-Ku, Sapporo, 0608638, Japan
| | - Ruijiang Li
- The Global Station for Quantum Medical Science and Engineering, Global Institution for collaborative research and education, N15 W8, Kita-Ku, Sapporo, 0608638, Japan.,Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, 94305-5847, CA, USA
| | - Kohsuke Kudo
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, 0608638, Japan
| | - Hiroki Shirato
- Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, N15 W7, Kita-Ku, Sapporo, 0608638, Japan.,The Global Station for Quantum Medical Science and Engineering, Global Institution for collaborative research and education, N15 W8, Kita-Ku, Sapporo, 0608638, Japan
| |
Collapse
|
19
|
Todd CS, Heeschen WA. New Image Texture Analysis, and Application to Polymer Membrane Surface Morphologies and Roughness. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2018; 24:471-477. [PMID: 30231944 DOI: 10.1017/s1431927618015155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
A new method of image texture analysis is presented, based on the mean and standard deviation of gray levels within domains in an image. The calculations are performed recursively on domains of various sizes within the images. These gray level calculations are used as the input matrix for principal component analysis. The technique analyzes the entire image as a whole and is not for image segmentation. The analysis routine operates across all distances, frequencies and directions in the image, and is not computationally burdensome. The method was applied to scanning electron microscope images of reverse osmosis membranes on domains from 23 nm to 3 µm. The texture analysis technique performed well in identifying the surface morphology and, once calibrated, in predicting the surface roughness as measured by atomic force microscopy.
Collapse
Affiliation(s)
- Clifford S Todd
- Analytical Science, Core R&D,The Dow Chemical Company,1897 Building,Midland, MI 48667,USA
| | - William A Heeschen
- Analytical Science, Core R&D,The Dow Chemical Company,1897 Building,Midland, MI 48667,USA
| |
Collapse
|
20
|
Rajković N, Li X, Plataniotis KN, Kanjer K, Radulovic M, Milošević NT. The Pan-Cytokeratin Staining Intensity and Fractal Computational Analysis of Breast Tumor Malignant Growth Patterns Prognosticate the Occurrence of Distant Metastasis. Front Oncol 2018; 8:348. [PMID: 30214894 PMCID: PMC6125390 DOI: 10.3389/fonc.2018.00348] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 08/08/2018] [Indexed: 11/13/2022] Open
Abstract
Improved prognosis of breast cancer outcome could prolong patient survival by reliable identification of patients at high risk of metastasis occurrence which could benefit from more aggressive treatments. Based on such clinical need, we prognostically evaluated the malignant cells in breast tumors, as the obvious potential source of unexploited prognostic information. The patient group was homogeneous, without any systemic treatments or lymph node spread, with smaller tumor size (pT1/2) and a long follow-up. Epithelial cells were labeled with AE1/AE3 pan-cytokeratin antibody cocktail and comprehensively analyzed. Monofractal and multifractal analyses were applied for quantification of distribution, shape, complexity and texture of malignant cell clusters, while mean pixel intensity and total area were measures of the pan-cytokeratin immunostaining intensity. The results surprisingly indicate that simple binary images and monofractal analysis provided better prognostic information then grayscale images and multifractal analysis. The key findings were that shapes and distribution of malignant cell clusters (by binary fractal dimension; AUC = 0.29), their contour shapes (by outline fractal dimension; AUC = 0.31) and intensity of the pan-cytokeratin immunostaining (by mean pixel intensity; AUC = 0.30) offered significant performance in metastasis risk prognostication. The results reveal an association between the lower pan-cytokeratin staining intensity and the high metastasis risk. Another interesting result was that multivariate analysis could confirm the prognostic independence only for fractal but not for immunostaining intensity features. The obtained results reveal several novel and unexpected findings highlighting the independent prognostic efficacy of malignant cell cluster distribution and contour shapes in breast tumors.
Collapse
Affiliation(s)
- Nemanja Rajković
- Department of Biophysics, School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Xingyu Li
- Multimedia Laboratory, The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Konstantinos N Plataniotis
- Multimedia Laboratory, The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Ksenija Kanjer
- Department of Experimental Oncology, Institute for Oncology and Radiology, Belgrade, Serbia
| | - Marko Radulovic
- Department of Experimental Oncology, Institute for Oncology and Radiology, Belgrade, Serbia
| | - Nebojša T Milošević
- Department of Biophysics, School of Medicine, University of Belgrade, Belgrade, Serbia
| |
Collapse
|
21
|
Tesic V, Perovic M, Zaletel I, Jovanovic M, Puskas N, Ruzdijic S, Kanazir S. A single high dose of dexamethasone increases GAP-43 and synaptophysin in the hippocampus of aged rats. Exp Gerontol 2017; 98:62-69. [DOI: 10.1016/j.exger.2017.08.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2017] [Revised: 07/06/2017] [Accepted: 08/08/2017] [Indexed: 01/14/2023]
|
22
|
KOLAREVIĆ D, VUJASINOVIĆ T, KANJER K, MILOVANOVIĆ J, TODOROVIĆ-RAKOVIĆ N, NIKOLIĆ-VUKOSAVLJEVIĆ D, RADULOVIC M. Effects of different preprocessing algorithms on the prognostic value of breast tumour microscopic images. J Microsc 2017; 270:17-26. [DOI: 10.1111/jmi.12645] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 09/01/2017] [Accepted: 09/04/2017] [Indexed: 01/17/2023]
Affiliation(s)
- D. KOLAREVIĆ
- Daily Chemotherapy Hospital; Institute for Oncology and Radiology of Serbia; Beograd Serbia
| | - T. VUJASINOVIĆ
- Department of Experimental Oncology; Institute for Oncology and Radiology of Serbia; Beograd Serbia
| | - K. KANJER
- Department of Experimental Oncology; Institute for Oncology and Radiology of Serbia; Beograd Serbia
| | - J. MILOVANOVIĆ
- Department of Experimental Oncology; Institute for Oncology and Radiology of Serbia; Beograd Serbia
| | - N. TODOROVIĆ-RAKOVIĆ
- Department of Experimental Oncology; Institute for Oncology and Radiology of Serbia; Beograd Serbia
| | - D. NIKOLIĆ-VUKOSAVLJEVIĆ
- Department of Experimental Oncology; Institute for Oncology and Radiology of Serbia; Beograd Serbia
| | - M. RADULOVIC
- Department of Experimental Oncology; Institute for Oncology and Radiology of Serbia; Beograd Serbia
| |
Collapse
|
23
|
Sahay P, Almabadi HM, Ghimire HM, Skalli O, Pradhan P. Light localization properties of weakly disordered optical media using confocal microscopy: application to cancer detection. OPTICS EXPRESS 2017; 25:15428-15440. [PMID: 28788968 PMCID: PMC5557329 DOI: 10.1364/oe.25.015428] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 01/31/2017] [Accepted: 01/31/2017] [Indexed: 06/07/2023]
Abstract
We have developed a novel technique to quantify submicron scale mass density fluctuations in weakly disordered heterogeneous optical media using confocal fluorescence microscopy. Our method is based on the numerical evaluation of the light localization properties of an 'optical lattice' constructed from the pixel intensity distributions of images obtained with confocal fluorescence microscopy. Here we demonstrate that the technique reveals differences in the mass density fluctuations of the fluorescently labeled molecules between normal and cancer cells, and that it has the potential to quantify the degree of malignancy of cancer cells. Potential applications of the technique to other disease situations or characterizing disordered samples are also discussed.
Collapse
Affiliation(s)
- Peeyush Sahay
- Department of Physics and Materials Science, BioNanoPhotonics Laboratory, University of Memphis, Memphis, Tennessee, 38152, USA
- These authors contributed equally to the work
| | - Huda M. Almabadi
- Department of Physics and Materials Science, BioNanoPhotonics Laboratory, University of Memphis, Memphis, Tennessee, 38152, USA
- Department of Biomedical Engineering, University of Memphis, Memphis, Tennessee, 38152, USA
- These authors contributed equally to the work
| | - Hemendra M. Ghimire
- Department of Physics and Materials Science, BioNanoPhotonics Laboratory, University of Memphis, Memphis, Tennessee, 38152, USA
| | - Omar Skalli
- Department of Biological Sciences and Integrated Microscopy Center, University of Memphis, Tennessee, 38152, USA
| | - Prabhakar Pradhan
- Department of Physics and Materials Science, BioNanoPhotonics Laboratory, University of Memphis, Memphis, Tennessee, 38152, USA
| |
Collapse
|
24
|
Kobayashi T, Sundaram D, Nakata K, Tsurui H. Gray-level co-occurrence matrix analysis of several cell types in mouse brain using resolution-enhanced photothermal microscopy. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:36011. [PMID: 28290595 DOI: 10.1117/1.jbo.22.3.036011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Accepted: 02/17/2017] [Indexed: 06/06/2023]
Abstract
Qualifications of intracellular structure were performed for the first time using the gray-level co-occurrence matrix (GLCM) method for images of cells obtained by resolution-enhanced photothermal imaging. The GLCM method has been used to extract five parameters of texture features for five different types of cells in mouse brain; pyramidal neurons and glial cells in the basal nucleus (BGl), dentate gyrus granule cells, cerebellar Purkinje cells, and cerebellar granule cells. The parameters are correlation, contrast, angular second moment (ASM), inverse difference moment (IDM), and entropy for the images of cells of interest in a mouse brain. The parameters vary depending on the pixel distance taken in the analysis method. Based on the obtained results, we identified that the most suitable GLCM parameter is IDM for pyramidal neurons and BGI, granule cells in the dentate gyrus, Purkinje cells and granule cells in the cerebellum. It was also found that the ASM is the most appropriate for neurons in the basal nucleus.
Collapse
Affiliation(s)
- Takayoshi Kobayashi
- University of Electro-Communications, Advanced Ultrafast Laser Research Center, Chofu, Tokyo, JapanbJapan Science and Technology Agency, Core Research for Evolutional Science and Technology, K's Gobancho, Chiyoda-ku, Tokyo, JapancNational Chiao-Tung University, Department of Electrophysics, Hsinchu, TaiwandOsaka University, Institute of Laser Engineering, Suita, Osaka, Japan
| | - Durga Sundaram
- University of Electro-Communications, Advanced Ultrafast Laser Research Center, Chofu, Tokyo, JapaneSastra University, Tirumalaisamudram, Thanjavur, Tamil Nadu, India
| | - Kazuaki Nakata
- University of Electro-Communications, Advanced Ultrafast Laser Research Center, Chofu, Tokyo, Japan
| | - Hiromichi Tsurui
- Juntendo University School of Medicine, Department of Pathoilogy, Bunkyo-ku, Tokyo, Japan
| |
Collapse
|
25
|
Rajković N, Vujasinović T, Kanjer K, Milošević NT, Nikolić-Vukosavljević D, Radulovic M. Prognostic biomarker value of binary and grayscale breast tumor histopathology images. Biomark Med 2016; 10:1049-1059. [PMID: 27680104 DOI: 10.2217/bmm-2016-0165] [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] [Indexed: 01/07/2023] Open
Abstract
AIM Breast cancer prognosis is in the spotlight owing to its potentially major clinical importance in effective therapeutic management. Following our recent prognostic establishment of the fractal features calculated on binary breast tumor histopathology images, this study aimed to accomplish the first optimization of this methodology by direct comparison of monofractal, multifractal and co-occurrence algorithms in analysis of binary versus grayscale image formats. PATIENTS & METHODS The study included 93 patients with invasive breast cancer, without systemic treatment and a long median follow-up of 150 months. RESULTS Grayscale images provided a better prognostic source in comparison to binary, while monofractal, multifractal and co-occurrence image analysis algorithms exerted a comparable performance. CONCLUSION The critical prognostic importance of the grayscale texture is revealed.
Collapse
Affiliation(s)
- Nemanja Rajković
- Department of Biophysics, School of Medicine, University of Belgrade, Višegradska 26/2, Belgrade 11000, Serbia
| | - Tijana Vujasinović
- Department of Experimental Oncology, Institute for Oncology & Radiology, Pasterova 14, Belgrade 11000, Serbia
| | - Ksenija Kanjer
- Department of Experimental Oncology, Institute for Oncology & Radiology, Pasterova 14, Belgrade 11000, Serbia
| | - Nebojša T Milošević
- Department of Biophysics, School of Medicine, University of Belgrade, Višegradska 26/2, Belgrade 11000, Serbia
| | | | - Marko Radulovic
- Department of Experimental Oncology, Institute for Oncology & Radiology, Pasterova 14, Belgrade 11000, Serbia
| |
Collapse
|
26
|
Rajković N, Kolarević D, Kanjer K, Milošević NT, Nikolić-Vukosavljević D, Radulovic M. Comparison of Monofractal, Multifractal and gray level Co-occurrence matrix algorithms in analysis of Breast tumor microscopic images for prognosis of distant metastasis risk. Biomed Microdevices 2016; 18:83. [DOI: 10.1007/s10544-016-0103-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
27
|
Pribic J, Vasiljevic J, Kanjer K, Konstantinovic ZN, Milosevic NT, Vukosavljevic DN, Radulovic M. Fractal dimension and lacunarity of tumor microscopic images as prognostic indicators of clinical outcome in early breast cancer. Biomark Med 2015; 9:1279-7. [PMID: 26612586 DOI: 10.2217/bmm.15.102] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
AIM Research in the field of breast cancer outcome prognosis has been focused on molecular biomarkers, while neglecting the discovery of novel tumor histology structural clues. We thus aimed to improve breast cancer prognosis by fractal analysis of tumor histomorphology. PATIENTS & METHODS This retrospective study included 92 breast cancer patients without systemic treatment. RESULTS Fractal dimension and lacunarity of the breast tumor microscopic histology possess prognostic value comparable to the major clinicopathological prognostic parameters. CONCLUSION Fractal analysis was performed for the first time on routinely produced archived pan-tissue stained primary breast tumor sections, indicating its potential for clinical use as a simple and cost-effective prognostic indicator of distant metastasis risk to complement the molecular approaches for cancer risk prognosis.
Collapse
Affiliation(s)
- Jelena Pribic
- Department of Experimental Oncology, Institute of Oncology & Radiology of Serbia, Pasterova 14, Belgrade, Serbia
| | | | - Ksenija Kanjer
- Department of Experimental Oncology, Institute of Oncology & Radiology of Serbia, Pasterova 14, Belgrade, Serbia
| | - Zora Neskovic Konstantinovic
- Department of Experimental Oncology, Institute of Oncology & Radiology of Serbia, Pasterova 14, Belgrade, Serbia
| | - Nebojsa T Milosevic
- Department of Biophysics, School of Medicine, University of Belgrade Visegradska 26/2, Belgrade, Serbia
| | | | - Marko Radulovic
- Department of Experimental Oncology, Institute of Oncology & Radiology of Serbia, Pasterova 14, Belgrade, Serbia
| |
Collapse
|