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Bhende M, Thakare A, Saravanan V, Anbazhagan K, Patel HN, Kumar A. Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3947434. [PMID: 35832843 PMCID: PMC9273435 DOI: 10.1155/2022/3947434] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 06/20/2022] [Indexed: 11/18/2022]
Abstract
At present, early lung cancer screening is mainly based on radiologists' experience in diagnosing benign and malignant pulmonary nodules by lung CT images. On the other hand, intraoperative rapid freezing pathology needs to analyse the invasive adenocarcinoma nodules with the worst recovery in adenocarcinoma. Moreover, rapid freezing pathology has a low diagnostic accuracy for small-diameter nodules. Because of the above problems, an algorithm for diagnosing invasive adenocarcinoma nodules in ground-glass pulmonary nodules is based on CT images. According to the nodule space information and plane features, sample data of different dimensions are designed, namely, 3D space and 2D plane feature samples. The network structure is designed based on the attention mechanism and residual learning unit; 2D and 3D neural networks are along built. By fusing the feature vectors extracted from networks of different dimensions, the diagnosis results of invasive adenocarcinoma nodules are finally obtained. The algorithm was studied on 1760 ground-glass nodules with 5-20 mm diameter collected from a city chest hospital with surgical and pathological results. There were 340 nodules with invasive adenocarcinoma and 340 with noninvasive adenocarcinoma. A total of 1420 invasive nodule samples were cross-validated on this example dataset. The classification accuracy of the algorithm was 82.7%, the sensitivity was 82.9%, and the specificity was 82.6%.
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Affiliation(s)
- Manisha Bhende
- Marathwada Mitra Mandal's Institute of Technology, Pune, India
| | - Anuradha Thakare
- Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India
| | | | - K. Anbazhagan
- Department of Computer Science and Engineering, Saveetha School of Engineering, SIMATS, Chennai, India
| | - Hemant N. Patel
- Computer Engineering, Sankalchand Patel College of Engineering, India
| | - Ashok Kumar
- Department of Computer Science, Banasthali Vidyapith, Banasthali-304022 (Rajasthan), India
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Janowczyk A, Zuo R, Gilmore H, Feldman M, Madabhushi A. HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides. JCO Clin Cancer Inform 2019; 3:1-7. [PMID: 30990737 PMCID: PMC6552675 DOI: 10.1200/cci.18.00157] [Citation(s) in RCA: 126] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/01/2019] [Indexed: 12/15/2022] Open
Abstract
PURPOSE Digital pathology (DP), referring to the digitization of tissue slides, is beginning to change the landscape of clinical diagnostic workflows and has engendered active research within the area of computational pathology. One of the challenges in DP is the presence of artefacts and batch effects, unintentionally introduced during both routine slide preparation (eg, staining, tissue folding) and digitization (eg, blurriness, variations in contrast and hue). Manual review of glass and digital slides is laborious, qualitative, and subject to intra- and inter-reader variability. Therefore, there is a critical need for a reproducible automated approach of precisely localizing artefacts to identify slides that need to be reproduced or regions that should be avoided during computational analysis. METHODS Here we present HistoQC, a tool for rapidly performing quality control to not only identify and delineate artefacts but also discover cohort-level outliers (eg, slides stained darker or lighter than others in the cohort). This open-source tool employs a combination of image metrics (eg, color histograms, brightness, contrast), features (eg, edge detectors), and supervised classifiers (eg, pen detection) to identify artefact-free regions on digitized slides. These regions and metrics are presented to the user via an interactive graphical user interface, facilitating artefact detection through real-time visualization and filtering. These same metrics afford users the opportunity to explicitly define acceptable tolerances for their workflows. RESULTS The output of HistoQC on 450 slides from The Cancer Genome Atlas was reviewed by two pathologists and found to be suitable for computational analysis more than 95% of the time. CONCLUSION These results suggest that HistoQC could provide an automated, quantifiable, quality control process for identifying artefacts and measuring slide quality, in turn helping to improve both the repeatability and robustness of DP workflows.
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Affiliation(s)
| | - Ren Zuo
- Case Western Reserve University, Cleveland, OH
| | - Hannah Gilmore
- University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Michael Feldman
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Anant Madabhushi
- Case Western Reserve University, Cleveland, OH
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH
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Zarella MD, Quaschnick MR, Breen DE, Garcia FU. Estimation of Fine-Scale Histologic Features at Low Magnification. Arch Pathol Lab Med 2018; 142:1394-1402. [PMID: 29911887 DOI: 10.5858/arpa.2017-0380-oa] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Whole-slide imaging has ushered in a new era of technology that has fostered the use of computational image analysis for diagnostic support and has begun to transfer the act of analyzing a slide to computer monitors. Due to the overwhelming amount of detail available in whole-slide images, analytic procedures-whether computational or visual-often operate at magnifications lower than the magnification at which the image was acquired. As a result, a corresponding reduction in image resolution occurs. It is unclear how much information is lost when magnification is reduced, and whether the rich color attributes of histologic slides can aid in reconstructing some of that information. OBJECTIVE.— To examine the correspondence between the color and spatial properties of whole-slide images to elucidate the impact of resolution reduction on the histologic attributes of the slide. DESIGN.— We simulated image resolution reduction and modeled its effect on classification of the underlying histologic structure. By harnessing measured histologic features and the intrinsic spatial relationships between histologic structures, we developed a predictive model to estimate the histologic composition of tissue in a manner that exceeds the resolution of the image. RESULTS.— Reduction in resolution resulted in a significant loss of the ability to accurately characterize histologic components at magnifications less than ×10. By utilizing pixel color, this ability was improved at all magnifications. CONCLUSIONS.— Multiscale analysis of histologic images requires an adequate understanding of the limitations imposed by image resolution. Our findings suggest that some of these limitations may be overcome with computational modeling.
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Affiliation(s)
| | | | | | - Fernando U Garcia
- From the Departments of Pathology & Laboratory Medicine (Dr Zarella) and Computer Science (Mr Quaschnick and Dr Breen), Drexel University, Philadelphia, Pennsylvania; and the Department of Pathology & Laboratory Medicine, Cancer Treatment Centers of America, Eastern Regional Medical Center, Philadelphia, Pennsylvania (Dr Garcia)
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Fractal analysis and the diagnostic usefulness of silver staining nucleolar organizer regions in prostate adenocarcinoma. Anal Cell Pathol (Amst) 2015; 2015:250265. [PMID: 26366372 PMCID: PMC4558419 DOI: 10.1155/2015/250265] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Accepted: 07/13/2015] [Indexed: 11/17/2022] Open
Abstract
Pathological diagnosis of prostate adenocarcinoma often requires complementary methods. On prostate biopsy tissue from 39 patients including benign nodular hyperplasia (BNH), atypical adenomatous hyperplasia (AAH), and adenocarcinomas, we have performed combined histochemical-immunohistochemical stainings for argyrophilic nucleolar organizer regions (AgNORs) and glandular basal cells. After ascertaining the pathology, we have analyzed the number, roundness, area, and fractal dimension of individual AgNORs or of their skeleton-filtered maps. We have optimized here for the first time a combination of AgNOR morphological denominators that would reflect best the differences between these pathologies. The analysis of AgNORs' roundness, averaged from large composite images, revealed clear-cut lower values in adenocarcinomas compared to benign and atypical lesions but with no differences between different Gleason scores. Fractal dimension (FD) of AgNOR silhouettes not only revealed significant lower values for global cancer images compared to AAH and BNH images, but was also able to differentiate between Gleason pattern 2 and Gleason patterns 3–5 adenocarcinomas. Plotting the frequency distribution of the FDs for different pathologies showed clear differences between all Gleason patterns and BNH. Together with existing morphological classifiers, AgNOR analysis might contribute to a faster and more reliable machine-assisted screening of prostatic adenocarcinoma, as an essential aid for pathologists.
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Yap CK, Kalaw EM, Singh M, Chong KT, Giron DM, Huang CH, Cheng L, Law YN, Lee HK. Automated image based prominent nucleoli detection. J Pathol Inform 2015; 6:39. [PMID: 26167383 PMCID: PMC4485194 DOI: 10.4103/2153-3539.159232] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Accepted: 01/07/2015] [Indexed: 11/19/2022] Open
Abstract
Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.
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Affiliation(s)
- Choon K Yap
- Imaging Informatics Division, Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix 138671, Novena, Singapore
| | - Emarene M Kalaw
- Imaging Informatics Division, Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix 138671, Novena, Singapore ; Department of Pathology, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, 308433, Novena, Singapore
| | - Malay Singh
- Imaging Informatics Division, Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix 138671, Novena, Singapore ; Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, 117417, Novena, Singapore
| | - Kian T Chong
- Department of Urology, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, 308433, Novena, Singapore
| | - Danilo M Giron
- Department of Pathology, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, 308433, Novena, Singapore
| | - Chao-Hui Huang
- Imaging Informatics Division, Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix 138671, Novena, Singapore
| | - Li Cheng
- Imaging Informatics Division, Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix 138671, Novena, Singapore
| | - Yan N Law
- Imaging Informatics Division, Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix 138671, Novena, Singapore
| | - Hwee Kuan Lee
- Imaging Informatics Division, Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix 138671, Novena, Singapore
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Taborri J, Rossi S, Palermo E, Patanè F, Cappa P. A novel HMM distributed classifier for the detection of gait phases by means of a wearable inertial sensor network. SENSORS (BASEL, SWITZERLAND) 2014; 14:16212-34. [PMID: 25184488 PMCID: PMC4208171 DOI: 10.3390/s140916212] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2014] [Revised: 08/05/2014] [Accepted: 08/26/2014] [Indexed: 11/28/2022]
Abstract
In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM) applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs) during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98) for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95) when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints.
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Affiliation(s)
- Juri Taborri
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana 18, I-00184 Roma, Italy.
| | - Stefano Rossi
- Department of Economics and Management, Industrial Engineering (DEIM), University of Tuscia, Via del Paradiso 47, I-01100 Viterbo, Italy.
| | - Eduardo Palermo
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana 18, I-00184 Roma, Italy.
| | - Fabrizio Patanè
- School of Mechanical Engineering, Niccolò Cusano University, Via Don Carlo Gnocchi 3, I-00166 Roma, Italy.
| | - Paolo Cappa
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana 18, I-00184 Roma, Italy.
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Kuznetsov V, Lee HK, Maurer-Stroh S, Molnár MJ, Pongor S, Eisenhaber B, Eisenhaber F. How bioinformatics influences health informatics: usage of biomolecular sequences, expression profiles and automated microscopic image analyses for clinical needs and public health. Health Inf Sci Syst 2013; 1:2. [PMID: 25825654 PMCID: PMC4336111 DOI: 10.1186/2047-2501-1-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2012] [Accepted: 10/05/2012] [Indexed: 01/25/2023] Open
Abstract
ABSTRACT The currently hyped expectation of personalized medicine is often associated with just achieving the information technology led integration of biomolecular sequencing, expression and histopathological bioimaging data with clinical records at the individual patients' level as if the significant biomedical conclusions would be its more or less mandatory result. It remains a sad fact that many, if not most biomolecular mechanisms that translate the human genomic information into phenotypes are not known and, thus, most of the molecular and cellular data cannot be interpreted in terms of biomedically relevant conclusions. Whereas the historical trend will certainly be into the general direction of personalized diagnostics and cures, the temperate view suggests that biomedical applications that rely either on the comparison of biomolecular sequences and/or on the already known biomolecular mechanisms have much greater chances to enter clinical practice soon. In addition to considering the general trends, we exemplarily review advances in the area of cancer biomarker discovery, in the clinically relevant characterization of patient-specific viral and bacterial pathogens (with emphasis on drug selection for influenza and enterohemorrhagic E. coli) as well as progress in the automated assessment of histopathological images. As molecular and cellular data analysis will become instrumental for achieving desirable clinical outcomes, the role of bioinformatics and computational biology approaches will dramatically grow. AUTHOR SUMMARY With DNA sequencing and computers becoming increasingly cheap and accessible to the layman, the idea of integrating biomolecular and clinical patient data seems to become a realistic, short-term option that will lead to patient-specific diagnostics and treatment design for many diseases such as cancer, metabolic disorders, inherited conditions, etc. These hyped expectations will fail since many, if not most biomolecular mechanisms that translate the human genomic information into phenotypes are not known yet and, thus, most of the molecular and cellular data collected will not lead to biomedically relevant conclusions. At the same time, less spectacular biomedical applications based on biomolecular sequence comparison and/or known biomolecular mechanisms have the potential to unfold enormous potential for healthcare and public health. Since the analysis of heterogeneous biomolecular data in context with clinical data will be increasingly critical, the role of bioinformatics and computational biology will grow correspondingly in this process.
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Affiliation(s)
- Vladimir Kuznetsov
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671 Singapore
- School of Computer Engineering (SCE), Nanyang Technological University (NTU), 50 Nanyang Drive, Singapore, 637553 Singapore
| | - Hwee Kuan Lee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671 Singapore
| | - Sebastian Maurer-Stroh
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671 Singapore
- School of Biological Sciences (SBS), Nanyang Technological University (NTU), 60 Nanyang Drive, Singapore, 637551 Singapore
| | - Maria Judit Molnár
- Institute of Genomic Medicine and Rare Disorders, Tömö Street 25-29, 1083 Budapest, Hungary
| | - Sandor Pongor
- Faculty of Information Technology, Pázmány Péter Catholic University, Budapest, Hungary (PPKE), Práter u. 50/a, 1083, Budapest, Hungary
| | - Birgit Eisenhaber
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671 Singapore
| | - Frank Eisenhaber
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671 Singapore
- School of Computer Engineering (SCE), Nanyang Technological University (NTU), 50 Nanyang Drive, Singapore, 637553 Singapore
- Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, Singapore, 117597 Singapore
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Mello-Thoms C, Mello CAB, Medvedeva O, Castine M, Legowski E, Gardner G, Tseytlin E, Crowley R. Perceptual analysis of the reading of dermatopathology virtual slides by pathology residents. Arch Pathol Lab Med 2012; 136:551-62. [PMID: 22540304 DOI: 10.5858/arpa.2010-0697-oa] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
CONTEXT The process by which pathologists arrive at a given diagnosis-a combination of their slide exploration strategy, perceptual information gathering, and cognitive decision making-has not been thoroughly explored, and many questions remain unanswered. OBJECTIVE To determine how pathology residents learn to diagnose inflammatory skin dermatoses, we contrasted the slide exploration strategy, perceptual capture of relevant histopathologic findings, and cognitive integration of identified features between 2 groups of residents, those who had and those who had not undergone their dermatopathology rotation. DESIGN Residents read a case set of 20 virtual slides (10 depicting nodular and diffuse dermatitis and 10 depicting subepidermal vesicular dermatitis), using an in-house-developed interface. We recorded residents' reports of diagnostic findings, conjectured diagnostic hypotheses, and final (or differential) diagnosis for each case, and time stamped each interaction with the interface. We created search maps of residents' slide exploration strategy. RESULTS No statistically significant differences were observed between the resident groups in the number of correctly or incorrectly reported diagnostic findings, but residents with dermatopathology training generated significantly more correct hypotheses (mean improvement of 88.5%) and correct diagnoses (70% of all correct diagnoses). CONCLUSIONS Two types of slide exploration strategy were identified for both groups: (1) a focused and efficient search, observed when the final diagnosis was correct; and (2) a more dispersed, time-consuming strategy, observed when the final diagnosis was incorrect. This difference was statistically significant, and it suggests that initial interpretation of a slide may bias further slide exploration.
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Affiliation(s)
- Claudia Mello-Thoms
- Department of Biomedical Informatics, University of Pittsburgh, UPMC Shadyside Hospital, Cancer Pavilion, 5150 Centre Avenue, Pittsburgh, PA 15232, USA.
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Basavanhally A, Feldman M, Shih N, Mies C, Tomaszewski J, Ganesan S, Madabhushi A. Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to Oncotype DX. J Pathol Inform 2012; 2:S1. [PMID: 22811953 PMCID: PMC3312707 DOI: 10.4103/2153-3539.92027] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2011] [Accepted: 11/08/2011] [Indexed: 12/27/2022] Open
Abstract
In this paper, we attempt to quantify the prognostic information embedded in multi-parametric histologic biopsy images to predict disease aggressiveness in estrogen receptor-positive (ER+) breast cancers (BCa). The novel methodological contribution is in the use of a multi-field-of-view (multi-FOV) framework for integrating image-based information from differently stained histopathology slides. The multi-FOV approach involves a fixed image resolution while simultaneously integrating image descriptors from many FOVs corresponding to different sizes. For each study, the corresponding risk score (high scores reflecting aggressive disease and vice versa), predicted by a molecular assay (Oncotype DX), is available and serves as the surrogate ground truth for long-term patient outcome. Using the risk scores, a trained classifier is used to identify disease aggressiveness for each FOV size. The predictions for each FOV are then combined to yield the final prediction of disease aggressiveness (good, intermediate, or poor outcome). Independent multi-FOV classifiers are constructed for (1) 50 image features describing the spatial arrangement of cancer nuclei (via Voronoi diagram, Delaunay triangulation, and minimum spanning tree graphs) in H and E stained histopathology and (2) one image feature describing the vascular density in CD34 IHC stained histopathology. In a cohort of 29 patients, the multi-FOV classifiers obtained by combining information from the H and E and CD34 IHC stained channels were able to distinguish low- and high-risk patients with an accuracy of 0.91 ± 0.02 and a positive predictive value of 0.94 ± 0.10, suggesting that a purely image-based assay could potentially replace more expensive molecular assays for making disease prognostic predictions.
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Affiliation(s)
- Ajay Basavanhally
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA
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Peng Y, Jiang Y, Eisengart L, Healy MA, Straus FH, Yang XJ. Computer-aided identification of prostatic adenocarcinoma: Segmentation of glandular structures. J Pathol Inform 2011; 2:33. [PMID: 21845231 PMCID: PMC3153693 DOI: 10.4103/2153-3539.83193] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2010] [Accepted: 04/24/2011] [Indexed: 11/24/2022] Open
Abstract
Background: Identification of individual prostatic glandular structures is an important prerequisite to quantitative histological analysis of prostate cancer with the aid of a computer. We have developed a computer method to segment individual glandular units and to extract quantitative image features, for computer identification of prostatic adenocarcinoma. Methods: Two sets of digital histology images were used: database I (n = 57) for developing and testing the computer technique, and database II (n = 116) for independent validation. The segmentation technique was based on a k-means clustering and a region-growing method. Computer segmentation results were evaluated subjectively and also compared quantitatively against manual gland outlines, using the Jaccard similarity measure. Quantitative features that were extracted from the computer segmentation results include average gland size, spatial gland density, and average gland circularity. Linear discriminant analysis (LDA) was used to combine quantitative image features. Classification performance was evaluated with receiver operating characteristic (ROC) analysis and the area under the ROC curve (AUC). Results: Jaccard similarity coefficients between computer segmentation and manual outlines of individual glands were between 0.63 and 0.72 for non-cancer and between 0.48 and 0.54 for malignant glands, respectively, similar to an interobserver agreement of 0.79 for non-cancer and 0.75 for malignant glands, respectively. The AUC value for the features of average gland size and gland density combined via LDA was 0.91 for database I and 0.96 for database II. Conclusions: Using a computer, we are able to delineate individual prostatic glands automatically and identify prostatic adenocarcinoma accurately, based on the quantitative image features extracted from computer-segmented glandular structures.
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Affiliation(s)
- Yahui Peng
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA
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Canada BA, Thomas GK, Cheng KC, Wang JZ. SHIRAZ: an automated histology image annotation system for zebrafish phenomics. MULTIMEDIA TOOLS AND APPLICATIONS 2011; 51:401-440. [PMID: 21461317 PMCID: PMC3066164 DOI: 10.1007/s11042-010-0638-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Histological characterization is used in clinical and research contexts as a highly sensitive method for detecting the morphological features of disease and abnormal gene function. Histology has recently been accepted as a phenotyping method for the forthcoming Zebrafish Phenome Project, a large-scale community effort to characterize the morphological, physiological, and behavioral phenotypes resulting from the mutations in all known genes in the zebrafish genome. In support of this project, we present a novel content-based image retrieval system for the automated annotation of images containing histological abnormalities in the developing eye of the larval zebrafish.
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Affiliation(s)
- Brian A. Canada
- Department of Science and Mathematics, University of South Carolina, Beaufort, SC USA
| | | | | | - James Z. Wang
- College of Information Sciences & Technology, The Pennsylvania State University, University Park, PA USA
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Gurcan MN, Boucheron L, Can A, Madabhushi A, Rajpoot N, Yener B. Histopathological image analysis: a review. IEEE Rev Biomed Eng 2009; 2:147-71. [PMID: 20671804 PMCID: PMC2910932 DOI: 10.1109/rbme.2009.2034865] [Citation(s) in RCA: 833] [Impact Index Per Article: 55.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.
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Affiliation(s)
- Metin N. Gurcan
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210 USA (phone: 614-292-1084; fax: 614-688-6600; )
| | - Laura Boucheron
- New Mexico State University, Klipsch School of Electrical and Computer Engineering, Las Cruces, NM 88003, USA ()
| | - Ali Can
- Global Research Center, General Electric Corporation, Niskayuna, NY 12309, USA ()
| | - Anant Madabhushi
- Biomedical Engineering Department, Rutgers University, Piscataway, NJ 08854, USA ()
| | - Nasir Rajpoot
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, England ()
| | - Bulent Yener
- Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY 12180, USA ()
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Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B. Histopathological image analysis: a review. IEEE Rev Biomed Eng 2009. [PMID: 20671804 DOI: 10.1109/rbme.2009.2034865.histopathological] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.
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Affiliation(s)
- Metin N Gurcan
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
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Madabhushi A. Digital pathology image analysis: opportunities and challenges. ACTA ACUST UNITED AC 2009; 1:7-10. [PMID: 30147749 DOI: 10.2217/iim.09.9] [Citation(s) in RCA: 131] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Abstract
Digital pathology represents an electronic environment for performing pathologic analysis and managing the information associated with this activity. The technology to create and support digital pathology has largely developed over the last decade. The use of digital pathology tools is essential to adapt and lead in the rapidly changing environment of 21st century neuropathology. The utility of digital pathology has already been demonstrated by pathologists in several areas including consensus reviews, quality assurance (Q/A), tissue microarrays (TMAs), education and proficiency testing. These utilities notwithstanding, interface issues, storage and image formatting all present challenges to the integration of digital pathology into the neuropathology work environment. With continued technologic improvements, as well as the introduction of fluorescent side scanning and multispectral detection, future developments in digital pathology offer the promise of adding powerful analytic tools to the pathology work environment. The integration of digital pathology with biorepositories offers particular promise for neuropathologists engaged in tissue banking. The utilization of these tools will be essential for neuropathologists to continue as leaders in diagnostics, translational research and basic science in the 21st century.
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Affiliation(s)
- Miguel Guzman
- Department of Pathology and Laboratory Medicine, Division of Neuropathology, University of Pennsylvania Medical Center, 3615 Civic Center Boulevard, Philadelphia, PA 19104-4318, USA
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