Contact us
|
Home
|
Login
| Users Online: 355
Feedback
Subscribe
Advertise
Search
Advanced Search
Month wise articles
Figures next to the month indicate the number of articles in that month
2021
April
[
4
]
March
[
7
]
February
[
3
]
January
[
6
]
2020
December
[
2
]
November
[
5
]
October
[
3
]
September
[
2
]
August
[
8
]
July
[
4
]
June
[
2
]
May
[
1
]
April
[
3
]
March
[
3
]
February
[
6
]
January
[
1
]
2019
December
[
6
]
November
[
4
]
September
[
4
]
August
[
3
]
July
[
6
]
June
[
1
]
May
[
2
]
April
[
6
]
March
[
3
]
February
[
4
]
January
[
2
]
2018
December
[
10
]
November
[
4
]
October
[
3
]
September
[
4
]
August
[
1
]
July
[
3
]
June
[
5
]
May
[
4
]
April
[
10
]
March
[
2
]
February
[
4
]
2017
December
[
5
]
November
[
4
]
October
[
3
]
September
[
9
]
July
[
5
]
June
[
2
]
May
[
4
]
April
[
6
]
March
[
6
]
February
[
7
]
2016
December
[
7
]
November
[
5
]
October
[
3
]
September
[
7
]
August
[
1
]
July
[
7
]
May
[
8
]
April
[
7
]
March
[
4
]
February
[
2
]
January
[
5
]
2015
November
[
4
]
October
[
5
]
September
[
5
]
August
[
4
]
July
[
3
]
June
[
19
]
May
[
5
]
April
[
1
]
March
[
5
]
February
[
9
]
January
[
3
]
2014
November
[
2
]
October
[
5
]
September
[
4
]
August
[
6
]
July
[
8
]
June
[
1
]
May
[
3
]
March
[
8
]
February
[
3
]
January
[
4
]
2013
December
[
5
]
November
[
2
]
October
[
4
]
September
[
4
]
August
[
3
]
July
[
3
]
June
[
5
]
May
[
7
]
March
[
18
]
February
[
1
]
January
[
1
]
2012
December
[
6
]
November
[
1
]
October
[
4
]
September
[
4
]
August
[
7
]
July
[
2
]
June
[
1
]
May
[
2
]
April
[
7
]
March
[
6
]
February
[
7
]
January
[
13
]
2011
December
[
3
]
November
[
1
]
October
[
7
]
August
[
9
]
July
[
3
]
June
[
7
]
May
[
3
]
March
[
6
]
February
[
8
]
January
[
6
]
2010
December
[
4
]
November
[
1
]
October
[
6
]
September
[
1
]
August
[
6
]
July
[
6
]
May
[
5
]
» Articles published in the past year
To view other articles click corresponding year from the navigation links on the left side.
All
|
Abstracts
|
Book Review
|
Commentaries
|
Commentary
|
Editorial
|
Letters to Editor
|
Original Article
|
Original Articles
|
Original Research
|
Original Research Article
|
Research Article
|
Research Articles
|
Review Articles
|
Symposium
|
Technical Note
|
Technical Note: Software
Export selected to
Endnote
Reference Manager
Procite
Medlars Format
RefWorks Format
BibTex Format
Show all abstracts
Show selected abstracts
Export selected to
Add to my list
Erratum:
Erratum: Antibody supervised deep learning for quantification of tumor infiltrating immune cells in hematoxylin and eosin stained breast cancer samples
J Pathol Inform
2016, 7:41 (28 September 2016)
DOI
:10.4103/2153-3539.191031
PMID
:27761297
[HTML Full text]
[PDF]
[Mobile Full text]
[EPub]
[PubMed]
[Sword Plugin for Repository]
Beta
Original Article:
Rates of provision of clinical information in the skin biopsy requisition form and corresponding encounter visit note
Meredith A Olson, Christine M Lohse, Nneka I Comfere
J Pathol Inform
2016, 7:40 (1 September 2016)
DOI
:10.4103/2153-3539.189705
PMID
:27688931
Background:
The skin biopsy requisition form (RF) serves as a key communication tool for transfer of relevant information related to skin biopsy between clinicians and pathologists. Clinical information in the skin biopsy RF is frequently missing or incomplete.
Objective:
To determine the rates of provision of critical clinical information necessary for histopathologic interpretation in the skin biopsy RF and encounter visit note (EVN).
Methods:
A retrospective review of 300 RFs and corresponding EVNs from May 1 to 7, 2012, in a tertiary care dermatology practice.
Results:
Age (100%), lesion location (100%), and clinical impression (93%) were the most commonly supplied elements in the RF and EVN. Clinical elements that were commonly not provided in the RF but present in the EVN included sampling method - partial versus complete (46%), duration of lesion (54%), morphology of lesion (97%), clinical symptoms (63%), clinical photos (63%), previous clinical (97%), and dermatopathologic diagnoses (82%).
Limitations:
Retrospective study design.
Conclusions:
These data suggest that while missing critical clinical information in the RF is often present in the EVN, some information is still not present in either source.
[ABSTRACT]
[HTML Full text]
[PDF]
[Mobile Full text]
[EPub]
[PubMed]
[Sword Plugin for Repository]
Beta
Letter to Editor:
A novel leadership fellowship in digital pathology
Bethany Jill Williams, Darren Treanor
J Pathol Inform
2016, 7:39 (1 September 2016)
DOI
:10.4103/2153-3539.189704
PMID
:27688930
[HTML Full text]
[PDF]
[Mobile Full text]
[EPub]
[PubMed]
[Sword Plugin for Repository]
Beta
Research Article:
Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples
Riku Turkki, Nina Linder, Panu E Kovanen, Teijo Pellinen, Johan Lundin
J Pathol Inform
2016, 7:38 (1 September 2016)
DOI
:10.4103/2153-3539.189703
PMID
:27688929
Background:
Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples.
Methods:
Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists.
Results:
In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92-0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87-0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (k = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (k = 0.78).
Conclusions:
Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists' visual assessment.
[ABSTRACT]
[HTML Full text]
[PDF]
[Mobile Full text]
[EPub]
[Citations (9) ]
[PubMed]
[Sword Plugin for Repository]
Beta
Technical Note:
Experience of maintaining laboratory educational website's sustainability
Izak B Dimenstein
J Pathol Inform
2016, 7:37 (1 September 2016)
DOI
:10.4103/2153-3539.189702
PMID
:27688928
Laboratory methodology websites are specialized niche websites. The visibility of a niche website transforms it into an authority site on a particular "niche of knowledge." This article presents some ways in which a laboratory methodology website can maintain its sustainability. The optimal composition of the website includes a basic content, a blog, and an ancillary part. This article discusses experimenting with the search engine optimization query results page. Strategic placement of keywords and even phrases, as well as fragmentation of the post's material, can improve the website's visibility to search engines. Hyperlinks open a chain reaction of additional links and draw attention to the previous posts. Publications in printed periodicals are a substantial part of a niche website presence on the Internet. Although this article explores a laboratory website on the basis of our hands-on expertise maintaining "Grossing Technology in Surgical Pathology" (www.grossing-technology.com) website with a high volume of traffic for more than a decade, the recommendations presented here for developing an authority website can be applied to other professional specialized websites. The authority websites visibility and sustainability are preconditions for aggregating them in a specialized educational laboratory portal.
[ABSTRACT]
[HTML Full text]
[PDF]
[Mobile Full text]
[EPub]
[PubMed]
[Sword Plugin for Repository]
Beta
Technical Note:
A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix
Akira Saito, Yasushi Numata, Takuya Hamada, Tomoyoshi Horisawa, Eric Cosatto, Hans-Peter Graf, Masahiko Kuroda, Yoichiro Yamamoto
J Pathol Inform
2016, 7:36 (1 September 2016)
DOI
:10.4103/2153-3539.189699
PMID
:27688927
Background:
Recent developments in molecular pathology and genetic/epigenetic analysis of cancer tissue have resulted in a marked increase in objective and measurable data. In comparison, the traditional morphological analysis approach to pathology diagnosis, which can connect these molecular data and clinical diagnosis, is still mostly subjective. Even though the advent and popularization of digital pathology has provided a boost to computer-aided diagnosis, some important pathological concepts still remain largely non-quantitative and their associated data measurements depend on the pathologist's sense and experience. Such features include pleomorphism and heterogeneity.
Methods and Results:
In this paper, we propose a method for the objective measurement of pleomorphism and heterogeneity, using the cell-level co-occurrence matrix. Our method is based on the widely used Gray-level co-occurrence matrix (GLCM), where relations between neighboring pixel intensity levels are captured into a co-occurrence matrix, followed by the application of analysis functions such as Haralick features. In the pathological tissue image, through image processing techniques, each nucleus can be measured and each nucleus has its own measureable features like nucleus size, roundness, contour length, intra-nucleus texture data (GLCM is one of the methods). In GLCM each nucleus in the tissue image corresponds to one pixel. In this approach the most important point is how to define the neighborhood of each nucleus. We define three types of neighborhoods of a nucleus, then create the co-occurrence matrix and apply Haralick feature functions. In each image pleomorphism and heterogeneity are then determined quantitatively. For our method, one pixel corresponds to one nucleus feature, and we therefore named our method Cell Feature Level Co-occurrence Matrix (CFLCM). We tested this method for several nucleus features.
Conclusion:
CFLCM is showed as a useful quantitative method for pleomorphism and heterogeneity on histopathological image analysis.
[ABSTRACT]
[HTML Full text]
[PDF]
[Mobile Full text]
[EPub]
[Citations (1) ]
[PubMed]
[Sword Plugin for Repository]
Beta
Editorial:
The coming paradigm shift: A transition from manual to automated microscopy
Navid Farahani, Corey E Monteith
J Pathol Inform
2016, 7:35 (1 September 2016)
DOI
:10.4103/2153-3539.189698
PMID
:27688926
The field of pathology has used light microscopy (LM) extensively since the mid-19
th
century for examination of histological tissue preparations. This technology has remained the foremost tool in use by pathologists even as other fields have undergone a great change in recent years through new technologies. However, as new microscopy techniques are perfected and made available, this reliance on the standard LM will likely begin to change. Advanced imaging involving both diffraction-limited and subdiffraction techniques are bringing nondestructive, high-resolution, molecular-level imaging to pathology. Some of these technologies can produce three-dimensional (3D) datasets from sampled tissues. In addition, block-face/tissue-sectioning techniques are already providing automated, large-scale 3D datasets of whole specimens. These datasets allow pathologists to see an entire sample with all of its spatial information intact, and furthermore allow image analysis such as detection, segmentation, and classification, which are impossible in standard LM. It is likely that these technologies herald a major paradigm shift in the field of pathology.
[ABSTRACT]
[HTML Full text]
[PDF]
[Mobile Full text]
[EPub]
[PubMed]
[Sword Plugin for Repository]
Beta
Sitemap
|
What's New
|
Feedback
|
Disclaimer
|
© Journal of Pathology Informatics | Published by Wolters Kluwer -
Medknow
Online since 10
th
March, 2010