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Month wise articles
Figures next to the month indicate the number of articles in that month
2021
January
[
5
]
2020
December
[
2
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November
[
5
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October
[
3
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September
[
2
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August
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8
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July
[
4
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June
[
2
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May
[
1
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April
[
3
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March
[
3
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February
[
6
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January
[
1
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2019
December
[
6
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November
[
4
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September
[
4
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August
[
3
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July
[
6
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June
[
1
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May
[
2
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April
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6
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March
[
3
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February
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4
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January
[
2
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2018
December
[
10
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November
[
4
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October
[
3
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September
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4
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August
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1
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July
[
3
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June
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5
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May
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4
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April
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10
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March
[
2
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February
[
4
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2017
December
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5
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November
[
4
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October
[
3
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September
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9
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July
[
5
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June
[
2
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May
[
4
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April
[
6
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March
[
6
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February
[
7
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2016
December
[
7
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November
[
5
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October
[
3
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September
[
7
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August
[
1
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July
[
7
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May
[
8
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April
[
7
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March
[
4
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February
[
2
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January
[
5
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2015
November
[
4
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October
[
5
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September
[
5
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August
[
4
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July
[
3
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June
[
19
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May
[
5
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April
[
1
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March
[
5
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February
[
9
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January
[
3
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2014
November
[
2
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October
[
5
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September
[
4
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August
[
6
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July
[
8
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June
[
1
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May
[
3
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March
[
8
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February
[
3
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January
[
4
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2013
December
[
5
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November
[
2
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October
[
4
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September
[
4
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August
[
3
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July
[
3
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June
[
5
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May
[
7
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March
[
18
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February
[
1
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January
[
1
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2012
December
[
6
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November
[
1
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October
[
4
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September
[
4
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August
[
7
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July
[
2
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June
[
1
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May
[
2
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April
[
7
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March
[
6
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February
[
7
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January
[
13
]
2011
December
[
3
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November
[
1
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October
[
7
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August
[
9
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July
[
3
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June
[
7
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May
[
3
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March
[
6
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February
[
8
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January
[
6
]
2010
December
[
4
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November
[
1
]
October
[
6
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September
[
1
]
August
[
6
]
July
[
6
]
May
[
5
]
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Original Article:
Using image analysis as a tool for assessment of prognostic and predictive biomarkers for breast cancer: How reliable is it?
Mark C Lloyd, Pushpa Allam-Nandyala, Chetna N Purohit, Nancy Burke, Domenico Coppola, Marilyn M Bui
J Pathol Inform
2010, 1:29 (23 December 2010)
DOI
:10.4103/2153-3539.74186
PMID
:21221174
Background
: Estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER2) are important and well-established prognostic and predictive biomarkers for breast cancers and routinely tested on patient's tumor samples by immunohistochemical (IHC) study. The accuracy of these test results has substantial impact on patient management. A critical factor that contributes to the result is the interpretation (scoring) of IHC. This study investigates how computerized image analysis can play a role in a reliable scoring, and identifies potential pitfalls with common methods.
Materials and Methods
: Whole slide images of 33 invasive ductal carcinoma (IDC) (10 ER and 23 HER2) were scored by pathologist under the light microscope and confirmed by another pathologist. The HER2 results were additionally confirmed by fluorescence
in situ
hybridization (FISH). The scoring criteria were adherent to the guidelines recommended by the American Society of Clinical Oncology/College of American Pathologists. Whole slide stains were then scored by commercially available image analysis algorithms from Definiens (Munich, Germany) and Aperio Technologies (Vista, CA, USA). Each algorithm was modified specifically for each marker and tissue. The results were compared with the semi-quantitative manual scoring, which was considered the gold standard in this study.
Results
: For HER2 positive group, each algorithm scored 23/23 cases within the range established by the pathologist. For ER, both algorithms scored 10/10 cases within range. The performance of each algorithm varies somewhat from the percentage of staining as compared to the pathologist's reading.
Conclusions
: Commercially available computerized image analysis can be useful in the evaluation of ER and HER2 IHC results. In order to achieve accurate results either manual pathologist region selection is necessary, or an automated region selection tool must be employed. Specificity can also be gained when strict quality assurance by a pathologist is invested. Quality assurance of image analysis by pathologists is always warranted. Automated image analysis should only be used as adjunct to pathologist's evaluation.
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Editorial:
The coming wave of change: ICD-10
Ji Yeon Kim, Bruce A Beckwith
J Pathol Inform
2010, 1:28 (23 December 2010)
DOI
:10.4103/2153-3539.74183
PMID
:21221173
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Book Review:
Lewis Paul D: R for Medicine and Biology
G William Moore
J Pathol Inform
2010, 1:27 (3 December 2010)
DOI
:10.4103/2153-3539.73505
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Original Article:
Informatics methods for laboratory evaluation of HPV ordering patterns with an example from a nationwide sample in the United States, 2003-2009
Brian H Shirts, Brian R Jackson
J Pathol Inform
2010, 1:26 (3 December 2010)
DOI
:10.4103/2153-3539.73504
PMID
:21189840
Background:
Laboratory data is a rich source of information that can be used to estimate adherence to physician guidelines and motivate improvement in clinical practice. Human papillomavirus (HPV) testing is an important component of cervical cancer screening programs with established screening guidelines. The purpose of this study was to develop methods to estimate concordance with published guidelines for HPV testing in order to provide clinicians and payors specific feedback about overscreening.
Methods:
This retrospective analysis of laboratory test ordering patterns evaluated 454,532 HPV tests ordered from September 2003 to October 2009 from 110 facilities and performed at ARUP laboratories. We used laboratory data including patient demographics, ordering frequency, timestamps and results to examine the proportion of HPV tests ordered on women under 21 years, ordered on women between 21 and 29 years apparently before cytological examination, repeated less than 1 year after a positive HPV result in women over 30 years, and repeated less than 3 years after a negative HPV result in women over 30 years.
Results:
The absolute number and proportion of HPV tests performed on women under 21 years declined from 20% in 2005 to 5% in October 2009. The proportion of HPV tests performed women between 21 and 29 years also declined during this period. Approximately one-third of HPV tests performed on women between 21 and 29 years arrived for HPV testing before cervical screening had presumably been completed. The most common follow-up intervals for HPV testing on women over 30 years were 6 months following a positive HPV result and 12 months following a negative HPV result. Only 6% of repeat HPV testing in women over 30 years followed a negative HPV result by 3 years or more. Approximately one-fourth of HPV tests ordered the year ending October 2009 were unnecessary based on the American Society for Colposcopy and Cervical Pathology guideline.
Conclusions:
We demonstrate simple methods to evaluate appropriate utilization of HPV testing using laboratory data. Our data illustrates that some aspects of HPV test ordering have become more consistent with guidelines over time. However, a large portion of HPV testing in the United States is unnecessary. This highlights opportunities for optimization of a rational cancer prevention strategy to reduce unnecessary screening, colposcopy and biopsies.
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© Journal of Pathology Informatics | Published by Wolters Kluwer -
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Online since 10
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March, 2010