Whole slide imaging (WSI) offers a convenient, tractable platform for measuring features of routine and special-stain histology or in immunohistochemistry staining by using digital image analysis (IA). We now routinely use IA for quantitative and qualitative analysis of theranostic markers such as human epidermal growth factor 2 (HER2/neu), estrogen and progesterone receptors, and Ki-67. Quantitative IA requires extensive validation, however, and may not always be the best approach, with pancreatic neuroendocrine tumors being one example in which a semiautomated approach may be preferable for patient care. We find that IA has great utility for objective assessment of gastrointestinal tract dysplasia, microvessel density in hepatocellular carcinoma, hepatic fibrosis and steatosis, renal fibrosis, and general quality analysis/quality control, although the applications of these to daily practice are still in development. Collaborations with bioinformatics specialists have explored novel applications to gliomas, including in silico approaches for mining histologic data and correlating with molecular and radiologic findings. We and many others are using WSI for rapid, remote-access slide reviews (telepathology), though technical factors currently limit its utility for routine, high-volume diagnostics. In our experience, the greatest current practical impact of WSI lies in facilitating long-term storage and retrieval of images while obviating the need to keep slides on site. Once the existing barriers of capital cost, validation, operator training, software design, and storage/back-up concerns are overcome, these technologies appear destined to be a cornerstone of precision medicine and personalized patient care, and to become a routine part of pathology practice.

Pathologists make crucial diagnoses and issue reports that directly affect patient care, primarily using microscopes and hematoxylin-eosin (H&E)–stained tissue with techniques that have not changed since the 19th century1,2  and that are likely to remain for decades to come with supplements from the advancing technology. Developing techniques such as immunohistochemistry (IHC) and molecular diagnostics/theranostics have brought up the necessity and ability to measure discrete molecules or “biomarkers” within tissue; however, there are a number of analytical variables that can affect the results of these tests, including the accurate measurement of signals.3,4  Given this inherent variability, there have been efforts to standardize these tests, and the US Food and Drug Administration, American Society of Clinical Oncology, and College of American Pathologists (CAP) have each provided special direction on some testing (eg, human epidermal growth factor 2 [HER2/neu]).5,6  Whole slide imaging (WSI) with algorithmic analysis is one potential technology to improve the assessment of such markers (as shown in the Table). This has ranged from studies on basic H&E slides to IHC to multiparametric quantum dot staining.

Selected Image Analysis Approaches in Our Department Use a Variety of Markers

Selected Image Analysis Approaches in Our Department Use a Variety of Markers
Selected Image Analysis Approaches in Our Department Use a Variety of Markers

Abbreviations: ER, estrogen receptor; IA, image analysis; HCC, hepatocellular carcinoma; HER2/neu, human epidermal growth factor 2; HLA-DR, human leukocyte antigen–antigen D related; IHC, immunohistochemistry; MVD, microvessel density; PR, progesterone receptor; WSI, whole slide imaging.

a

Genomic Health, Inc, Redwood City, California.

Currently, the most widely used application of WSI is arguably “telepathology,” that is, sharing of images with remote computers. This has enabled immediate access of pathologic evaluation from a remote observer. In addition to bringing expertise regarding tissue that may be located thousands of miles away for diagnostic purposes, it is gradually revolutionizing pathology education and pathology research as well. In this article, we will discuss the use of WSI in image analysis (IA), telepathology, and some of the challenges that can be encountered in the utilization of this technology.

The simple viewing of WSIs provides an application that can be used in a wide variety of settings. Whole slide images are used regularly for educational purposes. For example, slides are scanned for our departmental “Unknown Conferences” that are geared toward residents and other trainees. In this manner, trainees can view slides from any location, and multiple trainees can view the same slide simultaneously. Slides are shared among numerous individuals without the risk of slide breakage or loss. After these conferences are completed, these slides and other slides can be assembled into a bank of slides that can serve as study sets for the trainees. In the same manner, these slides can be used to present tumor boards and other clinical conferences. We have found this useful, since many clinical conference rooms do not have working microscopes; however, almost all conference rooms have a computer with projection capability and an Internet connection, allowing access to slides stored on network-connected servers.710 

Whole slide images can also be used to retain consult/second-opinion cases from other institutions. We have found this to be particularly useful when we are required to send the slides back owing to CAP or legal obligations. If scans are performed, the images can still be available for comparison with subsequent resections. Such images can be shared for a second opinion with other sites (especially multihospital centers such as ours), and in a similar manner, such images can be shared with our pathologists for another opinion by using techniques adopted at other centers.11 

Digital image capture has been used in our department for immediate cytology evaluations, since it is not cost-effective to send cytopathologists to all immediate evaluations, particularly for procedures lasting an hour or more. In addition, there are often simultaneous procedures ongoing at different departments and sites. For this reason, our cytologists have instead devised methods for trainees or cytotechnologists to send images from the rapid on-site evaluation site to the pathologist, similar to methods presented by others.1214 

We have used WSIs for many applications in research, as will be discussed further below. For example, we have conducted international multiobserver agreement studies that have been facilitated through the use of WSIs.1517  In a study of gallbladder epithelial atypia and early gallbladder cancer staging, WSI was used to circulate cases among 18 experts. If WSIs had not been used, boxes of slides would have had to be transferred from one expert to another; this would have been costly and would have taken approximately 2 years. Using WSIs, this was completed in 1 month.16,17 

Breast and Gynecologic Pathology

Image cytometry has been used in our department for some time.18  Perhaps the most common application has been in the breast. Early investigation in image analysis involved ploidy analysis, which at the time was believed to have a major role in patient stratification to treatment protocols.1922  In cases with no fresh tissue available, ploidy (nuclear DNA content) could be estimated by image analysis of Feulgen-stained slides.18  Currently, the most common use is in measurement of theranostic markers by IHC.20,2328  This includes studies on HER2/neu,2931  estrogen and progesterone receptors (ER and PR),2328  Ki-67 (MIB-1),32,33  proliferating cell nuclear antigen,20  epidermal growth factor receptor,20  and cathepsin.20 Most noteworthy, we currently analyze the membranous staining for HER2/neu and the nuclear staining for ER, PR, and Ki-67 by using digital algorithms (Figure 1, A through H). We have established that ER and PR by IA and also by visual inspection correlate with biochemical dextran-coated charcoal assays for ER and PR.2427  In many cases, we have found advantages of automated IA over visual inspection. For example, in a study on PR hormone status, image cytometric quantitation of PR immunohistochemical staining correlated with disease-free survival; however, visual quantitation of PR immunostaining did not relate to either overall or disease-free survival.23  We have found a similar result during an analysis of male breast carcinoma.20 

Figure 1.

Examples of tissue and stains for image analysis and their corresponding algorithm markup images are shown, including the following: (A) estrogen receptor and (B) a nuclear detection algorithm markup image; (C) human epidermal growth factor 2 (HER2/neu) and (D) a membrane analysis algorithm markup image; (E) hematoxylin-eosin–stained specimen of a liver with steatosis (fat) and (F) the markup image from a positive pixel count algorithm tuned to detect the degree of steatosis; and (G) a kidney biopsy specimen stained with a trichrome stain and (H) the markup image from a positive pixel count algorithm tuned to detect the fibrotic areas. In all of the markup images except for (F), red denotes areas conforming most exactly to algorithm parameters, followed by orange and then yellow; and blue corresponds to areas considered negative by the algorithm. In (F), the negative (blue) areas are a quantitation of the steatotic areas (original magnification approximately ×50 [A and B]; original magnification approximately ×200 [C, D, E, F, G, and H]).

Figure 1.

Examples of tissue and stains for image analysis and their corresponding algorithm markup images are shown, including the following: (A) estrogen receptor and (B) a nuclear detection algorithm markup image; (C) human epidermal growth factor 2 (HER2/neu) and (D) a membrane analysis algorithm markup image; (E) hematoxylin-eosin–stained specimen of a liver with steatosis (fat) and (F) the markup image from a positive pixel count algorithm tuned to detect the degree of steatosis; and (G) a kidney biopsy specimen stained with a trichrome stain and (H) the markup image from a positive pixel count algorithm tuned to detect the fibrotic areas. In all of the markup images except for (F), red denotes areas conforming most exactly to algorithm parameters, followed by orange and then yellow; and blue corresponds to areas considered negative by the algorithm. In (F), the negative (blue) areas are a quantitation of the steatotic areas (original magnification approximately ×50 [A and B]; original magnification approximately ×200 [C, D, E, F, G, and H]).

Close modal

Stains routinely used for breast carcinomas at our center include HER2/neu, ER, PR, and Ki-67, all of which are analyzed by digital methods (Figure 2). After using the Dako Automated Cellular Imaging System (ACIS; Dako, Carpinteria, California), we recently moved to a different platform, the Aperio whole slide scanner (currently marketed by Leica Biosystems Inc, Buffalo Grove, Illinois). There were challenges in reaching the optimum Aperio algorithm parameters to provide results that were equivalent to those of the ACIS.34  This transition was similar to other transitions that we have experienced in the past, since our department has also used other quantitation systems including the CAS 200 image analyzer (Becton Dickinson, San Jose, California).35  Our breast prognostic marker workflow represents a “computer-assisted” quantitation and is one input to the analytic process. For each breast cancer case, we digitally quantitate 6 scanned fields at ×20 magnification. The cases are also all visually reviewed by the pathologist to verify the accuracy of the image cytometric quantitation. In cases with unexpected results, the scanned images are revisited. The scoring in the final pathology report is ultimately at the discretion of the sign-out pathologist, although in most cases it is the one obtained by WSI and digital quantitation.

Figure 2.

Pathology image analysis workflow includes the following: (1) A technologist scans a stained slide, such as an IHC slide shown in this image. (2) Analysis area(s) is (are) selected to generally include multiple regions (and not 1 region as depicted) for analysis. (3) The image analysis algorithm is executed in the computer software. (4) The technologist or pathologist reviews the analysis “markup” of the detected parameter and the quantitative results. (5) The pathologist reports on the findings, which are conveyed in the electronic medical record for patient care. (A “sample” report is shown in which the name medical record number [MRN], date of birth [DOB], report accession number [SYY-XXXXX], procedure date [MM/DD/YY], and other identifying information are given at the top of the report.) In our current practice, the pathologists do not routinely review the scanned image electronically, since they are provided with the algorithm outputs, a printed representative field, and the corresponding glass slide, all of which are manually reviewed. Abbreviation: IHC, immunohistochemistry.

Figure 2.

Pathology image analysis workflow includes the following: (1) A technologist scans a stained slide, such as an IHC slide shown in this image. (2) Analysis area(s) is (are) selected to generally include multiple regions (and not 1 region as depicted) for analysis. (3) The image analysis algorithm is executed in the computer software. (4) The technologist or pathologist reviews the analysis “markup” of the detected parameter and the quantitative results. (5) The pathologist reports on the findings, which are conveyed in the electronic medical record for patient care. (A “sample” report is shown in which the name medical record number [MRN], date of birth [DOB], report accession number [SYY-XXXXX], procedure date [MM/DD/YY], and other identifying information are given at the top of the report.) In our current practice, the pathologists do not routinely review the scanned image electronically, since they are provided with the algorithm outputs, a printed representative field, and the corresponding glass slide, all of which are manually reviewed. Abbreviation: IHC, immunohistochemistry.

Close modal

In establishing the protocols and applying them to daily practice, close correlation with other standard parameters is warranted. For example, in our concordance studies correlating the cytometric scores and fluorescence in situ hybridization (FISH) amplification results, we have found that when the IA 2+ equivocal range is changed from a range of 1.8 to 2.2 to a range of 1.8 to 2.6, then low positive (3+) specimens (>2.2–2.6) are included in an equivocal (2+) group; this change in the upper equivocal range cutoff improved concordance (from 84.4% to 94.4%) and eliminated the need to triage the IHC 3+ cases to further FISH testing.36 

Image analysis and WSI utilization are also very valuable techniques to analyze the potential role of other ancillary markers. For example, we have investigated various markers including apoptotic markers in breast cancer. One study involved the quantitation of Bcl-2 and Bcl-x, which inhibit cell death, and Bax, which promotes cell death. This study revealed that a Bcl-2:Bcl-x ratio of 1 or greater is associated with an improved disease-free survival. However, the Bcl-2:Bax ratio was not predictive of overall or disease-free survival.35  Similar studies have been conducted in ovarian carcinoma, in which increased expression of Bax and Bcl-x was associated with increased overall and disease-free survival; Bcl-2:Bax and Bcl-2:Bcl-x ratios less than 1 had a non–statistically significant survival advantage.37  Quantitation of tumor-infiltrating lymphocytes may be important in breast carcinoma prognostics and theranostics38 ; and as we have shown with on-slide “flow cytometry” methods for counting lymphocytes on WSIs,3941  IA may be useful in quantitating tumor-infiltrating lymphocytes in breast cancer. These and similar studies are crucial to identify next-generation theranostic markers to supplement ER/PR/HER2/neu in patient-specific precision therapy of breast cancer and other cancers, and IA/WSI can have very important roles in their establishment.

Gastrointestinal, Pancreatobiliary, and Liver Pathology

Ki-67 IHC is often used as a surrogate marker of proliferation in a variety of tumors, including neuroendocrine tumors42,43  and hepatocellular carcinoma,44  and has become one of the most important parameters in diagnosis, classification, and grading. We have used computerized approaches extensively for this purpose; however, we have discovered several shortcomings that need to be addressed before this method can be used reliably.42,43,4547  For example, unless software modifications are made specifically or operators trained in pathology are engaged in the process, the scanner cannot distinguish other brown elements in the tissue (such as hemosiderin) from true positive units. For example, the scanner calculated the index as 68% in a case that was in reality only 3%, and this was due to the extensive hemorrhage in the tumor. Similarly, cells that are fairly abundant in the tissue, such as endothelial cells and lymphocytes, often show significant Ki-67 labeling, which leads to overcounting of the index unless some correction is applied. For these reasons, until the proper modifications are in place, we have had to revert for now to manual counting of printed images by a pathologist or other trained personnel, instead of using the automated count. Nonetheless, there is no question that with proper improvements the WSI approach will soon become the norm for this purpose. Two recent studies with sections and cytologic cell blocks show excellent correlation between results by manual counting and Aperio quantitation.48,49 

Image analysis has been used by our group for many other applications in gastrointestinal, liver, and pancreatobiliary pathology. Image analysis has also been used for the analysis of HER2/neu staining in gastric carcinoma.50  We have used algorithms in the assessment of microvessel density in hepatocellular carcinomas with the Aperio microvessel density algorithm applied to CD31 and CD34 IHC, showing decreased survival in tumors with high microvessel density.51  We have implemented algorithms in the quantitation of steatosis (Figure 1) and have worked with our radiology colleagues to correlate these findings with radiology measurements.52  Using special stains such as trichrome, we have explored the use of IA in the quantitation of hepatic fibrosis, primarily using positive pixel count algorithms applied to trichrome slides.5355  Using routine H&E slides, we have used basic measurements and also the positive pixel count algorithm to measure features of dysplastic nuclei in colonic adenomas and Barrett esophagus–associated dysplasia,56,57  and recently, we have used similar methods to recognize differences in sessile serrated adenomas.58  Investigators in our department have also used IA to measure apoptosis in colonic mucosa by using the apoptosis inhibitor Bcl-2 and the apoptosis promoter Bax.59  We have also used IA in an on-slide “flow cytometry”–type approach to quantitate inflammatory cells in autoimmune pancreatitis,39  and it is possible that such methods may become important in quantitating inflammatory cells in a variety of applications (eg, tumor-infiltrating lymphocytes in gastrointestinal tract cancers). These diverse applications demonstrate the many possibilities of WSI IA.

Renal and Genitourinary Pathology

Renal allograft rejection is one area in which IA has several potential applications that can be incorporated into daily practice. To establish more objective methods for the quantitation of inflammation in rejection, we have been engaged in efforts to establish on-slide “flow cytometry”–type methods, using cell-counting algorithms in the measurement of inflammatory cell density (eg, CD3+ cell density).40,41  Finding useful molecular correlates of rejection that can be easily probed on the tissue level has also been a goal of our group. For example, we have looked at the quantitation of human leukocyte antigen–antigen D related (HLA-DR) staining in renal allografts and correlated this with the severity of rejection.60  In an attempt to further characterize antibody-mediated rejection in the kidney, we also applied the microvessel density algorithm to C4d-stained slides and showed correlations of the C4d staining with mean fluorescence intensity of donor-specific antibody to HLA.61  Providing surrogate markers of allograft deterioration has been a goal of our department in studies using IA to measure renal fibrosis (Figure 1, A through H) in sections stained with trichrome, periodic acid–Schiff, Sirius red, and collagen III for IHC.15,62  Some of these studies have indicated that IA may provide benefits, given the challenge of standardizing fibrosis assessment among a number of reviewers in multicenter settings.15 

Individuals in our department have collaborated with informatics specialists to devise ways for computers to determine the Fuhrman grade of renal cell carcinoma63  and to normalize images of renal tumors so that they can be interpreted by computer algorithms.64  In another study,65  the mean epidermal growth factor staining density measured by IA correlated with skin adjacent to hypospadias.

Neuropathology

Neuropathologic diagnosis of tumors has also proven very amenable for the utilization of WSI,66  and Emory University pathologists and informaticists have explored a number of aspects of this technology.66,67  Earlier studies from our department established the role of Ki-67 quantitation in glial neoplasms.68,69  Recently, using multimodal, multiscale approaches and machine-based classification, researchers have devised ways to mine scanned histologic data on glioblastoma in The Cancer Genome Atlas Project and other sources; the resulting quantitative morphometric analysis findings have been further integrated with molecular data to provide in silico cancer research7080  and with radiologic data to provide clinicopathoradiologic correlation.81  Insights from this work also include findings on the importance of tumor-infiltrating lymphocytes in glioblastoma,72  and in silico approaches from these studies have uncovered novel findings regarding the regulation of asymmetric cell division in glioblastoma by such mediators as the human Brat ortholog TRIM3.80 

Image analysis has also garnered use in other organ systems. For example, angiogenesis measured by IA of CD31 IHC shows strong correlation with regional recurrence of laryngeal cancer.82  In an attempt to improve the quality analysis of our special stains, we used WSI of control slides to objectively measure the hue of trichrome stain so that “Westgard”-type rules could potentially be applied to histology staining quality control measures.83  On a daily basis, positive controls for ER, PR, HER2/neu, and Ki-67 are scanned and quantitated by IA. Results are graphed, and both IHC and automated quantitation are assessed.

There are different approaches to telepathology. The most simplistic approach involves the acquisition of static images that are then sent to the consultant. This can involve acquisition devices as ubiquitous as smart phones, as demonstrated by individuals at our institution,84,85  and forms of WSIs can be obtained using these methods.85  Some provide pathologists with the ability to move a microscope remotely by using a robotically controlled microscope stage (eg, the “Trestle” system).86,87  Others involve the acquisition of WSIs that are transferred over the network and reviewed remotely (eg, the Aperio system). All methods have their own advantages, including different costs and ease of implementation. Static image methods in which the image “donor” obtains the images may not benefit from the insight that an expert “recipient” may have in choosing the most diagnostic areas; however, these methods may be quite inexpensive to implement. Methods such as remote robotic control or WSIs provide recipients with the ability to view the slide at their leisure and speed and with the opportunity to use their own insight in choosing the proper diagnostic areas; however, these methods may be quite expensive.87 

Whole slide scanners have been used for telepathology at our institution; however, challenges can sometimes be encountered in their implementation.88,89  As one example, an Aperio scanner was installed at Emory University Midtown Hospital, which is separated from the Emory University Hospital (EUH) on the Emory University campus (all in Atlanta, Georgia). Telepathology was needed between the 2 campuses, primarily because neuropathologists were only routinely stationed at the EUH location. The solution implemented was a hybrid whole slide scan/robotic approach (Figure 3), in which WSI is rapidly obtained at relatively low magnification (approximately ×5). For higher optical magnification, the remote pathologist may optionally enable a “Telepath Live” feature in the Aperio ImageScope program to take control of the scanner and use it as a robotic microscope with the caveat that the scanned slide still has to be on the scanner stage. Anecdotally, reception from the pathologists using this system has been mixed. Some pathologists adapted quite well to the new technology; however, others had qualms about the solution. Pathologists who had difficulty with this solution complained about the delay needed for scanning. For the solution to be feasible, scanning had to be performed at ×20 magnification, and some pathologists did not feel that this provided enough detail, particularly when they needed to examine fine cytologic features (eg, on smear preparations examined in conjunction with the frozen sections). Some surgeons also noted an increase in frozen section turnaround time. However, all things considered, this telepathology implementation provided a solution where subspecialty expertise was needed and would have otherwise been unavailable.

Figure 3.

Schematic representation of our telepathology workflow. In our configuration, both the whole slide scanner and supporting server infrastructure are located at the facility where the specimen is processed. Our facilities communicate over a private data network. (1) The frozen section requisition is faxed to the facility where the pathologist is located with an identification number (ID No.). (2) The pathologists' assistant prepares the frozen section and performs a rapid low-power scan. (3) The pathologist views the low-power scan remotely. (4) Optionally, the pathologist may robotically control the slide scanner. (5) The pathologist's frozen section evaluation is faxed to the facility where the specimen was processed. Throughout the process, communication is usually also conducted via telephone, which is also typically used to convey the frozen section diagnosis to the surgeon in addition to the facsimile.

Figure 3.

Schematic representation of our telepathology workflow. In our configuration, both the whole slide scanner and supporting server infrastructure are located at the facility where the specimen is processed. Our facilities communicate over a private data network. (1) The frozen section requisition is faxed to the facility where the pathologist is located with an identification number (ID No.). (2) The pathologists' assistant prepares the frozen section and performs a rapid low-power scan. (3) The pathologist views the low-power scan remotely. (4) Optionally, the pathologist may robotically control the slide scanner. (5) The pathologist's frozen section evaluation is faxed to the facility where the specimen was processed. Throughout the process, communication is usually also conducted via telephone, which is also typically used to convey the frozen section diagnosis to the surgeon in addition to the facsimile.

Close modal

Our experience as well as that of other institutions has shown that in the implementation of telepathology, proper assessment of network connections and file server requirements maintaining Health Insurance Portability and Accountability Act (HIPAA) regulations should be considered carefully. The CAP has established guidelines for the validation of WSIs, and these should also be thoughtfully considered when implementing a WSI solution.90  At our institution, addressing the issues related to WSI implementation required a great investment of time from our pathologist and informatics leaders. Implementation of such a solution needs consideration of whether the projected utilization justifies the investment of time from the pathology and informatics teams.

Methods used in telepathology can eventually be used to essentially replace routine light microscopy.87,91  In multihospital centers, such implementations could eliminate the need to send thousands of slides and paperwork from one site to another and could also eliminate problems such as slide loss or breakage. Such methods may also improve the ergonomic issues that some pathologists may confront (eg, neck problems).92  Scanning speed and quality are now quite high; however, infrastructure issues and issues with economic storage of large numbers of images need to be solved before there will be widespread implementation.91 

Overall, WSI, novel stains, IA techniques, and telepathology offer a great deal of promise but also a number of challenges. Individuals at our institution have implemented clinical decision support tools93  and have been involved in efforts to define “computational pathology.” 94  It is anticipated that such methods will complement standard techniques even more in the future and will hopefully help complement WSI and IA.95  Overall, WSI and IA provide a wide variety of quantitative approaches for the examination of pathologic specimens. Once the perilous tasks of investment in technology and validation of these techniques are overcome, these techniques can potentially provide great promise for precision medicine and optimal personalized patient care.

1
King
DF,
King
LA.
A brief historical note on staining by hematoxylin and eosin
.
Am J Dermatopathol
.
1986
;
8
(
2
):
168
.
2
van den Tweel
JG,
Taylor
CR.
A brief history of pathology: preface to a forthcoming series that highlights milestones in the evolution of pathology as a discipline
.
Virchows Arch
.
2010
;
457
(
1
):
3
10
.
3
Dunstan
RW,
Wharton
KA
Jr,
Quigley
C,
Lowe
A.
The use of immunohistochemistry for biomarker assessment—can it compete with other technologies?
Toxicol Pathol
.
2011
;
39
(
6
):
988
1002
.
4
O'Hurley
G,
Sjostedt
E,
Rahman
A,
et al.
Garbage in, garbage out: a critical evaluation of strategies used for validation of immunohistochemical biomarkers
.
Mol Oncol
.
2014
;
8
(
4
):
783
798
.
5
Hardy
LB,
Fitzgibbons
PL,
Goldsmith
JD,
et al.
Immunohistochemistry validation procedures and practices: a College of American Pathologists survey of 727 laboratories
.
Arch Pathol Lab Med
.
2013
;
137
(
1
):
19
25
.
6
Wolff
AC,
Hammond
ME,
Hicks
DG,
et al.
Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update
.
Arch Pathol Lab Med
.
2014
;
138
(
2
):
241
256
.
7
Farahani
N,
Pantanowitz
L.
Overview of telepathology
.
Clin Lab Med
.
2016
;
36
(
1
):
101
112
.
8
Boyce
BF.
Whole slide imaging: uses and limitations for surgical pathology and teaching
.
Biotech Histochem
.
2015
;
90
(
5
):
321
330
.
9
Chen
ZW,
Kohan
J,
Perkins
SL,
Hussong
JW,
Salama
ME.
Web-based oil immersion whole slide imaging increases efficiency and clinical team satisfaction in hematopathology tumor board
.
J Pathol Inform
.
2014
;
5
:
41
.
10
Hamilton
PW,
Wang
Y,
McCullough
SJ.
Virtual microscopy and digital pathology in training and education
.
APMIS
.
2012
;
120
(
4
):
305
315
.
11
Pantanowitz
L,
McHugh
J,
Cable
W,
Zhao
C,
Parwani
AV.
Imaging file management to support international telepathology
.
J Pathol Inform
.
2015
;
6
:
17
.
12
Khurana
KK,
Kovalovsky
A,
Wang
D,
Lenox
R.
Feasibility of dynamic telecytopathology for rapid on-site evaluation of endobronchial ultrasound-guided transbronchial fine needle aspiration
.
Telemed J E Health
.
2013
;
19
(
4
):
265
271
.
13
Bott
MJ,
James
B,
Collins
BT,
et al.
A prospective clinical trial of telecytopathology for rapid interpretation of specimens obtained during endobronchial ultrasound-fine needle aspiration
.
Ann Thorac Surg
.
2015
;
100
(
1
):
201
205
.
14
Collins
BT.
Telepathology in cytopathology: challenges and opportunities
.
Acta Cytol
.
2013
;
57
(
3
):
221
232
.
15
Farris
AB,
Chan
S,
Climenhaga
J,
et al.
Banff fibrosis study: multicenter visual assessment and computerized analysis of interstitial fibrosis in kidney biopsies
.
Am J Transplant
.
2014
;
14
(
4
):
897
907
.
16
Adsay
V,
Roa
JC,
Basturk
O,
et al.
Epithelial atypia in the gallbladder: diagnosis and classification in an international consensus study. Seattle, WA: United States and Canadian Academy of Pathology 2016 Annual Meeting; March 12–18, 2016
.
Mod Pathol
.
2016
;
29(suppl 2):438A:1738.
17
Roa
JC,
Basturk
O,
Torres
J,
et al.
Marked geographic differences in the pathologic diagnosis of non-invasive (tis) vs minimally invasive (t1) gallbladder cancer: Santiago Consensus Conference Highlights the Need for the Unifying Category “Early Gallbladder Cancer” (EGBC). Seattle, WA: United States and Canadian Academy of Pathology 2016 Annual Meeting; March 12–18, 2016
.
Mod Pathol
.
2016
;
29(suppl 2):447A:1772.
18
Cohen
C.
Image cytometric analysis in pathology
.
Hum Pathol
.
1996
;
27
(
5
):
482
493
.
19
Muller
S,
DeRose
PB,
Cohen
C.
DNA ploidy of ameloblastoma and ameloblastic carcinoma of the jaws: analysis by image and flow cytometry
.
Arch Pathol Lab Med
.
1993
;
117
(
11
):
1126
1131
.
20
Moore
J,
Friedman
MI,
Gansler
T,
et al.
Prognostic indicators in male breast carcinoma
.
Breast J
.
1998
;
4
(
4
):
261
269
.
21
Rubin
EM,
DeRose
PB,
Cohen
C.
Comparative image cytometric DNA ploidy of liver cell dysplasia and hepatocellular carcinoma
.
Mod Pathol
.
1994
;
7
(
6
):
677
680
.
22
Cohen
C,
Santoianni
RA,
Tickman
RJ,
Kennedy
JC,
DeRose
PB.
Semiautomation of preparation of fixed paraffin-embedded tissue for DNA analysis
.
Anal Quant Cytol Histol
.
1991
;
13
(
3
):
177
181
.
23
Lohmann
C,
Gibney
E,
Cotsonis
G,
Lawson
D,
Cohen
C.
Progesterone receptor immunohistochemical quantitation compared with cytosolic assay: correlation with prognosis in breast cancer
.
Appl Immunohistochem Mol Morphol
.
2001
;
9
(
1
):
49
53
.
24
el-Badawy
N,
Cohen
C,
DeRose
PB,
Sgoutas
D.
Immunohistochemical estrogen receptor assay: quantitation by image analysis
.
Mod Pathol
.
1991
;
4
(
3
):
305
309
.
25
el-Badawy
N,
Cohen
C,
Derose
PB,
Check
IJ,
Sgoutas
D.
Immunohistochemical progesterone receptor assay: measurement by image analysis
.
Am J Clin Pathol
.
1991
;
96
(
6
):
704
710
.
26
Cohen
C,
Unger
ER,
Sgoutas
D,
Bradley
N,
Chenggis
M.
Automated immunohistochemical estrogen receptor in fixed embedded breast carcinomas
.
Am J Clin Pathol
.
1991
;
95
(
3
):
335
339
.
27
Baddoura
FK,
Cohen
C,
Unger
ER,
DeRose
PB,
Chenggis
M.
Image analysis for quantitation of estrogen receptor in formalin-fixed paraffin-embedded sections of breast carcinoma
.
Mod Pathol
.
1991
;
4
(
1
):
91
95
.
28
Cohen
C,
Unger
ER,
Sgoutas
D,
Bradley
N,
Chenggis
M.
Automated immunohistochemical estrogen receptor in fixed embedded breast carcinomas: comparison with manual immunohistochemistry on frozen tissues
.
Am J Clin Pathol
.
1989
;
92
(
5
):
669
672
.
29
Hanley
KZ,
Siddiqui
MT,
Lawson
D,
Cohen
C,
Nassar
A.
Evaluation of new monoclonal antibodies in detection of estrogen receptor, progesterone receptor, and Her2 protein expression in breast carcinoma cell block sections using conventional microscopy and quantitative image analysis
.
Diagn Cytopathol
.
2009
;
37
(
4
):
251
257
.
30
Bell
J,
Walsh
S,
Nusrat
A,
Cohen
C.
Zonula occludens-1 and Her-2/neu expression in invasive breast carcinoma
.
Appl Immunohistochem Mol Morphol
.
2003
;
11
(
2
):
125
129
.
31
Nassar
A,
Cohen
C,
Agersborg
SS,
et al.
Trainable immunohistochemical HER2/neu image analysis: a multisite performance study using 260 breast tissue specimens
.
Arch Pathol Lab Med
.
2011
;
135
(
7
):
896
902
.
32
Kennedy
JC,
el-Badawy
N,
DeRose
PB,
Cohen
C.
Comparison of cell proliferation in breast carcinoma using image analysis (Ki-67) and flow cytometric systems
.
Anal Quant Cytol Histol
.
1992
;
14
(
4
):
304
311
.
33
Williams
DJ,
Cohen
C,
Darrow
M,
Page
AJ,
Chastain
B,
Adams
AL.
Proliferation (Ki-67 and phosphohistone H3) and oncotype DX recurrence score in estrogen receptor-positive breast cancer
.
Appl Immunohistochem Mol Morphol
.
2011
;
19
(
5
):
431
436
.
34
Myers
CW,
Cohen
C,
Li X, et al
.
Validation of computer-assisted ER, PR, Her2, and Ki-67 IHC quantitation. Seattle, WA: United States and Canadian Academy of Pathology 2016 Annual Meeting; March 12–18, 2016
.
Mod Pathol
.
2016
;
29(suppl 2):500A:1989.
35
Schiller
AB,
Clark
WS,
Cotsonis
G,
Lawson
D,
DeRose
PB,
Cohen
C.
Image cytometric bcl-2:bax and bcl-2:bcl-x ratios in invasive breast carcinoma: correlation with prognosis
.
Cytometry
.
2002
;
50
(
4
):
203
209
.
36
Rong
Y,
Guerzon
GM,
Lawson
D,
Cohen
C.
Image cytometric HER2 quantitation: cut-off values for the equivocal range. San Diego, CA: United States and Canadian Academy of Pathology 2014 Annual Meeting; March 1–7, 2014
.
Mod Pathol
.
2014
;
27(suppl 2):511A:2093
.
37
Lohmann
CM,
League
AA,
Clark
WS,
Lawson
D,
DeRose
PB,
Cohen
C.
Bcl-2:bax and bcl-2:Bcl-x ratios by image cytometric quantitation of immunohistochemical expression in ovarian carcinoma: correlation with prognosis
.
Cytometry
.
2000
;
42
(
1
):
61
66
.
38
Ahn
SG,
Jeong
J,
Hong
S,
Jung
WH.
Current issues and clinical evidence in tumor-infiltrating lymphocytes in breast cancer
.
J Pathol Transl Med
.
2015
;
49
(
5
):
355
363
.
39
Farris
AB
III,
Lauwers
GY,
Deshpande
V.
Autoimmune pancreatitis-related diabetes: quantitative analysis of endocrine islet cells and inflammatory infiltrate
.
Virchows Arch
.
2010
;
457
(
3
):
329
336
.
40
Moon
A,
Smith
G,
Rogers
TE,
Ellis
CL,
Farris
AB.
Renal allograft biopsy CD3+ cell quantitation algorithm development for rejection assessment utilizing open source image analysis software. Seattle, WA: United States and Canadian Academy of Pathology 2016 Annual Meeting; March 12–18, 2016
.
Mod Pathol
.
2016
;
29(suppl 2):407A:1609.
41
Smith
GH,
Kong
J,
Farris
AB.
Renal allograft biopsy inflammatory cell quantitation using image analysis algorithms: correlation with pathologist assessment and rejection severity. Vancouver, Canada: United States and Canadian Academy of Pathology 2012 Annual Meeting; March 17–23, 2012
.
Mod Pathol
.
2012
;
25
(
suppl 2
):
407A
408A
.
42
Reid
MD,
Bagci
P,
Ohike
N,
et al.
Calculation of the Ki67 index in pancreatic neuroendocrine tumors: a comparative analysis of four counting methodologies
.
Mod Pathol
.
2015
;
28
(
5
):
686
694
.
43
Adsay
V.
Ki67 labeling index in neuroendocrine tumors of the gastrointestinal and pancreatobiliary tract: to count or not to count is not the question, but rather how to count
.
Am J Surg Pathol
.
2012
;
36
(
12
):
1743
1746
.
44
Wells
SJ,
DeRose
PB,
Cohen
C.
Image cytometric comparison of proliferating cell nuclear antigen and MIB-1 staining in hepatocellular carcinoma and adjacent liver tissue
.
Cytometry
.
1996
;
26
(
3
):
198
203
.
45
Klimstra
DS,
Modlin
IR,
Adsay
NV,
et al.
Pathology reporting of neuroendocrine tumors: application of the Delphic consensus process to the development of a minimum pathology data set
.
Am J Surg Pathol
.
2010
;
34
(
3
):
300
313
.
46
Basturk
O,
Yang
Z,
Tang
LH,
et al.
The high-grade (WHO G3) pancreatic neuroendocrine tumor category is morphologically and biologically heterogenous and includes both well differentiated and poorly differentiated neoplasms
.
Am J Surg Pathol
.
2015
;
39
(
5
):
683
690
.
47
Basturk
O,
Tang
L,
Hruban
RH,
et al.
Poorly differentiated neuroendocrine carcinomas of the pancreas: a clinicopathologic analysis of 44 cases
.
Am J Surg Pathol
.
2014
;
38
(
4
):
437
447
.
48
Burdette
EB,
Myers
CW,
Smith
G,
et al.
A comparison of manual counting with camera captured images and digital image analysis for Ki-67 proliferative index assessment in pancreatic neuroendocrine tumors. Seattle, WA: United States and Canadian Academy of Pathology 2016 Annual Meeting; March 12–18, 2016
.
Mod Pathol
.
2016
;
29(suppl 2):510A:2029.
49
Neely
CE,
Myers
CW,
Smith
G,
Cohen
C,
Reid
MD.
A comparison of automated digital image analysis (DIA) and manual count of camera-captured images in calculating Ki-67 proliferation index (PI) in cytologic samples from pancreatic neuroendocrine neoplasms (PanNENs). Seattle, WA: United States and Canadian Academy of Pathology 2016 Annual Meeting; March 12–18, 2016
.
Mod Pathol
.
2016
;
29(suppl 2):111A:435.
50
Ormenisan
C,
Wang
J,
Lawson
D,
Cohen
C.
Image cytometric HER2 in gastric carcinoma: is a new algorithm needed?
Appl Immunohistochem Mol Morphol
.
2013
;
21
(
5
):
414
419
.
51
Mohamed
A,
Caltharp
SA,
Wang
J,
Cohen
C,
Farris
AB.
Hepatocellular carcinoma microvessel density quantitation with image analysis: correlation with prognosis
.
J Anal Oncol
.
2013
;
2
(
3
):
135
141
.
52
Lee
MJ,
Bagci
P,
Kong
J,
et al.
Liver steatosis assessment: correlations among pathology, radiology, clinical data and automated image analysis software
.
Pathol Res Pract
.
2013
;
209
(
6
):
371
379
.
53
Mas-Moya
J,
Bagci
P,
Jang
KT,
et al.
Liver fibrosis quantitation via image analysis: correlation with pathologist assessment and clinical parameters. Baltimore, MD: United States and Canadian Academy of Pathology 2013 Annual Meeting; March 2–8, 2013
.
Mod Pathol
.
2013
;
26
(
suppl 2
):
407A
.
54
Jiang
K,
Dar
W,
Farris
AB.
Quantitative monitoring of hepatic fibrosis in orthotopic liver transplant patients utilizing digital image analysis: correlation with pathologic assessment. Boston, MA: American Society of Clinical Pathology Annual Meeting; October 30–November 3, 2012
.
Am J Clin Pathol
.
2012
;
138
:
A109
.
55
Mas-Moya
J,
Jiang
K,
McCabe
N,
Book
WM
,
Farris
AB
.
Fontan liver fibrosis quantitation using image analysis: a comparative study with other forms of cardiogenic hepatic fibrosis. Orlando, FL: College of American Pathologists Annual Meeting. October 13–16, 2013
.
Arch Pathol Lab Med
.
2013
;
137(October 2013):1409:Poster 67.
56
Martin
DR,
Braxton
DR,
Farris
AB.
Barrett esophagus dysplasia characterization through digital image analysis. San Diego, CA: United States and Canadian Academy of Pathology 2014 Annual Meeting; March 1–7, 2014
.
Mod Pathol
.
2014
;
27
(
suppl 2
):
193A
.
57
Martin
DR,
Farris
AB.
Dysplasia in colonic polyps: discrimination through digital image analysis. Baltimore, MD: United States and Canadian Academy of Pathology 2013 Annual Meeting; March 2–8, 2013
.
Mod Pathol
.
2013
;
26
(
suppl 2
):
166A
167A
.
58
Robinson
BS,
Martin
DR,
Farris
AB.
Digital image analysis of serrated lesions of the colorectum. Seattle, WA: United States and Canadian Academy of Pathology 2016 Annual Meeting; March 12–18, 2016
.
Mod Pathol
.
2016
;
29(suppl 2):195A:777.
59
Fedirko
V,
Bostick
RM,
Flanders
WD,
et al.
Effects of vitamin D and calcium supplementation on markers of apoptosis in normal colon mucosa: a randomized, double-blind, placebo-controlled clinical trial
.
Cancer Prev Res (Phila)
.
2009
;
2
(
3
):
213
223
.
60
Farris
AB,
Kong
J,
Chisolm
C,
et al.
HLA-DR immunohistochemistry quantitation in renal allograft biopsies: objective discrimination of rejection and other pathologic processes. San Antonio, Texas: United States and Canadian Academy of Pathology 2011 Annual Meeting; February 26–March 4, 2011
.
Mod Pathol
.
2011
;
24
(
suppl 2
):
344A
.
61
Stuart
LN,
Tumer
G,
Roberts-Wilson
T,
Gebel
HM,
Bray
RA,
Farris
AB.
Utility of whole slide imaging algorithms in the assessment of renal allograft biopsies: does automated analysis provide a better assessment of donor specific antibody than human scoring? Baltimore, MD: United States and Canadian Academy of Pathology 2013 Annual Meeting; March 2–8, 2013
.
Mod Pathol
.
2013
;
26
(
suppl 2
):
393A
394A
.
62
Farris
AB,
Adams
CD,
Brousaides
N,
et al.
Morphometric and visual evaluation of fibrosis in renal biopsies
.
J Am Soc Nephrol
.
2010
;
22
(
1
):
176
186
.
63
Champion
A,
Lu
G,
Walker
M,
Kothari
S,
Osunkoya
AO,
Wang
MD.
Semantic interpretation of robust imaging features for Fuhrman grading of renal carcinoma
.
Conf Proc IEEE Eng Med Biol Soc
.
2014
;
2014
:
6446
6449
.
64
Kothari
S,
Phan
JH,
Stokes
TH,
Osunkoya
AO,
Young
AN,
Wang
MD.
Removing batch effects from histopathological images for enhanced cancer diagnosis
.
IEEE J Biomed Health Inform
.
2014
;
18
(
3
):
765
772
.
65
el-Galley
RE,
Smith
E,
Cohen
C,
Petros
JA,
Woodard
J,
Galloway
NT.
Epidermal growth factor (EGF) and EGF receptor in hypospadias
.
Br J Urol
.
1997
;
79
(
1
):
116
119
.
66
Wang
F,
Kong
J,
Cooper
L,
et al.
A data model and database for high-resolution pathology analytical image informatics
.
J Pathol Inform
.
2011
;
2
:
32
.
67
Cooper
LA,
Carter
AB,
Farris
AB,
et al.
Digital pathology: data-intensive frontier in medical imaging
.
Proc IEEE Inst Electr Electron Eng
.
2012
;
100
(
4
):
991
1003
.
68
Kirkegaard
LJ,
DeRose
PB,
Yao
B,
Cohen
C.
Image cytometric measurement of nuclear proliferation markers (MIB-1, PCNA) in astrocytomas: prognostic significance
.
Am J Clin Pathol
.
1998
;
109
(
1
):
69
74
.
69
Coleman
KE,
Brat
DJ,
Cotsonis
GA,
Lawson
D,
Cohen
C.
Proliferation (MIB-1 expression) in oligodendrogliomas: assessment of quantitative methods and prognostic significance
.
Appl Immunohistochem Mol Morphol
.
2006
;
14
(
1
):
109
114
.
70
Kong
J,
Cooper
LA,
Wang
F,
et al.
Integrative, multimodal analysis of glioblastoma using TCGA molecular data, pathology images, and clinical outcomes
.
IEEE Trans Biomed Eng
.
2011
;
58
(
12
):
3469
3474
.
71
Kong
J,
Cooper
LA,
Wang
F,
et al.
Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates
.
PLoS One
.
2013
;
8
(
11
):
e81049
.
72
Rutledge
WC,
Kong
J,
Gao
J,
et al.
Tumor-infiltrating lymphocytes in glioblastoma are associated with specific genomic alterations and related to transcriptional class
.
Clin Cancer Res
.
2013
;
19
(
18
):
4951
4960
.
73
Kong
J,
Cooper
L,
Wang
F,
et al.
A comprehensive framework for classification of nuclei in digital microscopy imaging: an application to diffuse gliomas
.
Proc IEEE Int Symp Biomed Imaging
.
2011
;
2128
2131
.
74
Kong
J,
Cooper
L,
Moreno
C,
et al.
In silico analysis of nuclei in glioblastoma using large-scale microscopy images improves prediction of treatment response
.
Conf Proc IEEE Eng Med Biol Soc
.
2011
;
2011
:
87
90
.
75
Kong
J,
Cooper
L,
Kurc
T,
Brat
D,
Saltz
J.
Towards building computerized image analysis framework for nucleus discrimination in microscopy images of diffuse glioma
.
Conf Proc IEEE Eng Med Biol Soc
.
2011
;
2011
:
6605
6608
.
76
Cooper
LA,
Kong
J,
Gutman
DA,
et al.
Integrated morphologic analysis for the identification and characterization of disease subtypes
.
J Am Med Inform Assoc
.
2012
;
19
(
2
):
317
323
.
77
Cooper
LA,
Kong
J,
Gutman
DA,
et al.
An integrative approach for in silico glioma research
.
IEEE Trans Biomed Eng
.
2010
;
57
(
10
):
2617
2621
.
78
Cooper
LA,
Gutman
DA,
Long
Q,
et al.
The proneural molecular signature is enriched in oligodendrogliomas and predicts improved survival among diffuse gliomas
.
PLoS One
.
2010
;
5
(
9
):
e12548
.
79
Cooper
LA,
Gutman
DA,
Chisolm
C,
et al.
The tumor microenvironment strongly impacts master transcriptional regulators and gene expression class of glioblastoma
.
Am J Pathol
.
2012
;
180
(
5
):
2108
2119
.
80
Chen
G,
Kong
J,
Tucker-Burden
C,
et al.
Human Brat ortholog TRIM3 is a tumor suppressor that regulates asymmetric cell division in glioblastoma
.
Cancer Res
.
2014
;
74
(
16
):
4536
4548
.
81
Gutman
DA,
Cooper
LA,
Hwang
SN,
et al.
MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set
.
Radiology
.
2013
;
267
(
2
):
560
569
.
82
Murray
JD,
Carlson
GW,
McLaughlin
K,
et al.
Tumor angiogenesis as a prognostic factor in laryngeal cancer
.
Am J Surg
.
1997
;
174
(
5
):
523
526
.
83
Swadley
MJ,
Jones
ML,
Farris
AB.
Objective histologic stain quality and variability analysis through digital imaging: the effect of staining automation. Vancouver, Canada: United States and Canadian Academy of Pathology 2012 Annual Meeting; March 17–23, 2012
.
Mod Pathol
.
2012
;
25
(
suppl 2
):
507A
.
84
Morrison
AS,
Gardner
JM.
Smart phone microscopic photography: a novel tool for physicians and trainees
.
Arch Pathol Lab Med
.
2014
;
138
(
8
):
1002
.
85
Morrison
AO,
Gardner
JM.
Microscopic image photography techniques of the past, present, and future
.
Arch Pathol Lab Med
.
2015
;
139
(
12
):
1558
1564
.
86
Kaplan
KJ,
Burgess
JR,
Sandberg
GD,
Myers
CP,
Bigott
TR,
Greenspan
RB.
Use of robotic telepathology for frozen-section diagnosis: a retrospective trial of a telepathology system for intraoperative consultation
.
Mod Pathol
.
2002
;
15
(
11
):
1197
1204
.
87
Pantanowitz
L,
Wiley
CA,
Demetris
A,
et al.
Experience with multimodality telepathology at the University of Pittsburgh Medical Center
.
J Pathol Inform
.
2012
;
3
:
45
.
88
Carter
AB.
Stepping across borders into the future of telepathology
.
J Pathol Inform
.
2011
;
2
:
24
.
89
Williams
S,
Henricks
WH,
Becich
MJ,
Toscano
M,
Carter
AB.
Telepathology for patient care: what am I getting myself into?
Adv Anat Pathol
.
2010
;
17
(
2
):
130
149
.
90
Pantanowitz
L,
Sinard
JH,
Henricks
WH,
et al.
Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center
.
Arch Pathol Lab Med
.
2013
;
137
(
12
):
1710
1722
.
91
Pantanowitz
L,
Valenstein
PN,
Evans
AJ,
et al.
Review of the current state of whole slide imaging in pathology
.
J Pathol Inform
.
2011
;
2
:
36
.
92
Thorstenson
S,
Molin
J,
Lundstrom
C.
Implementation of large-scale routine diagnostics using whole slide imaging in Sweden: digital pathology experiences 2006-2013
.
J Pathol Inform
.
2014
;
5
(
1
):
14
.
93
Razavi
SA,
Carter
AB,
Puskas
JD,
Gregg
SR,
Aziz
IF,
Buchman
TG.
Reduced red blood cell transfusion in cardiothoracic surgery after implementation of a novel clinical decision support tool
.
J Am Coll Surg
.
2014
;
219
(
5
):
1028
1036
.
94
Louis
DN,
Feldman
M,
Carter
AB,
et al.
Computational pathology
.
Arch Pathol Lab Med
.
2015
;
140
(
1
):
41
50
.
95
Baron
JM,
Dighe
AS,
Arnaout
R,
et al.
The 2013 symposium on pathology data integration and clinical decision support and the current state of field
.
J Pathol Inform
.
2014
;
5
(
1
):
2
.
96
Bostick
RM,
Kong
KY,
Ahearn
TU,
Chaudry
Q,
Cohen
V,
Wang
MD.
Detecting and quantifying biomarkers of risk for colorectal cancer using quantum dots and novel image analysis algorithms
.
Conf Proc IEEE Eng Med Biol Soc
.
2006
;
1
:
3313
3316
.
97
Moon
A,
Yacoub
R,
Bostick
RM,
Campbell
PT,
Farris
AB.
Quantum dot multiparametric analysis of colorectal adenocarcinoma: critical examination of expression pattern scoring methods. Boston, MA: United States and Canadian Academy of Pathology 2015 Annual Meeting; March 21–27, 2015
.
Mod Pathol
.
2015
;
28(suppl 2):521A:2092.

Author notes

The authors have no relevant financial interest in the products or companies described in this article.