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21(st) century workflow: A proposal.

Fine JL - J Pathol Inform (2014)

Bottom Line: Pathologists have not yet developed a well-articulated plan for effectively utilizing digital imaging technology in their work.This paper outlines a proposal that is intended to begin meaningful progress toward achieving helpful computer-assisted pathology sign-out systems, such as pathologists' computer-assisted diagnosis (pCAD). pCAD is presented as a hypothetical intelligent computer system that would integrate advanced image analysis and better utilization of existing digital pathology data from lab information systems.This proposal provides stakeholders with a conceptual framework that can be used to facilitate development work, communication, and identification of new automation strategies.

View Article: PubMed Central - PubMed

Affiliation: Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.

ABSTRACT
Digital pathology is rapidly developing, but early systems have been slow to gain traction outside of niche applications such as: Second-opinion telepathology, immunostain interpretation, and intraoperative telepathology. Pathologists have not yet developed a well-articulated plan for effectively utilizing digital imaging technology in their work. This paper outlines a proposal that is intended to begin meaningful progress toward achieving helpful computer-assisted pathology sign-out systems, such as pathologists' computer-assisted diagnosis (pCAD). pCAD is presented as a hypothetical intelligent computer system that would integrate advanced image analysis and better utilization of existing digital pathology data from lab information systems. A detailed example of automated digital pathology is presented, as an automated breast cancer lymph node sign-out. This proposal provides stakeholders with a conceptual framework that can be used to facilitate development work, communication, and identification of new automation strategies.

No MeSH data available.


Related in: MedlinePlus

Breakdown of a tissue fragment image into individual regions of interests (ROIs), using example of lymph node for cancer staging. (a) Axillary lymph nodes can generally be divided into three compartments: Subcapsular sinus, lymph node interior and peri-nodal fat. (b) Rectangular ROI outlines are superimposed on the lymph node fragment. As subcapsular sinus is more likely to harbor small metastases, the ROIs there are smaller (i.e., higher magnification ROIs). Tumor is rarely only in the fat, so those ROIs are larger. This correlates with glass microscopy wherein such areas might be viewed at lower magnification, yielding larger fields of view. (c) Triaged ROIs are shown, arranged from most suspicious (orange outlined ROIs, left) to least suspicious (blue outlined ROIs, right). Although tumor is visible in the left-most ROI, other criteria could be also used for triage including anatomic compartment (i.e. lymph node vs fat), specimen title (i.e. lymph node #1 may be riskier than #3), etc. If no positive ROIs were present in a specimen, then these alternate rules would be used for triage
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Figure 4: Breakdown of a tissue fragment image into individual regions of interests (ROIs), using example of lymph node for cancer staging. (a) Axillary lymph nodes can generally be divided into three compartments: Subcapsular sinus, lymph node interior and peri-nodal fat. (b) Rectangular ROI outlines are superimposed on the lymph node fragment. As subcapsular sinus is more likely to harbor small metastases, the ROIs there are smaller (i.e., higher magnification ROIs). Tumor is rarely only in the fat, so those ROIs are larger. This correlates with glass microscopy wherein such areas might be viewed at lower magnification, yielding larger fields of view. (c) Triaged ROIs are shown, arranged from most suspicious (orange outlined ROIs, left) to least suspicious (blue outlined ROIs, right). Although tumor is visible in the left-most ROI, other criteria could be also used for triage including anatomic compartment (i.e. lymph node vs fat), specimen title (i.e. lymph node #1 may be riskier than #3), etc. If no positive ROIs were present in a specimen, then these alternate rules would be used for triage

Mentions: Next is the actual task of finding tumor. SLNB slides typically contain one or more histologic sections of lymph node and fat fragments, and the hypothetical computer system would be able to identify these [Figure 3]. Further, individual lymph node fragments can generally be divided into three compartments: Subcapsular sinus, lymph node interior, and peri-nodal fat [Figure 4]. These compartments can be further subdivided into ROIs that are small enough for rapid pathologist review. The computer would preview the ROIs; optimally it would dependably identify and prioritize tumor-positive ROIs. If no ROIs were positive, then the ROIs should be ranked or triaged based on risk and/or atypia assessment. Multiple factors could be used, but the goal is to deliver the most relevant ROIs to the pathologist as early as possible [Figure 5]. Clearly benign fat or lymphoid tissue should be reviewed later, if at all. The computer would show ROIs to the pathologist interactively, and the pathologist would confirm any tumor findings.


21(st) century workflow: A proposal.

Fine JL - J Pathol Inform (2014)

Breakdown of a tissue fragment image into individual regions of interests (ROIs), using example of lymph node for cancer staging. (a) Axillary lymph nodes can generally be divided into three compartments: Subcapsular sinus, lymph node interior and peri-nodal fat. (b) Rectangular ROI outlines are superimposed on the lymph node fragment. As subcapsular sinus is more likely to harbor small metastases, the ROIs there are smaller (i.e., higher magnification ROIs). Tumor is rarely only in the fat, so those ROIs are larger. This correlates with glass microscopy wherein such areas might be viewed at lower magnification, yielding larger fields of view. (c) Triaged ROIs are shown, arranged from most suspicious (orange outlined ROIs, left) to least suspicious (blue outlined ROIs, right). Although tumor is visible in the left-most ROI, other criteria could be also used for triage including anatomic compartment (i.e. lymph node vs fat), specimen title (i.e. lymph node #1 may be riskier than #3), etc. If no positive ROIs were present in a specimen, then these alternate rules would be used for triage
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4260324&req=5

Figure 4: Breakdown of a tissue fragment image into individual regions of interests (ROIs), using example of lymph node for cancer staging. (a) Axillary lymph nodes can generally be divided into three compartments: Subcapsular sinus, lymph node interior and peri-nodal fat. (b) Rectangular ROI outlines are superimposed on the lymph node fragment. As subcapsular sinus is more likely to harbor small metastases, the ROIs there are smaller (i.e., higher magnification ROIs). Tumor is rarely only in the fat, so those ROIs are larger. This correlates with glass microscopy wherein such areas might be viewed at lower magnification, yielding larger fields of view. (c) Triaged ROIs are shown, arranged from most suspicious (orange outlined ROIs, left) to least suspicious (blue outlined ROIs, right). Although tumor is visible in the left-most ROI, other criteria could be also used for triage including anatomic compartment (i.e. lymph node vs fat), specimen title (i.e. lymph node #1 may be riskier than #3), etc. If no positive ROIs were present in a specimen, then these alternate rules would be used for triage
Mentions: Next is the actual task of finding tumor. SLNB slides typically contain one or more histologic sections of lymph node and fat fragments, and the hypothetical computer system would be able to identify these [Figure 3]. Further, individual lymph node fragments can generally be divided into three compartments: Subcapsular sinus, lymph node interior, and peri-nodal fat [Figure 4]. These compartments can be further subdivided into ROIs that are small enough for rapid pathologist review. The computer would preview the ROIs; optimally it would dependably identify and prioritize tumor-positive ROIs. If no ROIs were positive, then the ROIs should be ranked or triaged based on risk and/or atypia assessment. Multiple factors could be used, but the goal is to deliver the most relevant ROIs to the pathologist as early as possible [Figure 5]. Clearly benign fat or lymphoid tissue should be reviewed later, if at all. The computer would show ROIs to the pathologist interactively, and the pathologist would confirm any tumor findings.

Bottom Line: Pathologists have not yet developed a well-articulated plan for effectively utilizing digital imaging technology in their work.This paper outlines a proposal that is intended to begin meaningful progress toward achieving helpful computer-assisted pathology sign-out systems, such as pathologists' computer-assisted diagnosis (pCAD). pCAD is presented as a hypothetical intelligent computer system that would integrate advanced image analysis and better utilization of existing digital pathology data from lab information systems.This proposal provides stakeholders with a conceptual framework that can be used to facilitate development work, communication, and identification of new automation strategies.

View Article: PubMed Central - PubMed

Affiliation: Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.

ABSTRACT
Digital pathology is rapidly developing, but early systems have been slow to gain traction outside of niche applications such as: Second-opinion telepathology, immunostain interpretation, and intraoperative telepathology. Pathologists have not yet developed a well-articulated plan for effectively utilizing digital imaging technology in their work. This paper outlines a proposal that is intended to begin meaningful progress toward achieving helpful computer-assisted pathology sign-out systems, such as pathologists' computer-assisted diagnosis (pCAD). pCAD is presented as a hypothetical intelligent computer system that would integrate advanced image analysis and better utilization of existing digital pathology data from lab information systems. A detailed example of automated digital pathology is presented, as an automated breast cancer lymph node sign-out. This proposal provides stakeholders with a conceptual framework that can be used to facilitate development work, communication, and identification of new automation strategies.

No MeSH data available.


Related in: MedlinePlus