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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 complete whole slide image (WSI) into its elemental fragments, using an example of lymph node tissue in a cancer staging case. (a) A complete clinical WSI ideally includes all tissue and the slide label; this is generally as far as current WSI system delve into the WSI. (b) However, if the label is machine-labeled, then the WSI can associate the label data with more extensive anatomic pathology lab information system data, depicted as a cloud. Further, the tissue could be divided into histologic levels or cuts (two cuts are pictured but cuts could be included from multiple slides). The levels are depicted side-by-side, as separate image objects. (c) Although there are many possible approaches, the author would prefer to view multiple tissue levels either as a stack (pictured) or simultaneously (pictured, top level is partially transparent). (d) Subdivision into individual tissue fragments is also possible. Here, the individual lymph node fragments are depicted as separate objects, each one with two levels (stacked). (e) If individual tissue fragments are separate objects, then they can be reviewed independently. The pathologist can focus on the review without having to track the fragments’ locations on the original slides, because the computer manages this information
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Figure 3: Breakdown of a complete whole slide image (WSI) into its elemental fragments, using an example of lymph node tissue in a cancer staging case. (a) A complete clinical WSI ideally includes all tissue and the slide label; this is generally as far as current WSI system delve into the WSI. (b) However, if the label is machine-labeled, then the WSI can associate the label data with more extensive anatomic pathology lab information system data, depicted as a cloud. Further, the tissue could be divided into histologic levels or cuts (two cuts are pictured but cuts could be included from multiple slides). The levels are depicted side-by-side, as separate image objects. (c) Although there are many possible approaches, the author would prefer to view multiple tissue levels either as a stack (pictured) or simultaneously (pictured, top level is partially transparent). (d) Subdivision into individual tissue fragments is also possible. Here, the individual lymph node fragments are depicted as separate objects, each one with two levels (stacked). (e) If individual tissue fragments are separate objects, then they can be reviewed independently. The pathologist can focus on the review without having to track the fragments’ locations on the original slides, because the computer manages this information

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 complete whole slide image (WSI) into its elemental fragments, using an example of lymph node tissue in a cancer staging case. (a) A complete clinical WSI ideally includes all tissue and the slide label; this is generally as far as current WSI system delve into the WSI. (b) However, if the label is machine-labeled, then the WSI can associate the label data with more extensive anatomic pathology lab information system data, depicted as a cloud. Further, the tissue could be divided into histologic levels or cuts (two cuts are pictured but cuts could be included from multiple slides). The levels are depicted side-by-side, as separate image objects. (c) Although there are many possible approaches, the author would prefer to view multiple tissue levels either as a stack (pictured) or simultaneously (pictured, top level is partially transparent). (d) Subdivision into individual tissue fragments is also possible. Here, the individual lymph node fragments are depicted as separate objects, each one with two levels (stacked). (e) If individual tissue fragments are separate objects, then they can be reviewed independently. The pathologist can focus on the review without having to track the fragments’ locations on the original slides, because the computer manages this information
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Breakdown of a complete whole slide image (WSI) into its elemental fragments, using an example of lymph node tissue in a cancer staging case. (a) A complete clinical WSI ideally includes all tissue and the slide label; this is generally as far as current WSI system delve into the WSI. (b) However, if the label is machine-labeled, then the WSI can associate the label data with more extensive anatomic pathology lab information system data, depicted as a cloud. Further, the tissue could be divided into histologic levels or cuts (two cuts are pictured but cuts could be included from multiple slides). The levels are depicted side-by-side, as separate image objects. (c) Although there are many possible approaches, the author would prefer to view multiple tissue levels either as a stack (pictured) or simultaneously (pictured, top level is partially transparent). (d) Subdivision into individual tissue fragments is also possible. Here, the individual lymph node fragments are depicted as separate objects, each one with two levels (stacked). (e) If individual tissue fragments are separate objects, then they can be reviewed independently. The pathologist can focus on the review without having to track the fragments’ locations on the original slides, because the computer manages this information
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