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Classification of follicular lymphoma: the effect of computer aid on pathologists grading.

Fauzi MF, Pennell M, Sahiner B, Chen W, Shana'ah A, Hemminger J, Gru A, Kurt H, Losos M, Joehlin-Price A, Kavran C, Smith SM, Nowacki N, Mansor S, Lozanski G, Gurcan MN - BMC Med Inform Decis Mak (2015)

Bottom Line: We also assess the effect of FLAGS on accuracy of expert and inexperienced readers.Access to FLAGS improved overall reader accuracy with the biggest improvement seen among residents.The results of this study show that FLAGS can be useful in increasing the pathologists' accuracy in grading the tissue.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia.

ABSTRACT

Background: Follicular lymphoma (FL) is one of the most common lymphoid malignancies in the western world. FL cases are stratified into three histological grades based on the average centroblast count per high power field (HPF). The centroblast count is performed manually by the pathologist using an optical microscope and hematoxylin and eosin (H&E) stained tissue section. Although this is the current clinical practice, it suffers from high inter- and intra-observer variability and is vulnerable to sampling bias.

Methods: In this paper, we present a system, called Follicular Lymphoma Grading System (FLAGS), to assist the pathologist in grading FL cases. We also assess the effect of FLAGS on accuracy of expert and inexperienced readers. FLAGS automatically identifies possible HPFs for examination by analyzing H&E and CD20 stains, before classifying them into low or high risk categories. The pathologist is first asked to review the slides according to the current routine clinical practice, before being presented with FLAGS classification via color-coded map. The accuracy of the readers with and without FLAGS assistance is measured.

Results: FLAGS was used by four experts (board-certified hematopathologists) and seven pathology residents on 20 FL slides. Access to FLAGS improved overall reader accuracy with the biggest improvement seen among residents. An average AUC value of 0.75 was observed which generally indicates "acceptable" diagnostic performance.

Conclusions: The results of this study show that FLAGS can be useful in increasing the pathologists' accuracy in grading the tissue. To the best of our knowledge, this study measure, for the first time, the effect of computerized image analysis on pathologists' grading of follicular lymphoma. When fully developed, such systems have the potential to reduce sampling bias by examining an increased proportion of HPFs within follicle regions, as well as to reduce inter- and intra-reader variability.

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Related in: MedlinePlus

Flowchart of the proposed high power fields detection and classification
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Related In: Results  -  Collection

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Fig1: Flowchart of the proposed high power fields detection and classification

Mentions: FigureĀ 1 (top) shows the flow chart of the proposed detection method. Given the CD20 and H&E stained images of a tissue sample, image registration is carried out to align the tissue boundaries as well as the follicle regions between the two images [11]. Since the classification of the FL tissues will eventually be carried out based on the H&E-stained images, the CD20 images are registered to the H&E images. In other words, the CD20 images are transformed so that they are spatially registered with the H&E images. The saturation channel from the HSV color model is used to register the two images as it provides a good gray level separation between the follicle regions, non-follicle regions, and the white background.Fig. 1


Classification of follicular lymphoma: the effect of computer aid on pathologists grading.

Fauzi MF, Pennell M, Sahiner B, Chen W, Shana'ah A, Hemminger J, Gru A, Kurt H, Losos M, Joehlin-Price A, Kavran C, Smith SM, Nowacki N, Mansor S, Lozanski G, Gurcan MN - BMC Med Inform Decis Mak (2015)

Flowchart of the proposed high power fields detection and classification
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4696238&req=5

Fig1: Flowchart of the proposed high power fields detection and classification
Mentions: FigureĀ 1 (top) shows the flow chart of the proposed detection method. Given the CD20 and H&E stained images of a tissue sample, image registration is carried out to align the tissue boundaries as well as the follicle regions between the two images [11]. Since the classification of the FL tissues will eventually be carried out based on the H&E-stained images, the CD20 images are registered to the H&E images. In other words, the CD20 images are transformed so that they are spatially registered with the H&E images. The saturation channel from the HSV color model is used to register the two images as it provides a good gray level separation between the follicle regions, non-follicle regions, and the white background.Fig. 1

Bottom Line: We also assess the effect of FLAGS on accuracy of expert and inexperienced readers.Access to FLAGS improved overall reader accuracy with the biggest improvement seen among residents.The results of this study show that FLAGS can be useful in increasing the pathologists' accuracy in grading the tissue.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia.

ABSTRACT

Background: Follicular lymphoma (FL) is one of the most common lymphoid malignancies in the western world. FL cases are stratified into three histological grades based on the average centroblast count per high power field (HPF). The centroblast count is performed manually by the pathologist using an optical microscope and hematoxylin and eosin (H&E) stained tissue section. Although this is the current clinical practice, it suffers from high inter- and intra-observer variability and is vulnerable to sampling bias.

Methods: In this paper, we present a system, called Follicular Lymphoma Grading System (FLAGS), to assist the pathologist in grading FL cases. We also assess the effect of FLAGS on accuracy of expert and inexperienced readers. FLAGS automatically identifies possible HPFs for examination by analyzing H&E and CD20 stains, before classifying them into low or high risk categories. The pathologist is first asked to review the slides according to the current routine clinical practice, before being presented with FLAGS classification via color-coded map. The accuracy of the readers with and without FLAGS assistance is measured.

Results: FLAGS was used by four experts (board-certified hematopathologists) and seven pathology residents on 20 FL slides. Access to FLAGS improved overall reader accuracy with the biggest improvement seen among residents. An average AUC value of 0.75 was observed which generally indicates "acceptable" diagnostic performance.

Conclusions: The results of this study show that FLAGS can be useful in increasing the pathologists' accuracy in grading the tissue. To the best of our knowledge, this study measure, for the first time, the effect of computerized image analysis on pathologists' grading of follicular lymphoma. When fully developed, such systems have the potential to reduce sampling bias by examining an increased proportion of HPFs within follicle regions, as well as to reduce inter- and intra-reader variability.

Show MeSH
Related in: MedlinePlus