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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

Average ROC curves obtained using a nonparametric average [26] of empirical ROC curves of (a) the four expert readers, (b) the seven resident readers, and (c) all 11 readers. See Table 3 for the corresponding average AUC values and statistical inference results
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Fig3: Average ROC curves obtained using a nonparametric average [26] of empirical ROC curves of (a) the four expert readers, (b) the seven resident readers, and (c) all 11 readers. See Table 3 for the corresponding average AUC values and statistical inference results

Mentions: Additional file 5: Figure S5 shows the ROC curves for each and every individual reader. Table 3 shows the results for statistical comparisons of the mean AUC performance without the computer aid versus with the computer aid for different reader populations. Figure 3 shows the average ROC curves corresponding to those average AUC values in Table 3, where the average was performed along the direction of the diagonal line connecting the upper-left point and the lower-right point in the ROC space. These results indicate that access to the computer improved reader accuracy with the biggest improvement seen among residents. However, even with the improvements provided by computer, the average AUC value was only 0.75, which is generally regarded as “fair” or “acceptable” diagnostic performance (see, for example, [29]).Fig. 3


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)

Average ROC curves obtained using a nonparametric average [26] of empirical ROC curves of (a) the four expert readers, (b) the seven resident readers, and (c) all 11 readers. See Table 3 for the corresponding average AUC values and statistical inference results
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: Average ROC curves obtained using a nonparametric average [26] of empirical ROC curves of (a) the four expert readers, (b) the seven resident readers, and (c) all 11 readers. See Table 3 for the corresponding average AUC values and statistical inference results
Mentions: Additional file 5: Figure S5 shows the ROC curves for each and every individual reader. Table 3 shows the results for statistical comparisons of the mean AUC performance without the computer aid versus with the computer aid for different reader populations. Figure 3 shows the average ROC curves corresponding to those average AUC values in Table 3, where the average was performed along the direction of the diagonal line connecting the upper-left point and the lower-right point in the ROC space. These results indicate that access to the computer improved reader accuracy with the biggest improvement seen among residents. However, even with the improvements provided by computer, the average AUC value was only 0.75, which is generally regarded as “fair” or “acceptable” diagnostic performance (see, for example, [29]).Fig. 3

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