Limits...
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.

Show MeSH

Related in: MedlinePlus

ROC Curve for Stand-alone Computer Diagnosis of Grade III
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: ROC Curve for Stand-alone Computer Diagnosis of Grade III

Mentions: Stand-alone computer ratings of the likelihood that a case was Grade III (0–100 scale) were compared to the ground truth by calculating the Area Under the ROC Curve (AUC) using the trapezoidal rule and, by viewing the AUC as a binomial proportion [24], an exact binomial 95 % confidence interval of the trapezoidal AUC was obtained. The confidence interval was also calculated using U-statistics to estimate the variance of AUC [25], followed by the logistic transform to find the confidence interval for logit (AUC), and transforming the confidence interval back to AUC [26]. The two methods resulted in similar 95 % confidence intervals. ROC curves of the computer and individual readers were generated using Intercooled Stata 11 (StataCorp, College Station, TX). The computer exhibited excellent performance in discriminating high grade from low grade cases, as indicated by the ROC curve shown in Fig. 2.Fig. 2


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)

ROC Curve for Stand-alone Computer Diagnosis of Grade III
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: ROC Curve for Stand-alone Computer Diagnosis of Grade III
Mentions: Stand-alone computer ratings of the likelihood that a case was Grade III (0–100 scale) were compared to the ground truth by calculating the Area Under the ROC Curve (AUC) using the trapezoidal rule and, by viewing the AUC as a binomial proportion [24], an exact binomial 95 % confidence interval of the trapezoidal AUC was obtained. The confidence interval was also calculated using U-statistics to estimate the variance of AUC [25], followed by the logistic transform to find the confidence interval for logit (AUC), and transforming the confidence interval back to AUC [26]. The two methods resulted in similar 95 % confidence intervals. ROC curves of the computer and individual readers were generated using Intercooled Stata 11 (StataCorp, College Station, TX). The computer exhibited excellent performance in discriminating high grade from low grade cases, as indicated by the ROC curve shown in Fig. 2.Fig. 2

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