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Low coherence interferometry approach for aiding fine needle aspiration biopsies.

Chang EW, Gardecki J, Pitman M, Wilsterman EJ, Patel A, Tearney GJ, Iftimia N - J Biomed Opt (2014)

Bottom Line: We present portable preclinical low-coherence interference (LCI) instrumentation for aiding fine needle aspiration biopsies featuring the second-generation LCI-based biopsy probe and an improved scoring algorithm for tissue differentiation.Our instrument and algorithm were tested on 38 mice with cultured tumor mass and we show the specificity, sensitivity, and positive predictive value of tumor detection of over 0.89, 0.88, and 0.96, respectively.

View Article: PubMed Central - PubMed

Affiliation: Physical Sciences, Inc., 20 New England Business Ctr. Drive, Andover, Massachusetts 01810, United States.

ABSTRACT
We present portable preclinical low-coherence interference (LCI) instrumentation for aiding fine needle aspiration biopsies featuring the second-generation LCI-based biopsy probe and an improved scoring algorithm for tissue differentiation. Our instrument and algorithm were tested on 38 mice with cultured tumor mass and we show the specificity, sensitivity, and positive predictive value of tumor detection of over 0.89, 0.88, and 0.96, respectively.

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(a) Comparison of the receiver operating characteristics (ROCs) for the current and previous algorithm; (b) ROC curves for two types of classifiers: with all six parameters and with one parameter sequentially eliminated. TPR, true positive rate and FPR, false positive rate.
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f9: (a) Comparison of the receiver operating characteristics (ROCs) for the current and previous algorithm; (b) ROC curves for two types of classifiers: with all six parameters and with one parameter sequentially eliminated. TPR, true positive rate and FPR, false positive rate.

Mentions: To test the performance of the modified algorithm against the previously tested algorithm, we generated receiver operating characteristic (ROC) curves (see Fig. 9). An ROC curve graphically illustrates the performance of each binary classifier by plotting the true positive rate (TPR) against the false positive rate (FPR) as its discrimination threshold is varied.22 TPR is defined as sensitivity and FPR as (1-specificity). The discrimination threshold is defined as the amount of tumor diagnosis within a single LCI frame above which the automated algorithm determines the tissue type as tumor and it varied from 49% to 80%. The dotted diagonal line in Fig. 9 represents the random guesses and ROC curves; points above the line of random guesses are considered good classifiers, with the best ones near the (0, 1) coordinate that maximize the area under the curve (AUC). The points along the ROC curves are generated from all samples used in this study, including both tumorous and normal.


Low coherence interferometry approach for aiding fine needle aspiration biopsies.

Chang EW, Gardecki J, Pitman M, Wilsterman EJ, Patel A, Tearney GJ, Iftimia N - J Biomed Opt (2014)

(a) Comparison of the receiver operating characteristics (ROCs) for the current and previous algorithm; (b) ROC curves for two types of classifiers: with all six parameters and with one parameter sequentially eliminated. TPR, true positive rate and FPR, false positive rate.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4222708&req=5

f9: (a) Comparison of the receiver operating characteristics (ROCs) for the current and previous algorithm; (b) ROC curves for two types of classifiers: with all six parameters and with one parameter sequentially eliminated. TPR, true positive rate and FPR, false positive rate.
Mentions: To test the performance of the modified algorithm against the previously tested algorithm, we generated receiver operating characteristic (ROC) curves (see Fig. 9). An ROC curve graphically illustrates the performance of each binary classifier by plotting the true positive rate (TPR) against the false positive rate (FPR) as its discrimination threshold is varied.22 TPR is defined as sensitivity and FPR as (1-specificity). The discrimination threshold is defined as the amount of tumor diagnosis within a single LCI frame above which the automated algorithm determines the tissue type as tumor and it varied from 49% to 80%. The dotted diagonal line in Fig. 9 represents the random guesses and ROC curves; points above the line of random guesses are considered good classifiers, with the best ones near the (0, 1) coordinate that maximize the area under the curve (AUC). The points along the ROC curves are generated from all samples used in this study, including both tumorous and normal.

Bottom Line: We present portable preclinical low-coherence interference (LCI) instrumentation for aiding fine needle aspiration biopsies featuring the second-generation LCI-based biopsy probe and an improved scoring algorithm for tissue differentiation.Our instrument and algorithm were tested on 38 mice with cultured tumor mass and we show the specificity, sensitivity, and positive predictive value of tumor detection of over 0.89, 0.88, and 0.96, respectively.

View Article: PubMed Central - PubMed

Affiliation: Physical Sciences, Inc., 20 New England Business Ctr. Drive, Andover, Massachusetts 01810, United States.

ABSTRACT
We present portable preclinical low-coherence interference (LCI) instrumentation for aiding fine needle aspiration biopsies featuring the second-generation LCI-based biopsy probe and an improved scoring algorithm for tissue differentiation. Our instrument and algorithm were tested on 38 mice with cultured tumor mass and we show the specificity, sensitivity, and positive predictive value of tumor detection of over 0.89, 0.88, and 0.96, respectively.

Show MeSH
Related in: MedlinePlus