Browsing by Author "Krishnamurthy, Savitri"

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    Confocal fluorescence microscopy for rapid evaluation of invasive tumor cellularity of inflammatory breast carcinoma core needle biopsies
    (Springer, 2014) Dobbs, Jessica; Krishnamurthy, Savitri; Kyrish, Matthew; Benveniste, Ana Paula; Yang, Wei; Richards-Kortum, Rebecca
    Tissue sampling is a problematic issue for inflammatory breast carcinoma, and immediate evaluation following core needle biopsy is needed to evaluate specimen adequacy. We sought to determine if confocal fluorescence microscopy provides sufficient resolution to evaluate specimen adequacy by comparing invasive tumor cellularity estimated from standard histologic images to invasive tumor cellularity estimated from confocal images of breast core needle biopsy specimens. Grayscale confocal fluorescence images of breast core needle biopsy specimens were acquired following proflavine application. A breast-dedicated pathologist evaluated invasive tumor cellularity in histologic images with hematoxylin and eosin staining and in grayscale and false-colored confocal images of cores. Agreement between cellularity estimates was quantified using a kappa coefficient. 23 cores from 23 patients with suspected inflammatory breast carcinoma were imaged. Confocal images were acquired in an average of less than 2 min per core. Invasive tumor cellularity estimated from histologic and grayscale confocal images showed moderate agreement by kappa coefficient: κ = 0.48 ± 0.09 (p < 0.001). Grayscale confocal images require less than 2 min for acquisition and allow for evaluation of invasive tumor cellularity in breast core needle biopsy specimens with moderate agreement to histologic images. We show that confocal fluorescence microscopy can be performed immediately following specimen acquisition and could indicate the need for additional biopsies at the initial visit.
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    Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues
    (BioMed Central, 2015) Dobbs, Jessica L.; Mueller, Jenna L.; Krishnamurthy, Savitri; Shin, Dongsuk; Kuerer, Henry; Yang, Wei; Ramanujam, Nirmala; Richards-Kortum, Rebecca
    Abstract Introduction Pathologists currently diagnose breast lesions through histologic assessment, which requires fixation and tissue preparation. The diagnostic criteria used to classify breast lesions are qualitative and subjective, and inter-observer discordance has been shown to be a significant challenge in the diagnosis of selected breast lesions, particularly for borderline proliferative lesions. Thus, there is an opportunity to develop tools to rapidly visualize and quantitatively interpret breast tissue morphology for a variety of clinical applications. Methods Toward this end, we acquired images of freshly excised breast tissue specimens from a total of 34 patients using confocal fluorescence microscopy and proflavine as a topical stain. We developed computerized algorithms to segment and quantify nuclear and ductal parameters that characterize breast architectural features. A total of 33 parameters were evaluated and used as input to develop a decision tree model to classify benign and malignant breast tissue. Benign features were classified in tissue specimens acquired from 30 patients and malignant features were classified in specimens from 22 patients. Results The decision tree model that achieved the highest accuracy for distinguishing between benign and malignant breast features used the following parameters: standard deviation of inter-nuclear distance and number of duct lumens. The model achieved 81 % sensitivity and 93 % specificity, corresponding to an area under the curve of 0.93 and an overall accuracy of 90 %. The model classified IDC and DCIS with 92 % and 96 % accuracy, respectively. The cross-validated model achieved 75 % sensitivity and 93 % specificity and an overall accuracy of 88 %. Conclusions These results suggest that proflavine staining and confocal fluorescence microscopy combined with image analysis strategies to segment morphological features could potentially be used to quantitatively diagnose freshly obtained breast tissue at the point of care without the need for tissue preparation.
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    Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues
    (BioMed Central, 2015) Dobbs, Jessica L.; Mueller, Jenna L.; Krishnamurthy, Savitri; Shin, Dongsuk; Kuerer, Henry; Yang, Wei; Ramanujam, Nirmala; Richards-Kortum, Rebecca
    Introduction: Pathologists currently diagnose breast lesions through histologic assessment, which requires fixation and tissue preparation. The diagnostic criteria used to classify breast lesions are qualitative and subjective, and inter-observer discordance has been shown to be a significant challenge in the diagnosis of selected breast lesions, particularly for borderline proliferative lesions. Thus, there is an opportunity to develop tools to rapidly visualize and quantitatively interpret breast tissue morphology for a variety of clinical applications. Methods: Toward this end, we acquired images of freshly excised breast tissue specimens from a total of 34 patients using confocal fluorescence microscopy and proflavine as a topical stain. We developed computerized algorithms to segment and quantify nuclear and ductal parameters that characterize breast architectural features. A total of 33 parameters were evaluated and used as input to develop a decision tree model to classify benign and malignant breast tissue. Benign features were classified in tissue specimens acquired from 30 patients and malignant features were classified in specimens from 22 patients. Results: The decision tree model that achieved the highest accuracy for distinguishing between benign and malignant breast features used the following parameters: standard deviation of inter-nuclear distance and number of duct lumens. The model achieved 81ﾠ% sensitivity and 93ﾠ% specificity, corresponding to an area under the curve of 0.93 and an overall accuracy of 90ﾠ%. The model classified IDC and DCIS with 92ﾠ% and 96ﾠ% accuracy, respectively. The cross-validated model achieved 75ﾠ% sensitivity and 93ﾠ% specificity and an overall accuracy of 88ﾠ%. Conclusions: These results suggest that proflavine staining and confocal fluorescence microscopy combined with image analysis strategies to segment morphological features could potentially be used to quantitatively diagnose freshly obtained breast tissue at the point of care without the need for tissue preparation.