The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Ashley M Laughney - One of the best experts on this subject based on the ideXlab platform.
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spectral discrimination of Breast pathologies in situ using spatial frequency domain imaging
Breast Cancer Research, 2013Co-Authors: Ashley M Laughney, Venkataramanan Krishnaswamy, Elizabeth J Rizzo, Mary C Schwab, Richard J Barth, David J Cuccia, Bruce J Tromberg, Keith D Paulsen, Brian W PogueAbstract:Introduction Nationally, 25% to 50% of patients undergoing lumpectomy for local management of Breast cancer require a secondary excision because of the persistence of residual tumor. Intraoperative assessment of specimen margins by frozen-section analysis is not widely adopted in Breast-conserving surgery. Here, a new approach to wide-field optical imaging of Breast Pathology in situ was tested to determine whether the system could accurately discriminate cancer from benign tissues before routine pathological processing.
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spectral discrimination of Breast pathologies in situ using spatial frequency domain imaging
Breast Cancer Research, 2013Co-Authors: Ashley M Laughney, Venkataramanan Krishnaswamy, Elizabeth J Rizzo, Mary C Schwab, Richard J Barth, David J Cuccia, Bruce J Tromberg, Keith D Paulsen, Brian W PogueAbstract:Nationally, 25% to 50% of patients undergoing lumpectomy for local management of Breast cancer require a secondary excision because of the persistence of residual tumor. Intraoperative assessment of specimen margins by frozen-section analysis is not widely adopted in Breast-conserving surgery. Here, a new approach to wide-field optical imaging of Breast Pathology in situ was tested to determine whether the system could accurately discriminate cancer from benign tissues before routine pathological processing. Spatial frequency domain imaging (SFDI) was used to quantify near-infrared (NIR) optical parameters at the surface of 47 lumpectomy tissue specimens. Spatial frequency and wavelength-dependent reflectance spectra were parameterized with matched simulations of light transport. Spectral images were co-registered to histoPathology in adjacent, stained sections of the tissue, cut in the geometry imaged in situ. A supervised classifier and feature-selection algorithm were implemented to automate discrimination of Breast pathologies and to rank the contribution of each parameter to a diagnosis. Spectral parameters distinguished all Pathology subtypes with 82% accuracy and benign (fibrocystic disease, fibroadenoma) from malignant (DCIS, invasive cancer, and partially treated invasive cancer after neoadjuvant chemotherapy) pathologies with 88% accuracy, high specificity (93%), and reasonable sensitivity (79%). Although spectral absorption and scattering features were essential components of the discriminant classifier, scattering exhibited lower variance and contributed most to tissue-type separation. The scattering slope was sensitive to stromal and epithelial distributions measured with quantitative immunohistochemistry. SFDI is a new quantitative imaging technique that renders a specific tissue-type diagnosis. Its combination of planar sampling and frequency-dependent depth sensing is clinically pragmatic and appropriate for Breast surgical-margin assessment. This study is the first to apply SFDI to Pathology discrimination in surgical Breast tissues. It represents an important step toward imaging surgical specimens immediately ex vivo to reduce the high rate of secondary excisions associated with Breast lumpectomy procedures.
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automated classification of Breast Pathology using local measures of broadband reflectance
Journal of Biomedical Optics, 2010Co-Authors: Ashley M Laughney, Venkataramanan Krishnaswamy, Keith D Paulsen, P B Garciaallende, Olga M Conde, Wendy A Wells, Brian W PogueAbstract:We demonstrate that morphological features pertinent to a tissue's Pathology may be ascertained from localized measures of broadband reflectance, with a mesoscopic resolution (100-μm lateral spot size) that permits scanning of an entire margin for residual disease. The technical aspects and optimization of a k-nearest neighbor classifier for automated diagnosis of pathologies are presented, and its efficacy is validated in 29 Breast tissue specimens. When discriminating between benign and malignant pathologies, a sensitivity and specificity of 91 and 77% was achieved. Furthermore, detailed subtissue-type analysis was performed to consider how diverse pathologies influence scattering response and overall classification efficacy. The increased sensitivity of this technique may render it useful to guide the surgeon or pathologist where to sample Pathology for microscopic assessment.
Brian W Pogue - One of the best experts on this subject based on the ideXlab platform.
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spectral discrimination of Breast pathologies in situ using spatial frequency domain imaging
Breast Cancer Research, 2013Co-Authors: Ashley M Laughney, Venkataramanan Krishnaswamy, Elizabeth J Rizzo, Mary C Schwab, Richard J Barth, David J Cuccia, Bruce J Tromberg, Keith D Paulsen, Brian W PogueAbstract:Introduction Nationally, 25% to 50% of patients undergoing lumpectomy for local management of Breast cancer require a secondary excision because of the persistence of residual tumor. Intraoperative assessment of specimen margins by frozen-section analysis is not widely adopted in Breast-conserving surgery. Here, a new approach to wide-field optical imaging of Breast Pathology in situ was tested to determine whether the system could accurately discriminate cancer from benign tissues before routine pathological processing.
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spectral discrimination of Breast pathologies in situ using spatial frequency domain imaging
Breast Cancer Research, 2013Co-Authors: Ashley M Laughney, Venkataramanan Krishnaswamy, Elizabeth J Rizzo, Mary C Schwab, Richard J Barth, David J Cuccia, Bruce J Tromberg, Keith D Paulsen, Brian W PogueAbstract:Nationally, 25% to 50% of patients undergoing lumpectomy for local management of Breast cancer require a secondary excision because of the persistence of residual tumor. Intraoperative assessment of specimen margins by frozen-section analysis is not widely adopted in Breast-conserving surgery. Here, a new approach to wide-field optical imaging of Breast Pathology in situ was tested to determine whether the system could accurately discriminate cancer from benign tissues before routine pathological processing. Spatial frequency domain imaging (SFDI) was used to quantify near-infrared (NIR) optical parameters at the surface of 47 lumpectomy tissue specimens. Spatial frequency and wavelength-dependent reflectance spectra were parameterized with matched simulations of light transport. Spectral images were co-registered to histoPathology in adjacent, stained sections of the tissue, cut in the geometry imaged in situ. A supervised classifier and feature-selection algorithm were implemented to automate discrimination of Breast pathologies and to rank the contribution of each parameter to a diagnosis. Spectral parameters distinguished all Pathology subtypes with 82% accuracy and benign (fibrocystic disease, fibroadenoma) from malignant (DCIS, invasive cancer, and partially treated invasive cancer after neoadjuvant chemotherapy) pathologies with 88% accuracy, high specificity (93%), and reasonable sensitivity (79%). Although spectral absorption and scattering features were essential components of the discriminant classifier, scattering exhibited lower variance and contributed most to tissue-type separation. The scattering slope was sensitive to stromal and epithelial distributions measured with quantitative immunohistochemistry. SFDI is a new quantitative imaging technique that renders a specific tissue-type diagnosis. Its combination of planar sampling and frequency-dependent depth sensing is clinically pragmatic and appropriate for Breast surgical-margin assessment. This study is the first to apply SFDI to Pathology discrimination in surgical Breast tissues. It represents an important step toward imaging surgical specimens immediately ex vivo to reduce the high rate of secondary excisions associated with Breast lumpectomy procedures.
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automated classification of Breast Pathology using local measures of broadband reflectance
Journal of Biomedical Optics, 2010Co-Authors: Ashley M Laughney, Venkataramanan Krishnaswamy, Keith D Paulsen, P B Garciaallende, Olga M Conde, Wendy A Wells, Brian W PogueAbstract:We demonstrate that morphological features pertinent to a tissue's Pathology may be ascertained from localized measures of broadband reflectance, with a mesoscopic resolution (100-μm lateral spot size) that permits scanning of an entire margin for residual disease. The technical aspects and optimization of a k-nearest neighbor classifier for automated diagnosis of pathologies are presented, and its efficacy is validated in 29 Breast tissue specimens. When discriminating between benign and malignant pathologies, a sensitivity and specificity of 91 and 77% was achieved. Furthermore, detailed subtissue-type analysis was performed to consider how diverse pathologies influence scattering response and overall classification efficacy. The increased sensitivity of this technique may render it useful to guide the surgeon or pathologist where to sample Pathology for microscopic assessment.
Kimberly H Allison - One of the best experts on this subject based on the ideXlab platform.
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prognostic and predictive parameters in Breast Pathology a pathologist s primer
Modern Pathology, 2021Co-Authors: Kimberly H AllisonAbstract:The pathologist's role in the Breast cancer treatment team has evolved from rendering a diagnosis of Breast cancer, to providing a growing list of prognostic and predictive parameters such that individualized treatment decisions can be made based on likelihood of benefit from additional treatments and potential benefit from specific therapies. In all stages, ER and HER2 status help segregate Breast cancers into treatment groups with similar outcomes and treatment response rates, however, traditional pathologic parameters such as favorable histologic subtype, size, lymph node status, and Nottingham grade also have remained clinically relevant in early stage disease decision-making. This is especially true for the most common subtype of Breast cancer; ER positive, HER2 negative disease. For this same group of Breast cancers, an ever-expanding list of gene-expression panels also can provide prediction and prognostication about potential chemotherapy benefit beyond standard endocrine therapies, with the 21-gene Recurrence Score, currently the only prospectively validated predictive test for this purpose. In the more aggressive ER-negative cancer subtypes, response to neoadjuvant therapy and` the extent of tumor infiltrating lymphocytes (TILs) are more recently recognized powerful prognostic parameters, and clinical guidelines now offer additional treatment options for those high-risk patients with residual cancer after standard neoadjuvant therapy. In stage four disease, predictive tests like germline BRCA status, tumor PIK3CA mutation status (in ER+ metastatic disease) and PDL-1 status (in triple negative metastatic disease) are now used to determine additional new treatment options. The objective of this review is to describe the latest in prognostic and predictive parameters in Breast cancer as they are relevant to standard Pathology reporting and how they are used in Breast cancer clinical treatment decisions.
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second opinion strategies in Breast Pathology a decision analysis addressing over treatment under treatment and care costs
Breast Cancer Research and Treatment, 2018Co-Authors: Anna N A Tosteson, Heidi D Nelson, Patricia A Carney, Tracy Onega, Berta M Geller, Qian Yang, Gary Longton, Samir Soneji, Margaret S Pepe, Kimberly H AllisonAbstract:To estimate the potential near-term population impact of alternative second opinion Breast biopsy Pathology interpretation strategies. Decision analysis examining 12-month outcomes of Breast biopsy for nine Breast Pathology interpretation strategies in the U.S. health system. Diagnoses of 115 practicing pathologists in the Breast Pathology Study were compared to reference-standard-consensus diagnoses with and without second opinions. Interpretation strategies were defined by whether a second opinion was sought universally or selectively (e.g., 2nd opinion if invasive). Main outcomes were the expected proportion of concordant Breast biopsy diagnoses, the proportion involving over- or under-interpretation, and cost of care in U.S. dollars within one-year of biopsy. Without a second opinion, 92.2% of biopsies received a concordant diagnosis. Concordance rates increased under all second opinion strategies, and the rate was highest (95.1%) and under-treatment lowest (2.6%) when all biopsies had second opinions. However, over-treatment was lowest when second opinions were sought selectively for initial diagnoses of invasive cancer, DCIS, or atypia (1.8 vs. 4.7% with no 2nd opinions). This strategy also had the lowest projected 12-month care costs ($5.907 billion vs. $6.049 billion with no 2nd opinions). Second opinion strategies could lower overall care costs while reducing both over- and under-treatment. The most accurate cost-saving strategy required second opinions for initial diagnoses of invasive cancer, DCIS, or atypia.
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the spectrum of risk lesions in Breast Pathology risk factors or cancer precursors
2018Co-Authors: Kimberly H Allison, Kelly L MooneyAbstract:Risk lesions of the Breast are histologically, biologically, and clinically diverse, with varying associated risks of cancer development. In this chapter, the definitions of risk markers and precursor lesions are defined and explored. We summarize the contemporary paradigm of Breast carcinogenesis and how atypical lesions of the Breast may fit into the estrogen receptor-positive cancer development pathways. For atypical ductal hyperplasia, atypical lobular hyperplasia, flat epithelial atypia, radial scar, papilloma, and fibroepithelial lesions, evidence is summarized regarding each lesion’s (1) association with long-term overall risk of developing Breast cancer and (2) possible precursor role in cancer development.
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identifying and processing the gap between perceived and actual agreement in Breast Pathology interpretation
Modern Pathology, 2016Co-Authors: Patricia A Carney, Paul D Frederick, Natalia V Oster, Donald L Weaver, Kimberly H Allison, Berta M Geller, Thomas R Morgan, Joann G ElmoreAbstract:We examined how pathologists' process their perceptions of how their interpretations on diagnoses for Breast Pathology cases agree with a reference standard. To accomplish this, we created an individualized self-directed continuing medical education program that showed pathologists interpreting Breast specimens how their interpretations on a test set compared with a reference diagnosis developed by a consensus panel of experienced Breast pathologists. After interpreting a test set of 60 cases, 92 participating pathologists were asked to estimate how their interpretations compared with the standard for benign without atypia, atypia, ductal carcinoma in situ and invasive cancer. We then asked pathologists their thoughts about learning about differences in their perceptions compared with actual agreement. Overall, participants tended to overestimate their agreement with the reference standard, with a mean difference of 5.5% (75.9% actual agreement; 81.4% estimated agreement), especially for atypia and were least likely to overestimate it for invasive Breast cancer. Non-academic affiliated pathologists were more likely to more closely estimate their performance relative to academic affiliated pathologists (77.6 vs 48%; P=0.001), whereas participants affiliated with an academic medical center were more likely to underestimate agreement with their diagnoses compared with non-academic affiliated pathologists (40 vs 6%). Before the continuing medical education program, nearly 55% (54.9%) of participants could not estimate whether they would overinterpret the cases or underinterpret them relative to the reference diagnosis. Nearly 80% (79.8%) reported learning new information from this individualized web-based continuing medical education program, and 23.9% of pathologists identified strategies they would change their practice to improve. In conclusion, when evaluating Breast Pathology specimens, pathologists do a good job of estimating their diagnostic agreement with a reference standard, but for atypia cases, pathologists tend to overestimate diagnostic agreement. Many participants were able to identify ways to improve.
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second opinion in Breast Pathology policy practice and perception
Journal of Clinical Pathology, 2014Co-Authors: Berta M Geller, Heidi D Nelson, Paul D Frederick, Patricia A Carney, Donald L Weaver, Kimberly H Allison, Anna N A Tosteson, Tracy Onega, Joann G ElmoreAbstract:Aims To assess the laboratory policies, pathologists’ clinical practice and perceptions about the value of second opinions for Breast Pathology cases among pathologists practising in the USA. Methods Cross-sectional data were collected from 252 pathologists who interpret Breast specimens in eight states using a web-based survey. Descriptive statistics were used to characterise findings. Results Most participants had >10 years of experience interpreting Breast specimens (64%), were not affiliated with academic centres (73%) and were not considered experts by their peers (79%). Laboratory policies mandating second opinions varied by diagnosis: invasive cancer 65%; ductal carcinoma in situ (DCIS) 56%; atypical ductal hyperplasia 36% and other benign cases 33%. 81% obtained second opinions in the absence of policies. Participants believed they improve diagnostic accuracy (96%) and protect from malpractice suits (83%), and were easy to obtain, did not take too much time and did not make them look less adequate. The most common (60%) approach to resolving differences between the first and second opinion is to ask for a third opinion, followed by reaching a consensus. Conclusions Laboratory-based second opinion policies vary for Breast Pathology but are most common for invasive cancer and DCIS cases. Pathologists have favourable attitudes towards second opinions, adhere to policies and obtain them even when policies are absent. Those without a formal policy may benefit from supportive clinical practices and systems that help obtain second opinions.
Jeffrey T Henderson - One of the best experts on this subject based on the ideXlab platform.
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virtual slide telePathology enables an innovative telehealth rapid Breast care clinic
Seminars in Diagnostic Pathology, 2009Co-Authors: Ana Maria Lopez, Gail P Barker, Lynne Richter, Elizabeth A Krupinski, Anna R Graham, Fangru Lian, Lauren L Grasso, Ashley Miller, Lindsay N Kreykes, Jeffrey T HendersonAbstract:An innovative telemedicine-enabled rapid Breast care service is described that bundles telemammography, telePathology, and teleoncology services into a single day process. The service is called the UltraClinics® Process. Since the core services are at four different physical locations a challenge has been to obtain STAT second opinion readouts on newly diagnosed Breast cancer cases. In order to provide same day QA re-review of Breast surgical Pathology cases, a DMetrix DX-40 ultrarapid virtual slide scanner (DMetrix, Inc., Tucson, AZ) was installed at the participating laboratory. Glass slides of Breast cancer and Breast hyperplasia cases were scanned the same day the slides were produced by the University Physicians Healthcare Hospital histology laboratory. Virtual slide telePathology was used for STAT quality assurance readouts at University Medical Center, 6 miles away. There was complete concurrence with the primary diagnosis in 139 (90.3%) of cases. There were 4 (2.3%) major discrepancies, which would have resulted in a different therapy and 3 (1.9%) minor discrepancies. Three cases (1.9%) were deferred for immunohistochemistry. In 2 cases (1.3%), the case was deferred for examination of the glass slides by the reviewing pathologists at University Medical Center. We conclude that the virtual slide telePathology QA program found a small number of significant diagnostic discrepancies. The virtual slide telePathology program service increased the job satisfaction of subspecialty pathologists without special training in Breast Pathology, assigned to cover the general surgical Pathology service at a small satellite university hospital.
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virtual slide telePathology enables an innovative telehealth rapid Breast care clinic
Human Pathology, 2009Co-Authors: Ana Maria Lopez, Gail P Barker, Lynne Richter, Elizabeth A Krupinski, Anna R Graham, Fangru Lian, Lauren L Grasso, Ashley Miller, Lindsay N Kreykes, Jeffrey T HendersonAbstract:Summary An innovative telemedicine-enabled rapid Breast care service is described that bundles telemammography, telePathology, and teleoncology services into a single day process. The service is called the UltraClinics® Process . Because the core services are at 4 different physical locations, a challenge has been to obtain stat second opinion readouts on newly diagnosed Breast cancer cases. To provide same day quality assurance rereview of Breast surgical Pathology cases, a DMetrix DX-40 ultrarapid virtual slide scanner (DMetrix Inc, Tucson, AZ) was installed at the participating laboratory. Glass slides of Breast cancer and Breast hyperplasia cases were scanned the same day the slides were produced by the University Physicians Healthcare Hospital histology laboratory. Virtual slide telePathology was used for stat quality assurance readouts at University Medical Center, 6 miles away. There was complete concurrence with the primary diagnosis in 139 (90.3%) of cases. There were 4 (2.3%) major discrepancies, which would have resulted in a different therapy and 3 (1.9%) minor discrepancies. Three cases (1.9%) were deferred for immunohistochemistry. In 2 cases (1.3%), the case was deferred for examination of the glass slides by the reviewing pathologists at University Medical Center. We conclude that the virtual slide telePathology quality assurance program found a small number of significant diagnostic discrepancies. The virtual slide telePathology program service increased the job satisfaction of subspecialty pathologists without special training in Breast Pathology, assigned to cover the general surgical Pathology service at a small satellite university hospital.
Venkataramanan Krishnaswamy - One of the best experts on this subject based on the ideXlab platform.
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spectral discrimination of Breast pathologies in situ using spatial frequency domain imaging
Breast Cancer Research, 2013Co-Authors: Ashley M Laughney, Venkataramanan Krishnaswamy, Elizabeth J Rizzo, Mary C Schwab, Richard J Barth, David J Cuccia, Bruce J Tromberg, Keith D Paulsen, Brian W PogueAbstract:Introduction Nationally, 25% to 50% of patients undergoing lumpectomy for local management of Breast cancer require a secondary excision because of the persistence of residual tumor. Intraoperative assessment of specimen margins by frozen-section analysis is not widely adopted in Breast-conserving surgery. Here, a new approach to wide-field optical imaging of Breast Pathology in situ was tested to determine whether the system could accurately discriminate cancer from benign tissues before routine pathological processing.
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spectral discrimination of Breast pathologies in situ using spatial frequency domain imaging
Breast Cancer Research, 2013Co-Authors: Ashley M Laughney, Venkataramanan Krishnaswamy, Elizabeth J Rizzo, Mary C Schwab, Richard J Barth, David J Cuccia, Bruce J Tromberg, Keith D Paulsen, Brian W PogueAbstract:Nationally, 25% to 50% of patients undergoing lumpectomy for local management of Breast cancer require a secondary excision because of the persistence of residual tumor. Intraoperative assessment of specimen margins by frozen-section analysis is not widely adopted in Breast-conserving surgery. Here, a new approach to wide-field optical imaging of Breast Pathology in situ was tested to determine whether the system could accurately discriminate cancer from benign tissues before routine pathological processing. Spatial frequency domain imaging (SFDI) was used to quantify near-infrared (NIR) optical parameters at the surface of 47 lumpectomy tissue specimens. Spatial frequency and wavelength-dependent reflectance spectra were parameterized with matched simulations of light transport. Spectral images were co-registered to histoPathology in adjacent, stained sections of the tissue, cut in the geometry imaged in situ. A supervised classifier and feature-selection algorithm were implemented to automate discrimination of Breast pathologies and to rank the contribution of each parameter to a diagnosis. Spectral parameters distinguished all Pathology subtypes with 82% accuracy and benign (fibrocystic disease, fibroadenoma) from malignant (DCIS, invasive cancer, and partially treated invasive cancer after neoadjuvant chemotherapy) pathologies with 88% accuracy, high specificity (93%), and reasonable sensitivity (79%). Although spectral absorption and scattering features were essential components of the discriminant classifier, scattering exhibited lower variance and contributed most to tissue-type separation. The scattering slope was sensitive to stromal and epithelial distributions measured with quantitative immunohistochemistry. SFDI is a new quantitative imaging technique that renders a specific tissue-type diagnosis. Its combination of planar sampling and frequency-dependent depth sensing is clinically pragmatic and appropriate for Breast surgical-margin assessment. This study is the first to apply SFDI to Pathology discrimination in surgical Breast tissues. It represents an important step toward imaging surgical specimens immediately ex vivo to reduce the high rate of secondary excisions associated with Breast lumpectomy procedures.
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automated classification of Breast Pathology using local measures of broadband reflectance
Journal of Biomedical Optics, 2010Co-Authors: Ashley M Laughney, Venkataramanan Krishnaswamy, Keith D Paulsen, P B Garciaallende, Olga M Conde, Wendy A Wells, Brian W PogueAbstract:We demonstrate that morphological features pertinent to a tissue's Pathology may be ascertained from localized measures of broadband reflectance, with a mesoscopic resolution (100-μm lateral spot size) that permits scanning of an entire margin for residual disease. The technical aspects and optimization of a k-nearest neighbor classifier for automated diagnosis of pathologies are presented, and its efficacy is validated in 29 Breast tissue specimens. When discriminating between benign and malignant pathologies, a sensitivity and specificity of 91 and 77% was achieved. Furthermore, detailed subtissue-type analysis was performed to consider how diverse pathologies influence scattering response and overall classification efficacy. The increased sensitivity of this technique may render it useful to guide the surgeon or pathologist where to sample Pathology for microscopic assessment.