Histological microdissection in diagnostic and investigative pathology

Going, James J.

doi:10.1016/j.mpdhp.2009.09.008

Cited by 6 publications

(7 citation statements)

References 50 publications

(23 reference statements)

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“…Coupled with a digital slide viewer, such a system can support fully remote pathology review of digitized WSIs, allowing NGS laboratories to widen their access to reviewing pathologists. Integration of SmartPath with automated microdissection systems 15 , 16 , 19 could allow for tissue extraction workflows which are almost entirely automated, with a pathologist needed only to approve or modify input parameters, and potentially be economically and clinically beneficial for NGS laboratories.…”

Section: Discussionmentioning

confidence: 99%

“…Laser-capture microdissection was introduced about two decades ago [11][12][13] , but has not been widely adopted in clinical laboratories because precise dissection of single tumor cells from FFPE slides is rarely necessary for clinical testing 14 . Lower resolution mechanical macrodissection systems have also been developed as more clinically pragmatic alternatives [15][16][17][18] . These systems can be combined with digital slide marking (digitally guided macrodissection), enabling integration with computer vision models for tumor enrichment 19,20 .…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides

Osinski

BenTaieb

et al. 2022

Modern Pathology

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To achieve minimum DNA input requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Unfortunately, misestimation may cause tissue waste and increased laboratory costs. We developed an artificial intelligence (AI)-augmented smart pathology review system (SmartPath) to empower pathologists with quantitative metrics for accurately determining tissue extraction parameters. SmartPath uses two deep learning architectures, a U-Net based network for cell segmentation and a multi-field-of-view convolutional network for tumor area segmentation, to extract features from digitized H&E-stained formalin-fixed paraffin-embedded slides. From the segmented tumor area, SmartPath suggests a macrodissection area. To predict DNA yield per slide, the extracted features from within the macrodissection area are correlated with known DNA yields to fit a regularized linear model ( R = 0.85). Then, a pathologist-defined target yield divided by the predicted DNA yield per slide gives the number of slides to scrape. Following model development, an internal validation trial was conducted within the Tempus Labs molecular sequencing laboratory. We evaluated our system on 501 clinical colorectal cancer slides, where half received SmartPath-augmented review and half traditional pathologist review. The SmartPath cohort had 25% more DNA yields within a desired target range of 100–2000 ng. The number of extraction attempts was statistically unchanged between cohorts. The SmartPath system recommended fewer slides to scrape for large tissue sections, saving tissue in these cases. Conversely, SmartPath recommended more slides to scrape for samples with scant tissue sections, especially those with degraded DNA, helping prevent costly re-extraction due to insufficient extraction yield. A statistical analysis was performed to measure the impact of covariates on the results, offering insights on how to improve future applications of SmartPath. With these improvements, AI-augmented histopathologic review has the potential to decrease tissue waste, sequencing time, and laboratory costs by optimizing DNA yields, especially for samples with scant tissue and/or degraded DNA.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides

Osinski

BenTaieb

et al. 2022

Modern Pathology

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“…8 Furthermore, we did not examine the addition of laser capture microdissection, although this is well described. 14 Importantly, once a positive actin screen result has been obtained, the specimen is shown to contain a sufficient quantity and quality of RNA for a variety of subsequent RT-PCR reactions.…”

Section: Discussionmentioning

confidence: 99%

Routinely Obtained Diagnostic Material as a Source of RNA for Personalized Medicine in Lung Cancer Patients

Andrews

Baird

Wallace

et al. 2011

Journal of Thoracic Oncology

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“…Laser capture microdissection (LCM) was introduced about two decades ago [14], but it has not been widely adopted in the clinical laboratory setting. LCM has been noted in multiple studies to diminish the yield and quality of retrieved material [9, 15-17], although others report that sample recovery is not impaired by the LCM process [18]. Even so, the precise dissection of single tumor cells from FFPE slides is seldom necessary for clinical molecular testing, whereas the rapid and cost effective retrieval of a sufficient DNA or RNA sample is paramount; therefore, lower resolution microdissection methods can be substituted for LCM.…”

Section: Introductionmentioning

confidence: 99%

Digitally guided microdissection aids somatic mutation detection in difficult to dissect tumors

Geiersbach

Adey

Welker³

et al. 2016

Cancer Genetics

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Molecular genetic testing on formalin fixed, paraffin embedded (FFPE) tumors frequently requires dissection of tumor from tissue sections mounted on glass slides. In a process referred to as “manual macrodissection,” the pathologist reviews an H&E stained slide at the light microscope and marks areas for dissection, and then the laboratory performs manual dissection from adjacent sections without the aid of a microscope, using the marked reference H&E slide as a guide. Manual macrodissection may be inadequate for tissue sections with low tumor content. We compared manual macrodissection to a new method, digitally guided microdissection, on a series of 32 FFPE pancreatic cancer samples. KRAS hotspot mutation profiling was performed using the Sequenom MassARRAY system (Agena Bioscience). Digitally guided microdissection was performed on multiple smaller areas of high tumor content selected from within the larger areas marked for manual macrodissection. The KRAS mutant allele fraction and estimated neoplastic cellularity were significantly higher in samples obtained by digitally guided microdissection (p <0.01), and 7 of the 32 samples (22%) showed a detectable mutation only with digitally guided microdissection. DNA quality and yield per cubic millimeter of dissected tissue were similar for both dissection methods. These results indicate a significant improvement in tumor content achievable with digitally guided microdissection.

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Histological microdissection in diagnostic and investigative pathology

Cited by 6 publications

References 50 publications

Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides

Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides

Routinely Obtained Diagnostic Material as a Source of RNA for Personalized Medicine in Lung Cancer Patients

Digitally guided microdissection aids somatic mutation detection in difficult to dissect tumors

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