Open-Top Light-Sheet Microscopy Image Atlas of Prostate Core Needle Biopsies

Reder, Nicholas P.; Glaser, Adam K.; McCarty, Erin; Chen, Ye; True, Lawrence D.; Liu, Jonathan

doi:10.5858/arpa.2018-0466-oa

Cited by 47 publications

(60 citation statements)

References 30 publications

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“…In particular, the code presented here is specifically optimized to generate H&E-like images of tissues stained with a two-channel fluorescent analog of H&E (i.e. a fluorescent nuclear stain that mimics hematoxylin, along with the stromal stain, eosin, which is naturally fluorescent) and imaged with 3D open-top light-sheet (OTLS) microscopy [12][13][14]. We show that FalseColor-Python enables accurate and reproducible H&E false-coloring that qualitatively and quantitatively matches the appearance of H&E whole slide images, but with less variability than is seen with standard histology.…”

Section: Plos Onementioning

confidence: 99%

“…Note that while H&E false-coloring is demonstrated here, FalseColor-Python can be tailored to accommodate other preferred fluorescent staining combinations and individual color preferences. For example, FalseColor-Python can be adapted to false-color fluorescence images to mimic other chromogens, such as DAB (used in standard immunohistochemistry [13]) or other special stains (e.g. PAS, Masson's Trichrome, Toluidine Blue).…”

Section: Plos Onementioning

confidence: 99%

“…Each channel was imaged separately, in succession, with a 16-bit sCMOS camera. For more information on OTLS imaging see our previous publications [12][13][14].…”

Section: Otls Imagingmentioning

confidence: 99%

“…For example, techniques such as confocal microscopy [1,2], multiphoton microscopy [3][4][5][6], microscopy with UV surface excitation (MUSE) [7][8][9], and structured illumination microscopy (SIM) [10,11] have been explored as slide-free alternatives to frozen-section histology for rapid interoperative guidance. Additionally, light-sheet microscopy technologies, such as open-top light-sheet (OTLS) microscopy [12][13][14], when used in conjunction with tissue-clearing techniques [15], have been explored for slide-free nondestructive 3D pathology. These techniques generally rely on fluorescence collected with a sensitive monochrome detector.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, this model can accommodate multiple fluorescent stains with overlapping spectra without loss of contrast or non-physical results. Note that in addition to H&E, other chromogens can be modeled, such as the DAB stain most-commonly used for immunohistochemistry (IHC) [13].…”

Section: Introductionmentioning

confidence: 99%

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FalseColor-Python: A rapid intensity-leveling and digital-staining package for fluorescence-based slide-free digital pathology

et al. 2020

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Slide-free digital pathology techniques, including nondestructive 3D microscopy, are gaining interest as alternatives to traditional slide-based histology. In order to facilitate clinical adoption of these fluorescence-based techniques, software methods have been developed to convert grayscale fluorescence images into color images that mimic the appearance of standard absorptive chromogens such as hematoxylin and eosin (H&E). However, these falsecoloring algorithms often require manual and iterative adjustment of parameters, with results that can be inconsistent in the presence of intensity nonuniformities within an image and/or between specimens (intra-and inter-specimen variability). Here, we present an open-source (Python-based) rapid intensity-leveling and digital-staining package that is specifically designed to render two-channel fluorescence images (i.e. a fluorescent analog of H&E) to the traditional H&E color space for 2D and 3D microscopy datasets. However, this method can be easily tailored for other false-coloring needs. Our package offers (1) automated and uniform false coloring in spite of uneven staining within a large thick specimen, (2) consistent color-space representations that are robust to variations in staining and imaging conditions between different specimens, and (3) GPU-accelerated data processing to allow these methods to scale to large datasets. We demonstrate this platform by generating H&E-like images from cleared tissues that are fluorescently imaged in 3D with open-top light-sheet (OTLS) microscopy, and quantitatively characterizing the results in comparison to traditional slide-based H&E histology.

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Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

“…Each channel was imaged separately, in succession, with a 16-bit sCMOS camera. For more information on OTLS imaging see our previous publications [12][13][14].…”

Section: Otls Imagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FalseColor-Python: A rapid intensity-leveling and digital-staining package for fluorescence-based slide-free digital pathology

et al. 2020

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Comparison of Real-Time Fluorescence Confocal Digital Microscopy With Hematoxylin-Eosin–Stained Sections of Core-Needle Biopsy Specimens

et al. 2020

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IMPORTANCE Strategies to procure high-quality core-needle biopsy (CNB) specimens are critical for making basic tissue diagnoses and for ancillary testing. OBJECTIVES To investigate acquisition of fluorescence confocal microscopy (FCM) images of interventional radiology (IR)-guided CNB in real time in the radiology suite and to compare the accuracy of FCM diagnoses with those of hematoxylin-eosin (H&E)-stained CNB sections. DESIGN, SETTING, AND PARTICIPANTS In this diagnostic study, FCM imaging of IR-guided CNBs was performed in the radiology suite at a major cancer center for patients with an imaging abnormality from August 1, 2016, to April 30, 2019. The time taken to acquire FCM images and the quality of FCM images based on percentage of interpretable tissue with optimal resolution was recorded. The FCM images were read by 2 pathologists and categorized as nondiagnostic, benign/ atypical, or suspicious/malignant; these diagnoses were compared with those made using H&E-stained tissue sections. Cases with discrepant diagnosis were reassessed by the pathologists together for a consensus diagnosis. Data were analyzed from June 3 to July 19, 2019. INTERVENTIONS Each IR-guided CNB was stained with 0.6mM acridine orange, subjected to FCM imaging, and then processed to generate H&E-stained sections. MAIN OUTCOMES AND MEASURES Mean time taken for acquisition of FCM images, quality of FCM images based on interpretable percentage of the image, and accuracy of diagnostic categorization based on FCM images compared with H&E-stained sections. RESULTS A total of 105 patients (57 male [54.3%]; mean [SD] age, 63 [13] years) underwent IR-guided CNBs in a mean (SD) of 7 (2) minutes each. The FCM images showed at least 20% of the tissue with optimal quality in 101 CNB specimens (96.2%). The FCM images were accurately interpreted by the 2 pathologists in 100 of 105 cases (95.2%) (2 false-positive and 3 false-negative) and 90 of 105 cases (85.7%) (6 false-positive and 9 false-negative). A reassessment of 14 discordant diagnoses resulted in consensus diagnoses that were accurate in 101 of 105 cases (96.2%) (1 falsepositive and 3 false-negative). CONCLUSIONS AND RELEVANCE The ease of acquisition of FCM images of acceptable quality and the high accuracy of the diagnoses suggest that FCM may be useful for rapid evaluation of IR-guided CNBs. This approach warrants further investigation.

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Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment

Serafin

Koyuncu

Xie

et al. 2023

The Journal of Pathology

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Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns orInternational Society of Urological Pathology (ISUP) grade groups] of limited numbers of two-dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over-and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three-dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape-based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open-top light-sheet (OTLS) microscopy of 102 cancercontaining biopsies extracted ex vivo from the prostatectomy specimens of 46 patients. A deep learning-based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape-based nuclear features were extracted, and a nested cross-validation scheme was used to train a supervised machine classifier based on 5-year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape-based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape-based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision-support tools.

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Open-Top Light-Sheet Microscopy Image Atlas of Prostate Core Needle Biopsies

Cited by 47 publications

References 30 publications

FalseColor-Python: A rapid intensity-leveling and digital-staining package for fluorescence-based slide-free digital pathology

FalseColor-Python: A rapid intensity-leveling and digital-staining package for fluorescence-based slide-free digital pathology

Comparison of Real-Time Fluorescence Confocal Digital Microscopy With Hematoxylin-Eosin–Stained Sections of Core-Needle Biopsy Specimens

Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment

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