Glandular architecture is currently the basis for the Gleason grading of prostate biopsies. To visualize and computationally analyze glandular features in large 3D pathology datasets, we developed an annotation-free segmentation method for 3D prostate glands that relies upon synthetic 3D immunofluorescence (IF) enabled by generative adversarial networks. By using a fluorescent analog of H and E (cheap and fast stain) as an input, our strategy allows for accurate glandular segmentation that does not rely upon subjective and tedious human annotations or slow and expensive 3D immunolabeling. We aim to demonstrate that this 3D segmentation will enable improved prostate cancer prognostication.
A current challenge is providing an accurate diagnosis in a timely manner for patients at risk of having prostate cancer. We developed and demonstrated a non-destructive procedure in which 12 biopsies can be cleared, fluorescently labeled, imaged with an open-top light-sheet (OTLS) microscope, and then diagnosed by a pathologist within an hour of biopsy. Using conventional histology as the gold standard, the accuracy, sensitivity, and specificity of 1Hr2Dx were all >90%. Such a method could potentially provide patients with a preliminary on-site diagnosis after a biopsy procedure, thereby alleviating anxiety and potentially expediting treatments.
Significance: Processing and diagnosing a set of 12 prostate biopsies using conventional histology methods typically take at least one day. A rapid and accurate process performed while the patient is still on-site could significantly improve the patient’s quality of life.
Aim: We develop and assess the feasibility of a one-hour-to-diagnosis (1Hr2Dx) method for processing and providing a preliminary diagnosis of a set of 12 prostate biopsies.
Approach: We developed a fluorescence staining, optical clearing, and 3D open-top light-sheet microscopy workflow to enable 12 prostate needle core biopsies to be processed and diagnosed within an hour of receipt. We analyzed 44 biopsies by the 1Hr2Dx method, which does not consume tissue. The biopsies were then processed for routine, slide-based 2D histology. Three pathologists independently evaluated the 3D 1Hr2Dx and 2D slide-based datasets in a blinded, randomized fashion. Turnaround times were recorded, and the accuracy of our method was compared with gold-standard slide-based histology.
Results: The average turnaround time for tissue processing, imaging, and diagnosis was 44.5 min. The sensitivity and specificity of 1Hr2Dx in diagnosing cancer were both >90 % .
Conclusions: The 1Hr2Dx method has the potential to improve patient care by providing an accurate preliminary diagnosis within an hour of biopsy.
Glandular features play an important role in the evaluation of prostate cancer. There has been significant interest in the use of 2D pathomics (feature extraction) approaches for detection, diagnosis, and characterization of prostate cancer on digitized tissue slide images. With the development of 3D microscopy techniques, such as open-top light-sheet (OTLS), there is an opportunity for rapid 3D imaging of large tissue specimens such as whole biopsies. In this study, we sought to investigate whether 3D features of gland morphology, namely volume and surface curvature, from OTLS images offer superior discrimination between malignant and benign glands compared to the traditional 2D gland features, namely area and curvature, alone. In this study, a cohort of 8 de-identified fresh prostate biopsies comprehensively imaged in 3D via the OTLS platform. A total of 367 glands were segmented from these images, of which 79 were identified as benign and 288 were identified as malignant. Glands were segmented using a 3D watershed algorithm followed by post-processing steps to filter out falsepositive regions. The 2D and 3D features were compared quantitatively and qualitatively. Our experiments demonstrated that a model using 3D features outperformed one using 2D features in differentiating benign and malignant glands. In 3D, both features, gland volume (p = 1.45 × 10−3) and surface curvature (p = 3.2 × 10−3), were found to be informative whereas in 2D, only gland area (p = 9 × 10−18) was found to be discriminating (p = 0.79 for 2D curvature). Notable visual and quantitative differences between 3D benign/malignant glands encourage the development of additional more sophisticated features in the future.
Intraoperative assessment of breast surgical margins will be of value for reducing the rate of re-excision surgeries for lumpectomy patients. While frozen-section histology is used for intraoperative guidance of certain cancers, it provides limited sampling of the margin surface (typically <1 % of the margin) and is inferior to gold-standard histology, especially for fatty tissues that do not freeze well, such as breast specimens. Microscopy with ultraviolet surface excitation (MUSE) is a nondestructive superficial optical-sectioning technique that has the potential to enable rapid, high-resolution examination of excised margin surfaces. Here, a MUSE system is developed with fully automated sample translation to image fresh tissue surfaces over large areas and at multiple levels of defocus, at a rate of ∼5 min / cm2. Surface extraction is used to improve the comprehensiveness of surface imaging, and 3-D deconvolution is used to improve resolution and contrast. In addition, an improved fluorescent analog of conventional H&E staining is developed to label fresh tissues within ∼5 min for MUSE imaging. We compare the image quality of our MUSE system with both frozen-section and conventional H&E histology, demonstrating the feasibility to provide microscopic visualization of breast margin surfaces at speeds that are relevant for intraoperative use.
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