Deep learning classification of deep ultraviolet fluorescence images toward intra-operative margin assessment in breast cancer.

Background: Breast-conserving surgery is aimed at removing all cancerous cells while minimizing the loss of healthy tissue. To ensure a balance between complete resection of cancer and preservation of healthy tissue, it is necessary to assess themargins of the removed specimen during the operation. Deep ultraviolet (DUV) fluorescence scanning microscopy provides rapid whole-surface imaging (WSI) of resected tissues with significant contrast between malignant and normal/benign tissue. Intra-operative margin assessment with DUV images would benefit from an automated breast cancer classification method. Methods: Deep learning has shown promising results in breast cancer classification, but the limited DUV image dataset presents the challenge of overfitting to train a robust network. To overcome this challenge, the DUV-WSI images are split into small patches, and features are extracted using a pre-trained convolutional neural network-afterward, a gradient-boosting tree trains on these features for patch-level classification. An ensemble learning approach merges patch-level classification results and regional importance to determine the margin status. An explainable artificial intelligence method calculates the regional importance values. Results: The proposed method's ability to determine the DUV WSI was high with 95% accuracy. The 100% sensitivity shows that the method can detect malignant cases efficiently. The method could also accurately localize areas that contain malignant or normal/benign tissue. Conclusion: The proposed method outperforms the standard deep learning classification methods on the DUV breast surgical samples. The results suggest that it can be used to improve classification performance and identify cancerous regions more effectively.

Analysis of Deep Ultraviolet Fluorescence Images for Intraoperative Breast Tumor Margin Assessment.

Positive margin status after breast-conserving surgery (BCS) is a predictor of higher rates of local recurrence. Intraoperative margin assessment aims to achieve negative surgical margin status at the first operation, thus reducing the re-excision rates that are usually associated with potential surgical complications, increased medical costs, and mental pressure on patients. Microscopy with ultraviolet surface excitation (MUSE) can rapidly image tissue surfaces with subcellular resolution and sharp contrasts by utilizing the nature of the thin optical sectioning thickness of deep ultraviolet light. We have previously imaged 66 fresh human breast specimens that were topically stained with propidium iodide and eosin Y using a customized MUSE system. To achieve objective and automated assessment of MUSE images, a machine learning model is developed for binary (tumor vs. normal) classification of obtained MUSE images. Features extracted by texture analysis and pre-trained convolutional neural networks (CNN) have been investigated for sample descriptions. A sensitivity, specificity, and accuracy better than 90% have been achieved for detecting tumorous specimens. The result suggests the potential of MUSE with machine learning being utilized for intraoperative margin assessment during BCS.

Automated assessment of breast margins in deep ultraviolet fluorescence images using texture analysis.

Microscopy with ultraviolet surface excitation (MUSE) is increasingly studied for intraoperative assessment of tumor margins during breast-conserving surgery to reduce the re-excision rate. Here we report a two-step classification approach using texture analysis of MUSE images to automate the margin detection. A study dataset consisting of MUSE images from 66 human breast tissues was constructed for model training and validation. Features extracted using six texture analysis methods were investigated for tissue characterization, and a support vector machine was trained for binary classification of image patches within a full image based on selected feature subsets. A weighted majority voting strategy classified a sample as tumor or normal. Using the eight most predictive features ranked by the maximum relevance minimum redundancy and Laplacian scores methods has achieved a sample classification accuracy of 92.4% and 93.0%, respectively. Local binary pattern alone has achieved an accuracy of 90.3%.

Sensitive and Specific Immunohistochemistry Protocol for Nucleocapsid Protein from All Common SARS-CoV-2 Virus Strains in Formalin-Fixed, Paraffin Embedded Tissues.

Human coronavirus disease 2019 (COVID-19) is a life-threatening and highly contagious disease caused by coronavirus SARS-CoV-2. Sensitive and specific detection of SARS-CoV-2 viral proteins in tissues and cells of COVID-19 patients will support investigations of the biologic behavior and tissue and cell tropism of this virus. We identified commercially available affinity-purified polyclonal antibodies raised against nucleocapsid and spike proteins of SARS-CoV-2 that provide sensitive and specific detection of the virus by immunohistochemistry in formalin-fixed, paraffin-embedded tissue. Two immunohistochemistry protocols are presented that are mutually validated by the matched detection patterns of the two distinct viral antigens in virus-infected cells within autopsy lung tissue of COVID-19 deceased patients. Levels of nucleocapsid protein in the lungs of COVID-19 decedents, as measured by quantitative histo-cytometry of immunohistochemistry images, showed an excellent log-linear relationship with levels of viral nucleocapsid RNA levels, as measured by qRT-PCR. Importantly, since the nucleocapsid protein sequence is conserved across all known viral strains, the nucleocapsid immunohistochemistry protocol is expected to recognize all common variants of SARS-CoV-2. Negative controls include autopsy lung tissues from patients who died from non-COVID-19 respiratory disease and control rabbit immunoglobulin. Sensitive detection of SARS-CoV-2 in human tissues will provide insights into viral tissue and cell distribution and load in patients with active infection, as well as provide insight into the clearance rate of virus in later COVID-19 disease stages. The protocols are also expected to be readily transferable to detect SARS-CoV-2 proteins in tissues of experimental animal models or animals suspected to serve as viral reservoirs.

Diffuse interstitial pneumonia-like/macrophage activation syndrome-like changes in patients with COVID-19 correlate with length of illness.

OBJECTIVES: Assess the pathologic changes in the lungs of COVID-19 decedents and correlate these changes with demographic data, clinical course, therapies, and duration of illness. METHODS: Lungs of 12 consecutive COVID-19 decedents consented for autopsy were evaluated for gross and histopathologic abnormalities. A complete Ghon "en block" dissection was performed on all cases; lung weights and gross characteristics recorded. Immunohistochemical studies were performed to characterize lymphocytic infiltrates and to assess SARS-CoV-2 capsid protein. RESULTS: Two distinct patterns of pulmonary involvement were identified. Three of 12 cases demonstrated a predominance of acute alveolar damage (DAD) while 9 of 12 cases demonstrated a marked increase in intra-alveolar macrophages in a fashion resembling desquamative interstitial pneumonia or macrophage activation syndrome (DIP/MAS). Two patterns were correlated solely with a statistically significant difference in the duration of illness. The group exhibiting DAD had duration of illness of 5.7 days while the group with DIP/MAS had duration of illness of 21.5 days (t-test p = 0.014). CONCLUSIONS: The pulmonary pathology of COVID-19 patients demonstrates a biphasic pattern, an acute phase demonstrating DAD changes while the patients with a more prolonged course exhibit a different pattern that resembles DIP/MAS-like pattern. The potential mechanisms and clinical significance are discussed.

Rapid assessment of breast tumor margins using deep ultraviolet fluorescence scanning microscopy.

SIGNIFICANCE: Re-excision rates for women with invasive breast cancer undergoing breast conserving surgery (or lumpectomy) have decreased in the past decade but remain substantial. This is mainly due to the inability to assess the entire surface of an excised lumpectomy specimen efficiently and accurately during surgery. AIM: The goal of this study was to develop a deep-ultraviolet scanning fluorescence microscope (DUV-FSM) that can be used to accurately and rapidly detect cancer cells on the surface of excised breast tissue. APPROACH: A DUV-FSM was used to image the surfaces of 47 (31 malignant and 16 normal/benign) fresh breast tissue samples stained in propidium iodide and eosin Y solutions. A set of fluorescence images were obtained from each sample using low magnification (4 × ) and fully automated scanning. The images were stitched to form a color image. Three nonmedical evaluators were trained to interpret and assess the fluorescence images. Nuclear-cytoplasm ratio (N/C) was calculated and used for tissue classification. RESULTS: DUV-FSM images a breast sample with subcellular resolution at a speed of 1.0 min / cm2. Fluorescence images show excellent visual contrast in color, tissue texture, cell density, and shape between invasive carcinomas and their normal counterparts. Visual interpretation of fluorescence images by nonmedical evaluators was able to distinguish invasive carcinoma from normal samples with high sensitivity (97.62%) and specificity (92.86%). Using N/C alone was able to differentiate patch-level invasive carcinoma from normal breast tissues with reasonable sensitivity (81.5%) and specificity (78.5%). CONCLUSIONS: DUV-FSM achieved a good balance between imaging speed and spatial resolution with excellent contrast, which allows either visual or quantitative detection of invasive cancer cells on the surfaces of a breast surgical specimen.