Optimizing immunofluorescence with high-dynamic-range imaging to enhance PD-L1 expression evaluation for 3D pathology assessment from NSCLC tumor tissue.

Assessing programmed death ligand 1 (PD-L1) expression through immunohistochemistry (IHC) is the golden standard in predicting immunotherapy response of non-small cell lung cancer (NSCLC). However, observation of heterogeneous PD-L1 distribution in tumor space is a challenge using IHC only. Meanwhile, immunofluorescence (IF) could support both planar and three-dimensional (3D) histological analyses by combining tissue optical clearing with confocal microscopy. We optimized clinical tissue preparation for the IF assay focusing on staining, imaging, and post-processing to achieve quality identical to traditional IHC assay. To overcome limited dynamic range of the fluorescence microscope's detection system, we incorporated a high dynamic range (HDR) algorithm to restore the post imaging IF expression pattern and further 3D IF images. Following HDR processing, a noticeable improvement in the accuracy of diagnosis (85.7%) was achieved using IF images by pathologists. Moreover, 3D IF images revealed a 25% change in tumor proportion score for PD-L1 expression at various depths within tumors. We have established an optimal and reproducible process for PD-L1 IF images in NSCLC, yielding high quality data comparable to traditional IHC assays. The ability to discern accurate spatial PD-L1 distribution through 3D pathology analysis could provide more precise evaluation and prediction for immunotherapy targeting advanced NSCLC.

A novel computer-assisted tool for 3D imaging of programmed death-ligand 1 expression in immunofluorescence-stained and optically cleared breast cancer specimens.

BACKGROUND: Programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) are the two most common immune checkpoints targeted in triple-negative breast cancer (BC). Refining patient selection for immunotherapy is non-trivial and finding an appropriate digital pathology framework for spatial analysis of theranostic biomarkers for PD-1/PD-L1 inhibitors remains an unmet clinical need. METHODS: We describe a novel computer-assisted tool for three-dimensional (3D) imaging of PD-L1 expression in immunofluorescence-stained and optically cleared BC specimens (n = 20). The proposed 3D framework appeared to be feasible and showed a high overall agreement with traditional, clinical-grade two-dimensional (2D) staining techniques. Additionally, the results obtained for automated immune cell detection and analysis of PD-L1 expression were satisfactory. RESULTS: The spatial distribution of PD-L1 expression was heterogeneous across various BC tissue layers in the 3D space. Notably, there were six cases (30%) wherein PD-L1 expression levels along different layers crossed the 1% threshold for admitting patients to PD-1/PD-L1 inhibitors. The average PD-L1 expression in 3D space was different from that of traditional immunohistochemistry (IHC) in eight cases (40%). Pending further standardization and optimization, we expect that our technology will become a valuable addition for assessing PD-L1 expression in patients with BC. CONCLUSION: Via a single round of immunofluorescence imaging, our approach may provide a considerable improvement in patient stratification for cancer immunotherapy as compared with standard techniques.

Panoramic Tissue Examination That Integrates 3-Dimensional Pathology Imaging and Gene Mutation: Potential Utility in Non-Small Cell Lung Cancer.

Novel therapeutics have significantly improved the survival and quality of life of patients with malignancies in this century. Versatile precision diagnostic data were used to formulate personalized therapeutic strategies for patients. However, the cost of extensive information depends on the consumption of the specimen, raising the challenges of effective specimen utilization, particularly in small biopsies. In this study, we proposed a tissue-processing cascaded protocol that obtains 3-dimensional (3D) protein expression spatial distribution and mutation analysis from an identical specimen. In order to reuse the thick section tissue evaluated after the 3D pathology technique, we designed a novel high-flatness agarose-embedded method that could improve tissue utilization rate by 1.52 fold, whereas it reduced the tissue-processing time by 80% compared with the traditional paraffin-embedding method. In animal studies, we demonstrated that the protocol would not affect the results of DNA mutation analysis. Furthermore, we explored the utility of this approach in non-small cell lung cancer because it is a compelling application for this innovation. We used 35 cases including 7 cases of biopsy specimens of non-small cell lung cancer to simulate future clinical application. The cascaded protocol consumed 150-µm thickness of formalin-fixed, paraffin-embedded specimens, providing 3D histologic and immunohistochemical information approximately 38 times that of the current paraffin-embedding protocol, and 3 rounds of DNA mutation analysis, offering both essential guidance for routine diagnostic evaluation and advanced information for precision medicine. Our designed integrated workflow provides an alternative way for pathological examination and paves the way for multidimensional tumor tissue assessment.

Deep-learning based breast cancer detection for cross-staining histopathology images.

Hematoxylin and eosin (H&E) staining is the gold standard for tissue characterization in routine pathological diagnoses. However, these visible light dyes do not exclusively label the nuclei and cytoplasm, making clear-cut segmentation of staining signals challenging. Currently, fluorescent staining technology is much more common in clinical research for analyzing tissue morphology and protein distribution owing to its advantages of channel independence, multiplex labeling, and the possibility of enabling 3D tissue labeling. Although both H&E and fluorescent dyes can stain the nucleus and cytoplasm for representative tissue morphology, color variation between these two staining technologies makes cross-analysis difficult, especially with computer-assisted artificial intelligence (AI) algorithms. In this study, we applied color normalization and nucleus extraction methods to overcome the variation between staining technologies. We also developed an available workflow for using an H&E-stained segmentation AI model in the analysis of fluorescent nucleic acid staining images in breast cancer tumor recognition, resulting in 89.6% and 80.5% accuracy in recognizing specific tumor features in H&E- and fluorescent-stained pathological images, respectively. The results show that the cross-staining inference maintained the same precision level as the proposed workflow, providing an opportunity for an expansion of the application of current pathology AI models.

A domain knowledge enhanced yield based deep learning classifier identifies perineural invasion in oral cavity squamous cell carcinoma.

Background: Perineural invasion (PNI), a form of local invasion defined as the ability of cancer cells to invade in, around, and through nerves, has a negative prognostic impact in oral cavity squamous cell carcinoma (OCSCC). Unfortunately, the diagnosis of PNI suffers from a significant degree of intra- and interobserver variability. The aim of this pilot study was to develop a deep learning-based human-enhanced tool, termed domain knowledge enhanced yield (Domain-KEY) algorithm, for identifying PNI in digital slides. Methods: Hematoxylin and eosin (H&E)-stained whole-slide images (WSIs, n = 85) were obtained from 80 patients with OCSCC. The model structure consisted of two parts to simulate human decision-making skills in diagnostic pathology. To this aim, two semantic segmentation models were constructed (i.e., identification of nerve fibers followed by the diagnosis of PNI). The inferred results were subsequently subjected to post-processing of generated decision rules for diagnostic labeling. Ten H&E-stained WSIs not previously used in the study were read and labeled by the Domain-KEY algorithm. Thereafter, labeling correctness was visually inspected by two independent pathologists. Results: The Domain-KEY algorithm was found to outperform the ResnetV2_50 classifier for the detection of PNI (diagnostic accuracy: 89.01% and 61.94%, respectively). On analyzing WSIs, the algorithm achieved a mean diagnostic accuracy as high as 97.50% versus traditional pathology. The observed accuracy in a validation dataset of 25 WSIs obtained from seven patients with oropharyngeal (cancer of the tongue base, n = 1; tonsil cancer, n = 1; soft palate cancer, n = 1) and hypopharyngeal (cancer of posterior wall, n = 2; pyriform sinus cancer, n = 2) malignancies was 96%. Notably, the algorithm was successfully applied in the analysis of WSIs to shorten the time required to reach a diagnosis. The addition of the hybrid intelligence model decreased the mean time required to reach a diagnosis by 15.0% and 23.7% for the first and second pathologists, respectively. On analyzing digital slides, the tool was effective in supporting human diagnostic thinking. Conclusions: The Domain-KEY algorithm successfully mimicked human decision-making skills and supported expert pathologists in the routine diagnosis of PNI.

Rapid Histological Assessment of Prostate Specimens in the Three-dimensional Space by Hydrophilic Tissue Clearing and Confocal Microscopy.

Microscopic examination of biopsied and resected prostatic specimens is the mainstay in the diagnosis of prostate cancer. However, conventional analysis of hematoxylin and eosin (H&E)-stained tissue is time-consuming and offers limited two-dimensional (2D) information. In the current study, we devised a method-termed Prostate Rapid Optical examination for cancer STATus (proSTAT)-for rapid screening of prostate cancer using high-resolution 2D and three-dimensional (3D) confocal images obtained after hydrophilic tissue clearing of 100-µm-thick tissue slices. The results of the proSTAT method were compared with those of traditional H&E stains for the analysis of cores (n=15) obtained from radical prostatectomy specimens (n=5). Gland lumen formation, consistent with Gleason pattern 3, was evident following tracking of multiple optical imaging sections. In addition, 3D rendering allowed visualizing a tubular network of interconnecting branches. Rapid 3D fluorescent labeling of tumor protein p63 accurately distinguished prostate adenocarcinoma from normal tissue and benign lesions. Compared with conventional stains, the 3D spatial and molecular information extracted from proSTAT may significantly increase the amount of available data for pathological assessment of prostate specimens. Our approach is amenable to automation and-subject to independent validation-can find a wide spectrum of clinical and research applications.

Computer-assisted three-dimensional quantitation of programmed death-ligand 1 in non-small cell lung cancer using tissue clearing technology.

Immune checkpoint blockade therapy has revolutionized non-small cell lung cancer treatment. However, not all patients respond to this therapy. Assessing the tumor expression of immune checkpoint molecules, including programmed death-ligand 1 (PD-L1), is the current standard in predicting treatment response. However, the correlation between PD-L1 expression and anti-PD-1/PD-L1 treatment response is not perfect. This is partly caused by tumor heterogeneity and the common practice of assessing PD-L1 expression based on limited biopsy material. To overcome this problem, we developed a novel method that can make formalin-fixed, paraffin-embedded tissue translucent, allowing three-dimensional (3D) imaging. Our protocol can process tissues up to 150 µm in thickness, allowing anti-PD-L1 staining of the entire tissue and producing high resolution 3D images. Compared to a traditional 4 µm section, our 3D image provides 30 times more coverage of the specimen, assessing PD-L1 expression of approximately 10 times more cells. We further developed a computer-assisted PD-L1 quantitation method to analyze these images, and we found marked variation of PD-L1 expression in 3D. In 5 of 33 needle-biopsy-sized specimens (15.2%), the PD-L1 tumor proportion score (TPS) varied by greater than 10% at different depth levels. In 14 cases (42.4%), the TPS at different depth levels fell into different categories (< 1%, 1-49%, or ≥ 50%), which can potentially influence treatment decisions. Importantly, our technology permits recovery of the processed tissue for subsequent analysis, including histology examination, immunohistochemistry, and mutation analysis. In conclusion, our novel method has the potential to increase the accuracy of tumor PD-L1 expression assessment and enable precise deployment of cancer immunotherapy.