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

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.

Application of Innovative 3D Pathological Tactic for Diagnosis of Organizing Pneumonia

BACKGROUND/AIM

The pathological diagnosis of organizing pneumonia (OP) relies on conventional traditional histopathological analysis, which involves examining stained thin slices of tissue. However, this method often results in suboptimal diagnostic objectivity due to low tissue sampling rates. This study aimed to assess the efficacy of tissue-clearing and infiltration-enhanced 3D spatial imaging techniques for elucidating the tissue architecture of OP.

MATERIALS AND METHODS

H&E staining, 3D imaging technology, and AI-assisted analysis were employed to facilitate the construction of a multidimensional tissue architecture using six OP patient specimens procured from Taichung Veterans General Hospital, enabling a comprehensive morphological assessment.

RESULTS

Specimens underwent H&E staining and exhibited Masson bodies and varying degrees of interstitial fibrosis. Furthermore, we conducted a comprehensive study of 3D images of the pulmonary histology reconstructed through an in-depth pathology analysis, and uncovered heterogenous distributions of fibrosis and Masson bodies across different depths of the OP specimens.

CONCLUSION

Integrating 3D imaging for OP with AI-assisted analysis permits a substantially enhanced visualization and delineation of complex histological pulmonary disorders such as OP. The synergistic application of conventional histopathology with novel 3D imaging elucidated the sophisticated spatial configuration of OP, revealing the presence of Masson bodies and interstitial fibrosis. This methodology transcends conventional pathology constraints and paves the way for advanced algorithmic approaches to enhance precision in the detection, classification, and clinical management of lung pathologies.

Schwann cells acquire a repair phenotype after assembling into spheroids and show enhanced in vivo therapeutic potential for promoting peripheral nerve repair

The prognosis for postinjury peripheral nerve regeneration remains suboptimal. Although transplantation of exogenous Schwann cells (SCs) has been considered a promising treatment to promote nerve repair, this strategy has been hampered in practice by the limited availability of SC sources and an insufficient postengraftment cell retention rate. In this study, to address these challenges, SCs were aggregated into spheroids before being delivered to an injured rat sciatic nerve. We found that the three-dimensional aggregation of SCs induced their acquisition of a repair phenotype, as indicated by enhanced levels of c-Jun expression/activation and decreased expression of myelin sheath protein. Furthermore, our in vitro results demonstrated the superior potential of the SC spheroid-derived secretome in promoting neurite outgrowth of dorsal root ganglion neurons, enhancing the proliferation and migration of endogenous SCs, and recruiting macrophages. Moreover, transplantation of SC spheroids into rats after sciatic nerve transection effectively increased the postinjury nerve structure restoration and motor functional recovery rates, demonstrating the therapeutic potential of SC spheroids. In summary, transplantation of preassembled SC spheroids may hold great potential for enhancing the cell delivery efficiency and the resultant therapeutic outcome, thereby improving SC-based transplantation approaches for promoting peripheral nerve regeneration.

Heterogeneity of cancer stem cell-related marker expression is associated with three-dimensional structures in malignant pleural effusion produced by lung adenocarcinoma

INTRODUCTION

Cancer stem cells have been described in lung adenocarcinoma-associated malignant pleural effusion. They show clinically important features, including the ability to initiate new tumours and resistance to treatments. However, their correlation with the three-dimensional tumour structures in the effusion is not well understood.

METHODS

Cell blocks produced from lung adenocarcinoma patients' pleural effusion were examined for cancer stem cell-related markers Nanog and CD133 using immunocytochemistry. The three-dimensional cancer cell structures and CD133 expression patterns were visualized with tissue-clearing technology. The expression patterns were correlated with tumour cell structures, genetic variants and clinical outcomes.

RESULTS

Thirty-nine patients were analysed. Moderate-to-strong Nanog expression was detected in 27 cases (69%), while CD133 was expressed by more than 1% of cancer cells in 11 cases (28%). Nanog expression was more homogenous within individual specimens, while CD133 expression was detected in single tumour cells or cells within small clusters instead of larger structures in 8 of the 11 positive cases (73%). Although no statistically significant correlation between the markers and tumour genetic variants or patient survival was observed, we recorded seven cases with follow-up specimens after cancer treatment, and four (57%) showed a change in stem cell-related marker expression corresponding to treatment response.

CONCLUSIONS

Lung adenocarcinoma cells in the pleural effusion show variable expression of cancer stem cell-related markers, some showing a correlation with the size of cell clusters. Their expression level is potentially correlated with cancer treatment effects.

Beyond 2D: A scalable and highly sensitive method for a comprehensive 3D analysis of kidney biopsy tissue

The spatial organization of various cell populations is critical for the major physiological and pathological processes in the kidneys. Most evaluation of these processes typically comes from a conventional 2D tissue cross-section, visualizing a limited amount of cell organization. Therefore, the 2D analysis of kidney biopsy introduces selection bias. The 2D analysis potentially omits key pathological findings outside a 1- to 10-μm thin-sectioned area and lacks information on tissue organization, especially in a particular irregular structure such as crescentic glomeruli. In this study, we introduce an easy-to-use and scalable method for obtaining high-quality images of molecules of interest in a large tissue volume, enabling a comprehensive evaluation of the 3D organization and cellular composition of kidney tissue, especially the glomerular structure. We show that CUBIC and ScaleS clearing protocols could allow a 3D analysis of the kidney tissues in human and animal models of kidney disease. We also demonstrate that the paraffin-embedded human biopsy specimens previously examined via 2D evaluation could be applicable to 3D analysis, showing a potential utilization of this method in kidney biopsy tissue collected in the past. In summary, the 3D analysis of kidney biopsy provides a more comprehensive analysis and a minimized selection bias than 2D tissue analysis. Additionally, this method enables a quantitative evaluation of particular kidney structures and their surrounding tissues, with the potential utilization from basic science investigation to applied diagnostics in nephrology.

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.

Volume imaging to interrogate cancer cell-tumor microenvironment interactions in space and time

Volume imaging visualizes the three-dimensional (3D) complexity of tumors to unravel the dynamic crosstalk between cancer cells and the heterogeneous landscape of the tumor microenvironment (TME). Tissue clearing and intravital microscopy (IVM) constitute rapidly progressing technologies to study the architectural context of such interactions. Tissue clearing enables high-resolution imaging of large samples, allowing for the characterization of entire tumors and even organs and organisms with tumors. With IVM, the dynamic engagement between cancer cells and the TME can be visualized in 3D over time, allowing for acquisition of 4D data. Together, tissue clearing and IVM have been critical in the examination of cancer-TME interactions and have drastically advanced our knowledge in fundamental cancer research and clinical oncology. This review provides an overview of the current technical repertoire of fluorescence volume imaging technologies to study cancer and the TME, and discusses how their recent applications have been utilized to advance our fundamental understanding of tumor architecture, stromal and immune infiltration, vascularization and innervation, and to explore avenues for immunotherapy and optimized chemotherapy delivery.

A Deep Learning-Based Framework for Uncertainty Quantification in Medical Imaging Using the DropWeak Technique: An Empirical Study with Baresnet

Lung cancer is a leading cause of cancer-related deaths globally. Early detection is crucial for improving patient survival rates. Deep learning (DL) has shown promise in the medical field, but its accuracy must be evaluated, particularly in the context of lung cancer classification. In this study, we conducted uncertainty analysis on various frequently used DL architectures, including Baresnet, to assess the uncertainties in the classification results. This study focuses on the use of deep learning for the classification of lung cancer, which is a critical aspect of improving patient survival rates. The study evaluates the accuracy of various deep learning architectures, including Baresnet, and incorporates uncertainty quantification to assess the level of uncertainty in the classification results. The study presents a novel automatic tumor classification system for lung cancer based on CT images, which achieves a classification accuracy of 97.19% with an uncertainty quantification. The results demonstrate the potential of deep learning in lung cancer classification and highlight the importance of uncertainty quantification in improving the accuracy of classification results. This study's novelty lies in the incorporation of uncertainty quantification in deep learning for lung cancer classification, which can lead to more reliable and accurate diagnoses in clinical settings.

Tissue clearing and 3D reconstruction of digitized, serially sectioned slides provide novel insights into pancreatic cancer

Pancreatic cancer is currently the third leading cause of cancer death in the United States. The clinical hallmarks of this disease include abdominal pain that radiates to the back, the presence of a hypoenhancing intrapancreatic lesion on imaging, and widespread liver metastases. Technologies such as tissue clearing and three-dimensional (3D) reconstruction of digitized serially sectioned hematoxylin and eosin-stained slides can be used to visualize large (up to 2- to 3-centimeter cube) tissues at cellular resolution. When applied to human pancreatic cancers, these 3D visualization techniques have provided novel insights into the basis of a number of the clinical characteristics of this disease. Here, we describe the clinical features of pancreatic cancer, review techniques for clearing and the 3D reconstruction of digitized microscope slides, and provide examples that illustrate how 3D visualization of human pancreatic cancer at the microscopic level has revealed features not apparent in 2D microscopy and, in so doing, has closed the gap between bench and bedside. Compared with animal models and 2D microscopy, studies of human tissues in 3D can reveal the difference between what can happen and what does happen in human cancers.

Assessment of histological and immunohistochemical features of retinal tissues using a novel tissue submission procedure

We introduce a novel tissue submission procedure without additional equipment or storage facilities for assessing the histological and immunohistochemical features of retinal tissues. In total, 150 specimens were collected from patients who underwent vitrectomy or macular surgery from January to December 2020. Ninety-eight specimens were submitted using the new procedure, and 58 specimens were submitted as flat-mount slides to compare specimen adequacy. The tissues submitted using the new procedure were subjected to paraffin-embedding and sectioning for hematoxylin & eosin staining. Additional immunohistochemical analysis was performed to assess the cellular composition in retinal tissues with diverse etiologies. The new submission procedure had an adequacy ratio of 75.51%, which was comparable to that of the flat-mount method (p = 0.1397). The new method could produce high-quality images of histological features of tissues and facilitated immunohistochemical analysis to demonstrate cell origins. More glial cells (p = 0.000) and myofibroblasts (p = 0.012) were detected in the epiretinal membranes (ERMs) than in the internal limiting membranes (ILMs). Subgroup analysis revealed that secondary ERMs contained more macrophage-like cells (p = 0.001) and retinal pigment epithelial cells (p = 0.000) than did idiopathic ERMs. Our novel tissue submission procedure can be applied to routine clinical practice. Our study provides additional histological and immunohistochemical evidence of cellular components in retinal tissues based on a large number of human tissue samples. Moreover, tissues submitted using the new method can be permanently preserved, enabling future investigation for potential prognostic or therapeutic targets.

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.

Artificial intelligence for prediction of response to cancer immunotherapy

Artificial intelligence (AI) indicates the application of machines to imitate intelligent behaviors for solving complex tasks with minimal human intervention, including machine learning and deep learning. The use of AI in medicine improves health-care systems in multiple areas such as diagnostic confirmation, risk stratification, analysis, prognosis prediction, treatment surveillance, and virtual health support, which has considerable potential to revolutionize and reshape medicine. In terms of immunotherapy, AI has been applied to unlock underlying immune signatures to associate with responses to immunotherapy indirectly as well as predict responses to immunotherapy responses directly. The AI-based analysis of high-throughput sequences and medical images can provide useful information for management of cancer immunotherapy considering the excellent abilities in selecting appropriate subjects, improving therapeutic regimens, and predicting individualized prognosis. In present review, we aim to evaluate a broad framework about AI-based computational approaches for prediction of response to cancer immunotherapy on both indirect and direct manners. Furthermore, we summarize our perspectives about challenges and opportunities of further AI applications on cancer immunotherapy relating to clinical practicability.

Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer

Lung cancer accounts for the main proportion of malignancy-related deaths and most patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have great advances in application in clinics to treat lung cancer patients, yet the efficacy is unstable. The response rate of these therapies varies among patients. Some biomarkers have been proposed to predict the outcomes of immunotherapy and targeted therapy, including programmed cell death-ligand 1 (PD-L1) expression and oncogene mutations. Nevertheless, the detection tests are invasive, time-consuming, and have high demands on tumor tissue. The predictive performance of conventional biomarkers is also unsatisfactory. Therefore, novel biomarkers are needed to effectively predict the outcomes of immunotherapy and targeted therapy. The application of artificial intelligence (AI) can be a possible solution, as it has several advantages. AI can help identify features that are unable to be used by humans and perform repetitive tasks. By combining AI methods with radiomics, pathology, genomics, transcriptomics, proteomics, and clinical data, the integrated model has shown predictive value in immunotherapy and targeted therapy, which significantly improves the precision treatment of lung cancer patients. Herein, we reviewed the application of AI in predicting the outcomes of immunotherapy and targeted therapy in lung cancer patients, and discussed the challenges and future directions in this field.