The screening, diagnosis, and management of patients with autosomal dominant polycystic kidney disease: A national consensus statement from Taiwan.

Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited cause of end-stage kidney disease (ESKD) worldwide. Guidelines for the diagnosis and management of ADPKD in Taiwan remains unavailable. In this consensus statement, we summarize updated information on clinical features of international and domestic patients with ADPKD, followed by suggestions for optimal diagnosis and care in Taiwan. Specifically, counselling for at-risk minors and reproductive issues can be important, including ethical dilemmas surrounding prenatal diagnosis and pre-implantation genetic diagnosis. Studies reveal that ADPKD typically remains asymptomatic until the fourth decade of life, with symptoms resulting from cystic expansion with visceral compression, or rupture. The diagnosis can be made based on a detailed family history, followed by imaging studies (ultrasound, computed tomography, or magnetic resonance imaging). Genetic testing is reserved for atypical cases mostly. Common tools for prognosis prediction include total kidney volume, Mayo classification and PROPKD/genetic score. Screening and management of complications such as hypertension, proteinuria, urological infections, intracranial aneurysms, are also crucial for improving outcome. We suggest that the optimal management strategies of patients with ADPKD include general medical care, dietary recommendations and ADPKD-specific treatments. Key points include rigorous blood pressure control, dietary sodium restriction and Tolvaptan use, whereas the evidence for somatostatin analogues and mammalian target of rapamycin (mTOR) inhibitors remains limited. In summary, we outline an individualized care plan emphasizing careful monitoring of disease progression and highlight the need for shared decision-making among these patients.

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.

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.

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.