The architecture and morphology of the intestinal tissue from mice or other small animals are difficult to preserve for histological and molecular analysis due to the fragile nature of this tissue. The intestinal mucosa consists of villi and crypts lined with epithelial cells. In between the epithelial folds extends the lamina propria, a loose connective tissue that contains blood and lymph vessels, fibroblasts, and immune cells. Underneath the mucosa are two layers of contractile smooth muscle and nerves. The tissue experiences significant changes during fixation, which can impair the reliability of histologic analysis. Poor-quality histologic sections are not suitable for quantitative image-based tissue analysis. This article offers a new fixative composed of neutral buffered formalin (NBF) and acetic acid, called FA. This fixative significantly improved the histology of mouse intestinal tissue compared to traditional NBF and enabled precise, reproducible histologic molecular analyses using QuPath software. Algorithmic training of QuPath allows for automated segmentation of intestinal compartments, which can be further interrogated for cellular composition and disease-related changes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Improved preservation of mouse intestinal tissue using a formalin/acetic acid fixative Support Protocol: Quantitative tissue analysis using QuPath.

OBJECTIVE: Automated measurement of immunostained samples can enable more convenient and objective prediction of treatment outcome from radiotherapy. We aimed to validate the performance of the QuPath image analysis software in immune cell markers detection by comparing QuPath cell counting results with those of physician manual cell counting.
METHODS: CD8- and FoxP3-stained cervical, CD8-stained oropharyngeal, and Ku70-stained prostate cancer tumor sections were analyzed in 104 cervical, 92 oropharyngeal, and 58 prostate cancer patients undergoing radiotherapy at our Institution.
RESULTS: QuPath and manual counts were highly correlated. When divided into two groups using ROC curves, the agreement between QuPath and manual counts was 89.4% for CD8 and 88.5% for FoxP3 in cervical cancer, 87.0% for CD8 in oropharyngeal cancer and 80.7% for Ku70 in prostate cancer. In cervical cancer, the high CD8 group based on QuPath counts had a better prognosis and the low CD8 group had a significantly worse prognosis [p=0.0003; 5-year overall survival (OS), 65.9% vs. 34.7%]. QuPath counts were more predictive than manual counts. Similar results were observed for FoxP3 in cervical cancer (p=0.002; 5-year OS, 62.1% vs. 33.6%) and CD8 in oropharyngeal cancer (p=0.013; 5-year OS, 80.2% vs. 47.2%). In prostate cancer, high Ku70 group had worse and low group significantly better outcome [p=0.007; 10-year progression-free survival (PFS), 56.0% vs. 93.8%].
CONCLUSIONS: QuPath showed a strong correlation with manual counting, confirming its utility and accuracy and potential applicability in clinical practice.

OBJECTIVE: The prognostic and predictive significance of pathologist-read tumor infiltrating lymphocytes (TILs) in head and neck cancers have been demonstrated through multiple studies over the years. TILs have not been broadly adopted clinically, perhaps due to substantial inter-observer variability. In this study, we developed a machine-based algorithm for TIL evaluation in head and neck cancers and validated its prognostic value in independent cohorts.
METHODS: A network classifier called NN3-17 was trained to identify and calculate tumor cells, lymphocytes, fibroblasts and \"other\" cells on hematoxylin-eosin stained sections using the QuPath software. These measurements were used to construct three predefined TIL variables. A retrospective collection of 154 head and neck squamous cell cancer cases was used as the discovery set to identify optimal association of TIL variables and survival. Two independent cohorts of 234 cases were used for validation.
RESULTS: We found that electronic TIL variables were associated with favorable prognosis in both the HPV-positive and -negative cases. After adjusting for clinicopathologic factors, Cox regression analysis demonstrated that electronic total TILs% (p = 0.025) in the HPV-positive and electronic stromal TILs% (p < 0.001) in the HPV-negative population were independent markers of disease specific outcomes (disease free survival).
CONCLUSIONS: Neural network TIL variables demonstrated independent prognostic value in validation cohorts of HPV-positive and HPV-negative head and neck cancers. These objective variables can be calculated by an open-source software and could be considered for testing in a prospective setting to assess potential clinical implications.

(1) Background: Nonalcoholic Steatohepatitis/Nonalcoholic Fatty Liver Disease (NASH/NAFLD) is the most recurrent chronic liver disease. NASH could present with a cholestatic (C) or hepatic (H) pattern of damage. Recently, we observed that increased Epithelial Cell Adhesion Molecule (EpCAM) expression was the main immunohistochemical feature to distinguish C from H pattern in NASH. (2) Methods: In the present study, we used digital pathology to compare the quantitative results of digital image analysis by QuPath software (Q-results), with the semi-quantitative results of observer assessment (S-results) for cytokeratin 7 and 19, (CK7, CK19) as well as EpCAM expression. Patients were classified into H or C group on the basis of the ratio between alanine transaminase (ALT) and alkaline phosphatase (ALP) values, using the \"R-ratio formula\". (3) Results: Q- and S-results showed a significant correlation for all markers (p < 0.05). Q-EpCAM expression was significantly higher in the C group than in the H group (p < 0.05). Importantly ALP, an indicator of hepatobiliary disorder, was the only biochemical parameter significantly correlated with Q-EpCAM. Instead, Q-CK7, but not Q-CK19, correlated only with γGlutamyl-Transferase (γGT). Of note, Stage 4 fibrosis correlated with Q-EpCAM, Q-CK19, and ALP but not with γGT or ALT. Conclusions: Image analysis confirms the relation between cholestatic-like pattern, associated with a worse prognosis, with increased ALP values, EpCAM positive biliary metaplasia, and advanced fibrosis. These preliminary data could be useful for the implementation of AI algorithms for the assessment of cholestatic NASH.

Triple negative breast cancers (TNBC) are an aggressive molecular subtype of breast carcinoma (BC) identified by the lack of receptor expression for estrogen, progesterone, & human epidermal growth factor receptor-2. Lack of tangible drug targets warrants further research in TNBC. LIV1, is a zinc (Zn) transporter known to be overexpressed in few cancer types including BCs. Recently, in the United States of America, FDA approved the use of a new drug targeting LIV1, antibody drug conjugate SGN-LIV1A for treatment of TNBC patients. Though LIV1 also has a role in modulating immune cells by its differential transport of Zn, a correlation between the tumor cell expression of LIV1 and immune cell infiltrations were scantily reported. Further adequate baseline data on LIV1 expression in other populations have not been documented. Our objective was to screen a large Indian cohort of TNBC patient samples for LIV1, categorize the immune cell infiltration using CD4/CD8 expression and correlate the findings with therapy outcomes. Further, we also investigated for LIV1 expression in matched samples of primary & secondary tumors; pre & postchemotherapy in TNBC patients. Results showed an elevated expression of LIV1 in TNBC samples as compared to adjacent normal, the mean Q scores being 183.06 ± 6.39 and 120.78 ± 7.37 (p < 0.0001), respectively. Similarly, LIV1 levels were elevated in secondary tumors than primary & in patient samples postchemotherapy as compared to naïve. In the TNBC cohort, using automated method, cell morphology parameters were computed and analysis showed LIV1 levels were elevated in grade 3 TNBC samples presenting with altered cell morphology parameters namely cell size, cell perimeter, & nucleus size. Thus indicating LIV1 expressing TNBC samples portrayed an aggressive phenotype. Finally, TNBC patients with 3+ staining intensity showed poor survival (4.44 year) as compared to patients with 2+ LIV1 expression (5.47 year), emphasizing that LIV1 expression is a poor prognostic factor in TNBC. In conclusion, the study reports elevated expression of LIV1 in a large Indian TNBC cohort; high expression is a poor prognostic factor and correlated with aggressive disease and indicating the need for LIV1 targeted therapies.

UNASSIGNED: In osteosarcoma, the most significant indicator of prognosis is the histologic changes related to tumor response to preoperative chemotherapy, such as necrosis. We have developed a method to measure the osteosarcoma treatment effect using whole slide image (WSI) with an open-source digital image analytical software Qupath.
UNASSIGNED: In Qupath, each osteosarcoma case was treated as a project. All H&E slides from the entire representative slice of osteosarcoma were scanned into WSIs and imported into a project in Qupath. The regions of tumor and tumor necrosis were annotated, and their areas were measured in Qupath. In order to measure the osteosarcoma treatment effect, we needed to calculate the percentage of total necrosis area over total tumor area. We developed a tool that can automatically extract all values of tumor and necrosis areas from a Qupath project into an Excel file, sum these values for necrosis and whole tumor respectively, and calculate necrosis/tumor percentage.
UNASSIGNED: Our method that combines WSI with Qupath can provide an objective measurement to facilitate pathologist\'s assessment of osteosarcoma response to treatment. The proposed approach can also be used for other types of tumors that have clinical need for post-treatment response assessment.

The accurate diagnosis of mixed-type gastric cancer from pathology images presents a formidable challenge for pathologists, given its intricate features and resemblance to other subtypes of gastric cancer. Artificial Intelligence has the potential to overcome this hurdle. This study aimed to leverage deep machine learning techniques to establish a precise and efficient diagnostic approach for this cancer type which can also predict the metastatic risk using two software, U-Net and QuPath, which have not been trialled in gastric cancers.
A U-Net neural network was trained to recognise, and segment differentiated components from 186 pathology images of mixed-type gastric cancer. Undifferentiated components in the same images were annotated using the open-source pathology imaging software QuPath. The outcomes from U-Net and QuPath were used to calculate the ratios of differentiation/undifferentiated components which were correlated to lymph node metastasis.
The models established by U-Net recognised ∼91% of the regions of interest, with precision, recall, and F1 values of 90.2%, 90.9% and 94.6%, respectively, indicating a high level of accuracy and reliability. Furthermore, the receiver operating characteristic curve analysis showed an area under the cure of 91%, indicating good performance. A bell-curve correlation between the differentiated/undifferentiated ratio and lymphatic metastasis was found (highest risk between 0.683 and 1.03), which is paradigm-shifting.
U-Net and QuPath exhibit promising accuracy in the identification of differentiated and undifferentiated components in mixed-type gastric cancer, as well as paradigm-shifting prediction of metastasis. These findings bring us one step closer to their potential clinical application.

Current methods for analysing immunohistochemistry are labour-intensive and often confounded by inter-observer variability. Analysis is time consuming when identifying small clinically important cohorts within larger samples. This study trained QuPath, an open-source image analysis program, to accurately identify MLH1-deficient inflammatory bowel disease-associated colorectal cancers (IBD-CRC) from a tissue microarray containing normal colon and IBD-CRC. The tissue microarray (n = 162 cores) was immunostained for MLH1, digitalised, and imported into QuPath. A small sample (n = 14) was used to train QuPath to detect positive versus no MLH1 and tissue histology (normal epithelium, tumour, immune infiltrates, stroma). This algorithm was applied to the tissue microarray and correctly identified tissue histology and MLH1 expression in the majority of valid cases (73/99, 73.74%), incorrectly identified MLH1 status in one case (1.01%), and flagged 25/99 (25.25%) cases for manual review. Qualitative review found five reasons for flagged cores: small quantity of tissue, diverse/atypical morphology, excessive inflammatory/immune infiltrations, normal mucosa, or weak/patchy immunostaining. Of classified cores (n = 74), QuPath was 100% (95% CI 80.49, 100) sensitive and 98.25% (95% CI 90.61, 99.96) specific for identifying MLH1-deficient IBD-CRC; κ = 0.963 (95% CI 0.890, 1.036) (p < 0.001). This process could be efficiently automated in diagnostic laboratories to examine all colonic tissue and tumours for MLH1 expression.

The wild Tasmanian devil (Sarcophilus harrisii) population has suffered a devastating decline due to two clonal transmissible cancers. The first devil facial tumor 1 (DFT1) was observed in 1996, followed by a second genetically distinct transmissible tumor, the devil facial tumor 2 (DFT2), in 2014. DFT1/2 frequently metastasize, with lymph nodes being common metastatic sites. MHC-I downregulation by DFT1 cells is a primary means of evading allograft immunity aimed at polymorphic MHC-I proteins. DFT2 cells constitutively express MHC-I, and MHC-I is upregulated on DFT1/2 cells by interferon gamma, suggesting other immune evasion mechanisms may contribute to overcoming allograft and anti-tumor immunity. Human clinical trials have demonstrated PD1/PDL1 blockade effectively treats patients showing increased expression of PD1 in tumor draining lymph nodes, and PDL1 on peritumoral immune cells and tumor cells. The effects of DFT1/2 on systemic immunity remain largely uncharacterized. This study applied the open-access software QuPath to develop a semiautomated pipeline for whole slide analysis of stained tissue sections to quantify PD1/PDL1 expression in devil lymph nodes. The QuPath protocol provided strong correlations to manual counting. PD-1 expression was approximately 10-fold higher than PD-L1 expression in lymph nodes and was primarily expressed in germinal centers, whereas PD-L1 expression was more widely distributed throughout the lymph nodes. The density of PD1 positive cells was increased in lymph nodes containing DFT2 metastases, compared to DFT1. This suggests PD1/PDL1 exploitation may contribute to the poorly immunogenic nature of transmissible tumors in some devils and could be targeted in therapeutic or prophylactic treatments.Abbreviations: PD1: programmed cell death protein 1; PDL1: programmed death ligand 1; DFT1: devil facial tumor 1; DFT2: devil facial tumor 2; DFTD: devil facial tumor disease; MCC: Matthew\'s correlation coefficient; DAB: diaminobenzidine; ROI: region of interest.

High-resolution scans of immunohistochemical (IHC) stains of Alzheimer\'s disease (AD) brain slices and radioligand autoradiography both provide information about the distribution of Aβ plaques and Tau, the two common proteinopathies in AD. Accurate assessment of the amount and regional location of Aβ plaques and Tau is essential to understand the progression of AD pathology. Our goal was to develop a quantitative method for the analysis of IHC-autoradiography images. Postmortem anterior cingulate (AC) and corpus callosum (CC) from AD and control (CN) subjects were IHC stained with anti-Aβ for Aβ plaques and autoradiography with [18F]flotaza and [125I]IBETA for Aβ plaques. For Tau, [124I]IPPI, a new radiotracer, was synthesized and evaluated in the AD brain. For Tau imaging, brain slices were IHC stained with anti-Tau and autoradiography using [125I]IPPI and [124I]IPPI. Annotations for Aβ plaques and Tau using QuPath for training and pixel classifiers were generated to measure the percent of the area of Aβ plaques and Tau in each slice. The binding of [124I]IPPI was observed in all AD brains with an AC/CC ratio > 10. Selectivity to Tau was shown by blocking [124I]IPPI with MK-6240. Percent positivity for Aβ plaques was 4-15%, and for Tau, it was 1.3 to 35%. All IHC Aβ plaque-positive subjects showed [18F]flotaza and [125I]IBETA binding with a positive linear correlation (r2 > 0.45). Tau-positive subjects showed [124/125I]IPPI binding with a stronger positive linear correlation (r2 > 0.80). This quantitative IHC-autoradiography approach provides an accurate measurement of Aβ plaques and Tau within and across subjects.