Elhalaby, Rofyda

Abstract Purpose: Pancreatic ductal adenocarcinoma (PDAC) frequently recurs after neoadjuvant therapy (NT) and curative-intent resection. Although major pathologic response predicts favorable outcomes, most patients achieve only minor response with a heterogeneous recurrence risk. We asked whether the spatial organization of residual PDAC encodes clinically relevant biology beyond residual tumor burden. Experimental Design: In a retrospective cohort of 203 patients with resected PDAC treated with NT and restricted to minor pathologic response, routine hematoxylin and eosin (H&E) whole-slide images were segmented into cancer and stroma using an artificial intelligence–enabled pipeline. Spatial composition (patch density, edge density) and configuration (compactness/complexity, intermixing) were quantified and tested for associations with disease-free survival (DFS) using multivariable models adjusted for standard clinicopathologic factors. Results: Shorter DFS was associated with a fragmented, interface-rich tumor–stroma ecology featuring higher edge density and diversity and reduced homotypic aggregation, independent of clinicopathologic variables. Two spatial risk models were independently prognostic: (i) cancer mean shape index plus stromal shape index variability [adjusted hazard ratio (HR), 1.71; P = 0.003] and (ii) mean stromal patch area plus edge density (adjusted HR, 2.19; P = 0.002). Both models stratified outcomes in which pathologic response grading and residual cancer area did not. High-risk configurations were further associated with reduced intratumoral tumor-infiltrating lymphocyte (TIL) density and infiltration ratio, with relative TIL accumulation at the cancer periphery and within the stroma, consistent with an immune-excluded phenotype. Conclusions: Residual cancer–stroma topology quantified from standard H&E slides yields independent prognostic signals after NT in PDAC, provides a cellular immune correlate for spatial risk, and motivates prospective validation and spatially informed adjuvant strategies.

INTRODUCTION: Esophageal adenocarcinoma (EAC) is an aggressive cancer with poor prognosis. Barrett's esophagus (BE) is a critical precursor of EAC. Patients with BE undergo endoscopic surveillance to monitor disease progression although only a small fraction develop EAC. These procedures are invasive and have limited accuracy in predicting BE progression. We evaluated the utility of an 8-protein mass spectrometry panel in predicting progression in patients with BE. METHODS: Eighty untreated controls and 20 cases were selected from our institutional tissue registry. Quantitative mass-spectrometry was performed on microdissected tissue sections. Data were split into 80% training and 20% test sets. We used Least Absolute Shrinkage and Selection Operator-regularized regression to train a logistic classifier on training data. Classifier performance was evaluated in test data. RESULTS: Ninety-two samples had sufficient tissue for mass spectrometry analysis (18 progressors, 74 nonprogressors). The multivariable regression model produced a sensitivity of 100% and a specificity of 39% in the overall cohort, with AUCs of 0.75 and 0.89 in the overall and test cohorts, respectively. Cox proportional hazards time-to-progression (TTP) showed a hazard ratio of 66.1 (95% CI 7.79–561, P = 0.00012) for the model prediction. DISCUSSION: The promising performance of the model generated here suggests that the test may aid in selecting patients most likely to benefit from active BE surveillance. Moreover, the association of this model's prediction with time-to-progression may offer decision support for management of patients likely to progress quickly. These results support continued development of this proteomic panel as a risk stratification tool for patients with BE.

Aims Schistosomiasis remains endemic in various parts of the world, and insights into pathogen immunobiology are mainly based on experimental models, while studies on human tissues are limited. Methods We explored the role of immune checkpoint pathway by evaluating the immunohistochemical expression of programmed death-ligand 1 (PD-L1) in a retrospective cohort of patients with bilharzial cystitis. Inflammation severity by conventional histology and staining intensity by immunohistochemistry were assigned three-tier scores (0/1+/2+), and a cut-off for staining percentage was set at 5%. Results 38 biopsies from 31 patients were considered adequate for evaluation, and positive staining was detected in 80.6% of patients (34 biopsies). High expressors (22.6%) showed strong positive membranous staining (score 2+) with high staining density (more than 5% of inflammatory cells). Low expressors (58.1%) showed mild/moderate staining (score 1+) predominantly in less than 5% of the cells (91.6%) or expressed restricted cytoplasmic staining (6/31). All high expressors showed severe inflammation (score 2+) (p<0.001), and viable ova were only observed in these cases. Calcified ova were associated with mild/moderate inflammation or absent/minimal inflammation, correlating with low expressors or non-expressors (19.4%), respectively. Conclusion Schistosomal granuloma exhibits upregulated PD-L1 expression proportional to inflammation severity and pathogen viability, highlighting a critical immune checkpoint engagement in disease pathology.

AimThe aim of this study was to compare the histopathologic features of MMF‐induced colitis and colonic GVHD and develop a digital tool using deep learning convolutional neural networks (CNNs) to semi‐automate the quantification of eosinophils.MethodsMMF and GVHD colitis cases were retrieved. A cohort of transplant patients who were at risk for developing GVHD and were on MMF at the time of biopsy was also identified. Cases were reviewed for histologic features and a deep learning CNN was trained to quantify lamina propria eosinophils. Clinical history was collected.ResultsNinety‐five patients were included in the study group: GVHD (n = 37), MMF (n = 25) and GVHD vs. MMF (n = 33). Within the GVHD vs. MMF group, one patient was thought to have definitive MMF colitis and one possible MMF colitis. The GVHD group had a significantly higher apoptotic count compared to the MMF group (median: 7 vs. 2, P = 0.03). Neuroendocrine aggregates were present in the GVHD (N = 12) and GVHD vs. MMF (N = 4) groups while absent in the MMF group. The MMF group had a higher semi‐quantitative eosinophil score and a higher automated eosinophil count as detected by the deep learning CNN (median eos/HPF: MMF 19.2, GVHD vs. MMF 3.6, GVHD 3.4, P < 0.001).ConclusionsLower eosinophil counts, higher apoptotic counts and neuroendocrine cell aggregates favour the diagnosis of GVHD. AI models for eosinophil quantification may serve as an ancillary tool for assessing this differential diagnosis. Given the rarity of MMF‐induced colitis in SCT patients, pathologists should have a high threshold for making this diagnosis in this population.

e14626 Background: Despite advancements in neoadjuvant therapy and surgical techniques, patients with pancreatic ductal adenocarcinoma (PDAC) often experience high recurrence rates after total neoadjuvant therapy (TNT) and resection. Existing tumor response scoring systems, such as the College of American Pathologists (CAP) guidelines, fail to stratify recurrence risk effectively, emphasizing the need for novel, objective approaches. Methods: This study evaluated spatial characteristics of residual cancer and stroma in a multi-site cohort of 203 patients with resected PDAC following TNT, not having achieved a major pathologic response (CAP 2 or 3). Whole slide images (WSI) of hematoxylin and eosin-stained sections were digitized and analyzed using artificial intelligence to perform tissue segmentation. Metrics quantifying spatial morphology and configuration, inspired by principles of landscape ecology, were computed to explore spatial features of the tumor microenvironment (TME). Statistical models (recursive partitioning and regression tree [RPRT] model and Cox model with backwards eliminations) incorporating these features were developed to stratify recurrence risk. Results: Five metrics were used for risk classification model build. Three models successfully classified patients into high- and low-risk groups based on spatial metrics. Models demonstrated moderate discriminatory power (C-statistics: 0.560–0.566) and showed that increased tumor fragmentation, like stromal patch density and cancer patch shape index, were strongly associated with improved outcomes. Conclusions: Integrating spatial metrics of cancer-stroma configuration enhances risk stratification beyond conventional scoring systems. Given increased tumor fragmentation is associated with improved outcomes, these data indirectly suggest ecological mechanisms of treatment resistance. This approach offers a pathway toward personalized post-operative management strategies to improve outcomes in PDAC. Risk classification models. Model Description Event/n Median DFS (95% CI) HR(95% CI) p-value C-statistics 1(RPRT) High risk: log standard deviation of the stroma shape index > 0.651 87 / 101 8.22(5.72 – 10.1) 1.64(1.20 – 2.25) 0.002 0.562 Low risk: log standard deviation of the stroma shape index ≤ 0.651 71 / 102 11.90(9.24 – 25.1) Ref 2(RPRT) High risk: log standard deviation of the stroma shape index > 0.651 & mean cancer shape index ≤ 1.775 58 / 63 7.23(5.19 – 9.63) 1.82(1.31 – 2.52) 0.0003 0.560 Low risk: (log standard deviation of the stroma shape index > 0.651 & mean cancer shape index > 1.775) or log standard deviation of the stroma shape index ≤ 0.651 100 / 140 11.57(9.24 – 17.92) Ref 3(Backwards elimination) High risk: PI * > median 86 / 101 8.02(6.08 – 9.86) 1.73(1.26 – 2.37) 0.0007 0.566 Low risk:PI * ≤ median 72 / 102 15.72(9.97 – 25.05) Ref *PI: (0.2701 * log mean stroma area) + (0.3905 * log edge density).