AI-guided histopathology predicts brain metastasis in lung cancer patients.

Brain metastases can occur in nearly half of patients with early and locally advanced (stage I-III) non-small cell lung cancer (NSCLC). There are no reliable histopathologic or molecular means to identify those who are likely to develop brain metastases. We sought to determine if deep learning (DL) could be applied to routine H&E-stained primary tumor tissue sections from stage I-III NSCLC patients to predict the development of brain metastasis. Diagnostic slides from 158 patients with stage I-III NSCLC followed for at least 5 years for the development of brain metastases (Met+, 65 patients) versus no progression (Met-, 93 patients) were subjected to whole-slide imaging. Three separate iterations were performed by first selecting 118 cases (45 Met+, 73 Met-) to train and validate the DL algorithm, while 40 separate cases (20 Met+, 20 Met-) were used as the test set. The DL algorithm results were compared to a blinded review by four expert pathologists. The DL-based algorithm was able to distinguish the eventual development of brain metastases with an accuracy of 87% (p < 0.0001) compared with an average of 57.3% by the four pathologists and appears to be particularly useful in predicting brain metastases in stage I patients. The DL algorithm appears to focus on a complex set of histologic features. DL-based algorithms using routine H&E-stained slides may identify patients who are likely to develop brain metastases from those who will remain disease free over extended (>5 year) follow-up and may thus be spared systemic therapy. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The fertility outcome of assisted oocyte activation combined with spindle view-assisted intracytoplasmic sperm injection in patients with low fertilization rate.

OBJECTIVE: To examine the possible synergic effect of spindle view-assisted intracytoplasmic sperm injection (SV-ICSI) with assisted oocyte activation (AOA) for low fertilization rate. MATERIALS AND METHODS: A single-center retrospective study from 2019/09-2023/06, a total of 47 patients, autologous IVF cycle, and low fertilization rate history, including control group (SV-ICSI, 33 patients) and intervention group (AOA-SV-ICSI, 14 patients), comparing fertilization rate, blastocyst formation rate, and clinical pregnancy rate. RESULTS: The blastocyst formation rate was significantly higher (p = 0.020) in the AOA-SV-ICSI group than in the SV-ICSI group. The fertilization rate (P = 0.468) and clinical pregnancy rate (p = 0.057) were non-significant between groups. CONCLUSION: The AOA-SV-ICSI group's blastocyst formation rate significantly improved in patients with previous low fertilization rates, which might help them obtain more useable embryos for further embryo implantation.

Induction of complementary immunogenic necroptosis and apoptosis cell death pathways inhibits cancer metastasis and relapse.

Interactions of light-sensitive drugs and materials with Cerenkov radiation-emitting radiopharmaceuticals generate cytotoxic reactive oxygen species (ROS) to inhibit localized and disseminated cancer progression, but the cell death mechanisms underlying this radionuclide stimulated dynamic therapy (RaST) remain elusive. Using ROS-regenerative nanophotosensitizers coated with a tumor-targeting transferrin-titanocene complex (TiO2-TC-Tf) and radiolabeled 2-fluorodeoxyglucose (18FDG), we found that adherent dying cells maintained metabolic activity with increased membrane permeabilization. Mechanistic assessment of these cells revealed that RaST activated the expression of RIPK-1 and RIPK-3, which mediate necroptosis cell death. Subsequent recruitment of the nuclear factors kappa B and the executioner mixed lineage kinase domain-like pseudo kinase (MLKL) triggered plasma membrane permeabilization and pore formation, respectively, followed by the release of cytokines and immunogenic damage-associated molecular patterns (DAMPs). In immune-deficient breast cancer models with adequate stroma and growth factors that recapitulate the human tumor microenvironment, RaST failed to inhibit tumor progression and the ensuing lung metastasis. A similar aggressive tumor model in immunocompetent mice responded to RaST, achieving a remarkable partial response (PR) and complete response (CR) with no evidence of lung metastasis, suggesting active immune system engagement. RaST recruited antitumor CD11b+, CD11c+, and CD8b+ effector immune cells after initiating dual immunogenic apoptosis and necroptosis cell death pathways in responding tumors in vivo. Over time, cancer cells upregulated the expression of negative immune regulating cytokine (TGF-ß) and soluble immune checkpoints (sICP) to challenge RaST effect in the CR mice. Using a signal-amplifying cancer-imaging agent, LS301, we identified latent minimal residual disseminated tumors in the lymph nodes (LNs) of the CR group. Despite increased protumor immunogens in the CR mice, RaST prevented cancer relapse and metastasis through dynamic redistribution of ROS-regenerative TiO2 from bones at the early treatment stage to the spleen and LNs, maintaining active immunity against cancer progression and migration. This study reveals the immune-mechanistic underpinnings of RaST-mediated antitumor immune response and highlights immunogenic reprogramming of tumors in response to RaST. Overcoming apoptosis resistance through complementary necroptosis activation paves the way for strategic drug combinations to improve cancer treatment.

A machine learning algorithm improves the diagnostic accuracy of the histologic component of antibody mediated rejection (AMR-H) in cardiac transplant endomyocardial biopsies.

BACKGROUND: Pathologic antibody mediated rejection (pAMR) remains a major driver of graft failure in cardiac transplant patients. The endomyocardial biopsy remains the primary diagnostic tool but presents with challenges, particularly in distinguishing the histologic component (pAMR-H) defined by 1) intravascular macrophage accumulation in capillaries and 2) activated endothelial cells that expand the cytoplasm to narrow or occlude the vascular lumen. Frequently, pAMR-H is difficult to distinguish from acute cellular rejection (ACR) and healing injury. With the advent of digital slide scanning and advances in machine deep learning, artificial intelligence technology is widely under investigation in the areas of oncologic pathology, but in its infancy in transplant pathology. For the first time, we determined if a machine learning algorithm could distinguish pAMR-H from normal myocardium, healing injury and ACR. MATERIALS AND METHODS: A total of 4,212 annotations (1,053 regions of normal, 1,053 pAMR-H, 1,053 healing injury and 1,053 ACR) were completed from 300 hematoxylin and eosin slides scanned using a Leica Aperio GT450 digital whole slide scanner at 40X magnification. All regions of pAMR-H were annotated from patients confirmed with a previous diagnosis of pAMR2 (>50% positive C4d immunofluorescence and/or >10% CD68 positive intravascular macrophages). Annotations were imported into a Python 3.7 development environment using the OpenSlide™ package and a convolutional neural network approach utilizing transfer learning was performed. RESULTS: The machine learning algorithm showed 98% overall validation accuracy and pAMR-H was correctly distinguished from specific categories with the following accuracies: normal myocardium (99.2%), healing injury (99.5%) and ACR (99.5%). CONCLUSION: Our novel deep learning algorithm can reach acceptable, and possibly surpass, performance of current diagnostic standards of identifying pAMR-H. Such a tool may serve as an adjunct diagnostic aid for improving the pathologist's accuracy and reproducibility, especially in difficult cases with high inter-observer variability. This is one of the first studies that provides evidence that an artificial intelligence machine learning algorithm can be trained and validated to diagnose pAMR-H in cardiac transplant patients. Ongoing studies include multi-institutional verification testing to ensure generalizability.

Development of a CD163-Targeted PET Radiotracer That Images Resident Macrophages in Atherosclerosis.

Tissue-resident macrophages are complementary to proinflammatory macrophages to promote the progression of atherosclerosis. The noninvasive detection of their presence and dynamic variation will be important to the understanding of their role in the pathogenesis of atherosclerosis. The goal of this study was to develop a targeted PET radiotracer for imaging CD163-positive (CD163+) macrophages in multiple mouse atherosclerosis models and assess the potential of CD163 as a biomarker for atherosclerosis in humans. Methods: CD163-binding peptide was identified using phage display and conjugated with a NODAGA chelator for 64Cu radiolabeling ([64Cu]Cu-ICT-01). CD163-overexpressing U87 cells were used to measure the binding affinity of [64Cu]Cu-ICT-01. Biodistribution studies were performed on wild-type C57BL/6 mice at multiple time points after tail vein injection. The sensitivity and specificity of [64Cu]Cu-ICT-01 in imaging CD163+ macrophages upregulated on the surface of atherosclerotic plaques were assessed in multiple mouse atherosclerosis models. Immunostaining, flow cytometry, and single-cell RNA sequencing were performed to characterize the expression of CD163 on tissue-resident macrophages. Human carotid atherosclerotic plaques were used to measure the expression of CD163+ resident macrophages and test the binding specificity of [64Cu]Cu-ICT-01. Results: [64Cu]Cu-ICT-01 showed high binding affinity to U87 cells. The biodistribution study showed rapid blood and renal clearance with low retention in all major organs at 1, 2, and 4 h after injection. In an ApoE-/- mouse model, [64Cu]Cu-ICT-01 demonstrated sensitive and specific detection of CD163+ macrophages and capability for tracking the progression of atherosclerotic lesions; these findings were further confirmed in Ldlr-/- and PCSK9 mouse models. Immunostaining showed elevated expression of CD163+ macrophages across the plaques. Flow cytometry and single-cell RNA sequencing confirmed the specific expression of CD163 on tissue-resident macrophages. Human tissue characterization demonstrated high expression of CD163+ macrophages on atherosclerotic lesions, and ex vivo autoradiography revealed specific binding of [64Cu]Cu-ICT-01 to human CD163. Conclusion: This work reported the development of a PET radiotracer binding CD163+ macrophages. The elevated expression of CD163+ resident macrophages on human plaques indicated the potential of CD163 as a biomarker for vulnerable plaques. The sensitivity and specificity of [64Cu]Cu-ICT-01 in imaging CD163+ macrophages warrant further investigation in translational settings.