A. T. Pearson

Generative Adversarial Networks Accurately Reconstruct Pan-Cancer Histology from Pathologic, Genomic, and Radiographic Latent Features

This paper introduces HistoXGAN, a GAN that reconstructs cancer histology from latent features, showing it retains tumor grade, histologic subtype, and gene expression patterns across 29 cancer types.

Mathematical Model Predicts Tumor Control Patterns Induced by Fast and Slow CTL Killing Mechanisms

The paper constructs a mathematical model to predict tumor control patterns based on CTL killing mechanisms and evaluates therapeutic outcomes under immune checkpoint blockade.

Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers

This paper evaluates ChatGPT-generated scientific abstracts against original ones using AI detectors, plagiarism tools, and human reviewers, finding they are original but identifiable as AI-generated.

Validating a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning

The paper validates a low-cost, open-source workstation and computational pipeline for automated histopathology evaluation using deep learning, showing comparable performance to expensive institutional hardware.

Latent transcriptional programs reveal histology-encoded tumor features spanning tissue origins

This paper uses machine learning to identify latent transcriptional programs in squamous cell carcinomas from histology images, linking them to clinically relevant biological processes.

Carcinoma associated mesenchymal stem cells promote ovarian cancer metastasis by increasing tumor heterogeneity through direct mitochondrial transfer

This study shows carcinoma-associated mesenchymal stem cells promote ovarian cancer metastasis by transferring mitochondria to cancer cells, increasing tumor heterogeneity and enhancing survival.

Self-supervised deep learning for pan-cancer mutation prediction from histopathology

This paper develops a self-supervised deep learning pipeline to predict genetic mutations from histopathology images across seven cancer types in two large datasets.

Multimodal Prediction of Breast Cancer Recurrence Assays and Risk of Recurrence

This paper develops a deep learning model that predicts breast cancer recurrence using digital histology and clinical factors, outperforming a clinical nomogram and identifying patients who may avoid genomic testing.

Classical Mathematical Models for Prediction of Response to Chemotherapy and Immunotherapy

This study evaluates classical mathematical models for predicting chemotherapy and immunotherapy responses using real-world patient tumor volume data, finding the Gompertz model provides the best balance between fit and complexity.

Deep learning detects virus presence in cancer histology

This paper uses deep learning to detect the presence of HPV and EBV in cancer histology images, achieving high accuracy in predicting virus-associated tumors.

Identifying predictors of HPV-related head and neck squamous cell carcinoma progression and survival through patient-derived models

This study identifies predictors of HPV-related head and neck squamous cell carcinoma progression and survival using patient-derived models and finds associations between mutational burden and E2F target dysregulation with clinical outcomes.