Siddharth Singi

Artificial intelligence models using digital histopathology slides stained with hematoxylin and eosin offer promising, tissue-preserving diagnostic tools for patients with cancer. Despite their advantages, their clinical utility in real-world settings remains unproven. Assessing EGFR mutations in lung adenocarcinoma demands rapid, accurate and cost-effective tests that preserve tissue for genomic sequencing. PCR-based assays provide rapid results but with reduced accuracy compared with next-generation sequencing and require additional tissue. Computational biomarkers leveraging modern foundation models can address these limitations. Here we assembled a large international clinical dataset of digital lung adenocarcinoma slides (N = 8,461) to develop a computational EGFR biomarker. Our model fine-tunes an open-source foundation model, improving task-specific performance with out-of-center generalization and clinical-grade accuracy on primary and metastatic specimens (mean area under the curve: internal 0.847, external 0.870). To evaluate real-world clinical translation, we conducted a prospective silent trial of the biomarker on primary samples, achieving an area under the curve of 0.890. The artificial-intelligence-assisted workflow reduced the number of rapid molecular tests needed by up to 43% while maintaining the current clinical standard performance. Our retrospective and prospective analyses demonstrate the real-world clinical utility of a computational pathology biomarker.

Cytomorphological analysis of the bone marrow aspirate (BMA) is pivotal for the diagnostic workup of a broad range of hematological disorders. However, this skill is error prone, highly complex, and time consuming. Deep learning-based models for the automatic classification of bone marrow cell morphology demonstrate the potential to improve diagnostic efficiency and accuracy. However, existing deep learning approaches in this field fall short of expert-level performance and lack generalizability beyond a single dataset. Working with multiple hematopathologists, we curated a dataset from the University of California, San Francisco, which included a training set of 30,394 images from 40 patients with morphologically normal marrows and a test set of 8507 images from 10 different patients, all derived from 400×-equivalent whole-slide images (WSIs). We then developed DeepHeme, a snapshot ensemble deep learning classifier, which outperformed previous models in accuracy while expanding the total number of differentiable cell classes. We externally validated DeepHeme using an independent dataset from the Memorial Sloan Kettering Cancer Center, which included 2694 images from 10 morphologically normal patients and 11,076 images from 655 patients with normal or diseased marrow, scanned using a different WSI system, demonstrating robust generalizability. At the level of individual cell classifications, we systematically compared DeepHeme's diagnostic performance with that of three medical experts from different academic hospitals, demonstrating that DeepHeme achieved accuracy comparable to, or exceeding, that of human experts. Accurate and generalizable cell classification represents a step toward automated analysis of hematopathology slides and the development of quantitative, morphology-based, predictive markers.

Background: (MIL) with pathology foundation models (PFMs) has become the standard baseline for CB development. While these methods, with architectural and optimization advances, have improved predictive performance, computational pathology lacks standardized intermediate data formats, provenance tracking, checkpointing conventions, and reproducible evaluation metrics required for clinical-grade deployment. Consequently, discipline-level standardization, including data representation, model versioning, evaluation protocols, and auditability, is essential to enable reliable, scalable, and regulatory-ready clinical translation of CBs. Methods: (www.artificialintelligencepathology.org), a standardized benchmarking framework built on a curated TCGA cohort with clinically anchored OncoKB level 1-3 biomarker labels. GOLDMARK distributes structured intermediate outputs, including tile coordinates, per-slide feature embeddings from canonical PFMs, embedding-level quality-control metadata, trained slide-level weights, and reference code. Multiple publicly available PFMs are benchmarked under a unified attention-based MIL head using predefined patient-level splits. Models are trained on TCGA and evaluated on an independent MSKCC cohort with reciprocal testing. Results: ) and showed the most stable cross-site performance. Differences between canonical encoders were modest relative to task-specific variability. Conclusions: Computational pathology is entering a translational phase in which reproducibility, transparency, and cross-institutional robustness are prerequisites for clinical trust. GOLDMARK establishes a reference framework that separates dataset curation from model evaluation and introduces structured intermediate artifacts, quality-control metadata, and symmetric cross-dataset testing as core components of benchmarking. Such infrastructure is essential for transforming computational biomarkers from research demonstrations into reproducible, clinically trusted workflows.

Computational biomarkers (CBs) are histopathology-derived patterns extracted from hematoxylin-eosin (H&E) whole-slide images (WSIs) using artificial intelligence (AI) to predict therapeutic response or prognosis. Recently, slide-level multiple-instance learning (MIL) with pathology foundation models (PFMs) has become the standard baseline for CB development. While these methods have improved predictive performance, computational pathology lacks standardized intermediate data formats, provenance tracking, checkpointing conventions, and reproducible evaluation metrics required for clinical-grade deployment. We introduce GOLDMARK (https://artificialintelligencepathology.org), a standardized benchmarking framework built on a curated TCGA cohort with clinically actionable OncoKB level 1-3 biomarker labels. GOLDMARK releases structured intermediate representations, including tile coordinate maps, per-slide feature embeddings from canonical PFMs, quality-control metadata, predefined patient-level splits, trained slide-level models, and evaluation outputs. Models are trained on TCGA and evaluated on an independent MSKCC cohort with reciprocal testing. Across 33 tumor-biomarker tasks, mean AUROC was 0.689 (TCGA) and 0.630 (MSKCC). Restricting to the eight highest-performing tasks yielded mean AUROCs of 0.831 and 0.801, respectively. These tasks correspond to established morphologic-genomic associations (e.g., LGG IDH1, COAD MSI/BRAF, THCA BRAF/NRAS, BLCA FGFR3, UCEC PTEN) and showed the most stable cross-site performance. Differences between canonical encoders were modest relative to task-specific variability. GOLDMARK establishes a shared experimental substrate for computational pathology, enabling reproducible benchmarking and direct comparison of methods across datasets and models.