Biology subjects

Kalpathy-Cramer, J.

Publications and source records attributed to Kalpathy-Cramer, J..

3 recordsLinked to original sources

bioRxiv2025

ESPWA: a deep learning-enabled tool for precision-based use of endocrine therapy in resource-limited settings

Immunohistochemistry for estrogen receptor (ER) expression is often unavailable in low-and-middle-income countries (LMICs), leading to empiric use of endocrine therapy (ET) and unnecessary toxicity in ER-negative patients. To address this unmet need, we developed ESPWA, a deep-learning model trained on 3448 H&E slides and tissue-matched ER status from breast cancer patients treated at Zanmi Lasante (ZL), Haiti. A model trained on The Cancer Genome Atlas (TCGA) exhibited substantial domain shift when applied to the ZL cohort, with AUROCs dropping from 0.846 on TCGA cross-validation to 0.671 on the ZL cohort. In contrast, ESPWA demonstrated improved performance on ZL cross-validation (AUROC=0.790; p=0.005). In an independent test set of 134 Haitian patients with parallel slides prepared and scanned in Mirebalais Hospital (Haiti) and Brigham and Womens Hospital, ESPWA was robust to variations in slide preparation, quality, and scanners, achieving AUROCs of 0.794 on BWH-prepared WSIs and 0.805 on Mirebalais-prepared WSIs. Prospective studies using ESPWA are underway in sub-Saharan Africa to evaluate its utility in informing precision-based use of ET.

cancer biology↗

bioRxiv2023

Structural and functional vascular dysfunction within brain metastases is linked to pembrolizumab inefficacy

Structurally and functionally aberrant vasculature is a hallmark of tumor angiogenesis and treatment resistance. Given the synergistic link between aberrant tumor vasculature and immunosuppression, we analyzed perfusion MRI for 44 patients with brain metastases (BM) undergoing treatment with pembrolizumab. To date, vascular-immune communication, or the relationship between immune checkpoint inhibitor (ICI) efficacy and vascular architecture, has not been well-characterized in human imaging studies. We found that ICI-responsive BM possessed a structurally balanced vascular makeup, which was linked to improved vascular efficiency and an immune-stimulatory microenvironment. In contrast, ICI-resistant BM were characterized by a lack of immune cell infiltration and a highly aberrant vasculature dominated by large-caliber vessels. Peri-tumor region analysis revealed early functional changes predictive of ICI resistance before radiographic evidence on conventional MRI. This study was one of the largest functional imaging studies for BM and establishes a foundation for functional studies that illuminate the mechanisms linking patterns of vascular architecture with immunosuppression, as targeting these aspects of cancer biology may serve as the basis for future combination treatments.

cancer biology↗

bioRxiv2021

DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics

Myocardial strain analysis from cinematic magnetic resonance imaging (cine-MRI) data could provide a more thorough characterization of cardiac mechanics than volumetric parameters such as left-ventricular ejection fraction, but sources of variation including segmentation and motion estimation have limited its wide clinical use. We designed and validated a deep learning (DL) workflow to generate both volumetric parameters and strain measures from cine-MRI data, including strain rate (SR) and regional strain polar maps, consisting of segmentation and motion estimation convolutional neural networks developed and trained using healthy and cardiovascular disease (CVD) subjects (n=150). DL-based volumetric parameters were correlated (>0.98) and without significant bias relative to parameters derived from manual segmentations in 50 healthy and CVD subjects. Compared to landmarks manually-tracked on tagging-MRI images from 15 healthy subjects, landmark deformation using DL-based motion estimates from paired cine-MRI data resulted in an end-point-error of 2.9 {+/-} 1.5 mm. Measures of end-systolic global strain from these cine-MRI data showed no significant biases relative to a tagging-MRI reference method. On 4 healthy subjects, intraclass correlation coefficient for intra-scanner repeatability was excellent (>0.95) for strain, moderate to excellent for SR (0.690-0.963), and good to excellent (0.826-0.994) in most polar map segments. Absolute relative change was within ~5% for strain, within ~10% for SR, and <1% in half of polar map segments. In conclusion, we developed and evaluated a DL-based, end-to-end fully-automatic workflow for global and regional myocardial strain analysis to quantitatively characterize cardiac mechanics of healthy and CVD subjects based on ubiquitously acquired cine-MRI data.

bioinformatics↗