Usage Information
Abstract
In clinical breast cancer intervention, selection of the optimal treatment protocol based on predictive biomarkers remains an elusive goal. Here, we present a modeling tool to predict the likelihood of breast cancer response to neoadjuvant chemotherapy using patient-specific tumor vasculature biomarkers. A semiautomated analysis was implemented and performed on 3990 histological images from 48 patients, with 10–208 images analyzed for each patient. We applied a histology-based mathematical model to 30 resected primary breast cancer tumors and then evaluated a cohort of 18 patients undergoing neoadjuvant chemotherapy, collecting pre- and posttreatment pathology specimens and MRI data. We found that core biopsy samples can be used with acceptable accuracy to determine histological parameters representative of the whole tissue region. Analysis of model histology parameters obtained from tumor vasculature measurements, specifically diffusion distance divided by the radius of the drug-delivering blood vessel (L/rb) and blood volume fraction (BVF), provides a statistically significant separation of patients obtaining a pathologic complete response (pCR) from those who do not. With this model, it is feasible to evaluate primary breast tumor vasculature biomarkers in a patient-specific manner, thereby allowing a precision approach to breast cancer treatment.
Authors
Terisse A. Brocato, Ursa Brown-Glaberman, Zhihui Wang, Reed G. Selwyn, Colin M. Wilson, Edward F. Wyckoff, Lesley C. Lomo, Jennifer L. Saline, Anupama Hooda-Nehra, Renata Pasqualini, Wadih Arap, C. Jeffrey Brinker, Vittorio Cristini
Usage data is cumulative from March 2025 through March 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 1,025 | 58 |
| 126 | 14 | |
| Figure | 248 | 0 |
| Table | 47 | 0 |
| Supplemental data | 47 | 0 |
| Citation downloads | 95 | 0 |
| Totals | 1,588 | 72 |
| Total Views | 1,660 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.
