Biology subjects

Egan, K. M.

Publications and source records attributed to Egan, K. M..

3 recordsLinked to original sources

bioRxiv2026

Comparison of Total X-Ray Energy Absorbed by Fibroglandular Tissue and its Mass Derived from Full Field Digital Mammography and Digital Breast Tomosynthesis

This study investigated whether total x-ray energy absorbed by fibroglandular tissue and fibroglandular tissue mass derived from raw and clinical full field digital mammography (FFDM) images corresponded with the respective quantities derived from digital breast tomosynthesis (DBT). A total of 840 paired FFDM and DBT observations were analyzed separately for the craniocaudal (CC) and mediolateral oblique (MLO) orientations. Total absorbed x-ray energy and fibroglandular tissue mass were calculated using an automated breast density detection method in conjunction with the recorded average glandular dose. Relationships between FFDM and DBT quantities were evaluated in the log-log domain using linear and quadratic models, with model performance assessed by R2, adjusted R2, mean squared error (MSE), chi-square goodness-of-fit tests, and residual analysis. Strong associations were observed between FFDM and respective DBT quantities. For raw FFDM, linear energy models produced R2 values of 0.734 and 0.901 for the CC and MLO views, respectively, while the corresponding mass models produced R2 values of 0.634 and 0.870. Inclusion of a quadratic term improved model performance modestly. For clinical FFDM, linear energy models produced R2 values of 0.854 and 0.919, while the corresponding mass models produced R2 values of 0.721 and 0.812. The first-order coefficients for the clinical-image energy models were approximately unity (0.99-1.05), and inclusion of the quadratic term produced negligible changes in model performance and residual distributions. The goodness-of-fit tests did not reject adequate fit for any model. Clinical FFDM images can therefore provide estimates of total absorbed x-ray energy and fibroglandular tissue mass that are strongly associated with corresponding quantities derived from DBT. The approximately unit first-order coefficients and minimal benefit from quadratic modeling support a simple linear log-log calibration for clinical FFDM. This approach may facilitate combining FFDM and DBT based data in larger retrospective and longitudinal studies of cumulative mammographic x-ray exposure. These findings demonstrate proof-of-concept internal consistency but do not establish absolute dosimetric accuracy.

bioinformatics↗

bioRxiv2018

ENvironmental Dynamics Underlying Responsive Extreme Survivors (ENDURES) of Glioblastoma: a Multi-disciplinary Team-based, Multifactorial Analytical Approach

Although glioblastoma is a fatal primary brain cancer with a short median survival of 15 months, a small number of patients survive more than 5 years after diagnosis; they are known as extreme survivors (ES). Due to their rarity, very little is known about what differentiates these outliers from other glioblastoma patients. For the purpose of identifying unknown drivers of extreme survivorship in glioblastoma, we developed the ENDURES consortium (ENvironmental Dynamics Underlying Responsive Extreme Survivors of glioblastoma). This consortium is a multicenter collaborative network of investigators focused on the integration of multiple types of clinical data and the creation of patient-specific models of tumor growth informed by radiographic and histological parameters. Leveraging our combined resources, the goals of the ENDURES consortium are two-fold: (1) to build a curated, searchable, multilayered repository housing clinical and outcome data on a large cohort of ES patients with glioblastoma and (2) to leverage the ENDURES repository for new insights on tumor behavior and novel targets for prolonging survival for all glioblastoma patients. In this article, we review the available literature and discuss what is already known about ES. We then describe the creation of our consortium and some of our preliminary results.\n\nFundingThis review was financially supported by a grant from the James S. McDonnell Foundation\n\nConflicts of InterestThe authors have declared that no conflicts of interest exist.\n\nAuthorshipConceptualized consortium: LW, RG, KME, PC, and KRS. Built consortium: SKJ, PK, NR, JS, KME, PC, and KRS. Wrote the manuscript: SKJ, PW, SCM, PK, AP, and KME. Reviewed and edited the manuscript: LFGC, MMM, AHD, PRJ, and LSH. Contributed to writing, provided feedback, and approved of final manuscript: All authors.\n\nLink to website for ENDUREShttp://mathematicalneurooncology.org/?page_id=2125

cancer biology↗

bioRxiv2018

Connecting Patterns of Tumor Growth with Sex Differences in Extreme Survivorship for Primary Glioblastoma Patients

BackgroundSex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences.\n\nMethodsCombining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients. Pretreatment MR images of 494 glioblastoma patients (299 males and 195 females) were segmented to quantify tumor volumes. Cox proportional hazard (CPH) models and Students t-tests were used to assess which variables were associated with survival outcomes.\n\nResultsAmong males, tumor (T1Gd) radius was a predictor of overall survival (HR=1.027, p=0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR=1.011, p<0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p=0.010 t-test), but tumor size was not correlated with female overall survival (p=0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p=0.004, F p=0.001, t-test). Additionally, extent of resection, tumor laterality, and IDH1 mutation status were also found to have sex-specific effects on overall survival.\n\nConclusionDespite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes, which emphasizes the importance of considering sex as a biological factor when determining patient prognosis and treatment approach.

cancer biology↗