Alan B. Hollingsworth

ORCID: 0000-0002-7856-6798
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About
Contact & Profiles
Research Areas
  • MRI in cancer diagnosis
  • Radiomics and Machine Learning in Medical Imaging
  • Global Cancer Incidence and Screening
  • Breast Cancer Treatment Studies
  • AI in cancer detection
  • Digital Radiography and Breast Imaging
  • Medical Imaging Techniques and Applications
  • Breast Lesions and Carcinomas
  • BRCA gene mutations in cancer
  • Molecular Biology Techniques and Applications
  • Advanced MRI Techniques and Applications
  • Colorectal Cancer Screening and Detection
  • Gene expression and cancer classification
  • Advanced Proteomics Techniques and Applications
  • MicroRNA in disease regulation
  • Cancer Risks and Factors
  • Advanced Biosensing Techniques and Applications
  • Nutrition, Genetics, and Disease
  • Breast Implant and Reconstruction
  • Bioinformatics and Genomic Networks
  • Monoclonal and Polyclonal Antibodies Research
  • Pediatric Hepatobiliary Diseases and Treatments
  • Peroxisome Proliferator-Activated Receptors
  • Genetic and Kidney Cyst Diseases
  • Cancer, Lipids, and Metabolism

Mercy Health
2009-2022

Sisters of Mercy Health System
2002-2021

Aurora Medical Center
2021

Kaohsiung Veterans General Hospital
2021

University Medical Center
2021

Boston University
2021

Tufts University
2021

Mercy Hospital
2015-2020

Vanderbilt University
2020

CHI Health Mercy Council Bluffs
2019

The hypothesis to use microRNAs (miRNAs) circulating in the blood as cancer biomarkers was formulated some years ago based on promising initial results. After exciting discoveries, however, it became evident that accurate quantification of cell-free miRNAs more challenging than expected. Difficulties were linked strong impact many, if not all, pre- and post- analytical variables have final In this study, we used currently available high-throughput technologies identify present plasma serum...

10.18632/oncotarget.3859 article EN Oncotarget 2015-05-02

In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims investigate advantages applying machine learning approach embedded with locally preserving projection (LPP) based feature combination regeneration algorithm predict short-term risk. A dataset involving negative mammograms acquired from 500 women was assembled. This divided into two age-matched classes 250 high cases in which detected...

10.1088/1361-6560/aaa1ca article EN Physics in Medicine and Biology 2017-12-14

Breast cancer circulating biomarkers include carcinoembryonic antigen and carbohydrate 15-3, which are used for patient follow-up. Since sensitivity specificity low, novel more useful needed. The presence of stable microRNAs (miRNAs) in serum or plasma suggested a promising role these tiny RNAs as biomarkers. To acquire an absolute concentration miRNAs reduce the impact preanalytical analytical variables, we droplet digital PCR (ddPCR) technique.We investigated panel five sera two...

10.1186/s40364-015-0037-0 article EN cc-by Biomarker Research 2015-06-05

Purpose To develop a new quantitative global kinetic breast magnetic resonance imaging (MRI) features analysis scheme and assess its feasibility to tumor response neoadjuvant chemotherapy. Materials Methods A dataset involving MR images acquired from 151 cancer patients before chemotherapy was used. Among them, 63 had complete (CR) 88 partial (PR) based on the RECIST criterion. computer‐aided detection (CAD) applied segment region depicted computed total of 10 image represent parenchyma...

10.1002/jmri.25276 article EN Journal of Magnetic Resonance Imaging 2016-04-15

This study aims to develop and evaluate a new computer-aided diagnosis (CADx) scheme based on analysis of global mammographic image features predict likelihood cases being malignant. An dataset involving 1,959 was retrospectively assembled. Suspicious lesions were detected biopsied in each case. Among them, 737 are malignant 1,222 benign. Each case includes four mammograms craniocaudal mediolateral oblique view left right breasts. CADx is applied pre-process mammograms, generate two maps...

10.1109/tmi.2019.2946490 article EN IEEE Transactions on Medical Imaging 2019-10-09

Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response neoadjuvant chemotherapy. Methods: The authors assembled dataset involving breast MR images acquired from 68 cancer patients before undergoing Among them, 25 had complete (CR) 43 partial nonresponse (NR) chemotherapy the evaluation criteria in solid tumors. developed computer‐aided detection scheme segment areas tumors depicted computed total...

10.1118/1.4933198 article EN Medical Physics 2015-10-19

To assess diagnostic performance of dedicated breast magnetic resonance (MR) imaging at centers by using a 1.5-T MR system that used high-spatial-resolution, high-contrast-resolution spiral trajectory acquisitions.The study was institutional review board approved and HIPAA compliant, with waiver informed consent. Diagnostic retrospectively assessed for 934 consecutive screening (n=347) (n=587) examinations performed from April 2006 to December 2007 in women aged 25-89 years old four sites...

10.1148/radiol.12110600 article EN Radiology 2012-08-25

Despite significant advances in breast imaging, the ability to accurately detect Breast Cancer (BC) remains a challenge. With discovery of key biomarkers and protein signatures for BC, proteomic technologies are currently poised serve as an ideal diagnostic adjunct imaging. Research studies have shown that tumors associated with systemic changes levels both serum (SPB) tumor autoantibodies (TAAb). However, independent contribution SPB TAAb expression data identifying BC relative...

10.1371/journal.pone.0157692 article EN cc-by PLoS ONE 2016-08-10

Abstract: Preoperative breast MRI in newly diagnosed cancer patients has several potential benefits. Improved survival for with invasive disease as the index lesion is unlikely to be one of these benefits, given what known from variations locoregional management historic conservation trials. However, this may not case ductal carcinoma situ (DCIS), discovery unsuspected located elsewhere biopsy-proven DCIS could result decreased if left undetected and untreated. In support hypothesis, a...

10.1111/j.1524-4741.2012.01273.x article EN The Breast Journal 2012-07-18

This study aims to investigate the feasibility of identifying a new quantitative imaging marker based on false-positives generated by computer-aided detection (CAD) scheme help predict short-term breast cancer risk. An image dataset including four view mammograms acquired from 1044 women was retrospectively assembled. All were originally interpreted as negative radiologists. In next subsequent mammography screening, 402 diagnosed with and 642 remained negative. existing CAD applied 'as is'...

10.1088/1361-6560/aabefe article EN Physics in Medicine and Biology 2018-04-18

Current guidelines for adding breast MRI to annual screening mammography are based entirely upon stratification of risk, with a heavy focus on lifetime calculations. This approach is fraught difficulty due the reliance mathematical models that vary widely in their calculations, inherent age discrimination using risks rather than short-term incidence, and failure incorporate mammographic density, latter being an independent risk as well greatest predictor failure. By utilizing system patient...

10.1111/tbj.12242 article EN The Breast Journal 2014-01-06

Overexpression of autocrine growth factors and their receptors has been reported in many human cancers. The study autocrine-regulated pathways using vitro culture systems can be hindered by the presence fetal bovine serum medium. A pancreatic cancer cell line (HPAF) was slowly weaned from its dependence on subsequently maintained serum-free conditions. Growth factor secretion studies showed that production such as transforming α, gastrin-releasing peptide, insulin-like I cells increased...

10.1097/00006676-200104000-00011 article EN Pancreas 2001-04-01
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