Dr. Obianuju S. S. Madueke-Laveaux Profile

Dr. Obianuju S. S. Madueke-Laveaux

at Univ of Chicago

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

SPIE Journal Paper | 12 September 2024 Open Access

Radiomics and quantitative multi-parametric MRI for predicting uterine fibroid growth

Karen Drukker, Milica Medved, Carla Harmath, Maryellen Giger, Obianuju Madueke-Laveaux

JMI, Vol. 11, Issue 05, 054501, (September 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.5.054501

KEYWORDS: Radiomics, Magnetic resonance imaging, Analog to digital converters, Principal component analysis, Cooccurrence matrices, Volume rendering, Tumors, Hazard analysis, Ultrasonography, Tissues

Read Abstract +

SignificanceUterine fibroids (UFs) can pose a serious health risk to women. UFs are benign tumors that vary in clinical presentation from asymptomatic to causing debilitating symptoms. UF management is limited by our inability to predict UF growth rate and future morbidity.AimWe aim to develop a predictive model to identify UFs with increased growth rates and possible resultant morbidity.ApproachWe retrospectively analyzed 44 expertly outlined UFs from 20 patients who underwent two multi-parametric MR imaging exams as part of a prospective study over an average of 16 months. We identified 44 initial features by extracting quantitative magnetic resonance imaging (MRI) features plus morphological and textural radiomics features from DCE, T2, and apparent diffusion coefficient sequences. Principal component analysis reduced dimensionality, with the smallest number of components explaining over 97.5% of the variance selected. Employing a leave-one-fibroid-out scheme, a linear discriminant analysis classifier utilized these components to output a growth risk score.ResultsThe classifier incorporated the first three principal components and achieved an area under the receiver operating characteristic curve of 0.80 (95% confidence interval [0.69; 0.91]), effectively distinguishing UFs growing faster than the median growth rate of 0.93 cm3/year/fibroid from slower-growing ones within the cohort. Time-to-event analysis, dividing the cohort based on the median growth risk score, yielded a hazard ratio of 0.33 [0.15; 0.76], demonstrating potential clinical utility.ConclusionWe developed a promising predictive model utilizing quantitative MRI features and principal component analysis to identify UFs with increased growth rates. Furthermore, the model’s discrimination ability supports its potential clinical utility in developing tailored patient and fibroid-specific management once validated on a larger cohort.

DOWNLOAD PAPER

Proceedings Article | 3 April 2024 Poster + Paper

Radiomics and quantitative multi-parametric MRI for predicting uterine fibroid growth

Karen Drukker, Milica Medved, Carla Harmath, Maryellen Giger, Obianuju Madueke-Laveaux

Proceedings Volume 12927, 129272Y (2024) https://doi.org/10.1117/12.3005190

KEYWORDS: Magnetic resonance imaging, Radiomics, Principal component analysis, Ultrasonography, Tumors, Tissues, Statistical analysis, Image enhancement, Hazard analysis

Read Abstract +

Uterine fibroids (UFs) are benign tumors that vary in clinical presentation from asymptomatic to causing debilitating symptoms. UF management is limited by our inability to predict UF growth rate and future morbidity. This study aimed to develop a predictive model to identify UFs with increased growth rate and possible resultant morbidity. We retrospectively analyzed 44 expertly-outlined UFs from 21 patients who underwent two multi-parametric MR imaging exams as part of a prospective study over an average of 16 months. We identified 100 initial features by extracting quantitative MRI, morphological and textural radiomics features from DCE, T2, and ADC sequences. Principal component analysis reduced dimensionality, with the smallest number of components explaining over 97.5% of variance selected. Employing a leave-one-fibroid-out scheme, a linear discriminant analysis classifier utilized these components to output a growth risk score. The classifier incorporated the first six principal components and achieved an area under the ROC curve of 0.80 (95% CI [0.69; 0.91]), effectively distinguishing UFs growing faster than the median growth rate of 0.93 cm3/year/fibroid from slower-growing ones within the cohort. Time-to-event analysis, dividing the cohort based on the median growth risk score, yielded a hazard ratio of 0.33 [0.15; 0.76], demonstrating potential clinical utility. In conclusion, this pilot study developed a promising predictive model utilizing quantitative MRI features and principal component analysis to identify UFs with increased growth rate. Furthermore, the model's discrimination ability supports its potential clinical utility in developing tailored patient and fibroid specific management once validated on a larger cohort.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years