Development and evaluation of machine learning models for voxel dose predictions in online adaptive magnetic resonance guided radiation therapy

Background and Aims: Endoscopic healing (EH), is a major treatment goal for Crohn's disease(CD). However, terminal ileum (TI) intubation failure is common, especially in children. We evaluated the added-value of machine-learning models in imputing a TI Simple Endoscopic Score for CD (SES-CD) from Magnetic Resonance Enterography (MRE) data of pediatric CD patients. Methods: This is a sub-study of the prospective ImageKids study. We developed machine-learning and baseline linear-regression models to predict TI SES-CD score from the Magnetic Resonance Index of Activity (MaRIA) and the Pediatric Inflammatory Crohn's MRE Index (PICMI) variables. We assessed TI SES-CD predictions' accuracy for intubated patients with a stratified 2-fold validation experimental setup, repeated 50 times. We determined clinical impact by imputing TI SES-CD in patients with ileal intubation failure during ileocolonscopy. Results: A total of 223 children were included (mean age 14.1+-2.5 years), of whom 132 had all relevant variables (107 with TI intubation and 25 with TI intubation failure). The combination of a machine-learning model with the PICMI variables achieved the lowest SES-CD prediction error compared to a baseline MaRIA-based linear regression model for the intubated patients (N=107, 11.7 (10.5-12.5) vs. 12.1 (11.4-12.9), p<0.05). The PICMI-based models suggested a higher rate of patients with TI disease among the non-intubated patients compared to a baseline MaRIA-based linear regression model (N=25, up to 25/25 (100%) vs. 23/25 (92%)). Conclusions: Machine-learning models with clinically-relevant variables as input are more accurate than linear-regression models in predicting TI SES-CD and EH when using the same MRE-based variables.

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Predicting common solid renal tumors using machine learning models of classification of radiologist-assessed magnetic resonance characteristics

Abdominal Radiology ◽

10.1007/s00261-020-02637-w ◽

2020 ◽

Vol 45 (9) ◽

pp. 2797-2809 ◽

Cited By ~ 2

Author(s):

Camila Lopes Vendrami ◽

Robert J. McCarthy ◽

Carolina Parada Villavicencio ◽

Frank H. Miller

Keyword(s):

Machine Learning ◽

Magnetic Resonance ◽

Renal Tumors ◽

Learning Models ◽

Machine Learning Models

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Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models

The Neuroradiology Journal ◽

10.1177/1971400921990766 ◽

2021 ◽

pp. 197140092199076

Author(s):

Sarv Priya ◽

Amit Agarwal ◽

Caitlin Ward ◽

Thomas Locke ◽

Varun Monga ◽

...

Keyword(s):

Machine Learning ◽

Magnetic Resonance ◽

Texture Analysis ◽

Diagnostic Performance ◽

Survival Prediction ◽

Learning Models ◽

Post Contrast ◽

Machine Learning Classification ◽

First Order ◽

Machine Learning Models

Objective Magnetic resonance texture analysis (MRTA) is a relatively new technique that can be a valuable addition to clinical and imaging parameters in predicting prognosis. In the present study, we investigated the efficacy of MRTA for glioblastoma survival using T1 contrast-enhanced (CE) images for texture analysis. Methods We evaluated the diagnostic performance of multiple machine learning models based on first-order histogram statistical parameters derived from T1-weighted CE images in the survival stratification of glioblastoma multiforme (GBM). Retrospective evaluation of 85 patients with GBM was performed. Thirty-six first-order texture parameters at six spatial scale filters (SSF) were extracted on the T1 CE axial images for the whole tumor using commercially available research software. Several machine learning classification models (in four broad categories: linear, penalized linear, non-linear, and ensemble classifiers) were evaluated to assess the survival prediction performance using optimal features. Principal component analysis was used prior to fitting the linear classifiers in order to reduce the dimensionality of the feature inputs. Fivefold cross-validation was used to partition the data iteratively into training and testing sets. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance. Results The neural network model was the highest performing model with the highest observed AUC (0.811) and cross-validated AUC (0.71). The most important variable was the age at diagnosis, with mean and mean of positive pixels (MPP) for SSF = 0 being the second and third most important, followed by skewness for SSF = 0 and SSF = 4. Conclusions First-order texture features, when combined with age at presentation, show good accuracy in predicting GBM survival.

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Automatic Planning of Whole Breast Radiation Therapy Using Machine Learning Models

Frontiers in Oncology ◽

10.3389/fonc.2019.00750 ◽

2019 ◽

Vol 9 ◽

Cited By ~ 3

Author(s):

Yang Sheng ◽

Taoran Li ◽

Sua Yoo ◽

Fang-Fang Yin ◽

Rachel Blitzblau ◽

...

Keyword(s):

Machine Learning ◽

Radiation Therapy ◽

Learning Models ◽

Automatic Planning ◽

Machine Learning Models

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Improving XGBoost with Imagination Sampling

Communications of the Blyth Institute ◽

10.33014/issn.2640-5652.2.1.holloway.1 ◽

2020 ◽

Vol 2 (1) ◽

pp. 3-6

Author(s):

Eric Holloway

Keyword(s):

Machine Learning ◽

General System ◽

Learning Models ◽

Starting Point ◽

Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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