Impact of rescanning and repositioning on radiomic features employing a multi-object phantom in magnetic resonance imaging

Bernatz, Simon; Zhdanovich, Yauheniya; Ackermann, Jörg; Koch, Ina; Wild, Peter J.; Santos, Daniel Pinto dos; Vogl, Thomas J.; Kaltenbach, Benjamin; Rosbach, Nicolas

doi:10.1038/s41598-021-93756-x

Cited by 21 publications

(23 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results provide indirect support of this finding and lay the foundation for further explainability of these radiomic features and clinical translation. Additionally, several of the top predictors are related to morphology and intensity profile, which have been identified to be the most reproducible radiomics features [52], again suggesting robustness of our results.…”

Section: Model Explanationssupporting

confidence: 73%

A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features

Severn

Suresh

Görg

et al. 2022

Sensors

View full text Add to dashboard Cite

Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as “black boxes”. Prediction models that provide no insight into how their predictions are obtained are difficult to trust for making important clinical decisions, such as medical diagnoses or treatment. Explainable machine learning (XML) methods, such as Shapley values, have made it possible to explain the behavior of ML algorithms and to identify which predictors contribute most to a prediction. Incorporating XML methods into medical software tools has the potential to increase trust in ML-powered predictions and aid physicians in making medical decisions. Specifically, in the field of medical imaging analysis the most used methods for explaining deep learning-based model predictions are saliency maps that highlight important areas of an image. However, they do not provide a straightforward interpretation of which qualities of an image area are important. Here, we describe a novel pipeline for XML imaging that uses radiomics data and Shapley values as tools to explain outcome predictions from complex prediction models built with medical imaging with well-defined predictors. We present a visualization of XML imaging results in a clinician-focused dashboard that can be generalized to various settings. We demonstrate the use of this workflow for developing and explaining a prediction model using MRI data from glioma patients to predict a genetic mutation.

show abstract

Section: Model Explanationssupporting

confidence: 73%

A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features

Severn

Suresh

Görg

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Therefore, they often lack biological meaning. This reduces reproducibility, that is, the features, and thus the models, often cannot be faithfully recreated if the study is conducted at other sites [ 18 ]. Feature selection is used to identify relevant features that could represent the underlying biology and thus be considered biomarkers [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the dependence of radiomic features on the machine learning model

Demircioğlu

2022

Insights Imaging

View full text Add to dashboard Cite

Background In radiomic studies, several models are often trained with different combinations of feature selection methods and classifiers. The features of the best model are usually considered relevant to the problem, and they represent potential biomarkers. Features selected from statistically similarly performing models are generally not studied. To understand the degree to which the selected features of these statistically similar models differ, 14 publicly available datasets, 8 feature selection methods, and 8 classifiers were used in this retrospective study. For each combination of feature selection and classifier, a model was trained, and its performance was measured with AUC-ROC. The best-performing model was compared to other models using a DeLong test. Models that were statistically similar were compared in terms of their selected features. Results Approximately 57% of all models analyzed were statistically similar to the best-performing model. Feature selection methods were, in general, relatively unstable (0.58; range 0.35–0.84). The features selected by different models varied largely (0.19; range 0.02–0.42), although the selected features themselves were highly correlated (0.71; range 0.4–0.92). Conclusions Feature relevance in radiomics strongly depends on the model used, and statistically similar models will generally identify different features as relevant. Considering features selected by a single model is misleading, and it is often not possible to directly determine whether such features are candidate biomarkers.

show abstract

“…In addition, the gray values were discretized using 25 bin width. The radiomic features were then extracted from ROI drawn by reader 1 using the 3D slicer software with an extended plug-in called “PyRadiomics package” ( ) ( 18 , 29 ).…”

Section: Methodsmentioning

confidence: 99%

CT-Based Radiomics Analysis for Noninvasive Prediction of Perineural Invasion of Perihilar Cholangiocarcinoma

Zhan

Lyu

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

PurposeThe study aimed to construct and evaluate a CT-Based radiomics model for noninvasive detecting perineural invasion (PNI) of perihilar cholangiocarcinoma (pCCA) preoperatively.Materials and MethodsFrom February 2012 to October 2021, a total of 161 patients with pCCA who underwent resection were retrospectively enrolled in this study. Patients were allocated into the training cohort and the validation cohort according to the diagnostic time. Venous phase images of contrast-enhanced CT were used for radiomics analysis. The intraclass correlation efficient (ICC), the correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression were applied to select radiomics features and built radiomics signature. Logistic regression analyses were performed to establish a clinical model, a radiomics model, and a combined model. The performance of the predictive models was measured by area under the receiver operating characteristic curve (AUC), and pairwise ROC comparisons between models were tested using the Delong method. Finally, the model with the best performance was presented as a nomogram, and its calibration and clinical usefulness were assessed.ResultsFinally, 15 radiomics features were selected to build a radiomics signature, and three models were developed through logistic regression. In the training cohort, the combined model showed a higher predictive capability (AUC = 0.950) than the radiomics model and the clinical model (AUC: radiomics = 0.914, clinical = 0.756). However, in the validation cohort, the AUC of the radiomics model (AUC = 0.885) was significantly higher than the other two models (AUC: combined = 0.791, clinical = 0.567). After comprehensive consideration, the radiomics model was chosen to develop the nomogram. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had a good consistency and clinical utility.ConclusionWe developed a CT-based radiomics model with good performance to noninvasively predict PNI of pCCA preoperatively.

show abstract

Impact of rescanning and repositioning on radiomic features employing a multi-object phantom in magnetic resonance imaging

Cited by 21 publications

References 37 publications

A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features

A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features

Evaluation of the dependence of radiomic features on the machine learning model

CT-Based Radiomics Analysis for Noninvasive Prediction of Perineural Invasion of Perihilar Cholangiocarcinoma

Contact Info

Product

Resources

About