To conduct a computational investigation to explore the influence of clinical reference uncertainty on magnetic resonance imaging(MRI)radiomics feature selection,modelling,and performance.This study used two sets of p...To conduct a computational investigation to explore the influence of clinical reference uncertainty on magnetic resonance imaging(MRI)radiomics feature selection,modelling,and performance.This study used two sets of publicly available prostate cancer MRI=radiomics data(Dataset 1:n=260;Dataset 2:n=100)with Gleason score clinical references.Each dataset was divided into training and holdout testing datasets at a ratio of 7:3 and analysed independently.The clinical references of the training set were permuted at different levels(increments of 5%)and repeated 20 times.Four feature selection algorithms and two classifiers were used to construct the models.Cross-validation was employed for training,while a separate hold-out testing set was used for evaluation.The Jaccard similarity coefficient was used to evaluate feature selection,while the area under the curve(AUC)and accuracy were used to assess model performance.An analysis of variance test with Bonferroni correction was conducted to compare the metrics of each model.The consistency of the feature selection performance decreased substantially with the clinical reference permutation.AUCs of the trained models with permutation particularly after 20%were significantly lower(Dataset 1(with≥20%permutation):0.67,and Dataset 2(≥20%permutation):0.74),compared to the AUC of models without permutation(Dataset 1:0.94,Dataset 2:0.97).The performances of the models were also associated with larger uncertainties and an increasing number of permuted clinical references.Clinical reference uncertainty can substantially influence MRI radiomic feature selection and modelling.The high accuracy of clinical references should be helpful in building reliable and robust radiomic models.Careful interpretation of the model performance is necessary,particularly for high-dimensional data.展开更多
Radiomics has increasingly been investigated as a potential biomarker in quantitative imaging to facilitate personalized diagnosis and treatment of head and neck cancer(HNC),a group of malignancies associated with hig...Radiomics has increasingly been investigated as a potential biomarker in quantitative imaging to facilitate personalized diagnosis and treatment of head and neck cancer(HNC),a group of malignancies associated with high heterogeneity.However,the feature reliability of radiomics is a major obstacle to its broad validity and generality in application to the highly heterogeneous head and neck(HN)tissues.In particular,feature repeatability of radiomics in magnetic resonance imaging(MRI)acquisition,which is considered a crucial confounding factor of radiomics feature reliability,is still sparsely investigated.This study prospectively investigated the acquisition repeatability of 93 MRI radiomics features in ten HN tissues of 15 healthy volunteers,aiming for potential magnetic resonance-guided radiotherapy(MRgRT)treatment of HNC.Each subject underwent four MRI acquisitions with MRgRT treatment position and immobilization using two pulse sequences of 3D T1-weighed turbo spin-echo and 3D T2-weighed turbo spin-echo on a 1.5T MRI simulator.The repeatability of radiomics feature acquisition was evaluated in terms of the intraclass correlation coefficient(ICC),whereas within-subject acquisition variability was evaluated in terms of the coefficient of variation(CV).The results showed that MRI radiomics features exhibited heterogeneous acquisition variability and uncertainty dependent on feature types,tissues,and pulse sequences.Only a small fraction of features showed excellent acquisition repeatability(ICC>0.9)and low within-subject variability.Multiple MRI scans improved the accuracy and confidence of the identification of reliable features concerning MRI acquisition compared to simple test-retest repeated scans.This study contributes to the literature on the reliability of radiomics features with respect to MRI acquisition and the selection of reliable radiomics features for use in modeling in future HNC MRgRT applications.展开更多
基金supported by hospital research project RC-2022-12.
文摘To conduct a computational investigation to explore the influence of clinical reference uncertainty on magnetic resonance imaging(MRI)radiomics feature selection,modelling,and performance.This study used two sets of publicly available prostate cancer MRI=radiomics data(Dataset 1:n=260;Dataset 2:n=100)with Gleason score clinical references.Each dataset was divided into training and holdout testing datasets at a ratio of 7:3 and analysed independently.The clinical references of the training set were permuted at different levels(increments of 5%)and repeated 20 times.Four feature selection algorithms and two classifiers were used to construct the models.Cross-validation was employed for training,while a separate hold-out testing set was used for evaluation.The Jaccard similarity coefficient was used to evaluate feature selection,while the area under the curve(AUC)and accuracy were used to assess model performance.An analysis of variance test with Bonferroni correction was conducted to compare the metrics of each model.The consistency of the feature selection performance decreased substantially with the clinical reference permutation.AUCs of the trained models with permutation particularly after 20%were significantly lower(Dataset 1(with≥20%permutation):0.67,and Dataset 2(≥20%permutation):0.74),compared to the AUC of models without permutation(Dataset 1:0.94,Dataset 2:0.97).The performances of the models were also associated with larger uncertainties and an increasing number of permuted clinical references.Clinical reference uncertainty can substantially influence MRI radiomic feature selection and modelling.The high accuracy of clinical references should be helpful in building reliable and robust radiomic models.Careful interpretation of the model performance is necessary,particularly for high-dimensional data.
基金This study was supported by hospital research project,No.REC-2019-09.
文摘Radiomics has increasingly been investigated as a potential biomarker in quantitative imaging to facilitate personalized diagnosis and treatment of head and neck cancer(HNC),a group of malignancies associated with high heterogeneity.However,the feature reliability of radiomics is a major obstacle to its broad validity and generality in application to the highly heterogeneous head and neck(HN)tissues.In particular,feature repeatability of radiomics in magnetic resonance imaging(MRI)acquisition,which is considered a crucial confounding factor of radiomics feature reliability,is still sparsely investigated.This study prospectively investigated the acquisition repeatability of 93 MRI radiomics features in ten HN tissues of 15 healthy volunteers,aiming for potential magnetic resonance-guided radiotherapy(MRgRT)treatment of HNC.Each subject underwent four MRI acquisitions with MRgRT treatment position and immobilization using two pulse sequences of 3D T1-weighed turbo spin-echo and 3D T2-weighed turbo spin-echo on a 1.5T MRI simulator.The repeatability of radiomics feature acquisition was evaluated in terms of the intraclass correlation coefficient(ICC),whereas within-subject acquisition variability was evaluated in terms of the coefficient of variation(CV).The results showed that MRI radiomics features exhibited heterogeneous acquisition variability and uncertainty dependent on feature types,tissues,and pulse sequences.Only a small fraction of features showed excellent acquisition repeatability(ICC>0.9)and low within-subject variability.Multiple MRI scans improved the accuracy and confidence of the identification of reliable features concerning MRI acquisition compared to simple test-retest repeated scans.This study contributes to the literature on the reliability of radiomics features with respect to MRI acquisition and the selection of reliable radiomics features for use in modeling in future HNC MRgRT applications.
