MIL normalization —— prerequisites for accurate MRI radiomics analysis

Hu, Zhaoyu; Zhuang, Qiyuan; Xiao, Yang; Wu, Guoqing; Shi, Zhifeng; Chen, Liang; Wang, Yuan‐Yuan; Yu, Jinhua

doi:10.1016/j.compbiomed.2021.104403

Cited by 14 publications

(21 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…X i indicates the signal of the voxel. According to the standardized workflow of radiomics, images should be preprocessed before radiomics features extraction ( 26 ).…”

Section: Methodsmentioning

confidence: 99%

A Radiomics Approach to Assess High Risk Carotid Plaques: A Non-invasive Imaging Biomarker, Retrospective Study

et al. 2022

View full text Add to dashboard Cite

ObjectiveThis study aimed to construct a radiomics-based MRI sequence from high-resolution magnetic resonance imaging (HRMRI), combined with clinical high-risk factors for non-invasive differentiation of the plaque of symptomatic patients from asyptomatic patients.MethodsA total of 115 patients were retrospectively recruited. HRMRI was performed, and patients were diagnosed with symptomatic plaques (SPs) and asymptomatic plaques (ASPs). Patients were randomly divided into training and test groups in the ratio of 7:3. T2WI was used for segmentation and extraction of the texture features. Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were employed for the optimized model. Radscore was applied to construct a diagnostic model considering the T2WI texture features and patient demography to assess the power in differentiating SPs and ASPs.ResultsSPs and ASPs were seen in 75 and 40 patients, respectively. Thirty texture features were selected by mRMR, and LASSO identified a radscore of 16 radiomics features as being related to plaque vulnerability. The radscore, consisting of eight texture features, showed a better diagnostic performance than clinical information, both in the training (area under the curve [AUC], 0.923 vs. 0.713) and test groups (AUC, 0.989 vs. 0.735). The combination model of texture and clinical information had the best performance in assessing lesion vulnerability in both the training (AUC, 0.926) and test groups (AUC, 0.898).ConclusionThis study demonstrated that HRMRI texture features provide incremental value for carotid atherosclerotic risk assessment.

show abstract

“…X i indicates the signal of the voxel. According to the standardized workflow of radiomics, images should be preprocessed before radiomics features extraction ( 26 ).…”

Section: Methodsmentioning

confidence: 99%

A Radiomics Approach to Assess High Risk Carotid Plaques: A Non-invasive Imaging Biomarker, Retrospective Study

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In this study, we used a so-called µ ± 3σ normalization technique with µ being the mean and σ the standard deviation of the image [ 52 , 53 , 54 , 55 , 56 ]. Two different discretization techniques were considered; the FBS and the FBN techniques.…”

Section: Methodsmentioning

confidence: 99%

Phantom Study on the Robustness of MR Radiomics Features: Comparing the Applicability of 3D Printed and Biological Phantoms

et al. 2022

View full text Add to dashboard Cite

The objectives of our study were to (a) evaluate the feasibility of using 3D printed phantoms in magnetic resonance imaging (MR) in assessing the robustness and repeatability of radiomic parameters and (b) to compare the results obtained from the 3D printed phantoms to metrics obtained in biological phantoms. To this end, three different 3D phantoms were printed: a Hilbert cube (5 × 5 × 5 cm3) and two cubic quick response (QR) code phantoms (a large phantom (large QR) (5 × 5 × 4 cm3) and a small phantom (small QR) (4 × 4 × 3 cm3)). All 3D printed and biological phantoms (kiwis, tomatoes, and onions) were scanned thrice on clinical 1.5 T and 3 T MR with 1 mm and 2 mm isotropic resolution. Subsequent analyses included analyses of several radiomics indices (RI), their repeatability and reliability were calculated using the coefficient of variation (CV), the relative percentage difference (RPD), and the interclass coefficient (ICC) parameters. Additionally, the readability of QR codes obtained from the MR images was examined with several mobile phones and algorithms. The best repeatability (CV ≤ 10%) is reported for the acquisition protocols with the highest spatial resolution. In general, the repeatability and reliability of RI were better in data obtained at 1.5 T (CV = 1.9) than at 3 T (CV = 2.11). Furthermore, we report good agreements between results obtained for the 3D phantoms and biological phantoms. Finally, analyses of the read-out rate of the QR code revealed better texture analyses for images with a spatial resolution of 1 mm than 2 mm. In conclusion, 3D printing techniques offer a unique solution to create textures for analyzing the reliability of radiomic data from MR scans.

show abstract

“…Regarding the application of synthesized images in glioblastoma, we are aware of two contributions 11,19 that have used synthesized images as input. In Reference 11 image synthesis is used to fill missing contrasts in MRI glioma databases. The overall performance is measured by means of tumor segmentation accuracy, as well as by the merit figures of two RSs, one for tumor grading and the other one for isocitrate dehydrogenase‐1 (IDH1) status prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Synthetic MRI has many potential applications as for image database management, such as retrospective completion of databases with missing weighted images or replacement of artifacted images. Additional applications such as data harmonization 11 or data augmentation 12,13 for segmentation or classification algorithms have recently been proposed. As for multicontrast acquisitions, synthetic MRI may be applied to replace some acquisitions with their synthesized versions, leading to MR protocol shortening, patient wellbeing and higher efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Synthetic MRI improves radiomics‐based glioblastoma survival prediction

et al. 2022

View full text Add to dashboard Cite

Glioblastoma is an aggressive and fast‐growing brain tumor with poor prognosis. Predicting the expected survival of patients with glioblastoma is a key task for efficient treatment and surgery planning. Survival predictions could be enhanced by means of a radiomic system. However, these systems demand high numbers of multicontrast images, the acquisitions of which are time consuming, giving rise to patient discomfort and low healthcare system efficiency. Synthetic MRI could favor deployment of radiomic systems in the clinic by allowing practitioners not only to reduce acquisition time, but also to retrospectively complete databases or to replace artifacted images. In this work we analyze the replacement of an actually acquired MR weighted image by a synthesized version to predict survival of glioblastoma patients with a radiomic system. Each synthesized version was realistically generated from two acquired images with a deep learning synthetic MRI approach based on a convolutional neural network. Specifically, two weighted images were considered for the replacement one at a time, a T2w and a FLAIR, which were synthesized from the pairs T1w and FLAIR, and T1w and T2w, respectively. Furthermore, a radiomic system for survival prediction, which can classify patients into two groups (survival >480 days and ≤ 480 days), was built. Results show that the radiomic system fed with the synthesized image achieves similar performance compared with using the acquired one, and better performance than a model that does not include this image. Hence, our results confirm that synthetic MRI does add to glioblastoma survival prediction within a radiomics‐based approach.

show abstract

MIL normalization —— prerequisites for accurate MRI radiomics analysis

Cited by 14 publications

References 37 publications

A Radiomics Approach to Assess High Risk Carotid Plaques: A Non-invasive Imaging Biomarker, Retrospective Study

A Radiomics Approach to Assess High Risk Carotid Plaques: A Non-invasive Imaging Biomarker, Retrospective Study

Phantom Study on the Robustness of MR Radiomics Features: Comparing the Applicability of 3D Printed and Biological Phantoms

Synthetic MRI improves radiomics‐based glioblastoma survival prediction

Contact Info

Product

Resources

About