Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Jiang, Zekun; Jin, Yixin; Han, Ping; Chen, Nan; Kang, Qingbo; Qiu, Yudong; Li, Yiyue; Lao, Qicheng; Sun, Miao; Yang, Dan; Huang, Shan; Qiu, Jiajun; Li, Kang

doi:10.21037/qims-22-252

Cited by 18 publications

(17 citation statements)

References 52 publications

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“…In addition to building machine learning models, we also found that the texture features of the images contributes to the prediction of NCRT response by LARC, consistent with previous studies (26,28). Moreover, wavelet transformation may enhance the texture characteristics of the images, improving the model performance (42), which may give some hints that this task can be verified in future studies.…”

Section: Discussionmentioning

confidence: 92%

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

Wang

Wu²,

Tian³

et al. 2023

Front. Oncol.

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ObjectivesTo develop and validate magnetic resonance imaging (MRI)-based pre-Radiomics and delta-Radiomics models for predicting the treatment response of local advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (NCRT).MethodsBetween October 2017 and August 2022, 105 LARC NCRT-naïve patients were enrolled in this study. After careful evaluation, data for 84 patients that met the inclusion criteria were used to develop and validate the NCRT response models. All patients received NCRT, and the post-treatment response was evaluated by pathological assessment. We manual segmented the volume of tumors and 105 radiomics features were extracted from three-dimensional MRIs. Then, the eXtreme Gradient Boosting algorithm was implemented for evaluating and incorporating important tumor features. The predictive performance of MRI sequences and Synthetic Minority Oversampling Technique (SMOTE) for NCRT response were compared. Finally, the optimal pre-Radiomics and delta-Radiomics models were established respectively. The predictive performance of the radionics model was confirmed using 5-fold cross-validation, 10-fold cross-validation, leave-one-out validation, and independent validation. The predictive accuracy of the model was based on the area under the receiver operator characteristic (ROC) curve (AUC).ResultsThere was no significant difference in clinical factors between patients with good and poor reactions. Integrating different MRI modes and the SMOTE method improved the performance of the radiomics model. The pre-Radiomics model (train AUC: 0.93 ± 0.06; test AUC: 0.79) and delta-Radiomcis model (train AUC: 0.96 ± 0.03; test AUC: 0.83) all have high NCRT response prediction performance by LARC. Overall, the delta-Radiomics model was superior to the pre-Radiomics model.ConclusionMRI-based pre-Radiomics model and delta-Radiomics model all have good potential to predict the post-treatment response of LARC to NCRT. Delta-Radiomics analysis has a huge potential for clinical application in facilitating the provision of personalized therapy.

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Section: Discussionmentioning

confidence: 92%

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

Wang

Wu²,

Tian³

et al. 2023

Front. Oncol.

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“…Although many researchers have used classical radiomic features (i.e., FO, GLCM, GLRLM, GLSZM, and GLDM), many others have achieved increased predictive power by exploiting high-level features, computed from filtered images by means of wavelet transforms, LoG filters, and intensity transformation. In particular, the wavelet-derived features have demonstrated their predictive capability in several contexts [ 7 , 8 , 9 , 11 , 12 ]. Despite their widespread use in the literature, comparing the different wavelets kernels is difficult because of the few studies [ 14 , 15 ] focused on the problem.…”

Section: Discussionmentioning

confidence: 99%

“…Wavelet-derived features showed strong predictive capabilities in several contexts: tumor-type prediction of early stage lung nodules in CT [ 7 ], neoadjuvant chemotherapy treatment prediction for breast cancer in MRI [ 8 ], low-dose rate radiotherapy treatment response prediction of gastric carcinoma in CT [ 9 ], liver cirrhosis detection [ 10 ], glioblastoma multiforme differentiation from brain metastases in MRI [ 11 ], and grading of COVID-19 pulmonary lesions in CT [ 12 ]. The wavelet-derived features are calculated on the image decompositions—four for 2D images (e.g., X-ray, mammography, ultrasound, etc.)…”

Section: Introductionmentioning

confidence: 99%

Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images

et al. 2023

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Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray images was used for COVID-19 prognosis prediction as a case study. Random forest, support vector machine, and XGBoost were trained (on a subset of 1103 images) after a rigorous feature selection strategy to build-up the predictive models. Next, to evaluate the models generalization capability on unseen data, a test phase was performed (on a subset of 486 images). The experimental findings showed that Bior1.5, Coif1, Haar, and Sym2 kernels guarantee better and similar performance for all three machine learning models considered. Support vector machine and random forest showed comparable performance, and they were better than XGBoost. Additionally, random forest proved to be the most stable model, ensuring an appropriate balance between sensitivity and specificity.

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“…Wavelet transforms can decompose image signals by using low-and high-pass filters and may amplify the heterogeneity information of texture features in radiological imaging, which is similar to previous studies. Jiang et al reported that wavelet transformation can enhance CT texture features and may be used to effectively assess the grade of pulmonary lesions caused by COVID-19 (29). Regarding the best performance in discriminating an expansive from an infiltrative front in tumour growth, Granata et al reported that wavelet transformation had the best performance in identifying tumour recurrence (30).…”

Section: Discussionmentioning

confidence: 99%

CT-based radiomics with various classifiers for histological differentiation of parotid gland tumors

Liu²,

Liu³

et al. 2023

Front. Oncol.

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ObjectiveThis study assessed whether radiomics features could stratify parotid gland tumours accurately based on only noncontrast CT images and validated the best classifier of different radiomics models.MethodsIn this single-centre study, we retrospectively recruited 249 patients with a diagnosis of pleomorphic adenoma (PA), Warthin tumour (WT), basal cell adenoma (BCA) or malignant parotid gland tumours (MPGTs) from June 2020 to August 2022. Each patient was randomly classified into training and testing cohorts at a ratio of 7:3, and then, pairwise comparisons in different parotid tumour groups were performed. CT images were transferred to 3D-Slicer software and the region of interest was manually drawn for feature extraction. Feature selection methods were performed using the intraclass correlation coefficient, t test and least absolute shrinkage and selection operator. Five common classifiers, namely, random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbours (KNN) and general Bayesian network (Gnb), were selected to build different radiomics models. The receiver operating characteristic curve, area under the curve (AUC), accuracy, sensitivity, specificity and F-1 score were used to assess the prediction performances of these models. The calibration of the model was calculated by the Hosmer–Lemeshow test. DeLong’s test was utilized for comparing the AUCs.ResultsThe radiomics model based on the RF, SVM, Gnb, LR, LR and RF classifiers obtained the highest AUC in differentiating PA from MPGTs, WT from MPGTs, BCA from MPGTs, PA from WT, PA from BCA, and WT from BCA, respectively. Accordingly, the AUC and the accuracy of the model for each classifier were 0.834 and 0.71, 0.893 and 0.79, 0.844 and 0.79, 0.902 and 0.88, 0.602 and 0.68, and 0.861 and 0.94, respectively.ConclusionOur study demonstrated that noncontrast CT-based radiomics could stratify refined pathological types of parotid tumours well but could not sufficiently differentiate PA from BCA. Different classifiers had the best diagnostic performance for different parotid tumours. Our study findings add to the current knowledge on the differential diagnosis of parotid tumours.

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Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Cited by 18 publications

References 52 publications

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images

CT-based radiomics with various classifiers for histological differentiation of parotid gland tumors

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