Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features

Chai, Rui; Qi, Wang; Qin, Peng; Zeng, Jianchao; Ren, Jiwei; Zhang, Ruiping; Cai, Lin; Zhang, Xuting; Guan, Yun

doi:10.1155/2021/5522452

Cited by 3 publications

(6 citation statements)

References 46 publications

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“…Both the training (70% data) and validation groups (30% data) were normalized using Z -scores. Intraobserver and interobserver intraclass correlation coefficients (ICCs) were applied to measure the reproducibility of each feature [ 18 , 19 ]. Reader 1 and Reader 2 performed image segmentation independently twice weekly to assess intraobserver reliability.…”

Section: Methodsmentioning

confidence: 99%

“…Reader 1 and Reader 2 performed image segmentation independently twice weekly to assess intraobserver reliability. Using the following steps, we selected significant radiomic features [ 18 , 19 ]. ICCs over 0.75 were kept for intraobserver and interobserver features.…”

Section: Methodsmentioning

confidence: 99%

“…In MRI images, brain tumors can be categorized in many ways [10][11][12][13][14]. One of the most prominent ones is fuzzy clustering means (FCM), support vector machine (SVM), artificial neural network (ANN), knowledge-based techniques, and the expectation-maximization (EM) algorithm methodology [14,[17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Differentiating Glioblastoma Multiforme from Brain Metastases Using Multidimensional Radiomics Features Derived from MRI and Multiple Machine Learning Models

Bijari

Jahanbakhshi

Hajishafiezahramini

et al. 2022

BioMed Research International

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Due to different treatment strategies, it is extremely important to differentiate between glioblastoma multiforme (GBM) and brain metastases (MET). It often proves difficult to distinguish between GBM and MET using MRI due to their similar appearance on the imaging modalities. Surgical methods are still necessary for definitive diagnosis, despite the importance of magnetic resonance imaging in detecting, characterizing, and monitoring brain tumors. We introduced an accurate, convenient, and user-friendly method to differentiate between GBM and MET through routine MRI sequence and radiomics analyses. We collected 91 patients from one institution, including 50 with GBM and 41 with MET, which were proven pathologically. The tumors separately were segmented on all MRI images (T1-weighted imaging (T1WI), contrast-enhanced T1-weighted imaging (T1C), T2-weighted imaging (T2WI), and fluid-attenuated inversion recovery (FLAIR)) to form the volume of interest (VOI). Eight ML models and feature reduction strategies were evaluated using routine MRI sequences (T1W, T2W, T1-CE, and FLAIR) in two methods with (second model) and without wavelet transform (first model) radiomics. The optimal model was selected based on each model’s accuracy, AUC-roc, and F1-score values. In this study, we have achieved the result of 0.98, 0.99, and 0.98 percent for accuracy, AUC-roc, and F1-score, respectively, which have yielded a better result than the first model. In most investigated models, there were significant improvements in the multidimensional wavelets model compared to the non-multidimensional wavelets model. Multidimensional discrete wavelet transform can analyze hidden features of the MRI from a different perspective and generate accurate features which are highly correlated with the model accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Differentiating Glioblastoma Multiforme from Brain Metastases Using Multidimensional Radiomics Features Derived from MRI and Multiple Machine Learning Models

Bijari

Jahanbakhshi

Hajishafiezahramini

et al. 2022

BioMed Research International

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“…Similarly, Chai et al. [26] used radiomic features in pulmonary CT images to facilitate the detection of lung atelectasis lesions. Xiao et al.…”

Section: Related Workmentioning

confidence: 99%

“…Radiomics converts image data into a high-resolution mineable data space using automatic or semiautomatic analysis methods [24], extracting numerous medical image features. Recent medical imaging studies have demonstrated its effectiveness [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

RFLSE: Joint radiomics feature‐enhanced level‐set segmentation for low‐contrast SPECT/CT tumour images

Guo,

Qin,

Zeng

et al. 2024

IET Image Processing

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Doctors typically use non‐contrast‐enhanced computed tomography (NCECT) in the treatment of kidney cancer to map kidney and tumour structural information to functional imaging single‐photon emission computed tomography, which is then used to assess patient kidney function and predict postoperative recovery. However, the assessment of kidney function and formulation of surgical plans is constrained by the low contrast of tumours in NCECT, which hinders the acquisition of accurate tumour boundaries. Therefore, this study designed a radiomics feature‐enhanced level‐set evolution (RFLSE) to precisely segment small‐sample low‐contrast kidney tumours. Integration of high‐dimensional radiomics features into the level‐set energy function enhances the edge detection capability of low‐contrast kidney tumours. The use of sensitive radiomics features to control the regional term parameters achieves adaptive adjustment of the curve evolution amplitude, improving the level‐set segmentation process. The experimental data used low‐contrast, limited‐sample tumours provided by hospitals, as well as the public datasets BUSI18 and KiTS19. Comparative results with advanced energy functionals and deep learning models demonstrate the precision and robustness of RFLSE segmentation. Additionally, the application value of RFLSE in assisting doctors with accurately marking tumours and generating high‐quality pseudo‐labels for deep learning datasets is demonstrated.

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Morphology-based Machine-Learning for Predicting Lymph Node Status in Oral Tongue Squamous Cell Carcinoma

Zhu,

Ren,

Song

et al. 2024

Preprint

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Purpose To develop machine-learning models based on morphological features extracted from preoperative magnetic resonance imaging (MRI) to predict lymph node status in oral tongue squamous cell carcinoma (OTSCC). Method This study retrospectively enrolled 90 OTSCC patients, of whom 45 and 13 patients, respectively, had confirmed lymph node metastasis (LNM) and extranodal extension (ENE). Fourteen morphological features and two customized metrics were derived from T2-weighted (T2W) images. Tumor maximum diameter and MRI-derived depth of invasion (DOI) were measured on contrast-enhanced T1-weighted (ceT1W) images. Information gain algorithm was applied to select the top five attributes. Models were created using six machine-learning methods, including neural network (NN), random forest (RF), logistic regression (LR), support vector machine (SVM), naïve bayes (NB), and AdaBoost. An internal stratified 10-fold cross-validation was performed to assess their performance. Results For predicting LNM, the NN classifier, which included Situation, Elongation, Top Bottom Area, Least Axis Length, and Minor Axis Length, yielded the best model, with an AUC of 0.746 and accuracy of 72.2%. The performance of the NN model was slightly superior to that of MRI-derived DOI (0.746 vs. 0.655), although the difference was not significant (P = 0.122). For predicting ENE, the SVM classifier, which included situation, Elongation, Top Bottom Area, Least Axis Length, and Minor Axis Length, performed the best, with an AUC of 0.750 and accuracy of 85.6%. Conclusions Machine-learning models using MRI morphological features have potential in preoperative evaluation of cervical lymph node status in OTSCC.

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Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features

Cited by 3 publications

References 46 publications

Differentiating Glioblastoma Multiforme from Brain Metastases Using Multidimensional Radiomics Features Derived from MRI and Multiple Machine Learning Models

Differentiating Glioblastoma Multiforme from Brain Metastases Using Multidimensional Radiomics Features Derived from MRI and Multiple Machine Learning Models

RFLSE: Joint radiomics feature‐enhanced level‐set segmentation for low‐contrast SPECT/CT tumour images

Morphology-based Machine-Learning for Predicting Lymph Node Status in Oral Tongue Squamous Cell Carcinoma

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