Deep learning and radiomics of longitudinal CT scans for early prediction of tuberculosis treatment outcomes

Nijiati, Mayidili; Guo, Lin; Abulizi, Abudoukeyoumujiang; Fan, Shiyu; Wubuli, Abulikemu; Tuersun, Abudouresuli; Nijiati, Pahatijiang; Xia, Li; Hong, Kunlei; Zou, Xiaoguang

doi:10.1016/j.ejrad.2023.111180

Cited by 1 publication

(1 citation statement)

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“…This approach helps mitigate overfitting issues caused by limited data availability and opens up new avenues for advancing radiomics ( 28 ). Existing studies have demonstrated that models combining DL features with radiomics features outperform those using either features alone in various clinical problems such as breast tumors ( 29 ), renal cystic lesions ( 30 ), meningiomas ( 31 ),and tuberculosis ( 32 ). In our study, while each individual model demonstrated satisfactory performance in isolation, the integration of DL with clinical and radiomics data yielded a more robust predictive tool, effectively capitalizing on the unique strengths of each individual component.…”

Section: Discussionmentioning

confidence: 99%

Differentiation of benign and malignant parotid gland tumors based on the fusion of radiomics and deep learning features on ultrasound images

Wang,

Gao,

Yin

et al. 2024

Front. Oncol.

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ObjectiveThe pathological classification and imaging manifestation of parotid gland tumors are complex, while accurate preoperative identification plays a crucial role in clinical management and prognosis assessment. This study aims to construct and compare the performance of clinical models, traditional radiomics models, deep learning (DL) models, and deep learning radiomics (DLR) models based on ultrasound (US) images in differentiating between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs).MethodsRetrospective analysis was conducted on 526 patients with confirmed PGTs after surgery, who were randomly divided into a training set and a testing set in the ratio of 7:3. Traditional radiomics and three DL models (DenseNet121, VGG19, ResNet50) were employed to extract handcrafted radiomics (HCR) features and DL features followed by feature fusion. Seven machine learning classifiers including logistic regression (LR), support vector machine (SVM), RandomForest, ExtraTrees, XGBoost, LightGBM and multi-layer perceptron (MLP) were combined to construct predictive models. The most optimal model was integrated with clinical and US features to develop a nomogram. Receiver operating characteristic (ROC) curve was employed for assessing performance of various models while the clinical utility was assessed by decision curve analysis (DCA).ResultsThe DLR model based on ExtraTrees demonstrated superior performance with AUC values of 0.943 (95% CI: 0.918-0.969) and 0.916 (95% CI: 0.861-0.971) for the training and testing set, respectively. The combined model DLR nomogram (DLRN) further enhanced the performance, resulting in AUC values of 0.960 (95% CI: 0.940- 0.979) and 0.934 (95% CI: 0.876-0.991) for the training and testing sets, respectively. DCA analysis indicated that DLRN provided greater clinical benefits compared to other models.ConclusionDLRN based on US images shows exceptional performance in distinguishing BPGTs and MPGTs, providing more reliable information for personalized diagnosis and treatment plans in clinical practice.

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