Objective To summarize the development of process and clinical practice for radiomics. Methods Relevant literatures about the development of process and clinical practice of radiomics were collected to make a review. Results Radiomics, which resulting from big data, had been used in diagnosis, assessment of prognosis, and predictionof therapy response for neoplasm. Conclusion Radiomics is an important part of precision medical imaging in the eraof big data.
ObjectiveTo summarize the application of radiomics in colorectal cancer.MethodsRelevant literatures about the therapeutic decision-making, therapeutic, and prognostic evaluation of colorectal cancer using radiomics were collected to make an review.ResultsRadiomics is of great value in preoperative stages, therapeutic, and prognostic evaluation in colorectal cancer.ConclusionRadiomics is an important part of precision medical imaging for colorectal cancer.
Currently, the types of kidney stones before surgery are mainly identified by human beings, which directly leads to the problems of low classification accuracy and inconsistent diagnostic results due to the reliance on human knowledge. To address this issue, this paper proposes a framework for identifying types of kidney stones based on the combination of radiomics and deep learning, aiming to achieve automated preoperative classification of kidney stones with high accuracy. Firstly, radiomics methods are employed to extract radiomics features released from the shallow layers of a three-dimensional (3D) convolutional neural network, which are then fused with the deep features of the convolutional neural network. Subsequently, the fused features are subjected to regularization, least absolute shrinkage and selection operator (LASSO) processing. Finally, a light gradient boosting machine (LightGBM) is utilized for the identification of infectious and non-infectious kidney stones. The experimental results indicate that the proposed framework achieves an accuracy rate of 84.5% for preoperative identification of kidney stone types. This framework can effectively distinguish between infectious and non-infectious kidney stones, providing valuable assistance in the formulation of preoperative treatment plans and the rehabilitation of patients after surgery.
ObjectiveTo investigate the value of a predictive model for sentinel lymph node (SLN) metastasis after neoadjuvant therapy (NAT) based on the radiomic features from multi-modality magnetic resonance imaging (MRI) in combination with clinicopathologic data. MethodsThe clinical data and MRI images of breast cancer patients (initially diagnosed with cN0, all underwent NAT and surgical treatment) from two hospitals (Affiliated Hospital of Southwest Medical University and Suining Central Hospital) from January 2018 to September 2024, were retrospectively collected. The radiomic features from the multi-modality images, including T2-weighted short tau inversion recovery (T2STIR), diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE), were extracted and selected. The predictive models for SLN metastasis after NAT were constructed using four algorithms: LightGBM, XGBoost, support vector machine (SVM), and logistic regression (LR), in combination with clinicopathologic data. The models were evaluated for performance and interpretability using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis, and Shapley additive explanation (SHAP) analysis. ResultsA total of 236 breast cancer patients were enrolled in this study. Among them, 216 patients from the Southwest Medical University were subdivided in an 8∶2 ratio into a training set (n=173) and internal validation set (n=43), while 20 patients from the Suining Central Hospital served as the external validation set. The multivariate logistic regression analysis showed that the lymphovascular invasion [OR (95%CI)=21.215 (4.404, 102.202), P ＜0.001] and perineural invasion [OR (95%CI)=25.867 (1.870, 357.790), P=0.002] were the risk factors, while high Ki-67 expression [OR (95%CI)=0.119 (0.035, 0.404), P＜0.001] was the protective factor of SLN metastasis after NAT. The predictive models utilizing multi-modality MRI and clinicopathologic data yielded area under the ROC curve values of the internal and external validation sets of 0.750 [95%CI=(0.395, 1.000)] / 0.625 [95%CI=(0.321, 0.926)] for LightGBM, 0.878 [95%CI=(0.707, 1.000)] / 0.778 [95%CI=(0.525, 0.986)] for XGBoost, 0.641 [95%CI=(0.488, 0.795)] / 0.681 [95%CI=(0.345, 1.000)] for SVM, and 0.667 [95%CI=(0.357, 0.945)] / 0.583 [95%CI=(0.196, 0.969)] for LR. The XGBoost demonstrated the best predictive performance. Further SHAP analysis revealed that the lymphovascular invasion, T2STAR-MRI_FIRSTORDER_Minimum, and platelet were the key features influencing the predictions of the models. ConclusionThe findings of this study suggest that XGBoost prediction model based on radiomic features derived from multi-modality MRI (T2STIR, DWI, and DCE) in combination with clinicopathologic data is able to predict SLN metastasis after NAT in patients with breast cancer.