Lung cancer is a leading cause of cancer-related deaths worldwide, with its high mortality rate primarily attributed to delayed diagnosis. Radiomics, by extracting abundant quantitative features from medical images, offers novel possibilities for early diagnosis and precise treatment of lung cancer. This article reviewed the latest advancements in radiomics for lung cancer management, particularly its integration with artificial intelligence (AI) to optimize diagnostic processes and personalize treatment strategies. Despite existing challenges, such as non-standardized image acquisition parameters and limitations in model reproducibility, the incorporation of AI significantly enhanced the precision and efficiency of image analysis, thereby improving the prediction of disease progression and the formulation of treatment plans. We emphasized the critical importance of standardizing image acquisition parameters and discussed the role of AI in advancing the clinical application of radiomics, alongside future research directions.
Humans ; Lung Neoplasms/diagnosis* ; Artificial Intelligence ; Early Detection of Cancer/methods* ; Precision Medicine ; Image Processing, Computer-Assisted/methods* ; Tomography, X-Ray Computed ; Radiomics

OBJECTIVES: To develop and validate a preoperative clinical-radiomics model for predicting overall survival (OS) and disease-free survival (DFS) in patients with extrahepatic cholangiocarcinoma (eCCA) undergoing radical resection. METHODS: In this retrospective study, consecutive patients with pathologically-confirmed eCCA who underwent radical resection at our institution from 2015 to 2022 were included. The patients were divided into a training cohort and a validation cohort according to the chronological order of their CT examinations. Least absolute shrinkage and selection operator (LASSO)-Cox regression was employed to select predictive radiomic features and clinical variables. The selected features and variables were incorporated into a Cox regression model. Model performance for 1-year OS and DFS prediction was assessed using calibration curves, area under receiver operating characteristic curve (AUC), and concordance index (C-index). RESULTS: This study included 123 patients (mean age 64.0 ± 8.4 years, 85 males/38 females), with 86 in the training cohort and 37 in the validation cohort. The OS-predicting model included four clinical variables and four radiomic features. It achieved a training cohort AUC of 0.858 (C-index = 0.800) and a validation cohort AUC of 0.649 (C-index = 0.605). The DFS-predicting model included four clinical variables and four other radiomic features. It achieved a training cohort AUC of 0.830 (C-index = 0.760) and a validation cohort AUC of 0.717 (C-index = 0.616). CONCLUSIONS The preoperative clinical-radiomics models show promise as a tool for predicting 1-year OS and DFS in eCCA patients after radical surgery.
Humans ; Male ; Female ; Retrospective Studies ; Middle Aged ; Cholangiocarcinoma/mortality* ; Prognosis ; Bile Duct Neoplasms/mortality* ; Tomography, X-Ray Computed/methods* ; Aged ; Radiomics

OBJECTIVES: To construct a nomogram for predicting the efficacy of immune checkpoint inhibitors (ICIs) in advanced non-small cell lung cancer (aNSCLC) by integrating chest CT radiomics signature that reflects the tumor microenvironment (TME) and clinical parameters of the patients. METHODS: Transcriptomic and CT imaging data from TCGA, GEO and TCIA databases were integrated for weighted gene co-expression network analysis (WGCNA) of the GEO cohort to identify the immunotherapy-related genes (IRGs) associated with ICIs response. A prognostic model was built using these IRGs in the TCGA cohort to assess immune microenvironment features across different risk groups. Radiomics features were extracted from TCIA lung_3 cohort using PyRadiomics, and 94 features showing strong association with IRGs (|r|>0.4) were selected. A retrospective cohort consisting of 210 aNSCLC patients receiving first-line ICIs at Guangdong Provincial People's Hospital was analyzed and divided into training (n=147) and validation (n=63) groups. Least absolute shrinkage and selection operator was used for radiomic features selection, and logistic regression was applied to construct a combined clinical-radiomic model and nomogram for predicting ICIs therapy response. The performance of the model was evaluated using ROC curve, calibration curve, and decision curve analysis. RESULTS: WGCNA identified 84 IRGs enriched in immune activation pathways. The combined model outperformed individual models in both the training (AUC=0.725, 95% CI: 0.644-0.807) and validation cohorts (AUC=0.706, 95% CI: 0.577-0.836). Calibration curve and decision curve analyses confirmed the clinical efficacy of the nomogram for predicting ICIs therapy response in aNSCLC patients. CONCLUSIONS The genomic-radiomic-clinical multidimensional predictive framework established in this study provides an interpretable biomarker combination and clinical decision-making tool for evaluating ICIs efficacy in aNSCLC, potentially facilitating personalized immunotherapy decision-making.
Humans ; Carcinoma, Non-Small-Cell Lung/therapy* ; Tumor Microenvironment ; Lung Neoplasms/therapy* ; Immunotherapy ; Tomography, X-Ray Computed ; Nomograms ; Retrospective Studies ; Immune Checkpoint Inhibitors/therapeutic use* ; Prognosis ; Male ; Female ; Radiomics

OBJECTIVES: To evaluate the effect of artificial intelligence-assisted compressed sensing (ACS) acceleration on MRI radiomic feature extraction and performance of diagnostic staging models for nasopharyngeal carcinoma (NPC) in comparison with conventional parallel imaging (PI). METHODS: A total of 64 patients with newly diagnosed NPC underwent 3.0T MRI using axial T1-weighted (T1W), T2-weighted (T2W), and contrast-enhanced T1-weighted (CE-T1W) sequences. Both PI and ACS protocols were performed using identical imaging parameters. The total scan time for the 3 sequences in ACS group was 227 s, representing a 30% reduction from 312 s in the PI group. Eighteen first-order and 75 texture features were extracted using Pyradiomics. Intraclass correlation coefficients (ICCs) were calculated to assess the agreement between the two acceleration methods. After feature selection using the least absolute shrinkage and selection operator (LASSO), random forest regression models were constructed to distinguish early-stage (T1 and T2) from advanced-stage (T3 and T4) NPC. The diagnostic performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. RESULTS: ACS-accelerated images demonstrated good radiomic reproducibility, with 86.0% (240/279) of features showing good agreement (ICC>0.75), with mean ICCs for T1W, T2W and CE-T1W sequences of 0.91±0.09, 0.89±0.13 and 0.88±0.11, respectively. The staging prediction models achieved similar AUCs for ACS and PI (0.89 vs 0.90, P=0.991). CONCLUSIONS The MRI radiomic features extracted using ACS and PI techniques are highly consistent, and the ACS-based model shows comparable diagnostic performance to the PI-based model, but ACS significantly reduces the scan time and provides an efficient and reliable acceleration strategy for radiomics in NPC.
Humans ; Nasopharyngeal Neoplasms/diagnosis* ; Magnetic Resonance Imaging/methods* ; Nasopharyngeal Carcinoma ; Neoplasm Staging ; Artificial Intelligence ; Carcinoma ; Female ; Male ; Middle Aged ; Adult ; Radiomics

Objective To develop a diagnostic model combining the CT angiography(CCTA)-derived myocardial radiomics signatures with the CT-derived fractional flow reserve(CT-FFR)based on coronary CCTA and investigate the diagnostic accuracy of the hybrid model for hemodynamically significant coronary artery disease(CAD).Methods The patients presenting stable angina pectoris,diagnosed with CAD,and clinically referred for CCTA examination and invasive coronary angiography were prospectively recruited.Radiomics features of the left ventricular myocardium were extracted from the three main perfusion territories demarcated according to the coronary blood supply.The extracted features were first selected by the minimum redundancy maximum relevance feature ranking method.A least absolute shrinkage and selection operator Logistic regression algorithm with leave-one-out cross-validation was then employed to construct a radiomics model.The CT-FFR value was generated for each blood vessel.The area under the receiver operating characteristics curve(AUC_ROC),sensitivity,and specificity were adopted to evaluate the performance of each model against the reference standard invasive coronary angiography/FFR.Results A total of 70 patients[42 men and 28 women;(61±10) years old] were included in this study and complemented CCTA examination,with 175 vessels and the corresponding myocardial territories undergoing invasive coronary angiography/FFR.A total of 1 656 specific radiomics parameters were extracted,from which 14 features were selected to establish the radiomics model.The AUC_ROC,sensitivity,and specificity were 0.797(95%CI=0.732-0.861),77.1%,and 73.7%for the radiomics model,0.892(95%CI=0.841-0.943),81.4%,and 88.8%for the CT-FFR model,and 0.928(95%CI=0.890-0.965),83.3%,and 88.4%for the hybrid model,respectively.The hybrid model outperformed the radiomics model and CT-FFR alone(P=0.040).Conclusions The radiomics signatures of the vessel-related myocardium from CCTA could provide incremental value to the diagnostic performance of CT-FFR and improve vessel-specific ischemia detection.The hybrid model combining CT-FFR with radiomics signatures is potentially feasible for improving the diagnostic accuracy for hemodynamically significant CAD.
Coronary Angiography/methods* ; Tomography, X-Ray Computed ; Humans ; Hemodynamics ; Coronary Artery Disease/diagnostic imaging* ; Male ; Female ; Middle Aged ; Aged ; Radiomics ; Angina Pectoris/diagnostic imaging* ; China ; Image Processing, Computer-Assisted ; Coronary Vessels/diagnostic imaging*

Hepatocellular carcinoma (HCC) is one of the most common malignancies and is a major cause of cancer-related mortalities worldwide (Forner et al., 2018; He et al., 2023). Sarcopenia is a syndrome characterized by an accelerated loss of skeletal muscle (SM) mass that may be age-related or the result of malnutrition in cancer patients (Cruz-Jentoft and Sayer, 2019). Preoperative sarcopenia in HCC patients treated with hepatectomy or liver transplantation is an independent risk factor for poor survival (Voron et al., 2015; van Vugt et al., 2016). Previous studies have used various criteria to define sarcopenia, including muscle area and density. However, the lack of standardized diagnostic methods for sarcopenia limits their clinical use. In 2018, the European Working Group on Sarcopenia in Older People (EWGSOP) renewed a consensus on the definition of sarcopenia: low muscle strength, loss of muscle quantity, and poor physical performance (Cruz-Jentoft et al., 2019). Radiological imaging-based measurement of muscle quantity or mass is most commonly used to evaluate the degree of sarcopenia. The gold standard is to measure the SM and/or psoas muscle (PM) area using abdominal computed tomography (CT) at the third lumbar vertebra (L3), as it is linearly correlated to whole-body SM mass (van Vugt et al., 2016). According to a "North American Expert Opinion Statement on Sarcopenia," SM index (SMI) is the preferred measure of sarcopenia (Carey et al., 2019). The variability between morphometric muscle indexes revealed that they have different clinical relevance and are generally not applicable to broader populations (Esser et al., 2019).
Humans ; Aged ; Sarcopenia/diagnostic imaging* ; Carcinoma, Hepatocellular/diagnostic imaging* ; Muscle, Skeletal/diagnostic imaging* ; Deep Learning ; Prognosis ; Radiomics ; Liver Neoplasms/diagnostic imaging* ; Retrospective Studies

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.
Humans ; Kidney Calculi/classification* ; Deep Learning ; Neural Networks, Computer ; Tomography, X-Ray Computed ; Imaging, Three-Dimensional ; Radiomics

OBJECTIVES: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC), and developing personalized treatment strategies is crucial for improving patient prognosis. This study aims to develop and validate a preoperative computer tomography (CT) radiomics-based predictive model to estimate Ki-67 expression in ccRCC patients, thereby assisting in clinical treatment decisions and prognosis prediction. METHODS: A retrospective analysis was conducted on 214 ccRCC patients who underwent surgical treatment at Gansu Provincial Hospital between January 2018 and November 2023. Patients were classified into high Ki-67 expression (n=123) and low Ki-67 expression (n=91) groups based on postoperative immunohistochemical staining results. The dataset was randomly divided in a 7꞉3 ratio into a training set (n=149) and a validation set (n=65). Preoperative contrast-enhanced urinary CT images and clinical data were collected. After preprocessing, 5 mm arterial-phase CT images were manually segmented layer by layer to delineate the region of interest (ROI) using ITK-SNAP 3.8 software. Radiomic features were then extracted using the FeAture Explorer (FAE) package. Dimensionality reduction and feature selection were performed using the least absolute shrinkage and selection operator (LASSO) algorithm, yielding the optimal feature set. Three classification models were constructed using logistic regression (LR), multilayer perceptron (MLP), and support vector machine (SVM). The receiver operating characteristic (ROC) curve, area under the curve (AUC), decision curve analysis (DCA), and calibration curves were used for model evaluation. RESULTS: A total of 107 radiomic features were extracted from 5 mm arterial-phase CT images, and twenty-one features significantly associated with Ki-67 expression were selected using the LASSO algorithm. Predictive models were developed using LR, MLP, and SVM classifiers. In the training and validation sets, the AUC values for each model were 0.904 (95% CI 0.852 to 0.956) and 0.818 (95% CI 0.710 to 0.926) for the LR model, 0.859 (95% CI 0.794 to 0.923) and 0.823 (95% CI 0.716 to 0.929) for the MLP model, and 0.917 (95% CI 0.865 to 0.969) and 0.857 (95% CI 0.760 to 0.953) for the SVM model. DCA demonstrated that all models had good clinical net benefit, while calibration curves indicated high accuracy of the predictions, supporting the robustness and reliability of the models. CONCLUSIONS A CT radiomics-based model for predicting Ki-67 expression in ccRCC was successfully developed. This model provides valuable guidance for treatment planning and prognostic assessment in ccRCC patients.
Humans ; Carcinoma, Renal Cell/surgery* ; Kidney Neoplasms/surgery* ; Tomography, X-Ray Computed/methods* ; Ki-67 Antigen/metabolism* ; Retrospective Studies ; Female ; Male ; Middle Aged ; Aged ; Prognosis ; Adult ; Preoperative Period ; Radiomics

Objective:To explore the relevant anatomical factors in the pathogenesis of chronic dacryocystitis based on CT radiomics. Methods:A retrospective analysis was conducted on the general data and sinus CT materials of 85 patients with chronic dacryocystitis（case group） admitted to our department from December 2020 to December 2023, and 85 individuals undergoing physical examination（control group） during the same period. The differences in anatomical parameters between the two groups were compared to study the morphological characteristics of the nasolacrimal duct in patients with chronic dacryocystitis. Univariate and multivariate logistic regression analyses were used to explore the relevant anatomical factors in the pathogenesis of chronic dacryocystitis. Results:There were statistically significant differences（P<0.05） in the proportion of combined nasal septal deviation, the distance between the anterior and posterior ridges of the lacrimal fossa, the angle between the long axis of the nasolacrimal duct and the projection on the midsagittal plane, the maximum transverse diameter of the bony nasolacrimal duct, the maximum cross-sectional area of the bony nasolacrimal duct, and the thickness of the frontal process of the maxilla between the two groups. There were no statistically significant differences（P>0.05） in whether there was a combined high-position nasal septal deviation, whether there was a combined non-high-position nasal septal deviation, and whether there was a combined pneumatized middle turbinate. Multivariate analysis showed that nasal septal deviation, the distance between the anterior and posterior ridges of the lacrimal fossa, the angle between the long axis of the nasolacrimal duct and the projection on the midsagittal plane, and the maximum cross-sectional area of the bony nasolacrimal duct are independent anatomical factors affecting the pathogenesis of chronic dacryocystitis. Conclusion:Nasal septal deviation, a large distance between the anterior and posterior ridges of the lacrimal fossa, a large angle between the long axis of the nasolacrimal duct and the projection on the midsagittal plane, and a small maximum transverse diameter of the bony nasolacrimal duct are important anatomical bases for the pathogenesis of chronic dacryocystitis, providing a basis for an in-depth understanding of the disease occurrence.
Humans ; Retrospective Studies ; Male ; Female ; Dacryocystitis/diagnostic imaging* ; Tomography, X-Ray Computed/methods* ; Nasolacrimal Duct/diagnostic imaging* ; Chronic Disease ; Nasal Septum/diagnostic imaging* ; Middle Aged ; Adult ; Radiomics