Gastric cancer remains one of the most prevalent and fatal malignancies in China. Peritoneal metastasis represents a frequent mode of dissemination or recurrence in patients with advanced disease and confers an extremely poor prognosis. In recent years, considerable progress has been made in imaging techniques, with modalities including CT, ultrasound, MRI and PET-CT being implemented to evaluate peritoneal metastasis. However, adequate detection remains challenging, particularly for occult peritoneal metastasis. With the advent of precision medicine, radiomics and artificial intelligence have undergone rapid development and show considerable promise for the early prediction of peritoneal metastasis in gastric cancer, providing a new means of diagnosis and treatment for patients with peritoneal metastasis.
Objective For potential patients with better prognosis of non-small-cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutations, a simpler and more effective model with easy-to-obtain histopathological parameters was established. MethodsThe computed tomography (CT) images of 158 patients with EGFR-mutant NSCLC who were first diagnosed in West China Hospital of Sichuan University were retrospectively analyzed, and the target areas of the lesions were described. Patients were randomly assigned to either a model training group or a test group.The radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) regression method was used to screen out the valuable radiomics features. The logistic regression method was used to establish a radiomic model, and the nomogram was used to evaluate the discrimination ability. Finally, the calibration curve, receiver characteristic curve (ROC), Kaplan-Meier curve and decision curve analysis (DCA) were employed to assess model efficacy. ResultsA nomogram combining three important clinical factors : gender, lesion location, treatment, and imaging risk score was established to predict the 3-year, 5-year, and 8-year survival rates of NSCLC patients with EGFR mutation. The calibration curve demonstrated highly consistent between model-predicted survival probabilities and observed overall survival (OS). The area under the curve (AUC) -ROC of the predicted 3-year, 5-year and 8-year OS was 0.70, 0.79 and 0.68, respectively. The Kaplan-Meier curve revealed significant OS disparities when comparing high- and low-risk patient subgroups. The DCA curve showed that the predicted 3-year and 5-year OS increased more clinical benefits than the treatment of all patients or no treatment.ConclusionThe nomogram for predicting the survival prognosis of NSCLC patients with EGFR mutation was constructed and verified, which can effectively predict the survival time range of NSCLC patients, and provide a reference for more individualized treatment decisions for such patients in clinical practice.
This study aims to predict expression of Ki67 molecular marker in pancreatic cystic neoplasm using radiomics. We firstly manually segmented tumor area in multi-detector computed tomography (MDCT) images. Then 409 high-throughput features were automatically extracted and the least absolute shrinkage selection operator (LASSO) regression model was used for feature selection. After 200 bootstrapping repetitions of LASSO, 20 most frequently selected features made up the optimal feature set. Then 200 bootstrapping repetitions of support vector machine (SVM) classifier with 10-fold cross-validation were used to avoid overfitting and accurately predict the Ki67 expression. The highest prediction accuracy could achieve 85.29% and the highest area under the receiver operating characteristic curve (AUC) was 91.54% with a sensitivity (SENS) of 81.88% and a specificity (SPEC) of 86.75%. According to the results of experiment, the feasibility of predicting expression of Ki67 in pancreatic cystic neoplasm based on radiomics was verified.
ObjectiveTo construct a multimodal imaging radiomics model based on enhanced CT features to predict tumor regression grade (TRG) in patients with locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (NCRT). MethodsA retrospective analysis was conducted on the Database from Colorectal Cancer (DACCA) at West China Hospital of Sichuan University, including 199 LARC patients treated from October 2016 to October 2023. All patients underwent total mesorectal excision after NCRT. Clinical pathological information was collected, and radiomics features were extracted from CT images prior to NCRT. Python 3.13.0 was used for feature dimension reduction, and univariate logistic regression (LR) along with Lasso regression with 5-fold cross-validation were applied to select radiomics features. Patients were randomly divided into training and testing sets at a ratio of 7∶3 for machine learning and joint model construction. The model’s performance was evaluated using accuracy, sensitivity, specificity, and the area under the curve (AUC). Receiver operating characteristic curve (ROC), confusion matrices, and clinical decision curves (DCA) were plotted to assess the model’s performance. ResultsAmong the 199 patients, 155 (77.89%) had poor therapeutic outcomes, while 44 (22.11%) had good outcomes. Univariate LR and Lasso regression identified 8 clinical pathological features and 5 radiomic features, including 1 shape feature, 2 first-order statistical features, and 2 texture features. LR, support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost) models were established. In the training set, the AUC values of LR, SVM, RF, XGBoost models were 0.99, 0.98, 1.00, and 1.00, respectively, with accuracy rates of 0.94, 0.93, 1.00, and 1.00, sensitivity rates of 0.98, 1.00, 1.00, and 1.00, and specificity rates of 0.80, 0.67, 1.00, and 1.00, respectively. In the testing set, the AUC values of 4 models were 0.97, 0.92, 0.96, and 0.95, with accuracy rates of 0.87, 0.87, 0.88, and 0.90, sensitivity rates of 1.00, 1.00, 1.00, and 0.95, and specificity rates of 0.50, 0.50, 0.56, and 0.75. Among the models, the XGBoost model had the best performance, with the highest accuracy and specificity rates. DCA indicated clinical benefits for all 4 models. ConclusionsThe multimodal imaging radiomics model based on enhanced CT has good clinical application value in predicting the efficacy of NCRT in LARC. It can accurately predict good and poor therapeutic outcomes, providing personalized clinical surgical interventions.
ObjectiveTo summarize the progress of radiomics in the diagnosis and treatment of hepatocellular carcinoma and discuss its future direction, limitations and challenges. MethodWe retrieved the literature related to radiomics in the diagnosis and treatment of hepatocellular carcinoma and made a review. ResultsTraditional hepatocellular carcinoma imaging examination, diagnosis and differential diagnosis had certain limitations. Radiomics as an emerging technology, it helped extract tissue biological information that could not be detected by the naked eye from high-throughput quantitative images and transform into high-dimensional qualitative quantitative data, and either alone or in combination with other clinical and molecular data such as demographics, histology, genomics or proteomics or other clinical and molecular data to solve clinical problems, such as hepatocellular carcinoma diagnosis and differential diagnosis, staging and grading, therapeutic regimen development and predicting prognosis and survival after therapy, etc. At present, there were still several problems to be solved in radiomics, such as insufficient interpretability of the combined artificial intelligence-medical imaging approach, lack of uniform standards and lack of external validation, etc.ConclusionsThe study of radiomics in the diagnosis and treatment of hepatocellular carcinoma has been deepened and expanded to different degrees with great potential and application prospects. Radiomics brings greater benefits to the diagnosis, treatment and management of hepatocellular carcinoma patients, provides a new direction for optimizing medical decision-making and promoting the development of precision medicine. However, there are still some deficiencies and challenges to overcome in the radiomics technology and methods, which require extensive validation and optimization through further clinical trials.
Biliary tract cancer is characterized by occult onset, highly malignancy and poor prognosis. Traditional medical imaging is an important tool for surgical strategies and prognostic assessment, but it can no longer meet the urgent need for accurate and individualized treatment in patients with biliary tract cancer. With the advent of the digital imaging era, the advancement of artificial intelligence technology has given a new vitality to digital imaging, and provided more possibilities for the development of medical imaging in clinical applications. The application of radiomics in the diagnosis and differential diagnosis of benign and malignant tumors of biliary tract, assessment of lymph node status, early recurrence and prognosis assessment provides new means for the diagnosis and treatment of patients with biliary tract cancer.
With the development of thin section axial computed tomography scan, the detection rate of pulmonary ground-glass nodules (GGN) continues increasing. GGN has a special natural growth history: pure ground-glass nodules (PGGN) smaller than 10 mm can hold steady for a long term, surgery resection is unnecessary, patients need regular follow up. Larger part solid ground-glass nodules (PSN) with a solid component can be malignant early stage lung cancer, which requires early surgery intervention. Establishment of a standard definition of GGN growth, investments in the long term natural growth history of GGN, validation of the clinical, radiology and genetic risk factors would be beneficial for the management of GGN patients.
The purpose of our study is to evaluate the diagnostic performance of radiomics in multi-class discrimination of lymphadenopathy based on elastography and B-mode dual-modal ultrasound images. We retrospectively analyzed a total of 251 lymph nodes (89 benign lymph nodes, 70 lymphoma and 92 metastatic lymph nodes) from 248 patients, which were examined by both elastography and B-mode sonography. Firstly, radiomic features were extracted from multimodal ultrasound images, including shape features, intensity statistics features and gray-level co-occurrence matrix texture features. Secondly, three feature selection methods based on information theory were used on the radiomic features to select different subsets of radiomic features, consisting of conditional infomax feature extraction, conditional mutual information maximization, and double input symmetric relevance. Thirdly, the support vector machine classifier was performed for diagnosis of lymphadenopathy on each radiomic subsets. Finally, we fused the results from different modalities and different radiomic feature subsets with Adaboost to improve the performance of lymph node classification. The results showed that the accuracy and overall F1 score with five-fold cross-validation were 76.09%±1.41% and 75.88%±4.32%, respectively. Moreover, when considering on benign lymph nodes, lymphoma or metastatic lymph nodes respectively, the area under the receiver operating characteristic curve of multi-class classification were 0.77, 0.93 and 0.84, respectively. This study indicates that radiomic features derived from multimodal ultrasound images are benefit for diagnosis of lymphadenopathy. It is expected to be useful in clinical differentiation of lymph node diseases.
The classification of lung tumor with the help of computer-aided diagnosis system is very important for the early diagnosis and treatment of malignant lung tumors. At present, the main research direction of lung tumor classification is the model fusion technology based on deep learning, which classifies the multiple fusion data of lung tumor with the help of radiomics. This paper summarizes the commonly used research algorithms for lung tumor classification, introduces concepts and technologies of machine learning, radiomics, deep learning and multiple data fusion, points out the existing problems and difficulties in the field of lung tumor classification, and looks forward to the development prospect and future research direction of lung tumor classification.
ObjectiveTo make a comprehensive review of the value of radiomics for prediction of therapeutic responses to neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced rectal cancer (LARC).MethodRelevant literatures about the therapeutic response evaluation of LARC by using radiomics were collected to make an review.ResultRadiomics had good predictive value in terms of complete pathologic response (pCR) and treatment effectiveness of NCRT in patients with LARC.ConclusionRadiomics, a new imaging diagnostic technique, plays an important role in the prediction of the efficacy of NCRT in LARC.