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.
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.
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.
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.
This study aims to predict expression of estrogen receptor (ER) in breast cancer by radiomics. Firstly, breast cancer images are segmented automatically by phase-based active contour (PBAC) method. Secondly, high-throughput features of ultrasound images are extracted and quantized. A total of 404 high-throughput features are divided into three categories, such as morphology, texture and wavelet. Then, the features are selected by R language and genetic algorithm combining minimum-redundancy-maximum-relevance (mRMR) criterion. Finally, support vector machine (SVM) and AdaBoost are used as classifiers, achieving the goal of predicting ER by breast ultrasound image. One hundred and four cases of breast cancer patients were conducted in the experiment and optimal indicator was obtained using AdaBoost. The prediction accuracy of molecular marker ER could achieve 75.96% and the highest area under the receiver operating characteristic curve (AUC) was 79.39%. According to the results of experiment, the feasibility of predicting expression of ER in breast cancer using radiomics was verified.
Objective To develop a radiomics nomogram based on contrast-enhanced CT (CECT) for preoperative prediction of high-risk and low-risk thymomas. Methods Clinical data of patients with thymoma who underwent surgical resection and pathological confirmation at Northern Jiangsu People's Hospital from January 2018 to February 2023 were retrospectively analyzed. Feature selection was performed using the Pearson correlation coefficient and least absolute shrinkage and selection operator (LASSO) method. An ExtraTrees classifier was used to construct the radiomics signature model and the radiomics signature. Univariate and multivariable logistic regression was applied to analyze clinical-radiological characteristics and identify variables for developing a clinical model. The radiomics nomogram model was developed by combining the radiomics signature and clinical features. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, and positive predictive value. Calibration curves and decision curves were plotted to assess model accuracy and clinical values. Results A total of 120 patients including 59 females and 61 males with an average age of 56.30±12.10 years. There were 84 patients in the training group and 36 in the validation group, 62 in the low-risk thymoma group and 58 in the high-risk thymoma group. Radiomics features (1 038 in total) were extracted from the arterial phase of CECT scans, among which 6 radiomics features were used to construct the radiomics signature. The radiomics nomogram model, combining clinical-radiological characteristics and the radiomics signature, achieved an AUC of 0.872 in the training group and 0.833 in the validation group. Decision curve analysis demonstrated better clinical efficacy of the radiomics nomogram than the radiomics signature and clinical model. Conclusion The radiomics nomogram based on CECT showed good diagnostic value in distinguishing high-risk and low-risk thymoma, which may provide a noninvasive and efficient method for clinical decision-making.
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.
ObjectiveTo establish a machine learning model based on computed tomography (CT) radiomics for preoperatively predicting invasive degree of lung ground-glass nodules (GGNs). MethodsWe retrospectively analyzed the clinical data of GGNs patients whose solid component less than 3 cm in the Department of Thoracic Surgery of Shanghai Pulmonary Hospital from March 2021 to July 2021 and the First Hospital of Lanzhou University from January 2019 to May 2022. The lesions were divided into pre-invasiveness and invasiveness according to postoperative pathological results, and the patients were randomly divided into a training set and a test set in a ratio of 7∶3. Radiomic features (1 317) were extracted from CT images of each patient, the max-relevance and min-redundancy (mRMR) was used to screen the top 100 features with the most relevant categories, least absolute shrinkage and selection operator (LASSO) was used to select radiomic features, and the support vector machine (SVM) classifier was used to establish the prediction model. We calculated the area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, positive predictive value to evaluate the performance of the model, drawing calibration and decision curves of the prediction model to evaluate the accuracy and clinical benefit of the model, analyzed the performance in the training set and subgroups with different nodule diameters, and compared the prediction performance of this model with Mayo and Brock models. Two primary thoracic surgeons were required to evaluate the invasiveness of GGNs to investigate the clinical utility of the model. ResultsA total of 400 patients were divided into the training set (n=280) and the test set (n=120) according to the admission criteria. There were 267 females and 133 males with an average age of 52.4±12.7 years. Finally, 8 radiomic features were screened out from the training set data to build SVM model. The AUC, sensitivity and specificity of the model in the training and test sets were 0.91, 0.89, 0.75 and 0.86, 0.92, 0.60, respectively. The model showed good prediction performance in the training set 0-10 mm, 10-20 mm and the test set 0-10 mm, 10-20 mm subgroups, with AUC values of 0.82, 0.88, 0.84, 0.72, respectively. The AUC of SVM model was significantly better than that of Mayo model (0.73) and Brock model (0.73). With the help of this model, the AUC value, sensitivity, specificity and accuracy of thoracic surgeons A and B in distinguishing invasive or non-invasive adenocarcinoma were significantly improved. ConclusionThe SVM model based on radiomics is helpful to distinguish non-invasive lesions from invasive lesions, and has stable predictive performance for GGNs of different sizes and has better prediction performance than Mayo and Brock models. It can help clinicians to more accurately judge the invasiveness of GGNs, to make more appropriate diagnosis and treatment decisions, and achieve accurate treatment.
The widespread application of low-dose computed tomography (LDCT) has significantly increased the detection of pulmonary small nodules, while accurate prediction of their growth patterns is crucial to avoid overdiagnosis or underdiagnosis. This article reviews recent research advances in predicting pulmonary nodule growth based on CT imaging, with a focus on summarizing key factors influencing nodule growth, such as baseline morphological parameters, dynamic indicators, and clinical characteristics, traditional prediction models (exponential and Gompertzian models), and the applications and limitations of radiomics-based and deep learning models. Although existing studies have achieved certain progress in predicting nodule growth, challenges such as small sample sizes and lack of external validation persist. Future research should prioritize the development of personalized and visualized prediction models integrated with larger-scale datasets to enhance predictive accuracy and clinical applicability.
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.