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Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To explore the application value of machine learning models in predicting acute coronary syndrome based on clinical indicators and pericoronary adipose tissue (PCAT) radiomics features in coronary computed tomography angiography (CCTA) of patients with coronary heart disease (CHD). Methods The clinical data and CCTA of 164 patients with CHD were analyzed retrospectively. Based on medical record follow-up, the patients were divided into two group: stable coronary artery disease (SCAD) group and acute coronary syndrome (ACS) group. The patients were randomly divided into a training group and a validation group in the ratio of 7:3. The region of interest for PCAT was delineated on CCTA images. Radiomics features were extracted and selected by least absolute shrinkage and selection operator to build the radiomies model. By integrating the clinical data and radiomics features, the predictive models were developed using five classifiers, including decision tree, extreme gradient boosting. support vector machine, random forest and logistic regression (LR). The predictive performance of the models was assessed by using the receiver operating characteristic curve, with differences compared by using DeLong test. Calibration curves were used to evaluate model accuracy, and the clinical benefits were assessed by decision curve analysis. Results Of 164 patients with CHD, there were 107 SCAD and 57 ACS. According to the univariate and multivariate logistic regression analysis, the history of diabetes mellitus and CT-derived fractional flow reserve were the independent risk factors of ACS (P<0.05). Among all models, the clinical-radiomics model based on LR showed the highest predictive performance with area under curves of 0.951(95% CI: 0.912, 0.990) in the training set and 0.845 (95% CI: 0.713, 0.977) in the validation set, significantly outperforming other classifiers (P<0.05). Calibration curves revealed good model accuracy, and decision curves demonstrated high clinical benefit for the combined clinical-radiomics model. Conclusion The machine learning model based on CCTA pericardial adipose tissue radiomics and clinical features can effectively predict the risk of ACS in CHD. [ABSTRACT FROM AUTHOR]

Objective: To explore the application value of radiomics based on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) in non-invasive preoperative prediction of pituitary adenoma consistency. Methods: The clinical and preoperative MRI data of 108 patients with pathologically confirmed pituitary adenoma were retrospectively analyzed. Two neurosurgeons evaluated tumor consistency intraoperatively and categorized them into soft and hard groups. Patients were randomly divided into a training set and a validation set in a 7:3 ratio. Volume of interest (VOI) representing the tumor solid component were manually delineated on T2WI and DWI images. Radiomics features were extracted by FeAture Explorer software. Unsupervised feature selection (UFS) was applied for feature selection. Support vector machine (SVM) was used to conduct the radiomics models. Area under curve (AUC) and calibration curve were used to assess the performance of the models. Results: In the combined T2WI and DWI radiomics model, the AUC for predicting the consistency of pituitary adenoma was 0.89 in the training set and 0.80 in the validation set. The calibration curve showed a good consistency between predicted and actual values. Conclusion: The combined T2WI and DWI radiomics model demonstrates good diagnostic performance and aids in preoperative prediction of the consistency of pituitary adenoma. [ABSTRACT FROM AUTHOR]

Objective To construct a radiomic model based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for preoperative prediction of hepatocellular carcinoma (HCC) differentiation and validate its clinical value. Methods Clinical and imaging data of 187 HCC patients who received surgical treatment in the Third Affiliated Hospital of Chongqing Medical University from June 2018 to January 2024 were collected and retrospectively analyzed. According to the postoperative pathological results, they were divided into low-differentiation group (n=58) and non-low-differentiation group (n=129), and randomly divided into the training group (n=149) and the validation group (n=38) with the ratio of 8 ∶2. Univariate analysis was used to assess the clinical indicators related to HCC differentiation, and then a clinical model was constructed. Pyramidimics software was used to extract the radiomic features of IVIM-DWI functional images, and minimum absolute contraction and selection operator logistic regression algorithm were employed to screen those highly correlated indicators with HCC differentiation. Support vector machine (SVM), logistic regression (LR) and random forest (RF) algorithms were utilized to construct different image omics models. SVM algorithm was applied to construct the combined imaging omics and clinical model. The internal verification of the model was carried out by using ten-fold cross-validation. Receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the diagnostic value and clinical benefits of clinical model, radiomic model, and their combination. Results A total of 4 060 radiological features were extracted, and after feature screening and dimensionality reduction, 24 features were finally included to construct the model. Among all models, the predictive performance of the radiomic model and the radiomic-clinical combined model was better than that of the clinical model. In the comparison between the radiomic model constructed by SVM algorithm and the radiomics-clinical combined model, the AUC value was 0.954 (0.908~1.000) for the former model, and was 0.943 (0.905~0.982) for the latter model in the training set, and there was no significant difference between them. In the validation set, the AUC value was 0.807 (0.640~0.975) and 0.876 (0.743~1.000), respectively, with statistical difference between the 2 models (P < 0.05). Calibration curve analysis showed that the radiomic model and the radiomics-clinical combined model had good goodness of fit. DCA indicated that the net benefit was higher and the threshold probability range was larger in both the radiomic model and the radiomics-clinical combined model, and the net benefit of the combined model was larger. Conclusion Both the radiomic model and the combined radiomics-clinical model constructed on the basis of IVIM-DWI functional images can better predict the severity of HCC differentiation before surgery.

Background and Purpose: The early prediction of local tumor progression-free survival (LTPFS) after radiofrequency ablation (RFA) for colorectal cancer (CRC) lung metastases has significant clinical importance. The application of radiomics in the prediction of tumor prognosis has been explored. This study aimed to construct a radiomics-based nomogram for predicting LTPFS after RFA in CRC patients with lung metastases. Methods: This study retrospectively analyzed 172 CRC patients with 401 lung metastases admitted to Department of Interventional Radiology, Fudan University Shanghai Cancer Center from August 2016 to January 2019. This study was reviewed by the medical ethics committee of Fudan University Shanghai Cancer Center (ethics number: 2402291-24). After augmentation of pre-ablation and immediate post-ablation computed tomography (CT) images, the target metastases and ablation regions were segmented manually to extract the radiomic features. Maximum relevance and minimum redundancy algorithm (MRMRA) and least absolute shrinkage and selection operator (LASSO) regression models were applied for feature selection. The clinical model, the radiomics model, and the fusion model were constructed based on the selected radiomic features and clinical variables screened by the multivariate analysis. The Harrell concordance index (C-index) and area under receiver operating characteristic (ROC) curves (AUC) were calculated to evaluate the prediction performance. Finally, the corresponding nomogram of the best model was drawn. Results: Among all the lung metastases, 102 (25.4%) had final recurrence, and 299 (74.6%) had complete response (CR). The median follow-up time was 21 months (95% CI: 19.466-22.534), and the LTPFS rates at 1, 2, and 3 years after RFA were 76.5% (95% CI: 72.0-80.4), 72.1% (95% CI: 66.6-76.9) and 69.9% (95% CI: 64.0-75.1). In both the training and test dataset, the fusion model based on the final 12 radiomic features through the LASSO regression and 4 clinical variables screened by multivariate analysis achieved the highest AUC values for LTPFS, with C-index values of 0.890 (95% CI: 0.854-0.927) and 0.843 (95% CI: 0.768-0.916), respectively. Conclusion: The fusion model based on radiomic features and clinical variables is feasible for predicting LTPFS after RFA of CRC patients with lung metastases, whose performance is superior to the single radiomic and clinical model. At the same time, the nomogram of the fusion model can intuitively predict the prognosis of CRC patients with lung metastases after RFA, thus assisting clinicians in developing individualized follow-up review plans for patients and adjusting treatment strategies flexibly.

大脑年龄作为一种个体化的脑健康影像学表型，是衡量大脑衰老程度的新型神经影像学生物标志物。个体大脑年龄与实际年龄之间的偏差，即脑年龄估值差，反映了大脑衰老的轨迹和速率。既往研究表明，缺血性卒中患者的脑年龄估值差升高与其预后不良存在显著关联。此外，缺血性脑损伤加速了卒中患者的大脑衰老过程，提示大脑衰老与缺血性卒中之间可能形成恶性循环。本文就大脑年龄预测的方法学框架、影响大脑衰老速率的因素、大脑年龄与缺血性卒中的关联及多组学数据联合分析的研究进展进行综述，旨在为卒中相关的衰老生物学研究提供新的视角。 Abstract: As an individualized brain health imaging phenotype, brain age is a new neuroimaging biomarker to assess the degree of brain aging. The discrepancy between an individual’s brain age and chronological age, known as the predicted age difference, reflects the trajectory and rate of brain aging. Previous studies have found that a greater predicted age difference in patients with ischemic stroke was associated with poor prognosis. Furthermore, ischemic brain injury may accelerate the brain aging process in stroke patients, suggesting the potential for a vicious cycle between brain aging and ischemic stroke. This article reviewed the progress in research on brain age prediction methodologies, factors influencing the rate of brain aging, the relationship between brain age and ischemic stroke, and the integration of multi-omics data analysis, providing a fresh perspective for biogerontological research related with stroke.

BACKGROUND: Radiomics is the newest and most promising direction in modern radiographic diagnostics. The number of head and neck cancer studies employing radiomics is increasing annually. A systematic review of recent publications (2021–2023) on computed tomography (CT) of head and neck malignancies was performed. AIM: To present systematized data on parameters for radiomic analysis for head and neck malignancies identified by CT data. MATERIALS AND METHODS: The literature search was carried out in PubMed. The basic characteristics of the selected articles were extracted, and their quality was assessed using RQS 2.0 and the modified QUADAS-CAD questionnaire. The reproducibility level of radiomic parameters selected for predictive models in different studies was assessed. Eleven articles were selected for the review. In most cases, a high risk of systematic error associated with data imbalance in terms of demographic parameters and level of pathologies was noted. RESULTS: The range of RQS 2.0 scores for the included articles varied from 19.44% to 50.00% of the maximum possible score. The decreasing research quality was mainly caused by the lack of external result validation (73% of the analyzed articles) and data accessibility and transparency (82%). Inter-study reproducibility of radiomic parameters was low owing to the wide variety of techniques used for image acquisition, image post-processing, extraction, and statistical processing of radiomic parameters. CONCLUSION: A set of stable radiomic parameters must be successfully introduced into clinical practice. The standardization of radiomics method and creation of an open radiomics database are necessary for this purpose.

Objective: To develop an MRI-based habitat radiomics model for the preoperative prediction of endometrial cancer (EC) molecular subtypes. Methods: Patients with pathologically proven EC from two hospitals were included in the training (n=270) and testing (n=70) cohorts. All patients had preoperative MRI and histological and molecular diagnoses. First, the tumor was divided into habitat subregions based on diffusion-weighted imaging (DWI) and contrast-enhanced (CE) images. Subsequently, habitat radiomic features were extracted from different subregions of T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), DWI, and CE images. Three machine learning classifiers, including logistic regression, support vector machines, and random forests, were applied to develop predictive models for p53-abnormal endometrial cancer, with model performance validated. The model demonstrating the best overall predictive performance was selected as the habitat radiomics model. Using the same procedure, a whole-region radiomics model based on T1WI, T2WI, DWI, and CE sequences and a clinical model were constructed. The performance of the models was evaluated using receiver operating characteristic curves, and DeLong's test was employed to compare differences between the models. Decision curve analysis was used to assess the clinical benefits of the models' application. Results: After feature selection, eight habitat radiomic features were retained to construct the habitat radiomics model, ten features for the whole-region radiomics model, and three clinical features for the clinical model. The habitat radiomics model achieved the highest area under the curve (AUC), with 0.855 (0.788-0.922) in the training cohort and 0.769 (0.631-0.907) in the testing cohort. DeLong's test showed that the habitat radiomics model outperformed the whole-region radiomics model in the training cohort (P=0.001), but there was no significant difference in the testing cohort (P=0.543). In both cohorts, the habitat radiomics model outperformed the clinical model (P=0.007, training cohort; P=0.038, testing cohort). Decision curve analysis (DCA) demonstrated that this model provided clinical benefit for diagnosis within a threshold probability range of approximately 0.2-0.8. Conclusion: The MRI-based habitat radiomics model can accurately predict p53-abnormal EC, outperforming both the whole-region radiomics model and the clinical model, and is useful for the non-invasive molecular subtyping of endometrial cancer before surgery. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To explore the application value of ultrasound radiomics technology based on grayscale ultrasound images in the differential diagnosis of histological classification of glomerulonephritis. Methods A total of 204 patients with renal biopsy were selected from our hospital, and according to pathological results, they were divided into the membranous nephropathy group (n=133) and the mesangial proliferative glomerulonephritis group (n=71). The ultrasound images were sketched and the image omics features were extracted by two physicians. The pathological results and ultrasound data of renal biopsy were collected from the two groups, and the ultrasound radiomics features were preliminarily screened by the maximum correlation and minimum redundancy algorithm (mRMR) algorithm for all the obtained omics feature data. Then the optimal effective features were selected from the screened features by minimum absolute shrinkage and selection operator (LASSO) algorithm, and random forest (RF), support vector machine (SVM), logistic regression (LR), four kinds of classifiers of K-nearest neighbor (KNN) method were used to establish a prediction model. All cases were randomly divided into the training set and the validation set according to the ratio of 7∶3, and the four models were trained by the training set, and then validated in the validation set, and the best prediction model was selected by comparing the receiver operating characteristic (ROC) curve, Delong test and GiViTI calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical utility of the model. Results The radiomics method was used to extract 837 radiomics features per image, and 16 meaningful features were finally screened out by using the mRMR + LASSO algorithm. Among the four prediction models of RF, SVM, LR and KNN, the best performing model was LR model, with the AUC of 0.944, the specificity of 0.867 and the sensitivity of 0.878. The GiViTI calibration curve showed that the model had good accuracy (P＞ 0.05), and the decision curve showed that the prediction model had good clinical practical value. Conclusion Ultrasound radiomics has a good ability to distinguish the more common histological types of glomerulonephritis, and is a non-invasive method with good application prospects. [ABSTRACT FROM AUTHOR]

Copyright of China Oncology is the property of Editorial Board of China Oncology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Stroke is the property of Chinese Journal of Stroke Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Acute pancreatitis (AP) is an acute abdominal disease that is prone to organ dysfunction, with high mortality. Timely prediction of the occurrence and development trend of the disease is the prerequisite for early treatment and intervention. Radiomics can extract quantitative features from medical images with high throughput and realize deep data mining. It can be used for the diagnosis of acute pancreatitis and prediction of severity, progression and recurrence of the disease. For the diagnosis of AP, CT radiomics can distinguish recurrent AP patients from functional abdominal pain, chronic pancreatitis, and recurrent AP patients, with an area under curve (AUC) of 0.88. For predicting AP recurrence, CT radiomics can accurately predict AP recurrence within 48 months, with an AUC of 0.93. For predicting the severity of AP, MRI radiomics can predict whether AP patients will progress to moderate to severe AP in the future, with an AUC of 0.85, which is better than clinical scoring systems. For predicting complications and progression of AP, MRI radiomics can effectively predict the occurrence of peripancreatic necrosis, with an AUC of 0.92. Metabolomics has confirmed that metabolic spectrum changes dynamically during the occurrence and development of AP. It has been reported that active metabolites can be used as early warning indicators for the diagnosis, etiology identification and severity assessment of AP. In addition, urinary metabolomics allows accurate diagnosis of AP, with an AUC of 0.91. For identifying the etiology of AP, the blood metabolomics models can identify patients with biliary AP, hyperlipidemic AP, and alcoholic AP, with AUCs of 0.89, 0.91, and 0.86, respectively. For predicting the severity of AP, the blood metabolomics models can accurately predict whether AP patients will progress to moderate to severe AP in the future, with an AUC of 0.99. The combination of the radiomics and metabolomics can complement each other's advantages and integrate multi-group data, which can jointly characterize the process and internal connections of disease occurrence and development from different levels, for achieving early warning and early intervention more effectively.

Lung cancer is the main cause of cancer-related deaths, with non-small cell lung cancer (NSCLC) being the predominant subtype. At present, immunotherapy represented by immune checkpoint inhibitors (ICIs) of programmed cell death receptor 1 or its ligand has been widely used in the clinical diagnosis and treatment of patients with NSCLC. However, only a few patients can benefit from it, and reliable predictive markers for immunotherapy are lacking. Radiomics is a tool that uses computer software and algorithms to extract a large amount of quantitative information from biomedical images. A large number of studies have confirmed that the radiomic model that predicts the immune efficacy of NSCLC can be used as a new type of immune efficacy predictive marker, which is expected to guide the individualized diagnosis and treatment decisions for patients with lung cancer and has a bright application prospect. This article reviews the research progress of radiomics in predicting the immune therapy response of NSCLC, identifying pseudo-progression and hyperprogression, ICIs-related pneumonia, cachexia risk, and combining with other genomics.

Pericoronary adipose tissue (PCAT) is a type of adipose tissue tightly wrapped in the adventitia of the coronary artery, which communicates bidirectionally with the coronary artery wall and promotes the development of coronary atherosclerosis during vascular inflammation. PCAT based on coronary computed tomography angiography can extract quantitative features from images with high throughput, hence has better diagnostic efficacy for analyzing plaque composition and identifying vulnerable plaques, predicting coronary artery and hemodynamic stenosis, and identifying and predicting acute coronary syndrome. This paper reviewed the research progress of PCAT based on CCTA in coronary atherosclerosis.

Pancreatic neuroendocrine neoplasm (pNEN) is the most common neuroendocrine tumor in the digestive system. The heterogeneity of pNEN makes it difficult to summarize diagnostic and therapeutic strategies only based on clinical experience. Over the past 20 years, basic research on pNEN has been well developed, which in turn has promoted its theoretical system and diagnostic and therapeutic strategies, significantly improving the therapeutic efficacy of pNEN. In this paper, we review the development of basic research on pNEN in recent years and discuss its role in clinical diagnosis and treatment. On the one hand, the development of basic research can further reveal the nature of the differential biological behavior of pNEN and provide clinical reference; on the other hand, reference to the migratory basic research results in other tumor fields is also conducive to improving the clinical diagnosis and treatment level of pNEN.

Objective To screen the risk factors related to primary trigeminal neuralgia (PTN) based on radiomics. Methods A total of 48 patients with PTN admitted to Guangdong Second Provincial General Hospital from January 2017 to December 2020 were selected. The mean arterial pressure (MAP) of the patients was measured, and the neurovascular compression model was constructed by head MRI examination. Univariate and multivariate Logistic regression analyses were used to screen for risk factors associated with PTN, and the predictive efficacy was evaluated by receiver operating characteristic (ROC) curve according to the risk factors. Results Among 48 patients, 42 patients (87.50%) had unilateral lesions and 6 patients (12.50%) had bilateral lesions. The MAP was 56.89-120.44 mm Hg, with an average of (94.32 ± 11.34) mm Hg. The neurovascular compression model of 54 cases was divided into the affected side (n = 40) and the healthy side (n = 14) according to whether the disease occurred. The neurovascular compression area (Z = -2.823, P = 0.005) and neurovascular pressure (Z = -0.365, P = 0.006) on the affected side were greater than those on the healthy side. Logistic regression analyses showed that high neurovascular pressure (OR = 1.001, 95%CI: 1.0003-1.0022; P = 0.011) was a risk factor for PTN. ROC showed the area under the curve (AUC) of neurovascular compression area for predicting PTN was 0.747 (95%CI: 0.605-0.890, P = 0.006), the sensitivity was 42.50%, the specificity was 100%, and the cut-off value was 25.34 mm². The AUC of neurovascular pressure was 0.755 (95%CI: 0.616-0.895, P = 0.005), the sensitivity was 67.50%, the specificity was 78.60%, and the cut-off value was 1672.99 mm Hg⋅mm². Conclusions The neurovascular compression area and neurovascular pressure are important in the diagnosis of PTN. [ABSTRACT FROM AUTHOR]

Osteoporosis (OP) is a common systemic bone disease in the middle-aged and elderly. Early screening based on medical images can improve the diagnosis and treatment rate of OP. Radiomics is a new digital medical technology that can be used to quantitatively analyze, diagnose and evaluate diseases through deep mining medical image feature data through machine learning. In recent years, with the continuous development of risk prediction of chronic disease, it has been gradually used in bone mineral density prediction,OP screening, bone microstructure analysis, prediction and diagnosis of osteoporotic fracture, etc. This article will review the relevant applications of radiomics in OP, in order to provide quantitative imaging references for the early diagnosis and treatment of OP. [ABSTRACT FROM AUTHOR]

Prostate cancer（PCa）is the second most common malignancy among men worldwide. A more comprehensive and integrated approach is needed for its early screening, auxiliary diagnosis, risk stratification, treatment guidance, and efficacy assessment. Radiomics, a technique that extracts and analyzes image features in a quantitative and high⁃throughput manner, has shown significant clinical value in the non⁃invasive diagnosis, aggressiveness assessment, treatment efficacy, and prognosis evaluation of PCa. With the rapid development of imaging modalities and artificial intelligence technologies, radiomics has tremendous potential. This article reviews the existing researches on radiomics based on multiparametric magnetic resonance imaging, positron emission tomography/computed tomography, computed tomography, ultrasound and other imaging modalities in PCa, emphasizing its potential in future applications. [ABSTRACT FROM AUTHOR]

Objective：To establish the applicability of a predictive model based on spectral computed tomography（CT）parameters and radiomics features through machine learning for differentiating benign and malignant thyroid nodules. Methods：A retrospective analysis was conducted on imaging and clinical data from 118 patients with thyroid nodules who underwent spectral CT enhancement scanning（143 nodules, comprise of 46 benign and 97 malignant nodules). These patients were randomly divided into a training set （n=100）and a validation set（n=43）in a 7∶3 ratio. Discriminative testing, intraclass correlation coefficient（ICC), and the least absolute shrinkage and selection operator（LASSO）were employed to select features and calculate a radiomics score（Radscore). Six machine learning algorithms including decision tree（DT), random forest（RF), extreme gradient boosting（XGBoost), support vector machine（SVM), K ⁃ nearest neighbors（KNN), and logistic regression（LR）were utilized to develop models. The optimal model was selected to construct nomograms. Results：The XGBoost model was demonstrated to be the best in the validation set（AUC：0.938； accuracy：86.05%；sensitivity：89.29%；specificity：80.00%), with normalized iodine concentration（NIC), Radscore, and age identified as significant predictive factors. The ensuing nomograms exhibited robust performance. Conclusion：The machine learning model that combines spectral CT and radiomics features with the nomograms provides a highly accurate reference for non ⁃invasive prediction of the benignity or malignancy of thyroid nodules. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision and postoperative adjuvant chemotherapy is the standard treatment for locally advanced rectal cancer (LARC). Some patients can achieve pathological complete response (PCR) after nCRT followed by local resection or non-surgical treatment. Therefore, it is essential to accurately predict whether LARC can achieve PCR before surgery to guide treatment decisions. High-resolution pelvic MRI has good soft tissue resolution, and has become the preferred imaging method for clinical evaluation of the efficacy of nCRT. This article reviews the research progress of MRI such as conventional MRI, functional MRI and MRI radiomics in predicting pCR. [ABSTRACT FROM AUTHOR]

Background and purpose: Preoperative risk categorization of thymoma is useful for treatment decisions but remains challenging. This study focused on training radiomics models using contrast-enhanced computed tomography (CECT) images for thymoma risk categorization and validating the model's performance, reliability and generalizability in a relatively large cohort. Methods: This retrospective cohort study analyzed the clinical data of thymoma patients (Masaoka Koga I-III) who underwent thymectomy surgery at the Affiliated Chest Hospital of Shanghai Jiao Tong University School of Medicine from January 2008 to December 2017. The cohort was divided into a training group (80%) and a test group (20%) using stratified random selection. The gold standard for histologic types was based on surgically resected specimens. Low-risk histologic types included A, AB and B1. High-risk histologic types included B2 and B3. Radiomics features were extracted from manually segmented regions of interest on preoperative CECT images. Interobserver correlation and least absolute shrinkage and selection operator (LASSO) regression were used for feature selection. Model performance metrics included area under the curve (AUC) of receiver operating characteristic (ROC) curve, sensitivity and specificity. Clinical characteristics were added to the combined model. Results: A total of 478 patients (mean age 51.3±12.3 years, 48.1% was male) were included. The AUC of the clinical model, the CECT-based model, and the model using both clinical and CECT features on the test set were 0.666, 0.831 and 0.850, respectively. The best performing model had a sensitivity of 0.829 and a specificity of 0.764. Conclusion: CECT-based radiomics models showed good performance in risk categorization of thymomas. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: Thyroid nodules are a prevalent issue, with an estimated incidence of 19% to 35% based on ultrasound examination and 8% to 65% based on autopsy findings [1]. In some cases, Plummer’s disease is observed, and nodular masses may be observed in 10% to 35% of Graves’ disease cases, with iodine accumulation of a different nature [2, 3]. One of the principal treatments for Graves’ and Plummer’s diseases is radioiodine therapy, which serves to exclude the possibility of malignancy in nodules. Furthermore, the pharmacokinetics of iodine is investigated, which represents the most time-consuming and labor-intensive stage of preparation for radioiodine therapy. In clinical practice, ultrasound is performed in accordance with the TI-RADS system, followed (if necessary) by fine-needle aspiration puncture biopsy, stratified according to the Bethesda system. However, the interpretation of ultrasound examinations is inherently subjective, whereas the use of decision support systems can reduce the number of fine-needle aspiration puncture biopsies by 27% and the number of missed malignant neoplasms by 1.9%. Furthermore, the quantitative characterization of nodal ultrasound may enhance the investigation of the pharmacokinetics of I-131 [4, 5]. AIM: The study aimed to develop a method for quantitatively characterizing ultrasound images of thyroid nodular masses for predicting malignancy and I-131 accumulation by nodular masses. MATERIALS AND METHODS: The study included 125 nodules with pathomorphologic findings (65 benign, 60 malignant) and 25 benign nodules (established by cytologic examination) of patients who underwent radioiodotherapy as part of the Russian Science Foundation grant project No. 22-15-00135. Longitudinal and transverse projections of thyroid nodules were obtained using GE Voluson E8 (36% of all benign nodules and 27% of malignant nodules) and GE Logiq E (64% of benign and 73% of malignant nodules). A pharmacokinetics study was conducted on 25 nodes obtained on a GE Logiq V2 device. The accumulation index of I-131 was determined after 24 hours. A spatial adjacency matrix, gray level line length matrix, gray level zone size matrix, and histogram were employed to investigate features based on ultrasound images. RESULTS: The malignancy prediction model, developed on the basis of the most significant features and after KNN correlation analysis, exhibited a diagnostic accuracy value of 72±3%, a sensitivity of 73±5%, and a specificity of 73±5%. An investigation of I-131 pharmacokinetics revealed that the maximum histogram intensity gradient (r=–0.48, p=0.08) and intensity entropy (r=–0.51, p=0.06) exhibited the highest Spearman correlation coefficient modulus with I-131 accumulation after 24 hours. CONCLUSIONS: The present study demonstrates the feasibility of using quantitative characterization of ultrasound images of nodal masses as a tool to monitor nodules before radioiodotherapy. This is with a view to subsequent adjunctive fine-needle aspiration puncture biopsy and prediction of I-131 accumulation after 24 hours.

BACKGROUND: Prostate cancer is currently the second most commonly diagnosed cancer in men. The second edition of the Prostate Imaging Magnetic Resonance Imaging Data Assessment and Reporting System (PI-RADS) was released in 2019 to standardize the diagnostic process. Within this classification, the PI-RADS 3 category indicates an intermediate risk of clinically significant prostate cancer. There is currently no consensus in the literature regarding the optimal treatment for patients in this category. Some researchers advocate for biopsy as a means of further evaluation, while others propose a strategy of active surveillance for these patients. AIM: The aim of this study is to analyze and compare existing diagnostic models based on radiomics to differentiate and detect clinically significant prostate cancer in patients with a PI-RADS 3 category. MATERIALS AND METHODS: A comprehensive search of the PubMed, Scopus, and Web of Science databases was conducted using the following keywords: PI-RADS 3, radiomics, texture analysis, clinically significant prostate cancer, with additional emphasis on studies evaluated by Radiology Quality Score. The selected studies were required to meet the following criteria: (1) identification of PI-RADS 3 according to version 2.1 guidelines, (2) use of systemic biopsy as a control, (3) use of tools compatible with the IBSI standard for analyzing radiologic features, and (4) detailed description of methodology. Consequently, four meta-analyses and 12 original articles were selected. RESULTS: Radiomics-based diagnostic models have demonstrated considerable potential for enhancing the accuracy of detecting clinically significant prostate cancer in the PI-RADS 3 category using the PI-RADS V2.1 system. However, studies by A. Stanzione A. et al. and J. Bleker et al. have identified quality issues with such models, which constrains their clinical application based on low Radiology Quality Score values. In contrast, the works of T. Li et al. and Y. Hou et al. proposed innovative methods, including nomogram development and the application of machine learning, which demonstrated the potential of radiomics in improving diagnosis for this category. This indicates the potential for further development and application of radiomics in clinical practice. CONCLUSIONS: Although the models developed today cannot completely replace PI-RADS, the inclusion of radiomics can greatly enhance the efficiency of the diagnostic process by providing radiologists with quantitative and qualitative criteria that will enable the diagnosis of prostate cancer with greater confidence.

. BACKGROUND: A limited number of studies have been conducted in Russian and world literature on the differential diagnosis of gastrointestinal stromal tumors with other intra-abdominal tumors. The treatment of gastric non-epithelial tumors is dependent on the histologic type. The standard treatment for localized forms of gastrointestinal stromal tumors is surgery. For subepithelial masses up to 2 cm in size, in the absence of endoscopic signs of high risk, a strategy of active surveillance with mandatory endoscopic ultrasound examination and compliance with short-term intervals may be considered. Leiomyomas, benign masses, do not typically necessitate surgical intervention in the absence of clinical symptoms. Therefore, preoperative determination of the tumor type may help to avoid unwarranted surgical intervention. However, the ability of computed tomography to differentiate these tumor types is limited due to the similar radiological picture. Therefore, new scientific and clinical methods are needed. One of the possible techniques is texture analysis (radiomics). AIM: The study aims to investigate the potential of texture analysis (radiomics) in the diagnosis and differential diagnosis of gastrointestinal stromal tumors and gastric leiomyomas by analyzing the available world scientific literature. MATERIALS AND METHODS: A search was conducted in PubMed, Scopus, and Web of Science databases for published articles using the following keywords gastrointestinal stromal tumors, leiomyomas, and radiomics. The review included 4 meta-analyses and 16 original articles. RESULTS: Texture analysis represents a promising tool for quantifying the heterogeneity of masses on radiologic images, thereby enabling the extraction of additional data that cannot be assessed by imaging analysis. The potential applications of texture analysis for differential diagnosis of gastrointestinal stromal tumors with other gastrointestinal neoplasms, risk stratification, and prediction of outcome after surgical treatment, as well as assessment of the mutational status of tumors, were explored. A differential diagnosis of gastrointestinal stromal tumors should be made with other mesenchymal tumors of the stomach (schwannoma, leiomyoma), as well as with malignant tumors (adenocarcinoma, lymphoma), although the number of such publications is limited. Some published studies on texture analysis of gastrointestinal stromal tumors have demonstrated excellent reproducibility of the obtained models. CONCLUSIONS: The lack of standardization and differences in study methodology present significant challenges to the clinical application of radiomics. Texture analysis may offer a valuable tool for the initial evaluation of gastric tumors, reducing the time required for diagnosis and determining patient management before biopsy. This approach could help to prevent inappropriate treatment.

BACKGROUND: Prostate cancer is one of the most common cancers among men [1, 2]. In recent years, a number of prognostic models based on texture analysis of biparametric magnetic resonance images have been created. The research has shown that radiomics features extracted from apparent diffusion coefficient maps are the most reproducible [3]. However, the models were limited in accuracy, since they are built using a single machine learning algorithm, which takes into account only linear dependences [4–6]. AIM: Increasing the accuracy of a prognostic model diagnosing prostate cancer through the use of stacking machine learning algorithms that takes into account not only linear, but also nonlinear dependencies based on radiomics of biparametric magnetic resonance imaging apparent diffusion coefficient maps. MATERIALS AND METHODS: A single-center cohort retrospective study of patients with suspected prostate cancer was conducted in the X-ray Diagnostics and Tomography Department of the United Hospital and Polyclinic (Moscow, Russia) from 2017 to 2023. The presence of prostate cancer was confirmed by biopsy or radical prostatectomy. Statistical analyses was performed using Python 3.11. RESULTS: The study involved 67 men aged 60 [54; 66] years, of which 57 were diagnosed with prostate cancer, and 10 — with benign prostate formation. The LIFEx software identified 96 radiomic features. Statistically significant differences were found for: PARAMS_ZSpatialResampling (the voxel size of the image: Z dimension) (p=0.001), SHAPE_Sphericity[onlyFor3DROI] (how spherical a Volume of Interest is) (p=0.006), SHAPE_Compacity[onlyFor3DROI] (how compact the Volume of Interest is) (p=0.004), GLRLM_HGRE (p=0.039), GLRLM_SRHGE (p=0.041), GLRLM_RLNU (p=0.039), where GLRLM — Grey-Level Run Length Matrix. Univariate logistic regression showed that SHAPE_Compacity[onlyFor3DROI] (R2=15%) and PARAMS_ZSpatialResampling (R2=18%) had a statistically significant effect on the outcome. First, using the multivariate logistic regression method, a prognostic model was built that takes into account only linear dependencies. The model includes 3 features that together have a statistically significant effect on the outcome (R2=23%): SHAPE_Sphericity[onlyFor3DROI], PARAMS_ZSpatialResampling and GLRLM_RLNU. To describe nonlinear relationships, another model was built based on the “Decision Tree” algorithm. It included 4 indicators (R2=58%): DISCRETIZED_HISTO_Entropy_log10 (the randomness of the distribution), SHAPE_Sphericity[onlyFor3DROI], PARAMS_ZSpatialResampling and GLRLM_SRE. Stacking of algorithms, which consists of calculating the arithmetic mean between the predictions of the multivariate logistic regression and “Decision Tree” algorithms, made it possible to construct a model that takes into account both linear and nonlinear dependencies. The model includes 5 features (R2=77%). The constructed model formed the basis of the developed calculator program [7], currently introduced into a radiology practice. CONCLUSION: The new model built on the basis of apparent diffusion coefficient maps performs better (area under ROC-curve 99.0% [97.7; 100.0]) than the existing models with area under ROC-curve 83.6% [78.3; 88.9], which also show high heterogeneity (I2=71%). The accuracy of the new model was increased due to the use of stacking machine learning technologies, which made it possible to take into account both linear and nonlinear effects from features on the outcome.

Esophageal cancer, a malignant tumor with devastatingly high incidence and mortality rates, necessitates prompt efficacy evaluation and accurate prognosis prediction. Radiomics is an algorithmic approach that extracts insightful quantitative parameters from medical images, offering a trove of high-throughput imaging features. This technology demonstrates significant predictive value in esophageal cancer, holding immense promise for personalized treatment and patient evaluation. This article delves into the latest research advancements of computed tomography radiomics in both prognosis prediction and efficacy evaluation of this aggressive disease.

Objective: To investigate the value of radiomics features based on spectral CT iodine map for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid carcinoma. Methods: Seventy-eight patients with papillary thyroid carcinoma who underwent cervical energy spectrum CT within two weeks before surgery were retrospectively analyzed. We included 117 metastatic, 176 non-metastatic lymph nodes, which were then randomly divided into a training set and a validation set in a 3:1 ratio. Radiomics features were extracted and screened from venous phase iodine maps. Logistic regression model was used to construct diagnostic models based on CT image features, radiomics signature, and a combination of the two, respectively; a nomogram was then drawn to visualize the combined model. The diagnostic performance, calibration ability and clinical practicability of each model were evaluated by ROC curve, calibration curve and decision curve analysis, respectively. Results: The combined model showed optimal diagnostic performance in both the training and validation sets, followed by radiomics model. These two models outperformed the CT image features model in both the training and validation sets. All models showed good calibration, and decision curve analysis demonstrated the superiority of the nomogram over the other two models in terms of clinical usefulness. Conclusion: The radiomics signature of spectral CT showed good performance in diagnosing lymph node metastasis of papillary thyroid carcinoma. The diagnostic performance was further improved when combined with CT image features, which can be a useful tool to assist in clinical decision-making.

Objective: The study aimed to develop and evaluate a clinical diagnostic model that combined computer tomography (CT) radiomic features with a nomogram for predicting invasiveness in subsolid pulmonary nodules. Methods: This retrospective study analyzed both clinical and imaging data from patients at our institution who were diagnosed with pathologically confirmed subsolid pulmonary nodules at our institution based on thin-slice CT images. Radiomic features were extracted from these CT images, and LASSO regression with K-fold cross-validation was used to select the most informative features. Three predictive models were constructed via multivariate logistic regression: the first incorporated clinical parameters and conventional imaging features; the second relied solely on radiomic characteristics; and the third was a hybrid clinical-radiomics model. The logistic regression results were visually represented using a nomogram. Receiver operating characteristic curves were utilized to compare the classification predictive performance of the three models for distinguishing ground-glass opacity lung adenocarcinoma IA and non-IA cases. Decision curve analysis (DCA) was employed to assess the clinical utility of these models across different cohorts. Results: A total of 204 subsolid pulmonary nodules from 192 patients were included. They were divided into invasive (n=114) and non-invasive groups (n=90) based on pathological typing. These nodules were divided into a training set (n=143, IA:non-IA 77∶66) and a test set (n=61, IA:non-IA 38∶23). A total of 1316 features were initially extracted from each subsolid nodule. Subsequently, two independent clinical predictors (mean CT value and maximum diameter) and three radiomic features were selected through feature selection and logistic regression for model building. The combined clinical-radiomics model demonstrated superior discriminative capability (AUC=0.920, 95%CI: 0.818~0.931) in distinguishing IA from non-IA within the training set compared to the radiomics model and the clinical model independently (AUC=0.907, 95%CI: 0.792~0.914; AUC=0.822, 95%CI: 0.764~0.895). In the test set, the inclusion of clinical data improved the diagnostic efficacy of the radiomics model. DCA demonstrated that the combined model generally provided greater clinical benefits in most scenarios. Conclusion: The developed clinical-radiomics joint model showed promising performance in predicting the subsolid pulmonary nodule invasiveness.

Lung cancer remains a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) as the most prevalent type. Accurately predicting NSCLC prognosis is crucial for optimizing patient survival. However, traditional assessment tools often lack the comprehensive and precise capability to effectively stratify patient risk. Recent research has focused on exploring the potential of imaging histology technology for NSCLC prognosis. This article delves into the core principles of imaging histology and reviews the current state of research on its application in predicting NSCLC outcomes.

Objective To investigate the efficacy of high-resolution computed tomography (HRCT) imaging radiomics for the classification and diagnosis of rheumatoid arthritis associated interstitial lung disease (RA-ILD). Methods A total of 261 patients diagnosed with RA-ILD admitted in our hospital from January 2019 to July 2023 were recruited in this study.There were 143 cases of usual interstitial pneumonia (UIP) and 118 cases of nonspecific interstitial pneumonia (NSIP).All the patients underwent HRCT.A U-net deep learning lung segmentation model was applied to obtain HRCT images for automatic lung segmentation, and 1 688 imaging histologic features were extracted from each lung segmentation.Variance thresholding, univariate feature selection, and least absolute shrinkage and selection operator (LASSO) were used for feature dimensionality reduction step by step, and various machine learning algorithms were conducted to construct the RA-ILD diagnostic histology model.The diagnostic value of each model was compared using receiver operating characteristic (ROC) curve and area under curve (AUC), and the accuracy, sensitivity and specificity of the models were evaluated. Results Feature screening finally identified 18 best features from the HRCT images of RA-ILD patients.Statistical difference was found in the distribution of Radiomics score (Radscore) between the UIP and NSIP patients in both the training and test sets (P < 0.01).Among the 5 imaging histology models, the support vector machine (SVM) algorithm had an AUC value of 0.943(95%CI: 0.916~0.966), a sensitivity of 0.787 and a specificity of 0.912, respectively for the training set, and an AUC value of 0.909(95%CI: 0.849~0.969), a sensitivity of 0.625 and a specificity of 0.897, respectively for the testing set. Conclusion Our constructed RA-ILD classification and diagnosis model performs well, and the model based on SVM algorithm shows the best potential in classifying and diagnosing RA-ILD.

This study aims to classify and differentiate mucinous and serous cystic neoplasms of the pancreas using a multi-source feature classification model based on deep learning for preoperative auxiliary diagnosis. Deep learning features and radiomics features were extracted from segmented images using deep learning and radiomics technology, respectively. Clinical features were also evaluated and quantified. LASSO (least absolute shrinkage and selection operator) and cross-validation methods were applied to screen the features, and two multi-source feature models were constructed: the radiomics combined with deep learning (RAD_DL) model and the clinical feature combined with RAD_DL (Clinical_RAD_DL) model. Traditional radiomics (RAD) and deep learning (DL) models were used as controls. SVM (support vector machine), ADAboost (adaptive boosting), Random Forest, and Logistic were selected for classification. The Clinical_RAD_DL feature model shows the best classification performance, with the accuracy of 0.923 1, recall rate of 0.882 4, precision of 0.882 0, F1-score of 0.882 2, and AUC value of 0.912 6. The experimental results indicate that the multi-source feature classification model based on deep learning has good performance in classifying pancreatic serous cystic neoplasms and pancreatic mucinous cystic neoplasms, and can assist clinical accurate diagnosis and treatment.

Objective To explore the value of a logistic regression (LR) model based on CT radiomics features for the stratified classification of isolated adenocarcinoma pulmonary nodules, tuberculous nodules and non-tuberculous inflammatory pulmonary nodules. Methods The clinical and CT data of the patients pathologically diagnosed with pulmonary adenocarcinoma, tuberculosis, and non-tuberculous inflammatory nodules in our hospital between January 2018 and January 2022 were collected and retrospectively analyzed. By contouring the region of interest for pulmonary nodules and extracting CT radiomics features, prediction models were established to distinguish pulmonary adenocarcinoma vs inflammatory nodules and tuberculosis vs non-tuberculous inflammatory nodules. The model's performance was assessed by plotting receiver operating characteristic (ROC) curves and calculating area under curve (AUC), sensitivity, and specificity. Results A total of 526 solitary pulmonary nodules were collected, including 263 cases of pulmonary adenocarcinoma, 99 cases of tuberculous nodules, and 164 cases of non-tuberculous inflammatory nodules. In the training and validation sets, the LR models based on CT radiomics features and clinical risk factors achieved an AUC value of 0.880 and 0.886, respectively, for distinguishing pulmonary adenocarcinoma from inflammatory nodules. For discriminating tuberculosis versus non-tuberculous inflammatory nodules, the LR models based on CT radiomics features yielded an AUC value of 0.921 in the training set and of 0.853 in the validation set. Conclusion The LR prediction models based on CT radiomics features demonstrate excellent performance in hierarchically identifying the 3 prevalent solitary pulmonary nodules, with substantial clinical significance.

Objective To explore the predictive value of different machine learning models based on MRI radiomics combined with clinical features for histological grade of cervical squamous cell carcinoma. Methods Clinical data of 150 patients with cervical squamous cell carcinoma confirmed by pathological biopsy were retrospectively analyzed. They were randomly divided into the training set and validation set at a ratio of 4∶1. Features were extracted from the regions of interest of T2WI fat suppression sequence （FS-T2WI） and enhanced T1WI （delayed phase）. After dimensionality reduction and feature selection， logistic regression （LR）， support vector machine （SVM）， naïve Bayes （NB）， random forest （RF）， Light Gradient Boosting Machine （LightGBM）， K-nearest neighbor （KNN） were used to construct a radiomics model for predicting the histological grade of cervical squamous cell carcinoma. The area under the receiver operating characteristic （ROC） curve （AUC） was used to evaluate the predictive performance of the six models. Univariate and multivariate logistic regression analyses were performed to predict the independent risk factors， and a combined model of clinical and radiomics was established. The differences of each model were compared by AUC， and the clinical value of the model was evaluated by decision curve （DCA）. Results In the radiomics model， the LightGBM model had the largest AUC （0.910 in the training set， and 0.839 in the validation set）. The AUC of clinical features combined with LightGBM model was the largest （0.935 in the training set， and 0.888 in the validation set）， which was higher than those of clinical model （0.762 in the training set， and 0.710 in the validation set） and LightGBM radiomics model. Conclusions The LightGBM model has a high predictive value in the radiomics model. The combined model has the optimal DCA effect and the highest clinical net benefit. The combined prediction model combining radiomics and clinical features has good predictive value for cervical squamous cell carcinoma with low differentiation， providing a non-invasive and efficient method for clinical decision-making.

Objective This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer. Methods A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness. Results The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model (P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model (P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort (P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort (P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer. [ABSTRACT FROM AUTHOR]

Objective To investigate the feasibility and value of a multi-parametric MRI radiomicsbased nomogram model for pretreatment predicting the lymphovascular space invasion (LVSI) of endometrial endometrioid adenocarcinoma (EEA). Methods Preoperative MRI and baseline clinical characteristics of 205 EEA patients were prospectively collected from Oct 2020 to Jan 2022 in the Obstetrics and Gynecology Hospital, Fudan University, and randomly divided into training set (n=123) and validation set (n=82) in a 6∶4 ratio. The whole-tumor region of interest was manually drawn on T2- weighted imaging, diffusion-weighted imaging (apparent diffusion coefficient), and dynamic contrastenhanced MRI, respectively, for radiomics features extraction. In the training set, univariate and multivariate Logistic regression analysis were used to select independent clinical predictors of LVSI (+) and construct the clinical model. The least absolute shrinkage and selection operator (LASSO) regression and multivariate Logistic regression analysis were used to select optimal radiomics features to form a radiomics signature. A combined nomogram model was established by integrating clinical independent predictors and the radiomics signature, and validated in the validation set. The predicting performance and clinical net benefit were evaluated by using the area under the receiver operating characteristic curve (AUC) and clinical decision curve analysis, respectively. Results Of the 205 EEA cases, 144 cases were LVSI (-) and 61 cases were LVSI (+). Menopausal status, CA125, and CA199 were independent clinical predictors for the LVSI (+), and contributing to a clinical model with AUCs of 0.714 (training) and 0.731 (validation). From 8 240 extracted radiomics features, five were selected to construct a MRI radiomics signature after de-redundancy and LASSO dimensionality reduction, yielding AUCs of 0.860 (training) and 0.759 (validation). The combined nomogram model showed AUCs of 0.887 (training) and 0.807 (validation), outperforming others and achieving maximum clinical benefit in a large range of threshold probability in both training and validation sets. Conclusion The multi-parametric MRI-based nomogram model has the potential for pretreatment predicting the LVSI status of EEA, providing valuable information for clinical management decision-making and improving patient’s clinical benefits. [ABSTRACT FROM AUTHOR]

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To investigate value of preoperative magnetic resonance imaging (MRI) T2 fat suppression sequence in predicting isocitrate dehydrogenase (IDH)-1 gene expression in gliomas. Methods 124 patients with glioma, who were confirmed by histopathology, were collected. Regions of interest (ROI) was delineated using ITK-SNAP software. Pyradiomics package was used to extract radiomic features from the imaging data, while a pre-trained ResNet50 deep. learning model was employed to extract deep learning features. Feature selection was performed using the Pearson correlation coefficient and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. Model performance was evaluated through 10-fold cross-validation. Traditional radiomics, deep transfer learning, and fusion models were separately constructed based on support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF) machine learning algorithms. The predictive performance of each model was assessed using receiver operating characteristic (ROC) curve. Results The area under curve (AUC) for the machine learning models SVM, KNN, and RF based on radiomic features were 0.699, 0.628, and 0.616, respectively. For the machine learning models SVM, KNN, and RF hased on deep transfer learning features, the AUC values were 0.853, 0.753, and 0.807, respectively. The machine learning models SVM, KNN, and RF based on fusion features achieved AUC values of 0. 868, 0.818, and 0. 787, respectively. Conclusion The SVM fusion model based on the T2 WI fat suppression sequence in routine MRI exhibits higher predictive performance in determining the IDH-1 gene expression status in gliomas. [ABSTRACT FROM AUTHOR]

Copyright of Journal of China Medical University is the property of Journal of China Medical University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Ductal carcinoma in situ (DCIS), a pathological type of breast cancer that is limited to the terminal ducts of the breast without breaking through the basement membrane, is considered as the precursor of invasive ductal carcinoma (IDC). When DCIS breaks through the basement membrane and invades surrounding tissues, it can form infiltrating lesions. If the maximum diameter of a single infiltrating lesion is less than 1mm or the maximum diameter of multiple infiltrating lesions is less than 1mm, it is defined as ductal carcinoma in situ with microinvasion (DCIS-Mi). About 12%-40% of untreated and intervened DCIS will progress to IDC, and DCIS and IDC can also coexist. However, there is a considerable portion of DCIS that never progresses with good prognosis. Recently, overdiagnosis and overtreatment of DCIS have become the research hotspots. The histological grade of DCIS is mainly based on the morphology of the nucleus, which is divided into three nuclear levels: low, medium, and high. There are also significant differences in receptor expression and molecular type distribution between DCIS, DCIS-Mi, and IDC. For DCIS with or without microinvasion as well as different histological grades, there are many controversies about the treatment regimen, clinical prognosis and risk. The development of modern imaging technology has achieved preliminary evaluation of histological grading, infiltration status, and prognosis prediction of DCIS. The most commonly used breast imaging techniques in clinical practice currently include mammography (MG), ultrasound (US), and magnetic resonance imaging (MRI). The imaging principles of these three techniques are different, and each has its own advantages and disadvantages in breast disease imaging diagnosis. However, they can complement each other and play an important role in disease diagnosis, treatment, and prognosis evaluation. Mammography has the advantages of safety, reliability and good repeatability. It is the preferred screening method for breast cancer recommended by international guidelines. The main manifestations of DCIS on MG can be divided into non calcified lesions and calcified lesions. On US, the main manifestations are lesions and non-lesion type, which can be further divided into hypoechoic changes, calcification, ductal changes, and structural disorders and distortions. MRI has higher sensitivity in detecting DCIS without calcification and multifocal DCIS compared with MG, and has higher accuracy in evaluating the lesion range. However, there are also shortcomings such as low diagnostic specificity and insensitivity to microcalcification display. In addition, radiomics has great potential in the histopathological evaluation, prediction, and guidance of individualized precision treatment of DCIS. In the current era of precision medicine, image features, histopathology, molecular genes, etc. are increasingly significant in predicting the prognosis of breast cancer. The early accurate diagnosis and molecular type of DCIS are also extremely important in clinical work. It has become a consensus in clinical treatment to predict the potential benefits of different treatments through molecular typing, histological grade, and imaging findings, in order to develop the most suitable personalized treatment plan. This article reviewed the correlation between imaging features and the molecular subtype, histopathology and prognosis of DCIS.

Objective To investigate the diagnostic performance of radiomics based on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) of MRI for preoperative prediction of extramural vascular invasion (EMVI) in rectal cancer. Methods A total of 168 patients with pathology-confirmed rectal adenocarcinoma were enrolled during January 2010 to June 2023. The patients underwent preoperative rectal MRI scans, and they were randomly divided into training set and validation set at a 7∶3 ratio. Radiomic features from T2WI and DWI were extracted and selected by dimensionality reduction using the maximum relevance minimum redundancy (mRMR) method and the least absolute shrinkage and selection operator (LASSO) regression analysis with ten-fold cross-validation. The radiomic total score (Radscore) for each patient was calculated to make radiomics model. The training set enrolled three clinical features [gender, age and preoperative level of carcinoembryonic antigen (CEA)] and six magnetic resonance imaging features [ADC value, depth of infiltration, tumor length, tumor location, T staging and magnetic resonance imaging-defined extramural vascular invasion (mrEMVI)].The clinical model was established through univariable and multivariable logistic regression analysis based on above clinical and imaging features， and the clinical-radiomics model (combined model) was established with Radscore and independent risk factors from the clinical model. The diagnostic efficacy of each model was assessed using receiver operating characteristic (ROC) curve. The differences in performance among the models were compared using the DeLong test. The Calibration curves were employed to evaluate the consistence between the preoperative predictive results obtained from the nomogram and the postoperative pathological results. Additionally, decision curve analysis (DCA) was applied to evaluate the clinical utility of the three models. Results The area under the curve (AUC) of the ROC curve for the combined model, clinical model, and radiomics model in the training were 0.926, 0.888, 0.756, and were 0.917, 0.896, 0.782 in validation sets, respectively. The DeLong test showed that the diagnostic efficacy of combined model was higher than that of radiomics model in both training and validation sets (P

Objective To investigate the value of a radiomic model of multiparametric MRI different regions of interest (ROI) in assessment of the invasion of prostate cancer (PCa), and explore the evaluation value of an integrated model based on above radiomic model combined with radiomics, PI-RADS 2.1 score, and clinical variables. Methods A total of 245 patients with pathologically-confirmed PCa admitted to 2 medical centers in our hospital from May 2018 to September 2022 were collected in this retrospective study. Among them, 176 cases were collected in Yuzhong Medical Center, including 77 cases in the low invasive group [Gleason score ≤7 (3+4)] and 99 cases in the high invasive group [Gleason score ≥7 (4+3)]; 69 cases were collected in Jiangnan Medical Center, including 33 cases in the low invasive group and 36 cases in the high invasive group. All patients underwent multiparametric MRI, and then 2 types of ROI, the tumor region (TR) and the prostate gland (PG), were segmented on the multiparametric MRI images. Clinical variables related with PCa invasion were assessed, and PI-RADS 2.1 score was recorded for each patient. Logistic regression algorithm was employed as a machine learning method to develop following invasive stratification models for PCa: radiomics models (ModelTR, ModelPG and ModelPG+TR), Radiomics-Clinical model, Radiomics-PIRADS model, PIRADS-Clinical model and Radiomics-PIRADS-Clinical combined model. Receiver operating characteristic (ROC) curve, area under ROC curve (AUC) and decision curve analysis (DCA) were applied to compare the diagnostic efficacy and clinical benefit of each model. A nomogram was constructed by combining Radiomics score (Radscore), PI-RADS 2.1 score and independent clinical variables, and its performance was evaluated by calibration, differentiation and clinical application. Results In the above 3 radiomics models, the AUC value was 0.919 for ModelPG+TR, which was higher than that of ModelTR (0.874) and of ModelPG (0.887). And the AUC value of the Radiomics-PIRADS-Clinical combined model was 0.954, superior to that of the radiomics model (0.919), Radiomics-PIRADS model (0.921), Radiomics-Clinical model (0.919), and PIRADS-Clinical model (0.769) respectively, The nomogram model showed good performance (AUC=0.919) and calibration efficacy in risk stratification. DSA revealed that both the ModelPG+TR model and Radiomics-PIRADS-Clinical combined models achieved a higher net clinical benefit. Conclusion Our radiomic model of combining features of the prostate and tumor regions can more accurately assess the invasion of PCa, and our integrated model of combining radiomics, PI-RADS 2.1 score and clinical variables can further improve the assessment.

Objective·To analyze the differences and classify hypertrophic cardiomyopathy (HCM) patients and healthy controls (HC) using short-axis cine cardiac magnetic resonance (CMR) images-derived radiomics features.Methods·One hundred HCM subjects were included, and fifty HC were randomly selected at 2∶1 ratio during January 2018 to December 2021 in the Department of Cardiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine. The CMR examinations were performed by experienced radiologists on these subjects. CVI 42 post-processing software was used to obtain left ventricular morphology and function measurements, including left ventricular ejection fraction (LVEF), left ventricular end-diastolic volume (LVEDV) and left ventricular end-diastolic mass (LVEDM). The 3D radiomic features of the end-diastolic myocardial region were extracted from short-axis images CMR cine. The distribution of the radiomic features in the two groups was analysed and machine learning models were constructed to classify the two groups.Results·One hundred and seven 3D radiomic features were selected and extracted. After exclusion of highly correlated features, least absolute shrinkage and selection operator (LASSO) was used, and a 5-fold cross-validation was performed. There were still 11 characteristics with non-zero coefficients. The K-best method was used to decide the top 8 features for subsequent analysis. Among them, four features were significantly different between the two groups (all P

Objective To construct a machine learning model based on radiomics features by combing conventional B-mode transrectal ultrasound (TRUS) and contrast-enhanced ultrasound (CEUS)in order to improve prostate cancer (PCa) detection in the peripheral zone, and to explore its clinical application value. Methods A retrospective study of 109 cases (45 benign, 64 malignant lesions) with targeted biopsy-confirmed pathology who underwent B-mode and CEUS examinations was performed. Time-intensity curves were obtained using Sonoliver software forall lesions in regions of interest. The following parameters were collected: the maximum intensity (IMAX), the rise time (RT), the time to peak (TTP), and the mean transit time (MTT). Clinical risk factors, including age, serum total prostate-specific antigen (tPSA), free PSA(fPSA), f/t PSA, prostate volume and prostate-specific antigen density (PSAD), were collected. Feature selection and model construction were carried out by feature correlation analysis and multivariable Logistic regression analysis, respectively. Results Finally, 32 features were screened out by the radiomics models. Three radiomics models (B model, CEUS model and B-CEUS combined model) were established by logistic regression, and radiomics scores were obtained at the same time. The risk factors like age, PSAD, and RT were significant independent predictors (P＜0.05). The diagnostic accuracy of B model, CEUS model, B-CEUS combined model, risk factor model and risk factor-radiomics combined model were 0.75, 0.71, 0.73, 0.70 and 0.84, respectively, and the area under the curves(AUC) were 0.79, 0.75, 0.84, 0.79 and 0.91, respectively. The risk factor-radiomics combined model had a significant significance in the validation cohort (P＜0.05). Conclusions The machine learning model combined with conventional B-mode TRUS, CEUS, and relevant clinical factors can better predict the benign and malignant prostate peripheral lesions, which may significantly support clinical decision-making.