This study aims to develop a radiomics machine learning (ML) model that uses preoperative computed tomography angiography (CTA) data to predict the prognosis of endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) patients.

In this retrospective study, 164 AAA patients underwent EVAR and were categorized into shrinkage (good prognosis) or stable (poor prognosis) groups based on post-EVAR sac regression. From preoperative AAA and perivascular adipose tissue (PVAT) image, radiomics features (RFs) were extracted for model creation. Patients were split into 80 % training and 20 % test sets. A support vector machine model was constructed for prediction. Accuracy is evaluated via the area under the receiver operating characteristic curve (AUC).

Demographics and comorbidities showed no significant differences between shrinkage and stable groups. The model containing 5 AAA RFs (which are original_firstorder_InterquartileRange, log-sigma-3-0-mm-3D_glrlm_GrayLevelNonUniformityNormalized, log-sigma-3-0-mm-3D_glrlm_RunPercentage, log-sigma-4-0-mm-3D_glrlm_ShortRunLowGrayLevelEmphasis, wavelet-LLH_glcm_SumEntropy) had AUCs of 0.86 (training) and 0.77 (test). The model containing 7 PVAT RFs (which are log-sigma-3-0-mm-3D_firstorder_InterquartileRange, log-sigma-3-0-mm-3D_glcm_Correlation, wavelet-LHL_firstorder_Energy, wavelet-LHL_firstorder_TotalEnergy, wavelet-LHH_firstorder_Mean, wavelet-LHH_glcm_Idmn, wavelet-LHH_glszm_GrayLevelNonUniformityNormalized) had AUCs of 0.76 (training) and 0.78 (test). Combining AAA and PVAT RFs yielded the highest accuracy: AUCs of 0.93 (training) and 0.87 (test).

Radiomics-based CTA model predicts aneurysm sac regression post-EVAR in AAA patients. PVAT RFs from preoperative CTA images were closely related to AAA prognosis after EVAR, enhancing accuracy when combined with AAA RFs. This preliminary study explores a predictive model designed to assist clinicians in optimizing therapeutic strategies during clinical decision-making processes.

Differentiation of benign myxomas and malignant myxoid sarcomas can be difficult with an overlapping spectrum of morphologic MR findings.

To assess the diagnostic utility of MRI radiomics in the differentiation of musculoskeletal myxomas and myxoid sarcomas.

Retrospective.

A total of 523 patients were included; histologically proven myxomas (N = 201) and myxoid sarcomas (N = 322), randomly divided (70:30) into training:test subsets.

T1-weighted (T1W), T2-weighted fat-suppressed (fluid-sensitive), and T1-weighted post-contrast (T1W + C) sequences at 1.0 T, 1.5 T, or 3.0 T.

Seven semantic (qualitative) tumor features were assessed in each case. Manual 3D tumor segmentations performed with radiomics features extracted from T1W, fluid-sensitive, and T1W + C acquisitions. Models were constructed based on radiomic features from individual sequences and from their combination, both with and without the addition of qualitative tumor features.

Intraclass correlation evaluated in 60 cases segmented by three readers. Features with intraclass correlation <0.7 excluded from further analysis. Boruta feature selection and Random Forest modeling performed using the training-dataset, with resultant models used to assess class discrimination (myxoma vs. myxoid sarcoma) in the test dataset. Radiomics score defined as probability class = myxoma. Logistic regression modeling employed to estimate performance of the radiomics score. Area under the receiver operating characteristic curve (AUC) was used to assess diagnostic performance, and DeLong's test to assess performance between constructed models. A P-value <0.05 was considered significant.

Four qualitative semantic features showed significant predictive power in class discrimination. Radiomic models demonstrated excellent differentiation of myxomas from myxoid sarcomas: AUC of 0.9271 (T1W), 0.9049 (fluid-sensitive), and 0.9179 (T1W + C). Incorporation of multiparametric data or semantic features did not significantly improve model performance (P ≥ 0.08) compared to radiomic models derived from any individual MRI sequence alone.

MRI radiomics appears to be accurate in the differentiation of myxomas from myxoid sarcomas. Classification performance did not improve when incorporating qualitative features or multiparametric imaging data.

Accurately distinguishing between benign soft tissue myxomas and malignant myxoid sarcomas is essential for guiding appropriate management but remains challenging with conventional MRI interpretation. This study utilized radiomics, a method that extracts quantitative mathematically derived features from images, to develop predictive models based on routine MRI examination. Analyzing over 500 cases, MRI radiomics demonstrated excellent diagnostic accuracy in differentiating between benign myxomas and malignant myxoid sarcomas, highlighting the potential of the technique, as a powerful non-invasive tool that could complement current diagnostic approaches, and enhance clinical decision-making in patients with soft tissue myxoid tumors of the musculoskeletal system.

3 TECHNICAL EFFICACY: Stage 2.

Radiomics involves the integration of computer technology, big data analysis, and clinical medicine. Currently, there have been initial advancements in the fields of acute cerebrovascular disease and cardiovascular disease. The objective of radiomics is to extract quantitative features from medical images for analysis to predict the risk or treatment outcome, help in differential diagnosis, and guide clinical decisions and management. Radiomics applied research has reached a more advanced stage yet encounters several obstacles, including the need for standardization of radiomics features and alignment with treatment requirements for acute and severe illnesses. Future research should aim to seamlessly incorporate radiomics with various disciplines, leverage big data and artificial intelligence advancements, cater to the requirements of acute and critical medicine, and enhance the effectiveness of technological innovation and application in diagnosing and treating acute and critical illnesses.

A radiomics model constructed from the intratumoral region of computed tomography (CT) can predict the pathologic T stage of clear cell renal cell carcinoma (ccRCC). However, the predictive capability of the radiomics model that incorporates both intra- and peritumoral regions of CT for the pathologic T stage in ccRCC patients has not been reported to date.

This study enrolled 250 patients with ccRCC who underwent laparoscopic surgery. Three radiomics models were developed based on the intra- and peritumoral regions. The sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curves of each model were analyzed. Decision curve analysis (DCA) and calibration curves were used to assess the net benefit and calibration ability of the models. Additionally, the diagnostic performance of the different models were compared with that of radiologists.

The radiomics model based on the intra- and peritumoral regions at 5 mm exhibited the strongest performance, with area under curve values of 0.91 (95 % confidence interval [CI], 0.8551-0.9650), 0.85 (95 % CI, 0.7490-0.9517), and 0.873 (95 % CI, 0.7612-0.9839) in distinguishing high and low T stages of ccRCC across the training, validation, and test sets, respectively. The model's accuracy in the training, validation, and test sets was 0.798, 0.732, and 0.769, with corresponding sensitivity values of 0.921, 0.857, and 0.882, and specificity values of 0.747, 0.690, and 0.729. The calibration curve demonstrated a high level of agreement between the predicted and actual outcomes, whereas the DCA showed that the model provided a meaningful net benefit.

The radiomics model based on the intra- and peritumoral regions of CT has certain value in distinguishing between high and low T stages of ccRCC.

To develop habitat and deep learning (DL) models from multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) habitat images categorized using the K-means clustering algorithm. Additionally, we aim to assess the predictive value of identified regions for early evaluation of the responsiveness of hepatocellular carcinoma (HCC) patients to treatment with transarterial chemoembolization (TACE) plus molecular targeted therapies (MTT) and anti-PD-(L)1.

A total of 102 patients with HCC from two institutions (A, n = 63 and B, n = 39) who received TACE plus systemic therapy were enrolled from September 2020 to January 2024. Multiple CE-MRI sequences were used to outline 3D volumes of interest (VOI) of the lesion. Subsequently, K-means clustering was applied to categorize intratumoral voxels into three distinct subgroups, based on signal intensity values of images. Using data from institution A, the habitat model was built with the ExtraTrees classifier after extracting radiomics features from intratumoral habitats. Similarly, the Crossformer model and ResNet50 model were trained on multi-channel data in institution A, and a DL model with Transformer-based aggregation was constructed to predict the response. Finally, all models underwent validation at institution B.

The Crossformer model and the habitat model both showed high area under the receiver operating characteristic curves (AUCs) of 0.869 and 0.877 (training cohort). In validation, AUC was 0.762 for the Crossformer model and 0.721 for the habitat model.

The habitat model and DL model based on CE-MRI possesses the capability to non-invasively predict the efficacy of TACE plus systemic therapy in HCC patients, which is critical for precision treatment and patient outcomes.

To evaluate the diagnostic value of magnetic resonance imaging (MRI) radiomics in distinguishing high-grade meningiomas (HGM) from low-grade meningiomas (LGM). A systematic search was conducted in PubMed, EMbase, Web of Science, and The Cochrane Library databases up to December 31, 2023. Two researchers independently screened studies, extracted data, and assessed risk of bias and quality of included studies as well. Meta-analysis was performed using Stata 14 software to calculate pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC). A total of 21 studies with 2253 patients were included (607 HGM, 1646 LGM). Meta-analysis showed an overall SEN of 0.82 (95% CI 0.74-0.88) and SPE of 0.85 (95% CI 0.81-0.89). The PLR and NLR were 5.64 (95% CI 4.17-7.64) and 0.21 (95% CI 0.14-0.31), respectively, with a pooled DOR of 26.66 (95% CI 14.42-49.27) and an AUC of 0.91 (95% CI 0.88-0.93), indicating high diagnostic accuracy. Although additional research is required to validate suitable techniques, MRI radiomics shows strong potential as an accurate tool for meningioma grading. Standardizing radiomics application could enhance diagnostic precision and clinical decision-making for meningioma grading in the future.Trial Registration: CRD42024500086.

This study aimed to develop and validate a predictive model for preoperative extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) using MRI features.

We retrospectively analyzed 140 confirmed PTC cases, divided into training (n = 84) and validation (n = 56) groups. MRI features such as T2-weighted imaging, multiphase contrast-enhanced MRI, and diffusion-weighted imaging were evaluated along with clinical data. Univariate and multivariate logistic regression identified independent predictors of ETE and developed a predictive nomogram. We evaluated the nomogram's discrimination, calibration, and clinical utility, and performed subgroup analyses to explore the relationships between risk factors and baseline data. Predictive performance was assessed using ROC curves and DeLong tests.

Age, protrusion value, and apparent diffusion coefficient_Brightest_rate (ADC_Best_rate) were independent predictors of ETE. The nomogram effectively differentiated ETE from no-ETE, showing strong discrimination, clinical utility, and calibration in both the training (AUC = 0.826, Hosmer-Lemeshow p = 0.882) and validation cohorts (AUC = 0.805, Hosmer-Lemeshow p = 0.585). The model performed consistently across different MRI systems (1.5 T and 3.0 T) and gender subgroups. Notably, ADC_Best_rate (AUC = 0.742) outperformed ADC_mean_rate and ADC_minimum_rate. A significant interaction between ADC_Best_rate and gender (p = 0.02) showed that ADC_Best_rate predicted ETE in PTC more accurately in males (AUC = 0.897) compared to females (AUC = 0.644).

Our nomogram model, incorporating age, protrusion value, and ADC_Best_rate, effectively predicted preoperative ETE in PTC patients, aiding surgeons in optimizing therapeutic decision-making. ADC_Best_rate may be a promising potential indicator in MRI functional imaging.

Question This study addresses the challenge of accurately predicting extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) to improve surgical decision-making. Findings A predictive nomogram incorporating age, protrusion value, and ADC_Best_rate effectively differentiates ETE from no-ETE, showing strong performance in both training and validation cohorts. Clinical relevance This nomogram aids surgeons in identifying patients at risk for ETE, enhancing therapeutic decision-making and potentially improving patient outcomes in PTC management.

To explore whether a CT-based AI framework, leveraging multi-scale features, can offer a non-invasive approach to accurately predict pathological grade and Ki67 index in clear cell renal cell carcinoma (ccRCC).

In this multicenter retrospective study, a total of 1073 pathologically confirmed ccRCC patients from seven cohorts were split into internal cohorts (training and validation sets) and an external test set. The AI framework comprised an image processor, a 3D-kidney and tumor segmentation model by 3D-UNet, a multi-scale features extractor built upon unsupervised learning, and a multi-task classifier utilizing XGBoost. A quantitative model interpretation technique, known as SHapley Additive exPlanations (SHAP), was employed to explore the contribution of multi-scale features.

The 3D-UNet model showed excellent performance in segmenting both the kidney and tumor regions, with Dice coefficients exceeding 0.92. The proposed multi-scale features model exhibited strong predictive capability for pathological grading and Ki67 index, with AUROC values of 0.84 and 0.87, respectively, in the internal validation set, and 0.82 and 0.82, respectively, in the external test set. The SHAP results demonstrated that features from radiomics, the 3D Auto-Encoder, and dimensionality reduction all made significant contributions to both prediction tasks.

The proposed AI framework, leveraging multi-scale features, accurately predicts the pathological grade and Ki67 index of ccRCC.

The CT-based AI framework leveraging multi-scale features offers a promising avenue for accurately predicting the pathological grade and Ki67 index of ccRCC preoperatively, indicating a direction for non-invasive assessment.

Non-invasively determining pathological grade and Ki67 index in ccRCC could guide treatment decisions. The AI framework integrates segmentation, classification, and model interpretation, enabling fully automated analysis. The AI framework enables non-invasive preoperative detection of high-risk tumors, assisting clinical decision-making.

The aim of this study was to thoroughly analyze the reproducibility of radiomics feature extraction across three Image Biomarker Standardization Initiative (IBSI)-compliant platforms using a digital phantom for benchmarking. It uncovers high consistency among common features while also pointing out the necessity for standardization in computational algorithms and mathematical definitions due to unique platform-specific features.

We selected three widely used radiomics platforms: LIFEx, Computational Environment for Radiological Research (CERR), and PyRadiomics. Using the IBSI digital phantom, we performed a comparative analysis to extract and benchmark radiomics features. The study design included testing each platform's ability to consistently reproduce radiomics features, with statistical analyses to assess the variability and agreement among the platforms.

The results indicated varying levels of feature reproducibility across the platforms. Although some features showed high consistency, others varied significantly, highlighting the need for standardized computational algorithms. Specifically, LIFEx and PyRadiomics performed consistently well across many features, whereas CERR showed greater variability in certain feature categories than LIFEx and PyRadiomics.

The study findings highlight the need for harmonized feature calculation methods to enhance the reliability and clinical usefulness of radiomics. Additionally, this study recommends incorporating clinical data and establishing benchmarking procedures in future studies to enhance the role of radiomics in personalized medicine.

Radiomics and artificial intelligence (AI) are rapidly evolving, significantly transforming the field of medical imaging. Despite their growing adoption, these technologies remain challenging to approach due to their technical complexity. This review serves as a practical guide for early-career radiologists and researchers seeking to integrate radiomics into their studies. It provides practical insights for clinical and research applications, addressing common challenges, limitations, and future directions in the field. This work offers a structured overview of the essential steps in the radiomics workflow, focusing on concrete aspects of each step, including indicative and practical examples. It covers the main steps such as dataset definition, image acquisition and preprocessing, segmentation, feature extraction and selection, and AI model training and validation. Different methods to be considered are discussed, accompanied by summary diagrams. This review equips readers with the knowledge necessary to approach radiomics and AI in medical imaging from a hands-on research perspective.

Radiomics allows for the quantification of medical images and facilitates precision medicine. Many radiomic features derived from computed tomography (CT) are sensitive to variations across scanners, reconstruction settings, and acquisition protocols. In this phantom study, eight different CT reconstruction parameters were varied to explore image- and feature-level harmonization approaches to improve tissue classification.

Varying reconstructions of an anthropomorphic radiopaque phantom containing three lesion categories (metastasis, hemangioma, and benign cyst) and normal liver tissue were used for evaluating two harmonization methods and their combination: (i) generative adversarial networks (GANs) at the image level; (ii) ComBat at the feature level, and (iii) a combination of (i) and (ii). A total of 93 texture and intensity features were extracted from each tissue class before and after image-level harmonization and were also harmonized at the feature level. Reproducibility and stability were assessed via the Concordance Correlation Coefficient (CCC) and pairwise comparisons using paired stability tests. The ability of features to discriminate between tissue classes was assessed by measuring the area under the receiver operating characteristic curve. The global reproducibility and discriminative power were assessed by averaging over the entire dataset and across all tissue types.

ComBat improved reproducibility by 31.58% and stability by 5.24%, while GAN increased reproducibility by 8% it reduced stability by 4.33%. Classification analysis revealed that ComBat increased average AUC by 15.19%, whereas GAN decreased AUC by 2.56%.

While GAN qualitatively enhances image harmonization, ComBat provides superior statistical improvements in feature stability and classification performance, highlighting the importance of robust feature-level harmonization in radiomics.

Small cell lung cancer (SCLC) is a highly aggressive form of lung cancer, representing about 15% of cases worldwide. Despite advances in imaging, such as low-dose CT, which have increased diagnostic rates, survival outcomes for SCLC patients have remained stagnant. Recent studies have only focused on radiomics, which extracts detailed quantitative features from imaging, with clinical risk factors to improve prognostic models. Therefore, this study aimed to develop a clinical-radiomics fusion nomogram based on computed tomography (CT) to estimate progression-free survival (PFS) in patients diagnosed with SCLC. By integrating radiomics features extracted from CT with clinical data, this model provides personalized prognostic assessment for clinicians. Its clinical utility lies in aiding treatment decision-making by offering more accurate prognostic evaluation, optimizing therapeutic strategies, and identifying high-risk patients at an early stage, ultimately improving overall survival and quality of life.

To develop the nomogram model, 95 patients diagnosed with pathologically confirmed SCLC between January 1, 2013, and December 31, 2023, were included in the study cohort. Participants were randomly divided into training and validation cohorts in a 7:3 ratio. Radiomics features associated with PFS were generated using the least absolute shrinkage and selection operator (LASSO) along with univariate and multivariate analyses. Additionally, in the training cohort, both univariate and multivariate analyses using Cox regression were conducted to identify the significant clinical risk factors influencing PFS. The predictive performance of the clinical and clinical-radiomics fusion nomogram were evaluated using the concordance index, calibration plots, and decision curve analysis (DCA).

Five radiomics features were selected and used to calculate the radiomics score (Rad-score). The radiomics features were significantly associated with PFS (hazard ratio: 0.5765, 95% confidence interval: 0.3641-0.9128, p < 0.05). Three clinical risk factors significantly associated with PFS were identified: neuron-specific enolase (NSE), carbohydrate antigen 125 levels (CA125), and treatment type, such as surgery. The clinical-radiomics fusion nomogram model (C-index:0.744) demonstrated superior performance compared to the clinical nomogram model (C-index: 0.718) in the training cohort. DCA indicated that the clinical-radiomics fusion nomogram outperformed the clinical nomogram in terms of clinical usefulness.

A CT-based clinical-radiomics fusion nomogram was developed to predict PFS in patients with SCLC, which is useful in providing individualized information.

A clinical-radiomics fusion nomogram was constructed to estimate the probability of PFS based on clinical risk factors and the rad-score.

This study aimed to develop a radiomics nomogram model using preoperative digital breast tomosynthesis (DBT) images to predict Ki-67 expression in patients with invasive breast cancer (IBC).

This retrospective study involved a cohort of 289 patients with IBC, who were randomly divided into a training dataset (N= 202) and a validation dataset (N= 87). Ki-67 expression was categorized into low and high groups using a 14% threshold. Radiomics features from both the intra- and peritumoral regions of DBT images were used to develop the radiomics model, referred to as Radscore. Clinical and nomogram models were constructed using multivariate logistic regression. The performance of the established models was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curve analysis, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

The clinical model was constructed using tumor size and DBT-reported lymph node metastasis (DBT_reported_LNM). By integrating Radscore_Combine-which incorporates both intra- and peritumoral radiomics features-along with tumor size and DBT_reported_LNM into the nomogram, the model achieved the highest area under the curve (AUC) values of 0.819 and 0.755 in the training and validation datasets, respectively. The notable improvement shown by the NRI and IDI suggests that Radscore_Combine could serve as a valuable biomarker for predicting Ki-67 expression effectively.

The nomogram offers a non-invasive method to predict Ki-67 expression in IBC patients, which could aid in creating personalized treatment plans.

Radiomics, an advanced medical imaging analysis technique introduced by Professor Lambin in 2012, has quickly become a key area of medical research. To explore emerging trends in cancer-related radiomics, we conducted a bibliometric analysis of the 100 most-cited articles (T100) in this field. Data were collected from the Web of Science Core Collection on October 7, 2023, and the articles were ranked by citation count. We extracted data such as authors, journals, citation counts, and publication years and analyzed it using Microsoft Excel 2019 and R 4.4.2. CiteSpace was used to create co-occurrence and citation burst maps to show the relationships between authors, countries, institutions, and keywords. The analysis revealed that the T100 came from 81 countries, with the U.S. contributing the most (56 articles). Harvard University was the leading institution, and the journal Radiology had the highest citation count. Aerts Hugo JWL was the most influential author. The study highlights that "lung cancer" and "artificial intelligence" are emerging as major research hotspots, shaping the future of cancer radiomics.

Gynecological cancer is the most common cancer that affects women's quality of life and well-being. Artificial intelligence (AI) technology enables us to exploit high-dimensional imaging data for precision oncology. Tremendous progress has been made with AI radiomics in cancers such as lung and breast cancers. Herein, we performed a literature review on AI radiomics in the management of gynecological cancer.

A search was performed in the databases of PubMed, Embase, and Web of Science for original articles written in English up to 10 September 2024, using the terms "gynecological cancer", "cervical cancer", "endometrial cancer", "ovarian cancer", AND "artificial intelligence", "AI", AND "radiomics". The included studies mainly focused on the current landscape of AI radiomics in the diagnosis, treatment, and prognosis of gynecological cancer.

A total of 128 studies were included, with 86 studies focusing on tumor diagnosis (n=23) and characterization (n=63), 15 on treatment response prediction, and 27 on recurrence and survival prediction. AI radiomics has shown potential value in tumor diagnosis and characterization [tumor staging, histological subtyping, lymph node metastasis (LNM), lymphovascular space invasion (LVSI), myometrial invasion (MI), and other molecular or clinicopathological factors], chemotherapy or chemoradiotherapy response evaluation, and prognosis (disease recurrence or metastasis, and survival) prediction. However, most included studies were single-center and retrospective. There was substantial heterogeneity in methodology and results reporting.

AI radiomics has been increasingly adopted in the management of gynecological cancer. Further validation in large-scale datasets is needed before clinical translation.

POPF after pancreaticoduodenectomy can be life-threatening. For risk stratification, prediction could be key. The aim of this study is to determine and validate a cut-off value for the Roberts Score, which is one of the few purely preoperative multicenter validated predictive models for POPF.

582 patients were included. The Youden index determined a cut-off in the exploratory cohort (n ​= ​466). The validation cohort's (n ​= ​116) ability to predict CR-POPF was tested using univariate and multivariate regression analysis.

AUC of Roberts Score for the exploration cohort was 0.768. The identified cut-off of 0.268 was confirmed in the validation cohort (p ​< ​0.001). Higher scores were significantly associated with longer time to drain removal and ICU stay. Multiple logistic regression showed the cut-off as an independent predictor of CR-POPF (p ​= ​0.038).

The scoring variables and the cut-off itself were both independent predictors, which may improve the identification of high-risk patients and help to investigate the development of POPF.

Glioblastoma (GBM) and solitary brain metastasis (SBM) exhibit similar radiomics features on magnetic resonance imaging (MRI), yet their treatment strategies and prognoses significantly differ. Therefore, accurate differentiation between these two types of tumors is crucial for clinical decision-making. This study aims to establish and validate an efficient diagnostic model based on the radiomic features of the T1-weighted contrast-enhanced (T1CE) sequence in the 10 mm brain-tumor interface region to achieve precise differentiation between GBM and SBM.

This study retrospectively collected contrast-enhanced T1-weighted imaging data from 226 GBM patients and 206 SBM patients at three centers between January 2010 and October 2024. Samples from centers 1 and 2 were used as the training set, while samples from center 3 were used as the test set. Two observers manually delineated the tumor edges on the T1CE images layer by layer to obtain the Region of Interest (ROI) covering the entire tumor volume. A 10 mm brain-to-tumor interface (BTI) was extracted using Python code. Radiomic features were extracted from the 10 mm BTI region, followed by feature selection and model construction. Finally, SHAP (SHapley Additive exPlanations) was used to visualize the model. Three radiologists with 2, 6, and 18 years of diagnostic experience independently evaluated the test set samples without knowing the patient information or pathology results, establishing three diagnostic models. The DeLong test was used to compare these models with the radiomic model.

Ultimately, ten radiomic features were used for modeling. The model established using the logistic regression (LR) algorithm had an AUC of 0.893 on the training set and 0.808 on the test set. The AUCs of the three radiologists with different diagnostic experiences on the test set were 0.699, 0.740, and 0.789, respectively, all lower than that of the radiomic model. The DeLong test showed that ModelBTI performed significantly better than Doctor 1 (p<0.05) in the test set, but there was no statistically significant difference in performance between ModelBTI and Doctors 2 and 3.

The radiomic model constructed based on the 10 mm brain-tumor interface can effectively differentiate between GBM and SBM, capturing tumor heterogeneity from a new perspective, thereby significantly improving diagnostic performance and providing assistance for clinical diagnosis.

The original contributions presented in the study are included in the article/Supplemental material, further inquiries can be directed to the corresponding authors.

Computed tomography (CT) imaging of parotid pleomorphic adenoma (PA) has been widely reported, nonetheless few reports have estimated the capsule characteristics of PA at length. This study aimed to establish and validate CT-based intratumoral and peritumoral radiomics models to clarify the characteristics between parotid PA with and without complete capsule.

In total, data of 129 patients with PA were randomly assigned to a training and test set at a ratio of 7:3. Quantitative radiomics features of the intratumoral and peritumoral regions of 2 mm and 5 mm on CT images were extracted, and radiomics models of Tumor, External2, External5, Tumor+ External2, and Tumor+External5 were constructed and used to train six different machine learning algorithms. Meanwhile, the prediction performances of different radiomics models (Tumor, External2, External5, Tumor+External2, Tumor+External5) based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared. The receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the prediction performance of each model.

Among all the established machine learning prediction radiomics models, the model based on a three-phase combination had better prediction performance, and the model using a combination of intratumoral and peritumoral radiomics features achieved a higher AUC than the model with only intratumoral or peritumoral radiomics features, and the Tumor+External2 model based on LR was the optimal model, the AUC of the test set was 0.817 (95% CI = 0.712, 0.847), and its prediction performance was significantly higher (p < 0.05, DeLong's test) than that with the Tumor model based on LDA (AUC of 0.772), the External2 model based on LR (AUC of 0.751), and the External5 model based on SVM (AUC of 0.667). And the Tumor+External2 model based on LR had a higher AUC than the Tumor+External5 model based on LDA (AUC = 0.817 vs. 0.796), but no statistically significant difference (P = 0.667).

The intratumoral and peritumoral radiomics model based on multiphasic CT images could accurately predict capsular characteristics of parotid of PA preoperatively, which may help in making treatment strategies before surgery, as well as avoid intraoperative tumor spillage and residuals.

This study aimed to identify distinct clusters of diabetic macular edema (DME) patients with differential anti-vascular endothelial growth factor (VEGF) treatment outcomes using an unsupervised machine learning (ML) approach based on radiomic features extracted from pre-treatment optical coherence tomography (OCT) images. Retrospective data from 234 eyes with DME treated with three anti-VEGF therapies between January 2020 and March 2024 were collected from two clinical centers. Radiomic analysis was conducted on pre-treatment OCT images. Following principal component analysis (PCA) for dimensionality reduction, two unsupervised clustering methods (K-means and hierarchical clustering) were applied. Baseline characteristics and treatment outcomes were compared across clusters to assess clustering efficacy. Feature selection employed a three-stage pipeline: exclusion of collinear features (Pearson's r > 0.8); sequential filtering through ANOVA (P < 0.05) and Boruta algorithm (500 iterations); multivariate stepwise regression (entry criteria: univariate P < 0.1) to identify outcome-associated predictors. From 1165 extracted radiomic features, four distinct DME clusters were identified. Cluster 4 exhibited a significantly lower incidence of residual/recurrent DME (RDME) (34.29%) compared to Clusters 1-3 (P = 0.003, P = 0.005 and P = 0.002, respectively). This cluster also demonstrated the highest proportion of eyes (71.43%) with best-corrected visual acuity (BCVA) exceeding 20/63 (P = 0.003, P = 0.005 and P = 0.002, respectively). Multivariate analysis identified logarithm_gldm_DependenceVariance as an independent risk factor for RDME (OR 1.75, 95% CI 1.28-2.40; P < 0.001), while Wavelet-LH_Firstorder_Mean correlated with worse visual outcomes (OR 8.76, 95% CI 1.22-62.84; P = 0.031). Unsupervised ML leveraging pre-treatment OCT radiomics successfully stratifies DME eyes into clinically distinct subgroups with divergent therapeutic responses. These quantitative features may serve as non-invasive biomarkers for personalized outcome prediction and retinal pathology assessment.

Cervical cancer, a prevalent gynecological malignancy, poses significant threats to women's health. Despite advances in treatment modalities, radiotherapy remains a cornerstone in managing cervical cancer. However, radiotherapy-induced complications, such as radiation proctitis, present substantial diagnostic and prognostic challenges. Accurate diagnosis are crucial for optimizing treatment strategies and improving patient outcomes. Deep learning has shown remarkable success in medical image segmentation, aiding clinicians in assessing patient conditions. In the other hand, radiomics excels in extracting diagnostically valuable features from medical images but requires extensive manual annotation and often lacks generalizability. Therefore, combining the strengths of deep learning and radiomics is pivotal in addressing these challenges. In this study, we propose a novel paradigm that leverages deep learning models for initial segmentation, followed by detailed radiomics analysis. Specifically, we utilize the Transformer-based SAM-Med2D model to extract visual features from CT images of cervical cancer patients. We apply T-tests and Lasso regression to identify features most correlated with radiation proctitis and build predictive models using logistic regression, random forest, and naive Gaussian Bayesian algorithms. Experimental results demonstrate that our method effectively extracts CT imaging features and exhibits excellent performance in diagnosis radiation proctitis. This approach not only enhances predictive accuracy but also provides a valuable tool for personalizing treatment plans and improving patient outcomes in cervical cancer radiotherapy.

High expression levels of programmed death receptor 1 (PD-1) and its ligand 1 (PD-L1) have been observed in thymic epithelial tumors (TET), suggesting their potential as prognostic indicators for disease progression and the effectiveness of immunotherapy in TET. The conventional method obtaining PD-L1 was challenging due to invasive sampling and tumor heterogeneity.

A total of 124 patients with pathologically confirmed TET (57 PD-L1 positive, 67 PD-L1 negative) were retrospectively enrolled and allocated into training and validation cohorts in a ratio of 7:3. Radiomics features were extracted from T1-weighted, T2-weighted fat suppression, and apparent diffusion coefficient (ADC) map images to establish a radiomics signature in the training cohort. Multivariate logistic regression analysis was conducted to develop a combined radiomics nomogram that incorporated clinical, conventional MR features, or ADC model for evaluation purposes. The performance of each model was compared using receiver operating characteristics analysis, while discrimination, calibration, and clinical efficiency of the combined radiomics nomogram were assessed.

The radiomics signature, consisting of four features, demonstrated a favorable ability to predict and differentiate between PD-L1 positive and negative TET patients. The combined radiomics nomogram, which incorporates the peri-cardial invasion sign, ADC value, WHO classification, and radiomics signature, showed excellent performance (training cohort: area under the curve [AUC] = 0.903; validation cohorts: AUC = 0.894). The calibration curve and decision curve analysis further confirmed the clinical usefulness of this combined model. The decision curve analysis demonstrated the clinical utility of the integrated radiomics nomogram.

The radiomics signature serves as a valuable tool for predicting the PD-L1 status of TET patients. Furthermore, the integration of radiomics nomogram enhances the personalized prediction capability.

Gamma-delta (γδ) T cells are a critical component of the tumor microenvironment and have been recognized as a promising biomarker and target for cancer therapy. Increasing evidence suggests that γδT cells play distinct roles in different cancers. However, the impact of γδT cells in breast cancer remains controversial.

In this study, we investigated the role of γδT cells in breast cancer using a comprehensive approach, including bulk and single-cell sequencing, radiomics based on magnetic resonance imaging (MRI), genomic data, and immunohistochemistry. Single-cell RNA profiling was used to infer the potential lineage evolution of γδT cells and their interactions with other immune cells. Bulk RNA sequencing was included to uncover the heterogeneity in signaling pathways, as well as radiotherapy and immunotherapy responses, among patients with varying levels of γδT cell abundance. Genomic analysis was used to recognize the critical gene mutations with the infiltration of γδT cells. Immunohistochemistry was performed to validate the prognostic value of γδT cells in breast cancer patients. Lastly, radiomics was used to establish a correlation between the abundance of γδT cells and the features of MRI images.

The γδT cell infiltration was closely associated with favorable prognosis in triple-negative breast cancer (TNBC) but not in other subtypes of breast cancer. γδT cells may exert antitumor effects through intrinsic lineage evolution or interact with antigen-presenting cells through ligand-receptor pairs. Patients with a high γδT cell abundance may benefit more from chemotherapy or radiotherapy alone than their combination. Additionally, patients with a high γδT cell abundance were more likely to benefit from immunotherapy. Finally, we established a radiomic model based on dynamic contrast-enhanced-MRI, which indicated the potential for estimating the γδT cell abundance for patients with TNBC.

Our study provides novel insight and a theoretical basis for individualized therapy of patients with TNBC based on γδT cells.

This study aimed to develop a prognostic model utilizing intratumoral and peritumoral radiomics from simulated localization CT images to predict overall survival (OS) in patients with advanced esophageal cancer, while evaluating its clinical applicability. We conducted a retrospective cohort study involving 151 patients with esophageal cancer who underwent radical radiotherapy between January 2017 and January 2023 (144 men, 7 women). Participants were randomly assigned to a training cohort (n = 105) and a validation cohort (n = 46) at a 7:3 ratio. The primary outcome measured was OS. We extracted 851 radiomic features from the radiotherapy target area of localized CT images. Univariate Cox and LASSO-Cox models were employed to identify features associated with OS. We developed four Cox proportional hazards regression models: a clinical model, a GTV radiomics model combined with the clinical model, a peritumoral radiomics model combined with the clinical model, and a comprehensive radiomics-clinical model. Model performance was assessed using receiver operating characteristic (ROC) curves, Kaplan-Meier survival curves, and nomograms. The median follow-up period was 22 months (range: 6-101). The clinical model exhibited C-index values of 0.540 and 0.590 for predicting OS in the training and validation cohorts, respectively. The GTV radiomics combined with the clinical model demonstrated improved performance with C-index values of 0.753 and 0.677. The peritumoral radiomics combined with the clinical model yielded C-index values of 0.662 and 0.587. The total radiomics-clinical model showed the best predictive capability, with C-index values of 0.762 and 0.704 in the training and validation cohorts. Calibration curves validated the accuracy and clinical relevance of the total radiomics-clinical model, which effectively stratified patient risk categories (p < 0.001). The total radiomics-clinical model, developed from simulated localization CT images, demonstrates a robust ability to predict overall survival (OS) in patients with advanced esophageal cancer. By accurately identifying high- and low-risk patients, this model empowers clinicians to tailor treatment strategies to individual patient profiles. This personalized approach enhances clinical decision-making, enabling more effective allocation of resources and interventions based on the unique prognostic factors of each patient.

Early prediction of treatment response to neoadjuvant therapy (NAT) in breast cancer patients can facilitate timely adjustment of treatment regimens. We aimed to develop and validate a MRI-based enhanced self-attention network (MESN) for predicting pathological complete response (pCR) based on longitudinal images at the early stage of NAT.

Two imaging datasets were utilized: a subset from the ACRIN 6698 trial (dataset A, n = 227) and a prospective collection from a Chinese hospital (dataset B, n = 245). These datasets were divided into three cohorts: an ACRIN 6698 training cohort (n = 153) from dataset A, an ACRIN 6698 test cohort (n = 74) from dataset A, and an external test cohort (n = 245) from dataset B. The proposed MESN allowed for the integration of multiple timepoint features and extraction of dynamic information from longitudinal MR images before and after early-NAT. We also constructed the Pre model based on pre-NAT MRI features. Clinicopathological characteristics were added to these image-based models to create integrated models (MESN-C and Pre-C), and their performance was evaluated and compared.

The MESN-C yielded area under the receiver operating characteristic curve (AUC) values of 0.944 (95% CI: 0.906 - 0.973), 0.903 (95%CI: 0.815 - 0.965), and 0.861 (95%CI: 0.811 - 0.906) in the ACRIN 6698 training, ACRIN 6698 test and external test cohorts, respectively, which were significantly higher than those of the clinical model (AUC: 0.720 [95%CI: 0.587 - 0.842], 0.738 [95%CI: 0.669 - 0.796] for the two test cohorts, respectively; p < 0.05) and Pre-C (AUC: 0.697 [95%CI: 0.554 - 0.819], 0.726 [95%CI: 0.666 - 0.797] for the two test cohorts, respectively; p < 0.05). High AUCs of the MESN-C maintained in the ACRIN 6698 standard (AUC = 0.853 [95%CI: 0.676 - 1.000]) and experimental (AUC = 0.905 [95%CI: 0.817 - 0.993]) subcohorts, and the interracial and external subcohort (AUC = 0.861 [95%CI: 0.811 - 0.906]). Moreover, the MESN-C increased the positive predictive value from 48.6 to 71.3% compared with Pre-C model, and maintained a high negative predictive value (80.4-86.7%).

The MESN-C using longitudinal multiparametric MRI after a short-term therapy achieved favorable performance for predicting pCR, which could facilitate timely adjustment of treatment regimens, increasing the rates of pCR and avoiding toxic effects.

Trial registration at https://www.chictr.org.cn/ .

ChiCTR2000038578, registered September 24, 2020.

Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be misdiagnosed during radiologic examination. Accurate and timely diagnosis of fracture is crucial for patients, and artificial intelligence that uses algorithms to imitate human intelligence to aid or enhance human performs is a promising solution to address this issue. In the last few years, numerous commercially available algorithms have been developed to enhance radiology practice and a large number of studies apply artificial intelligence to fracture detection. Recent contributions in literature have described numerous advantages showing how artificial intelligence performs better than doctors who have less experience in interpreting musculoskeletal X-rays, and assisting radiologists increases diagnostic accuracy and sensitivity, improves efficiency, and reduces interpretation time. Furthermore, algorithms perform better when they are trained with big data on a wide range of fracture patterns and variants and can provide standardized fracture identification across different radiologist, thanks to the structured report. In this review article, we discuss the use of artificial intelligence in fracture identification and its benefits and disadvantages. We also discuss its current potential impact on the field of radiology and radiomics.

With the intensification of population aging, the proportion of elderly breast cancer patients is continuously increasing, among which those with low HER2 expression account for approximately 45 %-55 % of all cases within traditional HER2-negative breast cancer. Concurrently, the significant therapeutic effect of T-DXd on patients with HER2-low tumors has brought this group into the public spotlight. Since the clinical approval of T-DXd in 2019, there has been a significant vertical surge in the volume of publications within this domain. We analyzed 512 articles on HER2-low breast cancer from the Web of Science Core Collection using bibliometrics, topic modeling, and knowledge graph techniques to summarize the current state and trends of research in this domain. Research efforts are particularly concentrated in the United States and China. Our analysis revealed six main research directions: HER2 detection, omics and clinical biomarkers, basic and translational research, neoadjuvant therapy and prognosis, progress of ADC drugs and clinical trials. To enhance the therapeutic efficacy and safety of antibodydrug conjugates (ADCs), researchers are actively exploring potential drug candidates other than T-DXd, with numerous ADC drugs emerging in clinical practice and trials. By incorporating emerging treatment strategies such as immunotherapy and employing circulating tumor cell (CTC) detection techniques, progress has been made toward improving the prognosis of patients with low HER2 expression. We believe that these research efforts hold promise as compelling evidence that HER2-low breast cancer may constitute a distinct and independent subtype.

This study aimed to investigate whether contrast-enhanced computed tomography (CECT) based radiomics analysis could noninvasively predict the perineural invasion (PNI) in esophageal squamous cell carcinoma (ESCC).

398 patients with ESCC who underwent resection between February 2016 and March 2020 were retrospectively enrolled in this study. Patients were randomly divided into training and testing cohorts in a 7:3 ratio. Radiomics analysis was performed on the arterial phase images of CECT scans. From these images, 1595 radiomics features were initially extracted. The intraclass correlation coefficient (ICC), wilcoxon rank-sum test, spearman correlation analysis, and boruta algorithm were used for feature selection. Logistic regression (LR), random forest (RF), and support vector machine (SVM) models were established to preidict the PNI status. The performance of these radiomics models was assessed by the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was conducted to evaluate their clinical utility.

Six radiomics features were retained to build the radiomics models. Among these models, the random forest (RF) model demonstrated superior performance. In the training cohort, the AUC value of the RF model was 0.773, compared to 0.627 for the logistic regression (LR) model and 0.712 for the support vector machine (SVM) model. Similarly, in the testing cohort, the RF model achieved an AUC value of 0.767, outperforming the LR model at 0.638 and the SVM model at 0.683. Decision curve analysis (DCA) suggested that the RF radiomics model exhibited the highest clinical utility.

CECT-based radiomics analysis, particularly utilizing the RF, can noninvasively predict the PNI in ESCC preoperatively. This novel approach could enhance patient management by providing personalized information, thereby facilitating the development of individualized treatment strategies for ESCC patients.

This study evaluates the accuracy of radiomics in predicting lymph node metastasis in non-small cell lung cancer, which is crucial for patient management and prognosis.

Adhering to PRISMA and AMSTAR guidelines, we systematically reviewed literature from March 2012 to December 2023 using databases including PubMed, Web of Science, and Embase. Radiomics studies utilizing computed tomography (CT) and positron emission tomography (PET)/CT imaging were included. The quality of studies was appraised with QUADAS-2 and RQS tools, and the TRIPOD checklist assessed model transparency. Sensitivity, specificity, and AUC values were synthesized to determine diagnostic performance, with subgroup and sensitivity analyses probing heterogeneity and a Fagan plot evaluating clinical applicability.

Our analysis incorporated 42 cohorts from 22 studies. CT-based radiomics demonstrated a sensitivity of 0.84 (95% CI: 0.79-0.88, p < 0.01) and specificity of 0.82 (95% CI: 0.75-0.87, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.92), indicating no publication bias (p-value = 0.54 > 0.05). PET/CT radiomics showed a sensitivity of 0.82 (95% CI: 0.76-0.86, p < 0.01) and specificity of 0.86 (95% CI: 0.81-0.90, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.93), with a slight publication bias (p-value = 0.03 < 0.05). Despite high clinical utility, subgroup analysis did not clarify heterogeneity sources, suggesting influences from possible factors like lymph node location and small subgroup sizes.

Radiomics models show accuracy in predicting lung cancer lymph node metastasis, yet further validation with larger, multi-center studies is necessary.

Radiomics models using CT and PET/CT imaging may improve the prediction of lung cancer lymph node metastasis, aiding personalized treatment strategies.

International Prospective Register of Systematic Reviews (PROSPERO), CRD42023494701. This study has been registered on the PROSPERO platform with a registration date of 18 December 2023. https://www.crd.york.ac.uk/prospero/ KEY POINTS: The study explores radiomics for lung cancer lymph node metastasis detection, impacting surgery and prognosis. Radiomics improves the accuracy of lymph node metastasis prediction in lung cancer. Radiomics can aid in the prediction of lymph node metastasis in lung cancer and personalized treatment.

Upper gastrointestinal cancers, mainly comprising esophageal and gastric cancers, are among the most prevalent cancers worldwide. There are many new cases of upper gastrointestinal cancers annually, and the survival rate tends to be low. Therefore, timely screening, precise diagnosis, appropriate treatment strategies, and effective prognosis are crucial for patients with upper gastrointestinal cancers. In recent years, an increasing number of studies suggest that artificial intelligence (AI) technology can effectively address clinical tasks related to upper gastrointestinal cancers. These studies mainly focus on four aspects: screening, diagnosis, treatment, and prognosis. In this review, we focus on the application of AI technology in clinical tasks related to upper gastrointestinal cancers. Firstly, the basic application pipelines of radiomics and deep learning in medical image analysis were introduced. Furthermore, we separately reviewed the application of AI technology in the aforementioned aspects for both esophageal and gastric cancers. Finally, the current limitations and challenges faced in the field of upper gastrointestinal cancers were summarized, and explorations were conducted on the selection of AI algorithms in various scenarios, the popularization of early screening, the clinical applications of AI, and large multimodal models.

Radiomics has widespread applications in the field of brain tumor research. However, radiomic analyses often function as a 'black box' due to their use of complex algorithms, which hinders the translation of brain tumor radiomics into clinical applications. In this review, we will elaborate extensively on the application of radiomics in brain tumors. Additionally, we will address the interpretability of handcrafted-feature radiomics and deep learning-based radiomics by integrating biological domain knowledge of brain tumors with interpretability methods. Furthermore, we will discuss the current challenges and prospects concerning the interpretability of brain tumor radiomics. Enhancing the interpretability of radiomics may make it more understandable for physicians, ultimately facilitating its translation into clinical practice. CRITICAL RELEVANCE STATEMENT: The interpretability of brain tumor radiomics empowers neuro-oncologists to make well-informed decisions from radiomic models. KEY POINTS: Radiomics makes a significant impact on the management of brain tumors in several key clinical areas. Transparent models, habitat analysis, and feature attribute explanations can enhance the interpretability of traditional handcrafted-feature radiomics in brain tumors. Various interpretability methods have been applied to explain deep learning-based models; however, there is a lack of biological mechanisms underlying these models.

Breast cancer is one of the most commonly diagnosed malignancies in women worldwide, and the disease burden continues to aggravate. The tumor-node-metastasis (TNM) staging information is crucial for oncology physicians to develop appropriate clinical strategies. This study aimed to investigate the value of a radiomics-clinical model for predicting TNM stage in breast cancer patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

DCE-MRI images from 166 patients with pathologically confirmed breast cancer were retrospectively collected, including early stage (TNM0-TNM2) and locally advanced or advanced stage (TNM3-TNM4). Included patients were divided into a training cohort (n=116) and a test cohort (n=50). The radiomics, clinical and integrated models were constructed and a nomogram was established to distinguish the TNM0-TNM2 stage from the TNM3-TNM4 stage. Receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) were employed to assess the predictability of the models.

Eighty-five patients were at the early stages, while 81 patients were at the other stages. In the training and test cohorts, the area under the curve (AUC) values for distinguishing early and advanced breast cancer were 0.870 and 0.818 for the nomogram, respectively. The nomogram calibration curves showed good agreement between the predicted and observed TNM stages in the training and test cohorts. The Hosmer-Lemeshow test showed that the nomogram fit perfectly in the two cohorts. DCA indicated that the nomogram displayed clear superiority in forecasting TNM staging over clinical and radiomic signatures.

Compared to traditional imaging methods, the clinical-radiomics nomogram acquired by DCE-MRI could potentially be utilized to preoperatively evaluate the TNM stage of breast cancer with relatively high accuracy. It can be an effective method to guide clinical decisions.

Breast cancer (BC) is a predominant malignancy among women globally, with its etiology remaining largely elusive. Diagnosis primarily relies on invasive histopathological methods, which are often limited by sample representation and processing time. Consequently, non-invasive imaging techniques such as mammography, ultrasound, and Magnetic Resonance Imaging (MRI) are indispensable for BC screening, diagnosis, staging, and treatment monitoring. Recent advancements in imaging technologies and artificial intelligence-driven radiomics have enhanced precision medicine by enabling early detection, accurate molecular subtyping, and personalized therapeutic strategies. Despite reductions in mortality through traditional treatments, challenges like tumor heterogeneity and therapeutic resistance persist. Immunotherapies, particularly PD-1/PD-L1 inhibitors, have emerged as promising alternatives. This review explores recent developments in BC imaging diagnostics and immunotherapeutic approaches, aiming to inform clinical practices and optimize therapeutic outcomes.

This study sought to clarify the utility of T2-weighted imaging (T2WI)-based radiomics to predict the recurrence of acute pancreatitis (AP) in subjects with metabolic syndrome (MetS).

Data from 196 patients with both AP and MetS from our hospital were retrospectively analyzed. These patients were separated into two groups according to their clinical follow-up outcomes, including those with first-onset AP (n = 114) and those with recurrent AP (RAP) (n = 82). The 196 cases were randomly divided into a training set (n = 137) and a test set (n = 59) at a 7:3 ratio. The clinical characteristics of these patients were systematically compiled for further analysis. For each case, the pancreatic parenchyma was manually delineated slice by slice using 3D Slicer software, and the appropriate radiomics characteristics were retrieved. The K-best approach, the least absolute shrinkage and selection operator (LASSO) algorithm, and variance thresholding were all used in the feature selection process. The establishment of clinical, radiomics, and combined models for forecasting AP recurrence in patients with MetS was then done using a random forest classifier. Model performance was measured using the area under the receiver operating characteristic curve (AUC), and model comparison was done using the DeLong test. The clinical utility of these models was evaluated using decision curve analysis (DCA), and the optimal model was determined via a calibration curve.

In the training set, the clinical, radiomics, and combined models yielded respective AUCs of 0.651, 0.825, and 0.883, with corresponding test sets of AUCs of 0.606, 0.776, and 0.878. Both the radiomics and combined models exhibited superior predictive effectiveness compared to the clinical model in both the training (p = 0.001, p < 0.001) and test sets (p = 0.04, p < 0.001). The combined model outperformed the radiomics model (training set: p = 0.025, test set: p = 0.019). The DCA demonstrated that the radiomics and combined models had greater clinical efficacy than the clinical model. The calibration curve for the combined model demonstrated good agreement between the predicted probability of AP recurrence and the observed outcomes.

These findings highlight the superior predictive power of a T2WI-based radiomics model for predicting AP recurrence in patients with MetS, potentially supporting early interventions that can mitigate or alleviate RAP.

The reproducibility of radiomic features (RFs) is essential in lung nodule diagnosis. This study aimed to prospectively investigate the effects of computed tomography (CT) scanning parameters on the reproducibility of RFs in pulmonary nodules.

Patients with pulmonary nodules who underwent chest CT scans at the Cancer Hospital of the Chinese Academy of Medical Sciences between July 2018 and March 2019 were prospectively included in the study. Six sequences with three pairs of different scanning parameters, including the reconstruction algorithm [filtered back projection (FBP) vs. 50% adaptive statistical iterative reconstruction-V (ASiR-V)], radiation dose (low dose vs. standard dose), and contrast agent [contrast-enhanced (CE) CT vs. non-contrast enhanced (NE) CT], were used for each patient. When one of the scanning parameters was changed, the other two remained fixed. The nodules were classified into pure ground-glass nodules (pGGNs), part-solid nodules (PSNs), and solid nodules (SNs) according to the nodule consistency. RFs with an intraclass correlation coefficient (ICC) >0.75 were considered to have good retest reliability. All the RF values of the different scanning parameters and nodule consistency were investigated and compared.

A total of 150 pulmonary nodules, including 50 pGGNs, 50 PSNs, and 50 SNs, in 96 patients (mean age: 52±10 years; 62 females) were included in the study. In total, 320 RFs with an ICC >0.75 were evaluated. The proportion of RFs showed significant difference between FBP and 50% ASiR-V, low dose and standard dose, and CE and NE CT scans was 38.4% (123/320), 63.1% (202/320) and 54.1% (173/320), respectively. The radiation dose and contrast agent affected more RFs than the reconstruction algorithm (both P<0.001). In the subgroup analysis of nodule consistency, regardless of changes in the reconstruction algorithms, radiation doses, or contrast agents, the RFs showed significant difference among the pGGNs, PSNs, and SNs (all P<0.001).

The scanning parameters affected the reproducibility of the RFs, and nodules of different consistency were affected differently. The effects of these parameters should be fully considered in radiomic analysis.

This study utilizes radiomics to explore imaging biomarkers for predicting the recurrence of chronic subdural hematoma (CSDH), aiming to improve the prediction of CSDH recurrence risk. Analyzing CT scans from 64 patients with CSDH, we extracted 107 radiomic features and employed recursive feature elimination (RFE) and the XGBoost algorithm for feature selection and model construction. The feature selection process identified six key imaging biomarkers closely associated with CSDH recurrence: flatness, surface area to volume ratio, energy, run entropy, small area emphasis, and maximum axial diameter. The selection of these imaging biomarkers was based on their significance in predicting CSDH recurrence, revealing deep connections between postoperative variables and recurrence. After feature selection, there was a significant improvement in model performance. The XGBoost model demonstrated the best classification performance, with the average accuracy improving from 46.82% (before feature selection) to 80.74% and the AUC value increasing from 0.5864 to 0.7998. These results prove that precise feature selection significantly enhances the model's predictive capability. This study not only reveals imaging biomarkers for CSDH recurrence but also provides valuable insights for future personalized treatment strategies.

Accurately predicting meningioma brain invasion preoperatively helps to select the appropriate surgical approach and predict prognosis, but there are few imaging features that are sufficient for discriminating it alone. We investigate the joint MR imaging features and apparent diffusion coefficient (ADC) to predict the risk of brain invasion of meningiomas preoperatively.

In this retrospective study, 143 patients (invasion group:51, non-invasion group: 92) diagnosed with meningioma by histopathology were included. The maximum (ADCmax), minimum (ADCmin) and mean (ADCmean) values of ADC and the mean ADC values of a comparative ROI in the normal appearing white matter (ADCNAWM) were calculated. Differences between clinical features, MRI morphological features, and all ADC values were assessed by Pearson's chi-square test and Kruskal-Wallis rank-sum test. Stepwise logistic regression analysis was used to select the optimal features and construct a prediction model. Furthermore, A nomogram was used to predict the risk of brain invasion, and a decision curve was used to verify the clinical utility of the nomogram.

According to stepwise logistic regression analysis, we found that sex, maximum diameter, peritumoral edema and ADCmin were closely related to brain invasion in meningioma. The model of the above four variables has the optimal discriminative ability to predict brain invasion, with an AUC of 0.924 (95 % CI, 0.879-0.969) and a sensitivity of 92.2 % (95 % CI, 74.5%-98.0 %).

Combining clinical features, MRI morphological characteristics and ADCmin, the model exhibits excellent discriminatory ability and high sensitivity, which can be used for predicting the risk of brain invasion of meningiomas.