Radscore Model Research Articles

A predictive model for survival outcomes of glioma patients based on multi-parametric, multi-regional MRI radiomics features and clinical features

To establish a predictive model for survival outcomes of glioma patients based on both brain radiomics features from preoperative MRI multi-sequence images and clinical features. We retrospectively analyzed the MRI images and clinical data of 388 glioma patients and extracted the radiomics features from the peritumoral edema zone, tumor core, and whole tumor on T1, T2, and T1-weighted contrast-enhanced (T1CE) and fluid attenuated inversion recovery (FLAIR) sequences. The cases were divided into a training set (271 cases) and a test set (117 cases). Random survival forest algorithms were used to select the radiomics features associated with overall survival (OS) in the training set to construct a radiomic score (Rad-score), based on which the patients were classified into high- and low-risk groups for Kaplan-Meier survival analysis. Cox proportional hazard regression models for the 3 different tumor zones were constructed, and their performance for predicting 1- and 3-year survival rates was evaluated using 5-fold cross-validation and AUC analysis followed by external validation using data from another 10 glioma patients. The best-performing model was used for constructing a nomogram for survival predictions. Five radiomics features from the tumor core, 7 from the peritumoral edema zone, and 5 from the whole tumor were selected. In both the training and test sets, the high- and low-risk groups had significantly different OS (P < 0.05), and age, IDH status and Rad-score were independent factors affecting OS. The combined model showed better performance than the Rad-score model with AUCs for 1-year and 3-year survival prediction of 0.750 and 0.778 in the training set, 0.764 and 0.800 in the test set, and 0.938 and 0.917 in external validation, respectively. The predictive model combining preoperative multi-modal MRI radiomics features and clinical features can effectively predict survival outcomes of glioma patients.

Clinical and radiomics integrated nomogram for preoperative prediction of tumor-infiltrating lymphocytes in patients with triple-negative breast cancer.

The present study aimed to develop a radiomics nomogram based on conventional ultrasound (CUS) to preoperatively distinguish high tumor-infiltrating lymphocytes (TILs) and low TILs in triple-negative breast cancer (TNBC) patients. In the present study, 145 TNBC patients were retrospectively included. Pathological evaluation of TILs in the hematoxylin and eosin sections was set as the gold standard. The patients were randomly allocated into training dataset and validation dataset with a ratio of 7:3. Clinical features (age and CUS features) and radiomics features were collected. Then, the Rad-score model was constructed after the radiomics feature selection. The clinical features model and clinical features plus Rad-score (Clin+RS) model were built using logistic regression analysis. Furthermore, the performance of the models was evaluated by analyzing the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Univariate analysis and LASSO regression were employed to identify a subset of 25 radiomics features from a pool of 837 radiomics features, followed by the calculation of Rad-score. The Clin+RS integrated model, which combined posterior echo and Rad-score, demonstrated better predictive performance compared to both the Rad-score model and clinical model, achieving AUC values of 0.848 in the training dataset and 0.847 in the validation dataset. The Clin+RS integrated model, incorporating posterior echo and Rad-score, demonstrated an acceptable preoperative evaluation of the TIL level. The Clin+RS integrated nomogram holds tremendous potential for preoperative individualized prediction of the TIL level in TNBC.

MRI-based radiomics for preoperative prediction of recurrence and metastasis in rectal cancer.

To explore the value of multi-parametric MRI (mp-MRI) radiomic model for preoperative prediction of recurrence and/or metastasis (RM) as well as survival benefits in patients with rectal cancer. A retrospective analysis of 234 patients from two centers with histologically confirmed rectal adenocarcinoma was conducted. All patients were divided into three groups: training, internal validation (in-vad) and external validation (ex-vad) sets. In the training set, radiomic features were extracted from T2WI, DWI, and contrast enhancement T1WI (CE-T1) sequence. Radiomic signature (RS) score was then calculated for feature screening to construct a rad-score model. Subsequently, preoperative clinical features with statistical significance were selected to construct a clinical model. Independent predictors from clinical and RS related to RM were selected to build the combined model and nomogram. After feature extraction, 26 features were selected to construct the rad-score model. RS (OR = 0.007, p < 0.01), MR-detected T stage (mrT) (OR = 2.92, p = 0.03) and MR-detected circumferential resection margin (mrCRM) (OR = 4.70, p = 0.01) were identified as independent predictors of RM. Then, clinical model and combined model were constructed. ROC curve showed that the AUC, accuracy, sensitivity and specificity of the combined model were higher than that of the other two models in three sets. Kaplan-Meier curves showed that poorer disease-free survival (DFS) time was observed for patients in pT3-4 stages with low RS score (p < 0.001), similar results were also found in pCRM-positive patients (p < 0.05). The mp-MRI radiomics model can be served as a noninvasive and accurate predictors of RM in rectal cancer that may support clinical decision-making.

A nomogram based on MRI radiomics features of mesorectal fat for diagnosing T2- and T3-stage rectal cancer.

To develop and validate a nomogram for the preoperative diagnosis of T2 and T3 stage rectal cancer using MRI radiomics features of mesorectal fat. The data of 288 patients with T2 and T3 stage rectal cancer were retrospectively collected. Radiomics features were extracted from the lesion region of interest (ROI) in the MRI high-resolution T2WI, apparent diffusion coefficient (ADC), and diffusion-weighted imaging (DWI) sequences. After using ICC inter-group consistency analysis and Pearson correlation analysis to reduce dimensions, LASSO regression analysis was performed to select features and calculate Rad-score for each sequence. Then, Combined_Radscore and nomogram were constructed based on the LASSO-selected features and clinical data for each sequence. Receiver operating characteristic curve (ROC) area under the curve (AUC) was used to evaluate the performance of the Rad-score model and nomogram. Decision curve analysis (DCA) was performed to evaluate the clinical usability of the radiomics nomogram, which were combined with calibration curves to evaluate the prediction accuracy. The nomogram based on MRI-report T status and Combined_Radscore achieved AUCs of 0.921 and 0.889 in the training and validation cohorts, respectively. The nomogram can be stated that the radiomics nomogram based on multi-sequence MRI imaging of the mesorectal fat has excellent diagnosing performance for preoperative differentiation of T2 and T3 stage rectal cancer.

Noninvasive Assessment of Tumor Histological Grade in Invasive Breast Carcinoma Based on Ultrasound Radiomics and Clinical Characteristics: A Multicenter Study.

Rationale and Objectives: We aimed to develop and validate prediction models for histological grade of invasive breast carcinoma (BC) based on ultrasound radiomics features and clinical characteristics. Materials and Methods: A number of 383 patients with invasive BC were retrospectively enrolled and divided into a training set (207 patients), internal validation set (90 patients), and external validation set (86 patients). Ultrasound radiomics features were extracted from all the eligible patients. The Boruta method was used to identify the most useful features. Seven classifiers were adopted to developed prediction models. The output of the classifier with best performance was labeled as the radiomics score (Rad-score) and the classifier was selected as the Rad-score model. A combined model combining clinical factors and Rad-score was developed. The performance of the models was evaluated using receiver operating characteristic curve. Results: Seven radiomics features were selected from 788 candidate features. The logistic regression model performing best among the 7 classifiers in the internal and external validation sets was considered as Rad-score model, with areas under the receiver operating characteristic curve (AUC) values of 0.731 and 0.738. The tumor size was screened out as the risk factor and the combined model was developed, with AUC values of 0.721 and 0.737 in the internal and external validation sets. Furthermore, the 10-fold cross-validation demonstrated that the 2 models above were reliable and stable. Conclusion: The Rad-score model and combined model were able to predict histological grade of invasive BC, which may enable tailored therapeutic strategies for patients with BC in routine clinical use.

Predicting Ki-67 expression levels in breast cancer using radiomics-based approaches on digital breast tomosynthesis and ultrasound.

To construct and validate radiomics models that utilize ultrasound (US) and digital breast tomosynthesis (DBT) images independently and in combination to non-invasively predict the Ki-67 status in breast cancer. 149 breast cancer women who underwent DBT and US scans were retrospectively enrolled from June 2018 to August 2023 in total. Radiomics features were acquired from both the DBT and US images, then selected and reduced in dimensionality using several screening approaches. Establish radiomics models based on DBT, and US separately and combined. The area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity were utilized to validate the predictive ability of the models. The decision curve analysis (DCA) was used to evaluate the clinical applicability of the models. The output of the classifier with the best AUC performance was converted into Rad-score and was regarded as Rad-Score model. A nomogram was constructed using the logistic regression method, integrating the Rad-Score and clinical factors. The model's stability was assessed through AUC, calibration curves, and DCA. Support vector machine (SVM), logistic regression (LR), and random forest (RF) were trained to establish radiomics models with the selected features, with SVM showing optimal results. The AUC values for three models (US_SVM, DBT_SVM, and merge_SVM) were 0.668, 0.704, and 0.800 respectively. The DeLong test indicated a notable disparity in the area under the curve (AUC) between merge_SVM and US_SVM (p = 0.048), while there was no substantial variability between merge_SVM and DBT_SVM (p = 0.149). The DCA curve indicates that merge_SVM is superior to unimodal models in predicting high Ki-67 level, showing more clinical values. The nomogram integrating Rad-Score with tumor size obtained the better performance in test set (AUC: 0.818) and had more clinical net. The fusion radiomics model performed better in predicting the Ki-67 expression level of breast carcinoma, but the gain effect is limited; thus, DBT is preferred as a preoperative diagnosis mode when resources are limited. Nomogram offers predictive advantages over other methods and can be a valuable tool for predicting Ki-67 levels in BC.

Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in invasive breast cancer.

To develop a nomogram for predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer. We included 307 patients with clinicopathologically confirmed invasive breast cancer. The cohort was divided into a training group (n=215) and a validation group (n=92). Ultrasound images were used to extract radiomics features. The least absolute shrinkage and selection operator (LASSO) algorithm helped select pertinent features, from which Radiomics Scores (Radscores) were calculated using the LASSO regression equation. We developed three logistic regression models based on Radscores and 2D image features, and assessed the models' performance in the validation group. A nomogram was created from the best-performing model. In the training set, the area under the curve (AUC) for the Radscore model, 2D feature model, and combined model were 0.76, 0.85, and 0.88, respectively. In the validation set, the AUCs were 0.71, 0.78, and 0.83, respectively. The combined model demonstrated good calibration and promising clinical utility. Our ultrasound-based radiomics nomogram can accurately and non-invasively predict ALNM in breast cancer, suggesting potential clinical applications to optimize surgical and medical strategies.

Application of a Radiomics Machine Learning Model for Differentiating Aldosterone-Producing Adenoma from Non-Functioning Adrenal Adenoma.

To evaluate the secretory function of adrenal incidentaloma, this study explored the usefulness of a contrast-enhanced computed tomography (CECT)-based radiomics model for distinguishing aldosterone-producing adenoma (APA) from non-functioning adrenal adenoma (NAA). Overall, 68 APA and 60 NAA patients were randomly assigned (8:2 ratio) to either a training or a test cohort. In the training cohort, univariate and least absolute shrinkage and selection operator regression analyses were conducted to select the significant features. A logistic regression machine learning (ML) model was then constructed based on the radiomics score and clinical features. Model effectiveness was evaluated according to the receiver operating characteristic, accuracy, sensitivity, specificity, F1 score, calibration plots, and decision curve analysis. In the test cohort, the area under the curve (AUC) of the Radscore model was 0.869 [95% confidence interval (CI), 0.734-1.000], and the accuracy, sensitivity, specificity, and F1 score were 0.731, 1.000, 0.583, and 0.900, respectively. The Clinic-Radscore model had an AUC of 0.994 [95% CI, 0.978-1.000], and the accuracy, sensitivity, specificity, and F1 score values were 0.962, 0.929, 1.000, and 0.931, respectively. In conclusion, the CECT-based radiomics and clinical radiomics ML model exhibited good diagnostic efficacy in differentiating APAs from NAAs; this non-invasive, cost-effective, and efficient method is important for the management of adrenal incidentaloma.

Radiogenomics for predicting microsatellite instability status and PD-L1 expression with machine learning in endometrial cancers: A multicenter study

PurposeTo evaluate the effectiveness of machine learning model based on magnetic resonance imaging (MRI) in identifying microsatellite instability (MSI) status and PD-L1 expression in endometrial cancer (EC). MethodsThis retrospective study included 82 EC patients from 2 independent centers. Radiomics features from the intratumoral and peritumoral regions, obtained from four conventional MRI sequences (T2-weighted images; contrast-enhanced T1-weighted images; diffusion-weighted images; apparent diffusion coefficient), were combined with clinicopathologic characteristics to develop machine learning model for predicting MSI status and PD-L1 expression. 60 patients from center 1 were used as the training set for model construction, while 22 patients from center 2 were used as an external validation set for model evaluation. ResultsFor predicting MSI status, the clinicopathologic model, radscore model, and combination model achieved area under the curves (AUCs) of 0.728, 0.833, and 0.889 in the training set, respectively, and 0.595, 0.790, and 0.848 in the validation set, respectively. For predicting PD-L1 expression, the clinicopathologic model, radscore model, and combination model achieved AUCs of 0.648, 0.814, and 0.834 in the training set, respectively, and 0.660, 0.708, and 0.764 in the validation set, respectively. Calibration curve analysis and decision curve analysis demonstrated good calibration and clinical utility of the combination model. ConclusionThe machine learning model incorporating MRI-based radiomics features and clinicopathologic characteristics could be a potential tool for predicting MSI status and PD-L1 expression in EC. This approach may contribute to precision medicine for EC patients.

Biological underpinnings of radiomic magnetic resonance imaging phenotypes for risk stratification in IDH wild-type glioblastoma

BackgroundTo develop and validate a conventional MRI-based radiomic model for predicting prognosis in patients with IDH wild-type glioblastoma (GBM) and reveal the biological underpinning of the radiomic phenotypes.MethodsA total of 801 adult patients (training set, N = 471; internal validation set, N = 239; external validation set, N = 91) diagnosed with IDH wild-type GBM were included. A 20-feature radiomic risk score (Radscore) was built for overall survival (OS) prediction by univariate prognostic analysis and least absolute shrinkage and selection operator (LASSO) Cox regression in the training set. GSEA and WGCNA were applied to identify the intersectional pathways underlying the prognostic radiomic features in a radiogenomic analysis set with paired MRI and RNA-seq data (N = 132). The biological meaning of the conventional MRI sequences was revealed using a Mantel test.ResultsRadscore was demonstrated to be an independent prognostic factor (P < 0.001). Incorporating the Radscore into a clinical model resulted in a radiomic-clinical nomogram predicting survival better than either the Radscore model or the clinical model alone, with better calibration and classification accuracy (a total net reclassification improvement of 0.403, P < 0.001). Three pathway categories (proliferation, DNA damage response, and immune response) were significantly correlated with the prognostic radiomic phenotypes.ConclusionOur findings indicated that the prognostic radiomic phenotypes derived from conventional MRI are driven by distinct pathways involved in proliferation, DNA damage response, and immunity of IDH wild-type GBM.

A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study

BackgroundTo explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer.MethodsData from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms.ResultsThree models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians.ConclusionThe multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making.

Feasibility of UTE-MRI-based radiomics model for prediction of histopathologic subtype of lung adenocarcinoma: in comparison with CT-based radiomics model.

To assess the feasibility of the UTE-MRI radiomic model in predicting the micropapillary and/or solid (MP/S) patterns of surgically resected lung adenocarcinoma. We prospectively enrolled 74 lesions from 71 patients who underwent UTE-MRI and CT before curative surgery for early lung adenocarcinoma. For conventional radiologic analysis, we analyzed the longest lesion diameter and lesion characteristics at both UTE-MRI and CT. Radiomic features were extracted from the volume of interest of the lesions and Rad-scores were generated using the least absolute shrinkage and selection operator with fivefold cross-validation. Six models were constructed by combining the conventional radiologic model, UTE-MRI Rad-score, and CT Rad-score. The areas under the curves (AUCs) of each model were compared using the DeLong method. Early recurrence after curative surgery was analyzed, and Kaplan-Meier survival analysis was performed. Twenty-four lesions were MP/S-positive, and 50 were MP/S-negative. The longitudinal size showed a small systematic difference between UTE-MRI and CT, with fair intermodality agreement of lesion characteristic (kappa = 0.535). The Rad-scores of the UTE-MRI and CT demonstrated AUCs of 0.84 and 0.841, respectively (p = 0.98). Among the six models, mixed conventional, UTE-MRI, and CT Rad-score model showed the highest diagnostic performance (AUC = 0.879). In the survival analysis, the high- and low-risk groups were successfully divided by the Rad-score in UTE-MRI (p = 0.01) and CT (p < 0.01). UTE-MRI radiomic model predicting MP/S positivity is feasible compared with the CT radiomic model. Also, it was associated with early recurrence in the survival analysis. A radiomic model utilizing UTE-MRI, which does not present a radiation hazard, was able to successfully predict the histopathologic subtype of lung adenocarcinoma, and it was associated with the patient's recurrence-free survival. • No studies have reported the ultrashort echo time (UTE)-MRI-based radiomic model for lung adenocarcinoma. • The UTE-MRI Rad-score showed comparable diagnostic performance with CT Rad-score for predicting micropapillary and/or solid histopathologic pattern. • UTE-MRI is feasible not only for conventional radiologic analysis, but also for radiomics analysis.

Predictive Modeling of Radiation Pneumonitis Induced by Chemoradiotherapy for Locally Advanced Non-Small Cell Lung Cancer Using Radiomics and Clinical Features

The purpose of this study is to establish a prediction model for the development of grade 2 or higher radiation pneumonitis (RP) using radiomics analysis of pretreatment CT images, PET images, and dose distribution, in addition to clinical factors, in patients with locally advanced non-small cell lung cancer (NSCLC) treated with radical chemoradiotherapy. We retrospectively evaluated 128 cases of locally advanced NSCLC treated with radical radiotherapy at our institution from 2008 to 2021. Clinical factors included age, sex, performance status (PS), KL-6, smoking history, histological type, clinical stage, and total radiation dose. Radiomics analysis was performed by analyzing treatment planning CT images, PET images, and dose distribution, and Rad-score (Radiomics-score) was calculated for the extracted features using Lasso-Cox regression. Rad-score (Radiomics-score) was calculated by Lasso-Cox regression for the extracted features. Risk factors were selected by univariate/multivariate analysis of clinical factors and Rad-score. Three models for predicting RP were developed from the identified risk factors using Nomogram: Clinical, Rad-score, and Combined model. The model was evaluated using area under the curve (AUC) based on receiver operating characteristic (ROC) curves and concordance index (C-index). RP was evaluated using the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. The cumulative incidence of Grade 2 or higher RP was evaluated using the Kaplan-Meier method. Of the 128 cases, grade 2 or higher RP was observed in 50 cases (39%). Regarding clinical factors, gender, smoking status, and histology were selected as significant predictors of RP. Lasso-Cox analysis of radiomics features selected 11 features from CT images, 7 features from PET images, and 16 features from dose distribution as predictors of RP, yielding a total of 34 factors. The combined model (C-index: 0.96, AUC: 0.92) showed the best discrimination performance compared to the clinical model (C-index: 0.73, AUC: 0.56) and the Rad-score model (C-index: 0.87, AUC: 0.92). Risk classification using the combined model showed that the 1-year cumulative incidence of grade 2 or higher RP was 65% in the high-risk group, significantly higher than 15% in the low-risk group (p<0.001). The combined model with Rad-score and clinical factors can predict grade 2 or higher RP in NSCLC patients with high accuracy.

Added value of CE-CT radiomics to predict high Ki-67 expression in hepatocellular carcinoma

BackgroundThis study aimed to develop a computed tomography (CT) model to predict Ki-67 expression in hepatocellular carcinoma (HCC) and to examine the added value of radiomics to clinico-radiological features.MethodsA total of 208 patients (training set, n = 120; internal test set, n = 51; external validation set, n = 37) with pathologically confirmed HCC who underwent contrast-enhanced CT (CE-CT) within 1 month before surgery were retrospectively included from January 2014 to September 2021. Radiomics features were extracted and selected from three phases of CE-CT images, least absolute shrinkage and selection operator regression (LASSO) was used to select features, and the rad-score was calculated. CE-CT imaging and clinical features were selected using univariate and multivariate analyses, respectively. Three prediction models, including clinic-radiologic (CR) model, rad-score (R) model, and clinic-radiologic-radiomic (CRR) model, were developed and validated using logistic regression analysis. The performance of different models for predicting Ki-67 expression was evaluated using the area under the receiver operating characteristic curve (AUROC) and decision curve analysis (DCA).ResultsHCCs with high Ki-67 expression were more likely to have high serum α-fetoprotein levels (P = 0.041, odds ratio [OR] 2.54, 95% confidence interval [CI]: 1.04–6.21), non-rim arterial phase hyperenhancement (P = 0.001, OR 15.13, 95% CI 2.87–79.76), portal vein tumor thrombus (P = 0.035, OR 3.19, 95% CI: 1.08–9.37), and two-trait predictor of venous invasion (P = 0.026, OR 14.04, 95% CI: 1.39–144.32). The CR model achieved relatively good and stable performance compared with the R model (AUC, 0.805 [95% CI: 0.683–0.926] vs. 0.678 [95% CI: 0.536–0.839], P = 0.211; and 0.805 [95% CI: 0.657–0.953] vs. 0.667 [95% CI: 0.495–0.839], P = 0.135) in the internal and external validation sets. After combining the CR model with the R model, the AUC of the CRR model increased to 0.903 (95% CI: 0.849–0.956) in the training set, which was significantly higher than that of the CR model (P = 0.0148). However, no significant differences were found between the CRR and CR models in the internal and external validation sets (P = 0.264 and P = 0.084, respectively).ConclusionsPreoperative models based on clinical and CE-CT imaging features can be used to predict HCC with high Ki-67 expression accurately. However, radiomics cannot provide added value.

Assessment of Hidden Blood Loss in Spinal Metastasis Surgery: A Comprehensive Approach with MRI-Based Radiomics Models.

Patients undergoing surgery for spinal metastasis are predisposed to hidden blood loss (HBL), which is associated with poor surgical outcomes but unpredictable. To evaluate the role of MRI-based radiomics models for assess the risk of HBL in patients undergoing spinal metastasis surgery. Retrospective. 202 patients (42.6% female) operated on for spinal metastasis with a mean age of 58 ± 11 years were divided into a training (n = 162) and a validation cohort (n = 40). 1.5T or 3.0T scanners. Sagittal T1-weighted and fat-suppressed T2-weighted imaging sequences. HBL was calculated using the Gross formula. Patients were classified as low and high HBL group, with 1000 mL as the threshold. Radiomics models were constructed with radiomics features. The radiomics score (Radscore) was obtained from the optimal radiomics model. Clinical variables were accessed using univariate and multivariate logistic regression analyses. Independent risk variables were used to build a clinical model. Clinical variables combined with Radscore were used to establish a combined model. Predictive performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score. Calibration curves and decision curves analyses were produced to evaluate the accuracy and clinical utility. Among the radiomics models, the fusion (T1WI + FS-T2WI) model demonstrated the highest predictive efficacy (AUC: 0.744, 95% confidence interval [CI]: 0.576-0.914). The Radscore model (AUC: 0.809, 95% CI: 0.664-0.954) performs slightly better than the clinical model (AUC: 0.721, 95% CI: 0.524-0.918; P = 0.418) and the combined model (AUC: 0.752, 95% CI: 0.593-0.911; P = 0.178). A radiomics model may serve as a promising assessment tool for the risk of HBL in patients undergoing spinal metastasis surgery, and guide perioperative planning to improve surgical outcomes. 3 TECHNICAL EFFICACY: Stage 2.

An integrated radiomics nomogram based on conventional ultrasound improves discriminability between fibroadenoma and pure mucinous carcinoma in breast.

To evaluate the ability of integrated radiomics nomogram based on ultrasound images to distinguish between breast fibroadenoma (FA) and pure mucinous carcinoma (P-MC). One hundred seventy patients with FA or P-MC (120 in the training set and 50 in the test set) with definite pathological confirmation were retrospectively enrolled. Four hundred sixty-four radiomics features were extracted from conventional ultrasound (CUS) images, and radiomics score (Radscore) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different models were developed by a support vector machine (SVM), and the diagnostic performance of the different models was assessed and validated. A comparison of the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) was performed to evaluate the incremental value of the different models. Finally, 11 radiomics features were selected, and then Radscore was developed based on them, which was higher in P-MC in both cohorts. In the test group, the clinic + CUS + radiomics (Clin + CUS + Radscore) model achieved a significantly higher area under the curve (AUC) value (AUC = 0.86, 95% CI, 0.733-0.942) when compared with the clinic + radiomics (Clin + Radscore) (AUC = 0.76, 95% CI, 0.618-0.869, P > 0.05), clinic + CUS (Clin + CUS) (AUC = 0.76, 95% CI, 0.618-0.869, P< 0.05), Clin (AUC = 0.74, 95% CI, 0.600-0.854, P< 0.05), and Radscore (AUC = 0.64, 95% CI, 0.492-0.771, P< 0.05) models, respectively. The calibration curve and DCA also suggested excellent clinical value of the combined nomogram. The combined Clin + CUS + Radscore model may help improve the differentiation of FA from P-MC.

Radiomics nomogram based on digital breast tomosynthesis: preoperative evaluation of axillary lymph node metastasis in breast carcinoma.

This study aimed to establish a radiomics nomogram model based on digital breast tomosynthesis (DBT) images, to predict the status of axillary lymph nodes (ALN) in patients with breast carcinoma. The data of 120 patients with confirmed breast carcinoma, including 49 cases with axillary lymph node metastasis (ALNM), were retrospectively analyzed in this study. The dataset was randomly divided into a training group consisting of 84 patients (37 with ALNM) and a validation group comprising 36 patients (12 with ALNM). Clinical information was collected for all cases, and radiomics features were extracted from DBT images. Feature selection was performed to develop the Radscore model. Univariate and multivariate logistic regression analysis were employed to identify independent risk factors for constructing both the clinical model and nomogram model. To evaluate the performance of these models, receiver operating characteristic (ROC) curve analysis, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI) were conducted. The clinical model identified tumor margin and DBT_reported_LNM as independent risk factors, while the Radscore model was constructed using 9 selected radiomics features. Incorporating tumor margin, DBT_reported_LNM, and Radscore, the radiomics nomogram model exhibited superior performance with AUC values of 0.933 and 0.920 in both datasets, respectively. The NRI and IDI showed a significant improvement, suggesting that the Radscore may serve as a useful biomarker for predicting ALN status. The radiomics nomogram based on DBT demonstrated effective preoperative prediction performance for ALNM in patients with breast cancer.

CT radiomics nomogram predicts pathological response after induced chemotherapy and overall survival in patients with advanced laryngeal cancer: A single-center retrospective study.

This study aimed to develop a radiomics nomogram to predict pathological response (PR) after induction chemotherapy (IC) and overall survival (OS) in patients with advanced laryngeal cancer (LC). This retrospective study included patients with LC (n = 114) who had undergone contrast computerized tomography (CT); patients were randomly assigned to training (n = 81) and validation cohorts (n = 33). Potential radiomics scores were calculated to establish a model for predicting the PR status using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression analyses were performed to select significant variables for predicting PR status. Kaplan-Meier analysis was performed to assess the risk stratification ability of PR and radiomics score (rad-score) for predicting OS. A prognostic nomogram was developed by integrating radiomics features and clinicopathological characteristics using multivariate Cox regression. All LC patients were stratified as low- and high-risk by the median CT radiomic score, C-index, calibration curve. Additionally, decision curve analysis (DCA) of the nomogram was performed to test model performance and clinical usefulness. Overall, PR rates were 45.6% (37/81) and 39.3% (13/33) in the training and validation cohorts, respectively. Eight features were optimally selected to build a rad-score model, which was significantly associated with PR and OS. The median OS in the PR group was significantly shorter than that in the non-PR group in both cohorts. Multivariate Cox analysis revealed that volume [hazard ratio, (HR) = 1.43], N stage (HR = 1.46), and rad-score (HR = 2.65) were independent risk factors associated with OS. The above four variables were applied to develop a nomogram for predicting OS, and the DCAs indicated that the predictive performance of the nomogram was better than that of the clinical model. For patients with advanced LC, CT radiomics score was an independent biomarker for estimating PR after IC. Moreover, the nomogram that incorporated radiomics features and clinicopathological factors performed better for individualized OS estimation.

Clinical features combined with ultrasound-based radiomics nomogram for discrimination between benign and malignant lesions in ultrasound suspected supraclavicular lymphadenectasis.

Conventional ultrasound (CUS) is the first choice for discrimination benign and malignant lymphadenectasis in supraclavicular lymph nodes (SCLNs), which is important for the further treatment. Radiomics provide more comprehensive and richer information than radiographic images, which are imperceptible to human eyes. This study aimed to explore the clinical value of CUS-based radiomics analysis in preoperative differentiation of malignant from benign lymphadenectasis in CUS suspected SCLNs. The characteristics of CUS images of 189 SCLNs were retrospectively analyzed, including 139 pathologically confirmed benign SCLNs and 50 malignant SCLNs. The data were randomly divided (7:3) into a training set (n=131) and a validation set (n=58). A total of 744 radiomics features were extracted from CUS images, radiomics score (Rad-score) built were using least absolute shrinkage and selection operator (LASSO) logistic regression. Rad-score model, CUS model, radiomics-CUS (Rad-score + CUS) model, clinic-radiomics (Clin + Rad-score) model, and combined CUS-clinic-radiomics (Clin + CUS + Rad-score) model were built using logistic regression. Diagnostic accuracy was assessed by receiver operating characteristic (ROC) curve analysis. A total of 20 radiomics features were selected from 744 radiomics features and calculated to construct Rad-score. The AUCs of Rad-score model, CUS model, Clin + Rad-score model, Rad-score + CUS model, and Clin + CUS + Rad-score model were 0.80, 0.72, 0.85, 0.83, 0.86 in the training set and 0.77, 0.80, 0.82, 0.81, 0.85 in the validation set. There was no statistical significance among the AUC of all models in the training and validation set. The calibration curve also indicated the good predictive performance of the proposed nomogram. The Rad-score model, derived from supraclavicular ultrasound images, showed good predictive effect in differentiating benign from malignant lesions in patients with suspected supraclavicular lymphadenectasis.

Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma.

(1) Objective: To evaluate the performance of ultrasound-based radiomics in the preoperative prediction of human epidermal growth factor receptor 2-positive (HER2+) and HER2- breast carcinoma. (2) Methods: Ultrasound images from 309 patients (86 HER2+ cases and 223 HER2- cases) were retrospectively analyzed, of which 216 patients belonged to the training set and 93 patients assigned to the time-independent validation set. The region of interest of the tumors was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analyses using the intra-class correlation coefficient (ICC), Mann-Whitney U test, and the least absolute shrinkage and selection operator (LASSO) algorithm. The radiomics score (Rad-score) for each patient was calculated through a linear combination of the nonzero coefficient features. The support vector machine (SVM), K nearest neighbors (KNN), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction models based on the Rad-score. A clinical model based on significant clinical features was also established. In addition, the logistic regression method was used to integrate Rad-score and clinical features to generate the nomogram model. The leave-one-out cross validation (LOOCV) method was used to validate the reliability and stability of the model. (3) Results: Among the seven classifier models, the LR achieved the best performance in the validation set, with an area under the receiver operating characteristic curve (AUC) of 0.786, and was obtained as the Rad-score model, while the RF performed the worst. Tumor size showed a statistical difference between the HER2+ and HER2- groups (p = 0.028). The nomogram model had a slightly higher AUC than the Rad-score model (AUC, 0.788 vs. 0.786), but no statistical difference (Delong test, p = 0.919). The LOOCV method yielded a high median AUC of 0.790 in the validation set. (4) Conclusion: The Rad-score model performs best among the seven classifiers. The nomogram model based on Rad-score and tumor size has slightly better predictive performance than the Rad-score model, and it has the potential to be utilized as a routine modality for preoperatively determining HER2 status in BC patients non-invasively.