Radiomics Nomogram Research Articles

Radiomics Nomogram Derived from Gated Myocardial Perfusion SPECT for Identifying Ischemic Cardiomyopathy.

Personalized management involving heart failure (HF) etiology is crucial for better prognoses. We aim to evaluate the utility of a radiomics nomogram based on gated myocardial perfusion imaging (GMPI) in distinguishing ischemic from non-ischemic origins of HF. A total of 172 heart failure patients with reduced left ventricular ejection fraction (HFrEF) who underwent GMPI scan were divided into training (n = 122) and validation sets (n = 50) based on chronological order of scans. Radiomics features were extracted from the resting GMPI. Four machine learning algorithms were used to construct radiomics models, and the model with the best performances were selected to calculate the Radscore. A radiomics nomogram was constructed based on the Radscore and independent clinical factors. Finally, the model performance was validated using operating characteristic curves, calibration curve, decision curve analysis, integrated discrimination improvement values (IDI), and the net reclassification index (NRI). Three optimal radiomics features were used to build a radiomics model. Total perfusion deficit (TPD) was identified as the independent factors of conventional GMPI metrics for building the GMPI model. In the validation set, the radiomics nomogram integrating the Radscore, age, systolic blood pressure, and TPD significantly outperformed the GMPI model in distinguishing ischemic cardiomyopathy (ICM) from non-ischemic cardiomyopathy (NICM) (AUC 0.853 vs. 0.707, p = 0.038). IDI analysis indicated that the nomogram improved diagnostic accuracy by 28.3% compared to the GMPI model in the validation set. By combining radiomics signatures with clinical indicators, we developed a GMPI-based radiomics nomogram that helps to identify the ischemic etiology of HFrEF.

Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients

PurposeDeveloped and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients.MethodsThis multi-center study retrospectively included 322 patients diagnosed with gastric cancer from January 2013 to June 2023 at two hospitals. Handcrafted radiomics technique and the EfficientNet V2 neural network were applied to arterial, portal venous, and delayed phase CT images to extract two-dimensional handcrafted and deep learning features. A nomogram model was built by integrating the handcrafted signature, the deep learning signature, with clinical features. Discriminative ability was assessed using the receiver operating characteristics (ROC) curve and the precision-recall (P-R) curve. Model fitting was evaluated using calibration curves, and clinical utility was assessed through decision curve analysis (DCA).ResultsThe nomogram exhibited excellent performance. The area under the ROC curve (AUC) was 0.848 [95% confidence interval (CI), 0.793–0.893)], 0.802 (95% CI 0.688–0.889), and 0.751 (95% CI 0.652–0.833) for the training, internal validation, and external validation sets, respectively. The AUCs of the P-R curves were 0.838 (95% CI 0.756–0.895), 0.541 (95% CI 0.329–0.740), and 0.556 (95% CI 0.376–0.722) for the corresponding sets. The nomogram outperformed the clinical model and handcrafted signature across all sets (all P < 0.05). The nomogram model demonstrated good calibration and provided greater net benefit within the relevant threshold range compared to other models.ConclusionThis study created a deep learning nomogram using CECT images and clinical data to predict NAC response in LAGC patients undergoing surgical resection, offering personalized treatment insights.Graphical abstract

Prediction of programmed death-1 expression status in non–small cell lung cancer based on intratumoural and peritumoral computed tomography (CT) radiomics nomogram

This study aimed to predict the expression of programmed death-1 (PD-1) in non-small cell lung cancer (NSCLC) using intratumoral and peritumoral computed tomography (CT) radiomics nomogram. Two hundred patients pathologically diagnosed with NSCLC from two hospitals were retrospectively analyzed. Of these, 159 NSCLC patients from our hospital were randomly divided into a training cohort (n=96) and an internal validation cohort (n=63) at a ratio of 6:4, while 41 NSCLC patients from another medical institution served as the external validation cohort. The radiomic features of the gross tumor volume (GTV) and peritumoral volume (PTV) were extracted from the CT images. Optimal radiomics features were selected using least absolute shrinkage and selection operator regression analysis. Finally, a CT radiomics nomogram of clinically independent predictors combined with the best rad-score was constructed. Compared with the 'GTV' and 'PTV' radiomics models, the combined 'GTV+PTV' radiomics model showed better predictive performance, and its area under the curve (AUC) values in the training, internal validation, and external validation cohorts were 0.90 (95% confidence interval [CI]: 0.83-0.97), 0.85 (95% CI: 0.74-0.96) and 0.78 (95% CI: 0.63-0.92). The nomogram constructed by the rad-score of the 'GTV+PTV' radiomics model combined with clinical independent predictors (prealbumin and monocyte) had the best performance, with AUC values in each cohort being 0.92 (95% CI: 0.85-0.98), 0.88 (95% CI: 0.78-0.97), and 0.80 (95% CI: 0.66-0.94), respectively. The intratumoral and peritumoral CT radiomics nomogram may facilitate individualized prediction of PD-1 expression status in patients with NSCLC.

MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer

Background and purposeAdjuvant treatments are valuable to decrease the recurrence rate and improve survival for early-stage cervical cancer patients (ESCC), Therefore, recurrence risk evaluation is critical for the choice of postoperative treatment. A magnetic resonance imaging (MRI) based radiomics nomogram integrating postoperative adjuvant treatments was constructed and validated externally to improve the recurrence risk prediction for ESCC. Material and methods212 ESCC patients underwent surgery and adjuvant treatments from three centers were enrolled and divided into the training, internal validation, and external validation cohorts. Their clinical data, pretreatment T2-weighted images (T2WI) were retrieved and analyzed. Radiomics models were constructed using machine learning methods with features extracted and screen from sagittal and axial T2WI. A nomogram for recurrence prediction was build and evaluated using multivariable logistic regression analysis integrating radiomic signature and adjuvant treatments. ResultsA total of 8 radiomic features were screened out of 1020 extracted features. The extreme gradient boosting (XGboost) model based on MRI radiomic features performed best in recurrence prediction with an area under curve (AUC) of 0.833, 0.822 in the internal and external validation cohorts, respectively. The nomogram integrating radiomic signature and clinical factors achieved an AUC of 0.806, 0.718 in the internal and external validation cohorts, respectively, for recurrence risk prediction for ESCC. ConclusionIn this study, the nomogram integrating T2WI radiomic signature and clinical factors is valuable to predict the recurrence risk, thereby allowing timely planning for effective treatments for ESCC with high risk of recurrence.

Combined radiomics nomogram of different machine learning models for preoperative distinguishing intraspinal schwannomas and meningiomas: a multicenter and comparative study

Combined radiomics nomogram of different machine learning models for preoperative distinguishing intraspinal schwannomas and meningiomas: a multicenter and comparative study

Concomitant Prediction of the Ki67 and PIT-1 Expression in Pituitary Adenoma Using Different Radiomics Models.

To preoperatively predict the high expression of Ki67 and positive pituitary transcription factor 1 (PIT-1) simultaneously in pituitary adenoma (PA) using three different radiomics models. A total of 247 patients with PA (training set: n = 198; test set: n = 49) were included in this retrospective study. The imaging features were extracted from preoperative contrast-enhanced T1WI (T1CE), T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI). Feature selection was performed using Spearman's rank correlation coefficient and least absolute shrinkage and selection operator (LASSO). The classic machine learning (CML), deep learning (DL), and deep learning radiomics (DLR) models were constructed using logistic regression (LR), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and test sets. In addition, combined with clinical characteristics, the best CML and the best DL models (SVM classifier), the DL radiomics nomogram (DLRN) was constructed to aid clinical decision-making. Seven CML features, 96 DL features, and 107 DLR features were selected to construct CML, DL and DLR models. Compared to CML and DL model, the DLR model had the best performance. The AUC, sensitivity, specificity, accuracy, NPV and PPV were 0.827, 0.792, 0.800, 0.796, 0.800 and 0.792 in the test set, respectively. Compared with CML and DL models, the DLR model shows the best performance in predicting the Ki67 and PIT-1 expression in PAs simultaneously.

A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma

BackgroundThis study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC).MethodsA total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA).ResultsThe PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit.ConclusionsThe radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.

Comparison of Different Fusion Radiomics for Predicting Benign and Malignant Sacral Tumors: A Pilot Study.

Differentiating between benign and malignant sacral tumors is crucial for determining appropriate treatment options. This study aims to develop two benchmark fusion models and a deep learning radiomic nomogram (DLRN) capable of distinguishing between benign and malignant sacral tumors using multiple imaging modalities. We reviewed axial T2-weighted imaging (T2WI) and non-contrast computed tomography (NCCT) of 134 patients pathologically confirmed as sacral tumors. The two benchmark fusion models were developed using fusion deep learning (DL) features and fusion classical machine learning (CML) features from multiple imaging modalities, employing logistic regression, K-nearest neighbor classification, and extremely randomized trees. The two benchmark models exhibiting the most robust predictive performance were merged with clinical data to formulate the DLRN. Performance assessment involved computing the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV). The DL benchmark fusion model demonstrated superior performance compared to the CML fusion model. The DLRN, identified as the optimal model, exhibited the highest predictive performance, achieving an accuracy of 0.889 and an AUC of 0.961 in the test sets. Calibration curves were utilized to evaluate the predictive capability of the models, and decision curve analysis (DCA) was conducted to assess the clinical net benefit of the DLR model. The DLRN could serve as a practical predictive tool, capable of distinguishing between benign and malignant sacral tumors, offering valuable information for risk counseling, and aiding in clinical treatment decisions.

Predicting long-term outcomes in patients with classical trigeminal neuralgia following microvascular decompression with an MRI-based radiomics nomogram: a multicentre study.

This study aimed to develop a clinical-radiomics nomogram to predict the long-term outcomes of patients with classical trigeminal neuralgia (CTN) following microvascular decompression (MVD). This retrospective study included 455 patients with CTN who underwent MVD from three independent institutions A total of 2030 radiomics features from the cistern segment of the trigeminal nerve were extracted computationally from the three-dimensional steady-state free precession and three-dimensional time-of-flight magnetic resonance angiography sequences. Using the least absolute shrinkage and selection operator regression, 16 features were chosen to develop radiomics signatures. A clinical-radiomics nomogram was subsequently developed in the development cohort of 279 patients via multivariate Cox regression. The predictive performance and clinical application of the nomogram were assessed in an external cohort consisting of 176 patients. Sixteen highly outcome-related radiomics features extracted from multisequence images were used to construct the radiomics model, with concordance indices (C-index) of 0.804 and 0.796 in the development and test cohorts, respectively. Additionally, a clinical-radiomics nomogram was developed by incorporating both radiomics features and clinical characteristics (i.e., pain type and degree of neurovascular compression) and yielded higher C-indices of 0.865 and 0.834 in the development and test cohorts, respectively. K‒M survival analysis indicated that the nomogram successfully stratified patients with CTN into high-risk and low-risk groups for poor outcomes (hazard ratio: 37.18, p < 0.001). Our study findings indicated that the clinical-radiomics nomogram exhibited promising performance in accurately predicting long-term pain outcomes following MVD. This model had the potential to aid clinicians in making well-informed decisions regarding the treatment of patients with CTN. Trigeminal neuralgia recurs in about one-third of patients after undergoing MVD. The clinical-radiomics nomogram stratified patients into high- and low-risk groups for poor surgical outcomes. Using this nomogram could better inform patients of recurrence risk and allow for discussion of alternative treatments.

Ultrasound-based radiomics nomogram for distinguishing invasive breast cancer (IBC) from invasive breast cancer with intraductal component (IBC-IC)

ObjectiveThe objective was to devise a nomogram integrating radiomics features, sonographic characteristics, and immunologic indices to distinguish between IBC and IBC-IC. MethodsThe preoperative ultrasonography (US) findings and pathological data from 244 BCE patients (115 IBC-IC and 129 IBC) diagnosed with IDC through surgical confirmation were retrospectively gathered. The study utilized the BI-RADS lexicon from the ACR to interpret the findings. 1125 radiomics features were derived from the regions of breast lesions, coupled with pathological data, to establish a predictive model employing the LASSO technique. The model's performance was examined by the AUC, along with sensitivity, and specificity measures, to evaluate its effectiveness in differentiating between the two groups of subjects. ResultsFour prognostic models for IDC-IC were developed, comprising a conventional ultrasound (CUS) signature, pathology signature nomogram, radiomics signature, and combined signature. The combined model exhibited the highest diagnostic efficacy, achieving the AUC of 0.893, sensitivity of 0.875, and specificity of 0.835 in the training set, and 0.835, 0.800, and 0.789 in the testing set. ConclusionThe combined model, integrating CUS, pathology information, and radiomics features derived from CUS, exhibited a notable capacity for preoperatively distinguish IDC-IC. This capability holds promise for guiding personalized surgical strategies for BC individuals undergoing breast-conserving surgery (BCS).

Abstract PO4-07-06: Development of MRI-based Radiomics Nomogram for the Prediction of Recurrence in Patients with Luminal-type Breast Cancer: A Nested Case-Control Study

Abstract Purpose: The aim of this study was to develop a radiomics nomogram using breast MRI data and preoperative pathologic information to predict oncotype DX recurrence score (RS) in patients with estrogen receptor-positive early-stage breast cancer (EBC). Materials and methods: In this retrospective study, a total of 493 patients with EBC, diagnosed through core needle biopsy and who underwent preoperative breast MRI at a single institution between 2011 and 2017, were included. The patients were categorized based on RS, age at surgery, and nodal status, as suggested by the TAILORX trial, and out of the included patients, 249 were identified as likely to benefit from chemotherapy, while 244 were deemed safe to forgo chemotherapy. Radiomic features were extracted from three-dimensional segmentations of each tumor, and computer-extracted image phenotypes (CEIP) were generated from early post-contrast T1-weighted images, percent enhancement (PE) maps, and signal enhancement ratio (SER) maps. The patient cohort was divided into a training set (n=329) and a validation set (n=164). Feature selection and radiomics score construction were performed using elastic net, and a prediction model was developed using multivariate logistic regression analysis. The radiomics score, along with independent pathologic risk factors, was incorporated into a radiomics nomogram. Internal validation was conducted using an independent validation set (n=164). Results: The radiomics score, composed of 24 selected CEIPs, demonstrated a significant association with recurrence prediction (C-index, 0.769 for training set and 0.745 for validation set). The independent pathologic predictors included in the nomogram were histology, estrogen receptor status, progesterone receptor status, nuclear grade, histologic grade, HER1, CK56, P53, and Ki67 status, with a C-index of 0.858 for the training set and 0.774 for the validation set. Adding the radiomics score to the pathologic nomogram resulted in incremental values of 0.054 and 0.092, respectively. The radiomics nomogram showed favorable prediction of low-risk RS, with a C-index of 0.912 for the training set and 0.866 for the validation set. Conclusion: This study demonstrated that an MRI-based radiomics nomogram incorporating the radiomics score and preoperatively obtained pathologic data can effectively predict oncotype DX RS and the benefit of adjuvant chemotherapy in EBC patients. The model improves risk assessment availability and timeliness for patients with EBC. Citation Format: Minji Song, Hee Jung Shin. Development of MRI-based Radiomics Nomogram for the Prediction of Recurrence in Patients with Luminal-type Breast Cancer: A Nested Case-Control Study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-06.

A radiomics-based nomogram may be useful for predicting telomerase reverse transcriptase promoter mutation status in adult glioblastoma.

As a crucial diagnostic and prognostic biomarker, telomerase reverse transcriptase (TERT) promoter mutation holds immense significance for personalized treatment of patients with glioblastoma (GBM). In this study, we developed a radiomics nomogram to determine the TERT promoter mutation status and assessed its prognostic efficacy in GBM patients. The study retrospectively included 145 GBM patients. A comprehensive set of 3736 radiomics features was extracted from preoperative magnetic resonance imaging, including T2-weighted imaging, T1-weighted imaging (T1WI), contrast-enhanced T1WI, and fluid-attenuated inversion recovery. The construction of the radiomics model was based on integrating the radiomics signature (rad-score)with clinical features. Receiver-operating characteristic curve analysis was employed to evaluate the discriminative ability of the prediction model, and the risk score was used to stratify patient outcomes. The least absolute shrinkage and selection operator classifier identified 10 robust features for constructing the prediction model, and the radiomics nomogram exhibited excellent performance in predicting TERT promoter mutation status, with area under the curve values of.906 (95% confidence interval [CI]:.850-.963) and.899 (95% CI:.708-.966) in the training and validation sets, respectively. The clinical utility of the radiomics nomogram is further supported by calibration curve and decision curve analyses. Additionally, the radiomics nomogram effectively stratified GBM patients with significantly different prognoses (HR=1.767, p=.019). The radiomics nomogram holds promise as a modality for evaluating TERT promoter mutations and prognostic outcomes in patients with GBM.

Clinical study on forecasting the prognosis of patients with cerebellar hemorrhage based on CT radiomics models

Background and purposeWe created and verified a clinical–radiomics nomogram to forecast the prognosis of patients with cerebellar hemorrhage. Materials and MethodsEighty-eight patients with cerebellar hemorrhage from two different hospitals were retrospectively analyzed (62 cases from first hospital (training cohort) and 27 cases from second hospital (external validation cohort)). All patients underwent computed tomography scanning within 72 hours of the onset of cerebellar hemorrhage and were split into two categories based on their prognosis. Clinically independent risk factors were identified using the independent samples t-test and binary logistic regression analysis. Radiomic features were obtained using the SelectkBest, variance threshold, least absolute shrinkage, and selection operator. Radiomics scores (rad-scores) were produced from eight radiomics features of perihematomal edema (PHE) and hematoma associated with cerebellar hemorrhage prognosis. Logistic regression analyses of the rad-scores against clinical risk factors yielded clinical–radiomics column line plots. Predictive performance of the column line plots was validated using decision curve analysis analyses in both the training and external validation cohorts. ResultsTo construct rad-scores, eight radiomics traits and four clinical risk factors were combined to create clinical–radiomics models. In both the training cohort (area under the curve [AUC], 0.96 versus vs. 0.87/0.84) and the external validation cohort (AUC: 0.93 vs. 0.74/0.69), the clinical–radiomics nomogram achieved superior performance to the clinical and radiomics nomogram models. Clinical–radiomics column line plot demonstrated satisfactory calibration and clinical advantages in both cohorts. ConclusionsRadiomics features of PHE and hematoma can predict cerebellar hemorrhage prognosis. Clinical–radiomics models can help clinicians make individualized clinical treatment decisions and efficacy assessments for patients with cerebellar hemorrhage, help physicians prepare for possible deterioration, and promptly communicate treatment goals and efficacy expectations to patients and their families.

Are Radiomic Spleen Features Useful for Assessing the Response to Infliximab in Patients With Crohn's Disease? A Multicenter Study.

To develop and validate a radiomics nomogram for assessing the response of patients with Crohn's disease (CD) to infliximab. Radiomics features of the spleen were extracted from computed tomography enterography images of each patient's arterial phase. The feature selection process was performed using the least absolute shrinkage and selection operator algorithm, and a radiomics score was calculated based on the radiomics signature formula. Subsequently, the radiomic model and the clinical risk factor model were separately established based on the radiomics score and clinically significant features, respectively. The performance of both models was evaluated using receiver operating characteristic curves, decision curve analysis curves, and clinical impact curves. Among the 175 patients with CD, 105 exhibited a clinical response, and 60 exhibited clinical remission after receiving infliximab treatment. Our radiomic model, comprising 20 relevant features, demonstrated excellent predictive performance. The radiomic nomogram for predicting clinical response showed good calibration and discrimination in the training cohort (area under the curve [AUC] 0.909, 95% confidence interval [CI] 0.840-0.978), the validation cohort (AUC 0.954, 95% CI 0.889-1), and the external cohort (AUC = 0.902, 95% CI 0.83-0.974). Accordingly, the nomogram was also suitable for predicting clinical remission. Decision curve analysis and clinical impact curves highlighted the clinical utility of our nomogram. Our radiomics nomogram is a noninvasive predictive tool constructed from radiomic features of the spleen. It also demonstrated good predictive accuracy in evaluating CD patients' response to infliximab treatment. Multicenter validation provided high-level evidence for its clinical application.

Construction and Validation of a MRI‑Based Radiomic Nomogram to Predict Overall Survival in Patients with Local Advanced Cervical Cancer: A Multicenter Study

BackgroundCervical cancer is the fourth most common cancer among women. Radiomics has emerged as a new approach providing valuable information for cancer management. The aim of this study was to construct a radiomics nomogram to accurately predict survival outcomes in patients with locally advanced cervical cancer. MethodsThis retrospective study enrolled a total of 582 locally advanced cervical cancer patients from three center (training cohort: n=228; internal validation cohort: n=98; external validation cohort: n=256). Radiomic features were extracted from pretreatment MRI images. Least absolute shrinkage and selection operator logistic regression (LASSO) was applied to select radiomic features and calculated the radiomic scores. Univariate and multivariate Cox proportional hazards regression analyses were used to identify the independent prognostic clinic-radiological factors for cervical cancer, which were incorporated into the nomogram. ResultsA total of 6 radiomic features were found to be associated with overall survival of locally advanced cervical cancer patients. The AUC of radiomic scores in the training cohort was 0.634-0.708 for the training cohort, 0.725-0.762 for internal validation cohort and 0.788-0.881 for the external validation cohort. Age, parametrial invasion, and radiomic score were the independent prognostic indicators for cervical cancer patients (Age: HR=1.041, 95% CI=1.012-1.071, p=0.006; Parametrial invasion: HR=4.755, 95% CI=1.493-15.144, p=0.008; HR=2.324, 95% CI=1.050-5.143, p=0.037). The nomogram model incorporating these factors showed favorable discrimination in predicting the overall survival rates of cervical cancer patients, with the AUC values of 0.809, 0.808, and 0.862 for 1-, 2-, and 3-year predictions. The decision curve analysis (DCA) indicated that the nomogram model achieved the highest clinical net benefit across the entire range of reasonable threshold probabilities. ConclusionThe nomogram, incorporating clinicopathological factors and radiomic features derived from MRI images, showed satisfactory discrimination in predicting the overall survival rates of locally advanced cervical cancer patients.

Multiparametric MRI-based radiomic nomogram for predicting HER-2 2+ status of breast cancer

ObjectiveTo explore the application of multiparametric MRI-based radiomic nomogram for assessing HER-2 2+ status of breast cancer (BC) MethodsPatients with pathology-proven HER-2 2+ invasive BC, who underwent preoperative MRI were divided into training (72 patients, 21 HER-2-positive and 51 HER-2-negative) and validation (32 patients, 9 HER-2-positive and 23 HER-2-negative) sets by randomization. All were classified as HER-2 2+ FISH-positive (HER-2-positive) or -negative (HER-2-negative) according to IHC and FISH. The 3D VOI was drawn on MR images by two radiologists. ADC, T2WI, and DCE images were analyzed separately to extract features (n = 1906). L1 regularization, F-test, and other methods were used to reduce dimensionality. Binary radiomics prediction models using features from single or combined imaging sequences were constructed using logistic regression (LR) classifier then and validated on a validation dataset. To build a radiomics nomogram, multivariate LR analysis was conducted to identify independent indicators. An evaluation of the model's predictive efficacy was made using AUC. ResultsOn the basis of combined ADC, T2WI, and DCE images, ten radiomic features were extracted following feature dimensionality reduction. There was superior diagnostic efficiency of radiomic signature using all three sequences compared to either one or two sequences (AUC for training group: 0.883; AUC for validation group: 0.816). Based on multivariate LR analysis, radiomic signature and peritumoral edema were independent predictors for identifying HER-2 2 +. In both training and validation datasets, nomograms combining peritumoral edema and radiomics signature demonstrated an effective discrimination (AUCs were respectively 0.966 and 0. 884). ConclusionThe nomogram that incorporated peritumoral edema and multiparametric MRI-based radiomic signature can be used to effectively predict the HER-2 2+ status of BC.

Predicting preoperative muscle invasion status for bladder cancer using computed tomography-based radiomics nomogram

ObjectivesThe aim of the study is to assess the efficacy of the established computed tomography (CT)-based radiomics nomogram combined with radiomics and clinical features for predicting muscle invasion status in bladder cancer (BCa).MethodsA retrospective analysis was conducted using data from patients who underwent CT urography at our institution between May 2018 and April 2023 with urothelial carcinoma of the bladder confirmed by postoperative histology. There were 196 patients enrolled in all, and each was randomized at random to either the training cohort (n = 137) or the test cohort (n = 59). Eight hundred fifty-one radiomics features in all were retrieved. For feature selection, the significance test and least absolute shrinkage and selection operator (LASSO) approaches were utilized. Subsequently, the radiomics score (Radscore) was obtained by applying linear weighting based on the selected features. The clinical and radiomics model, as well as radiomics-clinical nomogram were all established using logistic regression. Three models were evaluated using analysis of the receiver operating characteristic curve. An area under the curve (AUC) and 95% confidence intervals (CI) as well as specificity, sensitivity, accuracy, negative predictive value, and positive predictive value were included in the analysis. Radiomics-clinical nomogram’s performance was assessed based on discrimination, calibration, and clinical utility.ResultsAfter obtaining 851 radiomics features, 12 features were ultimately selected. Histopathological grading and tortuous blood vessels were included in the clinical model. The Radscore and clinical histopathology grading were among the final predictors in the unique nomogram. The three models had an AUC of 0.811 (95% CI, 0.742–0.880), 0.845 (95% CI, 0.781–0.908), and 0.896 (95% CI, 0.846–0.947) in the training cohort and in the test cohort they were 0.808 (95% CI, 0.703–0.913), 0.847 (95% CI, 0.739–0.954), and 0.887 (95% CI, 0.803–0.971). According to the DeLong test, the radiomics-clinical nomogram’s AUC in the training cohort substantially differed from that of the clinical model (AUC: 0.896 versus 0.845, p = 0.015) and the radiomics model (AUC: 0.896 versus 0.811, p = 0.002). The Delong test in the test cohort revealed no significant difference among the three models.ConclusionsCT-based radiomics-clinical nomogram can be a useful tool for quantitatively predicting the status of muscle invasion in BCa.

A PET-based radiomics nomogram for individualized predictions of seizure outcomes after temporal lobe epilepsy surgery

PurposeTo establish and validate a novel nomogram based on clinical characteristics and [18F]FDG PET radiomics for the prediction of postsurgical seizure freedom in patients with temporal lobe epilepsy (TLE). Patients and Methods234 patients with drug-refractory TLE patients were included with a median follow-up time of 24 months after surgery. The correlation coefficient redundancy analysis and LASSO Cox regression were used to characterize risk factors. The Cox model was conducted to develop a Clinic-PET nomogram to predict the relapse status in the training set (n = 171). The nomogram's performance was estimated through discrimination, calibration, and clinical utility. The prognostic prediction model was validated in the test set (n = 63). ResultsEight radiomics features were selected to assess the radiomics score (radscore) of the operation side (Lat_radscore) and the asymmetric index (AI) of the radiomics score (AI_radscore). AI_radscor, Lat_radscor, secondarily generalized seizures (SGS), and duration between seizure onset and surgery (Durmon) were significant predictors of seizure-free outcomes. The final model had a C-index of 0.68 (95 %CI: 0.59–0.77) for complete freedom from seizures and time-dependent AUROC was 0.65 at 12 months, 0.65 at 36 months, and 0.59 at 60 months in the test set. A web application derived from the primary predictive model was displayed for economic and efficient use. ConclusionsA PET-based radiomics nomogram is clinically promising for predicting seizure outcomes after temporal lobe epilepsy surgery.

Preliminary study on diagnosis of gallbladder neoplastic polyps based on contrast-enhanced ultrasound and grey scale ultrasound radiomics.

Neoplastic gallbladder polyps (GPs), including adenomas and adenocarcinomas, are considered absolute indications for surgery; however, the distinction of neoplastic from non-neoplastic GPs on imaging is often challenging. This study thereby aimed to develop a CEUS radiomics nomogram, and evaluate the role of a combined grey-scale ultrasound and CEUS model for the prediction and diagnosis of neoplastic GPs. Patients with GPs of ≥ 1cm who underwent CEUS between January 2017 and May 2022 were retrospectively enrolled. Grey-scale ultrasound and arterial phase CEUS images of the largest section of the GPs were used for radiomics feature extraction. Features with good reproducibility in terms of intraclass correlation coefficient were selected. Grey-scale ultrasound and CEUS Rad-score models were first constructed using the Mann-Whitney U and LASSO regression test, and were subsequently included in the multivariable logistic regression analysis as independent factors for construction of the combined model. A total of 229 patients were included in our study. Among them, 118 cholesterol polyps, 68 adenomas, 33 adenocarcinomas, 6 adenomyomatoses, and 4 inflammatory polyps were recorded. A total of 851 features were extracted from each patient. Following screening, 21 and 15 features were retained in the grey-scale and CEUS models, respectively. The combined model demonstrated AUCs of 0.88 (95% CI: 0.83 - 0.93) and 0.84 (95% CI: 0.74 - 0.93) in the training and testing set, respectively. When applied to the whole dataset, the combined model detected 111 of the 128 non-neoplastic GPs, decreasing the resection rate of non-neoplastic GPs to 13.3%. Our proposed combined model based on grey-scale ultrasound and CEUS radiomics features carries the potential as a non-invasive, radiation-free, and reproducible tool for the prediction and identification of neoplastic GPs. Our model may not only guide the treatment selection for GPs, but may also reduce the surgical burden of such patients.

Radiomic nomogram based on bi-parametric magnetic resonance imaging to predict the International Society of Urological Pathology grading ≥ 3 prostate cancer: a multicenter study

To create a reliable radiomic nomogram for the prediction of the International Society of Urological Pathology (ISUP) grading ≥ 3 prostate cancer (PCa) patients. patients with verified PCa were obtained from three different hospitals. The patients were divided into training, internal validation, and two external validation groups. A radiomic signature (rad-score) extracted from T2WI, diffusion-weighted imaging, and apparent diffusion coefficient (ADC) maps were constructed in the training cohort. Eight clinical features were performed to develop a clinical model using univariate and multivariate logistic regression. The combined model incorporated the radiomic signature and clinical model. The model's performance was assessed by the receiver operating characteristic (ROC) curve. Rad-score, magnetic resonance imaging T-stage, and ADC value were significant predictors of ISUP≥3 PCa. A nomogram of these three factors was shown to have greater diagnostic accuracy than using only the radiomic signature or clinical model alone. The area under the ROC curve was 0.85, 0.88, 0.81, 0.81 for the training, internal, and two external validation cohorts, respectively. In the stratified analysis based on the MR scanner model, the area under the ROC curve of predicting ISUP≥3 PCa for GE, Siemens, and combined groups were 0.84, 0.83, and 0.84, respectively, in the combined training group and an internal validation group. The proposed nomogram has the potential to predict the differentiation degree of ISUP PCa patients.