Selected Radiomics Features Research Articles

Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors

BackgroundOvarian cancer is the most women malignancy in the whole world. It is difficult to differentiate ovarian cancers from ovarian borderline tumors because of some similar imaging findings.Radiomics study may help clinicians to make a proper diagnosis before invasive surgery.PurposeTo evaluate the ability of T2-weighted imaging (T2WI)-based radiomics to discriminate ovarian borderline tumors (BOTs) from malignancies based on two-dimensional (2D) and three-dimensional (3D) lesion segmentation methods.MethodsA total of 95 patients with pathologically proven ovarian BOTs and 101 patients with malignancies were retrospectively included in this study. We evaluated the diagnostic performance of the signatures derived from T2WI-based radiomics in their ability to differentiate between BOTs and malignancies and compared the performance differences in the 2D and 3D segmentation models. The least absolute shrinkage and selection operator method (Lasso) was used for radiomics feature selection and machine learning processing.ResultsThe radiomics score between BOTs and malignancies in four types of selected T2WI-based radiomics models differed significantly at the statistical level (p < 0.0001). For the classification between BOTs and malignant masses, the 2D and 3D coronal T2WI-based radiomics models yielded accuracy values of 0.79 and 0.83 in the testing group, respectively; the 2D and 3D sagittal fat-suppressed (fs) T2WI-based radiomics models yielded an accuracy of 0.78 and 0.99, respectively.ConclusionsOur results suggest that T2WI-based radiomic features were highly correlated with ovarian tumor subtype classification. 3D-sagittal MRI radiomics features may help clinicians differentiate ovarian BOTs from malignancies with high ACC.

Radiomic analysis for predicting prognosis of colorectal cancer from preoperative 18F-FDG PET/CT

BackgroundTo develop and validate a survival model with clinico-biological features and 18F- FDG PET/CT radiomic features via machine learning, and for predicting the prognosis from the primary tumor of colorectal cancer.MethodsA total of 196 pathologically confirmed patients with colorectal cancer (stage I to stage IV) were included. Preoperative clinical factors, serum tumor markers, and PET/CT radiomic features were included for the recurrence-free survival analysis. For the modeling and validation, patients were randomly divided into the training (n = 137) and validation (n = 59) set, while the 78 stage III patients [training (n = 55), and validation (n = 23)] was divided for the further experiment. After selecting features by the log-rank test and variable-hunting methods, random survival forest (RSF) models were built on the training set to analyze the prognostic value of selected features. The performance of models was measured by C-index and was tested on the validation set with bootstrapping. Feature importance and the Pearson correlation were also analyzed.ResultsRadiomics signature (containing four PET/CT features and four clinical factors) achieved the best result for prognostic prediction of 196 patients (C-index 0.780, 95% CI 0.634–0.877). Moreover, four features (including two clinical features and two radiomics features) were selected for prognostic prediction of the 78 stage III patients (C-index was 0.820, 95% CI 0.676–0.900). K–M curves of both models significantly stratified low-risk and high-risk groups (P < 0.0001). Pearson correlation analysis demonstrated that selected radiomics features were correlated with tumor metabolic factors, such as SUVmean, SUVmax.ConclusionThis study presents integrated clinico-biological-radiological models that can accurately predict the prognosis in colorectal cancer using the preoperative 18F-FDG PET/CT radiomics in colorectal cancer. It is of potential value in assisting the management and decision making for precision treatment in colorectal cancer.Trial registration The retrospectively registered study was approved by the Ethics Committee of Fudan University Shanghai Cancer Center (No. 1909207-14-1910) and the data were analyzed anonymously.

Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study.

PURPOSE Knowing the genetic phenotype of gastrointestinal stromal tumors (GISTs) is essential for patients who receive therapy with tyrosine kinase inhibitors. The aim of this study was to develop a radiomic algorithm for predicting GISTs with KIT exon 11 mutation. METHODS We enrolled 106 patients (80 in the training set, 26 in the validation set) with clinicopathologically confirmed GISTs from two centers. Preoperative and postoperative clinical characteristics were selected and analyzed to construct the clinical model. Arterial phase, venous phase, delayed phase, and tri-phase combined radiomics algorithms were generated from the training set based on contrast-enhanced computed tomography (CE-CT) images. Various radiomics feature selection methods were used, namely least absolute shrinkage and selection operator (LASSO); minimum redundancy maximum relevance (mRMR); and generalized linear model (GLM) as a machine-learning classifier. Independent predictive factors were determined to construct preoperative and postoperative radiomics nomograms by multivariate logistic regression analysis. The performances of the clinical model, radiomics algorithm, and radiomics nomogram in distinguishing GISTs with the KIT exon 11 mutation were evaluated by area under the curve (AUC) of the receiver operating characteristics. RESULTS Of 106 patients who underwent genetic analysis, 61 had the KIT exon 11 mutation. The combined radiomics algorithm was found to be the best prediction model for differentiating the expression status of the KIT exon 11 mutation (AUC = 0.836; 95% confidence interval [CI], 0.640-0.951) in the validation set. The clinical model, and preoperative and postoperative radiomics nomograms had AUCs of 0.606 (95% CI, 0.397-0.790), 0.715 (95% CI, 0.506-0.873), and 0.679 (95% CI, 0.468-0.847), respectively, with the validation set. CONCLUSION The radiomics algorithm could distinguish GISTs with the KIT exon 11 mutation based on CE-CT images and could potentially be used for selective genetic analysis to support the precision medicine of GISTs.

Benchmarking Eliminative Radiomic Feature Selection for Head and Neck Lymph Node Classification.

Simple SummaryPathologic cervical lymph nodes (LN) in head and neck squamous cell carcinoma (HNSCC) deteriorate prognosis. Current radiologic criteria for LN-classification are primarily shape-based. Radiomics is an emerging data-driven technique that aids in extraction, processing and analyzing features and is potentially capable of LN-classification. Currently available sets of features are too complex for clinical applicability. We identified the combination of sparse discriminant analysis and genetic algorithms as a potentially useful algorithm for eliminative feature selection. In this retrospective, cohort-study, from 252 LNs with over extracted 30,000 features, this algorithm retained a classification accuracy of up to 90% with only 10% of the original number of features. From a clinical perspective, the selected features appeared plausible and potentially capable of correctly classifying LNs. Both the identified algorithm and features need further exploration of their potential as prospective classifiers for LNs in HNSCC.In head and neck squamous cell carcinoma (HNSCC) pathologic cervical lymph nodes (LN) remain important negative predictors. Current criteria for LN-classification in contrast-enhanced computed-tomography scans (contrast-CT) are shape-based; contrast-CT imagery allows extraction of additional quantitative data (“features”). The data-driven technique to extract, process, and analyze features from contrast-CTs is termed “radiomics”. Extracted features from contrast-CTs at various levels are typically redundant and correlated. Current sets of features for LN-classification are too complex for clinical application. Effective eliminative feature selection (EFS) is a crucial preprocessing step to reduce the complexity of sets identified. We aimed at exploring EFS-algorithms for their potential to identify sets of features, which were as small as feasible and yet retained as much accuracy as possible for LN-classification. In this retrospective cohort-study, which adhered to the STROBE guidelines, in total 252 LNs were classified as “non-pathologic” (n = 70), “pathologic” (n = 182) or “pathologic with extracapsular spread” (n = 52) by two experienced head-and-neck radiologists based on established criteria which served as a reference. The combination of sparse discriminant analysis and genetic optimization retained up to 90% of the classification accuracy with only 10% of the original numbers of features. From a clinical perspective, the selected features appeared plausible and potentially capable of correctly classifying LNs. Both the identified EFS-algorithm and the identified features need further exploration to assess their potential to prospectively classify LNs in HNSCC.

Preoperative Prediction of BRAF Mutation Status in Colorectal Cancer Using a Clinical-radiomics Model

This study aimed to develop a clinically practical model to predict V-raf murine sarcoma viral oncogene homolog B1 (BRAF) mutation in colorectal cancer according to radiomic signatures based on computed tomography (CT) and clinical risk factors, and to determine the model's diagnostic accuracy for BRAF mutation status. This retrospective study included 140 patients with colorectal cancer. The significant clinical risk factors were used to build the clinical model; the least absolute shrinkage and selection operator algorithm was adopted to construct a radiomics signature according to imaging features of the tumor lesion, and stepwise logistic regression was applied to select the significant variables to develop the clinical-radiomics model. The predictive performance was evaluated by receiver operating characteristic curve analysis, calibration curve analysis, and decision curve analysis. The radscore, generated by 5 selected radiomics features, demonstrated a favorable ability to predict BRAF mutation in both the training (area under the receiver operating characteristic curve [AUC] 0.93) and validation (AUC 0.87) cohorts. Subsequently, integrating two independent predictors (including the radscore and clinical risk factors) into a nomogram exhibited more favorable discriminatory performance, with the AUC improved to 0.95 and 0.88 in both cohorts. Moreover, the accuracy for predicting BRAF mutations was higher than that of the clinical model, ranging from 0.70 to 0.89. The proposed CT-based radiomics signature is associated with BRAF mutations. The present study also proposes a combined model can potentially be applied in the individual preoperative prediction of BRAF mutation status in colorectal cancer. CT-based radiomics showed satisfactory diagnostic significance for the BRAF status in colorectal cancer, the clinical-combined model may be applied in the individual preoperative prediction of BRAF mutation.

Comparison of Comprehensive Morphological and Radiomics Features of Subsolid Pulmonary Nodules to Distinguish Minimally Invasive Adenocarcinomas and Invasive Adenocarcinomas in CT Scan.

ObjectiveTo investigative the diagnostic performance of the morphological model, radiomics model, and combined model in differentiating invasive adenocarcinomas (IACs) from minimally invasive adenocarcinomas (MIAs).MethodsThis study retrospectively involved 307 patients who underwent chest computed tomography (CT) examination and presented as subsolid pulmonary nodules whose pathological findings were MIAs or IACs from January 2010 to May 2018. These patients were randomly assigned to training and validation groups in a ratio of 4:1 for 10 times. Eighteen categories of morphological features of pulmonary nodules including internal and surrounding structure were labeled. The following radiomics features are extracted: first-order features, shape-based features, gray-level co-occurrence matrix (GLCM) features, gray-level size zone matrix (GLSZM) features, gray-level run length matrix (GLRLM) features, and gray-level dependence matrix (GLDM) features. The chi-square test and F1 test selected morphology features, and LASSO selected radiomics features. Logistic regression was used to establish models. Receiver operating characteristic (ROC) curves evaluated the effectiveness, and Delong analysis compared ROC statistic difference among three models.ResultsIn validation cohorts, areas under the curve (AUC) of the morphological model, radiomics model, and combined model of distinguishing MIAs from IACs were 0.88, 0.87, and 0.89; the sensitivity (SE) was 0.68, 0.81, and 0.83; and the specificity (SP) was 0.93, 0.79, and 0.87. There was no statistically significant difference in AUC between three models (p > 0.05).ConclusionThe morphological model, radiomics model, and combined model all have a high efficiency in the differentiation between MIAs and IACs and have potential to provide non-invasive assistant information for clinical decision-making.

Multimodality MRI-based radiomics approach to predict the posttreatment response of lung cancer brain metastases to gamma knife radiosurgery.

To develop and validate a multimodality MRI-based radiomics approach to predicting the posttreatment response of lung cancer brain metastases (LCBM) to gamma knife radiosurgery (GKRS). We retrospectively analyzed 213 lesions from 137 patients with LCBM who received GKRS between January 2017 and November 2020. The data were divided into a primary cohort (102 patients with 173 lesions) and an independent validation cohort (35 patients with 40 lesions) according to the time of treatment. Benefit result was defined using pretreatment and 3-month follow-up MRI images based on the Response Assessment in Neuro-Oncology Brain Metastases criteria. Valuable radiomics features were extracted from pretreatment multimodality MRI images using random forests. Prediction performance among the radiomics features of tumor core (RFTC) and radiomics features of peritumoral edema (RFPE) together was evaluated separately. Then, the random forest radiomics score and nomogram were developed through the primary cohort and evaluated through an independent validation cohort. Prediction performance was evaluated by ROC curve, calibration curve, and decision curve. Gender (p = 0.018), histological subtype (p = 0.009), epidermal growth factor receptor mutation (p = 0.034), and targeted drug treatment (p = 0.021) were significantly associated with posttreatment response. Adding RFPE to RFTC showed improved prediction performance than RFTC alone in primary cohort (AUC = 0.848 versus AUC = 0.750; p < 0.001). Finally, the radiomics nomogram had an AUC of 0.930, a C-index of 0.930 (specificity of 83.1%, sensitivity of 87.3%) in primary cohort, and an AUC of 0.852, a C-index of 0.848 (specificity of 84.2%, sensitivity of 76.2%) in validation cohort. Multimodality MRI-based radiomics models can predict the posttreatment response of LCBM to GKRS. • Among the selected radiomics features, texture features basically contributed the dominant force in prediction tasks (80%), especially gray-level co-occurrence matrix features (40%). • Adding RFPE to RFTC showed improved prediction performance than RFTC alone in primary cohort (AUC = 0.848 versus AUC = 0.750; p < 0.001). • The multimodality MRI-based radiomics nomogram showed high accuracy for distinguishing the posttreatment response of LCBM to GKRS (AUC = 0.930, in primary cohort; AUC = 0.852, in validation cohort).

Retrospective Analysis of the Value of Enhanced CT Radiomics Analysis in the Differential Diagnosis Between Pancreatic Cancer and Chronic Pancreatitis

PurposeTo investigate the feasibility of enhanced computed tomography (CT) radiomics analysis to differentiate between pancreatic cancer (PC) and chronic pancreatitis.Methods and materialsThe CT images of 151 PCs and 24 chronic pancreatitis were retrospectively analyzed in the three-dimensional regions of interest on arterial phase (AP) and venous phase (VP) and segmented by MITK software. A multivariable logistic regression model was established based on the selected radiomics features. The radiomics score was calculated, and the nomogram was established. The discrimination of each model was analyzed by the receiver operating characteristic curve (ROC). Decision curve analysis (DCA) was used to evaluate clinical utility. The precision recall curve (PRC) was used to evaluate whether the model is affected by data imbalance. The Delong test was adopted to compare the diagnostic efficiency of each model.ResultsSignificant differences were observed in the distribution of gender (P = 0.034), carbohydrate antigen 19-9 (P < 0.001), and carcinoembryonic antigen (P < 0.001) in patients with PC and chronic pancreatitis. The area under the ROC curve (AUC) value of AP multivariate regression model, VP multivariate regression model, AP combined with VP features model (Radiomics), clinical feature model, and radiomics combined with clinical feature model (COMB) was 0.905, 0.941, 0.941, 0.822, and 0.980, respectively. The sensitivity and specificity of the COMB model were 0.947 and 0.917, respectively. The results of DCA showed that the COMB model exhibited net clinical benefits and PRC shows that COMB model have good precision and recall (sensitivity).ConclusionThe COMB model could be a potential tool to distinguish PC from chronic pancreatitis and aid in clinical decisions.

Development of a Radiotherapy Localisation Computed Tomography-Based Radiomic Model for Predicting Survival in Patients With Nasopharyngeal Carcinoma Treated With Intensity-Modulated Radiotherapy Following Induction Chemotherapy.

BackgroundOur purpose is to develop a model combining radiomic features of radiotherapy localisation computed tomography and clinical characteristics that can be used to estimate overall survival in patients with nasopharyngeal carcinoma treated with intensity-modulated radiotherapy following induction chemotherapy.MethodsWe recruited 145 patients with pathologically confirmed nasopharyngeal carcinoma between February 2012 and April 2015. In total, 851 radiomic features were extracted from radiotherapy localisation computed tomography images for the gross tumour volume of the nasopharynx and the gross tumour volume of neck metastatic lymph nodes. The least absolute shrinkage and selection operator algorithm was applied to select radiomics features, build the model and calculate the Rad-score. The patients were divided into high- and low-risk groups based on their Rad-scores. A nomogram for estimating overall survival based on both radiomic and clinical features was generated using multivariate Cox regression hazard models. Prediction reliability was evaluated using Harrell’s concordance index.ResultsIn total, seven radiomic features and one clinical characteristic were extracted for survival analysis, and the combination of radiomic and clinical features was a better predictor of overall survival (concordance index = .849 [confidence interval: .782-.916]) than radiomic features (concordance index = .793 [confidence interval: .697-.890]) or clinical characteristics (concordance index = .661 [confidence interval: .673-.849]) alone.ConclusionOur results show that a nomogram combining radiomic features of radiotherapy localisation computed tomography and clinical characteristics can predict overall survival in patients with nasopharyngeal carcinoma treated with intensity-modulated radiotherapy following induction chemotherapy more effectively than radiomic features or clinical characteristics alone.

Novel Deep Learning Radiomics Model for Preoperative Evaluation of Hepatocellular Carcinoma Differentiation Based on Computed Tomography Data

Introduction: The evaluation of tumor differentiation is an urgent clinical issue that would facilitate the establishment of individualized therapeutic strategies. Our aims were to develop a deep learning radiomics model based on computed tomography(CT) data for preoperative evaluation of hepatocellular carcinoma(HCC) differentiation(low vs high grade), and to preliminarily explore the biological basis of the radiomics model. Methods: A total of 1234 HCC patients with contrast-enhanced CT images were recruited from two institutions. Radiomics features were extracted from preoperative venous-phase CT data and selected in terms of reproducibility, relevance and redundancy. The random forest(RF) algorithm was applied to establish a radiomics signature. The deep learning model was constructed based on a VGG network. Clinical characteristics of the subjects were used to construct a clinical model. The fused model integrated a tripartite prediction based on a logistic regression algorithm. Analysis was performed to explore the association between radiomics features and biological variables based on multiomics levels. Results: The radiomics signature established with the RF algorithm comprised 25 radiomics features. The AUCs in the training, internal validation, and independent test cohorts were 0.82, 0.76 and 0.75 respectively for the radiomics signature; 0.85, 0.81 and 0.75 respectively for the deep learning model; and 0.89, 0.83 and 0.80 respectively for the fused model. Multiomics analysis showed the selected radiomics features contained abundant biological information was related to tumor differentiation. Conclusions: Our deep learning radiomics model can serve as a noninvasive tool of preoperative HCC differentiation evaluation to guide clinical decision-making and prognostic stratification.

Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model.

Background/Aim: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumours. Previous data suggest that, in the central nervous system (CNS) tumours, amino acid PET may more accurately demarcate the active disease than paramagnetic enhanced MRI, which is currently the standard method of evaluation in brain tumours and helps in the assessment of disease grading, as a fundamental basis for proper clinical patient management. The aim of this study is to evaluate the feasibility of ML on 11[C]-MET PET/CT scan images and to propose a radiomics workflow using a machine-learning method to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours. Materials and Methods: In this retrospective study, fifty-six patients affected by a primary brain tumour who underwent 11[C]-MET PET/CT were selected from January 2016 to December 2019. Pathological examination was available in all patients to confirm the diagnosis and grading of disease. PET/CT acquisition was performed after 10 min from the administration of 11C-Methionine (401–610 MBq) for a time acquisition of 15 min. 11[C]-MET PET/CT images were acquired using two scanners (24 patients on a Siemens scan and 32 patients on a GE scan). Then, LIFEx software was used to delineate brain tumours using two different semi-automatic and user-independent segmentation approaches and to extract 44 radiomics features for each segmentation. A novel mixed descriptive-inferential sequential approach was used to identify a subset of relevant features that correlate with the grading of disease confirmed by pathological examination and clinical outcome. Finally, a machine learning model based on discriminant analysis was used in the evaluation of grading prediction (low grade CNS vs. high-grade CNS) of 11[C]-MET PET/CT. Results: The proposed machine learning model based on (i) two semi-automatic and user-independent segmentation processes, (ii) an innovative feature selection and reduction process, and (iii) the discriminant analysis, showed good performance in the prediction of tumour grade when the volumetric segmentation was used for feature extraction. In this case, the proposed model obtained an accuracy of ~85% (AUC ~79%) in the subgroup of patients who underwent Siemens tomography scans, of 80.51% (AUC 65.73%) in patients who underwent GE tomography scans, and of 70.31% (AUC 64.13%) in the whole patients’ dataset (Siemens and GE scans). Conclusions: This preliminary study on the use of an ML model demonstrated to be feasible and able to select radiomics features of 11[C]-MET PET with potential value in prediction of grading of disease. Further studies are needed to improve radiomics algorithms to personalize predictive and prognostic models and potentially support the medical decision process.

Radiomics Analysis Derived From LGE-MRI Predict Sudden Cardiac Death in Participants With Hypertrophic Cardiomyopathy

Objectives: To identify significant radiomics features derived from late gadolinium enhancement (LGE) images in participants with hypertrophic cardiomyopathy (HCM) and assess their prognostic value in predicting sudden cardiac death (SCD) endpoint.Method: The 157 radiomic features of 379 sequential participants with HCM who underwent cardiovascular magnetic resonance imaging (MRI) were extracted. CoxNet (Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net) and Random Forest models were applied to optimize feature selection for the SCD risk prediction and cross-validation was performed.Results: During a median follow-up of 29 months (interquartile range, 20–42 months), 27 participants with HCM experienced SCD events. Cox analysis revealed that two selected features, local binary patterns (LBP) (19) (hazard ratio (HR), 1.028, 95% CI: 1.032–1.134; P = 0.001) and Moment (1) (HR, 1.212, 95%CI: 1.032–1.423; P = 0.02) provided significant prognostic value to predict the SCD endpoints after adjustment for the clinical risk predictors and late gadolinium enhancement. Furthermore, the univariately significant risk predictor was improved by the addition of the selected radiomics features, LBP (19) and Moment (1), to predict SCD events (P < 0.05).Conclusion: The radiomics features of LBP (19) and Moment (1) extracted from LGE images, reflecting scar heterogeneity, have independent prognostic value in identifying high SCD risk patients with HCM.

Overall Survival Prognostic Modelling of Non-small Cell Lung Cancer Patients Using Positron Emission Tomography/Computed Tomography Harmonised Radiomics Features: The Quest for the Optimal Machine Learning Algorithm

AimsDespite the promising results achieved by radiomics prognostic models for various clinical applications, multiple challenges still need to be addressed. The two main limitations of radiomics prognostic models include information limitation owing to single imaging modalities and the selection of optimum machine learning and feature selection methods for the considered modality and clinical outcome. In this work, we applied several feature selection and machine learning methods to single-modality positron emission tomography (PET) and computed tomography (CT) and multimodality PET/CT fusion to identify the best combinations for different radiomics modalities towards overall survival prediction in non-small cell lung cancer patients. Materials and methodsA PET/CT dataset from The Cancer Imaging Archive, including subjects from two independent institutions (87 and 95 patients), was used in this study. Each cohort was used once as training and once as a test, followed by averaging of the results. ComBat harmonisation was used to address the centre effect. In our proposed radiomics framework, apart from single-modality PET and CT models, multimodality radiomics models were developed using multilevel (feature and image levels) fusion. Two different methods were considered for the feature-level strategy, including concatenating PET and CT features into a single feature set and alternatively averaging them. For image-level fusion, we used three different fusion methods, namely wavelet fusion, guided filtering-based fusion and latent low-rank representation fusion. In the proposed prognostic modelling framework, combinations of four feature selection and seven machine learning methods were applied to all radiomics modalities (two single and five multimodalities), machine learning hyper-parameters were optimised and finally the models were evaluated in the test cohort with 1000 repetitions via bootstrapping. Feature selection and machine learning methods were selected as popular techniques in the literature, supported by open source software in the public domain and their ability to cope with continuous time-to-event survival data. Multifactor ANOVA was used to carry out variability analysis and the proportion of total variance explained by radiomics modality, feature selection and machine learning methods was calculated by a bias-corrected effect size estimate known as ω2. ResultsOptimum feature selection and machine learning methods differed owing to the applied radiomics modality. However, minimum depth (MD) as feature selection and Lasso and Elastic-Net regularized generalized linear model (glmnet) as machine learning method had the highest average results. Results from the ANOVA test indicated that the variability that each factor (radiomics modality, feature selection and machine learning methods) introduces to the performance of models is case specific, i.e. variances differ regarding different radiomics modalities and fusion strategies. Overall, the greatest proportion of variance was explained by machine learning, except for models in feature-level fusion strategy. ConclusionThe identification of optimal feature selection and machine learning methods is a crucial step in developing sound and accurate radiomics risk models. Furthermore, optimum methods are case specific, differing due to the radiomics modality and fusion strategy used.

A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma.

ObjectiveThis study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs).Methods370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).ResultsUsing multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness.ConclusionThe CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.

MRI-Based Bone Marrow Radiomics Nomogram for Prediction of Overall Survival in Patients With Multiple Myeloma.

PurposeTo develop and validate a radiomics nomogram for predicting overall survival (OS) in multiple myeloma (MM) patients.Material and MethodsA total of 121 MM patients was enrolled and divided into training (n=84) and validation (n=37) sets. The radiomics signature was established by the selected radiomics features from lumbar MRI. The radiomics signature and clinical risk factors were integrated in multivariate Cox regression model for constructing radiomics nomogram to predict MM OS. The predictive ability and accuracy of the nomogram were evaluated by the index of concordance (C-index) and calibration curves, and compared with other four models including the clinical model, radiomics signature model, the Durie-Salmon staging system (D-S) and the International Staging System (ISS). The potential association between the radiomics signature and progression-free survival (PFS) was also explored.ResultsThe radiomics signature, 1q21 gain, del (17p), and β2-MG≥5.5 mg/L showed significant association with MM OS. The predictive ability of radiomics nomogram was better than the clinical model, radiomics signature model, the D-S and the ISS (C-index: 0.793 vs. 0.733 vs. 0.742 vs. 0.554 vs. 0.671 in training set, and 0.812 vs. 0.799 vs.0.717 vs. 0.512 vs. 0.761 in validation set). The radiomics signature lacked the predictive ability for PFS (log-rank P=0.001 in training set and log-rank P=0.103 in validation set), whereas the 1-, 2- and 3-year PFS rates all showed significant difference between the high and low risk groups (P ≤ 0.05).ConclusionThe MRI-based bone marrow radiomics may be an additional useful tool for MM OS prediction.

Predicting haemodynamic significance of coronary stenosis with radiomics-based pericoronary adipose tissue characteristics

To investigate the diagnostic performance of the radiomics features of pericoronary adipose tissue (PCAT) in determining haemodynamically significant coronary artery stenosis as evaluated by fractional flow reserve (FFR). A total of 92 patients with clinically suspected coronary artery disease who underwent coronary computed tomography (CT) angiography (CCTA), invasive coronary angiography (ICA), and FFR examination within 1 month were included retrospectively, and 121 lesions were randomly assigned to the training and testing set. Based on manual segmentation of PCAT, 1,116 radiomics features were computed. After radiomics robustness assessment and feature selection, radiomics models were established using the different machine-learning algorithms. The area under the receiver operating characteristic (ROC) curve (AUC) and net reclassification index (NRI) were analysed to compare the discrimination and reclassification abilities of radiomics models. Two radiomics features were selected after exclusions, and both were significantly higher in coronary arteries with FFR ≤0.8 than those with FFR >0.8. ROC analysis showed that the combination of CCTA and decision tree radiomics model achieved significantly higher diagnostic performance (AUC: 0.812) than CCTA alone (AUC: 0.599, p=0.015). Furthermore, the NRI of the combined model was 0.820 and 0.775 in the training and testing sets, respectively, suggesting the radiomics features of PCAT had were effective in classifying the haemodynamic significance of coronary stenosis. Adding PCAT radiomics features to CCTA enabled identification of haemodynamically significant coronary artery stenosis.

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

PurposeTo investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs).MethodsThis retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses.ResultsSVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model.ConclusionA combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.

Quantification of Cancer-Developing Idiopathic Pulmonary Fibrosis Using Whole-Lung Texture Analysis of HRCT Images.

Simple SummaryIdiopathic pulmonary fibrosis (IPF) patients have a significantly higher risk of developing lung cancer. Traditional risk factors including age, male gender, smoking status, and emphysema have been reported. However, there are only limited data on radiomics features from HRCT images useful for risk stratification of IPF patients for lung cancer. In this study, we found that texture-based radiomics features can be differentiated between IPF patients with and without cancer development, and their diagnostic accuracy is not inferior to that of traditional risk factors. By combining radiomics features and traditional risk factors, the diagnostic accuracy can be improved.Idiopathic pulmonary fibrosis (IPF) patients have a significantly higher risk of developing lung cancer (LC). There is only limited evidence of the use of texture-based radiomics features from high-resolution computed tomography (HRCT) images for risk stratification of IPF patients for LC. We retrospectively enrolled subjects who suffered from IPF in this study. Clinical data including age, gender, smoking status, and pulmonary function were recorded. Non-contrast chest CT for fibrotic score calculation and determination of three dimensional measures of whole-lung texture and emphysema were performed using a promising deep learning imaging platform. The results revealed that among 116 subjects with IPF (90 non-cancer and 26 lung cancer cases), the radiomics features showed significant differences between non-cancer and cancer patients. In the training cohort, the diagnostic accuracy using selected radiomics features with AUC of 0.66–0.73 (sensitivity of 80.0–85.0% and specificity of 54.2–59.7%) was not inferior to that obtained using traditional risk factors, such as gender, smoking status, and emphysema (%). In the validation cohort, the combination of radiomics features and traditional risk factors produced a diagnostic accuracy of 0.87 AUC and an accuracy of 75.0%. In this study, we found that whole-lung CT texture analysis is a promising tool for LC risk stratification of IPF patients.

Incorporating Robustness to Imaging Physics into Radiomic Feature Selection for Breast Cancer Risk Estimation.

Simple SummaryMammographic density estimates can be combined with radiomic texture features to offer an even better assessment of breast cancer risk. However, some feature variations will be due to true parenchymal differences between women, but others will be due to imaging physics effects (contrast, noise, and image sharpness); features robust to imaging physics effects should better model risk. To investigate this, we imaged an anthropomorphic phantom at various x-ray technique settings, allowing us to directly measure the effects of imaging physics on feature values. We compared these variations, for each feature, with the inter-woman variation in a screening population (552 cancer-free women) and the intra-woman variation between each woman’s left and right breasts, to assess which features were relatively robust to physics settings. We then tested more- versus less-robust features in modeling cancer risk on an independent case-control data set, and demonstrated that more-robust features were indeed better at risk prediction.Digital mammography has seen an explosion in the number of radiomic features used for risk-assessment modeling. However, having more features is not necessarily beneficial, as some features may be overly sensitive to imaging physics (contrast, noise, and image sharpness). To measure the effects of imaging physics, we analyzed the feature variation across imaging acquisition settings (kV, mAs) using an anthropomorphic phantom. We also analyzed the intra-woman variation (IWV), a measure of how much a feature varies between breasts with similar parenchymal patterns—a woman’s left and right breasts. From 341 features, we identified “robust” features that minimized the effects of imaging physics and IWV. We also investigated whether robust features offered better case-control classification in an independent data set of 575 images, all with an overall BI-RADS® assessment of 1 (negative) or 2 (benign); 115 images (cases) were of women who developed cancer at least one year after that screening image, matched to 460 controls. We modeled cancer occurrence via logistic regression, using cross-validated area under the receiver-operating-characteristic curve (AUC) to measure model performance. Models using features from the most-robust quartile of features yielded an AUC = 0.59, versus 0.54 for the least-robust, with p < 0.005 for the difference among the quartiles.

A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules.

Lung cancer is the most common cancer and the leading cause of cancer-related death worldwide. The optimal management of computed tomography (CT)-indeterminate pulmonary nodules is important. To optimize individualized follow-up strategies, we developed a radiomics nomogram for predicting 2-year growth in case of indeterminate small pulmonary nodules. A total of 215 histopathology-confirmed small pulmonary nodules (21 benign and 194 malignant) in 205 patients with ultra-high-resolution CT (U-HRCT) were divided into growth and nongrowth nodules and were randomly allocated to the primary (n = 151) or validation (n = 64) group. The least absolute shrinkage and selection operator (LASSO) method was used for radiomics feature selection and radiomics signature determination. Multivariable logistic regression analysis was used to develop a radiomics nomogram that integrated the radiomics signature with significant clinical parameters (sex and nodule type). The area under the curve (AUC) was applied to assess the predictive performance of the radiomics nomogram. The net benefit of the radiomics nomogram was assessed using a clinical decision curve. The radiomics signature and nomogram yielded AUCs of 0.892 (95% confidence interval [CI]: 0.843-0.940) and 0.911 (95% CI: 0.867-0.955), respectively, in the primary group and 0.826 (95% CI: 0.727-0.926) and 0.843 (95% CI: 0.749-0.937), respectively, in the validation group. The clinical usefulness of the nomogram was demonstrated by decision curve analysis. A radiomics nomogram was developed by integrating the radiomics signature with clinical parameters and was easily used for the individualized prediction of two-year growth in case of CT-indeterminate small pulmonary nodules. • A radiomics nomogram was developed for predicting the two-year growth of CT-indeterminate small pulmonary nodules. • The nomogram integrated a CT-based radiomics signature with clinical parameters and was valuable in developing an individualized follow-up strategy for patients with indeterminate small pulmonary nodules.