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Radiomics and artificial intelligence have the potential to become a valuable tool in clinical applications. Frequently, radiomic analyses through machine learning methods present issues caused by high dimensionality and multicollinearity, and redundant radiomic features are usually removed based on correlation analysis. We assessed the effect of preprocessing—in terms of voxel size resampling, discretization, and filtering—on correlation-based dimensionality reduction in radiomic features from cardiac T1 and T2 maps of patients with hypertrophic cardiomyopathy. For different combinations of preprocessing parameters, we performed a dimensionality reduction of radiomic features based on either Pearson's or Spearman's correlation coefficient, followed by the computation of the stability index. With varying resampling voxel size and discretization bin width, for both T1 and T2 maps, Pearson's and Spearman's dimensionality reduction produced a slightly different percentage of remaining radiomic features, with a relatively high stability index. For different filters, the remaining features' stability was instead relatively low. Overall, the percentage of eliminated radiomic features through correlation-based dimensionality reduction was more dependent on resampling voxel size and discretization bin width for textural features than for shape or first-order features. Notably, correlation-based dimensionality reduction was less sensitive to preprocessing when considering radiomic features from T2 compared with T1 maps. [ABSTRACT FROM AUTHOR]

This study aims to review the current literature on methods of preoperative prediction of pituitary adenoma consistency. Pituitary adenoma consistency may be a limiting factor for successful surgical removal of tumors. Efforts have been made to investigate the possibility of an accurate assessment of the preoperative consistency to allow for safer and more effective surgery planning. We searched major scientific databases and systematically analyzed the results. A total of 54 relevant articles were identified and selected for inclusion. These studies evaluated methods based on either MRI intensity, enhancement, radiomics, MR elastometry, or CT evaluation. The results of these studies varied widely. Most studies used the average intensity of either T2WI or ADC maps. Firm tumors appeared hyperintense on T2WI, although only 55% of the studies reported statistically significant results. There are mixed reports on ADC values in firm tumors with findings of increased values (28%), decreased values (22%), or no correlation (50%). Multiple contrast enhancement-based methods showed good results in distinguishing between soft and firm tumors. There were mixed reports on the utility of MR elastography. Attempts to develop radiomics and machine learning-based models have achieved high accuracy and AUC values; however, they are prone to overfitting and need further validation. Multiple methods of preoperative consistency assessment have been studied. None demonstrated sufficient accuracy and reliability in clinical use. Further efforts are needed to enable reliable surgical planning. [ABSTRACT FROM AUTHOR]

Background: Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy.Methods: A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results.Results: First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression.Conclusions: 18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction. [ABSTRACT FROM AUTHOR]

Background: Identifying high-risk intracranial plaques is significant for the treatment and prevention of stroke. Objective: To develop a high-risk plaque model using three-dimensional (3D) high-resolution magnetic resonance imaging (HRMRI) based radiomics features and machine learning. Methods: 136 patients with documented symptomatic intracranial artery stenosis and available HRMRI data were included. Among these patients, 136 and 92 plaques were identified as symptomatic and asymptomatic plaques, respectively. A conventional model was developed by recording and quantifying the radiological plaque characteristics. Radiomics features from T1-weighted images (T1WI) and contrast-enhanced T1WI (CE-T1WI) were used to construct a high-risk plaque model with linear support vector classification (linear SVC). The radiological and radiomics features were combined to build a combined model. Receiver operating characteristic (ROC) curves were used to evaluate these models. Results: Plaque length, burden, and enhancement were independently associated with clinical symptoms and were included in the conventional model, which had an AUC of 0.853 vs. 0.837 in the training and test sets. While the radiomics and the combined model showed an improved AUC: 0.923 vs. 0.925 for the training sets and 0.906 vs. 0.903 in the test sets. Both the radiomics model (p = 0.024, p = 0.018) and combined model (p = 0.042, p = 0.049) outperformed the conventional model in the two sets, whereas the performance of the combined model was not significantly different from that of the radiomics model in the two sets (p = 0.583 and p = 0.606). Conclusion: The radiomics model based on 3D HRMRI can accurately differentiate symptomatic from asymptomatic intracranial arterial plaques and significantly outperforms the conventional model. [ABSTRACT FROM AUTHOR]

Objective: To explore the characteristics of peripheral blood, high resolution computed tomography (HRCT) imaging and the radiomics signature (RadScore) in patients infected with delta variant virus under different coronavirus disease (COVID-19) vaccination status. Methods: 123 patients with delta variant virus infection collected from November 1, 2021 to March 1, 2022 were analyzed retrospectively. According to COVID-19 vaccination Status, they were divided into three groups: Unvaccinated group, partially vaccinated group and full vaccination group. The peripheral blood, chest HRCT manifestations and RadScore of each group were analyzed and compared. Results: The mean lymphocyte count 1.22 ± 0.49 × 10^9/L, CT score 7.29 ± 3.48, RadScore 0.75 ± 0.63 in the unvaccinated group; The mean lymphocyte count 1.55 ± 0.70 × 10^9/L, CT score 5.27 ± 2.72, RadScore 1.03 ± 0.46 in the partially vaccinated group; The mean lymphocyte count 1.87 ± 0.70 × 10^9/L, CT score 3.59 ± 3.14, RadScore 1.23 ± 0.29 in the fully vaccinated group. There were significant differences in lymphocyte count, CT score and RadScore among the three groups (all p < 0.05); Compared with the other two groups, the lung lesions in the unvaccinated group were more involved in multiple lobes, of which 26 cases involved the whole lung. Conclusions: Through the analysis of clinical features, pulmonary imaging features and radiomics, we confirmed the positive effect of COVID-19 vaccine on pulmonary inflammatory symptoms and lymphocyte count (immune system) during delta mutant infection. [ABSTRACT FROM AUTHOR]

Objectives: To develop and evaluate magnetic resonance imaging (MRI)-based radiomics models for predicting residual myoma regrowth within 1 year after high-intensity focused ultrasound (HIFU) ablation of uterine myomas.Methods: A retrospective analysis of residual myoma regrowth within 1 year was performed on 428 myomas in 339 patients who were diagnosed with uterine myoma and treated with HIFU ablation in two hospital centers. In total, 851 radiomics features were extracted from T2-weighted images (T2WI) obtained 1 day after HIFU ablation, and the least absolute shrinkage and selection operator in the training cohort (n = 243) was employed to select radiomics features. Support vector machines were adopted to develop radiomics, clinicoradiological and combined radiomics-clinical models to predict residual myoma regrowth, defined as an increase in residual myoma volume of > 10% between that at day 1 post HIFU and that at follow-up MRI within 1 year. These models were validated in both internal (n = 81) and external (n = 104) test cohorts. The predictive performance and clinical application of these models were assessed using receiver-operating-characteristics-curve analysis, the area under the curve (AUC) and decision-curve analysis.Results: The AUCs of the T2WI-based radiomics prediction model in the internal and external test cohorts were 0.834 (95% CI, 0.747-0.920) and 0.801 (95% CI, 0.712-0.889), respectively, and those of the clinicoradiological model were 0.888 (95% CI, 0.816-0.960) and 0.912 (95% CI, 0.851-0.973), respectively. The combined model had better predictive performance than either the radiomics or the clinicoradiological model, with AUC values of 0.922 (95% CI, 0.857-0.987) and 0.930 (95% CI, 0.880-0.980) in the internal and external test cohorts, respectively. Decision-curve analysis also indicated that application of the combined model has clinical value, this model achieving more net benefits than the other two models.Conclusion: T2WI-based radiomics features can predict effectively the occurrence of residual myoma regrowth within 1 year after HIFU ablation of uterine myomas, which serves as an accurate and convenient reference for clinical decision-making. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Background: Radiomics is the high throughput analysis of medical images using computer algorithms, which specifically assess textural features. It has increasingly been proposed as a tool for the development of imaging biomarkers. However, an important acknowledged limitation of radiomics is the lack of reproducibility of features produced. Purpose: To assess reproducibility and repeatability of radiomics variables in brain MRI through a multivisit, multicenter study. Study Type: Retrospective. Population: Fourteen individuals visiting three institutions twice, 10 males with the mean age of 36.3 years and age range 25–51. Field Strength 3D T1W inversion recovery on three 1.5‐T General Electric scanners. Assessment Radiomics analysis by a consultant radiologist performed on the T1W images of the whole brain on all visits. All possible radiomics features were generated. Statistical Test: Concordance correlation coefficient (CCC) and dynamic range (DR) for all variables were calculated to assess the test–retest repeatability. Intraclass correlation coefficients (ICCs) were calculated to investigate the reproducibility of features across centers. Results: Of 1596 features generated, 57 from center 1, 15 from center 2, and 22 from center 3 had a CCC > 0.9 and DR > 0.9. Eight variables had CCC > 0.9 and DR > 0.9 in all centers. Forty‐one variables had an ICC of >0.9. No variables had CCC > 0.9, DR > 0.9, and ICC > 0.9. Data Conclusion: Repeatability and reproducibility of variables is a significant limitation of radiomics analysis in 3DT1W brain MRI. Careful selection of radiomic features is required. Level of Evidence: 4 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) imaging data can be used to develop computer-assisted diagnostic tools for neurodegenerative diseases such as aspartylglucosaminuria (AGU) and other lysosomal storage disorders. MR images contain features that are suitable for the classification and differentiation of affected individuals from healthy persons. Here, comparisons were made between MRI features extracted from different types of magnetic resonance images. Random forest classifiers were trained to classify AGU patients (n = 22) and healthy controls (n = 24) using volumetric features extracted from T1-weighted MR images, the zone variance of gray level size zone matrix (GLSZM) calculated from magnitude susceptibility-weighted MR images, and the caudate–thalamus intensity ratio computed from T2-weighted MR images. The leave-one-out cross-validation and area under the receiver operating characteristic curve were used to compare different models. The left–right-averaged, normalized volumes of the 25 nuclei of the thalamus and the zone variance of the thalamus demonstrated equal and excellent performance as classifier features for binary organization between AGU patients and healthy controls. Our findings show that texture-based features of susceptibility-weighted images and thalamic volumes can differentiate AGU patients from healthy controls with a very low error rate. [ABSTRACT FROM AUTHOR]

Defining the exact histological features of salivary gland malignancies before treatment remains an unsolved problem that compromises the ability to tailor further therapeutic steps individually. Radiomics, a new methodology to extract quantitative information from medical images, could contribute to characterizing the individual cancer phenotype already before treatment in a fast and non-invasive way. Consequently, the standardization and implementation of radiomic analysis in the clinical routine work to predict histology of salivary gland cancer (SGC) could also provide improvements in clinical decision-making. In this study, we aimed to investigate the potential of radiomic features as imaging biomarker to distinguish between high grade and low-grade salivary gland malignancies. We have also investigated the effect of image and feature level harmonization on the performance of radiomic models. For this study, our dual center cohort consisted of 126 patients, with histologically proven SGC, who underwent curative-intent treatment in two tertiary oncology centers. We extracted and analyzed the radiomics features of 120 pre-therapeutic MRI images with gadolinium (T1 sequences), and correlated those with the definitive post-operative histology. In our study the best radiomic model achieved average AUC of 0.66 and balanced accuracy of 0.63. According to the results, there is significant difference between the performance of models based on MRI intensity normalized images + harmonized features and other models (p value < 0.05) which indicates that in case of dealing with heterogeneous dataset, applying the harmonization methods is beneficial. Among radiomic features minimum intensity from first order, and gray level-variance from texture category were frequently selected during multivariate analysis which indicate the potential of these features as being used as imaging biomarker. The present bicentric study presents for the first time the feasibility of implementing MR-based, handcrafted radiomics, based on T1 contrast-enhanced sequences and the ComBat harmonization method in an effort to predict the formal grading of salivary gland carcinoma with satisfactory performance., (© 2024. The Author(s).)

Objective: To construct a prognostic model based on MR features and clinical data to evaluate the progression free survival (PFS), overall survival (OS) and objective response rate (ORR) of pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy., Methods: 105 pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy were assigned to the training set (n = 52), validation set (n = 22), and testing set (n = 31). Multi-lesion volume of interest were delineated, multi-sequence radiomics features were extracted, and the radiomics models for predicting PFS, OS and ORR were constructed, respectively. Clinical variables were extracted, and the clinical models for predicting PFS, OS and ORR were constructed, respectively. The nomogram was jointly constructed by radiomics model and clinical model., Result: The ORR exhibits no significant correlation with either PFS or OS. The area under the curve (AUC) of nomogram for predicting 6-month PFS reached 0.847 (0.737-0.957), 0.786 (0.566-1.000) and 0.864 (0.735-0.994) in the training set, validation set and testing set, respectively. The AUC of nomogram for predicting 1-year OS reached 0.770 (0.635-0.906), 0.743 (0.479-1.000) and 0.818 (0.630-1.000), respectively. The AUC of nomogram for predicting ORR reached 0.914 (0.828-1.00), 0.938 (0.840-1.00) and 0.846 (0.689-1.00), respectively., Conclusion: The prognostic models based on MR imaging features and clinical data are effective in predicting the PFS, OS and ORR of chemoimmunotherapy in pancreatic cancer patients with hepatic metastasis, and can be used to evaluate the prognosis of patients., (© 2024. The Author(s).)

This study assesses the feasibility of using a sample-efficient model to investigate radiomics changes over time for predicting progression-free survival in rare diseases. Eighteen high-grade glioma patients underwent two L-3,4-dihydroxy-6-[ 18 F]-fluoro-phenylalanine positron emission tomography (PET) dynamic scans: the first during treatment and the second at temozolomide chemotherapy discontinuation. Radiomics features from static/dynamic parametric images, alongside conventional features, were extracted. After excluding highly correlated features, 16 different models were trained by combining various feature selection methods and time-to-event survival algorithms. Performance was assessed using cross-validation. To evaluate model robustness, an additional dataset including 35 patients with a single PET scan at therapy discontinuation was used. Model performance was compared with a strategy extracting informative features from the set of 35 patients and applying them to the 18 patients with 2 PET scans. Delta-absolute radiomics achieved the highest performance when the pipeline was directly applied to the 18-patient subset (support vector machine (SVM) and recursive feature elimination (RFE): C-index = 0.783 [0.744-0.818]). This result remained consistent when transferring informative features from 35 patients (SVM + RFE: C-index = 0.751 [0.716-0.784], p = 0.06). In addition, it significantly outperformed delta-absolute conventional (C-index = 0.584 [0.548-0.620], p < 0.001) and single-time-point radiomics features (C-index = 0.546 [0.512-0.580], p < 0.001), highlighting the considerable potential of delta radiomics in rare cancer cohorts., (© 2024. The Author(s).)

Here, we establish a CT-radiomics based method for application in invasive, orthotopic rodent brain tumour models. Twenty four NOD/SCID mice were implanted with U87R-Luc2 GBM cells and longitudinally imaged via contrast enhanced (CE-CT) imaging. Pyradiomics was employed to extract CT-radiomic features from the tumour-implanted hemisphere and non-tumour-implanted hemisphere of acquired CT-scans. Inter-correlated features were removed (Spearman correlation > 0.85) and remaining features underwent predictive analysis (recursive feature elimination or Boruta algorithm). An area under the curve of the receiver operating characteristic curve was implemented to evaluate radiomic features for their capacity to predict defined outcomes. Firstly, we identified a subset of radiomic features which distinguish the tumour-implanted hemisphere and non- tumour-implanted hemisphere (i.e, tumour presence from normal tissue). Secondly, we successfully translate preclinical CT-radiomic pipelines to GBM patient CT scans (n = 10), identifying similar trends in tumour-specific feature intensities (E.g. 'glszm Zone Entropy'), thereby suggesting a mouse-to-human species conservation (a conservation of radiomic features across species). Thirdly, comparison of features across timepoints identify features which support preclinical tumour detection earlier than is possible by visual assessment of CT scans. This work establishes robust, preclinical CT-radiomic pipelines and describes the application of CE-CT for in-depth orthotopic brain tumour monitoring. Overall we provide evidence for the role of pre-clinical 'discovery' radiomics in the neuro-oncology space., (© 2024. The Author(s).)

To explore the value of CT-based radiomics model in the differential diagnosis of benign ovarian tumors (BeOTs), borderline ovarian tumors (BOTs), and early malignant ovarian tumors (eMOTs). The retrospective research was conducted with pathologically confirmed 258 ovarian tumor patients from January 2014 to February 2021. The patients were randomly allocated to a training cohort (n = 198) and a test cohort (n = 60). By providing a three-dimensional (3D) characterization of the volume of interest (VOI) at the maximum level of images, 4238 radiomic features were extracted from the VOI per patient. The Wilcoxon-Mann-Whitney (WMW) test, least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were employed to select the radiomic features. Five machine learning (ML) algorithms were applied to construct three-class diagnostic models. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the radiomics models. The test cohort was used to verify the generalization ability of the radiomics models. The receiver-operating characteristic (ROC) was used to evaluate diagnostic performance of radiomics model. Global and discrimination performance of five models was evaluated by average area under the ROC curve (AUC). The average ROC indicated that random forest (RF) diagnostic model in training cohort demonstrated the best diagnostic performance (micro/macro average AUC, 0.98/0.99), which was then confirmed with by LOOCV (micro/macro average AUC, 0.89/0.88) and external validation (test cohort) (micro/macro average AUC, 0.81/0.79). Our proposed CT-based radiomics diagnostic models may effectively assist in preoperatively differentiating BeOTs, BOTs, and eMOTs., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Recurrence occurs in up to 36% of patients treated with curative-intent radiotherapy for NSCLC. Identifying patients at higher risk of recurrence for more intensive surveillance may facilitate the earlier introduction of the next line of treatment. We aimed to use radiotherapy planning CT scans to develop radiomic classification models that predict overall survival (OS), recurrence-free survival (RFS) and recurrence two years post-treatment for risk-stratification. A retrospective multi-centre study of >900 patients receiving curative-intent radiotherapy for stage I-III NSCLC was undertaken. Models using radiomic and/or clinical features were developed, compared with 10-fold cross-validation and an external test set, and benchmarked against TNM-stage. Respective validation and test set AUCs (with 95% confidence intervals) for the radiomic-only models were: (1) OS: 0.712 (0.592–0.832) and 0.685 (0.585–0.784), (2) RFS: 0.825 (0.733–0.916) and 0.750 (0.665–0.835), (3) Recurrence: 0.678 (0.554–0.801) and 0.673 (0.577–0.77). For the combined models: (1) OS: 0.702 (0.583–0.822) and 0.683 (0.586–0.78), (2) RFS: 0.805 (0.707–0.903) and 0·755 (0.672–0.838), (3) Recurrence: 0·637 (0.51–0.·765) and 0·738 (0.649–0.826). Kaplan-Meier analyses demonstrate OS and RFS difference of >300 and >400 days respectively between low and high-risk groups. We have developed validated and externally tested radiomic-based prediction models. Such models could be integrated into the routine radiotherapy workflow, thus informing a personalised surveillance strategy at the point of treatment. Our work lays the foundations for future prospective clinical trials for quantitative personalised risk-stratification for surveillance following curative-intent radiotherapy for NSCLC. [ABSTRACT FROM AUTHOR]

Background and purpose: The aim of the study is to predict the subthalamic nucleus (STN) deep brain stimulation (DBS) outcomes for Parkinson's disease (PD) patients using the radiomic features extracted from pre-operative magnetic resonance images (MRI). Methods: The study included 34 PD patients who underwent DBS implantation in the STN. Five patients (15%) showed poor DBS motor outcome. All together 9 amygdalar nuclei and 12 hippocampus subfields were segmented using Freesurfer 7.0 pipeline from pre-operative MRI images. Furthermore, PyRadiomics platform was used to extract 120 radiomic features for each nuclei and subfield resulting in 5,040 features. Minimum Redundancy Maximum Relevance (mRMR) feature selection method was employed to reduce the number of features to 20, and 8 machine learning methods (regularized binary logistic regression (LR), decision tree classifier (DT), linear discriminant analysis (LDA), naive Bayes classifier (NB), kernel support vector machine (SVM), deep feed-forward neural network (DNN), one-class support vector machine (OC-SVM), feed-forward neural network-based autoencoder for anomaly detection (DNN-A)) were applied to build the models for poor vs. good and very good STN-DBS motor outcome prediction. Results: The highest mean prediction accuracy was obtained using regularized LR (96.65 ± 7.24%, AUC 0.98 ± 0.06) and DNN (87.25 ± 14.80%, AUC 0.87 ± 0.18). Conclusion: The results show the potential power of the radiomic features extracted from hippocampus and amygdala MRI in the prediction of STN-DBS motor outcomes for PD patients. [ABSTRACT FROM AUTHOR]

Radiomics is a challenging development area in imaging field that is greatly capturing interest of radiologists and neuroscientists. However, radiomics features show a strong non-biological variability determined by different facilities and imaging protocols, limiting the reproducibility and generalizability of analysis frameworks. Our study aimed to investigate the usefulness of harmonization to reduce site-effects on radiomics features over specific brain regions. We selected T1-weighted magnetic resonance imaging (MRI) by using the MRI dataset Parkinson's Progression Markers Initiative (PPMI) from different sites with healthy controls (HC) and Parkinson's disease (PD) patients. First, the investigation of radiomics measure discrepancies were assessed on healthy brain regions-of-interest (ROIs) via a classification pipeline based on LASSO feature selection and support vector machine (SVM) model. Then, a ComBat-based harmonization approach was applied to correct site-effects. Finally, a validation step on PD subjects evaluated diagnostic accuracy before and after harmonization of radiomics data. Results on healthy subjects demonstrated a dependence from site-effects that could be corrected with ComBat harmonization. LASSO regressor after harmonization was unable to select any feature to distinguish controls by site. Moreover, harmonized radiomics features achieved an area under the receiving operating characteristic curve (AUC) of 0.77 (compared to AUC of 0.71 for raw radiomics measures) in distinguish Parkinson's patients from HC. We found a not-negligible site-effect studying radiomics of HC preand post-harmonization of features. Our validation study on PD patients demonstrated a significant influence of non-biological noise source in diagnostic performances. Finally, harmonization of multicenter radiomic data represent a necessary step to make analysis pipelines reliable and replicable for multisite neuroimaging studies. [ABSTRACT FROM AUTHOR]

Radiomics in neuroimaging uses fully automatic segmentation to delineate the anatomical areas for which radiomic features are computed. However, differences among these segmentation methods affect radiomic features to an unknown extent. A scan-rescan dataset (n = 46) of T1-weighted and diffusion tensor images was used. Subjects were split into a sleep-deprivation and a control group. Scans were segmented using four segmentation methods from which radiomic features were computed. First, we measured segmentation agreement using the Dice-coefficient. Second, robustness and reproducibility of radiomic features were measured using the intraclass correlation coefficient (ICC). Last, difference in predictive power was assessed using the Friedman-test on performance in a radiomics-based sleep deprivation classification application. Segmentation agreement was generally high (interquartile range = 0.77–0.90) and median feature robustness to segmentation method variation was higher (ICC > 0.7) than scan-rescan reproducibility (ICC 0.3–0.8). However, classification performance differed significantly among segmentation methods (p < 0.001) ranging from 77 to 84%. Accuracy was higher for more recent deep learning-based segmentation methods. Despite high agreement among segmentation methods, subtle differences significantly affected radiomic features and their predictive power. Consequently, the effect of differences in segmentation methods should be taken into account when designing and evaluating radiomics-based research methods. [ABSTRACT FROM AUTHOR]

Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic features (RFs). In this study, we investigate the potential of RFs to predict overall survival in patients with clear cell renal cell carcinoma, as well as the impact of ComBat harmonization on the performance of RF models. We assessed the robustness of the results by performing the analyses a thousand times. Publicly available CT scans of 179 patients were retrospectively collected and analyzed. The scans were acquired using different imaging vendors and parameters in different medical centers. The performance was calculated by averaging the metrics over all runs. On average, the clinical model significantly outperformed the radiomic models. The use of ComBat harmonization, on average, did not significantly improve the performance of radiomic models. Hence, the variability in image acquisition and reconstruction parameters significantly affect the performance of radiomic models. The development of radiomic specific harmonization techniques remain a necessity for the advancement of the field. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the value of multiphase computed tomography (CT)-based radiomics for predicting lymph node metastasis in patients with colorectal cancer (CRC).Methods: This study included 191 patients enrolled in our hospital who underwent non-contrast, arterial, and portal venous phase CT scans between June 2017 and December 2019. Segmented regions of interest in each slice of CT images were used to extract radiomics features. Redundant features were ruled out using the least absolute shrinkage and selection operator (LASSO) regression. The multiphase CT-combined radiomics signature (Com-RS) was constructed based on the selected radiomics features from the three CT phases weighted by the respective LASSO coefficients. The nomogram was created by combining the Com-RS with key clinical parameters. The performance of the nomogram was evaluated using receiver operating characteristics, calibration, and decision curve analyses (DCA).Results: Nine features were demonstrated to be the most significant and used to build the Com-RS: two from non-contrast CT, four from arterial CT, and three from portal venous CT. Tumor length has been identified as a key clinical parameter. A radiomics nomogram was constructed by integrating the Com-RS with tumor length and generated good performance with areas under the curve of 0.830 (95% confidence interval [CI], 0.758 - 0.902) and 0.712 (95% CI, 0.585 - 0.839) in the training and validation cohorts, respectively. Calibration and DCA confirmed the potential clinical relevance and applicability of the nomogram.Conclusions: The developed multiphase CT-based radiomics nomogram can potentially serve as an effective tool for the preoperative prediction of lymph node status in CRC. [ABSTRACT FROM AUTHOR]

Simple Summary: Acute radiation pneumonitis (ARP) is one of the common radiation toxicities in patients with non-small-cell lung cancer (NSCLC) treated by radiotherapy. The prediction for ARP can benefit clinical management and thereby improve the quality of life of patients. Markers derived from radiomics and dosiomics features can be adopted to predict ARP. Moreover, the features from lung subregions can improve the prediction for ARP compared to the whole lung region. The previous studies using lung subregion features for predicting ARP ignored the low-dose covered lung region. In this manuscript, we proposed an incremental-dose-interval-based lung subregion segmentation method by covering the low-dose lung region for ARP modeling and evaluated its effectiveness in improving ARP predictions using pre-treatment CT radiomics features and planning dose features for patients with NSCLC treated by radiotherapy. The performance among ARP models using features from our proposed lung subregion and the whole lung were compared for both multi-omics and single-omics. The results could provide insight into the subregion's ability to predict the ARP and could be useful in predicting the risk of radiation-induced toxicity. This manuscript and its results were neither published in any conferences nor journals. Purpose: To evaluate the effectiveness of features obtained from our proposed incremental-dose-interval-based lung subregion segmentation (IDLSS) for predicting grade ≥ 2 acute radiation pneumonitis (ARP) in lung cancer patients upon intensity-modulated radiotherapy (IMRT). (1) Materials and Methods: A total of 126 non-small-cell lung cancer patients treated with IMRT were retrospectively analyzed. Five lung subregions (SRs) were generated by the intersection of the whole lung (WL) and five sub-regions receiving incremental dose intervals. A total of 4610 radiomics features (RF) from pre-treatment planning computed tomographic (CT) and 213 dosiomics features (DF) were extracted. Six feature groups, including WL-RF, WL-DF, SR-RF, SR-DF, and the combined feature sets of WL-RDF and SR-RDF, were generated. Features were selected by using a variance threshold, followed by a Student t-test. Pearson's correlation test was applied to remove redundant features. Subsequently, Ridge regression was adopted to develop six models for ARP using the six feature groups. Thirty iterations of resampling were implemented to assess overall model performance by using the area under the Receiver-Operating-Characteristic curve (AUC), accuracy, precision, recall, and F1-score. (2) Results: The SR-RDF model achieved the best classification performance and provided significantly better predictability than the WL-RDF model in training cohort (Average AUC: 0.98 ± 0.01 vs. 0.90 ± 0.02, p < 0.001) and testing cohort (Average AUC: 0.88 ± 0.05 vs. 0.80 ± 0.04, p < 0.001). Similarly, predictability of the SR-DF model was significantly stronger than that of the WL-DF model in training cohort (Average AUC: 0.88 ± 0.03 vs. 0.70 ± 0.030, p < 0.001) and in testing cohort (Average AUC: 0.74 ± 0.08 vs. 0.65 ± 0.06, p < 0.001). By contrast, the SR-RF model significantly outperformed the WL-RF model only in the training set (Average AUC: 0.93 ± 0.02 vs. 0.85 ± 0.03, p < 0.001), but not in the testing set (Average AUC: 0.79 ± 0.05 vs. 0.77 ± 0.07, p = 0.13). (3) Conclusions: Our results demonstrated that the IDLSS method improved model performance for classifying ARP with grade ≥ 2 when using dosiomics or combined radiomics-dosiomics features. [ABSTRACT FROM AUTHOR]

Simple Summary: Radiomics could increase the value of medical images for oncologic patients, allowing for the identification of novel imaging biomarkers and building prediction models. Unfortunately, despite the many promises and encouraging findings, the translation of radiomics into clinical practice appears as a distant goal. Indeed, challenges such as generalizability and reproducibility are slowing down the process and must be faced with a rigorous and robust radiomic methodology. In this review, we turn the spotlight to the methodological complexity of radiomics, providing an outline of dos and don'ts aimed at facilitating state-of-the-art research. Imaging plays a crucial role in the management of oncologic patients, from the initial diagnosis to staging and treatment response monitoring. Recently, it has been suggested that its importance could be further increased by accessing a new layer of previously hidden quantitative data at the pixel level. Using a multi-step process, radiomics extracts potential biomarkers from medical images that could power decision support tools. Despite the growing interest and rising number of research articles being published, radiomics is still far from fulfilling its promise of guiding oncologic imaging toward personalized medicine. This is, at least partly, due to the heterogeneous methodological quality in radiomic research, caused by the complexity of the analysis pipelines. In this review, we aim to disentangle this complexity with a stepwise approach. Specifically, we focus on challenges to face during image preprocessing and segmentation, how to handle imbalanced classes and avoid information leaks, as well as strategies for the proper validation of findings. [ABSTRACT FROM AUTHOR]

Purpose. We aim to develop and validate a machine learning model by enhanced MRI to determine the pathological grading of meningiomas with unsupervised clustering image analysis method, which are multihabitat to reflect the inherent heterogeneity of tumors. Materials and Methods. A total of 120 patients with meningiomas confirmed by postoperative pathology were included in the study, including 60 patients with low-grade meningiomas (WHO grade I) and 60 patients with high-grade meningiomas (WHO grade II and WHO grade III). All patients underwent complete head enhanced magnetic resonance scans before surgery or any anti-tumor treatment. Enrolled patients in the group received surgical resection and obtained postoperative pathological data. The patients in the training group (84 people) and the test group (36 people) were randomly divided into two groups according to the ratio of 7 to 3. Multi-habitat features were extracted from MRI images based on enhanced T1. Machine learning method was used to model, which was used to distinguish high-grade meningioma from low-grade meningioma. At the same time, the obtained machine learning model was calibrated and evaluated. Results. In patients with low-grade meningioma and high-grade meningioma, we found significant differences in Silhouette coefficient (P<0.05). In the machine learning model, the area under the curve was 0.838 in the training group (sensitivity, 67.65%; specificity, 88.82%) and 0.73 in the test group (sensitivity, 69.05%; specificity, 71.43%). After the analysis of calibration curve and decision curve analysis, the model had shown the potential of great application value. Conclusions. Multi-habitat analysis based on enhanced MRI (T1) could accurately predict the pathological grading of meningiomas. This unsupervised image-based method could reflect the direct heterogeneity between high-grade meningiomas and low-grade meningiomas, which is of great significance for patients' treatment and prevention of recurrence. [ABSTRACT FROM AUTHOR]

Photoacoustic imaging is an increasingly popular method of exploring the tumour microenvironment, which can provide insight into tumour oxygenation status and potentially treatment response assessment. Currently, the measurements most commonly performed on such images are the mean and median of the pixel values of the tumour volumes of interest. We investigated expanding the set of measurements that can be extracted from these images by adding radiomic features. In particular, we found that Skewness was sensitive to differences between basal and luminal patient derived xenograft cancer models with an η 2 of 0.86, and that it was robust to variations in confounding factors such as reconstruction type and wavelength. We also built discriminant models with radiomic features that were correlated with the underlying tumour model and were independent from each other. We then ranked features by their importance in the model. Skewness was again found to be an important feature, as were 10th Percentile, Root Mean Squared, and several other texture-based features. In summary, this paper proposes a methodology to select radiomic features extracted from photoacoustic images that are robust to changes in acquisition and reconstruction parameters, and discusses features found to have discriminating power between the underlying tumour models in a pre-clinical dataset. [ABSTRACT FROM AUTHOR]

The objectives of our study were to (a) evaluate the feasibility of using 3D printed phantoms in magnetic resonance imaging (MR) in assessing the robustness and repeatability of radiomic parameters and (b) to compare the results obtained from the 3D printed phantoms to metrics obtained in biological phantoms. To this end, three different 3D phantoms were printed: a Hilbert cube (5 × 5 × 5 cm3) and two cubic quick response (QR) code phantoms (a large phantom (large QR) (5 × 5 × 4 cm3) and a small phantom (small QR) (4 × 4 × 3 cm3)). All 3D printed and biological phantoms (kiwis, tomatoes, and onions) were scanned thrice on clinical 1.5 T and 3 T MR with 1 mm and 2 mm isotropic resolution. Subsequent analyses included analyses of several radiomics indices (RI), their repeatability and reliability were calculated using the coefficient of variation (CV), the relative percentage difference (RPD), and the interclass coefficient (ICC) parameters. Additionally, the readability of QR codes obtained from the MR images was examined with several mobile phones and algorithms. The best repeatability (CV ≤ 10%) is reported for the acquisition protocols with the highest spatial resolution. In general, the repeatability and reliability of RI were better in data obtained at 1.5 T (CV = 1.9) than at 3 T (CV = 2.11). Furthermore, we report good agreements between results obtained for the 3D phantoms and biological phantoms. Finally, analyses of the read-out rate of the QR code revealed better texture analyses for images with a spatial resolution of 1 mm than 2 mm. In conclusion, 3D printing techniques offer a unique solution to create textures for analyzing the reliability of radiomic data from MR scans. [ABSTRACT FROM AUTHOR]

Background: Radiomics is a noninvasive method using machine learning to support personalised medicine. Preprocessing filters such as wavelet and Laplacian-of-Gaussian filters are commonly used being thought to increase predictive performance. However, the use of preprocessing filters increases the number of features by up to an order of magnitude and can produce many correlated features. Both substantially increase the dataset complexity, which in turn makes modeling with machine learning techniques more challenging, possibly leading to poorer performance. We investigated the impact of these filters on predictive performance. Methods: Using seven publicly available radiomic datasets, we measured the impact of adding features preprocessed with eight different preprocessing filters to the unprocessed features on the predictive performance of radiomic models. Modeling was performed using five feature selection methods and five classifiers, while predictive performance was measured using area-under-the-curve at receiver operating characteristics analysis (AUC-ROC) with nested, stratified 10-fold cross-validation. Results: Significant improvements of up to 0.08 in AUC-ROC were observed when all image preprocessing filters were applied compared to using only the original features (up to p = 0.024). Decreases of -0.04 and -0.10 were observed on some data sets, but these were not statistically significant (p > 0.179). Tuning of the image preprocessing filters did not result in decreases in AUC-ROC but further improved results by up to 0.1; however, these improvements were not statistically significant (p > 0.086) except for one data set (p = 0.023). Conclusions: Preprocessing filters can have a significant impact on the predictive performance and should be used in radiomic studies. [ABSTRACT FROM AUTHOR]

The ionospheric sporadic E layer, the ionospheric irregularities of enhanced electron density, appears in the Earth's ionosphere at altitudes between 90 and 120 km, which supports the real‐world radio communication needs of many sectors reliant on ionosphere‐dependent decision‐making. The prediction of the occurrence of sporadic E layers has been extremely difficult due to the highly complex behavior. Conventional numerical methods are limited because of the inability to extract high‐level information from data. Deep learning is a powerful tool for mining latent features from data, which can theoretically avoid assumptions constraining physical methods. Inspired by feature extraction, we applied deep learning to explore latent relationships between mapping observable lower atmospheric data and ionospheric data from limited observations. The proposed model was trained with high‐resolution ERA5 data during 1 January 2007–30 August 2018 as input and corresponding ionospheric sporadic E data collected from COSMIC RO measurements as output. The results show that the model can learn complex relevance as bridges connecting the input and the desired output and obtain excellent performance and generalization capability by applying multiple evaluation criteria. Additionally, we established several model architecture training methods to explore the performance of the model with different input data. The statistic results show that model inference performance is proportional to the abundance of input information and is impacted by intraseasonal variability. The inference capability of the model achieves the best performance in the June–August (JJA) and December–February (DJF) seasons, which is the exact period of sporadic E layer significant occurrence, although different models are evaluated. Plain Language Summary: Deep learning has been shown to be an efficient technique in atmospheric sciences as well as weather and climate prediction applications, such as forecasting the occurrence of ionospheric irregularities, which is a challenging task because of their highly complex behavior. In this study, we present an image‐based artificial intelligence (AI) approach to estimate global ionospheric irregularity intensity by combining lower atmospheric information and satellite measurements. The trained deep learning model can correctly estimate the spatial structure and seasonal variation in ionospheric irregularities. Furthermore, the novel technique demonstrates that the accuracy of model inference is correlated with the amount of supplied atmospheric information. The inference ability of the model performs better in summer and winter due to significant ionospheric irregularities in such seasons according to statistical classification metrics. This inspiring model could provide support in advanced warning for space weather and satellite navigation communities. Key Points: An artificial intelligence model was proposed to extract the latent correlation between the lower atmosphere and ionospheric irregularitiesThe model prediction performance depends on the abundance of input information, including the lower atmosphere and solar activityThe intraseasonal variability is found to influence the predictive capability of the model by applying classification evaluation criteria [ABSTRACT FROM AUTHOR]

Objectives: Radiomic features derived from routine medical images show great potential for personalized medicine in gastric cancer (GC). We aimed to evaluate the current status and quality of radiomic research as well as its potential for identifying biomarkers to predict therapy response and prognosis in patients with GC. Methods: We performed a systematic search of the PubMed and Embase databases for articles published from inception through July 10, 2021. The phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool were applied to evaluate scientific and reporting quality. Results: Twenty-five studies consisting of 10,432 patients were included. 96% of studies extracted radiomic features from CT images. Association between radiomic signature and therapy response was evaluated in seven (28%) studies; association with survival was evaluated in 17 (68%) studies; one (4%) study analyzed both. All results of the included studies showed significant associations. Based on the phase classification criteria for image mining studies, 18 (72%) studies were classified as phase II, with two, four, and one studies as discovery science, phase 0 and phase I, respectively. The median RQS score for the radiomic studies was 44.4% (range, 0 to 55.6%). There was extensive heterogeneity in the study population, tumor stage, treatment protocol, and radiomic workflow amongst the studies. Conclusions: Although radiomic research in GC is highly heterogeneous and of relatively low quality, it holds promise for predicting therapy response and prognosis. Efforts towards standardization and collaboration are needed to utilize radiomics for clinical application. Key Points: • Radiomics application of gastric cancer is increasingly being reported, particularly in predicting therapy response and survival. • Although radiomics research in gastric cancer is highly heterogeneous and relatively low quality, it holds promise for predicting clinical outcomes. • Standardized imaging protocols and radiomic workflow are needed to facilitate radiomics into clinical use. [ABSTRACT FROM AUTHOR]

Radiomics is a newer approach for analyzing radiological images obtained from conventional imaging modalities such as computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography. Radiomics involves extracting quantitative data from the images and assessing them to identify diagnostic or prognostic features such as tumor grade, resectability, tumor response to neoadjuvant therapy, and survival. The purpose of this review is to discuss the basic principles of radiomics and provide an overview of the current clinical applications of radiomics in the field of pancreatic tumors. [ABSTRACT FROM AUTHOR]

The relationship between the stress-strain characteristics of ice-containing unsaturated soil and its various physical properties has important implications for the design and construction of transportation and building projects. The study also reveals the varying phases of the substance that can be deposited within the pore space. During winter, the freezing-thawing cycles affect the various media that can be deposited within the soil pore space, such as water and other water. The presence of water and others in the soil's pores can cause a freeze-thawing phenomenon and a significant change in the material's elastic modulus. This paper presents a method that uses the representative elementary volume (REV) model to perform a representative state variable analysis on an unsaturated ice-containing soil with porosity. The three stress-strain REV models that were developed for the study were designed to analyze the properties of an unsaturated ice-containing soil under freezing-thawing conditions. Then a comprehensive analysis of the soil's various components was performed. The effects of frozen soil on the change law of the relationship between different porous medium are studied. The study also explores the effects of the presence of porosity on the variation law of the frozen soil relationship between the stress-strain. The paper presents the study of the variation characteristics of the elastic modulus of permafrost soil. It also explores the various factors (porosity, moisture content, ice percentage, and tortuosity) that influence the stress-strain characteristics of unsaturated soil. [ABSTRACT FROM AUTHOR]

In the Selective Laser Sintering (SLS) technique, the great majority of the powder involved is not included in the final printed parts, being just used as a support material. However, the quality of this powder is negatively affected during the process since it is subjected to high temperatures (close to its melting temperature) during a long time, i.e., the printing cycle time, especially in the neighborhood of the printed part contour. This type of powder is relatively expensive and large amounts of used powder result after each printing cycle. The present paper focuses on the reuse of Polyamide 12 (PA 12) powder. For this sake, the same PA 12 powder was used in consecutive printing cycles. After each cycle, the remaining non-used powder was milled and filtered before subsequent use. Properties of the powder and corresponding prints were characterized in each cycle, using differential scanning calorimetry (DSC), scanning electron microscopy (SEM), computed tomography (CT), and tensile tests. It was concluded that subjecting the same powder to multiple SLS printing cycles affects the properties of the printed parts essentially regarding their morphology (voids content), mechanical properties reproducibility, and aesthetical aspect. However, post-processing treatment of the powder enabled to maintain the mechanical performance of the prints during the first six printing cycles without the need to add virgin powder. [ABSTRACT FROM AUTHOR]

Simple Summary: Lung cancer remains the leading cause of cancer-related mortality worldwide. Although early-stage non-small cell lung cancer (NSCLC) is likely to be controlled with stereotactic body radiation therapy (SBRT), approximately 18% of patients lead to recurrence. The aim of this study was to identify prognostic factors and establish a predictive model for survival outcomes of patients with non-metastatic NSCLC treated with SBRT. Several radiomic features were selected as predictive factors and two prediction models were established from the pre-treatment computed tomography images of 250 patients in the training cohort. One radiomic factor remained a significant prognostic factor of overall survival (OS) (p = 0.044), and one predicting model could estimate OS time (mean: 37.8 months) similar to the real OS time (33.7 months). In this study, we identified one radiomic factor and one prediction model that can be widely used. Stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC) leads to recurrence in approximately 18% of patients. We aimed to extract the radiomic features, with which we predicted clinical outcomes and to establish predictive models. Patients with primary non-metastatic NSCLC who were treated with SBRT between 2002 and 2022 were retrospectively reviewed. The 358 primary tumors were randomly divided into a training cohort of 250 tumors and a validation cohort of 108 tumors. Clinical features and 744 radiomic features derived from primary tumor delineation on pre-treatment computed tomography were examined as prognostic factors of survival outcomes by univariate and multivariate analyses in the training cohort. Predictive models of survival outcomes were established from the results of the multivariate analysis in the training cohort. The selected radiomic features and prediction models were tested in a validation cohort. We found that one radiomic feature showed a significant difference in overall survival (OS) in the validation cohort (p = 0.044) and one predicting model could estimate OS time (mean: 37.8 months) similar to the real OS time (33.7 months). In this study, we identified one radiomic factor and one prediction model that can be widely used. [ABSTRACT FROM AUTHOR]

Quantitative estimation of growth patterns is important for diagnosis of lung adenocarcinoma and prediction of prognosis. However, the growth patterns of lung adenocarcinoma tissue are very dependent on the spatial organization of cells. Deep learning for lung tumor histopathological image analysis often uses convolutional neural networks to automatically extract features, ignoring this spatial relationship. In this paper, a novel fully automated framework is proposed for growth pattern evaluation in lung adenocarcinoma. Specifically, the proposed method uses graph convolutional networks to extract cell structural features; that is, cells are extracted and graph structures are constructed based on histopathological image data without graph structure. A deep neural network is then used to extract the global semantic features of histopathological images to complement the cell structural features obtained in the previous step. Finally, the structural features and semantic features are fused to achieve growth pattern prediction. Experimental studies on several datasets validate our design, demonstrating that methods based on the spatial organization of cells are appropriate for the analysis of growth patterns. [ABSTRACT FROM AUTHOR]

Objective: To explore the feasibility of predicting distant metastasis (DM) of nasopharyngeal carcinoma (NPC) patients based on MRI radiomics model. Methods: A total of 146 patients with NPC pathologically confirmed, who did not exhibit DM before treatment, were retrospectively reviewed and followed up for at least one year to analyze the DM risk of the disease. The MRI images of these patients including T2WI and CE-T1WI sequences were extracted. The cases were randomly divided into training group (n=116) and validation group (n=30). The images were filtered before radiomics feature extraction. The least absolute shrinkage and selection operator (LASSO) regression was used to develop the dimension of texture parameters and the logistic regression was used to construct the prediction model. The ROC curve and calibration curve were used to evaluate the predictive performance of the model, and the area under curve (AUC), accuracy, sensitivity, and specificity were calculated. Results: 72 patients had DM and 74 patients had no DM. The AUC, accuracy, sensitivity and specificity of the model were 0. 80 (95% CI: 0.72~0. 88), 75.0%, 76.8%, 73.3%. and0.70 (95% CI: 0.51~0.90), 66.7%, 72.7%, 63.2% in training group and validation group, respectively. Conclusion: The radiomics model based on logistic regression algorithm has application potential for evaluating the DM risk of patients with NPC. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to evaluate the ability of peritumoral, intratumoral, or combined computed tomography (CT) radiomic features to predict chemotherapy response in non-small cell lung cancer (NSCLC). Methods: After excluding subjects with incomplete data or other types of treatments, 272 (Dataset 1) and 43 (Dataset 2, external validation) NSCLC patients who were only treated with chemotherapy as the first-line treatment were enrolled between 2015 and 2019. All patients were divided into response and nonresponse based on the response evaluation criteria in solid tumors, version 1.1. By using 3D slicer and morphological operations in python, the intra- and peritumoral regions of lung tumors were segmented from pretreatment CT images (unenhanced) and confirmed by two experienced radiologists. Then radiomic features (the first order, texture, shape, et al.) were extracted from the above regions of interest. The models were trained and tested in Dataset 1 and further validated in Dataset 2. The performance of models was compared using the area under curve (AUC), confusion matrix, accuracy, precision, recall, and F1-score. Results: The radiomic model using features from the peritumoral region of 0-3 mm outperformed that using features from 3-6, 6-9, 9-12 mm peritumoral region, and intratumoral region (AUC: 0.95 versus 0.87, 0.86, 0.85, and 0.88). By the fusion of features from 0-3 and 3-6 mm peritumoral regions, the logistic regression model achieved the best performance, with an AUC of 0.97. This model achieved an AUC of 0.85 in the external cohort. Moreover, among the 20 selected features, seven features differed significantly between the two groups (p < 0.05). Conclusions: CT radiomic features from both the peri- and intratumoral regions can predict chemotherapy response in NSCLC using machine learning models. Combined features from two peritumoral regions yielded better predictions. [ABSTRACT FROM AUTHOR]

Purpose: To propose a deep learning network with subregion partition for predicting metastatic origins and EGFR/HER2 status in patients with brain metastasis. Methods: We retrospectively enrolled 140 patients with clinico-pathologically confirmed brain metastasis originated from primary NSCLC (n = 60), breast cancer (BC, n = 60) and other tumor types (n = 20). All patients underwent contrast-enhanced brain MRI scans. The brain metastasis was subdivided into phenotypically consistent subregions using patient-level and population-level clustering. A residual network with a global average pooling layer (RN-GAP) was proposed to calculate deep learning-based features. Features from each subregion were selected with least absolute shrinkage and selection operator (LASSO) to build logistic regression models (LRs) for predicting primary tumor types (LR-NSCLC for the NSCLC origin and LR-BC for the BC origin), EGFR mutation status (LR-EGFR) and HER2 status (LR-HER2). Results: The brain metastasis can be partitioned into a marginal subregion (S1) and an inner subregion (S2) in the MRI image. The developed models showed good predictive performance in the training (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.860 vs. 0.909 vs. 0.850 vs. 0.900) and validation (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.819 vs. 0.872 vs. 0.750 vs. 0.830) set. Conclusion: Our proposed deep learning network with subregion partitions can accurately predict metastatic origins and EGFR/HER2 status of brain metastasis, and hence may have the potential to be non-invasive and preoperative new markers for guiding personalized treatment plans in patients with brain metastasis. [ABSTRACT FROM AUTHOR]

Infarct volume (FIV) on follow-up diffusion-weighted imaging (FU-DWI) is only moderately associated with functional outcome in acute ischemic stroke patients. However, FU-DWI may contain other imaging biomarkers that could aid in improving outcome prediction models for acute ischemic stroke. We included FU-DWI data from the HERMES, ISLES, and MR CLEAN-NO IV databases. Lesions were segmented using a deep learning model trained on the HERMES and ISLES datasets. We assessed the performance of three classifiers in predicting functional independence for the MR CLEAN-NO IV trial cohort based on: (1) FIV alone, (2) the most important features obtained from a trained convolutional autoencoder (CAE), and (3) radiomics. Furthermore, we investigated feature importance in the radiomic-feature-based model. For outcome prediction, we included 206 patients: 144 scans were included in the training set, 21 in the validation set, and 41 in the test set. The classifiers that included the CAE and the radiomic features showed AUC values of 0.88 and 0.81, respectively, while the model based on FIV had an AUC of 0.79. This difference was not found to be statistically significant. Feature importance results showed that lesion intensity heterogeneity received more weight than lesion volume in outcome prediction. This study suggests that predictions of functional outcome should not be based on FIV alone and that FU-DWI images capture additional prognostic information. [ABSTRACT FROM AUTHOR]

Objectives: The primary aim of this study was to develop and validate radiomics models, applied to ultrasound images, capable of differentiating from other cancers high-risk endometrial cancer, as defined jointly by the European Society for Medical Oncology, European Society of Gynaecological Oncology and European Society for Radiotherapy & Oncology (ESMO-ESGO-ESTRO) in 2016. The secondary aim was to develop and validate radiomics models for differentiating low-risk endometrial cancer from other endometrial cancers.Methods: This was a multicenter, retrospective, observational study. From two participating centers, we identified consecutive patients with histologically confirmed diagnosis of endometrial cancer who had undergone preoperative ultrasound examination by an experienced examiner between 2016 and 2019. Patients recruited in Center 1 (Rome) were included as the training set and patients enrolled in Center 2 (Milan) formed the external validation set. Radiomics analysis (extraction of a high number of quantitative features from medical images) was applied to the ultrasound images. Clinical (including preoperative biopsy), ultrasound and radiomics features that were statistically significantly different in the high-risk group vs the other groups and in the low-risk group vs the other groups on univariate analysis in the training set were considered for multivariate analysis and for developing ultrasound-based machine-learning risk-prediction models. For discriminating between the high-risk group and the other groups, a random forest model from the radiomics features (radiomics model), a binary logistic regression model from clinical and ultrasound features (clinical-ultrasound model) and another binary logistic regression model from clinical, ultrasound and previously selected radiomics features (mixed model) were created. Similar models were created for discriminating between the low-risk group and the other groups. The models developed in the training set were tested in the validation set. The performance of the models in discriminating between the high-risk group and the other groups, and between the low-risk group and the other risk groups for both validation and training sets was compared.Results: The training set comprised 396 patients and the validation set 102 patients. In the validation set, for predicting high-risk endometrial cancer, the radiomics model had an area under the receiver-operating-characteristics curve (AUC) of 0.80, sensitivity of 58.7% and specificity of 85.7% (using the optimal risk cut-off of 0.41); the clinical-ultrasound model had an AUC of 0.90, sensitivity of 80.4% and specificity of 83.9% (using the optimal cut-off of 0.32); and the mixed model had an AUC of 0.88, sensitivity of 67.3% and specificity of 91.0% (using the optimal cut-off of 0.42). For the prediction of low-risk endometrial cancer, the radiomics model had an AUC of 0.71, sensitivity of 65.0% and specificity of 64.5% (using the optimal cut-off of 0.38); the clinical-ultrasound model had an AUC of 0.85, sensitivity of 70.0% and specificity of 80.6% (using the optimal cut-off of 0.46); and the mixed model had an AUC of 0.85, sensitivity of 87.5% and specificity of 72.5% (using the optimal cut-off of 0.36).Conclusions: Radiomics seems to have some ability to discriminate between low-risk endometrial cancer and other endometrial cancers and better ability to discriminate between high-risk endometrial cancer and other endometrial cancers. However, the addition of radiomics features to the clinical-ultrasound models did not result in any notable increase in performance. Other efficacy studies and further effectiveness studies are needed to validate the performance of the models. © 2021 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Background: Radiomic features extracted from breast MRI have potential for diagnostic, prognostic, and predictive purposes. However, before they can be used as biomarkers in clinical decision support systems, features need to be repeatable and reproducible. Objective: Identify repeatable radiomics features within breast tissue on prospectively collected MRI exams through multiple test–retest measurements. Study Type: Prospective. Population: 11 healthy female volunteers. Field Strength/Sequence: 1.5 T; MRI exams, comprising T2‐weighted turbo spin‐echo (T2W) sequence, native T1‐weighted turbo gradient‐echo (T1W) sequence, diffusion‐weighted imaging (DWI) sequence using b‐values 0/150/800, and corresponding derived ADC maps. Assessment: 18 MRI exams (three test–retest settings, repeated on 2 days) per healthy volunteer were examined on an identical scanner using a fixed clinical breast protocol. For each scan, 91 features were extracted from the 3D manually segmented right breast using Pyradiomics, before and after image preprocessing. Image preprocessing consisted of 1) bias field correction (BFC); 2) z‐score normalization with and without BFC; 3) grayscale discretization using 32 and 64 bins with and without BFC; and 4) z‐score normalization + grayscale discretization using 32 and 64 bins with and without BFC. Statistical Tests: Features' repeatability was assessed using concordance correlation coefficient(CCC) for each pair, i.e. each MRI was compared to each of the remaining 17 MRI with a cut‐off value of CCC > 0.90. Results: Images without preprocessing produced the highest number of repeatable features for both T1W sequence and ADC maps with 15 of 91 (16.5%) and 8 of 91 (8.8%) repeatable features, respectively. Preprocessed images produced between 4 of 91 (4.4%) and 14 of 91 (15.4%), and 6 of 91 (6.6%) and 7 of 91 (7.7%) repeatable features, respectively for T1W and ADC maps. Z‐score normalization produced highest number of repeatable features, 26 of 91 (28.6%) in T2W sequences, in these images, no preprocessing produced 11 of 91 (12.1%) repeatable features. Data Conclusion: Radiomic features extracted from T1W, T2W sequences and ADC maps from breast MRI exams showed a varying number of repeatable features, depending on the sequence. Effects of different preprocessing procedures on repeatability of features were different for each sequence. Level of Evidence: 2 Technical Efficacy Stage: 1 [ABSTRACT FROM AUTHOR]

Background: While magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of patients with brain tumors, it may still be challenging to differentiate glioblastoma multiforme (GBM) from solitary brain metastasis (SBM) due to their similar imaging features. This study aimed to evaluate the features extracted of dual-tree complex wavelet transform (DTCWT) from routine MRI protocol for preoperative differentiation of glioblastoma (GBM) and solitary brain metastasis (SBM). Methods: A total of 51 patients were recruited, including 27 GBM and 24 SBM patients. Their contrast-enhanced T1-weighted images (CET1WIs), T2 fluid-attenuated inversion recovery (T2FLAIR) images, diffusion-weighted images (DWIs), and apparent diffusion coefficient (ADC) images were employed in this study. The statistical features of the pre-transformed images and the decomposed images of the wavelet transform and DTCWT were utilized to distinguish between GBM and SBM. Results: The support vector machine (SVM) showed that DTCWT images have a better accuracy (82.35%), sensitivity (77.78%), specificity (87.50%), and the area under the curve of the receiver operating characteristic curve (AUC) (89.20%) than the pre-transformed and conventional wavelet transform images. By incorporating DTCWT and pre-transformed images, the accuracy (86.27%), sensitivity (81.48%), specificity (91.67%), and AUC (93.06%) were further improved. Conclusions: Our studies suggest that the features extracted from the DTCWT images can potentially improve the differentiation between GBM and SBM. [ABSTRACT FROM AUTHOR]

Objective: The performance and stability of radiomics model caused by dimension reduction remain being confronted with major challenges. In this study, we aimed to propose a new scheme of global feature management independent of dimension reduction to improve it. Methods: The non-contrast computed tomography (NCCT) images of acute brainstem infarction (ABI) from two medical centers were used as test and validation sets. A new scheme was constructed based on global feature management, and the traditional scheme dependent on dimension reduction was used as control. The radiomic features of NCCT images were extracted in Matlab R2013a. The performance of prediction model was evaluated by the generalized linear model (GLM) and multivariate logistic regression. And, the stability of radiomics model was evaluated with the difference of area under curve (AUC) between the test and validation sets. Results: Compared with the traditional scheme, the new scheme presented a similar detection performance (AUC: 0.875 vs. 0.883), yet a better performance in predicting prognosis (AUC: 0.864, OR = 0.917, p = 0.021 vs. AUC:0.806, OR = 0.972, p = 0.007). All these results were well verified in an independent validation set. Moreover, the new scheme showed stronger stability in both the detection model (ΔAUC: 0.013 vs. 0.039) and prediction model (ΔAUC = 0.004 vs. 0.044). Conclusion: Although there might be several limitations, this study proved that the scheme of global feature management independent of dimension reduction could be a powerful supplement to the radiomics methodology. Key Points: • The new scheme (Swavelet) presented similar detection performances for ABI with the traditional scheme. • A better predictive performance for END was found in the new scheme (Swavelet) compared with the traditional scheme. • Stronger model stability was found in both the detection and prediction models based on the new scheme. [ABSTRACT FROM AUTHOR]

Ultrasound (US) imaging has been recognized and widely used as a screening and diagnostic imaging modality for cervical cancer all over the world. However, few studies have investigated the U-net-based automatic segmentation models for cervical cancer on US images and investigated the effects of automatic segmentations on radiomics features. A total of 1102 transvaginal US images from 796 cervical cancer patients were collected and randomly divided into training (800), validation (100) and test sets (202), respectively, in this study. Four U-net models (U-net, U-net with ResNet, context encoder network (CE-net), and Attention U-net) were adapted to segment the target of cervical cancer automatically on these US images. Radiomics features were extracted and evaluated from both manually and automatically segmented area. The mean Dice similarity coefficient (DSC) of U-net, Attention U-net, CE-net, and U-net with ResNet were 0.88, 0.89, 0.88, and 0.90, respectively. The average Pearson coefficients for the evaluation of the reliability of US image–based radiomics were 0.94, 0.96, 0.94, and 0.95 for U-net, U-net with ResNet, Attention U-net, and CE-net, respectively, in their comparison with manual segmentation. The reproducibility of the radiomics parameters evaluated by intraclass correlation coefficients (ICC) showed robustness of automatic segmentation with an average ICC coefficient of 0.99. In conclusion, high accuracy of U-net-based automatic segmentations was achieved in delineating the target area of cervical cancer US images. It is feasible and reliable for further radiomics studies with features extracted from automatic segmented target areas. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate an MRI-based radiomic texture classifier alone and combined with radiologist qualitative assessment in predicting pathological complete response (pCR) using restaging MRI with internal training and external validation. Methods: Consecutive patients with locally advanced rectal cancer (LARC) who underwent neoadjuvant therapy followed by total mesorectal excision from March 2012 to February 2016 (Memorial Sloan Kettering Cancer Center/internal dataset, n = 114, 41% female, median age = 55) and July 2014 to October 2015 (Instituto do Câncer do Estado de São Paulo/external dataset, n = 50, 52% female, median age = 64.5) were retrospectively included. Two radiologists (R1, senior; R2, junior) independently evaluated restaging MRI, classifying patients (radiological complete response vs radiological partial response). Model A (n = 33 texture features), model B (n = 91 features including texture, shape, and edge features), and two combination models (model A + B + R1, model A + B + R2) were constructed. Pathology served as the reference standard for neoadjuvant treatment response. Comparison of the classifiers' AUCs on the external set was done using DeLong's test. Results: Models A and B had similar discriminative ability (P = 0.3; Model B AUC = 83%, 95% CI 70%–97%). Combined models increased inter-reader agreement compared with radiologist-only interpretation (κ = 0.82, 95% CI 0.70–0.89 vs k = 0.25, 95% CI 0.11–0.61). The combined model slightly increased junior radiologist specificity, positive predictive value, and negative predictive values (93% vs 90%, 57% vs 50%, and 91% vs 90%, respectively). Conclusion: We developed and externally validated a combined model using radiomics and radiologist qualitative assessment, which improved inter-reader agreement and slightly increased the diagnostic performance of the junior radiologist in predicting pCR after neoadjuvant treatment in patients with LARC. [ABSTRACT FROM AUTHOR]

Objective: Although the guideline indicates that total kidney volume (TKV) is an important detection indicator in patients with autosomal dominant polycystic kidney disease (ADPKD), this study attempted to demonstrate that renal parenchymal information, combined with parenchymal volume and radiomics features, may have more valuable clinical guiding significance. Methods: A totals of 340 ADPKD patients with normal renal function were prospectively collected and followed-up for five years, with renal function tests and non-contrast computed tomography (CT) performed every six months. The relationship between renal function impairment and renal parenchymal volume (RPV) as along with radiomics features was explored using a multiple linear regression model and multiple logistic regression. Then, a combined model of RPV with radiomics features was constructed to comprehensively evaluate its predictive value. Results: Compared with TKV, decreased RPV presented a closer relationship with renal function impairment, namely, with the impairment rate (RPV: 82.3% vs. TVK: 67.1%) and eGFR (RPV: r = 0.614, p < 0.001 vs. TKV: r = -0.452, p < 0.001), and showed higher predictive power (RPV: AUC = 0.752 [95%CI 0.692–0.805], p < 0.001 vs. TKV: AUC = 0.711 [95%CI 0.649–0.768], p < 0.001). Correspondingly, the radiomics analysis that was derived from the renal parenchyma also showed a satisfactory result (AUC = 0.849 [95%Cl 0.797–0.892], p < 0.001). Importantly, the predictive power for renal function impairment was further improved in the combined model of RPV and radiomics features (AUC = 0.902 [95%Cl 0.857–0.937], p < 0.001). Conclusion: Renal parenchyma information may be a sensitive biomarker of renal function impairment in ADPKD, which can provide a new approach for clinically monitoring renal function, and may greatly improve the pre-hospital prevention and treatment effects. [ABSTRACT FROM AUTHOR]

Objective. With the progress of artificial intelligence (AI) in magnetic resonance imaging (MRI), large-scale multi-center MRI datasets have a great influence on diagnosis accuracy and model performance. However, multi-center images are highly variable due to the variety of scanners or scanning parameters in use, which has a negative effect on the generality of AI-based diagnosis models. To address this problem, we propose a self-supervised harmonization (SSH) method. Approach. Mapping the style of images between centers allows harmonization without traveling phantoms to be formalized as an unpaired image-to-image translation problem between two domains. The mapping is a two-stage transform, consisting of a modified cycle generative adversarial network (cycleGAN) for style transfer and a histogram matching module for structure fidelity. The proposed algorithm is demonstrated using female pelvic MRI images from two 3 T systems and compared with three state-of-the-art methods and one conventional method. In the absence of traveling phantoms, we evaluate harmonization from three perspectives: image fidelity, ability to remove inter-center differences, and influence on the downstream model. Main results. The improved image sharpness and structure fidelity are observed using the proposed harmonization pipeline. It largely decreases the number of features with a significant difference between two systems (from 64 to 45, lower than dualGAN: 57, cycleGAN: 59, ComBat: 64, and CLAHE: 54). In the downstream cervical cancer classification, it yields an area under the receiver operating characteristic curve of 0.894 (higher than dualGAN: 0.828, cycleGAN: 0.812, ComBat: 0.685, and CLAHE: 0.770). Significance. Our SSH method yields superior generality of downstream cervical cancer classification models by significantly decreasing the difference in radiomics features, and it achieves greater image fidelity. [ABSTRACT FROM AUTHOR]

Background: Size-based assessments are inaccurate indicators of tumor response in soft-tissue sarcoma (STS), motivating the requirement for new response imaging biomarkers for this rare and heterogeneous disease. In this study, we assess the test-retest repeatability of radiomic features from MR diffusion-weighted imaging (DWI) and derived maps of apparent diffusion coefficient (ADC) in retroperitoneal STS and compare baseline repeatability with changes in radiomic features following radiotherapy (RT). Materials and methods: Thirty patients with retroperitoneal STS received an MR examination prior to treatment, of whom 23/30 were investigated in our repeatability analysis having received repeat baseline examinations and 14/30 patients were investigated in our post-treatment analysis having received an MR examination after completing pre-operative RT. One hundred and seven radiomic features were extracted from the full manually delineated tumor region using PyRadiomics. Test-retest repeatability was assessed using an intraclass correlation coefficient (baseline ICC), and post-radiotherapy variance analysis (post-RT-IMS) was used to compare the change in radiomic feature value to baseline repeatability. Results: For the ADC maps and DWI images, 101 and 102 features demonstrated good baseline repeatability (baseline ICC > 0.85), respectively. Forty-three and 2 features demonstrated both good baseline repeatability and a high post-RT-IMS (>0.85), respectively. Pearson correlation between the baseline ICC and post-RT-IMS was weak (0.432 and 0.133, respectively). Conclusions: The ADC-based radiomic analysis shows better test-retest repeatability compared with features derived from DWI images in STS, and some of these features are sensitive to post-treatment change. However, good repeatability at baseline does not imply sensitivity to post-treatment change. [ABSTRACT FROM AUTHOR]

Simple Summary: Modern, personalized therapy approaches are increasingly changing advanced cancer into a chronic disease. Compared to imaging, novel omics methodologies in molecular biology have already achieved an individual characterization of cancerous lesions. With quantitative imaging biomarkers, analyzed by radiomics or deep learning, an imaging-based assessment of tumoral biology can be brought into clinical practice. Combining these with other non-invasive methods, e.g., liquid profiling, could allow for more individual decision making regarding therapies and applications. Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed. [ABSTRACT FROM AUTHOR]

Radiomics-guided prediction of overall survival (OS) in brain gliomas is seen as a significant problem in Neuro-oncology. The ultimate goal is to develop a robust MRI based approach (i.e., a radiomics model) that can accurately classify a novel subject as a short-term survivor, a medium-term survivor, or a long-term survivor. The BraTS 2020 challenge provides radiological imaging and clinical data (178 subjects) to develop and validate radiomics-based methods for OS classification in brain gliomas. In this study, we empirically evaluated the efficacy of four multiregional radiomic models, for OS classification, and quantified the robustness of predictions to variations in automatic segmentation of brain tumor volume. More specifically, we evaluated four radiomic models, namely, the Whole Tumor (WT) radiomics model, the 3-subregions radiomics model, the 6-subregions radiomics model, and the 21-subregions radiomics model. The 3-subregions radiomics model is based on a physiological segmentation of whole tumor volume (WT) into three non-overlapping subregions. The 6-subregions and 21- subregions radiomic models are based on an anatomical segmentation of the brain tumor into 6 and 21 anatomical regions, respectively. Moreover, we employed six segmentation schemes – five CNNs and one STAPLE-fusion method – to quantify the robustness of radiomic models. Our experiments revealed that the 3-subregions radiomics model had the best predictive performance (mean AUC = 0.73) but poor robustness (RSD = 1.99) and the 6-subregions and 21-subregions radiomics models were more robust (RSD 1.39) with lower predictive performance (mean AUC 0.71). The poor robustness of the 3-subregions radiomics model was associated with highly variable and inferior segmentation of tumor core and active tumor sub regions as quantified by the Hausdorff distance metric (4.4 − 6.5 mm) across six segmentation schemes. Failure analysis revealed that the WT radiomics model, the 6-subregions radiomics model, and the 21-subregions radiomics model failed for the same subjects which is attributed to the common requirement of accurate segmentation of the WT volume. Moreover, short-term survivors were largely misclassified by the radiomic models and had large segmentation errors (average Hausdorff distance of 7.09 mm). Lastly, we concluded that while STAPLE-fusion can reduce segmentation errors, it is not a solution to learning accurate and robust radiomic models. [ABSTRACT FROM AUTHOR]

Background: Reproducibility and generalization are major challenges for clinically significant prostate cancer modeling using MRI radiomics. Multicenter data seem indispensable to deal with these challenges, but the quality of such studies is currently unknown. The aim of this study was to systematically review the quality of multicenter studies on MRI radiomics for diagnosing clinically significant PCa. Methods: This systematic review followed the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Multicenter studies investigating the value of MRI radiomics for the diagnosis of clinically significant prostate cancer were included. Quality was assessed using the checklist for artificial intelligence in medical imaging (CLAIM) and the radiomics quality score (RQS). CLAIM consisted of 42 equally important items referencing different elements of good practice AI in medical imaging. RQS consisted of 36 points awarded over 16 items related to good practice radiomics. Final CLAIM and RQS scores were percentage-based, allowing for a total quality score consisting of the average of CLAIM and RQS. Results: Four studies were included. The average total CLAIM score was 74.6% and the average RQS was 52.8%. The corresponding average total quality score (CLAIM + RQS) was 63.7%. Conclusions: A very small number of multicenter radiomics PCa classification studies have been performed with the existing studies being of bad or average quality. Good multicenter studies might increase by encouraging preferably prospective data sharing and paying extra care to documentation in regards to reproducibility and clinical utility. [ABSTRACT FROM AUTHOR]