First PACS‐integrated artificial intelligence‐based software tool for rapid and fully automatic analysis of body composition from CT in clinical routine

Background: To externally evaluate the first picture archiving communications system (PACS)-integrated artificial intelligence (AI)-based workflow, trained to automatically detect a predefined computed tomography (CT) slice at the third lumbar vertebra (L3) and automatically perform complete image segmentation for analysis of CT body composition and to compare its performance with that of an established semi-automatic segmentation tool regarding speed and accuracy of tissue area calculation. Methods: For fully automatic analysis of body composition with L3 recognition, U-Nets were trained (Visage) and compared with a conventional image segmentation software (TomoVision). Tissue was differentiated into psoas muscle, skeletal muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Mid-L3 level images from randomly selected DICOM slice files of 20 CT scans acquired with various imaging protocols were segmented with both methods. Results: Success rate of AI-based L3 recognition was 100%. Compared with semi-automatic, fully automatic AI-based image segmentation yielded relative differences of 0.22% and 0.16% for skeletal muscle, 0.47% and 0.49% for psoas muscle, 0.42% and 0.42% for VAT and 0.18% and 0.18% for SAT. AI-based fully automatic segmentation was significantly faster than semi-automatic segmentation (3 ± 0 s vs. 170 ± 40 s, P < 0.001, for User 1 and 152 ± 40 s, P < 0.001, for User 2). Conclusion: Rapid fully automatic AI-based, PACS-integrated assessment of body composition yields identical results without transfer of critical patient data. Additional metabolic information can be inserted into the patient’s image report and offered to the referring clinicians

Radiomics in radiation oncology-basics, methods, and limitations

Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy

Evaluation of FET PET Radiomics Feature Repeatability in Glioma Patients

Amino acid PET using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine (FET) has attracted considerable interest in neurooncology. Furthermore, initial studies suggested the additional diagnostic value of FET PET radiomics in brain tumor patient management. However, the conclusiveness of radiomics models strongly depends on feature generalizability. We here evaluated the repeatability of feature-based FET PET radiomics. A test–retest analysis based on equivalent but statistically independent subsamples of FET PET images was performed in 50 newly diagnosed and histomolecularly characterized glioma patients. A total of 1,302 radiomics features were calculated from semi-automatically segmented tumor volumes-of-interest (VOIs). Furthermore, to investigate the influence of the spatial resolution of PET on repeatability, spherical VOIs of different sizes were positioned in the tumor and healthy brain tissue. Feature repeatability was assessed by calculating the intraclass correlation coefficient (ICC). To further investigate the influence of the isocitrate dehydrogenase (IDH) genotype on feature repeatability, a hierarchical cluster analysis was performed. For tumor VOIs, 73% of first-order features and 71% of features extracted from the gray level co-occurrence matrix showed high repeatability (ICC 95% confidence interval, 0.91–1.00). In the largest spherical tumor VOIs, 67% of features showed high repeatability, significantly decreasing towards smaller VOIs. The IDH genotype did not affect feature repeatability. Based on 297 repeatable features, two clusters were identified separating patients with IDH-wildtype glioma from those with an IDH mutation. Our results suggest that robust features can be obtained from routinely acquired FET PET scans, which are valuable for further standardization of radiomics analyses in neurooncology

Radiomic analysis of planning computed tomograms for predicting radiation-induced lung injury and outcome in lung cancer patients treated with robotic stereotactic body radiation therapy

Objectives To predict radiation-induced lung injury and outcome in non-small cell lung cancer (NSCLC) patients treated with robotic stereotactic body radiation therapy (SBRT) from radiomic features of the primary tumor. Methods In all, 110 patients with primary stage I/IIa NSCLC were analyzed for local control (LC), disease-free survival (DFS), overall survival (OS) and development of local lung injury up to fibrosis (LF). First-order (histogram), second-order (GLCM, Gray Level Co-occurrence Matrix) and shape-related radiomic features were determined from the unprocessed or filtered planning CT images of the gross tumor volume (GTV), subjected to LASSO (Least Absolute Shrinkage and Selection Operator) regularization and used to construct continuous and dichotomous risk scores for each endpoint. Results Continuous scores comprising 1-5 histogram or GLCM features had a significant (p= 0.0001-0.032) impact on all endpoints that was preserved in a multifactorial Cox regression analysis comprising additional clinical and dosimetric factors. At 36 months, LC did not differ between the dichotomous risk groups (93% vs. 85%, HR 0.892, 95%CI 0.222-3.590), while DFS (45% vs. 17%, p< 0.05, HR 0.457, 95%CI 0.240-0.868) and OS (80% vs. 37%, p< 0.001, HR 0.190, 95%CI 0.065-0.556) were significantly lower in the high-risk groups. Also, the frequency of LF differed significantly between the two risk groups (63% vs. 20% at 24 months, p< 0.001, HR 0.158, 95%CI 0.054-0.458). Conclusion Radiomic analysis of the gross tumor volume may help to predict DFS and OS and the development of local lung fibrosis in early stage NSCLC patients treated with stereotactic radiotherapy

Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning

Purpose Liver Imaging Reporting and Data System (LI-RADS) uses multiphasic contrast-enhanced imaging for hepatocellular carcinoma (HCC) diagnosis. The goal of this feasibility study was to establish a proof-of-principle concept towards automating the application of LI-RADS, using a deep learning algorithm trained to segment the liver and delineate HCCs on MRI automatically. Methods In this retrospective single-center study, multiphasic contrast-enhanced MRIs using T1-weighted breath-hold sequences acquired from 2010 to 2018 were used to train a deep convolutional neural network (DCNN) with a U-Net architecture. The U-Net was trained (using 70% of all data), validated (15%) and tested (15%) on 174 patients with 231 lesions. Manual 3D segmentations of the liver and HCC were ground truth. The dice similarity coefficient (DSC) was measured between manual and DCNN methods. Postprocessing using a random forest (RF) classifier employing radiomic features and thresholding (TR) of the mean neural activation was used to reduce the average false positive rate (AFPR). Results 73 and 75% of HCCs were detected on validation and test sets, respectively, using > 0.2 DSC criterion between individual lesions and their corresponding segmentations. Validation set AFPRs were 2.81, 0.77, 0.85 for U-Net, U-Net + RF, and U-Net + TR, respectively. Combining both RF and TR with the U-Net improved the AFPR to 0.62 and 0.75 for the validation and test sets, respectively. Mean DSC between automatically detected lesions using the DCNN + RF + TR and corresponding manual segmentations was 0.64/0.68 (validation/test), and 0.91/0.91 for liver segmentations. Conclusion Our DCNN approach can segment the liver and HCCs automatically. This could enable a more workflow efficient and clinically realistic implementation of LI-RADS

Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios as predictors of tumor response in hepatocellular carcinoma after DEB-TACE

Objectives To investigate the predictive value of quantifiable imaging and inflammatory biomarkers in patients with hepatocellular carcinoma (HCC) for the clinical outcome after drug-eluting bead transarterial chemoembolization (DEB-TACE) measured as volumetric tumor response and progression-free survival (PFS). Methods This retrospective study included 46 patients with treatment-naive HCC who received DEB-TACE. Laboratory work-up prior to treatment included complete and differential blood count, liver function, and alpha-fetoprotein levels. Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were correlated with radiomic features extracted from pretreatment contrast-enhanced magnetic resonance imaging (MRI) and with tumor response according to quantitative European Association for the Study of the Liver (qEASL) criteria and progression-free survival (PFS) after DEB-TACE. Radiomic features included single nodular tumor growth measured as sphericity, dynamic contrast uptake behavior, arterial hyperenhancement, and homogeneity of contrast uptake. Statistics included univariate and multivariate linear regression, Cox regression, and Kaplan-Meier analysis. Results Accounting for laboratory and clinical parameters, high baseline NLR and PLR were predictive of poorer tumor response (p = 0.014 and p = 0.004) and shorter PFS (p = 0.002 and p < 0.001). When compared to baseline imaging, high NLR and PLR correlated with non-spherical tumor growth (p = 0.001 and p < 0.001). Conclusions This study establishes the prognostic value of quantitative inflammatory biomarkers associated with aggressive non-spherical tumor growth and predictive of poorer tumor response and shorter PFS after DEB-TACE

Evaluating body composition by combining quantitative spectral detector computed tomography and deep learning-based image segmentation

Purpose: Aim of this study was to develop and evaluate a software toolkit, which allows for a fully automated body composition analysis in contrast enhanced abdominal computed tomography leveraging the strengths of both, quantitative information from dual energy computed tomography and simple detection and segmentation tasks performed by deep convolutional neuronal networks (DCNN). Methods and materials: Both, public and private datasets were used to train and validate DCNN. A combination of two DCNN and quantitative thresholding was used to classify axial CT slices to the abdominal region, classify voxels as fat and muscle and to differentiate between subcutaneous and visceral fat. For validation, patients undergoing repetitive examination (+/- 21 days) and patients who underwent concurrent bioelectrical impedance analysis (BIA) were analyzed. Concordance correlation coefficient (CCC), linear regression and Bland-Altman-Analysis were used as statistical tests. Results: Results provided from the algorithm toolkit were visually validated. The automated classifier was able to extract slices of interest from the full body scans with an accuracy of 98.7 %. DCNN-based segmentation for subcutaneous fat reached a mean dice similarity coefficient of 0.95. CCCs were 0.99 for both muscle and subcutaneous fat and 0.98 for visceral fat in patients undergoing repetitive examinations (n = 48). Further linear regression and Bland-Altman-Analyses suggested good agreement (r(2):0.67-0.88) between the software toolkit and patients who underwent concurrent BIA (n = 39). Conclusion: We describe a software toolkit allowing for an accurate analysis of body composition utilizing a combination of DCNN- and threshold-based segmentations from spectral detector computed tomography

Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

Introduction Deep learning-based algorithms have demonstrated enormous performance in segmentation of medical images. We collected a dataset of multiparametric MRI and contour data acquired for use in radiosurgery, to evaluate the performance of deep convolutional neural networks (DCNN) in automatic segmentation of brain metastases (BM). Methods A conventional U-Net (cU-Net), a modified U-Net (moU-Net) and a U-Net trained only on BM smaller than 0.4 ml (sU-Net) were implemented. Performance was assessed on a separate test set employing sensitivity, specificity, average false positive rate (AFPR), the dice similarity coefficient (DSC), Bland-Altman analysis and the concordance correlation coefficient (CCC). Results A dataset of 509 patients (1223 BM) was split into a training set (469 pts) and a test set (40 pts). A combination of all trained networks was the most sensitive (0.82) while maintaining a specificity 0.83. The same model achieved a sensitivity of 0.97 and a specificity of 0.94 when considering only lesions larger than 0.06 ml (75% of all lesions). Type of primary cancer had no significant influence on the mean DSC per lesion (p = 0.60). Agreement between manually and automatically assessed tumor volumes as quantified by a CCC of 0.87 (95% CI, 0.77-0.93), was excellent. Conclusion Using a dataset which properly captured the variation in imaging appearance observed in clinical practice, we were able to conclude that DCNNs reach clinically relevant performance for most lesions. Clinical applicability is currently limited by the size of the target lesion. Further studies should address if small targets are accurately represented in the test data

Radiomics for prediction of radiation-induced lung injury and oncologic outcome after robotic stereotactic body radiotherapy of lung cancer: results from two independent institutions

Objectives To generate and validate state-of-the-art radiomics models for prediction of radiation-induced lung injury and oncologic outcome in non-small cell lung cancer (NSCLC) patients treated with robotic stereotactic body radiation therapy (SBRT). Methods Radiomics models were generated from the planning CT images of 110 patients with primary, inoperable stage I/IIa NSCLC who were treated with robotic SBRT using a risk-adapted fractionation scheme at the University Hospital Cologne (training cohort). In total, 199 uncorrelated radiomic features fulfilling the standards of the Image Biomarker Standardization Initiative (IBSI) were extracted from the outlined gross tumor volume (GTV). Regularized models (Coxnet and Gradient Boost) for the development of local lung fibrosis (LF), local tumor control (LC), disease-free survival (DFS) and overall survival (OS) were built from either clinical/ dosimetric variables, radiomics features or a combination thereof and validated in a comparable cohort of 71 patients treated by robotic SBRT at the Radiosurgery Center in Northern Germany (test cohort). Results Oncologic outcome did not differ significantly between the two cohorts (OS at 36 months 56% vs. 43%, p = 0.065; median DFS 25 months vs. 23 months, p = 0.43; LC at 36 months 90% vs. 93%, p = 0.197). Local lung fibrosis developed in 33% vs. 35% of the patients (p = 0.75), all events were observed within 36 months. In the training cohort, radiomics models were able to predict OS, DFS and LC (concordance index 0.77-0.99, p < 0.005), but failed to generalize to the test cohort. In opposite, models for the development of lung fibrosis could be generated from both clinical/dosimetric factors and radiomic features or combinations thereof, which were both predictive in the training set (concordance index 0.71- 0.79, p < 0.005) and in the test set (concordance index 0.59-0.66, p < 0.05). The best performing model included 4 clinical/dosimetric variables (GTV-D-mean, PTV-D-95%, Lung-D-1ml, age) and 7 radiomic features (concordance index 0.66, p < 0.03). Conclusion Despite the obvious difficulties in generalizing predictive models for oncologic outcome and toxicity, this analysis shows that carefully designed radiomics models for prediction of local lung fibrosis after SBRT of early stage lung cancer perform well across different institutions

Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities.

Technological innovation has enabled the development of machine learning (ML) tools that aim to improve the practice of radiologists. In the last decade, ML applications to neuro-oncology have expanded significantly, with the pre-operative prediction of glioma grade using medical imaging as a specific area of interest. We introduce the subject of ML models for glioma grade prediction by remarking upon the models reported in the literature as well as by describing their characteristic developmental workflow and widely used classifier algorithms. The challenges facing these models-including data sources, external validation, and glioma grade classification methods -are highlighted. We also discuss the quality of how these models are reported, explore the present and future of reporting guidelines and risk of bias tools, and provide suggestions for the reporting of prospective works. Finally, this review offers insights into next steps that the field of ML glioma grade prediction can take to facilitate clinical implementation