Reliability of Automated RECIST 1.1 and Volumetric RECIST Target Lesion Response Evaluation in Follow-Up CT-A Multi-Center, Multi-Observer Reading Study.

Objectives: To evaluate the performance of a custom-made convolutional neural network (CNN) algorithm for fully automated lesion tracking and segmentation, as well as RECIST 1.1 evaluation, in longitudinal computed tomography (CT) studies compared to a manual Response Evaluation Criteria in Solid Tumors (RECIST 1.1) evaluation performed by three radiologists.

Can Delta Radiomics Improve the Prediction of Best Overall Response, Progression-Free Survival, and Overall Survival of Melanoma Patients Treated with Immune Checkpoint Inhibitors?

Background: The prevalence of metastatic melanoma is increasing, necessitating the identification of patients who do not benefit from immunotherapy. This study aimed to develop a radiomic biomarker based on the segmentation of all metastases at baseline and the first follow-up CT for the endpoints best overall response (BOR), progression-free survival (PFS), and overall survival (OS), encompassing various immunotherapies. Additionally, this study investigated whether reducing the number of segmented metastases per patient affects predictive capacity.

Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics.

Objectives: Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation.

Materials And Methods: The consensus was achieved by a multi-stage process.

Improving assessment of lesions in longitudinal CT scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow.

Purpose: AI-assisted techniques for lesion registration and segmentation have the potential to make CT-based tumor follow-up assessment faster and less reader-dependent. However, empirical evidence on the advantages of AI-assisted volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans is lacking. The aim of this study was to assess the efficiency, quality, and inter-reader variability of an AI-assisted workflow for volumetric segmentation of lymph node and soft tissue metastases in follow-up CT scans.

Pre-treatment 18F-FDG-PET/CT parameters as biomarkers for progression free survival, best overall response and overall survival in metastatic melanoma patients undergoing first-line immunotherapy.

Background: Checkpoint inhibitors have drastically improved the therapy of patients with advanced melanoma. 18F-FDG-PET/CT parameters might act as biomarkers for response and survival and thus can identify patients that do not benefit from immunotherapy. However, little literature exists on the association of baseline 18F-FDG-PET/CT parameters with progression free survival (PFS), best overall response (BOR), and overall survival (OS).

Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study.

Background: The aim of this study was to investigate whether the combination of radiomics and clinical parameters in a machine-learning model offers additive information compared with the use of only clinical parameters in predicting the best response, progression-free survival after six months, as well as overall survival after six and twelve months in patients with stage IV malignant melanoma undergoing first-line targeted therapy.

Methods: A baseline machine-learning model using clinical variables (demographic parameters and tumor markers) was compared with an extended model using clinical variables and radiomic features of the whole tumor burden, utilizing repeated five-fold cross-validation. Baseline CTs of 91 stage IV malignant melanoma patients, all treated in the same university hospital, were identified in the Central Malignant Melanoma Registry and all metastases were volumetrically segmented ( = 4727).

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients.

Combination of Whole-Body Baseline CT Radiomics and Clinical Parameters to Predict Response and Survival in a Stage-IV Melanoma Cohort Undergoing Immunotherapy.

Background: This study investigated whether a machine-learning-based combination of radiomics and clinical parameters was superior to the use of clinical parameters alone in predicting therapy response after three months, and overall survival after six and twelve months, in stage-IV malignant melanoma patients undergoing immunotherapy with PD-1 checkpoint inhibitors and CTLA-4 checkpoint inhibitors.

Methods: A random forest model using clinical parameters (demographic variables and tumor markers = baseline model) was compared to a random forest model using clinical parameters and radiomics (extended model) via repeated 5-fold cross-validation. For this purpose, the baseline computed tomographies of 262 stage-IV malignant melanoma patients treated at a tertiary referral center were identified in the Central Malignant Melanoma Registry, and all visible metastases were three-dimensionally segmented ( = 6404).

Radiomics Features of the Spleen as Surrogates for CT-Based Lymphoma Diagnosis and Subtype Differentiation.

The spleen is often involved in malignant lymphoma, which manifests on CT as either splenomegaly or focal, hypodense lymphoma lesions. This study aimed to investigate the diagnostic value of radiomics features of the spleen in classifying malignant lymphoma against non-lymphoma as well as the determination of malignant lymphoma subtypes in the case of disease presence-in particular Hodgkin lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), mantle-cell lymphoma (MCL), and follicular lymphoma (FL). Spleen segmentations of 326 patients (139 female, median age 54.

Quantitative Imaging Biomarkers of the Whole Liver Tumor Burden Improve Survival Prediction in Metastatic Pancreatic Cancer.

Finding prognostic biomarkers with high accuracy in patients with pancreatic cancer (PC) remains a challenging problem. To improve the prediction of survival and to investigate the relevance of quantitative imaging biomarkers (QIB) we combined QIB with established clinical parameters. In this retrospective study a total of 75 patients with metastatic PC and liver metastases were analyzed.

CNN-based lung CT registration with multiple anatomical constraints.

Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional registration methods because they are either limited to small deformation or they fail to handle a superposition of large and small deformations without producing implausible deformation fields with foldings inside. In this paper, we identify important strategies of conventional registration methods for lung registration and successfully developed the deep-learning counterpart.

Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach.

Objectives: To develop and validate a deep learning-based algorithm for segmenting and quantifying the physiological and diseased aorta in computed tomography angiographies.

Methods: CTA exams of the aorta of 191 patients (68.1 ± 14 years, 128 male), performed between 2015 and 2018, were retrospectively identified from our imaging archive and manually segmented by two investigators.

Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge.

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B).

MRI-based radiomic feature analysis of end-stage liver disease for severity stratification.

Purpose: We aimed to develop a predictive model of disease severity for cirrhosis using MRI-derived radiomic features of the liver and spleen and compared it to the existing disease severity metrics of MELD score and clinical decompensation. The MELD score is compiled solely by blood parameters, and so far, it was not investigated if extracted image-based features have the potential to reflect severity to potentially complement the calculated score.

Methods: This was a retrospective study of eligible patients with cirrhosis ([Formula: see text]) who underwent a contrast-enhanced MR screening protocol for hepatocellular carcinoma (HCC) screening at a tertiary academic center from 2015 to 2018.

The International Radiomics Platform - An Initiative of the German and Austrian Radiological Societies - First Application Examples.

Purpose:  The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets.

The relevance of CT-based geometric and radiomics analysis of whole liver tumor burden to predict survival of patients with metastatic colorectal cancer.

Objectives: To investigate the prediction of 1-year survival (1-YS) in patients with metastatic colorectal cancer with use of a systematic comparative analysis of quantitative imaging biomarkers (QIBs) based on the geometric and radiomics analysis of whole liver tumor burden (WLTB) in comparison to predictions based on the tumor burden score (TBS), WLTB volume alone, and a clinical model.

Methods: A total of 103 patients (mean age: 61.0 ± 11.

Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing.

Automatic liver tumor segmentation would have a big impact on liver therapy planning procedures and follow-up assessment, thanks to standardization and incorporation of full volumetric information. In this work, we develop a fully automatic method for liver tumor segmentation in CT images based on a 2D fully convolutional neural network with an object-based postprocessing step. We describe our experiments on the LiTS challenge training data set and evaluate segmentation and detection performance.

Role of CT Density in PET/CT-Based Assessment of Lymphoma.

Purpose: In patients with Hodgkin (HL) and non-Hodgkin lymphoma (NHL), primary staging, as well as intermediate and late response assessment, is often performed by integrated 2-deoxy-2-[F]fluoro-D-glucose ([F]FDG) positron emission tomography/X-ray computed tomography (PET/CT). The purpose of this analysis was to evaluate if findings in patients with histopathologically proven HL or NHL might correlate with semi-automated density measurements of target lesions (TLs) in the CT component of the integrated PET/CT examination.

Procedures: After approval by the institutional review board, 176 lymph nodes (LN) in 90 PET/CT examinations of 90 patients were retrospectively analyzed (HL, 108 TLs out of 55 patients; NHL, 68 TLs out of 35 patients).

Ga-PSMA PET/CT and Volumetric Morphology of PET-Positive Lymph Nodes Stratified by Tumor Differentiation of Prostate Cancer.

Ga-prostate-specific membrane antigen (PSMA) PET/CT is a new method to detect early nodal metastases in patients with biochemical relapse of prostate cancer. In this retrospective investigation, the dimensions, volume, localization, and SUV of nodes identified by Ga-PSMA were correlated to their Gleason score (GS) at diagnosis. All PET/CT images were acquired 60 ± 10 min after intravenous injection of Ga-PSMA (mean dose, 176 MBq).

Correlation Between SUVmax and CT Radiomic Analysis Using Lymph Node Density in PET/CT-Based Lymph Node Staging.

Unlabelled: In patients with lung cancer (LC), malignant melanoma (MM), gastroenteropancreatic neuroendocrine tumors (GEP NETs), and prostate cancer (PCA), lymph node (LN) staging is often performed by F-FDG PET/CT (LC and MM), Ga-DOTATOC PET/CT (GEP NET), and Ga-labeled prostate-specific membrane antigen PET/CT (PCA) but is sometimes not accurate because of indeterminate PET findings. To better evaluate malignant LN infiltration, additional surrogate parameters, especially in cases with indeterminate PET findings, would be helpful. The purpose of this study was to evaluate whether SUV in the PET examination might correlate with semiautomated density measurements of LNs in the CT component of the PET/CT examination.

Radiomic Analysis using Density Threshold for FDG-PET/CT-Based N-Staging in Lung Cancer Patients.

Purpose: Mediastinal nodal (N)-staging done by integrated 2-deoxy-2-[F]fluoro-D-glucose ([F]FDG) positron emission tomography/x-ray computed tomography (PET/CT) in lung cancer patients is not always accurate. In order to reduce the need for invasive staging procedures, additional surrogate parameters for the detection of malignant lymph node infiltration would be helpful. The purpose of this study was to evaluate if radiomic semi-automated density profiling in mediastinal lymph nodes can improve preclinical N-staging, irrespective of the specific lung cancer entity.

Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge.

Rationale And Objectives: Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA).

Semi-automatic 3D-volumetry of liver metastases from neuroendocrine tumors to improve combination therapy with 177Lu-DOTATOC and 90Y-DOTATOC.

Purpose: Patients with neuroendocrine tumors (NET) often present with disseminated liver metastases and can be treated with a number of different nuclides or nuclide combinations in peptide receptor radionuclide therapy (PRRT) depending on tumor load and lesion diameter. For quantification of disseminated liver lesions, semi-automatic lesion detection is helpful to determine tumor burden and tumor diameter in a time efficient manner. Here, we aimed to evaluate semi-automated measurement of total metastatic burden for therapy stratification.