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Objectives: In women with high-grade serous ovarian cancer (HGSOC), a CT-based radiomic prognostic vector (RPV) predicted stromal phenotype and survival after primary surgery. The study's purpose was to fully externally validate RPV and its biological correlate., Materials and Methods: In this retrospective study, ovarian masses on CT scans of HGSOC patients, who underwent primary cytoreductive surgery in an ESGO-certified Center between 2002 and 2017, were segmented for external RPV score calculation and then correlated with overall survival (OS) and progression-free survival (PFS). A subset of tissue samples subjected to fibronectin immunohistochemistry were evaluated by a gynaeco-pathologist for stromal content. Kaplan-Meier log-rank test and a Cox proportional hazards model were used for outcome analysis., Results: Among 340 women with HGSOC, 244 ovarian lesions were available for segmentation in 198 women (mean age 59.8 years, range 34-92). Median OS was 48.69 months (IQR: 27.0-102.5) and PFS was 19.3 months (IQR: 13-32.2). Using multivariate Cox analysis, poor OS was associated with RPV-high (HR 3.17; 95% CI: 1.32-7.60; p = 0.0099), post-operative residual disease (HR 2.04; 95% CI: 1.30-3.20; p = 0.0020), and FIGO stage III/IV (HR 1.79; 95% CI: 1.11-2.86; p = 0.016). Age did not influence OS. RPV-high tissue had higher stromal content based on fibronectin expression (mean 48.9%, SD 10.5%) compared to RPV-low cases (mean 14.9%, SD 10.5%, p < 0.0001). RPV score was not significantly associated with PFS., Conclusion: Patients with HGSOC and RPV-high ovarian mass on pre-operative CT had significantly worse OS following primary surgery and a higher stromal content compared to RPV-low masses, externally validating the RPV and its biological interpretation., Key Points: Question Can the performance of a previously described RPV in women with HGSOC be replicated when licenced to an external institution? Findings External validation of RPV among 244 ovarian lesions demonstrated that, on multivariate analysis, OS was associated with RPV, stage, and postoperative residual disease, replicating previous findings. Clinical relevance External validation of a radiomic tool is an essential step in translation to clinical applicability and provides the basis for prospective validation. In clinical practice, this RPV may allow more personalized decision-making for women with ovarian cancer being considered for extensive cytoreductive surgery., Competing Interests: Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Andrea G. Rockall. Conflict of interest: The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. Statistics and biometry: Haonan Lu kindly provided statistical advice for this manuscript. Informed consent: Written informed consent was obtained from all subjects (patients) in this study. Ethical approval: Institutional Review Board approval was obtained. Methodology: Retrospective Cross-sectional study External validation study Performed at one institution, (© 2024. The Author(s).)

Objectives: This study aims to identify repeated previous shortcomings in medical imaging data collection, curation, and AI-based analysis during the early phase of respiratory pandemics. Based on the results, it seeks to highlight essential steps for improving future pandemic preparedness., Materials and Methods: We searched PubMed/MEDLINE, Scopus, and Cochrane Reviews for articles published from January 1, 2000, to December 31, 2021, using the terms "imaging" or "radiology" or "radiography" or "CT" or "x-ray" combined with "SARS," "MERS," "H1N1," or "COVID-19." WHO and CDC Databases were searched for case definitions., Results: Over the last 20 years, the world faced several international health emergencies caused by respiratory diseases such as SARS, MERS, H1N1, and COVID-19. During the same period, major technological advances enabled the analysis of vast amounts of imaging data and the continual development of artificial intelligence algorithms to support radiological diagnosis and prognosis. Timely availability of data proved critical, but so far, data collection attempts were initialized only as individual responses to each outbreak, leading to long delays and hampering unified guidelines and data-driven technology to support the management of pandemic outbreaks. Our findings highlight the multifaceted role of imaging in the early stages of SARS, MERS, H1N1, and COVID-19, and outline possible actions for advancing future pandemic preparedness., Conclusions: Advancing international cooperation and action on these topics is essential to create a functional, effective, and rapid counteraction system to future respiratory pandemics exploiting state of the art imaging and artificial intelligence., Key Points: Question What has been the role of radiological data for diagnosis and prognosis in early respiratory pandemics and what challenges were present? Findings International cooperation is essential to developing an effective rapid response system for future respiratory pandemics using advanced imaging and artificial intelligence. Clinical relevance Strengthening global collaboration and leveraging cutting-edge imaging and artificial intelligence are crucial for developing rapid and effective response systems. This approach is essential for improving patient outcomes and managing future respiratory pandemics more effectively., Competing Interests: Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is G.L. Conflict of interest: G.L. is co-founder and shareholder of contextflow GmbH. H.P. declares the following interests: Speaker honoria (AstraZeneca, BMS, Boehringer Ingelheim, Janssen, MSD, Novartis, Roche/Intermune, Sanofi, Siemens Healthcare, Takeda), Advisory Boards (BMS, Boehringer Ingelheim, MSD, Roche/Intermune, Sanofi), Travel grants (Boehringer Ingelheim), Research support (Boehringer Ingelheim, AstraZeneca, Siemens, EU4Health). H.P. is a deputy editor for European Radiology, they have not participated in the selection nor review processes for this article. The remaining authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Statistics and biometry: No complex statistical methods were necessary for this paper. Informed consent: Written informed consent was not required for this study because it is a scoping review. Ethical approval: Institutional Review Board approval was not required because it is a scoping review. Study subjects or cohorts overlap: There were no study subjects or cohorts used for this study. Methodology: Scoping review, (© 2024. The Author(s).)

Objective: To compare unsupervised deep clustering (UDC) to fat fraction (FF) and relative liver enhancement (RLE) on Gd-EOB-DTPA-enhanced MRI to distinguish simple steatosis from non-alcoholic steatohepatitis (NASH), using histology as the gold standard., Materials and Methods: A derivation group of 46 non-alcoholic fatty liver disease (NAFLD) patients underwent 3-T MRI. Histology assessed steatosis, inflammation, ballooning, and fibrosis. UDC was trained to group different texture patterns from MR data into 10 distinct clusters per sequence on unenhanced T1- and Gd-EOB-DTPA-enhanced T1-weighted hepatobiliary phase (T1-Gd-EOB-DTPA-HBP), then on T1 in- and opposed-phase images. RLE and FF were quantified on identical sequences. Differences of these parameters between NASH and simple steatosis were evaluated with χ 2 - and t-tests, respectively. Linear regression and Random Forest classifier were performed to identify associations between histological NAFLD features, RLE, FF, and UDC patterns, and then determine predictors able to distinguish simple steatosis from NASH. ROC curves assessed diagnostic performance of UDC, RLE, and FF. Finally, we tested these parameters on 30 validation cohorts., Results: For the derivation group, UDC-derived features from unenhanced and T1-Gd-EOB-DTPA-HBP, plus from T1 in- and opposed-phase, distinguished NASH from simple steatosis (p ≤ 0.001 and p = 0.02, respectively) with 85% and 80% accuracy, respectively, while RLE and FF distinguished NASH from simple steatosis (p ≤ 0.001 and p = 0.004, respectively), with 83% and 78% accuracy, respectively. On multivariate regression analysis, RLE and FF correlated only with fibrosis (p = 0.040) and steatosis (p ≤ 0.001), respectively. Conversely, UDC features, using Random Forest classifier predictors, correlated with all histologic NAFLD components. The validation group confirmed these results for both approaches., Conclusion: UDC, RLE, and FF could independently separate NASH from simple steatosis. UDC may predict all histologic NAFLD components., Clinical Relevance Statement: Using gadoxetic acid-enhanced MR, fat fraction (FF > 5%) can diagnose NAFLD, and relative liver enhancement can distinguish NASH from simple steatosis. Adding AI may let us non-invasively estimate the histologic components, i.e., fat, ballooning, inflammation, and fibrosis, the latter the main prognosticator., Key Points: • Unsupervised deep clustering (UDC) and MR-based parameters (FF and RLE) could independently distinguish simple steatosis from NASH in the derivation group. • On multivariate analysis, RLE could predict only fibrosis, and FF could predict only steatosis; however, UDC could predict all histologic NAFLD components in the derivation group. • The validation cohort confirmed the findings for the derivation group., (© 2023. The Author(s).)

Objectives: To identify and evaluate predictive lung imaging markers and their pathways of change during progression of idiopathic pulmonary fibrosis (IPF) from sequential data of an IPF cohort. To test if these imaging markers predict outcome., Methods: We studied radiological disease progression in 76 patients with IPF, including overall 190 computed tomography (CT) examinations of the chest. An algorithm identified candidates for imaging patterns marking progression by computationally clustering visual CT features. A classification algorithm selected clusters associated with radiological disease progression by testing their value for recognizing the temporal sequence of examinations. This resulted in radiological disease progression signatures, and pathways of lung tissue change accompanying progression observed across the cohort. Finally, we tested if the dynamics of marker patterns predict outcome, and performed an external validation study on a cohort from a different center., Results: Progression marker patterns were identified and exhibited high stability in a repeatability experiment with 20 random sub-cohorts of the overall cohort. The 4 top-ranked progression markers were consistently selected as most informative for progression across all random sub-cohorts. After spatial image registration, local tracking of lung pattern transitions revealed a network of tissue transition pathways from healthy to a sequence of disease tissues. The progression markers were predictive for outcome, and the model achieved comparable results on a replication cohort., Conclusions: Unsupervised learning can identify radiological disease progression markers that predict outcome. Local tracking of pattern transitions reveals pathways of radiological disease progression from healthy lung tissue through a sequence of diseased tissue types., Key Points: • Unsupervised learning can identify radiological disease progression markers that predict outcome in patients with idiopathic pulmonary fibrosis. • Local tracking of pattern transitions reveals pathways of radiological disease progression from healthy lung tissue through a sequence of diseased tissue types. • The progression markers achieved comparable results on a replication cohort., (© 2022. The Author(s).)

Objectives: Content-based image retrieval systems (CBIRS) are a new and potentially impactful tool for radiological reporting, but their clinical evaluation is largely missing. This study aimed at assessing the effect of CBIRS on the interpretation of chest CT scans from patients with suspected diffuse parenchymal lung disease (DPLD)., Materials and Methods: A total of 108 retrospectively included chest CT scans with 22 unique, clinically and/or histopathologically verified diagnoses were read by eight radiologists (four residents, four attending, median years reading chest CT scans 2.1± 0.7 and 12 ± 1.8, respectively). The radiologists read and provided the suspected diagnosis at a certified radiological workstation to simulate clinical routine. Half of the readings were done without CBIRS and half with the additional support of the CBIRS. The CBIRS retrieved the most likely of 19 lung-specific patterns from a large database of 6542 thin-section CT scans and provided relevant information (e.g., a list of potential differential diagnoses)., Results: Reading time decreased by 31.3% (p < 0.001) despite the radiologists searching for additional information more frequently when the CBIRS was available (154 [72%] vs. 95 [43%], p < 0.001). There was a trend towards higher overall diagnostic accuracy (42.2% vs 34.7%, p = 0.083) when the CBIRS was available., Conclusion: The use of the CBIRS had a beneficial impact on the reading time of chest CT scans in cases with DPLD. In addition, both resident and attending radiologists were more likely to consult informational resources if they had access to the CBIRS. Further studies are needed to confirm the observed trend towards increased diagnostic accuracy with the use of a CBIRS in practice., Key Points: • A content-based image retrieval system for supporting the diagnostic process of reading chest CT scans can decrease reading time by 31.3% (p < 0.001). • The decrease in reading time was present despite frequent usage of the content-based image retrieval system. • Additionally, a trend towards higher diagnostic accuracy was observed when using the content-based image retrieval system (42.2% vs 34.7%, p = 0.083)., (© 2022. The Author(s).)

Objectives: Acute respiratory distress syndrome (ARDS) constitutes a major factor determining the clinical outcome in polytraumatized patients. Early prediction of ARDS is crucial for timely supportive therapy to reduce morbidity and mortality. The objective of this study was to develop and test a machine learning-based method for the early prediction of ARDS derived from the first computed tomography scan of polytraumatized patients after admission to the hospital., Materials and Methods: One hundred twenty-three patients (86 male and 37 female, age 41.2 ± 16.4) with an injury severity score (ISS) of 16 or higher (31.9 ± 10.9) were prospectively included and received a CT scan within 1 h after the accident. The lungs, including air pockets and pleural effusions, were automatically segmented using a deep learning-based algorithm. Subsequently, we extracted radiomics features from within the lung and trained an ensemble of gradient boosted trees (GBT) to predict future ARDS., Results: Cross-validated ARDS prediction resulted in an area under the curve (AUC) of 0.79 for the radiomics score compared to 0.66 for ISS, and 0.68 for the abbreviated injury score of the thorax (AIS-thorax). Prediction using the radiomics score yielded an f1-score of 0.70 compared to 0.53 for ISS and 0.57 for AIS-thorax. The radiomics score achieved a sensitivity and specificity of 0.80 and 0.76., Conclusions: This study proposes a radiomics-based algorithm for the prediction of ARDS in polytraumatized patients at the time of admission to hospital with an accuracy that competes and surpasses conventional scores despite the heterogeneous, and therefore more realistic, scanning protocols., Key Points: • Early prediction of acute respiratory distress syndrome in polytraumatized patients is possible, even when using heterogenous data. • Radiomics-based prediction resulted in an area under the curve of 0.79 compared to 0.66 for the injury severity score, and 0.68 for the abbreviated injury score of the thorax. • Highlighting the most relevant lung regions for prediction facilitates the understanding of machine learning-based prediction., (© 2021. The Author(s).)

Objectives: To evaluate the influence of baseline parameters on the occurrence of stent-graft surface movement after endovascular aneurysm repair (EVAR) and to investigate its association with migration and stent-graft-related endoleaks (srEL)., Methods: In this retrospective, cross-sectional study, three-dimensional surface models of the stent-graft, delimited by landmarks using custom-built software, were derived from the pre-discharge and last follow-up computed tomography angiography (CTA). Stent-graft surface movement in the proximal anchoring zone between these examinations was considered significant at a threshold of 9 mm. The Cox proportional hazards model was used to determine baseline variables associated with the occurrence of stent-graft surface movement. The association between migration and srEL with stent-graft surface movement was tested with the chi-square and the Fisher exact test, respectively., Results: Stent-graft surface movement was observed in 54 (28.9%) of 187 patients. Multivariate analysis revealed that age ([HR] 1.05; p = 0.017), proximal neck diameter ([HR] 5.07; p < 0.001), infrarenal aortic neck angulation ([HR] 1.02, p = 0.002), and proximal neck length ([HR] 0.62, p < 0.001) were significantly associated with the occurrence of stent-graft surface movement. Migration and srEL occurred in 17 (31.5%) and 5 (9.3%) patients, with and 11 (8.3%) and 2 (1.5%) without stent-graft surface movement (p < 0.001, p = 0.022)., Conclusions: Age, neck diameter, infrarenal neck angulation, and proximal neck length were significantly associated with the occurrence of stent-graft surface movement. Apart from possible use of adjunctive sealing systems, concerned patients may benefit from regular CTA surveillance, enabling timely diagnosis of subtle changes of stent-graft position., Key Points: • Stent-graft surface movement, demonstrating subtle, three-dimensional changes in stent-graft position in the proximal anchoring zone, can be derived from CTA examinations. • Age, proximal neck diameter, and infrarenal neck angulation were significantly associated with an increased incidence of stent-graft surface movement. Stent-graft surface movement is significantly more frequent in patients with stent-graft migration and stent-graft-related endoleaks. • Consideration of risk factors for stent-graft surface movement may help to identify patients who might benefit from regular CTA surveillance and timely diagnosis of subtle changes of stent-graft position, enabling re-interventions to prevent migration and srEL.

Objectives: To demonstrate the feasibility of contrast-enhanced dual-energy mammography (CEDEM) using titanium (Ti) filtering at 49 kVp for high-energy images and a novel artefact reducing image-subtraction post-processing algorithm., Methods: Fifteen patients with suspicious findings (ACR BI-RADS 4 and 5) detected with digital mammography (MG) that required biopsy were included. CEDEM examinations were performed on a modified prototype machine. Acquired HE and low-energy raw data images were registered non-rigidly to compensate for possible subtle tissue motion. Subtracted CEDEM images were generated via weighted subtraction, using a fully automatic, locally adjusted tissue thickness-dependent subtraction factor to avoid over-subtraction at the breast border. Two observers evaluated the MG and CEDEM images according to ACR BI-RADS in two reading sessions. Results were correlated with histopathology., Results: Seven patients with benign and eight with malignant findings were included. All malignant lesions showed a strong contrast enhancement. BI-RADS assessment was altered in 66.6 % through the addition of CEDEM, resulting in increased overall accuracy. With CEDEM, additional lesions were depicted and false-positive rate was reduced compared to MG., Conclusions: CEDEM using Ti filtering with 49 kVp for HE exposures is feasible in a clinical setting. The proposed image-processing algorithm has the potential to reduce artefacts and improve CEDEM images., Key Points: • CEDEM with a titanium filter is feasible in a clinical setting. • Breast thickness-dependent image subtraction has the potential to improve CEDEM images. • The proposed image-processing algorithm reduces artefacts.