By the time its legendary, long-serving (1869–1919) editor, Henry Mills Alden, accepted "An Anarchist" in 1906, Harper's was the preeminent literary magazine in the United States. Harper's New Monthly Magazine began in 1850 as a "'compendium' for those of their customers who didn't have time to read through 'scores and hundreds of magazines and journals'" (Lapham xii). Copyright laws did not protect European authors and publishers in the American market, so Harper's early issues [End Page 179] were full of pirated pieces by Dickens, Thackeray, George Eliot, and others—American authors were relegated to the back. Though the magazine achieved extraordinary popularity very soon—with fifty thousand subscribers after six months of publication (Exman 69–70)—its European orientation inspired a backlash:

Radiology reports can change during workflows, especially when residents draft preliminary versions that attending physicians finalize. We explored how large language models (LLMs) and embedding techniques can categorize these changes into textual, semantic, or clinically actionable types. We evaluated 400 adult CT reports drafted by residents against finalized versions by attending physicians. Changes were rated on a five-point scale from no changes to critical ones. We examined open-source LLMs alongside traditional metrics like normalized word differences, Levenshtein and Jaccard similarity, and text embedding similarity. Model performance was assessed using quadratic weighted Cohen's kappa (κ), (balanced) accuracy, F₁, precision, and recall. Inter-rater reliability among evaluators was excellent (κ = 0.990). Of the reports analyzed, 1.3 % contained critical changes. The tested methods showed significant performance differences (P < 0.001). The Qwen3-235B-A22B model using a zero-shot prompt, most closely aligned with human assessments of changes in clinical reports, achieving a κ of 0.822 (SD 0.031). The best conventional metric, word difference, had a κ of 0.732 (SD 0.048), the difference between the two showed statistical significance in unadjusted post-hoc tests (P = 0.038) but lost significance after adjusting for multiple testing (P = 0.064). Embedding models underperformed compared to LLMs and classical methods, showing statistical significance in most cases. Large language models like Qwen3-235B-A22B demonstrated moderate to strong alignment with expert evaluations of the clinical significance of changes in radiology reports. LLMs outperformed embedding methods and traditional string and word approaches, achieving statistical significance in most instances. This demonstrates their potential as tools to support peer review.

Background/Objectives: The aim of this study was to investigate the prognostic value of established response assessment tools for hepatocellular carcinoma (HCC) treated with high-dose-rate interstitial brachytherapy (iBT) alone or with transarterial chemoembolization (cTACE). Methods: (Non-)responders were categorized using size-based RECIST 1.1 and WHO criteria, enhancement-based mRECIST and EASL criteria, and the LI-RADS Treatment Response Algorithm (LR-TRA). The outcomes were the overall survival (OS), progression-free survival (PFS), and time to progression (TTP). The statistics used included Fisher’s exact test, a t-test, the Mann–Whitney-U test, and a Kaplan–Meier analysis. The median OS, PFS, and TTP were higher in patients following iBT (26.3, 9.1, and 13.0 months) than following cTACE/iBT (23.3, 7.6, and 9.2 months). Results: The enhancement-based criteria identified more responders and predicted PFS and TTP better compared to the size-based criteria. At two months, the cTACE/iBT responders showed improved PFS (mRECIST and EASL: 11.3 vs. 2.3 and 11.0 vs. 2.3, p < 0.01) and TTP (mRECIST and EASL: 11.9 vs. 2.4 months, p < 0.01) by the enhancement-based criteria. An EASL assessment at five months predicted improved survival following both cTACE/iBT (PFS: 11.9 vs. 5.1 months, p = 0.03; TTP: 12.4 vs. 5.0, p < 0.01) and iBT (11.1 vs. 5.1 months, p = 0.04; 13.0 vs. 5.3, p < 0.01). The LR-TRA showed OS benefits at five months for cTACE/iBT responders. Size-based criteria were not prognostic. Conclusions: Extending follow-up post-iBT or post-iBT/cTACE may improve responder stratification and prognostication.

Purpose: This study evaluates the prognostic significance of pleural effusion (PE) in COVID-19 patients across thirteen centers in Germany, aiming to clarify its role in predicting clinical outcomes. Methods: In this retrospective analysis within the RACOON project (Radiological Cooperative Network of the COVID-19 pandemic), 1183 patients (29.3 % women, 70.7 % men) underwent chest CT to assess PE. We investigated PE's association with 30-day mortality, ICU admission, and the need for mechanical ventilation. Results: PE was detected in 31.5 % of patients, showing a significant correlation with 30-day mortality (47.5 % in non-survivors vs. 27.3 % in survivors, p < 0.001), with a hazard ratio of 2.22 (95% CI 1.65-2.99, p < 0.001). No significant association was found between PE volume or density and mortality. ICU admissions were noted in 46.8 % of patients, while mechanical ventilation was required for 26.7 %. Conclusion: Pleural effusion is present in a significant portion of COVID-19 patients and independently predicts increased 30-day mortality, underscoring its value as a prognostic marker. Its identification, irrespective of volume or density, should be a priority in radiological reports to guide clinical decision-making.

Background: Tracking differential growth of secondary liver metastases is important for early detection of progression but remains challenging due to variable tumor growth rates. We aimed to automate accurate, consistent, and efficient longitudinal monitoring. Methods: We developed an automatic 3D segmentation and tracking algorithm to quantify differential growth, tested on contrast-enhanced MRI follow-ups of patients with neuroendocrine liver metastases (NELMs). The output was integrated into a decision support tool to distinguish between progressive disease, stable disease, and partial/complete response. A user study involving an expert group of seven expert radiologists evaluated its impact. Group comparisons used the Friedman test with post hoc analyses. Results: Our algorithm detected 991 metastases in 30 patients: 13% new, 30% progressive, 18% stable, and 18% regressive; the remainder were either too small to measure (15%) or merged with another metastasis in the follow-up assessment (6%). Diagnostic accuracy improved with additional information on hepatic tumor load and differential growth, albeit not significantly (p = 0.72). The diagnosis time increased (p < 0.001). All radiologists found the method useful and expressed a desire to integrate it in existing diagnostic tools. Conclusions: We automated segmentation and quantification of individual NELMs, enabling comprehensive longitudinal analysis of differential tumor growth with the potential to enhance clinical decision-making.