Philipp Raffler

Objectives: Opportunistic CT-based screening for osteoporosis has been shown to be cost-effective and may help to reduce the existing treatment gap in patients. Reproducibility has not yet been investigated at thoracic levels, which is becoming increasingly relevant with upcoming lung cancer screening programs. Methods: Scans from 178 patients with 2 consecutive CT examinations within 1 month were retrospectively included. In all available reconstructions, automated extraction of trabecular volumetric bone mineral density (vBMD) was performed, including HU-to-BMD conversion and contrast phase correction. Mean trabecular vBMD was calculated for 2 thoracic regions (T5-T7 and T8-T10). Reproducibility was calculated as the root mean square coefficient of variation (RMSCV) under 6 reproducibility scenarios, ranging from different reconstructions of the same scan to measurements across different scanners and contrast phases. Results: In total, 914 vBMD observations were analyzed. RMSCV values ranged from 0.83% to 4.90% for T5-T7 and from 1.4% to 6.8% for T8-T10. One-way ANOVA showed a significant overall difference between reproducibility scenarios in both vertebral regions (all P < .001), with post hoc analyses demonstrating a stepwise increase in RMSCV with technical complexity. Conclusions: Our results support the clinical applicability of opportunistic osteoporosis screening at the thoracic spine across diverse CT acquisition protocols. With the upcoming national lung cancer screening programs utilizing low-dose CT (LDCT), an extraordinary opportunity emerges to address the substantial osteoporosis treatment gap through opportunistic bone health assessment. Advances in knowledge: This work is novel in extending reproducibility validation for opportunistic osteoporosis screening from the lumbar spine to thoracic vertebrae.

Background: Quantitative MRI (qMRI) might detect subtle changes in recurrent high-grade glioma earlier than conventional imaging. The purpose of this study was to investigate whether WHO grade 4 glioma patients demonstrate alterations in multiparameter mapping (MPM) and diffusion tensor imaging (DTI)-derived measures within radiologically normal-appearing brain regions that subsequently exhibit tumor recurrence in follow-up scans. Methods: For 16 WHO grade 4 glioma patients with confirmed recurrence at follow-up, qMRI parametric maps (proton density (PD), longitudinal relaxation rate (R1), transverse relaxation rate (R2*)) were generated using a MPM protocol. Additionally, diffusion tensor imaging (DTI) -derived free water (FW) and FW-corrected tissue fractional anisotropy (FAt) were evaluated. We mapped recurrent tumor areas onto baseline scans to identify regions that subsequently progressed to contrast-enhancing tumor (CET) or FLAIR-hyperintense areas. Results: Normal-appearing grey matter (NAGM) that progressed into FLAIR-hyperintense areas in follow-up scans showed significantly lower median PD values, while median FW was significantly elevated across all subregions compared to their reference regions. This potentially reflects an early redistribution of water from bound tissue compartments into the extracellular space, most evident in gray matter. R1 demonstrated significantly lower median values in NAGM progressing to tumor compared to stable NAGM. Variance was significantly increased for PD, R2*, and FW in multiple subregions, consistent with heterogeneous tumor infiltration. Conclusions: MPM and DTI-derived metrics reveal subtle alterations in radiologically normal-appearing brain tissue months before recurrence becomes visible on conventional imaging. This could support earlier identification of subclinical progression and allow for personalized surveillance strategies.

Purpose: To evaluate the impact of a structured tutorial on the use of a large language model (LLM)-based search engine on radiology residents’ performance in brain MRI differential diagnosis. Methods: In this study, nine radiology residents determined the three most likely differential diagnoses for three sets of ten brain MRI cases with a challenging yet definite diagnosis. Each set was assessed (1) with the support of conventional internet search, (2) using an LLM-based search engine (© Perplexity AI) without prior tutorial, or (3) using the LLM-based search engine after a structured 10-minute tutorial. Reader responses were rated using a binary and numeric scoring system. Reading times and confidence levels (measured on a 5-point Likert scale) were recorded for each case. Search engine logs were examined to quantify user interaction metrics, and to identify hallucinations and misinterpretations in LLM responses. Results: Radiology residents achieved the highest accuracy when employing the LLM-based search engine following the tutorial, indicating the correct diagnosis among the top three differential diagnoses in 62.5% of cases (55/88). This was followed by the LLM-assisted workflow before the tutorial (44.8%; 39/87) and the conventional internet search workflow (32.2%; 28/87). The LLM tutorial led to significantly higher performance (binary scores: p = 0.042, numeric scores: p = 0.016) and confidence ( p = 0.006) but resulted in no relevant differences in reading times. Hallucinations were found in 5.1% of LLM queries. Conclusion: Our findings demonstrate the considerable benefits that even low-effort educational interventions on LLMs can provide, highlighting their potential role in radiology training programs.

Background: The purpose of this retrospective study was to evaluate the value of contrast-enhanced computed tomography (CE-CT) image features at baseline and after neoadjuvant chemotherapy in predicting histopathological response in patients with adenocarcinoma of the gastroesophageal junction (GEJ). Methods: A total of 105 patients with a diagnosis of adenocarcinoma of the GEJ were examined by CE-CT at baseline and preoperatively after neoadjuvant chemotherapy. All patients underwent surgical resection. Histopathological parameters and tumor regression grading according to Becker et al. were collected in 93 patients. Line profiles of the primary tumor area in baseline and preoperative CE-CT were generated using ImageJ. Maximum tumor density and tumor-to-wall density delta were calculated and correlated with the histopathological tumor response. In addition, tumor response was assessed according to standard RECIST measurements in all patients and by endoscopy in 72 patients. Results: Baseline and change in baseline to preoperative CE-CT parameters showed no significant differences between responders (Becker grade 1a, 1b) and non-responders (Becker grade 2, 3). After neoadjuvant therapy, responders and non-responders showed significant differences in maximum density and tumor-to-wall density delta values. Line profile measurements showed excellent inter-rater agreement. In comparison, neither RECIST nor endoscopy showed significant differences between these groups. Conclusions: Posttreatment CE-CT can predict histopathological therapy response to neoadjuvant treatment in adenocarcinoma of GEJ patients with high accuracy and thus may improve patient management.