Citations

ABSTRACT Approximately 30% of patients with acute graft-versus-host disease (GVHD) develop steroid-refractory disease and have very poor outcomes. Ruxolitinib has become the standard of care for steroid-refractory acute GVHD, but it is unclear which patients derive benefit. The MAGIC Composite Score (MCS), an algorithm that combines clinical symptoms and biomarkers, has been validated to predict outcomes at the start of primary GVHD treatment. Here, we evaluated its performance at the initiation of second-line treatment in 278 patients. MCS stratified patients into three risk groups (MCS1-3), with the majority (88%) classified as intermediate or high risk. Increasing MCS score was associated with progressively higher 1-year non-relapse mortality (NRM) rates (16%, 41%, and 73%; p<0.001), lower 1-year survival (77%, 56%, and 24%; p<0.001), and lower complete response (CR) rates at day 28 (47%, 38%, and 20%, respectively; p<0.01). The area under the receiver operating characteristic curve (AUROC) for 1-year NRM was significantly higher with MCS compared to clinical symptoms alone (0.70 vs. 0.63; p=0.023). Among patients treated with ruxolitinib, higher MCS similarly predicted higher NRM and lower survival and CR rates. Patients classified as MCS2/3 had poor outcomes despite ruxolitinib, underscoring the need for novel therapies in this patient population. In conclusion the MCS is an accurate predictor of outcomes for patients who require second-line treatment and may be of use as an eligibility criterion for future clinical trials in this high-risk population. Key Points MAGIC Composite Scores that integrate clinical symptoms and biomarker scores predict outcomes of second-line therapy for acute GVHD more accurately than clinical symptoms alone. MCS identifies patients with high NRM despite treatment with ruxolitinib.

Purpose: The aim of this study was to evaluate the ability of computer-aided detection (CAD) and human readers to detect pulmonary nodules ≥5 mm using 100 kV ultra–low-dose computed tomography (ULDCT) utilizing a tin filter. Materials and Methods: After informed consent, 55 patients prospectively underwent standard-dose chest CT (SDCT) using 120 kV followed by ULDCT using 100 kV/tin. Reference nodules ≥5 mm were identified by a thoracic radiologist using SDCT. Four thoracic radiologists marked detected nodules on SDCT and ULDCT examinations using a dedicated computer workstation. After a 6-month memory extinction, readers were shown the same ULDCT cases with all CAD markings as well as their original detections, and characterized CAD detections as true positive or false positive. Results: Volume CT Dose index (CTDIvol) for SDCT and ULDCT were 5.3±2 and 0.4±0.2 mGy (P<0.0001), respectively. Forty-five reference nodules were detected in 30 patients. Reader sensitivity varied widely but similarly for SDCT (ranging from 45% to 87%) and ULDCT (45% to 83%). CAD sensitivity was 76% (34/45) for SDCT and 71% (32/45) for ULDCT. After CAD, reader sensitivity substantially improved by 19% and 18% for 2 readers, and remained nearly unchanged for the other 2 readers (0% and 2%), despite reader perception that many more nodules were identified with CAD. There was a mean of 2 false-positive CAD detections/case. Conclusions: ULDCT with 100 kV/tin reduced patient dose by over 90% without compromising pulmonary nodule detection sensitivity. CAD can substantially improve nodule detection sensitivity at ULDCT for some readers, maintaining interobserver performance.