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BackgroundThere is no accurate method distinguishing different types of pulmonary nodules.PurposeTo investigate whether multiparametric 3T MRI biomarkers can distinguish malignant from benign pulmonary nodules, differentiate different types of neoplasms, and compare MRI‐derived measurements with values from commonly used noninvasive imaging modalities.Study TypeProspective.SubjectsSixty‐eight adults with pulmonary nodules undergoing resection.SequencesRespiratory triggered diffusion‐weighted imaging (DWI), periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) fat saturated T2‐weighted imaging, T1‐weighted 3D volumetric interpolated breath‐hold examination (VIBE) using CAIPIRINHA (controlled aliasing in parallel imaging results in a higher acceleration).Assessment/StatisticsApparent diffusion coefficient (ADC), T1, T2, T1 and T2 normalized to muscle (T1/M and T2/M), and dynamic contrast enhancement (DCE) values were compared with histology to determine whether they could distinguish malignant from benign nodules and discern primary from secondary malignancies using logistic regression. Predictability of primary neoplasm types was assessed using two‐sample t‐tests. MRI values were compared with positron emission tomography / computed tomography (PET/CT) to examine if they correlated with standardized uptake value (SUV) or CT Hounsfield unit (HU). Intra‐ and interreader agreements were assessed using intraclass correlations.ResultsForty‐nine of 74 nodules were malignant. There was a significant association between ADC and malignancy (odds ratio 4.47, P < 0.05). ADC ≥1.3 μm2/ms predicted malignancy. ADC, T1, and T2 together predicted malignancy (P = 0.003). No MRI parameter distinguished primary from metastatic neoplasms. T2 predicted PET positivity (P = 0.016). T2 and T1/M correlated with SUV (P < 0.05). Of 18 PET‐negative malignant nodules, 12 (67%) had an ADC ≥1.3 μm2/ms. With the exception of T2, all noncontrast MRI parameters distinguished adenocarcinomas from carcinoid tumors (P < 0.05). T1, T2, T1/M, and T2/M correlated with HU and therefore can predict nodule density. Combined with ADC, washout enhancement, arrival time (AT), peak enhancement intensity (PEI), Ktrans, Kep, Ve collectively were predictive of malignancy (P = 0.012). Combined washin, washout, time to peak (TTP), AT, and PEI values predicted malignancy (P = 0.043). There was good observer agreement for most noncontrast MRI biomarkers.Data ConclusionMRI can contribute to pulmonary nodule analysis. Multiparametric MRI might be better than individual MRI biomarkers in pulmonary nodule risk stratification.Level of Evidence: 1Technical Efficacy: Stage 2J. Magn. Reson. Imaging 2018.

Objectives The aim of this study was to evaluate if a high-resolution photon-counting detector computed tomography (PCD-CT) system with a 1024×1024 matrix reconstruction can improve the visualization of fine structures in the lungs compared with conventional high-resolution CT (HRCT). Materials and Methods Twenty-two adult patients referred for clinical chest HRCT (mean CTDI vol, 13.58 mGy) underwent additional dose-matched PCD-CT (mean volume CT dose index, 13.37 mGy) after written informed consent. Computed tomography images were reconstructed at a slice thickness of 1.5 mm and an image increment of 1 mm with our routine HRCT reconstruction kernels (B46 and Bv49) at 512 and 1024 matrix sizes for conventional energy-integrating detector (EID) CT scans. For PCD-CT, routine B46 kernel and an additional sharp kernel (Q65, unavailable for EID) images were reconstructed at 1024 matrix size. Two thoracic radiologists compared images from EID and PCD-CT noting the highest level bronchus clearly identified in each lobe of the right lung, and rating bronchial wall conspicuity of third- and fourth-order bronchi. Lung nodules were also compared with the B46/EID/512 images using a 5-point Likert scale. Statistical analysis was performed using a Wilcoxon signed rank test with a P < 0.05 considered significant. Results Compared with B46/EID/512, readers detected higher-order bronchi using B46/PCD/1024 and Q65/PCD/1024 images for every lung lobe (P < 0.0015), but in only the right middle lobe for B46/EID/1024 (P = 0.007). Readers were able to better identify bronchial walls of the third- and fourth-order bronchi better using the Q65/PCD/1024 images (mean Likert scores of 1.1 and 1.5), which was significantly higher compared with B46/EID/1024 or B46/PCD/1024 images (mean difference, 0.8; P < 0.0001). The Q65/PCD/1024 images had a mean nodule score of 1 ± 1.3 for reader 1, and −0.1 (0.9) for reader 2, with one reader having improved nodule evaluation scores for both PCD kernels (P < 0.001), and the other reader not identifying any increased advantage over B46/EID/1024 (P = 1.0). Conclusions High-resolution lung PCD-CT with 1024 image matrix reconstruction increased radiologists' ability to visualize higher-order bronchi and bronchial walls without compromising nodule evaluation compared with current chest CT, creating an opportunity for radiologists to better evaluate airway pathology.

11534 Background: To date, there are no validated blood-based biomarkers of predicting response to PD-1 blockade. We previously reported that Bim is a downstream signaling molecule of the PD-1 pathway, and that measurement of Bim levels in circulating T-cells may predict and monitor responses to anti–PD-1 therapy in melanoma. We have identified the existence of sPD-L1 in cancer patients and showed that the sPD-L1 is biologically active and capable of triggering apoptosis in activated T-cells. Here we evaluated T cell Bim and sPD-L1 in the peripheral blood (PB) as biomarkers of response in a cohort of patients with metastatic melanoma and lung cancer undergoing anti-PD1 therapy. Methods: 60 pts treated with anti-PD-1 had PB collected at baseline and at radiographic tumor evaluation. Frequencies of Bim+ T cells and Bim median fluorescence intensity (MFI) were measured by flow cytometry in gated tumor-reactive CD11ahighPD1+ CD8+ T cells. We also measured levels of sPD-L1 at baseline and serially during treatment with sPD-L1 ELISA. Baseline Bim and sPD-L1 levels and percent change in Bim levels in patients (pts) who had a radiographic response (CR/PR) were compared to those who had progressive disease (PD) at 12 wks. Results: Similarly to previously reported preliminary data, pts with objective response (CR/PR, 15/60) after 4 cycles of anti-PD1 therapy had higher frequency of Bim T cells at baseline compared to pts with PD (16/60) (mean 43% vs. 30%, P = 0.0484). The frequencies of Bim+ T cells decreased significantly after the first 3 months of treatment in responders compared with progressors (mean -16% vs. + 40% P = 0.0111). High baseline sPD-L1 were associated with progression on anti-PD1 therapy (mean 2.8 ng/mL vs. 0.7 ng/mL, p = 0.07, n = 13) and the levels increased by the first tumor assessment in patients resistant to anti-PD-1. Conclusions: Measurements of Bim and sPD-L1 levels may help to select patients who are likely to benefit from anti-PD1 monotherapy versus combinatorial strategies, and provide a new non-invasive way to monitor response to anti-PD-1 blockade. A larger validation study is underway.

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.

Though congenital pericardial absence is often asymptomatic, complications can be life threatening. To date, few short case reports, primarily from the pre-CT and MR era, describe congenital absence of the right pericardium. We present a more comprehensive discussion of the embryologic derangements causing such defects and offer an up-to-date review of characteristic radiologic findings. Recognition of characteristic imaging findings of congenital pericardial absence is crucial in guiding diagnosis and management.