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A Multicenter Study Of Volumetric Computed Tomography For Staging Malignant Pleural Mesothelioma.
Published 2016 · Medicine
BACKGROUND Standard imaging modalities are inaccurate in staging malignant pleural mesothelioma (MPM). Single-institution studies suggest that volumetric computed tomography (CT) is more accurate but labor intensive. We established a multicenter network to test interobserver variability, accuracy (relative to pathologic stage), and the prognostic significance of semiautomated volumetric CT. METHODS Six institutions electronically submitted to an established multicenter database clinical and pathologic data for patients with MPM who had operations. Institutional radiologists reviewed preoperative CT scans for quality and then submitted by electronic network (AG Mednet, www.agmednet.com) to the biostatistical center. Two reference radiologists blinded to clinical data performed semiautomated tumor volume calculations using Vitrea Enterprise 6.0 software (Vital Images, Minnetonka, MN) and then submitted readings to the biostatistical center. Study end points included feasibility of the network, interobserver variability for volumetric CT, correlation of tumor volume to pTN stages, and overall survival (OS). RESULTS Of 164 patients, the CT scans for 129 were analyzable and read by reference radiologists. Most tumors were less than 500 cm(3). A small bias was observed between readers because one provided consistently larger measurements than the other (mean difference, 47.9; p = .0027), but for 80%, the absolute difference was 200 cm(3) or less. Spearman correlation between readers was 0.822. Volume correlated with pTN stages and OS, best defined by three groups with average volumes of 91.2, 245.3, and 511.3 cm(3) associated with median OS of 37, 18, and 8 months, respectively. CONCLUSIONS For the first time, a multicenter network was established and initial correlations of tumor volume with pTN stages and OS are shown. A larger multicenter international study is planned to confirm the results and refine correlations.