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Updated: Jun 10, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
A computed tomography-based score indicative of lung cancer aggression (SILA) predicts lung adenocarcinomas with low
Dylan Steiner1, Ju Ae Park2, Sarah Singh3
1Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, USA.
Abstract:
BackgroundHistologic grading of lung adenocarcinoma (LUAD) is predictive of outcome but is only possible after surgical resection. A radiomic biomarker predictive of grade has the potential to improve preoperative management of early-stage LUAD.ObjectiveValidate a prognostic radiomic score indicative of lung cancer aggression (SILA) in surgically resected stage I LUAD (n = 161) histologically graded as indolent low malignant potential (LMP), intermediate, or aggressive vascular invasive (VI) subtypes.MethodsThe SILA scores were generated from preoperative CT-scans using the previously validated Computer-Aided Nodule Assessment and Risk Yield (CANARY) software.ResultsCox proportional regression showed significant association between the SILA and 7-year recurrence-free survival (RFS) in a univariate (P < 0.05) and multivariate (P < 0.05) model incorporating age, gender, smoking status, pack years, and extent of resection. The SILA was positively correlated with invasive size (spearman r = 0.54, P = 8.0 × 10-14) and negatively correlated with percentage of lepidic histology (spearman , P = 7.1 × 10-10). The SILA predicted indolent LMP with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.74 and aggressive VI with an AUC of 0.71, the latter remaining significant when invasive size was included as a covariate in a logistic regression model (P < 0.01).ConclusionsThe SILA scoring of preoperative CT scans was prognostic and predictive of resected pathologic grade.
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