Enterprise AI Analysis
Single and multi-site CT-based radiogenomics analysis of metastatic lung adenocarcinoma and correlations with outcome
This study leverages unsupervised CT-based radiomic clustering to evaluate associations between single-site and multi-site features, oncogenic alterations (OAs), and treatment response in metastatic lung adenocarcinoma (MLUAD). Identifying robust radiomic patterns linked to molecular profiles and patient outcomes, it suggests a powerful, non-invasive adjunct to guide molecular testing and optimize treatment selection.
Executive Impact
Our AI-driven analysis of this research reveals key metrics demonstrating the potential for enhanced precision oncology, enabling more tailored and effective patient management strategies.
361+
Patients Analyzed
1721+
Tumor Lesions Segmented
0.849
Improved OA Prediction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow: Study Workflow
CT Post-processing
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Radiomics Features Extraction at Lesion level
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Integration at Patient level
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Identification of the biopsied lesion
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Radiogenomics and outcomes
Radiomic Homogeneity Across Subgroups
Lower Dispersion for nsOA patients, indicating more homogeneous radiomic profiles. This supports the radiogenomic hypothesis that macroscopic radiologic heterogeneity reflects underlying molecular heterogeneity.| Prediction Task | Clinical-Radiological AUROC | Clinical+Radiomics AUROC | Improvement |
|---|---|---|---|
| WT vs any OA | 0.593 (95% CI = 0.533-0.657) | 0.655 (95% CI = 0.591-0.718) | +0.062 |
| WT vs nsOA | 0.838 (95% CI = 0.749-0.916) | 0.849 (95% CI = 0.755-0.922) | +0.011 |
| WT vs sOA | 0.555 (95% CI = 0.505-0.603) | 0.637 (95% CI = 0.573–0.701) | +0.082 |
Radiomics-based models consistently outperformed clinical-radiological models in discriminating oncogenic profiles across all subgroups in the 1000 out-of-bag test sets of the Monte Carlo cross-validation, providing significant added discriminatory power. |
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Radiomics Predicts Treatment Response & Survival
Higher ORR & Longer OS independently associated with Cluster-M2 + M5, reflecting nsOA biology, and with lower intra-patient radiophenotypic dispersion.Non-Invasive AI Biomarker Potential
Baseline CT-based single- and multi-site radiomics capture patterns associated with key Oncogenic Alterations (OAs) in Metastatic Lung Adenocarcinoma (MLUAD). This suggests their potential role as a non-invasive adjunct to guide molecular testing and optimize treatment selection. Radiomic clustering, whatever the initial disease staging, may serve as an AI biomarker that complements molecular testing, helping identify actionable tumor profiles and stratify patients for treatment selection and prognostication in MLUAD.
Advancing Radiogenomics in Metastatic LUAD
This study extends prior findings in stage I LUAD, demonstrating that CT-based radiomic clustering is associated with key OAs, response to treatment, and OS in stage IIIB-IV disease. It differs from a prior study on a subset of the same cohort by employing an unsupervised radiomic clustering framework to explore intrinsic imaging phenotypes and their associations with OAs and treatment response. It also enhances predictive value in a multivariable setting, including clinical and radiological covariates and a resampling scheme, which were not fully evaluated previously.
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Your AI Implementation Roadmap
We guide you through a structured process to integrate AI seamlessly into your operations, from initial assessment to ongoing optimization.
Phase 1: Discovery & Strategy
Comprehensive assessment of your current workflows, identification of high-impact AI opportunities, and development of a tailored AI strategy aligned with your business objectives.
Phase 2: Pilot & Proof-of-Concept
Deployment of a targeted AI pilot project to demonstrate value, refine models, and gather initial performance metrics in a controlled environment.
Phase 3: Full-Scale Integration
Seamless integration of validated AI solutions across your enterprise, ensuring robust infrastructure, data security, and user adoption.
Phase 4: Optimization & Scaling
Continuous monitoring, performance tuning, and iterative enhancement of AI models. Expansion to additional use cases and departments to maximize long-term ROI.
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