Clinical Scorecard: CT-Based Radiogenomics Evaluation of Metastatic Lung Adenocarcinoma: A Study of Single and Multi-Site Analysis and Its Impact on Patient Outcomes

At a Glance

Key Highlights

• Radiomics enables non-invasive prediction of oncogenic alterations, potentially reducing repeat biopsies in MLUAD.
• Multi-site radiomics integrates imaging features from multiple metastatic lesions to improve patient stratification.
• Distinct radiomic clusters correlate with smoking status, oncogenic profiles, treatment response, and overall survival.

Guideline-Based Recommendations

Diagnosis

• Use contrast-enhanced whole-body CT as standard imaging modality for metastatic lung adenocarcinoma.
• Perform routine molecular profiling on pre-treatment tumor samples using next-generation sequencing panels for key gene alterations.
• Incorporate radiomics analysis of CT images to assist in predicting oncogenic alterations and guide precision oncology.

Management

• Base first-line treatment decisions on oncogenic profiles derived from molecular testing and supported by radiogenomic data.
• Consider smoking history to interpret oncogenic alteration patterns and tailor therapeutic approaches.
• Utilize radiomics-derived patient stratification to optimize treatment selection and resource allocation.

Monitoring & Follow-up

• Evaluate overall response rate (ORR) to first-line therapy using RECIST v1.1 criteria.
• Monitor overall survival (OS) from diagnosis to disease-related death or last follow-up.
• Use follow-up CT and PET imaging to assess metastatic patterns and treatment response.

Risks

• Recognize limitations of TP53 mutations as non-specific markers of genomic instability rather than actionable targets.
• Be aware of variability in CT acquisition parameters across centers that may affect radiomic feature extraction.
• Consider potential heterogeneity in tumor molecular profiles between metastatic sites.

Patient & Prescribing Data

Patients with stage IIIB–IV metastatic lung adenocarcinoma undergoing molecular profiling and imaging evaluation

Radiogenomic clusters derived from CT radiomics correlate with oncogenic alterations and clinical outcomes, informing personalized treatment strategies and potentially reducing invasive biopsy requirements.

Clinical Best Practices

• Ensure high-quality contrast-enhanced CT imaging with standardized acquisition protocols for reliable radiomics analysis.
• Segment and analyze multiple metastatic lesions ≥1 cm3 to capture tumor heterogeneity.
• Integrate radiomic data with molecular profiling and clinical parameters including smoking status for comprehensive patient stratification.
• Use supervised and unsupervised machine learning approaches to identify imaging phenotypes linked to oncogenic drivers and prognosis.
• Maintain multidisciplinary collaboration among radiologists, oncologists, and pathologists for optimal interpretation and application of radiogenomic findings.

References

• World Health Organization Lung Cancer Statistics
• French National Guidelines for Molecular Profiling in Lung Cancer
• RECIST v1.1 Criteria for Response Evaluation
• Perez-Johnston et al. Radiomics and Lung Adenocarcinoma Study