Contrast Enhanced CT Radiogenomics in a Retrospective NSCLC Cohort: Models, Attempted Validation of a Published Model and the Relevance of the Clinical Context

A Kohan; R Hinzpeter; R Kulanthaivelu; S A Mirshahvalad; L Avery; M Tsao; Q Li; C Ortega; U Metser; A Hope; P Veit-Haibach

doi:10.1016/j.acra.2024.01.031

Contrast Enhanced CT Radiogenomics in a Retrospective NSCLC Cohort: Models, Attempted Validation of a Published Model and the Relevance of the Clinical Context

Acad Radiol. 2024 Feb 20:S1076-6332(24)00053-9. doi: 10.1016/j.acra.2024.01.031. Online ahead of print.

Authors

A Kohan¹, R Hinzpeter², R Kulanthaivelu³, S A Mirshahvalad³, L Avery⁴, M Tsao⁵, Q Li⁵, C Ortega³, U Metser³, A Hope⁶, P Veit-Haibach³

Affiliations

¹ Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada. Electronic address: Andres.Kohan@uhn.ca.
² Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
³ Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada.
⁴ Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada.
⁵ University Health Network, Ontario Cancer Institute/Princess Margaret Cancer Centre, Toronto, ON, Canada.
⁶ Department of Radiation Oncology, University Health Network, University of Toronto, ON, Canada.

PMID: 38383258
DOI: 10.1016/j.acra.2024.01.031

Abstract

Rationale and objective: To develop a radiogenomic predictive model for non-small cell lung cancer (NSCLC) patients studied through contrast enhanced chest computed tomography (CE-CT) targeting the most frequent gene alterations. M&M: A retrospective study of patients with NSCLC imaged with CE-CT before treatment and had their tumor genomics sequenced at our institution was performed. Data was gathered from their imaging studies, their electronic medical records and a web-based database search (cBioPortal.ca). All of the patient data was tabulated for analysis. Two predictive models (M1 & M2) were created using different approaches and a third model was extracted from the literature to also be tested in our population.

Results: Out of 157 patients, eighty were male (51%) and 124 (79%) had a history of smoking. The three most prevalent genes were KRAS, TP53 and EGFR. The M1 radiomics-only model median AUC were 0.61 (TP53), 0.53 (KRAS) and 0.64 (EGFR) and for M1 radiomics + clinical were 0.61 (TP53), 0.61 (KRAS) and 0.80 (EGFR). The M2 radiomics-only model median AUC were 0.63 (TP53), 0.60 (KRAS) and 0.65 (EGFR) and for M2 radiomics + clinical were 0.64 (TP53), 0.62 (KRAS) and 0.81 (EGFR). The external EGFR radiomic model showed an AUC of 0.69 and 0.86 for the radiomics-only and combined radiomics + clinical respectively.

Conclusion: Our study was able to provide robust predictive radiomics model evaluation for the detection of TP53, KRAS and EGFR. We also compared our performance with an already published model and observed how impactful clinical variables can be on models' performance.

Clinical relevance statement: Identifying tumor mutations in patients that can't undergo biopsy is critical for their outcomes.

Keypoints: • Tumor genomic profiling is critical for treatment selection • CE-CT radiomics produce robust predictive models comparable to those already published • Clinical variables should be considered/included in predictive models.

Keywords: CT; Genomics; NSCLC; Radiomics.