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
; 31(7): 2953-2961, 2024 Jul.
Article
en En
| MEDLINE
| ID: mdl-38383258
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.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
1_ASSA2030
Problema de salud:
1_sistemas_informacao_saude
Asunto principal:
Tomografía Computarizada por Rayos X
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Carcinoma de Pulmón de Células no Pequeñas
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Medios de Contraste
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Neoplasias Pulmonares
Límite:
Adult
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Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Acad Radiol
Asunto de la revista:
RADIOLOGIA
Año:
2024
Tipo del documento:
Article