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2024 conference-abstract

Prediction of recurrence in stage I EGFR mutation-positive NSCLC: Combination of CT appearance and selected co-occurring gene alterations by machine learning.

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Le résumé fourni par la source

8053 Background: Predicting the risk of postoperative recurrence is becoming increasingly important in patients (pts) with resectable EGFR mutation positive ( EGFRm) non-small cell lung cancer (NSCLC) after the ADAURA trial. The presence of non-solid ground-grass opacity (GGO) component on computed tomography (CT) images is known to affect the prognosis of stage I lung cancer. Only a few reports investigating the relationship between gene alterations and CT images in stage I EGFRm NSCLC. Methods: We have developed a machine learning-based model to predict recurrence within five years in TNM stage I (UICC 8th) EGFRm NSCLC pts who underwent surgery between 1985 and 2019 using whole exome sequencing in the PRISM project. We evaluated the pts’ characteristics, recurrence-free survival (RFS), the CT appearance (pure GGO and part solid [GGO]), without GGO [pure solid]), and consolidation tumor ratio (CTraio). Then, we analyzed the correlation between the image findings and gene alterations predicting a high risk of recurrence. Results: Among 1351 pts, stage I, EGFRm were 308 (22.8%). The median RFS for stage I EGFR-m pts was 123.2 months (m). In the prediction model, TP53 and RBM10 genes were among the gene alterations that had a high impact on the high-risk recurrence within five years. Among the 302 stage I, EGFRm pts for whom CT images were available, 166 (55.6%) pts had GGO, and 137 (45.4%) pts had pure solid appearance. The median CTratio was 0.87. The median RFS was not reached (NR) for GGO, 86.5m for pure solid (hazard ratio [HR] 2.42 [1.58-3.73], p<0.0001). There was a negative correlation indicating that the larger the CTratio, the shorter the RFS (p<0.0047). The proportion of TP53 mutation (TP53m) was lower in GGO and higher in pure solid (p<0.0001, Table). The median RFS was NR for GGO plus TP53m-negative pts, was 119.7m for GGO plus TP53m-positive pts, 84.8m for pure solid plus TP53m-negative pts, and 98.8m for pure solid plus TP53m-positive pts, respectively. Among pts with 0≦CTratio<0.5 46 (15.2%), with 0.5≦CTratio<1 119 (39.4%), and with CTratio=1 137 (45.4%), the proportion of TP53m increased as the CTratio increased. Conclusions: We found a linear relationship between CTratio and proportion of co-occurring TP53m with EGFRm. Combination of CT appearance (GGO or pure solid) and co-occurring TP53m can predict recurrence in stage I, EGFRm NSCLC. [Table: see text]

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Le contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
Prediction of recurrence in stage I EGFR mutation-positive NSCLC: Combination of CT appearance and selected co-occurring gene alterations by machine learning.
Date Crossref
01/06/2024
Éditeur
American Society of Clinical Oncology (ASCO)
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.

Où se fait cette recherche

  • Tokyo National Hospital pays non établi dans la notice
    Établissement de santé
  • National Cancer Center Hospital Department of Thoracic Oncology pays non établi dans la notice
    Établissement de santé
  • National Cancer Center Research Institute Division of Medical AI Research and Development pays non établi dans la notice
    Structure de recherche

Tokyo National Hospital, Department of Thoracic Oncology — National Cancer Center Hospital et Division of Medical AI Research and Development — National Cancer Center Research Institute.

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

Radiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and TreatmentLung Cancer Treatments and Mutations

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