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Evaluation of radiomics as a predictor of efficacy and the tumor immune microenvironment in anti-PD-1 mAb treated recurrent/metastatic squamous cell carcinoma of the head and neck patients.
Zandberg, Dan P; Zenkin, Serafettin; Ak, Murat; Mamindla, Priyadarshini; Peddagangireddy, Vishal; Hsieh, Ronan; Anderson, Jennifer L; Delgoffe, Greg M; Menk, Ashely; Skinner, Heath D; Duvvuri, Umamaheswar; Ferris, Robert L; Colen, Rivka R.
Afiliación
  • Zandberg DP; UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA.
  • Zenkin S; Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Ak M; Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Mamindla P; Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Peddagangireddy V; Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Hsieh R; UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA.
  • Anderson JL; UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA.
  • Delgoffe GM; UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA.
  • Menk A; UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA.
  • Skinner HD; UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA.
  • Duvvuri U; UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA.
  • Ferris RL; UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA.
  • Colen RR; Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Head Neck ; 2024 Jul 30.
Article en En | MEDLINE | ID: mdl-39080968
ABSTRACT

BACKGROUND:

We retrospectively evaluated radiomics as a predictor of the tumor microenvironment (TME) and efficacy with anti-PD-1 mAb (IO) in R/M HNSCC.

METHODS:

Radiomic feature extraction was performed on pre-treatment CT scans segmented using 3D slicer v4.10.2 and key features were selected using LASSO regularization method to build classification models with XGBoost algorithm by incorporating cross-validation techniques to calculate accuracy, sensitivity, and specificity. Outcome measures evaluated were disease control rate (DCR) by RECIST 1.1, PFS, and OS and hypoxia and CD8 T cells in the TME.

RESULTS:

Radiomics features predicted DCR with accuracy, sensitivity, and specificity of 76%, 73%, and 83%, for OS 77%, 86%, 70%, PFS 82%, 75%, 89%, and in the TME, for high hypoxia 80%, 88%, and 72% and high CD8 T cells 91%, 83%, and 100%, respectively.

CONCLUSION:

Radiomics accurately predicted the efficacy of IO and features of the TME in R/M HNSCC. Further study in a larger patient population is warranted.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Head Neck Asunto de la revista: NEOPLASIAS Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Head Neck Asunto de la revista: NEOPLASIAS Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos