Molecular Characterization and Therapeutic Approaches to Small Cell Lung Cancer: Imaging Implications

Hyesun Park1, Shu-Chi Tseng1, Lynette M Sholl1

  • 1From the Departments of Radiology (H.P., S.C.T., H.H., M.N.), Pathology (L.M.S.), Medical Oncology (M.M.A.), and Medicine (M.M.A.), Dana-Farber Cancer Institute and Brigham and Women's Hospital, 450 Brookline Ave, Boston, MA 02215.

Radiology
|October 25, 2022
PubMed

Insights

Small cell lung cancer (SCLC) is aggressive, but new treatments and imaging offer hope. This review covers SCLC molecular subtypes, therapies, and advanced imaging techniques like radiomics for better patient outcomes.

Area of Science:

  • Oncology
  • Radiology
  • Genomics

Background:

  • Small cell lung cancer (SCLC) is an aggressive malignancy with a poor prognosis, accounting for ~15% of lung cancers.
  • Advances in understanding SCLC's molecular and genomic landscape are driving precision oncology.
  • Imaging is critical for SCLC diagnosis, staging, and monitoring treatment response.

Purpose of the Study:

  • To provide a state-of-the-art review of SCLC.
  • To focus on molecular subtyping and emerging systemic therapies.
  • To discuss the implications of these advances for SCLC imaging.

Main Methods:

  • Literature review of recent advancements in SCLC.
  • Analysis of molecular and genomic data relevant to SCLC.
  • Evaluation of current and emerging systemic therapeutic approaches.
  • Assessment of novel imaging manifestations of treatment response and toxicities.

Main Results:

  • New systemic agents and precision oncology approaches are improving SCLC treatment.
  • Immune checkpoint inhibitors are leading to novel imaging findings.
  • Molecular characterization is crucial for SCLC subtyping and targeted therapy.

Conclusions:

  • Understanding SCLC molecular subtypes is key to personalized treatment.
  • Advanced imaging techniques are essential for monitoring novel therapies and identifying treatment effects.
  • Future directions include the integration of radiomics and machine learning in SCLC imaging.