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Related Concept Videos

Tumor Immunotherapy01:27

Tumor Immunotherapy

Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
Treatment Resistent Cancers02:56

Treatment Resistent Cancers

Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...

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Toward Response-Adaptive Therapy in Locally Advanced NSCLC: Integrating ctDNA and Radiomics for Risk Stratification.

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A new model combines ctDNA changes and imaging features during treatment to predict outcomes for locally advanced non-small cell lung cancer patients. This approach shows promise for tailoring chemoradiation therapy in real-time.

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Area of Science:

  • Oncology
  • Radiology
  • Molecular Diagnostics

Background:

  • Locally advanced non-small cell lung cancer (NSCLC) requires effective risk stratification for personalized treatment.
  • Chemoradiation is a standard treatment, but predicting individual patient response remains challenging.
  • Integrating diverse data sources can improve treatment decisions.

Purpose of the Study:

  • To develop and validate a multimodal risk stratification model for locally advanced NSCLC patients undergoing chemoradiation.
  • To assess the utility of combining circulating tumor DNA (ctDNA) kinetics with radiomic features for predicting treatment outcomes.
  • To explore the potential for real-time, response-adaptive therapy in NSCLC.

Main Methods:

  • Patients with locally advanced NSCLC undergoing chemoradiation were included.
  • Mid-treatment ctDNA levels and kinetics were analyzed.
  • Radiomic features were extracted from baseline imaging (e.g., CT scans).
  • A multimodal model integrating ctDNA and radiomic data was developed to predict treatment response and outcomes.

Main Results:

  • The novel multimodal model demonstrated proof-of-concept for accurate risk stratification.
  • Integration of ctDNA kinetics with baseline radiomics improved predictive performance compared to individual modalities.
  • The model shows potential for identifying patients who may benefit from adaptive therapy strategies.

Conclusions:

  • A multimodal risk stratification model combining ctDNA and radiomic features is feasible and promising for locally advanced NSCLC.
  • This approach supports the development of real-time, response-adaptive chemoradiation therapy.
  • Further research is needed to standardize biomarkers and overcome implementation barriers for clinical adoption.