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  11. Delta Magnetic Resonance Imaging Radiomics Features‑based Nomogram Predicts Long‑term Efficacy After Induction Chemotherapy In Locoregionally Advanced Nasopharyngeal Carcinoma

Delta magnetic resonance imaging radiomics features‑based nomogram predicts long‑term efficacy after induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma

Guang-Sen Pan1, Xiao-Ming Sun1, Fang-Fang Kong1

  • 1Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; Shanghai Clinical Research Center for Radiation Oncology, China; Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, China.

Oral Oncology
|August 12, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

A new delta-radiomics model accurately predicts progression-free survival (PFS) in locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients after induction chemotherapy (IC). This tool aids in personalizing treatment strategies for improved outcomes.

Area of Science:

  • Radiomics and Medical Imaging
  • Oncology and Cancer Research
  • Radiotherapy and Chemotherapy

Background:

  • Locoregionally advanced nasopharyngeal carcinoma (LA-NPC) requires effective prognostic tools post-induction chemotherapy (IC).
  • Predicting progression-free survival (PFS) is crucial for tailoring treatment strategies in LA-NPC.
  • Current methods may not fully capture treatment response dynamics.

Purpose of the Study:

  • To develop and validate a delta-radiomics model for predicting PFS in LA-NPC patients after IC.
  • To integrate radiomic changes from MRI scans into a predictive tool.
  • To assess the model's utility in guiding treatment decisions.

Main Methods:

  • Utilized MRI scans from 250 LA-NPC patients (145 training, 105 validation) before and after IC.
Keywords:
ChemoradiotherapyDelta-radiomicsMagnetic resonance imagingNasopharyngeal carcinoma

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  • Extracted and analyzed radiomic features, calculating changes (delta-radiomics).
  • Constructed a delta-radiomics signature using LASSO-Cox regression and developed a prognostic nomogram with clinical factors.

    • Main Results:

    • A 12-feature delta-radiomics signature independently predicted prognosis in LA-NPC patients.
    • The nomogram demonstrated excellent calibration and discrimination (C-index 0.848 training, 0.820 validation).
    • Risk stratification identified distinct PFS groups, with high-risk patients benefiting significantly from intensified treatment (CCRT/RT+AC) over RT alone.

    Conclusions:

    • A delta MRI-based radiomics model shows promise for predicting PFS in LA-NPC after IC.
    • The model can potentially guide personalized treatment decisions, optimizing therapeutic strategies.
    • Further validation may enhance its clinical applicability in managing LA-NPC.
    Nomogram