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Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging.

Esha Baidya Kayal1, Devasenathipathy Kandasamy2, Kedar Khare3

  • 1Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India.

NMR in Biomedicine
|October 20, 2020
PubMed
Summary

Statistical texture analysis (TA) using MRI effectively identified aggressive osteosarcoma and predicted chemotherapy response. These imaging markers show promise for improving patient outcomes in osteosarcoma treatment.

Keywords:
chemotherapy response assessment, imaging biomarkers, IVIM diffusion-weighted MRI, multi-parametric MRI, osteosarcoma, texture analysis, tumor aggressiveness, tumor heterogeneity

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Osteosarcoma is a challenging bone cancer, and predicting chemotherapy response is crucial for treatment planning.
  • Neoadjuvant chemotherapy (NACT) is a standard treatment, but its effectiveness varies among patients.
  • Accurate prediction of treatment response and tumor aggressiveness can significantly improve patient survival rates.

Purpose of the Study:

  • To assess the efficacy of MRI-based statistical texture analysis (TA) in predicting chemotherapy response in osteosarcoma patients.
  • To identify imaging markers for tumor aggressiveness and patient mortality risk at baseline.
  • To evaluate TA features for predicting NACT response during treatment.

Main Methods:

  • Prospective study of 40 osteosarcoma patients undergoing NACT.
  • Diffusion-weighted MRI (including ADC and IVIM parameters) and structural T1W/T2W images acquired at baseline, after first, and after third NACT cycles.
  • 3D statistical TA (GLCM, NGTDM, RLM) applied to parametric maps and images.
  • Receiver-operating-characteristic (ROC) curve analysis to assess TA features for predicting mortality and NACT response.

Main Results:

  • NGTDM features (coarseness, busyness, strength) on D, D*, f maps, and T1W/T2W images identified nonsurvivors with high accuracy (AUC = 0.82-0.88) at baseline.
  • GLCM (contrast, correlation), NGTDM (contrast, complexity), and RLM (short-run-low-gray-level-emphasis) features predicted NACT response using D, D*, T2W at baseline (t0) and D*, f at t1 (AUC = 0.70-0.80).

Conclusions:

  • 3D statistical TA features derived from MRI are valuable imaging markers for characterizing osteosarcoma aggressiveness.
  • These TA features can predict chemotherapy response in osteosarcoma patients.
  • MRI-based TA holds potential for non-invasive assessment of tumor behavior and treatment efficacy.