A computed tomography radiomics-based model for predicting osteoporosis after breast cancer treatment

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Many treatments against breast cancer decrease the level of estrogen in blood, resulting in bone loss, osteoporosis and fragility fractures in breast cancer patients. This retrospective study aimed to evaluate a novel opportunistic screening for cancer treatment-induced bone loss (CTIBL) in breast cancer patients using CT radiomics. Between 2011 and 2021, a total of 412 female breast cancer patients who received treatment and were followed up in our institution, had post-treatment dual-energy X-ray absorptiometry (DXA) examination of the lumbar vertebrae and had post-treatment chest CT scan that encompassed the L1 vertebra, were included in this study. Results indicated that the T-score of L1 vertebra had a strongly positive correlation with the average T-score of L1–L4 vertebrae derived from DXA (r = 0.91, p < 0.05). On multivariable analysis, four clinical variables (age, body weight, menopause status, aromatase inhibitor exposure duration) and three radiomic features extracted from the region of interest of L1 vertebra (original_firstorder_RootMeanSquared, wavelet.HH_glcm_InverseVariance, and wavelet.LL_glcm_MCC) were selected for building predictive models of L1 T-score and bone health. The predictive model combining clinical and radiomic features showed the greatest adjusted R2 value (0.557), sensitivity (83.6%), specificity (74.2%) and total accuracy (79.4%) compared to models that relied solely on clinical data, radiomic features, or Hounsfield units. In conclusion, the clinical-radiomic predictive model may be used as an opportunistic screening tool for early identification of breast cancer survivors at high risk of CTIBL based on non-contrast CT images of the L1 vertebra, thereby facilitating early intervention for osteoporosis.

Original languageEnglish
Pages (from-to)239-248
Number of pages10
JournalPhysical and Engineering Sciences in Medicine
Issue number1
Publication statusPublished - 2024 Mar

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Radiological and Ultrasound Technology
  • Biophysics
  • Biomedical Engineering
  • Instrumentation
  • Radiology Nuclear Medicine and imaging


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