Computed tomography Hounsfield units can predict breast cancer metastasis to axillary lymph nodes

Masakazu Urata, Yuko Kijima, Munetsugu Hirata, Yoshiaki Shinden, Hideo Arima, Akihiro Nakajo, Chihaya Koriyama, Takaaki Arigami, Yoshikazu Uenosono, Hiroshi Okumura, Kosei Maemura, Sumiya Ishigami, Heiji Yoshinaka, Shoji Natsugoe

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


Background: Axillary lymph node (ALN) status is an important prognostic factor for breast cancer. We retrospectively used contrast-enhanced computed tomography (CE-CT) to evaluate the presence of ALN, metastasis based on size, shape, and contrasting effects.Methods: Of 131 consecutive patients who underwent CE-CT followed by surgery for breast cancer between 2005 and 2012 in our institution, 49 were histologically diagnosed with lymph node metastasis. Maximum Hounsfield units (HU) and mean HU were measured in non-contrasting CT (NC-CT) and CE-CT of ALNs.Results: Of 12 examined measurements, we found significant differences between negative and metastatic ALNs in mean and maximum NC-CT HU, and mean and maximum CE-CT HU (P < 0.05). We used a receiver operating curve, to determine cut-off values of four items in which significant differences were observed. The highest accuracy rate was noted for the cut-off value of 54 as maximum NC-CT HU for which sensitivity, specificity, and accuracy rate were 79.6%, 80.5% and 80.2%, respectively.Conclusions: CT HU of a patient with breast cancer are absolute values that offer objective disease management data that are not influenced by the screener's ability.

Original languageEnglish
Article number730
Pages (from-to)1-8
Number of pages8
JournalBMC Cancer
Issue number1
Publication statusPublished - 30-09-2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Genetics
  • Oncology
  • Cancer Research


Dive into the research topics of 'Computed tomography Hounsfield units can predict breast cancer metastasis to axillary lymph nodes'. Together they form a unique fingerprint.

Cite this