Abstract
BACKGROUND AND PURPOSE: Differentiating nodal metastases from reactive adenopathy in HIV-infected patients with [18F] FDG-PET/CT can be challenging because lymph nodes in HIV-positive patients often show increased [18F] FDG uptake. The purpose of this study was to assess CT textural analysis characteristics of HIV-positive and HIV-negative lymph nodes on [18F] FDG-PET/CT to differentiate nodal metastases from disease-specific nodal reactivity.
MATERIALS AND METHODS: Nine HIV-positive patients with head and neck squamous cell carcinoma (7 men, 2 women; 29–62 years of age; median age, 48 years) with 22 lymph nodes (≥1 cm) who underwent contrast-enhanced CT with [18F] FDG-PET followed by pathologic evaluation of cervical lymph nodes were retrospectively reviewed. Twenty-six HIV-negative patients with head and neck squamous cell carcinoma with 61 lymph nodes were evaluated as a control group. Each lymph node was manually segmented, and an in-house-developed Matlab-based texture analysis program extracted 41 texture features from each segmented volume. A mixed linear regression model was used to compare the pathologically proved malignant lymph nodes with benign nodes in the 2 enrolled groups.
RESULTS: Thirteen (59%) lymph nodes in the HIV-positive group and 22 (36%) lymph nodes in the HIV-negative control group were confirmed as positive for metastases. There were 7 histogram features (P = .017–0.032), 3 gray-level co-occurrence features (P = .009-.025), and 9 gray-level run-length features (P < .001–.033) that demonstrated a significant difference in HIV-positive patients with either benign or malignant lymph nodes.
CONCLUSIONS: CT texture analysis may be useful as a noninvasive method of obtaining additional quantitative information to differentiate nodal metastases from disease-specific nodal reactivity in HIV-positive patients with head and neck squamous cell carcinoma.
ABBREVIATIONS:
- AUC
- area under receiver operating characteristic curve
- HNSCC
- head and neck squamous cell carcinoma
- GLCM
- gray-level co-occurrence matrix
- GLGM
- gray-level gradient matrix
- GLN
- gray-level nonuniformity
- GLRL
- gray-level run-length
- HGRE
- high gray-level run emphasis
- LGRE
- low gray-level run emphasis
- LRE
- long-run emphasis
- LRHGE
- long-run high gray-level emphasis
- max
- maximum
- RLN
- run-length nonuniformity
- RP
- run percentage
- SRE
- short-run emphasis
- SRLGE
- short-run low gray-level emphasis
- SUV
- standard uptake value
Footnotes
Disclosures: Hirofumi Kuno—UNRELATED: Grants/Grants Pending: Grant-in-Aid for Young Scientists KAKEN (#18K15573). Osamu Sakai—UNRELATED: Consultancy: Boston Imaging Core Lab.
- © 2019 by American Journal of Neuroradiology