Purpose of ReviewTo explain the technique of Dual-energy CT (DECT) and highlight its applications and advantages in head and neck radiology.Recent FindingsUsing DECT, additional datasets can be created next to conventional images. In head and neck radiology, three material decomposition algorithms can be used for improved lesion detection and delineation of the tumor. Iodine concentration measurements can aid in differentiating malignant from nonmalignant lymph nodes and benign posttreatment changes from tumor recurrence. Virtual non-calcium images can be used for detection of bone marrow edema. Virtual mono-energetic imaging can be useful for improved iodine conspicuity at lower keV and for reduction of metallic artifacts and increase in signal-to-noise ratio at higher keV.SummaryDECT and its additional reconstructions can play an important role in head and neck cancer patients, from initial diagnosis and staging, to therapy planning, evaluation of treatment response and follow-up. Moreover, it can be helpful in imaging of infections and inflammation and parathyroid imaging as supplementary reconstructions can be obtained at lower or equal radiation dose compared with conventional single energy scanning.
Radical resection
for patients with oral cavity cancer remains
challenging. Rapid evaporative ionization mass spectrometry (REIMS)
of electrosurgical vapors has been reported for real-time classification
of normal and tumor tissues for numerous surgical applications. However,
the infiltrative pattern of invasion of oral squamous cell carcinomas
(OSCC) challenges the ability of REIMS to detect low amounts of tumor
cells. We evaluate REIMS sensitivity to determine the minimal amount
of detected tumors cells during oral cavity cancer surgery. A total
of 11 OSCC patients were included in this study. The tissue classification
based on 185 REIMS
ex vivo
metabolic profiles from
five patients was compared to histopathology classification using
multivariate analysis and leave-one-patient-out cross-validation.
Vapors were analyzed
in vivo
by REIMS during four
glossectomies. Complementary desorption electrospray ionization–mass
spectrometry imaging (DESI-MSI) was employed to map tissue heterogeneity
on six oral cavity sections to support REIMS findings. REIMS sensitivity
was assessed with a new cell-based assay consisting of mixtures of
cell lines (tumor, myoblasts, keratinocytes). Our results depict REIMS
classified tumor and soft tissues with 96.8% accuracy.
In
vivo
REIMS generated intense mass spectrometric signals.
REIMS detected 10% of tumor cells mixed with 90% myoblasts with 83%
sensitivity and 82% specificity. DESI-MSI underlined distinct metabolic
profiles of nerve features and a metabolic shift phosphatidylethanolamine
PE(O-16:1/18:2))/cholesterol sulfate common to both mucosal maturation
and OSCC differentiation. In conclusion, the assessment of tissue
heterogeneity with DESI-MSI and REIMS sensitivity with cell mixtures
characterized sensitive metabolic profiles toward
in vivo
tissue recognition during oral cavity cancer surgeries.
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