Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning.

OBJECTIVES: This study was conducted in order to evaluate a novel risk stratification model using dual-energy CT (DECT) texture analysis of head and neck squamous cell carcinoma (HNSCC) with machine learning to (1) predict associated cervical lymphadenopathy and (2) compare the accuracy of spectral versus single-energy (65 keV) texture evaluation for endpoint prediction. METHODS: Eighty-seven patients with HNSCC were evaluated. Texture feature extraction was performed on virtual monochromatic images (VMIs) at 65 keV alone or different sets of multi-energy VMIs ranging from 40 to 140 keV, in addition to iodine material decomposition maps and other clinical information. Random forests (RF) models were constructed for outcome prediction with internal cross-validation in addition to the use of separate randomly selected training (70%) and testing (30%) sets. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined for predicting positive versus negative nodal status in the neck. RESULTS: Depending on the model used and subset of patients evaluated, an accuracy, sensitivity, specificity, PPV, and NPV of up to 88, 100, 67, 83, and 100%, respectively, could be achieved using multi-energy texture analysis. Texture evaluation of VMIs at 65 keV alone or in combination with only iodine maps had a much lower accuracy. CONCLUSIONS: Multi-energy DECT texture analysis of HNSCC is superior to texture analysis of 65 keV VMIs and iodine maps alone and can be used to predict cervical nodal metastases with relatively high accuracy, providing information not currently available by expert evaluation of the primary tumor alone. KEY POINTS: • Texture features of HNSCC tumor are predictive of nodal status. • Multi-energy texture analysis is superior to analysis of datasets at a single energy. • Dual-energy CT texture analysis with machine learning can enhance noninvasive diagnostic tumor evaluation.

Giant metastatic mixed-type spinal ependymoma presenting with hydrocephalus.

Ependymomas are rare nervous system tumors that can arise anywhere in the neuraxis. While having a high propensity for leptomeningeal dissemination, retrograde dissemination (from the spine to the CNS) remains infrequent. We describe the case of a 31-year-old female who presented with hydrocephalus secondary to an intracranial leptomeningeal metastasis of a giant spinal ependymoma with mixed (classic and myxopapillary) histopathologic features, successfully treated with surgical resection and radiotherapy of the entire neuraxis. This case highlights the importance of including ependymomas in the differential diagnosis for lesions in atypical extra-axial locations, of systematically obtaining imaging of the entire neuraxis when suspecting it, and of considering retrograde dissemination when both intracranial and spinal lesions are present.