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The authors performed a retrospective data base search that queried neck CT reports with Neck Imaging Reporting and Data Systems scores of 2–4 from June 2014 to March 2018. The electronic medical records were reviewed to determine outcomes of clinical and radiologic follow-up, including symptoms, physical examination findings, pathologic correlation, and clinical notes within 3 months of imaging. A total of 255 cases all with NIRADS scores of 2 or 3 met the inclusion criteria. Fifty-nine patients (23%) demonstrated recurrence, and 21 patients (36%) had clinically occult recurrence. The median overall time to radiologically detected, clinically occult recurrence was 11.4 months from treatment completion. They conclude that imaging surveillance beyond the first posttreatment baseline study was critical for detecting clinically occult recurrent disease in patients with head and neck squamous cell carcinoma. More than one-third of all recurrences were seen in patients without clinical evidence of disease.

The authors used resting-state fMRI in a prospective study to compare whole-brain functional connectivity between patients with good and poor outcomes, implementing support vector machine learning. They automatically predicted coma outcome using resting-state fMRI and also compared the prediction based on resting-state fMRI with the outcome prediction based on DWI. Of 17 eligible patients who completed the study procedure (among 351 patients screened), 9 regained consciousness and 8 remained comatose. They found higher functional connectivity in patients recovering consciousness, with greater changes occurring within and between the occipitoparietal and temporofrontal regions. Coma outcome prognostication based on resting-state fMRI machine learning was very accurate, notably for identifying patients with good outcome. They conclude that resting-state fMRI might bridge the gap left in early prognostication of postanoxic patients in a coma by identifying those with both good and poor outcomes.