BACKGROUND: Since the pioneering reports of the so-called "leonine face" in cluster headache (CH) patients, cranial and facial features of these patients have been poorly investigated with conflicting results. We aimed to investigate whether abnormalities in craniometric measurements could characterize male CH patients and represent reliable and reproducible diagnostic biomarkers able to identify CH patients. METHODS: Brain CT images were recorded between 2018 and 2020 in 24 male patients with CH, and in 24 matched healthy controls (HC). Then, craniometric measurements were obtained, and logistic regression and Receiver Operating Charateristic curves (ROC) analyses were used to identify the craniometrics abnormalities able to distinguish CH patients from HC. RESULTS: Logistic regression analyses showed that Frontal Bone Height and Facial Width were able to discriminate, one independently from the other, CH patients from HC with an overall accuracy of 77.00%. The optimal cut-off score in detecting the probable presence of CH was 11.50 cm for Frontal Bone Height and 13.30 cm for Facial Width. DISCUSSION: In the present study we found, for the first time by means of brain 3 D-TC approach, abnormal craniometric measurements in CH patients when compared with HC. The absence of differences in smoke and alcohol intake suggests that the observed craniometric abnormalities may represent a specific feature of CH patients. CONCLUSION: The craniometric evaluation by means of brain 3 D-TC could represent a widespread, noninvasive and accurate tool to support CH diagnosis to avoid frequent misdiagnosis or delay in the diagnostic process.
BACKGROUND: Since the pioneering reports of the so-called "leonine face" in cluster headache (CH) patients, cranial and facial features of these patients have been poorly investigated with conflicting results. We aimed to investigate whether abnormalities in craniometric measurements could characterize male CH patients and represent reliable and reproducible diagnostic biomarkers able to identify CH patients. METHODS: Brain CT images were recorded between 2018 and 2020 in 24 male patients with CH, and in 24 matched healthy controls (HC). Then, craniometric measurements were obtained, and logistic regression and Receiver Operating Charateristic curves (ROC) analyses were used to identify the craniometrics abnormalities able to distinguish CH patients from HC. RESULTS: Logistic regression analyses showed that Frontal Bone Height and Facial Width were able to discriminate, one independently from the other, CH patients from HC with an overall accuracy of 77.00%. The optimal cut-off score in detecting the probable presence of CH was 11.50 cm for Frontal Bone Height and 13.30 cm for Facial Width. DISCUSSION: In the present study we found, for the first time by means of brain 3 D-TC approach, abnormal craniometric measurements in CH patients when compared with HC. The absence of differences in smoke and alcohol intake suggests that the observed craniometric abnormalities may represent a specific feature of CH patients. CONCLUSION: The craniometric evaluation by means of brain 3 D-TC could represent a widespread, noninvasive and accurate tool to support CH diagnosis to avoid frequent misdiagnosis or delay in the diagnostic process.