Neurosurgical Anatomy of the Internal Carotid Artery: Magnetic Resonance Imaging Study of the Sellar Region

The Significance of the Intercarotid Distances for Transsphenoidal Pituitary Surgery: A Magnetic Resonance Imaging Study

OBJECTIVE

We performed magnetic resonance imaging (MRI) to analyze the distance between the left and right internal carotid arteries (ICD) around pituitary tumors for transsphenoidal surgery (TSS).

METHODS

Measurements were performed using thin slice T2-weighted MRI, and/or time-of-flight (TOI) MR angiography imaging for 64 nonfunctioning pituitary neuroendocrine tumors (NF-pitNETs), 22 growth hormone-producing pitNETs (GH producing pitNETs), and 46 normal controls. We measured the ICD at 3 anatomic levels: at the distal dural ring (ICD-A); at the most concave point of the C4-C5 bend (ICD-B); and at the most convex point of the C4 bend (ICD-C). Additionally, we measured the sagittal distance between the tuberculum selle and the junction of the sellar floor and clivus on the midsagittal MRI (TS-C).

RESULTS

We found that ICD-B was the longest and that ICD-A was the shortest among the 3 distance parameters in all groups (P < 0.001). The comparison of the groups revealed that the NF-pitNET group had longer distances than the normal control group at all ICDs (P < 0.001). The GH producing pitNET group had longer distance than the normal control group at ICD-B (P < 0.001). Tumor volume was correlated with ICD-A and ICD-B in the NF-pitNET, and was correlated with ICD-C in the GH producing pitNET group.

CONCLUSIONS

Among the distance parameters, ICD-B is the longest, and was approximately twice the width of the TS-C. The shape of the sella is an ellipse that is widest in the lateral dimension in TSS. A horizontal based dural incision may be more rational than a conventional X-shaped dural incision.

Anatomical variations of the cavernous Internal Carotid Artery: Validation of an MRI-based classification

OBJECTIVE

Variations of the cavernous Internal Carotid Artery (ICA) angulation (C4-bend) have been classified into 4 anatomical subtypes with particular surgical relevance, as a very angulated ICA comes in closest contact with the pituitary gland leading to higher risk of iatrogenic vascular injury. This study aimed to validate this classification using current routine imaging techniques.

METHODS

The different cavernous ICA bending angles were measured on 109 MRI TOF sequences, within a retrospective database of patients with no sellar lesions. Each ICA was classified into one of 4 anatomical subtypes as already defined in a previous study [1]. Interrater agreement was assessed by a Kappa Correlation Coefficient.

RESULTS

Kappa Correlation Coefficient was of 0.90 [0.82-0.95], validating the strong level of agreement between all observers when using the present classification.

CONCLUSION

Classification of the cavernous ICA in 4 subtypes appears statistically valid on routinely used pre-operative MRI, providing an efficient tool to estimate the vascular iatrogenic risk prior to endoscopic endonasal transsphenoidal surgery.

Prediction of carotid plaque by blood biochemical indices and related factors based on Fisher discriminant analysis

OBJECTIVE

This study aims to establish the predictive model of carotid plaque formation and carotid plaque location by retrospectively analyzing the clinical data of subjects with carotid plaque formation and normal people, and to provide technical support for screening patients with carotid plaque.

METHODS

There were 4300 subjects in the ultrasound department of Maanshan People's Hospital collected from December 2013 to December 2018. We used demographic and biochemical data from 3700 subjects to establish predictive models for carotid plaque and its location. The leave-one-out cross-validated classification, 600 external data validation, and area under the receiver operating characteristic curve (AUC) were used to verify the accuracy, sensitivity, specificity, and application value of the model.

RESULTS

There were significant difference of age (F = - 34.049, p < 0.01), hypertension (χ² = 191.067, p < 0.01), smoking (χ² = 4.762, p < 0.05) and alcohol (χ² = 8.306, p < 0.01), Body mass index (F = 15.322, p < 0.01), High-density lipoprotein (HDL) (F = 13.840, p < 0.01), Lipoprotein a (Lp a) (F = 52.074, p < 0.01), Blood Urea Nitrogen (F = 2.679, p < 0.01) among five groups. Prediction models were built: carotid plaque prediction model (Model CP); Prediction model of left carotid plaque only (Model CP Left); Prediction model of right carotid plaque only (Model CP Right). Prediction model of bilateral carotid plaque (Model CP Both). Model CP (Wilks' lambda = 0.597, p < 0.001, accuracy = 78.50%, sensitivity = 78.07%, specificity = 79.07%, AUC = 0.917). Model CP Left (Wilks' lambda = 0.605, p < 0.001, accuracy = 79.00%, sensitivity = 86.17%, specificity = 72.70%, AUC = 0.880). Model CP Right (Wilks' lambda = 0.555, p < 0.001, accuracy = 83.00%, sensitivity = 81.82%, specificity = 84.44%, AUC = 0.880). Model CP Both (Wilks' lambda = 0.651, p < 0.001, accuracy = 82.30%, sensitivity = 89.50%, specificity = 72.70%, AUC = 0.880).

CONCLUSION

Demographic characteristics and blood biochemical indexes were used to establish the carotid plaque and its location discriminant models based on Fisher discriminant analysis (FDA), which has high application value in community screening.