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Luis E. Savastano, Todd C. Hollon, Ariel L. Barkan and Stephen E. Sullivan

Korsakoff syndrome is a chronic memory disorder caused by a severe deficiency of thiamine that is most commonly observed in alcoholics. However, some have proposed that focal structural lesions disrupting memory circuits—in particular, the mammillary bodies, the mammillothalamic tract, and the anterior thalamus—can give rise to this amnestic syndrome. Here, the authors present 4 patients with reversible Korsakoff syndromes caused by suprasellar retrochiasmatic lesions compressing the mammillary bodies and adjacent caudal hypothalamic structures.

Three of the patients were found to have large pituitary macroadenomas in their workup for memory deficiency and cognitive decline with minimal visual symptoms. These tumors extended superiorly into the suprasellar region in a retrochiasmatic position and caused significant mass effect in the bilateral mammillary bodies in the base of the brain. These 3 patients had complete and rapid resolution of amnestic problems shortly after initiation of treatment, consisting of resection in 1 case of nonfunctioning pituitary adenoma or cabergoline therapy in 2 cases of prolactinoma. The fourth patient presented with bizarre and hostile behavior along with significant memory deficits and was found to have a large cystic craniopharyngioma filling the third ventricle and compressing the midline diencephalic structures. This patient underwent cyst fenestration and tumor debulking, with a rapid improvement in his mental status. The rapid and dramatic memory improvement observed in all of these cases is probably due to a reduction in the pressure imposed by the lesions on structures contiguous to the third ventricle, rather than a direct destructive effect of the tumor, and highlights the essential role of the caudal diencephalic structures—mainly the mammillary bodies—in memory function.

In summary, large pituitary lesions with suprasellar retrochiasmatic extension and third ventricular craniopharyngiomas can cause severe Korsakoff-like amnestic syndromes, probably because of bilateral pressure on or damage to mammillary bodies, anterior thalamic nuclei, or their major connections. Neuropsychiatric symptoms may rapidly and completely reverse shortly after initiation of therapy via surgical decompression of tumors or pharmacological treatment of prolactinomas. Early identification of these lesions with timely treatment can lead to a favorable prognosis for this severe neuropsychiatric disorder.

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Todd C. Hollon, Adish Parikh, Balaji Pandian, Jamaal Tarpeh, Daniel A. Orringer, Ariel L. Barkan, Erin L. McKean and Stephen E. Sullivan

OBJECTIVE

Pituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. The authors sought to build a predictive model using supervised machine learning to accurately predict early outcomes of pituitary adenoma surgery.

METHODS

A retrospective cohort of 400 consecutive pituitary adenoma patients was used. Patient variables/predictive features were limited to common patient characteristics to improve model implementation. Univariate and multivariate odds ratio analysis was performed to identify individual risk factors for common postoperative complications and to compare risk factors with model predictors. The study population was split into 300 training/validation patients and 100 testing patients to train and evaluate four machine learning models using binary classification accuracy for predicting early outcomes.

RESULTS

The study included a total of 400 patients. The mean ± SD patient age was 53.9 ± 16.3 years, 59.8% of patients had nonfunctioning adenomas and 84.7% had macroadenomas, and the mean body mass index (BMI) was 32.6 ± 7.8 (58.0% obesity rate). Multivariate odds ratio analysis demonstrated that age < 40 years was associated with a 2.86 greater odds of postoperative diabetes insipidus and that nonobese patients (BMI < 30) were 2.2 times more likely to develop postoperative hyponatremia. Using broad criteria for a poor early postoperative outcome—major medical and early surgical complications, extended length of stay, emergency department admission, inpatient readmission, and death—31.0% of patients met criteria for a poor early outcome. After model training, a logistic regression model with elastic net (LR-EN) regularization best predicted early postoperative outcomes of pituitary adenoma surgery on the 100-patient testing set—sensitivity 68.0%, specificity 93.3%, overall accuracy 87.0%. The receiver operating characteristic and precision-recall curves for the LR-EN model had areas under the curve of 82.7 and 69.5, respectively. The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing’s disease.

CONCLUSIONS

Early postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.

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Noah S. Cutler, Sudharsan Srinivasan, Bryan L. Aaron, Sharath Kumar Anand, Michael S. Kang, David B. Altshuler, Thomas C. Schermerhorn, Todd C. Hollon, Cormac O. Maher and Siri Sahib S. Khalsa

OBJECTIVE

Normal percentile growth charts for head circumference, length, and weight are well-established tools for clinicians to detect abnormal growth patterns. Currently, no standard exists for evaluating normal size or growth of cerebral ventricular volume. The current standard practice relies on clinical experience for a subjective assessment of cerebral ventricular size to determine whether a patient is outside the normal volume range. An improved definition of normal ventricular volumes would facilitate a more data-driven diagnostic process. The authors sought to develop a growth curve of cerebral ventricular volumes using a large number of normal pediatric brain MR images.

METHODS

The authors performed a retrospective analysis of patients aged 0 to 18 years, who were evaluated at their institution between 2009 and 2016 with brain MRI performed for headaches, convulsions, or head injury. Patients were excluded for diagnoses of hydrocephalus, congenital brain malformations, intracranial hemorrhage, meningitis, or intracranial mass lesions established at any time during a 3- to 10-year follow-up. The volume of the cerebral ventricles for each T2-weighted MRI sequence was calculated with a custom semiautomated segmentation program written in MATLAB. Normal percentile curves were calculated using the lambda-mu-sigma smoothing method.

RESULTS

Ventricular volume was calculated for 687 normal brain MR images obtained in 617 different patients. A chart with standardized growth curves was developed from this set of normal ventricular volumes representing the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles. The charted data were binned by age at scan date by 3-month intervals for ages 0–1 year, 6-month intervals for ages 1–3 years, and 12-month intervals for ages 3–18 years. Additional percentile values were calculated for boys only and girls only.

CONCLUSIONS

The authors developed centile estimation growth charts of normal 3D ventricular volumes measured on brain MRI for pediatric patients. These charts may serve as a quantitative clinical reference to help discern normal variance from pathologic ventriculomegaly.