Outcome of Transsphenoidal Surgery for Cushing Disease

Chandler, William F.; Barkan, Ariel L.; Hollon, Todd; Sakharova, Alla; Sack, Jayson; Brahma, Barunashish; Schteingart, D.E.

doi:10.1227/neu.0000000000001011

Cited by 65 publications

(38 citation statements)

References 25 publications

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“…17 Moreover, they are the second most common nonmalignant brain tumor with surgical resection as a potential curative treatment. While scoring systems have been developed that use radiographic features to classify invasion into adjacent structures 4,10,11 and hormone levels to predict treatment response, 2,25 no scoring system has been developed to comprehensively include patient characteristics and stratify surgical risk. Such scoring systems have been developed for meningiomas, 22 gliomas (both low-grade 3,20 and malignant 13,18 ), brain metastases, 6,7 and arteriovenous malformations 12,23,24 to predict both early and long-term outcomes.…”

Section: Discussionmentioning

confidence: 99%

A machine learning approach to predict early outcomes after pituitary adenoma surgery

Hollon¹,

Parikh

Pandian

et al. 2018

Neurosurgical Focus

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OBJECTIVEPituitary 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.METHODSA 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.RESULTSThe 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.CONCLUSIONSEarly 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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Section: Discussionmentioning

confidence: 99%

A machine learning approach to predict early outcomes after pituitary adenoma surgery

Hollon¹,

Parikh

Pandian

et al. 2018

Neurosurgical Focus

Self Cite

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“…For patients in immediate remission after surgery, recurrence was considered based on the recurrence symptoms and biochemical evidence of CD, including an elevated morning serum cortisol level or 24-h UFC and a lack of suppression of the morning cortisol after a late evening dose of dexamethasone [12]. MRI, as mentioned above, was also used to confirm recurrence.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Recurrence after Transsphenoidal Surgery for Cushing’s Disease: The Use of Machine Learning Algorithms

Liu

Hong

et al. 2019

Neuroendocrinology

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Background: There are no reliable predictive models for recurrence after transsphenoidal surgery (TSS) for Cushing’s disease (CD). Objectives: This study aimed to develop machine learning (ML)-based predictive models for CD recurrence after initial TSS and to evaluate their performance. Method: A total of 354 CD patients were included in this retrospective, supervised learning, data mining study. Predictive models for recurrence were developed according to 17 variables using 7 algorithms. Models were evaluated based on the area under the receiver operating characteristic curve (AUC). Results: All patients were followed up for over 12 months (mean ± SD 43.80 ± 35.61). The recurrence rate was 13.0%. Age (p < 0.001), postoperative morning serum cortisol nadir (p = 0.002), and postoperative (p < 0.001) and preoperative (p = 0.04) morning adrenocorticotropin (ACTH) level were significantly related to recurrence. AUCs of the 7 models ranged from 0.608 to 0.781. The best performance (AUC = 0.781, 95% CI 0.706, 0.856) appeared when 8 variables were introduced to the random forest (RF) algorithm, which was much better than that of logistic regression (AUC = 0.684, p = 0.008) and that of using only postoperative morning serum cortisol (AUC = 0.635, p < 0.001). According to the feature selection algorithms, the top 3 predictors were age, postoperative serum cortisol, and postoperative ACTH. Conclusions: Using ML-based models for prediction of the recurrence after initial TSS for CD is feasible, and RF performs best. The performance of most of ML-based models was significantly better than that of some conventional models.

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“…33 Remission rates at experienced pituitary centers range between 70% and 90%. 1,6,9,10,11,18,19,24,27,[29][30][31] While many diagnostic tests aid in the localization of ACTH-dependent disease to the pituitary gland (such as ACTH level, pituitary MRI, and inferior petrosal sinus sampling [IPSS]), all of these studies have known weaknesses, and presence of a tumor is not always confirmed. 2 Ultimately, it falls to the discretion of the surgeon on how to manage presumed CD cases when no obvious adenoma is discovered at the time of surgery.…”

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confidence: 99%

Negative surgical exploration in patients with Cushing’s disease: benefit of two-thirds gland resection on remission rate and a review of the literature

Carr¹,

Kleinschmidt‐DeMasters²,

Kerr

et al. 2018

Journal of Neurosurgery

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OBJECTIVE The authors report their single-institution experience with the pathological findings, rates of remission, and complications in patients with presumed Cushing's disease (CD) who underwent a two-thirds pituitary gland resection when no adenoma was identified at the time of transsphenoidal surgery (TSS). The authors also review the literature on patients with CD, negative surgical exploration, and histological findings. METHODS This study is a retrospective analysis of cases found in neurosurgery and pathology department databases between 1989 and 2011. In all cases, patients had been operated on by the same neurosurgeon (K.O.L.). Twenty-two (13.6%) of 161 patients who underwent TSS for CD had no adenoma identified intraoperatively after systematic exploration of the entire gland; these patients all underwent a two-thirds pituitary gland resection. A chart review was performed to assess treatment data points as well as clinical and biochemical remission status. RESULTS Of the 22 patients who underwent two-thirds gland resection, 6 (27.3%) ultimately had lesions found on final pathology. All 6 patients were found to have a distinct adrenocorticotropic hormone (ACTH) cell adenoma. Sixteen (72.7%) of the patients had no tumor identified, with 3 of these patients suspected of having ACTH cell hyperplasia. The follow-up duration for the entire group was between 14 and 315 months (mean 98.9 months, median 77 months). Remission rates were 100% (6/6 patients) for the ACTH cell adenoma group and 75% (12/16) for the group without adenoma. Overall, 18 (81.8%) of the 22 patients had no evidence of hypercortisolism at last follow-up, and 4 patients (18%) had persistent hypercortisolism, defined as a postoperative cortisol level > 5 μg/dl. Of these 4 patients, 1 was suspected of harboring a cavernous sinus adenoma, 2 were found to have lung tumors secreting ACTH, and 1 remained with an undiagnosed etiology. Rates of postoperative complications were low. CONCLUSIONS The diagnosis and treatment of CD can be challenging for neurosurgeons, endocrinologists, and pathologists alike. Failure to find a discrete adenoma at the time of surgery occurs in at least 10%-15% of cases, even in experienced centers. The current literature provides little guidance regarding rational intraoperative approaches in such cases. The authors' experience with 161 patients with CD, when no intraoperative tumor was localized, demonstrates the utility of a two-thirds pituitary gland resection with a novel and effective surgical strategy, as suggested by a high initial remission rate and a low operative morbidity.

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Outcome of Transsphenoidal Surgery for Cushing Disease

Abstract: Transsphenoidal surgery provides a safe and effective treatment for Cushing disease. For both adults and children, the best outcomes occurred in patients with microadenomas and/or those with type 1 remission.

Cited by 65 publications

References 25 publications

A machine learning approach to predict early outcomes after pituitary adenoma surgery

A machine learning approach to predict early outcomes after pituitary adenoma surgery

Prediction of Recurrence after Transsphenoidal Surgery for Cushing’s Disease: The Use of Machine Learning Algorithms

Negative surgical exploration in patients with Cushing’s disease: benefit of two-thirds gland resection on remission rate and a review of the literature

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