Lateral one-third gland resection in Cushing patients with failed adenoma identification leads to low remission rates: long-term observations from a small, single-center cohort

The diagnostic yield of inferior petrosal sinus sampling in Cushing syndrome in the era of ovine CRH shortage

PURPOSE

The ovine corticotropin-releasing hormone (oCRH) stimulation test has been routinely used in the diagnostic work-up of ACTH-dependent Cushing syndrome (CS). With oCRH currently being out-of-stock in Europe, we aimed at evaluating the diagnostic performance of inferior petrosal sinus sampling (IPSS) without oCRH stimulation.

METHODS

We compared the values of 40 patients with ACTH-dependent CS and negative MRI findings in whom ACTH was measured before and after oCRH stimulation.

RESULTS

The ratio of central-to-peripheral ACTH measurement (IPS:P) before the combined 3, 5, and 10 min of oCRH stimulation yielded diminished sensitivity (85% vs. 97%), alongside markedly decreased specificity (57% vs. 71%), as well as reduced positive and negative predictive values (90% vs. 94% and 44% vs. 83%), respectively.

CONCLUSIONS

With the current drug shortages in Europe, ACTH measurements without oCRH stimulation in IPSS cannot be recommended. Thus, we call for desmopressin or the commercially available human CRH as a potential alternative in the confirmation of ACTH excess by IPSS in equivocal MRI findings.

Current and Future Advances in Surgical Therapy for Pituitary Adenoma

The vital physiological role of the pituitary gland, alongside its proximity to critical neurovascular structures, means that pituitary adenomas can cause significant morbidity or mortality. While enormous advancements have been made in the surgical care of pituitary adenomas, numerous challenges remain, such as treatment failure and recurrence. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (eg, endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient's journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, surgical abilities will be augmented by the future operative armamentarium, including advanced optical devices, smart instruments, and surgical robotics. Intraoperative support to surgical team members will benefit from a data science approach, utilizing machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, neural networks leveraging multimodal datasets will allow early detection of individuals at risk of complications and assist in the prediction of treatment failure, thus supporting patient-specific discharge and monitoring protocols. While these advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of the translation of such technologies, ensuring systematic assessment of risk and benefit prior to clinical implementation. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future.

Prolactinomas in adolescent and elderly patients-A comparative long-term analysis

Objectives

Prolactinomas represent the most common type of secreting pituitary adenomas, yet are rarely encountered in adolescent-onset (AO; i.e. <18 years) or elderly-onset (EO; i.e. ≥65 years) cohorts. As a result, it is not clear whether long-term strategies should be focused differently at both age extremes when comparing their therapeutic outcomes. We aimed at investigating long-term endocrinological outcomes, looking for differences between the two cohorts and evaluating the dependence on continued dopamine agonist (DA) therapy.

Methods

Retrospective cross-sectional comparative study analyzing prolactinoma patients with a follow-up of ≥4 years. Clinical, radiological and biochemical characteristics were assessed at diagnosis and last follow-up. Longitudinal endocrinological outcomes between groups of extreme ages (i.e. AO and EO) and middle age (i.e. ≥18 years to 65 years) were compared. Independent risk factors for long-term dependence on DAs were calculated.

Results

Follow-up at ≥4 years was recorded for 108 prolactinoma patients; 10 patients with AO and 10 patients with EO. Compared to AO patients, EO patients were predominantly men (p = 0.003), and presented with significantly higher prolactin (PRL) levels (p = 0.05) and higher body mass index (p = 0.03). We noted a significant positive correlation between patients' PRL values and their age (r = 0.5, p = 0.03) or BMI (r = 0.6, p = 0.03). After a median follow-up of 115 months, remission was noted in 87 (83%) patients; 9 (90%) in AO patients, and 7 (70%) in EO patients (p = 0.58). Continuation of DAs was required in 4 patients (40%) with AO and 7 patients (70%) with EO (p = 0.37). Patients with elderly-onset were an independent predictor of long-term dependence on DAs (HR 2.8, 95% CI 1.1-7.2, p = 0.03).

Conclusions

Long-term control of hyperprolactinemia and hypogonadism does not differ between members of the AO and EO cohorts, and can be attained by the majority of patients. However, adjuvant DAs are often required, independent of the age of onset. Considering the clinical significance of persistent DA therapy for the control of hyperprolactinemia in many patients at both extremes of age, long-term monitoring may become recommended, in particular in patients with elderly-onset.

Surgical strategies in the treatment of MR-negative Cushing's Disease: a systematic review and treatment algorithm

PURPOSE

Several surgical strategies have been proposed to treat MRI-negative Cushing's Disease. These include tumor removal, if identified, and if a tumor is not identified, resection of varying degrees of the pituitary gland, often guided by inferior petrosal sinus sampling (IPSS). The relative risks and benefits of each strategy have never been compared.

METHODS

This systematic review of the literature included only studies on the results of surgery for MRI-negative patients with Cushing's Disease in which the surgical strategy was clearly described and associated remission and/or hypopituitarism rates detailed for each strategy.

RESULTS

We identified 12 studies that met inclusion criteria for remission rates and 5 studies for hypopituitarism rates. We divided cases into 6 resection strategies. Remission and hypopituitarism rates for each strategy were: (1) tumor identified, resect tumor only (68%, 0%); (2) resect tumor and surrounding capsule (85%, 0%); and if the tumor was not identified (3) resect inferior 1/3 of gland (78%, no data); (4) resect 30-50% of gland based on IPSS (68%, 13%); (5) resect > 50% but < 100% of gland (65%, 9%); (6) resect entire gland (66%, 67%). Strategy 3 only contained 9 patients.

CONCLUSION

Remission rates for MRI-negative Cushing's Disease support surgery as a reasonable approach. Results are best if a tumor is found. If a tumor is not identified, one can either remove one-third of the gland guided by IPSS lateralization, or remove both lateral portions along with the inferior portion leaving sufficient central gland to preserve function. Our recommendations are limited by the lack of rigorous and objective data.

Copeptin Levels Before and After Transsphenoidal Surgery for Cushing Disease: A Potential Early Marker of Remission

Context

Arginine-vasopressin (AVP) and corticotropin-releasing hormone (CRH) act synergistically to stimulate secretion of adrenocorticotropic hormone (ACTH). There is evidence that glucocorticoids act via negative feedback to suppress AVP secretion.

Objective

Our hypothesis was that a post-operative increase in plasma copeptin may serve as a marker of remission of Cushing Disease (CD).

Design

Plasma copeptin was obtained in patients with CD before and daily on post-operative days 1-8 after transsphenoidal surgery. Peak post-operative copeptin levels and Δcopeptin values were compared among those in remission versus no remission.

Results

Forty-four patients (64% female, 7-55 years old) were included, and 19 developed neither DI nor SIADH. Thirty-three had follow-up at least 3 months post-operatively. There was no difference in peak post-operative copeptin in remission [6.1 pmol/L (4.3-12.1)] versus no remission [7.3 pmol/L (5.4-8.4), p=0.88].. Excluding those who developed DI or SIADH, there was no difference in peak post-operative copeptin in remission [10.2 pmol/L (6.9-21.0)] versus no remission [5.4 pmol/L (4.6-7.3), p= 0.20]. However, a higher peak post-operative copeptin was found in those in remission [14.6 pmol/L (±10.9) vs. 5.8 (±1.4), p=0.03]] with parametric testing. There was no difference in the Δcopeptin by remission status.

Conclusions

A difference in peak post-operative plasma copeptin as an early marker to predict remission of CD was not consistently present, although the data point to the need for a larger sample size to further evaluate this. However, the utility of this test may be limited to those who develop neither DI nor SIADH post-operatively.

Machine Learning for Outcome Prediction in First-Line Surgery of Prolactinomas

BACKGROUND

First-line surgery for prolactinomas has gained increasing acceptance, but the indication still remains controversial. Thus, accurate prediction of unfavorable outcomes after upfront surgery in prolactinoma patients is critical for the triage of therapy and for interdisciplinary decision-making.

OBJECTIVE

To evaluate whether contemporary machine learning (ML) methods can facilitate this crucial prediction task in a large cohort of prolactinoma patients with first-line surgery, we investigated the performance of various classes of supervised classification algorithms. The primary endpoint was ML-applied risk prediction of long-term dopamine agonist (DA) dependency. The secondary outcome was the prediction of the early and long-term control of hyperprolactinemia.

METHODS

By jointly examining two independent performance metrics - the area under the receiver operating characteristic (AUROC) and the Matthews correlation coefficient (MCC) - in combination with a stacked super learner, we present a novel perspective on how to assess and compare the discrimination capacity of a set of binary classifiers.

RESULTS

We demonstrate that for upfront surgery in prolactinoma patients there are not a one-algorithm-fits-all solution in outcome prediction: different algorithms perform best for different time points and different outcomes parameters. In addition, ML classifiers outperform logistic regression in both performance metrics in our cohort when predicting the primary outcome at long-term follow-up and secondary outcome at early follow-up, thus provide an added benefit in risk prediction modeling. In such a setting, the stacking framework of combining the predictions of individual base learners in a so-called super learner offers great potential: the super learner exhibits very good prediction skill for the primary outcome (AUROC: mean 0.9, 95% CI: 0.92 - 1.00; MCC: 0.85, 95% CI: 0.60 - 1.00). In contrast, predicting control of hyperprolactinemia is challenging, in particular in terms of early follow-up (AUROC: 0.69, 95% CI: 0.50 - 0.83) vs. long-term follow-up (AUROC: 0.80, 95% CI: 0.58 - 0.97). It is of clinical importance that baseline prolactin levels are by far the most important outcome predictor at early follow-up, whereas remissions at 30 days dominate the ML prediction skill for DA-dependency over the long-term.

CONCLUSIONS

This study highlights the performance benefits of combining a diverse set of classification algorithms to predict the outcome of first-line surgery in prolactinoma patients. We demonstrate the added benefit of considering two performance metrics jointly to assess the discrimination capacity of a diverse set of classifiers.

Pituitary surgery for Cushing's disease

BACKGROUND

Surgery is currently the first-line treatment of Cushing's disease. Surgery for Cushing's patients requires technical specificity, especially if no adenoma is identified on dedicated preoperative pituitary MRI.

METHOD

From 2006 to 2020, 683 patients with Cushing's disease were operated on with a mononostril endoscopic endonasal approach by the same two senior neurosurgeons. Here, we report the particularities of this challenging surgery.

CONCLUSION

A rigorous and planned surgical strategy avoids the pitfalls of Cushing's disease surgery and leads to a high rate of endocrine remission.

Impact of primary medical or surgical therapy on prolactinoma patients' BMI and metabolic profile over the long-term

•

High prolactin (PRL) levels are associated with weight gain and impaired metabolic profiles.

•

Long-term control of hyperprolactinemia can be attained by first-line surgery and medical therapy.

•

Normalization of PRL improves patients’ BMI and fasting glucose levels.

•

Marginal changes in patients’ metabolic profiles are noted regardless of the primary therapy.

•

Not dopamine agonists per se, but rather the control of hyperprolactinemia plays a role in metabolic profile alterations.

Objectives

High prolactin levels have been associated with weight gain and impaired metabolic profiles. While treatment with dopamine agonists (DAs) has been shown to improve these parameters, there is a lack of surgical series on its comparative effect in prolactinoma patients.

Methods

In this retrospective, comparative study, consecutive patients with a prolactinoma were enrolled if treated with first-line transsphenoidal surgery (TSS) or with DAs. Patients with prolactinomas of Knosp grade >2 and those with a follow-up <24 months were excluded, as were patients with missing laboratory metabolic parameters at baseline and over the long-term. Effects of either treatment on BMI and the metabolic profile were analyzed, and independent risk factors for long-term obesity were calculated.

Results

Primary treatment was TSS for 12 patients (40%) and DAs for 18 patients (60%). At diagnosis, no significant differences between the two cohorts were observed with regard to adenoma size, Knosp grading, baseline prolactin (PRL) levels, prevalence of hypogonadism, or laboratory metabolic parameters. Mean follow-up was 51.9 months (range, 24–158). Over the long-term, both TSS and DAs led to the control of hyperprolactinemia (92% vs. 72%) and hypogonadism (78% vs. 83%) in the majority of patients. While a significant decrease in patients’ BMI and fasting glucose were observed, changes in the lipid profile were marginal and independent of the treatment modality. At baseline, increased BMI—but not the primary treatment strategy—was an independent predictor of long-term obesity.

Conclusions

Over the long-term, patients’ BMI and FG improve, but changes in the metabolic profile are marginal and independent of the primary treatment. It is presumable that not DAs per se, but rather the control of hyperprolactinemia plays a role in patients’ metabolic profile alterations.