Pre-operative MRI predictors of hormonal remission status post pituitary adenoma resection

Predictors of Durable Remission After Successful Surgery for Cushing Disease: Results From the Multicenter RAPID Registry

BACKGROUND AND OBJECTIVE

Cushing disease (CD) affects mortality and quality of life along with limited long-term remission, underscoring the need to better identify recurrence risk. The identification of surgical or imaging predictors for CD remission after transsphenoidal surgery has yielded some inconsistent results and has been limited by single-center, single-surgeon, or meta-analyses studies. We sought to evaluate the multicenter Registry of Adenomas of the Pituitary and Related Disorders (RAPID) database of academic US pituitary centers to assess whether robust nonhormonal recurrence predictors could be elucidated.

METHODS

Patients with treated CD from 2011 to 2023 were included. The perioperative and long-term characteristics of CD patients with and without recurrence were assessed using univariable and multivariable analyses.

RESULTS

Of 383 patients with CD from 26 surgeons achieving postoperative remission, 288 (75.2%) maintained remission at last follow-up while 95 (24.8%) showed recurrence (median time to recurrence 9.99 ± 1.34 years). Patients with recurrence required longer postoperative hospital stays (5 ± 3 vs 4 ± 2 days, P = .002), had larger average tumor volumes (1.76 ± 2.53 cm3 vs 0.49 ± 1.17 cm3, P = .0001), and more often previously failed prior treatment (31.1% vs 14.9%, P = .001) mostly being prior surgery. Multivariable hazard prediction models for tumor recurrence found younger age (odds ratio [OR] = 0.95, P = .002) and Knosp grade of 0 (OR = 0.09, reference Knosp grade 4, P = .03) to be protective against recurrence. Comparison of Knosp grade 0 to 2 vs 3 to 4 showed that lower grades had reduced risk of recurrence (OR = 0.27, P = .04). Other factors such as length of stay, surgeon experience, prior tumor treatment, and Knosp grades 1, 2, or 3 failed to reach levels of statistical significance in multivariable analysis.

CONCLUSION

This multicenter study centers suggests that the strongest predictors of recurrence include tumor size/invasion and age. This insight can help with patient counseling and prognostication. Long-term follow-up is necessary for patients, and early treatment of small tumors may improve outcomes.

The State of Machine Learning in Outcomes Prediction of Transsphenoidal Surgery: A Systematic Review

The purpose of this analysis is to assess the use of machine learning (ML) algorithms in the prediction of postoperative outcomes, including complications, recurrence, and death in transsphenoidal surgery. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically reviewed all papers that used at least one ML algorithm to predict outcomes after transsphenoidal surgery. We searched Scopus, PubMed, and Web of Science databases for studies published prior to May 12, 2021. We identified 13 studies enrolling 5,048 patients. We extracted the general characteristics of each study; the sensitivity, specificity, area under the curve (AUC) of the ML models developed as well as the features identified as important by the ML models. We identified 12 studies with 5,048 patients that included ML algorithms for adenomas, three with 1807 patients specifically for acromegaly, and five with 2105 patients specifically for Cushing's disease. Nearly all were single-institution studies. The studies used a heterogeneous mix of ML algorithms and features to build predictive models. All papers reported an AUC greater than 0.7, which indicates clinical utility. ML algorithms have the potential to predict postoperative outcomes of transsphenoidal surgery and can improve patient care. Ensemble algorithms and neural networks were often top performers when compared with other ML algorithms. Biochemical and preoperative features were most likely to be selected as important by ML models. Inexplicability remains a challenge, but algorithms such as local interpretable model-agnostic explanation or Shapley value can increase explainability of ML algorithms. Our analysis shows that ML algorithms have the potential to greatly assist surgeons in clinical decision making.

Development and external validation of clinical prediction models for pituitary surgery

Introduction

Gross total resection (GTR), Biochemical Remission (BR) and restitution of a priorly disrupted hypothalamus pituitary axis (new improvement, IMP) are important factors in pituitary adenoma (PA) resection surgery. Prediction of these metrics using simple and preoperatively available data might help improve patient care and contribute to a more personalized medicine.

Research question

This study aims to develop machine learning models predicting GTR, BR, and IMP in PA resection surgery, using preoperatively available data.

Material and methods

With data from patients undergoing endoscopic transsphenoidal surgery for PAs machine learning models for prediction of GTR, BR and IMP were developed and externally validated. Development was carried out on a registry from Bologna, Italy while external validation was conducted using patient data from Zurich, Switzerland.

Results

The model development cohort consisted of 1203 patients. GTR was achieved in 207 (17.2%, 945 (78.6%) missing), BR in 173 (14.4%, 992 (82.5%) missing) and IMP in 208 (17.3%, 167 (13.9%) missing) cases. In the external validation cohort 206 patients were included and GTR was achieved in 121 (58.7%, 32 (15.5%) missing), BR in 46 (22.3%, 145 (70.4%) missing) and IMP in 42 (20.4%, 7 (3.4%) missing) cases. The AUC at external validation amounted to 0.72 (95% CI: 0.63-0.80) for GTR, 0.69 (0.52-0.83) for BR, as well as 0.82 (0.76-0.89) for IMP.

Discussion and conclusion

All models showed adequate generalizability, performing similarly in training and external validation, confirming the possible potentials of machine learning in helping to adapt surgical therapy to the individual patient.

Prognostic Models in Growth-Hormone- and Prolactin-Secreting Pituitary Neuroendocrine Tumors: A Systematic Review

Growth-hormone (GH)- and prolactin (PRL)-secreting PitNETs (pituitary neuroendocrine tumors) are divided into multiple histological subtypes, which determine their clinical and biological variable behavior. Proliferation markers alone have a questionable degree of prediction, so we try to identify validated prognostic models as accurately as possible. (1) Background: The data available so far show that the use of staging and clinical-pathological classification of PitNETs, along with imaging, are useful in predicting the evolution of these tumors. So far, there is no consensus for certain markers that could predict tumor evolution. The application of the WHO (World Health Organisation) classification in practice needs to be further evaluated and validated. (2) Methods: We performed the CRD42023401959 protocol in Prospero with a systematic literature search in PubMed and Web of Science databases and included original full-text articles (randomized control trials and clinical trials) from the last 10 years, published in English, and the search used the following keywords: (i) pituitary adenoma AND (prognosis OR outcome OR prediction), (ii) growth hormone pituitary adenoma AND (prognosis OR outcome OR prediction), (iii) prolactin pituitary adenoma AND (prognosis OR outcome OR prediction); (iv) mammosomatotroph adenoma AND (prognosis OR outcome OR prediction). (3) Results: Two researchers extracted the articles of interest and if any disagreements occurred in the selection process, these were settled by a third reviewer. The articles were then assessed using the ROBIS bias assessment and 75 articles were included. (4) Conclusions: the clinical-pathological classification along with factors such as GH, IGF-1, prolactin levels both preoperatively and postoperatively offer valuable information.

Usefulness of subtraction images for accurate diagnosis of pituitary microadenomas in dynamic contrast-enhanced magnetic resonance imaging

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (MRI) is the modality of choice for the diagnosis of pituitary microadenomas; however, it may be associated with a relatively high false-negative rate, especially in small lesions.

PURPOSE

To evaluate the usefulness of subtraction images for enhancing the visual detection of pituitary microadenomas.

MATERIAL AND METHODS

In total, 50 patients with clinically established diagnosis hyperprolactinemia, acromegaly, and Cushing's disease were enrolled. Ten patients referred for brain MRI for reasons other than pituitary abnormality were selected as control group. Routine dynamic MRI of the pituitary gland and obtained subtraction MRI scans were scrutinized separately on different sessions by an experienced radiologist blinded to the study design and patient's data. The investigator's opinion on the presence or absence of a lesion and lesion size were collected.

RESULTS

In patients with pituitary microadenoma, dynamic MRI images were reported positive in 42 (84%) patients and negative in 8 (16%). Subtraction images were described as positive in all patients (100%)-including all patients with negative dynamic MRI-and the difference was statistically significant (P=0.016). Undetected lesions on dynamic MRI had a mean size of 2.84 ± 1.79 mm (median= 2.20 mm, interquartile range=1.62-4.62 mm) and a significant inverse correlation was noted between lesion size and negative report of dynamic MRI (P=0.018). Brain MRI scans in the control group were reported negative for pituitary microadenoma in both dynamic contrast-enhanced and subtraction images.

CONCLUSION

Subtraction images can successfully identify all lesions detectable with conventional dynamic MRI as well as improving visualization of lesions undetected on dynamic MRI, especially in small lesions.

Radiological Knosp, Revised-Knosp, and Hardy–Wilson Classifications for the Prediction of Surgical Outcomes in the Endoscopic Endonasal Surgery of Pituitary Adenomas: Study of 228 Cases

Purpose

To evaluate which radiological classification, Knosp, revised-Knosp, or Hardy–Wilson classification, is better for the prediction of surgical outcomes in the endoscopic endonasal transsphenoidal (EET) surgery of pituitary adenomas (PAs).

Methods

This is a retrospective study of patients with PAs who underwent EET PA resection for the first time between January 2009 and December 2020. Radiological cavernous sinus invasiveness was defined as a Knosp or revised-Knosp grade >2 or a grade E in the Hardy–Wilson classification.

Results

A total of 228 patients with PAs were included. Cavernous sinus invasion was evident in 35.1% and suprasellar extension was evident in 74.6%. Overall, surgical cure was achieved in 64.3% of patients. Surgical cure was lower in invasive PAs than in non-invasive PAs (28.8% vs. 83.1%, p < 0.0001), and the risk of major complications was higher (13.8% vs. 3.4%, p = 0.003). The rate of surgical cure decreased as the grade of Knosp increased (p < 0.001), whereas the risk of complications increased (p < 0.001). Patients with Knosp 3B PAs tended to achieve surgical cure less commonly than Knosp 3A PAs (30.0% vs. 56.0%, p = 0.164). Similar results were observed based on the invasion and extension of Hardy–Wilson classification (stage A–C 83.1% vs. E 28.8% p < 0.0001, grade 0–II 81.1% vs. III–IV 59.7% p = 0.008). The Knosp classification offered the greatest diagnostic accuracy for the prediction of surgical cure (AUC 0.820), whereas the invasion Hardy–Wilson classification lacked utility for this purpose (AUC 0.654).

Conclusion

The Knosp classifications offer a good orientation for the estimation of surgical cure and the risk of complications in patients with PAs submitted to EET surgery. However, the invasion Hardy–Wilson scale lacks utility for this purpose.

Optimizing MR imaging for intraoperative image guidance in sellar pathologies

PURPOSE

With the advancement of extended endonasal approaches, the ability to surgically reach parasellar tumor extensions increase. The aim of the study was to propose an optimized imaging protocol for surgical guidance in the cavernous sinus (CS) for proper visualization structures at risk.

METHODS

Prospective case control analysis of 20 consecutive pituitary adenoma patients scheduled for endoscopic transnasal surgery. Assessment of the capability of three different MRI sequences (MPRAGE, VIBE, CISS) by 4 investigators to correctly visualize sellar and parasellar structures. Invasiveness and position of the normal pituitary gland were compared with the intraoperative findings.

RESULTS

The consensus between the 4 examiners to achieve the same results for all modalities was 40% for MPRAGE, 70% for VIBE and 60% for CISS sequences (p = 0.155). A consensus of Knosp Grade per patient was 80% for MPRAGE, 100% for VIBE and 90% for CISS (overall kappa 0.60). A higher Knosp Grade was found in MPRAGE sequences compared to the other sequences. Intraoperative status of invasiveness was correctly identified in 12/20 (60%) with MPRAGE, 19/20 (95%) with VIBE and 11/20 (55%) with CISS sequences. The position of the normal pituitary gland was most frequent evaluable in 15/20 (75%) and correctly identified in 12/15 (80%) cases.

CONCLUSION

Our data showed that VIBE sequences obtain the highest degree of consensus with intraoperative findings of invasiveness and position of the normal pituitary gland. VIBE sequences, due to their high spatial resolution and at the same time fast image acquisition could provide improved imaging for neuronavigation.

MRI texture analysis in acromegaly and its role in predicting response to somatostatin receptor ligands

PURPOSE

Given the paucity of reliable predictors of tumor recurrence, progression, or response to somatostatin receptor ligand (SRL) therapy in acromegaly, we attempted to determine whether preoperative MR image texture was predictive of these clinical outcomes. We also determined whether image texture could differentiate somatotroph adenomas from non-functioning pituitary adenomas (NFPAs).

METHODS

We performed a retrospective study of patients with acromegaly due to a macroadenoma who underwent transsphenoidal surgery at our institution between 2007 and 2015. Clinical data were extracted from electronic medical records. MRI texture analysis was performed on preoperative non-enhanced T1-weighted images using ImageJ (NIH). Logistic and Cox models were used to determine if image texture parameters predicted outcomes.

RESULTS

Eighty-nine patients had texture parameters measured, which were compared to that of NFPAs, while 64 of these patients had follow-up and were included in the remainder of analyses. Minimum pixel intensity, skewness, and kurtosis were significantly different in somatotroph adenomas versus NFPAs (area under the receiver operating characteristic curve, 0.7771, for kurtosis). Furthermore, those with a maximum pixel intensity above the median had an increased odds of IGF-I normalization on SRL therapy (OR 5.96, 95% CI 1.33-26.66), which persisted after adjusting for several potential predictors of response. Image texture did not predict tumor recurrence or progression.

CONCLUSION

Our data suggest that MRI texture analysis can distinguish NFPAs from somatotroph macroadenomas with good diagnostic accuracy and can predict normalization of IGF-I with SRL therapy.

Predictive modeling for pituitary adenomas: single center experience in 501 consecutive patients

BACKGROUND

Personalized postoperative management of patients with pituitary adenomas requires an early risk stratification system.

METHODS

We reviewed 501 cases operated between 10/27/2011 and 5/5/2016 by a single neurosurgeon. We determined biochemical remission and tumor resection at 3 months, and biochemical recurrence, tumor recurrence, radiation and reoperation during follow-up. We considered age, gender, tumor diameter, cavernous sinus invasion (CSI) by MRI, diagnostic category (clinical, biochemical and immunohistochemical), and proliferation markers in a Cox proportional hazards model. We built predictive models with the significant parameters and used Kaplan-Meier survival curves for time-dependent analyses.

RESULTS

The 501 cases comprised 141 functional and 360 nonfunctional adenomas. Tumor diameter, CSI, and ki-67 index predicted long-term events. Model 1 (CSI, diameter ≥ 2.9 cm and ki-67 > 3%) identified 18 (3.6%) adenomas and predicted persistent hypersecretory syndrome and residual tumor with 98.7% specificity (OR 8.6; CI 3.0-24.7). Model 2 (ki-67 > 3% and CSI) identified 48 (9.6%) adenomas and had 93.1% specificity (OR 3.3; CI 1.8-6.0). Model 3 (ki-67 > 3%, mitoses and p53, former "atypical" adenoma) identified 26 (5.2%) adenomas and had 96.0% specificity (OR 2.3; CI 1.0-5.0). Model 1 best predicted the long-term event-free survival and was strengthened when Knosp 3-4 CSI grades were used. Model 2 better identified the smaller adenomas at risk. Among the WHO 2017 special PA subtypes, patients with silent corticotroph adenoma had a lower event-free survival than ACTH-negative nonfunctional adenomas.

CONCLUSION

Use of CSI, ki-67 and tumor diameter in prediction models facilitates tailored surveillance and management of patients with pituitary adenomas.