Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence

Development and validation of a predictive machine learning model for postoperative long-term diabetes insipidus following transsphenoidal surgery for sellar lesions

OBJECTIVE

Diabetes Insipidus (DI) is a common complication that occurs following transsphenoidal surgery for sellar lesions. DI is usually transient but can be permanent in select patients. Prior studies have described preoperative risk factors for developing postoperative DI. However, no predictive risk score has been created to risk stratify these patients.

METHODS

A single-center retrospective review from 2017 - 2022 was performed, reviewing all patients who underwent transsphenoidal surgery for resection of a sellar lesion. Longterm DI was defined as a patient who met DI criteria for at least six months and required desmopressin therapy. Baseline patient, operative, and radiographic characteristics were obtained. A machine learning method (Risk-SLIM) was utilized to create a risk stratification score to identify patients at high risk for DI.

RESULTS

In total, 252 patients were identified to have sellar lesions treated with transsphenoidal surgery. Of these, 27 (10.7 %) patients developed long-term DI and required desmopressin therapy. The DI after Transsphenoidal Surgery score (DITSS) was created with an area under the curve of 0.81 and a calibration error (CAL) error of 7.3 %. Predicative factors were tumor pathology, Tumor size, patient age, and endoscopic approach. The probability of developing DI requiring long-term desmopressin therapy ranged from < 1 % for a score of 0 and > 95 % for a score of 10 CONCLUSIONS: The DITSS model is a concise and accurate tool to assist in clinical decision-making for risk stratifying which patients undergoing transsphenoidal surgery for sellar lesions may go on to develop DI.

Identification of Prolactinoma in Pituitary Neuroendocrine Tumors Using Radiomics Analysis Based on Multiparameter MRI

This study aims to investigate the feasibility of preoperatively predicting histological subtypes of pituitary neuroendocrine tumors (PitNETs) using machine learning and radiomics based on multiparameter MRI. Patients with PitNETs from January 2016 to May 2022 were retrospectively enrolled from four medical centers. A cfVB-Net network was used to automatically segment PitNET multiparameter MRI. Radiomics features were extracted from the MRI, and the radiomics score (Radscore) of each patient was calculated. To predict histological subtypes, the Gaussian process (GP) machine learning classifier based on radiomics features was performed. Multi-classification (six-class histological subtype) and binary classification (PRL vs. non-PRL) GP model was constructed. Then, a clinical-radiomics nomogram combining clinical factors and Radscores was constructed using the multivariate logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic (ROC) curves. The PitNET auto-segmentation model eventually achieved the mean Dice similarity coefficient of 0.888 in 1206 patients (mean age 49.3 ± SD years, 52% female). In the multi-classification model, the GP of T2WI got the best area under the ROC curve (AUC), with 0.791, 0.801, and 0.711 in the training, validation, and external testing set, respectively. In the binary classification model, the GP of T2WI combined with CE T1WI demonstrated good performance, with AUC of 0.936, 0.882, and 0.791 in training, validation, and external testing sets, respectively. In the clinical-radiomics nomogram, Radscores and Hardy' grade were identified as predictors for PRL expression. Machine learning and radiomics analysis based on multiparameter MRI exhibited high efficiency and clinical application value in predicting the PitNET histological subtypes.

Role of artificial intelligence in brain tumour imaging

Artificial intelligence (AI) is a rapidly evolving field with many neuro-oncology applications. In this review, we discuss how AI can assist in brain tumour imaging, focusing on machine learning (ML) and deep learning (DL) techniques. We describe how AI can help in lesion detection, differential diagnosis, anatomic segmentation, molecular marker identification, prognostication, and pseudo-progression evaluation. We also cover AI applications in non-glioma brain tumours, such as brain metastasis, posterior fossa, and pituitary tumours. We highlight the challenges and limitations of AI implementation in radiology, such as data quality, standardization, and integration. Based on the findings in the aforementioned areas, we conclude that AI can potentially improve the diagnosis and treatment of brain tumours and provide a path towards personalized medicine and better patient outcomes.

Oral octreotide capsules for acromegaly treatment: application of clinical trial insights to real-world use

INTRODUCTION

Acromegaly is a rare endocrine disorder usually caused by a benign growth hormone‒secreting pituitary adenoma. Surgical adenoma resection is typically the first line of treatment, and medical therapy is used for patients with persistent disease following surgery, for adenoma recurrence, or for patients ineligible for, or declining, surgery. Approved somatostatin receptor ligands (SRLs) have been limited to injectable options, until recently. Oral octreotide capsules (OOC) are the first approved oral SRL for patients with acromegaly.

AREAS COVERED

We review published reports and provide case study examples demonstrating practical considerations on the use of OOC. Using two hypothetical case scenarios, we discuss current treatment patterns, breakthrough symptoms and quality of life (QoL), efficacy of SRLs, OOC dose titration, evaluation of OOC treatment response, and incidence and management of adverse events.

EXPERT OPINION

OOC are an option for patients with acromegaly including those who experience breakthrough symptoms, who have preference for oral therapies, or other reasons for declining injectable SRLs. OOC have been associated with improved patient-reported QoL measures compared with those reported for lanreotide and octreotide. Continued real-world experience will determine whether OOC, alone or in combination with other therapies, provides further advantages over current injectable acromegaly treatments.