Deep Learning for Prediction of Progression and Recurrence in Nonfunctioning Pituitary Macroadenomas: Combination of Clinical and MRI Features

MRI based radiomics nomogram for predict recurrence of non functioning pituitary macroadenomas post surgery

OBJECTIVE

To establish and validate a comprehensive predictive model combining clinical data and radiomics features to improve the accuracy of predicting recurrence within five years after surgery in patients with non-functioning pituitary macroadenomas (NFMA).

METHODS

This retrospective study included 292 NFMA patients who underwent surgery between January 2012 and January 2018, with an additional 123 patients as an external test set. Clinical, pathological, and conventional imaging features were collected and analyzed using univariate and multivariate logistic regression to identify independent risk factors for postoperative recurrence. Radiomic features were extracted from preoperative T1-weighted (T1WI), T2-weighted (T2WI), and T1-enhanced images using 3D Slicer software. A radiomics prediction model was developed, and a combined model integrating clinical and radiomics features was established. The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

The clinical model (Cli-score), radiomics model (Rad-score) and combined model were developed. The diagnostic performance of the clinical model in the external test set, showed an AUC of 0.757 (95%CI: 0.671-0.830), with SEN, SPE, and ACC of 82.5%, 59.04%, and 71.54%, respectively. The diagnostic performance of the radiomics model in the external test set showed an AUC of 0.835 (95% CI: 0.757-0.896), with 80%, 79.52% and 63.41% for SEN, SPE and ACC%, respectively. The diagnostic performance of the combined model in the external test set showed an AUC of 0.863 (95% CI: 0.790-0.919), with SEN, SPE, and ACC of 80%, 81.93%, and 68.30%, respectively. The calibration curve indicated good predictive performance, and DCA confirmed the high clinical utility of the combined model.

CONCLUSION

The combined model provides a more accurate prediction of NFMA recurrence. This model can guide postoperative follow-up strategies and aid in early initiation of adjuvant therapy for high-risk patients.

Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review

PURPOSE

Pituitary adenoma surgery is a complex procedure due to critical adjacent neurovascular structures, variations in size and extensions of the lesions, and potential hormonal imbalances. The integration of artificial intelligence (AI) and machine learning (ML) has demonstrated considerable potential in assisting neurosurgeons in decision-making, optimizing surgical outcomes, and providing real-time feedback. This scoping review comprehensively summarizes the current status of AI/ML technologies in pituitary adenoma surgery, highlighting their strengths and limitations.

METHODS

PubMed, Embase, Web of Science, and Scopus were searched following the PRISMA-ScR guidelines. Studies discussing the use of AI/ML in pituitary adenoma surgery were included. Eligible studies were grouped to analyze the different outcomes of interest of current AI/ML technologies.

RESULTS

Among the 2438 identified articles, 44 studies met the inclusion criteria, with a total of seventeen different algorithms utilized across all studies. Studies were divided into two groups based on their input type: clinicopathological and imaging input. The four main outcome variables evaluated in the studies included: outcome (remission, recurrence or progression, gross-total resection, vision improvement, and hormonal recovery), complications (CSF leak, readmission, hyponatremia, and hypopituitarism), cost, and adenoma-related factors (aggressiveness, consistency, and Ki-67 labeling) prediction. Three studies focusing on workflow analysis and real-time navigation were discussed separately.

CONCLUSION

AI/ML modeling holds promise for improving pituitary adenoma surgery by enhancing preoperative planning and optimizing surgical strategies. However, addressing challenges such as algorithm selection, performance evaluation, data heterogeneity, and ethics is essential to establish robust and reliable ML models that can revolutionize neurosurgical practice and benefit patients.

Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery

This chapter explores current artificial intelligence (AI), radiomics, and computational modeling applications in skull base surgery. AI advancements are providing opportunities to improve diagnostic accuracy, surgical planning, and postoperative care. Currently, computational models can assist in diagnosis, simulate surgical scenarios, and improve safety during surgical procedures by identifying critical structures. AI-powered technologies, such as liquid biopsy, machine learning, radiomic analysis, computer vision, and label-free optical imaging, aim to revolutionize skull base surgery. AI-driven advancements promise safer, more precise, and effective surgeries, improving patient outcomes and preoperative assessment.

Risk factors for recurrence of chronic subdural hematoma after surgical evacuation: a systematic review and meta-analysis

Chronic subdural hematoma (CSDH) is a common neurosurgical condition. Surgical evacuation has remained the primary treatment despite many advancements in the endovascular field. Regardless, recurrence requiring reoperation is commonly observed during the postoperative follow-up. Herein, we aimed to investigate risk factors for recurrence after surgical evacuation. A review of MEDLINE, EMBASE, Web of Science, and Scopus was conducted using the designed search string. Studies were reviewed based on the predefined eligibility criteria. Data regarding sixty potential risk factors along with operational information were extracted for analysis. A meta-analysis using the random-effect model was conducted, and each risk factor affecting the postoperative recurrence of CSDH was then evaluated and graded. A total of 198 records met the eligibility criteria. A total number of 8523 patients with recurrent CSDH and 56,096 with non-recurrent CSDH were included in the study. The recurrence rate after surgical evacuation was 12%. Fifteen preoperative, nine radiologic, four hematoma-related, and three operative and postoperative factors were associated with recurrence. Risk factors associated with recurrence after surgical evacuation are important in neurosurgical decision-making and treatment planning. Found risk factors in this study may be used as the basis for pre-operative risk assessment to choose patients who would benefit the most from surgical evacuation.