Machine learning–based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming?

Zoli, Matteo; Staartjes, Victor E.; Guaraldi, Federica; Friso, Filippo; Rustici, Arianna; Asioli, Sofia; Sollini, Giacomo; Pasquini, Ernesto; Regli, Luca; Serra, Carlo; Mazzatenta, Diego

doi:10.3171/2020.3.focus2060

Cited by 26 publications

(36 citation statements)

References 59 publications

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“…This recommendation may be extended to incorporate the application of high performing predictive models to forecast and optimise future QoL. The utility of appropriately developed supervised learning-based decision support systems for neurosurgeons and their patients is becoming clearer [ 14 , 16 , 25 , 62 ].…”

Section: Discussionmentioning

confidence: 99%

“…Supervised learning techniques have demonstrated efficacy across a wide range of application domains and the application of machine learning to neurosurgery is growing rapidly [ 14 , 18 , 19 ]. With regard to skull base surgery, supervised learning has been applied to predict early postoperative outcomes [ 20 ], hyponatremia [ 21 ], the risk of experiencing intraoperative cerebrospinal fluid (CSF) leaks [ 22 ], remission after surgery [ 23 , 24 ] and long-term postoperative control of Cushing’s disease [ 25 ]. It has been used to classify adenoma subtypes using magnetic resonance imaging data [ 26 ] and predict radiotherapeutic response in patients with acromegaly [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

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Predictors of improvement in quality of life at 12-month follow-up in patients undergoing anterior endoscopic skull base surgery

et al. 2022

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Background Patients with pituitary lesions experience decrements in quality of life (QoL) and treatment aims to arrest or improve QoL decline. Objective To detect associations with QoL in trans-nasal endoscopic skull base surgery patients and train supervised learning classifiers to predict QoL improvement at 12 months. Methods A supervised learning analysis of a prospective multi-institutional dataset (451 patients) was conducted. QoL was measured using the anterior skull base surgery questionnaire (ASBS). Factors associated with QoL at baseline and at 12-month follow-up were identified using multivariate logistic regression. Multiple supervised learning models were trained to predict postoperative QoL improvement with five-fold cross-validation. Results ASBS at 12-month follow-up was significantly higher (132.19,SD = 24.87) than preoperative ASBS (121.87,SD = 25.72,p<0.05). High preoperative scores were significantly associated with institution, diabetes and lesions at the planum sphenoidale / tuberculum sella site. Patients with diabetes were five times less likely to report high preoperative QoL. Low preoperative QoL was significantly associated with female gender, a vision-related presentation, diabetes, secreting adenoma and the cavernous sinus site. Top quartile change in postoperative QoL at 12-month follow-up was negatively associated with baseline hypercholesterolemia, acromegaly and intraoperative CSF leak. Positive associations were detected for lesions at the sphenoid sinus site and deficient preoperative endocrine function. AdaBoost, logistic regression and neural network classifiers yielded the strongest predictive performance. Conclusion It was possible to predict postoperative positive change in QoL at 12-month follow-up using perioperative data. Further development and implementation of these models may facilitate improvements in informed consent, treatment decision-making and patient QoL.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictors of improvement in quality of life at 12-month follow-up in patients undergoing anterior endoscopic skull base surgery

et al. 2022

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“…TSS is the first-line treatment for patients with CD; however, surgical outcomes are usually the most difficult to predict preoperatively. Zoli and colleagues ( 23 ) trained and internally validated robust models using ML algorithms to make accurate preoperative surgical outcome predictions for CD patients. Zhang ( 24 ) also developed a readily available ML-based model for the preoperative prediction of immediate remission in patients with histology-positive CD.…”

Section: Magnetic Resonance Imaging-based Radiomics and ML In Pasmentioning

confidence: 99%

The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas

et al. 2021

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Pituitary adenomas (PAs) are a group of tumors with complex and heterogeneous clinical manifestations. Early accurate diagnosis, individualized management, and precise prediction of the treatment response and prognosis of patients with PA are urgently needed. Artificial intelligence (AI) and machine learning (ML) have garnered increasing attention to quantitatively analyze complex medical data to improve individualized care for patients with PAs. Therefore, we critically examined the current use of AI and ML in the management of patients with PAs, and we propose improvements for future uses of AI and ML in patients with PAs. AI and ML can automatically extract many quantitative features based on massive medical data; moreover, related diagnosis and prediction models can be developed through quantitative analysis. Previous studies have suggested that AI and ML have wide applications in early accurate diagnosis; individualized treatment; predicting the response to treatments, including surgery, medications, and radiotherapy; and predicting the outcomes of patients with PAs. In addition, facial imaging-based AI and ML, pathological picture-based AI and ML, and surgical microscopic video-based AI and ML have also been reported to be useful in assisting the management of patients with PAs. In conclusion, the current use of AI and ML models has the potential to assist doctors and patients in making crucial surgical decisions by providing an accurate diagnosis, response to treatment, and prognosis of PAs. These AI and ML models can improve the quality and safety of medical services for patients with PAs and reduce the complication rates of neurosurgery. Further work is needed to obtain more reliable algorithms with high accuracy, sensitivity, and specificity for the management of PA patients.

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“…Classification of CS using gene expression data of tumor tissues has been demonstrated [14]. The use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing disease (CD) has been studied [15]. Another study has aimed to identify facial anomalies associated with endocrinal disorders including CS using ML approach to facilitate the process of diagnosis and follow-up [16].…”

Section: A Related Workmentioning

confidence: 99%

Machine Learning Models for Classification of Cushing's Syndrome Using Retrospective Data

Isci

Kalender

Bayraktar

et al. 2021

IEEE J. Biomed. Health Inform.

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Accurate classification of Cushing's Syndrome (CS) plays a critical role in providing the early and correct diagnosis of CS that may facilitate treatment and improve patient outcomes. Diagnosis of CS is a complex process, which requires careful and concurrent interpretation of signs and symptoms, multiple biochemical test results, and findings of medical imaging by physicians with a high degree of specialty and knowledge to make correct judgments. In this article, we explore the state of the art machine learning algorithms to demonstrate their potential as a clinical decision support system to analyze and classify CS to facilitate the diagnosis, prognosis, and treatment of CS. Prominent algorithms are compared using nested cross-validation and various class comparison strategies including multiclass, one vs. all, and one vs. one binary classification. Our findings show that Random Forest (RF) algorithm is most suitable for the classification of CS. We demonstrate that the proposed approach can classify CS with an average accuracy of 92% and an average F1 score of 91.5%, depending on the class comparison strategy and selected features. RF-based one vs. all binary classification model achieves sensitivity of 97.6%, precision of 91.1%, and specificity of 87.1% to discriminate CS from non-CS on the test dataset. RF-based multiclass classification model achieves average per class sensitivity of 91.8%, average per class specificity of 97.1%, and average per class precision of 92.1% to classify different subtypes of CS on the test dataset. Clinical performance evaluation suggests that the developed models can help improve physicians' judgment in diagnosing CS.

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Machine learning–based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming?

Cited by 26 publications

References 59 publications

Predictors of improvement in quality of life at 12-month follow-up in patients undergoing anterior endoscopic skull base surgery

Predictors of improvement in quality of life at 12-month follow-up in patients undergoing anterior endoscopic skull base surgery

The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas

Machine Learning Models for Classification of Cushing's Syndrome Using Retrospective Data

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