Pituitary Tumors in the Computational Era, Exploring Novel Approaches to Diagnosis, and Outcome Prediction with Machine Learning

Soldozy, Sauson; Farzad, Faraz; Young, Steven D.; Yağmurlu, Kaan; Norat, Pedro; Sokolowski, Jennifer D.; Park, Min; Jane, John A.; Syed, Hasan R.

doi:10.1016/j.wneu.2020.07.104

Cited by 7 publications

(5 citation statements)

References 31 publications

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“…Twelve articles did not focus on ML techniques [ 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 ]. Eight articles were not original reports but reviews or editorials [ 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 ]. Three articles used semi-automatic segmentation techniques [ 96 , 97 , 98 ].…”

Section: Resultsmentioning

confidence: 99%

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Windisch

Koechli

Rogers

et al. 2022

Cancers

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Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusion: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Windisch

Koechli

Rogers

et al. 2022

Cancers

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“…ML algorithms can be particularly helpful in clinical practice for issues where there are unclear predictive risk factors, such as for outcomes after TSS. Several papers have been published recently using ML to predict outcomes in TSS [10,11,12]. The application of ML to predictive modeling in TSS is novel, with a small but significant number of papers being published in recent years.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Several papers have been published recently using ML to predict outcomes in TSS. 10 11 12 The application of ML to predictive modeling in TSS is novel, with a small but significant number of papers being published in recent years. In this present systematic review, we compile the known research on the application of ML for TSS, and we investigate the features used by these ML algorithms to predict postoperative outcomes, looking at which are deemed most predictive of poor postoperative outcomes.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Smith

et al. 2022

J Neurol Surg B Skull Base

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The purpose of this analysis is to assess the use of machine learning (ML) algorithms in the prediction of post-operative outcomes, including complications, recurrence, and death in transsphenoidal surgery. Following 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 12th, 2021. We identified 13 studies enrolling 5048 patients. We extracted the general characteristics of each study; the sensitivity, specificity, AUC of the ML models developed as well as the features identified as important by the ML models. We identified 12 studies with 5048 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 .7, which indicates clinical utility. ML algorithms have the potential to predict post-operative outcomes of transsphenoidal surgery and can improve patient care. Ensemble algorithms and neural networks were often top performers when compared to other ML algorithms. Biochemical and pre-operative 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.

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“…Patients could benefit from noninvasive identification processes to clarify tumor characteristics, such as recurrence and remission, therapeutic effect, tumor aggressiveness, drug adverse‐reactions, and gross total resection. However, the standard statistical methods and normative data‐mining process require attention (Soldozy et al, 2020).…”

Section: Current Status Of Multiomics Studies Of Pasmentioning

confidence: 99%

The use of mass spectrometry in a proteome‐centered multiomics study of human pituitary adenomas

Desiderio

Zhan

2021

Mass Spectrometry Reviews

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A pituitary adenoma (PA) is a common intracranial neoplasm, and is a complex, chronic, and whole-body disease with multicausing factors, multiprocesses, and multiconsequences. It is very difficult to clarify molecular mechanism and treat PAs from the single-factor strategy model. The rapid development of multiomics and systems biology changed the paradigms from a traditional single-factor strategy to a multiparameter systematic strategy for effective management of PAs.A series of molecular alterations at the genome, transcriptome, proteome, peptidome, metabolome, and radiome levels are involved in pituitary tumorigenesis, and mutually associate into a complex molecular network system. Also, the center of multiomics is moving from structural genomics to phenomics, including proteomics and metabolomics in the medical sciences. Mass spectrometry (MS) has been extensively used in phenomics studies of human PAs to clarify molecular mechanisms, and to discover biomarkers and therapeutic targets/drugs. MS-based

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Pituitary Tumors in the Computational Era, Exploring Novel Approaches to Diagnosis, and Outcome Prediction with Machine Learning

Cited by 7 publications

References 31 publications

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

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

The use of mass spectrometry in a proteome‐centered multiomics study of human pituitary adenomas

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