Neural network modeling for prediction of recurrence, progression, and hormonal non-remission in patients following resection of functional pituitary adenomas

Shahrestani, Shane; Cardinal, Tyler; Micko, Alexander; Strickland, Ben A.; Pangal, Dhiraj J.; Kugener, Guillaume; Weiss, Martin H.; Carmichael, John D.; Zada, Gabriel

doi:10.1007/s11102-021-01128-5

Cited by 17 publications

(25 citation statements)

References 42 publications

(38 reference statements)

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“…In recent years, research on clinical event prediction using machine learning algorithms has been actively conducted, and more complicated and reliable classification methods have become possible. 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 Liu et al 21 developed a machine learning triage system that can be used to detect severity of patients’ injuries using basic clinical information. Therefore, we hypothesized that machine learning approaches might make it possible to produce reliable prediction models even when using insufficient information that was collected on the scene.…”

Section: Introductionmentioning

confidence: 99%

A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms

et al. 2022

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Key Points Question Can machine learning algorithms be used to triage patients with head trauma according to their severity before transportation? Findings In this cohort study of 2123 patients with head trauma, a machine learning–based prediction model detected traumatic intracranial hemorrhage with a sensitivity of 74% and a specificity of 75% by using only the patient’s prehospital information. Meaning This study suggests that a machine learning algorithm can accurately stratify patients with head trauma according to severity in prehospital settings and may improve the prognosis of patients with severe traumatic head injury.

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Section: Introductionmentioning

confidence: 99%

A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms

et al. 2022

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“…Thirty three articles were excluded due to predicting only pathological features, e.g., grade (n = 16), or differentiating between tumor entities (n = 8) [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Thirty four articles were excluded due to predicting only clinical parameters, e.g., tumor consistency (n = 7), response/treatment outcome (n = 12) or brain/bone invasion (n = 4) [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , …”

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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“…The results of clinical evaluation in NFMAs by MRI-based CNN models are excellent, and most studies report accuracy up to 90% and AUC up to 0.80 ( 22 – 30 ). Compared with the previously reported studies, the application of DL for predicting clinical outcomes in NFMAs have not yet been reported, and no similar studies can be compared.…”

Section: Discussionmentioning

confidence: 99%

“…U-Net and derived DL models are currently considered as optimal for image segmentation ( 29 ). Recently, DL showed high accuracy in predicting suboptimal postoperative outcomes in functional pituitary adenomas ( 30 ). However, the DL gmodels for predicting tumor recurrence in NFMAs have not yet been reported.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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ObjectivesA subset of non-functioning pituitary macroadenomas (NFMAs) may exhibit early progression/recurrence (P/R) after tumor resection. The purpose of this study was to apply deep learning (DL) algorithms for prediction of P/R in NFMAs.MethodsFrom June 2009 to December 2019, 78 patients diagnosed with pathologically confirmed NFMAs, and who had undergone complete preoperative MRI and postoperative MRI follow-up for more than one year, were included. DL classifiers including multi-layer perceptron (MLP) and convolutional neural network (CNN) were used to build predictive models. Categorical and continuous clinical data were fed into the MLP model, and images of preoperative MRI (T2WI and contrast enhanced T1WI) were analyzed by the CNN model. MLP, CNN and multimodal CNN-MLP architectures were performed to predict P/R in NFMAs.ResultsForty-two (42/78, 53.8%) patients exhibited P/R after surgery. The median follow-up time was 42 months, and the median time to P/R was 25 months. As compared with CNN using MRI (accuracy 83%, precision 87%, and AUC 0.84) or MLP using clinical data (accuracy 73%, precision 73%, and AUC 0.73) alone, the multimodal CNN-MLP model using both clinical and MRI features showed the best performance for prediction of P/R in NFMAs, with accuracy 83%, precision 90%, and AUC 0.85.ConclusionsDL architecture incorporating clinical and MRI features performs well to predict P/R in NFMAs. Pending more studies to support the findings, the results of this study may provide valuable information for NFMAs treatment planning.

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Neural network modeling for prediction of recurrence, progression, and hormonal non-remission in patients following resection of functional pituitary adenomas

Cited by 17 publications

References 42 publications

A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms

A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms

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

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

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