Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing’s Disease

Zhang, Wentai; Sun, Mengke; Fan, Yanghua; Wang, He; Feng, Ming; Zhou, S. Kevin; Wang, Renzhi

doi:10.3389/fendo.2021.635795

Cited by 13 publications

(29 citation statements)

References 36 publications

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“…An intriguing observation is that tumor size was not a predictor of IR, which is inconsistent with previous studies (9,19,25,26). In our previous study (7), tumor size was strongly correlated with IR when two surgeons performed operations over several decades. However, in the present study, only MF performed the operation.…”

Section: Discussioncontrasting

confidence: 89%

“…In our previous study, we used several ML algorithms to build ML-based models to preoperatively predict IR (7). In that study, we only included structured data in the ML-based model, whereas in the present study, we introduced not only structured data into the models but also unstructured data.…”

Section: Discussionmentioning

confidence: 99%

“…In both the training and test datasets, 10-fold crossvalidation was performed. Then, a grid search was used to select the best hyperparameters, as discussed elsewhere (7).…”

Section: Algorithmsmentioning

confidence: 99%

“…Several studies have been conducted to investigate perioperative risk factors for the prediction of postoperative prognosis using both traditional biostatistical and machine learning (ML) methods (6)(7)(8)(9). ML is a computer-based method for data analysis based on the theory that there are patterns hidden in data, and it helps to predict the prognosis of diseases (10).…”

Section: Introductionmentioning

confidence: 99%

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Electronic Medical Records as Input to Predict Postoperative Immediate Remission of Cushing’s Disease: Application of Word Embedding

Zhang

Feng

et al. 2021

Front. Oncol.

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BackgroundNo existing machine learning (ML)-based models use free text from electronic medical records (EMR) as input to predict immediate remission (IR) of Cushing’s disease (CD) after transsphenoidal surgery.PurposeThe aim of the present study is to develop an ML-based model that uses EMR that include both structured features and free text as input to preoperatively predict IR after transsphenoidal surgery.MethodsA total of 419 patients with CD from Peking Union Medical College Hospital were enrolled between January 2014 and August 2020. The EMR of the patients were embedded and transformed into low-dimensional dense vectors that can be included in four ML-based models together with structured features. The area under the curve (AUC) of receiver operating characteristic curves was used to evaluate the performance of the models.ResultsThe overall remission rate of the 419 patients was 75.7%. From the results of logistic multivariate analysis, operation (p < 0.001), invasion of cavernous sinus from MRI (p = 0.046), and ACTH (p = 0.024) were strongly correlated with IR. The AUC values for the four ML-based models ranged from 0.686 to 0.793. The highest AUC value (0.793) was for logistic regression when 11 structured features and “individual conclusions of the case by doctor” were included.ConclusionAn ML-based model was developed using both structured and unstructured features (after being processed using a word embedding method) as input to preoperatively predict postoperative IR.

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Section: Discussioncontrasting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

“…In both the training and test datasets, 10-fold crossvalidation was performed. Then, a grid search was used to select the best hyperparameters, as discussed elsewhere (7).…”

Section: Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Electronic Medical Records as Input to Predict Postoperative Immediate Remission of Cushing’s Disease: Application of Word Embedding

Zhang

Feng

et al. 2021

Front. Oncol.

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“…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. After TSS, a subset of patients with CD do not achieve immediate remission but achieve remission without further postoperative therapy during long-term follow-up, which is defined as postoperative delayed remission ( 39 ).…”

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

Suero Molina,

Di Ieva

2024

Advances in Experimental Medicine and Biology

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Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing’s Disease

Cited by 13 publications

References 36 publications

Electronic Medical Records as Input to Predict Postoperative Immediate Remission of Cushing’s Disease: Application of Word Embedding

Electronic Medical Records as Input to Predict Postoperative Immediate Remission of Cushing’s Disease: Application of Word Embedding

The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas

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

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