Electronic Resource
Interpretable Machine Learning-Based Prediction of Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Transsphenoidal Pituitary Surgery: A Pilot Study
| العنوان: | Interpretable Machine Learning-Based Prediction of Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Transsphenoidal Pituitary Surgery: A Pilot Study |
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| المؤلفون: | Mattogno, Pier Paolo, Caccavella, V. M., Giordano, M., D'Alessandris, Quintino Giorgio, Chiloiro, Sabrina, Tariciotti, L., Olivi, Alessandro, Lauretti, Liverana, Mattogno P. P., D'Alessandris Q. G. (ORCID:0000-0002-2953-9291), Chiloiro S. (ORCID:0000-0001-9241-2392), Olivi A. (ORCID:0000-0002-4489-7564), Lauretti L. (ORCID:0000-0002-6463-055X) |
| بيانات النشر: | Thieme Medical Publishers, Inc. 2022 |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | Purpose Transsphenoidal surgery (TSS) for pituitary adenomas can be complicated by the occurrence of intraoperative cerebrospinal fluid (CSF) leakage (IOL). IOL significantly affects the course of surgery predisposing to the development of postoperative CSF leakage, a major source of morbidity and mortality in the postoperative period. The authors trained and internally validated the Random Forest (RF) prediction model to preoperatively identify patients at high risk for IOL. A locally interpretable model-agnostic explanations (LIME) algorithm is employed to elucidate the main drivers behind each machine learning (ML) model prediction. Methods The data of 210 patients who underwent TSS were collected; first, risk factors for IOL were identified via conventional statistical methods (multivariable logistic regression). Then, the authors trained, optimized, and audited a RF prediction model. Results IOL reported in 45 patients (21.5%). The recursive feature selection algorithm identified the following variables as the most significant determinants of IOL: Knosp's grade, sellar Hardy's grade, suprasellar Hardy's grade, tumor diameter (on X, Y, and Z axes), intercarotid distance, and secreting status (nonfunctioning and growth hormone [GH] secreting). Leveraging the predictive values of these variables, the RF prediction model achieved an area under the curve (AUC) of 0.83 (95% confidence interval [CI]: 0.78; 0.86), significantly outperforming the multivariable logistic regression model (AUC = 0.63). Conclusion A RF model that reliably identifies patients at risk for IOL was successfully trained and internally validated. ML-based prediction models can predict events that were previously judged nearly unpredictable; their deployment in clinical practice may result in improved patient care and reduced postoperative morbidity and healthcare costs. |
| مصطلحات الفهرس: | cerebrospinal fluid leak, machine learning, pituitary adenoma, pituitary surgery, random forest, transsphenoidal surgery, Settore MED/27 - NEUROCHIRURGIA, info:eu-repo/semantics/article |
| URL: | info:eu-repo/semantics/altIdentifier/wos/WOS:000742889500002 volume:83 issue:5 firstpage:485 lastpage:495 numberofpages:11 issueyear:2022 journal:JOURNAL OF NEUROLOGICAL SURGERY. PART B, SKULL BASE |
| الاتاحة: | Open access content. Open access content |
| ملاحظة: | English |
| Other Numbers: | SYC oai:publicatt.unicatt.it:10807/218993 10.1055/s-0041-1740621 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85138922176 1355229475 |
| المصدر المساهم: | UNIV CATTOLICA DEL SACRO CUORE From OAIster®, provided by the OCLC Cooperative. |
| رقم الانضمام: | edsoai.on1355229475 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |