The Role of Machine Learning and Radiomics for Treatment Response Prediction in Idiopathic Normal Pressure Hydrocephalus

Abstract

IntroductionVentricular shunting remains the standard of care for patients with idiopathic normal pressure hydrocephalus (iNPH); however, not all patients benefit from the shunting. Prediction of response in advance can result in improved patient selection for ventricular shunting. This study aims to develop a machine learning predictive model for treatment response after shunt placement using the clinical and radiomics features. MethodsIn this retrospective pilot study, the medical records of iNPH patients who underwent ventricular shunting were evaluated. In each patient, the "idiopathic normal pressure hydrocephalus grading scale" (iNPHGS) and a "Modified Rankin Scale" were calculated before and after surgery. The subsequent treatment response was calculated as the difference between the iNPHGS scores before and after surgery. iNPHGS score reduction of two or more than two were considered as treatment response. The presurgical MRI scans were evaluated by radiologists, the ventricular systems were segmented on the T2-weighted images, and the radiomics features were extracted from the segmented ventricular system. Using Orange data mining open-source platform, different machine learning models were then developed based on the presurgical clinical features and the selected radiomics features to predict treatment response after shunt placement. ResultsAfter the implementation of the inclusion criteria, 78 patients were included in this study. One hundred twenty radiomics features were extracted, and the 12 best predictive radiomics features were selected. Using only clinical data (iNPHGS and Modified Rankin Scale), the random forest model achieved the best performance in treatment prediction with an area under the curve (AUC) of 0.71. Adding the Radiomics analysis to the clinical data improved the prediction performance, with the support vector machine (SVM) achieving the highest rank in treatment prediction with an AUC of 0.8. Adding age and sex to the analysis did not improve the prediction. ConclusionUsing machine learning models for treatment response prediction in patients with iNPH is feasible with acceptable accuracy. Adding the Radiomics analysis to the clinical features can further improve the predictive performance. SVM is likely the best model for this task.

Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis

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Characterization of patients with idiopathic normal pressure hydrocephalus using natural language processing within an electronic healthcare record system

“…There is growing recognition of the utility of big data and AI in the detection of neurological disorders; however, work in iNPH to date has focused on automated detection using AI-driven radiomics. 11,12 Irie et al demonstrated the effective use of deep learning-enabled analysis of MR images acquired in patients with iNPH or Alzheimer's disease and healthy controls. They demonstrated a high sensitivity and specificity for iNPH (each 91%), which is not significantly different from the sensitivity and specificity found during examination by a radiologist.…”

“…9,10 Several studies have explored the use of artificial intelligence (AI) in identifying features of iNPH via diagnostic imaging and distinguishing them from mimicking disorders in patients; however, to our knowledge, no group has attempted to detect patients with iNPH using AI-driven analysis of written medical records. 11,12 Natural language processing (NLP) is a branch of AI that involves training machines to analyze and interpret unstructured written information within its intended context. 13 Within healthcare, NLP is particularly relevant to the interpretation and amalgamation of information from clinical notes, masses of which are stored as unstructured entries within electronic health record (EHR) systems.…”