03232nas a2200481   4500008004100000022001400041245011400055210006900169260001300238300001200251490000800263520183300271653001002104653000902114653001002123653002002133653001102153653001102164653001102175653002102186653003102207653000902238653001602247653002102263653001402284653000902298653002602307100002402333700002402357700001802381700002302399700001902422700003302441700001702474700001602491700001602507700001802523700002102541700002302562700002302585700002302608856011902631       2024                        eng d  a1573-737300aPredicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning.0 aPredicting postsurgical functional status in highgrade glioma wi  c2024 Aug  a175-1850 v1693 a<p><b>PURPOSE: </b>High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. The current standard of care includes surgical resection of the tumor, which can lead to functional and cognitive deficits. The aim of this study is to develop models capable of predicting functional outcomes in HGG patients before surgery, facilitating improved disease management and informed patient care.</p><p><b>METHODS: </b>Adult HGG patients (N = 102) from the neurosurgery brain tumor service at Washington University Medical Center were retrospectively recruited. All patients completed structural neuroimaging and resting state functional MRI prior to surgery. Demographics, measures of resting state network connectivity (FC), tumor location, and tumor volume were used to train a random forest classifier to predict functional outcomes based on Karnofsky Performance Status (KPS < 70, KPS ≥ 70).</p><p><b>RESULTS: </b>The models achieved a nested cross-validation accuracy of 94.1% and an AUC of 0.97 in classifying KPS. The strongest predictors identified by the model included FC between somatomotor, visual, auditory, and reward networks. Based on location, the relation of the tumor to dorsal attention, cingulo-opercular, and basal ganglia networks were strong predictors of KPS. Age was also a strong predictor. However, tumor volume was only a moderate predictor.</p><p><b>CONCLUSION: </b>The current work demonstrates the ability of machine learning to classify postoperative functional outcomes in HGG patients prior to surgery accurately. Our results suggest that both FC and the tumor's location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients.</p>10aAdult10aAged10aBrain10aBrain Neoplasms10aFemale10aGlioma10aHumans10amachine learning10aMagnetic Resonance Imaging10aMale10aMiddle Aged10aNeoplasm Grading10aPrognosis10aRest10aRetrospective Studies1 aLuckett, Patrick, H1 aOlufawo, Michael, O1 aPark, Ki, Yun1 aLamichhane, Bidhan1 aDierker, Donna1 aVerastegui, Gabriel, Trevino1 aLee, John, J1 aYang, Peter1 aKim, Albert1 aButt, Omar, H1 aChheda, Milan, G1 aSnyder, Abraham, Z1 aShimony, Joshua, S1 aLeuthardt, Eric, C  uhttps://neurotechcenter.org/publications/2024/predicting-post-surgical-functional-status-high-grade-glioma-resting