Predictive biomarkers of intra-ocular pressure decrease after cataract surgery associated with trabecular washout in patients with pseudo exfoliative glaucoma.

To investigate biomarkers of intra-ocular pressure (IOP) decrease after cataract surgery with trabecular washout in pseudo-exfoliative (PEX) glaucoma. A single-center observational prospective study in PEX glaucoma patients undergoing cataract surgery with trabecular washout (Goniowash) was performed from 2018 to 2021. Age, gender, visual acuity, IOP, endothelial cell count, central corneal thickness, medications, were collected over 16-month follow-up. Multivariable binomial regression models were implemented. 54 eyes (35 subjects) were included. Mean preoperative IOP (IOPBL) was 15.9 ± 3.5 mmHg. Postoperative IOP reduction was significant at 1-month and throughout follow-up (p < 0.01, respectively). IOPBL was a predictive biomarker inversely correlated to IOP decrease throughout follow-up (p < 0.001). At 1 and 12 months of follow-up, IOP decrease concerned 31 (57.4%) and 34 (63.0%) eyes with an average IOP decrease of 17.5% (from 17.6 ± 3.1 to 14.3 ± 2.2 mmHg) and 23.0% (from 17.7 ± 2.8 to 13.5 ± 2.6 mmHg), respectively. Performance (AUC) of IOPBL was 0.85 and 0.94 (p < 0.0001, respectively), with IOPBL threshold ≥ 15 mmHg for 82.1% and 96.8% sensitivity, 84.2% and 75.0% specificity, 1.84 and 3.91 IOP decrease odds-ratio, respectively. All PEX glaucoma patients with IOPBL greater than or equal to the average general population IOP were likely to achieve a significant sustainable postoperative IOP decrease.

Machine learning decision tree models for multiclass classification of common malignant brain tumors using perfusion and spectroscopy MRI data.

Background: To investigate the contribution of machine learning decision tree models applied to perfusion and spectroscopy MRI for multiclass classification of lymphomas, glioblastomas, and metastases, and then to bring out the underlying key pathophysiological processes involved in the hierarchization of the decision-making algorithms of the models. Methods: From 2013 to 2020, 180 consecutive patients with histopathologically proved lymphomas (n = 77), glioblastomas (n = 45), and metastases (n = 58) were included in machine learning analysis after undergoing MRI. The perfusion parameters (rCBVmax, PSRmax) and spectroscopic concentration ratios (lac/Cr, Cho/NAA, Cho/Cr, and lip/Cr) were applied to construct Classification and Regression Tree (CART) models for multiclass classification of these brain tumors. A 5-fold random cross validation was performed on the dataset. Results: The decision tree model thus constructed successfully classified all 3 tumor types with a performance (AUC) of 0.98 for PCNSLs, 0.98 for GBM and 1.00 for METs. The model accuracy was 0.96 with a RSquare of 0.887. Five rules of classifier combinations were extracted with a predicted probability from 0.907 to 0.989 for that end nodes of the decision tree for tumor multiclass classification. In hierarchical order of importance, the root node (Cho/NAA) in the decision tree algorithm was primarily based on the proliferative, infiltrative, and neuronal destructive characteristics of the tumor, the internal node (PSRmax), on tumor tissue capillary permeability characteristics, and the end node (Lac/Cr or Cho/Cr), on tumor energy glycolytic (Warburg effect), or on membrane lipid tumor metabolism. Conclusion: Our study shows potential implementation of machine learning decision tree model algorithms based on a hierarchical, convenient, and personalized use of perfusion and spectroscopy MRI data for multiclass classification of these brain tumors.