An explainable machine learning based prediction model for Alzheimer's disease in China longitudinal aging study.

Alzheimer's disease (AD) is the most common cause of dementia. Accurate prediction and diagnosis of AD and its prodromal stage, i.e., mild cognitive impairment (MCI), is essential for the possible delay and early treatment for the disease. In this paper, we adopt the data from the China Longitudinal Aging Study (CLAS), which was launched in 2011, and includes a joint effort of 15 institutions all over the country. Four thousand four hundred and eleven people who are at least 60 years old participated in the project, where 3,514 people completed the baseline survey. The survey collected data including demographic information, daily lifestyle, medical history, and routine physical examination. In particular, we employ ensemble learning and feature selection methods to develop an explainable prediction model for AD and MCI. Five feature selection methods and nine machine learning classifiers are applied for comparison to find the most dominant features on AD/MCI prediction. The resulting model achieves accuracy of 89.2%, sensitivity of 87.7%, and specificity of 90.7% for MCI prediction, and accuracy of 99.2%, sensitivity of 99.7%, and specificity of 98.7% for AD prediction. We further utilize the SHapley Additive exPlanations (SHAP) algorithm to visualize the specific contribution of each feature to AD/MCI prediction at both global and individual levels. Consequently, our model not only provides the prediction outcome, but also helps to understand the relationship between lifestyle/physical disease history and cognitive function, and enables clinicians to make appropriate recommendations for the elderly. Therefore, our approach provides a new perspective for the design of a computer-aided diagnosis system for AD and MCI, and has potential high clinical application value.

Prediction of intraoperative cerebrospinal fluid leaks in endoscopic endonasal transsphenoidal pituitary surgery based on a deep neural network model trained with MRI images: a pilot study.

Objective: This study aimed to investigate the reliability of a deep neural network (DNN) model trained only on contrast-enhanced T1 (T1CE) images for predicting intraoperative cerebrospinal fluid (ioCSF) leaks in endoscopic transsphenoidal surgery (EETS). Methods: 396 pituitary adenoma (PA) cases were reviewed, only primary PAs with Hardy suprasellar Stages A, B, and C were included in this study. The T1CE images of these patients were collected, and sagittal and coronal T1CE slices were selected for training the DNN model. The model performance was evaluated and tested, and its interpretability was explored. Results: A total of 102 PA cases were enrolled in this study, 51 from the ioCSF leakage group, and 51 from the non-ioCSF leakage group. 306 sagittal and 306 coronal T1CE slices were collected as the original dataset, and data augmentation was applied before model training and testing. In the test dataset, the DNN model provided a single-slice prediction accuracy of 97.29%, a sensitivity of 98.25%, and a specificity of 96.35%. In clinical test, the accuracy of the DNN model in predicting ioCSF leaks in patients reached 84.6%. The feature maps of the model were visualized and the regions of interest for prediction were the tumor roof and suprasellar region. Conclusion: In this study, the DNN model could predict ioCSF leaks based on preoperative T1CE images, especially in PAs in Hardy Stages A, B, and C. The region of interest in the model prediction-making process is similar to that of humans. DNN models trained with preoperative MRI images may provide a novel tool for predicting ioCSF leak risk for PA patients.

Deep Learning model-based approach for preoperative prediction of Ki67 labeling index status in a noninvasive way using magnetic resonance images: A single-center study.

OBJECTIVES: Ki67 is an important biomarker of pituitary adenoma (PA) aggressiveness. In this study, PA invasion of surrounding structures is investigated and deep learning (DL) models are established for preoperative prediction of Ki67 labeling index (Ki67LI) status using conventional magnetic resonance (MR) images. METHODS: We reviewed 362 consecutive patients with PAs who underwent endoscopic transsphenoidal surgery, of which 246 patients with primary PA are selected for PA invasion analysis. MRI data from 234 of these PA patients are collected to develop DL models to predict Ki67LI status, and DL models were tested on 27 PA patients in the clinical setting. RESULTS: PA invasion is observed in 46.8% of cases in the Ki67 ≥ 3% group and 33.3% of cases in the Ki67 < 3% group. Three deep-learning models are developed using contrast-enhanced T1-weighted images (ceT1WI), T2-weighted images (T2WI), and multimodal images (ceT1WI+T2WI), respectively. On the validation dataset, the prediction accuracy of the ceT1WI model, T2WI model, and multimodal model were 87.4%, 89.4%, and 89.2%, respectively. In the clinical test, 27 MR slices with the largest tumors from 27 PA patients were tested using the ceT1WI model, T2WI model, and multimodal model, the average accuracy of Ki67LI status prediction was 63%, 77.8%, and 70.4%, respectively. CONCLUSION: Preoperative prediction of PA Ki67LI status in a noninvasive way was realized with the DL model by using MRI. T2WI model outperformed the ceT1WI model and multimodal model. This end-to-end model-based approach only requires a single slice of T2WI to predict Ki67LI status and provides a new tool to help clinicians make better PA treatment decisions.