25 Background: [F18]DCFPyL (PyL) is a PSMA targeted imaging agent for prostate cancer. Independent of manual feature selection, a deep learning algorithm might offer additional insight into the disease biology. We explored the performance of a deep learning algorithm on PyL images of the primary tumor to predict co-existing distant metastases. Methods: 74 veterans with high risk primary prostate cancer tumors were imaged with both PyL PSMA PET/CT and conventional imaging (bone scan and CT or MRI of the abdomen/pelvis). 26% were confirmed with metastatic disease (M1) by conventional imaging. The PyL images of the primary tumor were analyzed with EXINI’s PyL-AI algorithm. Location of the prostate was defined on low dose CT via automatic segmentation using a deep convolutional network. The segmentations were used to map the PyL PET image of the prostate. The image based PyL-AI model was made up of a Conv3D layer of 4 kernels, a Conv3D layer of 8 kernels, a dense layer of 64 nodes followed by a final dense layer with 2 nodes. The model training was performed on the images using 5-fold cross validation with non-overlapping validation sets. The test predictions were compared with ground truth (M1); the area under ROC curve (AUC) was computed to determine the performance of the model in predicting the presence of distant metastases. A logistical regression model from baseline clinicopathologic features of the primary tumor (baseline PSA, biopsy gleason score, percent cores positive, T stage) was created as a comparator. Results: The logistical regression model using clinicopathologic features had an AUC of 0.71, while the PyL-AI model based on intra-prostatic PyL Images alone had an AUC of 0.81 for prediction of metastatic disease as defined by conventional imaging. Adding clinical parameters in the image based PyL-AI model incrementally increased the AUC to 0.82. Conclusions: The image based PyL-AI deep learning model demonstrates a higher predictive accuracy over the logistic model using classical clinicopathologic features. The study is hypothesis generating observation that needs prospective validation in an independent data set.
Deep Learning Algorithm Trained on Brain Magnetic Resonance Images and Clinical Data to Predict Motor Outcomes of Patients With Corona Radiata Infarct
