Automated Liver Lesion Detection in 68Ga DOTATATE PET / CT Using a Deep Fully Convolutional Neural Network
Abstract Purpose: Gastroenteropancreatic neuroendocrine tumors most commonly metastasize to the liver, however, high normal background 68Ga-DOTATATE activity, and high image noise make metastatic lesions difficult to detect. The purpose of this study is to develop a rapid, automated, and highly specific method to identify 68Ga-DOTATATE PET/CT hepatic lesions using a 2D U-Net convolutional neural network. Methods: 68Ga-DOTATATE PET/CT patient studies (n=125; 57 with 68Ga-DOTATATE hepatic lesions and 68 without) were physician annotated using a modified PERCIST threshold, and boundary definition by gradient edge detection. The 2D U-Net was trained independently five times for 100,000 iterations using a linear combination of binary cross entropy and dice losses with a stochastic gradient descent algorithm. Performance metrics included: positive predictive value (PPV), sensitivity, F1 score and area under the precision-recall curve (PR-AUC). Five different pixel area thresholds were used to filter noisy predictions.Results: A total of 233 lesions were annotated with each abnormal study containing a mean of 4 ± 2.75 lesions. A pixel filter of 20 produced the highest mean PPV 0.94±0.01. A pixel filter of 5 produced the highest mean sensitivity 0.74±0.02. The highest mean F1 score 0.79±0.01 was produced with a 20 pixel filter. The highest mean PR-AUC 0.73±0.03 was produced with a 15 pixel filter.Conclusion: Deep neural networks can automatically detect hepatic lesions in 68Ga-DOTATATE PET. Ongoing improvements in data annotation methods, increasing sample sizes, and training methods are anticipated to further improve detection performance.