Automated liver lesion detection in 68Ga DOTATATE PET/CT using a deep fully convolutional neural network

Abstract

Background 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 A retrospective study of 68Ga-DOTATATE PET/CT patient studies (n = 125; 57 with 68Ga-DOTATATE hepatic lesions and 68 without) was evaluated. The dataset was randomly divided into 75 studies for the training set (36 abnormal, 39 normal), 25 for the validation set (11 abnormal, 14 normal) and 25 for the testing set (11 abnormal, 14 normal). Hepatic lesions 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.

Artificial Intelligence in Oncological Hybrid Imaging

“…However, these first-order features do not necessarily reflect true radiomic features to use the hidden potential of imaging data. Wehrend et al developed a DL algorithm to automatically detect tumor lesions in 68 Ga-DOTA-TATE PET in a study of 125 patients [72]. Despite promising results, high physiological liver uptake and comparably low spatial resolution hamper the diagnostic accuracy of PET with SSTR analogs in the detection of liver lesions, making hybrid imaging with MRI desirable.…”

An update on the diagnosis of gastroenteropancreatic neuroendocrine neoplasms

“…This area has been more extensively studied for 18 F-FDG PET/CT[ 110 , 111 ]. A retrospective 2021 study retrospectively demonstrated that deep learning could facilitate the automation of detection of hepatic metastases, though future studies with larger sample sizes are required for further validation[ 112 ]. Continued optimization of imaging techniques and development of more selective tracers will continue to improve diagnostic yield and ability of functional imaging to guide the management of GEP-NENs effectively.…”

Artificial Intelligence in Oncological Hybrid Imaging

“…However, these firstorder features do not necessarily reflect true radiomic features to use the hidden potential of imaging data. Wehrend et al developed a DL algorithm to automatically detect tumor lesions in 68 Ga-DOTA-TATE PET in a study of 125 patients [72]. Despite promising results, high physiological liver uptake and comparably low spatial resolution hamper the diagnostic accuracy of PET with SSTR analogs in the detection of liver lesions, making hybrid imaging with MRI desirable.…”