Deep Learning Based Lymph Node Gross Tumor Volume Detection via Distance-Guided Gating Using CT and 18F-FDG PET in Esophageal Cancer Radiotherapy.

<bold>Purpose/objective(s): </bold>Identifying suspicious cancer metastasized lymph nodes (GTV is an essential task in esophageal cancer radiotherapy. Manual delineation requires high-level sophisticated clinical reasoning in multi-modality imaging, which is challenging and suffers from high subjectivity and inter-observer variability. We propose an automated esophageal GTV_LN detection method via integrating a distance-based decision stratification into a multi-task deep network, which is able to effectively learn the GTV_LN oncology features. This is inspired by the fact that lymph node involvements often follow certain distance patterns from the primary tumors.<bold>Materials/methods: </bold>We collected and curated 2 esophageal cancer datasets with PET and RTCT images. The 1st dataset contained 141 patients and 651 annotated GTV_LN for training and evaluating our deep network (60%, 10% and 30% for training, validation and testing), while the 2nd dataset contained 10 patients aiming for the multi-user agreement analysis. A distance-based decision stratification is designed to divide GTV_LN into "tumor-proximal" and "tumor-distal" categories and a multi-branch deep network is proposed to solve each of them. The multi-branch network has a shared encoder and separate decoders to detect and segment two GTV_LN subcategories, respectively. The distance-based decision stratification enables the automated multi-branch method to specialize in GTV_LN oncology features of each subcategory. We use both PET and RTCT as the network inputs to exploit the complementary information in each imaging modality.<bold>Results: </bold>In the testing set of the 1st esophageal cancer dataset (34 patients with 138 GTV_LNs), our method achieves a sensitivity of 73.8% and 79.1% at 6 and 12 false positives (FPs) per patient as compared to 72.0% and 75.9% by MULAN, the state-of-the-art deep learning based universal lesion detection algorithm. On the 2nd dataset containing 10 patients, 3 radiation oncologists annotated 53 GTV_LNs in total, among which 42 GTV_LNs were agreed with all 3 physicians leading to 79.2% multi-user agreement. Our method achieves a sensitivity of 77.4% at 6 FPs per patient on the 2nd dataset. This shows that the sensitivity achieved by our automated deep learning approach can be close to the multi-user agreement.<bold>Conclusion: </bold>We proposed a multi-task deep learning method with distance-based decision stratification to effectively detect and segment the GTV_LN in esophageal cancer radiotherapy. It significantly improves on previous state-of-the-art and the achieved performance has potential clinical values considering the large multi-user variability in this challenging task. [ABSTRACT FROM AUTHOR]