ESTRO 2023 - Abstract Book

S307

Sunday 14 May 2023

ESTRO 2023

Conclusion This study demonstrates the significant acute and late toxicity following radical treatment of NPC, which can profoundly negatively impact patients’ quality of life. Excellent cancer survival outcomes, in line with published data, highlight the importance of toxicity reduction and support the evaluation of potential toxicity sparing technologies, such as proton beam radiotherapy. PD-0405 3D-Resnet: a model for lymph node metastasis recognition from HNSCC on pre-treatment tomography Y. Lin 1 , C. Lin 2,4 , H. Hsie 1 , J. Chen 2 , W. You 1 , Y. Hsu 3 1 Taichung Veterans General Hospital, Department of Radiation Oncology, Taichung, Taiwan; 2 National Yang Ming Chiao Tung University, College of Artificial Intelligence, Tainan, Taiwan; 3 Taichung Veterans General Hospital, Cancer Prevention and Control Center, Taichung, Taiwan; 4 Taichung Veterans General Hospital, Department of Orthopedics, Taichung, Taiwan Purpose or Objective Head and neck squamous cell carcinoma (HNSCC) is a global major cancer, especially in southeast Asia. The pre-treatment evaluation of lymph node status remained a challenge for oncologists. To build a deep learning neural network for HNSCC lymph node recognition from pre-treatment tomography and avoid human specialist bias, we designed this study. Materials and Methods We designed a retrospective study, collect newly diagnosed HNSCC patients from 2019-2021, with available pre-treatment computed tomography (CT) scans and pathology report to build ground truth of the LNM status. We experiment 7 three dimensional convolutional neural networks (3D-CNNs) with two different input images s. We compared classical 3D-CNNs and 3D-Resnets to find out best architecture for LNM recognition. Results From 2019-2021, 158 patients with 158 CT scans meet our criteria. The median duration from CT scan to surgery was 10 days (range: 1–28 days). Following pathologic correlation with CT scans, 350 lymph nodes were segmented in total (range: 1–8 per patient): 241 negative nodes, 111 nodes contained tumor cells. Along the 7 examined models, dual-pathway 3D-Resnet (Figure 1), input image -preserved pathway containing 18 layers and -invariant pathway containing 10 layers yielded the best performance. The prediction area under the receiver operating characteristic curve (AUC) achieved 0.93 (Figure 2), accuracy 0.89, sensitivity 0.80, specificity 0.99, positive predictive value (PPV) 0.97 and negative predictive value (NPV) 0.82.