Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-Based Gating Using 3D CT/PET Imaging in Radiothera
Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance. In radiotherapy, they are referred to as Lymph Node Gross Tumor Volume (GTV\(_{LN}\) ). Determining a
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PAII Inc., Bethesda, MD, USA [email protected] 2 Johns Hopkins University, Baltimore, MD, USA 3 Chang Gung Memorial Hospital, Linkou, Taiwan, ROC 4 National Tsing Hua University, Hsinchu City, Taiwan, ROC The First Affiliated Hospital Zhejiang University, Hangzhou, China 6 Ping An Technology, Shenzhen, China
Abstract. Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance. In radiotherapy, they are referred to as Lymph Node Gross Tumor Volume (GTVLN ). Determining and delineating the spread of GTVLN is essential in defining the corresponding resection and irradiating regions for the downstream workflows of surgical resection and radiotherapy of various cancers. In this work, we propose an effective distance-based gating approach to simulate and simplify the highlevel reasoning protocols conducted by radiation oncologists, in a divideand-conquer manner. GTVLN is divided into two subgroups of “tumorproximal” and “tumor-distal”, respectively, by means of binary or soft distance gating. This is motivated by the observation that each category can have distinct though overlapping distributions of appearance, size and other LN characteristics. A novel multi-branch detection-by-segmentation network is trained with each branch specializing on learning one GTVLN category features, and outputs from multi-branch are fused in inference. The proposed method is evaluated on an in-house dataset of 141 esophageal cancer patients with both PET and CT imaging modalities. Our results validate significant improvements on the mean recall from 72.5% to 78.2%, as compared to previous state-of-the-art work. The highest achieved GTVLN recall of 82.5% at 20% precision is clinically relevant and valuable since human observers tend to have low sensitivity (∼80% for the most experienced radiation oncologists, as reported by literature [5]).
Keyword: Lymph Node Gross Tumor Volume (GTVLN ), CT/PET Imaging, 3D Distance Transformation, Distance-based Gating
c Springer Nature Switzerland AG 2020 A. L. Martel et al. (Eds.): MICCAI 2020, LNCS 12267, pp. 753–762, 2020. https://doi.org/10.1007/978-3-030-59728-3_73
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Introduction
Assessing the lymph node (LN) status in oncology clinical workflows is an indispensable step for the precision cancer diagnosis and treatment planning, e.g., radiation therapy or surgical resection. The class of enlarged LN is defined by the revised RECIST guideline [15] if its short axial axis is more than 10-15 mm in computed tomography (CT). In radiotherapy treatment, both the primary tumor and all metastasis suspicious LNs must be sufficiently treated within the clinical target volume with the proper doses [7]. We refer these LNs as lymph node gross tumor volume or GTVLN , which includes enlarged LNs, as well as smaller ones that are associated with a high positron emission tomography (PET) signal or any metastasis signs in CT [14]. Accurately identifying and delineating GTVLN , to be spatially included in
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