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首页> 外文会议>Conference on Biophotonics and Immune Responses >Developing a Low Cost Image Marker to Identify Lymph Node Metastasis for Cervical Cancer Patients: An Initial Study
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Developing a Low Cost Image Marker to Identify Lymph Node Metastasis for Cervical Cancer Patients: An Initial Study

机译：开发低成本的图像标记以鉴定宫颈癌患者的淋巴结转移：初步研究

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This study aims to utilize the primary tumor characteristics from CT images to detect lymph node (LN) metastasisfor accurately categorizing locally advanced cervical cancer patients (LACC). In clinical practice, LN metastasis is acritical indicator for patients’ prognostic assessment, which is usually investigated by PET/CT (i.e., positron emissiontomography/computed tomography) examination. However, the high cost of the PET/CT imaging modality limits itsapplication and also leads to heavy financial burden on patients. Thus it is clinically imperative to develop an economicsolution for the LN metastasis identification. For this purpose, a novel image marker was developed, which is based onthe primary cervical tumors segmented from CT images. Accordingly, a total of 99 handcrafted features were computed,and an optimal feature set was determined by Laplacian Score (LS) method. Next, a logistic regression model was appliedon the optimal feature set to generate a likelihood score for the identification of LN metastasis. Using a retrospectivedataset that contains a total of 82 LACC patients, this new model was trained and optimized by leave one out crossvalidation (LOOCV) strategy. The marker performance was assessed by receiver operator characteristic curve (ROC). Theresults indicate that the area under the ROC curve (AUC) of this identification model was 0.774±0.050, whichdemonstrates its strong discriminative power. This study may be able to provide gynecologic oncologists a CT imagebased low cost clinical marker to identify LN metastasis occurred on LACC patients.
机译：本研究旨在利用来自CT图像的主要肿瘤特征来检测淋巴结（LN）转移准确分类局部晚期宫颈癌患者（LACC）。在临床实践中，LN转移是一个患者预后评估的关键指标，通常由PET / CT（即，正电子发射）研究断层扫描/计算断层扫描）检查。然而，PET / CT成像模块的高成本限制了其申请并导致患者的沉重财务负担。因此，它在临床上必须发展经济LN转移鉴定的溶液。为此目的，开发了一种新的图像标记，基于从CT图像中分段的主要颈椎肿瘤。因此，计算了总共99个手绘功能，并且通过拉普拉斯评分（LS）方法确定了最佳特征集。接下来，应用了逻辑回归模型在最佳特征集上，以生成识别LN转移的似然分数。使用回顾DataSet包含共82例LACC患者，这一新模型受到培训和优化，留出了一个交叉验证（LOOCV）策略。通过接收器操作员特征曲线（ROC）评估标记性能。这结果表明，该识别模型的ROC曲线（AUC）下的面积为0.774±0.050，证明了其强烈的歧视力。本研究可能能够提供妇科肿瘤学家CT图像基于低成本的临床标记，以鉴定LACC患者发生的LN转移。

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