Developing a Low Cost Image Marker to Identify Lymph Node Metastasis for Cervical Cancer Patients: An Initial Study

This study aims to utilize the primary tumor characteristics from CT images to detect lymph node (LN) metastasis for accurately categorizing locally advanced cervical cancer patients (LACC). In clinical practice, LN metastasis is a critical indicator for patients' prognostic assessment, which is usually investigated by PET/CT (i.e., positron emission tomography/computed tomography) examination. However, the high cost of the PET/CT imaging modality limits its application and also leads to heavy financial burden on patients. Thus it is clinically imperative to develop an economic solution for the LN metastasis identification. For this purpose, a novel image marker was developed, which is based on the 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 applied on the optimal feature set to generate a likelihood score for the identification of LN metastasis. Using a retrospective dataset that contains a total of 82 LACC patients, this new model was trained and optimized by leave one out cross validation (LOOCV) strategy. The marker performance was assessed by receiver operator characteristic curve (ROC). The results indicate that the area under the ROC curve (AUC) of this identification model was 0.774 +/- 0.050, which demonstrates its strong discriminative power. This study may be able to provide gynecologic oncologists a CT image based low cost clinical marker to identify LN metastasis occurred on LACC patients.