Abstract
BackgroundOur aim was to establish a deep learning radiomics method to preoperatively evaluate regional lymph node (LN) staging for hilar cholangiocarcinoma (HC) patients. Methods and MaterialsOf the 179 enrolled HC patients, 90 were pathologically diagnosed with lymph node metastasis. Quantitative radiomic features and deep learning features were extracted. An LN metastasis status classifier was developed through integrating support vector machine, high-performance deep learning radiomics signature, and three clinical characteristics. An LN metastasis stratification classifier (N1 vs. N2) was also proposed with subgroup analysis.ResultsThe average areas under the receiver operating characteristic curve (AUCs) of the LN metastasis status classifier reached 0.866 in the training cohort and 0.870 in the external test cohorts. Meanwhile, the LN metastasis stratification classifier performed well in predicting the risk of LN metastasis, with an average AUC of 0.946.ConclusionsTwo classifiers derived from computed tomography images performed well in predicting LN staging in HC and will be reliable evaluation tools to improve decision-making.
Highlights
Hilar cholangiocarcinoma (HC) is one of the malignant tumors with poor prognosis, accounting for approximately two-thirds of all biliary tract tumors [1, 2]
Studies confirm that lymph node (LN) status is an important biomarker for HC prognosis [5]
Except for the carcinoembryonic antigen (CEA) level, there was no significant difference in the characteristics between the two groups
Summary
Hilar cholangiocarcinoma (HC) is one of the malignant tumors with poor prognosis, accounting for approximately two-thirds of all biliary tract tumors [1, 2]. The stage of metastatic LNs (N1: one to three; N2: four or more) as a factor of poor prognosis was Access LN Staging via Deep-Learning Radiomics incorporated into the tumor node metastasis staging system [6]. LN dissection is a critical surgical step for suspicious LN-positive HC [7]. Precise evaluation of regional LN staging in HC is critical to formulating individualized clinical treatment strategies. Our aim was to establish a deep learning radiomics method to preoperatively evaluate regional lymph node (LN) staging for hilar cholangiocarcinoma (HC) patients
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