Dataset Information

A Preoperative Risk Prediction Model for Lymph Node Examination of Stage I-III Colon Cancer Patients: A Population-Based Study.

ABSTRACT: Background: Lymph node examination is a prognostic indicator for colon cancer (CC) patients. The aim of this study was to develop and validate a preoperative risk prediction model for inadequate lymph node examination. Methods: 24284 patients diagnosed as stage I-III CC between 2010-2014 were extracted from SEER database and randomly divided into development cohort (N=12142) and internal validation cohort (N=12142). 680 patients diagnosed as stage I-III CC between 2012-2014 were extracted from our hospital as external validation cohort. Logistic regression analysis was performed and risk score of each factor was calculated according to model formula. Model discrimination was assessed using C-statistics. Results: Preoperative risk factors were identified as gender, age, tumor site and tumor size. Patients with total risk score of 0-6 were considered as low risk group while patients scored ≥13 were considered as high risk group. The model had good discrimination and calibration in all cohorts and could apply to patients in the SEER database (American population) and patients in our hospital (Chinese population). Conclusions: The model could accurately predict the risk of inadequate lymph node examination before surgery and might provide useful reference for surgeons and pathologists.

SUBMITTER: Wang Y

PROVIDER: S-EPMC7097944 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A Preoperative Risk Prediction Model for Lymph Node Examination of Stage I-III Colon Cancer Patients: A Population-Based Study.

Wang Yuliuming Y Guan Xu X Zhang Yukun Y Zhao Zhixun Z Gao Zhifeng Z Chen Haipeng H Zhang Weiyuan W Liu Zheng Z Jiang Zheng Z Chen Yinggang Y Wang Guiyu G Wang Xishan X

Journal of Cancer 20200305 11

<b>Background</b>: Lymph node examination is a prognostic indicator for colon cancer (CC) patients. The aim of this study was to develop and validate a preoperative risk prediction model for inadequate lymph node examination. <b>Methods</b>: 24284 patients diagnosed as stage I-III CC between 2010-2014 were extracted from SEER database and randomly divided into development cohort (N=12142) and internal validation cohort (N=12142). 680 patients diagnosed as stage I-III CC between 2012-2014 were ex ...[more]

PMID: 32231735

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Project description:Background: The lymph node ratio (LNR), involved nodes/ lymph nodes examined, is associated with survival in colon cancer. Previous studies investigated the prognostic role of LNR regardless of TNM N staging or compared LNR and TNM N stages for prognostic strength. However, LNR may be utilized to obtain additional prognostic information rather than replacing TNM staging in daily practice. This study aimed to evaluate the role of LNR in TNM N stages to provide further prognostic information in daily practice. Methods: Patients with stage-III colon cancer who underwent surgery and adjuvant chemotherapy were included. pN1c tumors (tumor deposits without node involvement) and rectal cancers were excluded. Clinicopathological parameters and LNR in pN1a-b and pN2 groups were evaluated for recurrence-free survival (RFS). Results: A total of 97 patients were included [pN1a-b: n=69 (71.1%) and pN2: n=28 (28.9%)]. Median LNR in the entire population was 0.09 (0.01-0.84) with a median lymph node examined of 22 (8-89) and involved of 2 (1-17). Median RFS was not reached in the pN1a-b and pN2 groups during a median follow-up of 20.8 months (1.13-101.03), with significantly better survival of the pN1a-b group (p=0.003). Among the pN1a-b group, the LNR cut-off was set as 0.10. LNR significantly discriminated RFS (Median not-reached, p=0.001). Among the pN2 group, the LNR cut-off was set as 0.25 and LNR significantly discriminated RFS [Not reached vs. 11.40 months (95%CI: 3.57-16.83), p=0.004]. Combined pN-LNR groups revealed significant discrimination in RFS (p<0.001). RFS was not statistically different between pN2-LNR≤0.25 and pN1-LNR>0.10 groups (p=0.282). In multivariable analysis with clinicopathological parameters, only LNR was significant (p=0.023), whereas the pN stage did not remain significant (p=0.637). Conclusion: LNR adds further prognostication in pN1a-b and N2 groups. LNR may be utilized to detect patient subgroups in different TNM N sages (pN1a-b and pN2) but with similar prognoses. This further prognostic information may assist clinical decisions in practice. The results of this study emphasize an adequate and higher number of lymph node samples in surgery.

Project description:Lymph node involvement increases the risk of breast cancer recurrence. An accurate non-invasive assessment of nodal involvement is valuable in cancer staging, surgical risk, and cost savings. Radiomics has been proposed to pre-operatively predict sentinel lymph node (SLN) status; however, radiomic models are known to be sensitive to acquisition parameters. The purpose of this study was to develop a prediction model for preoperative prediction of SLN metastasis using deep learning-based (DLB) features and compare its predictive performance to state-of-the-art radiomics. Specifically, this study aimed to compare the generalizability of radiomics vs DLB features in an independent test set with dissimilar resolution. Dynamic contrast-enhancement images from 198 patients (67 positive SLNs) were used in this study. Of these subjects, 163 had an in-plane resolution of 0.7 × 0.7 mm2, which were randomly divided into a training set (approximately 67%) and a validation set (approximately 33%). The remaining 35 subjects with a different in-plane resolution (0.78 × 0.78 mm2) were treated as independent testing set for generalizability. Two methods were employed: (1) conventional radiomics (CR), and (2) DLB features which replaced hand-curated features with pre-trained VGG-16 features. The threshold determined using the training set was applied to the independent validation and testing dataset. Same feature reduction, feature selection, model creation procedures were used for both approaches. In the validation set (same resolution as training), the DLB model outperformed the CR model (accuracy 83% vs 80%). Furthermore, in the independent testing set of the dissimilar resolution, the DLB model performed markedly better than the CR model (accuracy 77% vs 71%). The predictive performance of the DLB model outperformed the CR model for this task. More interestingly, these improvements were seen particularly in the independent testing set of dissimilar resolution. This could indicate that DLB features can ultimately result in a more generalizable model.

Dataset Information

A Preoperative Risk Prediction Model for Lymph Node Examination of Stage I-III Colon Cancer Patients: A Population-Based Study.

Publications

A Preoperative Risk Prediction Model for Lymph Node Examination of Stage I-III Colon Cancer Patients: A Population-Based Study.

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