Dataset Information

A Novel Predictive Model of Pathological Lymph Node Metastasis Constructed with Preoperative Independent Predictors in Patients with Renal Cell Carcinoma.

ABSTRACT: Introduction: Renal cell carcinoma (RCC) is one of the most common urinary tumors. The risk of metastasis for patients with RCC is about 1/3, among which 30-40% have lymph node metastasis, and the existence of lymph node metastasis will greatly reduce the survival rate of patients. However, the necessity of lymph node dissection is still controversial at present. Therefore, a new predictive model is urgently needed to judge the risk of lymph node metastasis and guide clinical decision making before operation. Method: We retrospectively collected the data of 189 patients who underwent retroperitoneal lymph node dissection or enlarged lymph node resection due to suspected lymph node metastasis or enlarged lymph nodes found during an operation in Tongji Hospital from January 2016 to October 2021. Univariate and multivariate logistic regression and least absolute shrinkage and selection operator (lasso) regression analyses were used to identify preoperative predictors of pathological lymph node positivity. A nomogram was established to predict the probability of lymph node metastasis in patients with RCC before surgery according to the above independent predictors, and its efficacy was evaluated with a calibration curve and a DCA analysis. Result: Among the 189 patients, 54 (28.60%) were pN1 patients, and 135 (71.40%) were pN0 patients. Three independent impact factors were, finally, identified, which were the following: age (OR = 0.3769, 95% CI = 0.1864-0.7622, p < 0.01), lymph node size according to pre-operative imaging (10-20 mm: OR = 15.0040, 95% CI = 1.5666-143.7000, p < 0.05; >20 mm: OR = 4.4013, 95% CI = 1.4892-7.3134, p < 0.01) and clinical T stage (cT1-2 vs. cT3-4) (OR = 3.1641, 95% CI = 1.0336-9.6860, p < 0.05). The calibration curve and DCA (Decision Curve Analysis) showed the nomogram of this predictive model had good fitting. Conclusions: Low age, large lymph node size in pre-operative imaging and high clinical T stage can be used as independent predictive factors of pathological lymph node metastasis in patients with RCC. Our predictive nomogram using these factors exhibited excellent discrimination and calibration.

SUBMITTER: Sun JX

PROVIDER: S-EPMC9866659 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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A Novel Predictive Model of Pathological Lymph Node Metastasis Constructed with Preoperative Independent Predictors in Patients with Renal Cell Carcinoma.

Sun Jian-Xuan JX Liu Chen-Qian CQ Zhang Zong-Biao ZB Xia Qi-Dong QD Xu Jin-Zhou JZ An Ye Y Xu Meng-Yao MY Zhong Xing-Yu XY Zeng Na N Ma Si-Yang SY He Hao-Dong HD Guan Wei W Wang Shao-Gang SG

Journal of clinical medicine 20230105 2

Introduction: Renal cell carcinoma (RCC) is one of the most common urinary tumors. The risk of metastasis for patients with RCC is about 1/3, among which 30−40% have lymph node metastasis, and the existence of lymph node metastasis will greatly reduce the survival rate of patients. However, the necessity of lymph node dissection is still controversial at present. Therefore, a new predictive model is urgently needed to judge the risk of lymph node metastasis and guide clinical decision making bef ...[more]

PMID: 36675368

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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 Novel Predictive Model of Pathological Lymph Node Metastasis Constructed with Preoperative Independent Predictors in Patients with Renal Cell Carcinoma.

Publications

A Novel Predictive Model of Pathological Lymph Node Metastasis Constructed with Preoperative Independent Predictors in Patients with Renal Cell Carcinoma.

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