Risk Factors for Lymph Node Metastasis of Colorectal T2 Cancer and Prediction Model for Metastasis Using Virtual Slides
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ABSTRACT:
DISEASE(S): Colorectal Cancer
PROVIDER: 34476 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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Project description:BackgroundThis study aimed to explore the risk factors for lymph node metastasis (LNM) in patients with endometrial cancer (EC) and develop a clinically useful nomogram based on clinicopathological parameters to predict it.MethodsClinical information of patients who underwent staging surgery for EC was abstracted from Qilu Hospital of Shandong University from January 1st, 2005 to June 31st, 2019. Parameters including patient-related, tumor-related, and preoperative hematologic examination-related were analyzed by univariate and multivariate logistic regression to determine the correlation with LNM. A nomogram based on the multivariate results was constructed and underwent internal and external validation to predict the probability of LNM.ResultsThe overall data from the 1517 patients who met the inclusion criteria were analyzed. 105(6.29%) patients had LNM. According the univariate analysis and multivariate logistic regression analysis, LVSI is the most predictive factor for LNM, patients with positive LVSI had 13.156-fold increased risk for LNM (95%CI:6.834-25.324; P < 0.001). The nomogram was constructed and incorporated valuable parameters including histological type, histological grade, depth of myometrial invasion, LVSI, cervical involvement, parametrial involvement, and HGB levels from training set. The nomogram was cross-validated internally by the 1000 bootstrap sample and showed good discrimination accuracy. The c-index for internal and external validation of the nomogram are 0.916(95%CI:0.849-0.982) and 0.873(95%CI:0.776-0.970), respectively.ConclusionsWe developed and validated a 7-variable nomogram with a high concordance probability to predict the risk of LNM in patients with EC.
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Project description:The surgical management of papillary thyroid microcarcinoma (PTMC), especially regarding the necessity of central/lateral lymph node dissection, remains controversial. This study investigated the clinicopathologic factors predictive of lymph node metastasis (LNM) in patients diagnosed with PTMC. Multivariate logistic regression analysis was used for PTMC patients identified from the Surveillance, Epidemiology, and End Results database who were treated by surgery between 2002 and 2012, to determine the association of clinicopathologic factors with LNM. According to the results, a total of 31,017 patients met the inclusion criteria of the study. Final histology confirmed 2,135 (6.9%) cases of N1a disease and 1,684 cases (5.4%) of N1b disease. Our multivariate logistic regression analysis identified variables associated with both central LNM and lateral lymph node metastasis (LLNM), including a younger age (<45 years), male sex, non-Hispanic white and other race, classical papillary histology, larger tumor size, multifocality, and extrathyroidal extension; distant metastasis was also significantly associated with LLNM. The significant predictors identified from multivariable logistic regression were integrated into a statistical model that showed that extrathyroidal extension had maximum weight in the predictive role for LNM. LLNM was validated to be a significant risk factor for cancer-specific death in Cox regression analyses, whereas central LNM failed to predict a worse cancer-specific survival according to our data. Therefore, we suggested that central lymph node dissection could be performed in certain patients with risk factors. Given the prevalence of LLNM in PTMC, a thorough inspection of the lateral compartment is recommended in PTMC patients with risk factors for precise staging; from the viewpoint of a radical treatment for tumors, prophylactic lateral lymph node dissection that aims to remove the occult lateral lymph nodes may be an option for PTMC with risk factors. Multicenter studies with long-term follow-up are recommended to better understand the risk factors and surgical management for cervical nodes in PTMC.
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Project description:OBJECTIVE:To build and validate a CT radiomic model for pre-operatively predicting lymph node metastasis in early cervical carcinoma. METHODS AND MATERIALS:A data set of 150 patients with Stage IB1 to IIA2 cervical carcinoma was retrospectively collected from the Nanfang hospital and separated into a training cohort (n = 104) and test cohort (n = 46). A total of 348 radiomic features were extracted from the delay phase of CT images. Mann-Whitney U test, recursive feature elimination, and backward elimination were used to select key radiomic features. Ridge logistics regression was used to build a radiomic model for prediction of lymph node metastasis (LNM) status by combining radiomic and clinical features. The area under the receiver operating characteristic curve (AUC) and ? test were applied to verify the model. RESULTS:Two radiomic features from delay phase CT images and one clinical feature were associated with LNM status: log-sigma-2-0?mm-3D_glcm_Idn (p = 0.01937), wavelet-HL_firstorder_Median (p = 0.03592), and Stage IB (p = 0.03608). Radiomic model was built consisting of the three features, and the AUCs were 0.80 (95% confidence interval: 0.70 ~ 0.90) and 0.75 (95% confidence intervalI: 0.53 ~ 0.93) in training and test cohorts, respectively. The ? coefficient was 0.84, showing excellent consistency. CONCLUSION:A non-invasive radiomic model, combining two radiomic features and a International Federation of Gynecology and Obstetrics stage, was built for prediction of LNM status in early cervical carcinoma. This model could serve as a pre-operative tool. ADVANCES IN KNOWLEDGE:A noninvasive CT radiomic model, combining two radiomic features and the International Federation of Gynecology and Obstetrics stage, was built for prediction of LNM status in early cervical carcinoma.
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Project description:This study aimed to establish a model for predicting lateral neck lymph node metastasis in patients with papillary thyroid carcinoma. A total of 106 patients with papillary thyroid carcinoma undergoing initial treatment of neck lymph node dissection (including central and lateral neck lymph nodes) at the thyroid surgery department were enrolled from January 2009 to April 2017. Logistic regression analysis was used to screen the factors influencing lateral neck lymph node metastasis and develop a prediction model. The receiver operating characteristic curve was used to evaluate the predictive power and boundary value of the model for lateral neck lymph node metastasis. Prediction model: Logistic(P)?=?-5.699?+?0.681?×?(TSH)?+?0.342?×?(Metastatic rate of central lymph nodes)?+?1.463?×?(Combined with Hashimoto's disease)?+?1.525?×?(Number of tumors). When logistic (P) was???0.821, it was predicted that lateral neck lymph node metastasis occurred in patients with papillary thyroid carcinoma. When logistic (P) was <0.821, it was predicted that no metastasis was found in the lateral neck lymph node. The prediction accuracy was 78.3%. The model helped in evaluating lateral neck lymph node metastasis in patients with papillary thyroid carcinoma. Also, it had significance in determining reasonable surgical range, reducing unnecessary lateral neck lymph node dissection, and further improving the quality of life of patients.
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Project description:BackgroundPapillary thyroid cancer (PTC) in clinically lymph node-negative (cN0) patients is prone toward lymph node metastasis. As a risk factor for tumor persistence and local recurrence, lateral lymph node metastasis (LLNM) is related to the number of central lymph node metastases (CLNMs).MethodsWe performed LLNM risk stratification based on the number of CLNMs for cN0 PTC patients who underwent thyroidectomy and lymph node dissection between January 2013 and December 2018. A retrospective analysis was applied to the 274 collected patients with 1-2 CLNMs. We examined the clinicopathological characteristics of the patients and constructed a LASSO model.ResultsIn the 1-2 CLNM group, tumors >10 mm located in the upper region and nodular goiters were independent risk factors for LLNM. Specifically, tumors >20 mm and located in the upper region contributed to metastasis risk at level II. Hashimoto's thyroiditis reduced this risk (p = 0.045, OR = 0.280). Age ≤ 30 years and calcification (microcalcification within thyroid nodules) correlated with LLNM. The LASSO model divided the population into low- (25.74%) and high-risk (57.25%) groups for LLNM, with an AUC of 0.715.ConclusionsFor patients with 1-2 CLNMs, young age, calcification, nodular goiter, tumor >10 mm, and tumor in the upper region should alert clinicians to considering a higher occult LLNM burden. Close follow-up and therapy adjustment may be warranted for high-risk patients.
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Project description:Background and aimEndoscopic submucosal dissection (ESD) for undifferentiated type early gastric cancer is regarded as an investigational treatment. Few studies have tried to identify the risk factors that predict lymph-node metastasis (LNM) in intramucosal poorly differentiated adenocarcinomas (PDC). This study was designed to develop a risk scoring system (RSS) for predicting LNM in intramucosal PDC.MethodsFrom January 2002 to July 2015, patients diagnosed with mucosa-confined PDC, among those who underwent curative gastrectomy with lymph node dissection were reviewed. A risk model based on independent predicting factors of LNM was developed, and its performance was internally validated using a split sample approach.ResultsOverall, LNM was observed in 5.2% (61) of 1169 patients. Four risk factors [Female sex, tumor size ≥ 3.2 cm, muscularis mucosa (M3) invasion, and lymphatic-vascular involvement] were significantly associated with LNM, which were incorporated into the RSS. The area under the receiver operating characteristic curve for predicting LNM after internal validation was 0.69 [95% confidence interval (CI), 0.59-0.79]. A total score of 2 points corresponded to the optimal RSS threshold with a discrimination of 0.75 (95% CI 0.69-0.81). The LNM rates were 1.6% for low risk (<2 points) and 8.9% for high-risk (≥2 points) patients, with a negative predictive value of 98.6% (95% CI 0.98-1.00).ConclusionsA RSS could be useful in clinical practice to determine which patients with intramucosal PDC have low risk of LNM.
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