scholarly journals Development and Validation of a Combined Model for Preoperative Prediction of Lymph Node Metastasis in Peripheral Lung Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Qi Li ◽  
Xiao-qun He ◽  
Xiao Fan ◽  
Chao-nan Zhu ◽  
Jun-wei Lv ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Tumor Size ◽  
Validation Cohort ◽  
Morphological Features ◽  
Combined Model ◽  
Training Cohort ◽  
Node Metastasis ◽  
Peripheral Lung

BackgroundBased on the “seed and soil” theory proposed by previous studies, we aimed to develop and validate a combined model of machine learning for predicting lymph node metastasis (LNM) in patients with peripheral lung adenocarcinoma (PLADC).MethodsRadiomics models were developed in a primary cohort of 390 patients (training cohort) with pathologically confirmed PLADC from January 2016 to August 2018. The patients were divided into the LNM (−) and LNM (+) groups. Thereafter, the patients were subdivided according to TNM stages N0, N1, N2, and N3. Radiomic features from unenhanced computed tomography (CT) were extracted. Radiomic signatures of the primary tumor (R1) and adjacent pleura (R2) were built as predictors of LNM. CT morphological features and clinical characteristics were compared between both groups. A combined model incorporating R1, R2, and CT morphological features, and clinical risk factors was developed by multivariate analysis. The combined model’s performance was assessed by receiver operating characteristic (ROC) curve. An internal validation cohort containing 166 consecutive patients from September 2018 to November 2019 was also assessed.ResultsThirty-one radiomic features of R1 and R2 were significant predictors of LNM (all P < 0.05). Sex, smoking history, tumor size, density, air bronchogram, spiculation, lobulation, necrosis, pleural effusion, and pleural involvement also differed significantly between the groups (all P < 0.05). R1, R2, tumor size, and spiculation in the combined model were independent risk factors for predicting LNM in patients with PLADC, with area under the ROC curves (AUCs) of 0.897 and 0.883 in the training and validation cohorts, respectively. The combined model identified N0, N1, N2, and N3, with AUCs ranging from 0.691–0.927 in the training cohort and 0.700–0.951 in the validation cohort, respectively, thereby indicating good performance.ConclusionCT phenotypes of the primary tumor and adjacent pleura were significantly associated with LNM. A combined model incorporating radiomic signatures, CT morphological features, and clinical risk factors can assess LNM of patients with PLADC accurately and non-invasively.

scholarly journals Consolidation Tumor Ratio Combined With Pathological Features Could Predict Status of Lymph Nodes of Early-Stage Lung Adenocarcinoma

2022 ◽  
Vol 11 ◽  
Author(s):  
Liang Zhao ◽  
Guangyu Bai ◽  
Ying Ji ◽  
Yue Peng ◽  
Ruochuan Zang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Validation Cohort ◽  
Frozen Sections ◽  
Paraffin Sections ◽  
Training Cohort ◽  
Node Metastasis ◽  
The Status

IntroductionStage IA lung adenocarcinoma manifested as part-solid nodules (PSNs), has attracted immense attention owing to its unique characteristics and the definition of its invasiveness remains unclear. We sought to develop a nomogram for predicting the status of lymph nodes of this kind of nodules.MethodsA total of 2,504 patients between September 2018 to October 2020 with part-solid nodules in our center were reviewed. Their histopathological features were extracted from paraffin sections, whereas frozen sections were reviewed to confirm the consistency of frozen sections and paraffin sections. Univariate and multivariate logistic regression analyses and Akaike information criterion (AIC) variable selection were performed to assess the risk factors of lymph node metastasis and construct the nomogram. The nomogram was subjected to bootstrap internal validation and external validation. The concordance index (C-index) was applied to evaluate the predictive accuracy and discriminative ability.ResultsWe enrolled 215 and 161 eligible patients in the training cohort and validation cohort, respectively. The sensitivity between frozen and paraffin sections on the presence of micropapillary/solid subtype was 78.4%. Multivariable analysis demonstrated that MVI, the presence of micropapillary/solid subtype, and CTR >0.61 were independently associated with lymph node metastasis (p < 0.01). Five risk factors were integrated into the nomogram. The nomogram demonstrated good accuracy in estimating the risk of lymph node metastasis, with a C-index of 0.945 (95% CI: 0.916–0.974) in the training cohort and a C-index of 0.975 (95% CI: 0.954–0.995) in the validation cohort. The model’s calibration was excellent in both cohorts.ConclusionThe nomogram established showed excellent discrimination and calibration and could predict the status of lymph nodes for patients with ≤3 cm PSNs. Also, this prediction model has the prediction potential before the end of surgery.

scholarly journals Development of Nomograms for Predicting Lymph Node Metastasis and Distant Metastasis in Newly Diagnosed T1-2 Non-Small Cell Lung Cancer: A Population-Based Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Yiming Qi ◽  
Shuangshuang Wu ◽  
Linghui Tao ◽  
Yunfu Shi ◽  
Wenjuan Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Small Cell Lung Cancer ◽  
Distant Metastasis ◽  
Tumor Size ◽  
Small Cell ◽  
Small Cell Lung ◽  
Node Metastasis

BackgroundFor different lymph node metastasis (LNM) and distant metastasis (DM), the diagnosis, treatment and prognosis of T1-2 non-small cell lung cancer (NSCLC) are different. It is essential to figure out the risk factors and establish prediction models related to LNM and DM.MethodsBased on the surveillance, epidemiology, and end results (SEER) database from 1973 to 2015, a total of 43,156 eligible T1-2 NSCLC patients were enrolled in the retrospective study. Logistic regression analysis was used to determine the risk factors of LNM and DM. Risk factors were applied to construct the nomograms of LNM and DM. The predictive nomograms were discriminated against and evaluated by Concordance index (C-index) and calibration plots, respectively. Decision curve analysis (DCAs) was accepted to measure the clinical application of the nomogram. Cumulative incidence function (CIF) was performed further to detect the prognostic role of LNM and DM in NSCLC-specific death (NCSD).ResultsEight factors (age at diagnosis, race, sex, histology, T-stage, marital status, tumor size, and grade) were significant in predicting LNM and nine factors (race, sex, histology, T-stage, N-stage, marital status, tumor size, grade, and laterality) were important in predicting DM(all, P< 0.05). The calibration curves displayed that the prediction nomograms were effective and discriminative, of which the C-index were 0.723 and 0.808. The DCAs and clinical impact curves exhibited that the prediction nomograms were clinically effective.ConclusionsThe newly constructed nomograms can objectively and accurately predict LNM and DM in patients suffering from T1-2 NSCLC, which may help clinicians make individual clinical decisions before clinical management.

scholarly journals Development and Validation a Nomogram for Predicting the Third Station Lymph Node Metastasis in Early Gastric Cancer

2021 ◽  
Author(s):  
Xiaoxiao Wang ◽  
Cong Li ◽  
Mengjie Fang ◽  
Liwen Zhang ◽  
Lianzhen Zhong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Validation Cohort ◽  
Predictive Accuracy ◽  
Clinical Risk Factors ◽  
Node Metastasis ◽  
Clinical Risk

Abstract Background:This study aimed to evaluate the value of radiomic nomogram in predicting lymph node metastasis in T1-2 gastric cancer according to the No. 3 station lymph nodes.Methods:A total of 159 T1-2 gastric cancer (GC) patients who had undergone surgery with lymphadenectomy between March 2012 and November 2017 were retrospectively collected and divided into a primary cohort (n = 80) and a validation cohort (n = 79). Radiomic features were extracted from both tumor region and No. 3 station lymph nodes (LN) based on computed tomography (CT) images per patient. Then, key features were selected using minimum redundancy maximum relevance algorithm and fed into two radiomic signatures, respectively. Meanwhile, the predictive performance of clinical risk factors was studied. Finally, a nomogram was built by merging radiomic signatures and clinical risk factors and evaluated by the area under the receiver operator characteristic curve (AUC) as well as decision curve.Results: Two radiomic signatures, reflecting phenotypes of the tumor and LN respectively, were significantly associated with LN metastasis. A nomogram incorporating two radiomic signatures and CT-reported LN metastasis status showed good discrimination of LN metastasis in both the primary cohort (AUC: 0.915; 95% confidence interval [CI]: 0.832-0.998) and validation cohort (AUC: 0.908; 95%CI: 0.814-1.000). The decision curve also indicated its potential clinical usefulness.Conclusions:The nomogram received favorable predictive accuracy in predicting No.3 station LN metastasis in T1-2 GC, and could assist the choice of therapy.

scholarly journals Indications of Lymph Node Metastasis and Survival Analysis in T1N+M0 Gastric Cancer: a Population-Based Study

2020 ◽  
Author(s):  
Peng Jin ◽  
Yang Li ◽  
Shuai Ma ◽  
Wenzhe Kang ◽  
Hao Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Survival Analysis ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Tumor Size ◽  
Multivariate Analyses ◽  
Invasion Depth ◽  
Node Metastasis ◽  
Definition Of

Abstract Background Since the definition of early gastric cancer (EGC) was first proposed in 1971, the treatment of gastric cancer with or without lymph node metastasis (LNM) has changed a lot. The present study aims to identify risk factors for LNM and prognosis, and to further evaluate the indications for adjuvant chemotherapy (AC) in T1N + M0 gastric cancer. Methods A total of 1291 patients with T1N + M0 gastric cancer were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate analyses were performed to identify risk factors for LNM. The effect of LNM on overall survival (OS) and cancer-specific survival (CSS) was compared with patients grouped into T1N0-1 and T1N2-3, as the indications for AC. Results The rate of LNM was 19.52%. Multivariate analyses showed age, tumor size, invasion depth, and type of differentiation and retrieved LNs were associated with LNM (p < 0.05). Cox multivariate analyses indicated age, sex, tumor size, N stage were independent predictors of OS and CSS (p < 0.05), while race was indicator for OS (HR 0.866; 95%CI 0.750–0.999, p = 0.049), but not for CSS (HR 0.878; 95% CI 0.723–1.065, p = 0.187). In addition, survival analysis showed the proportion of patients in N+/N0 was better distributed than N0-1/N2-3b. There were statistically significant differences in OS and CSS between patients with and without chemotherapy in pT1N1M0 patients (p༜0.05). Conclusions Both tumor size and invasion depth are associated with LNM and prognosis. LNM is an important predictor of prognosis. pT1N + M0 may be appropriate candidates for AC. Currently, the treatment and prognosis of T1N0M0/T1N + M0 are completely different. An updated definition of EGC, taking into tumor size, invasion depth and LNM, may be more appropriate in an era of precision medicine.

Identification of risk factors associated with pelvic lymph node metastasis in patients with stage IB1 cervical cancer.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e18005-e18005
Author(s):  
Ping Jiang ◽  
Jing Cai ◽  
Xiaoqi He ◽  
Hongbo Wang ◽  
Weihong Dong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cervical Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Tumor Size ◽  
Node Metastasis ◽  
Parametrial Invasion ◽  
Pelvic Lymph Node Metastasis ◽  
Internal Iliac

e18005 Background: Evaluation the distribution of nodal metastases in the stage IB1 cervical cancer and the risk factors associated with pelvic lymph node metastasis (LNM) at each anatomic location. Methods: 728 patients with stage IB1 cervical cancer who underwent radical hysterectomies and systemic pelvic lymphadenectomies from January 2008 to December 2017 were retrospectively studied. All removed pelvic lymph nodes were pathologically examined, and the risk factors for LNM at the obturator, internal iliac, external iliac, and common iliac regions were evaluated by univariate and multivariate logistic regression analyses. Results: 20,134 lymph nodes were analysed with the average number of 27.80 (± SD 9.43) lymph nodes per patient. Nodal metastases were present in 266 (14.6%) patients. The obturator was the most common site for nodal metastasis (42.5%) followed by the internal iliac nodes (20.3%) and the external iliac nodes (19.9%), while the common iliac (9.8%) and parametrial (7.5%) nodes were the least likely to be involved. Tumor size more than 2 cm, histologically proven lymphovascular space involvement (LVSI) and parametrial invasion correlated independently significantly with the higher risk of the lymphatic metastasis. Obesity (BMI≥25) was independently significantly negatively correlated with the risk of lymphatic metastases. All the positive common iliac nodes were found in patients with tumors greater than 2 cm. The multivariate analysis showed that tumor size greater than 3 cm was associated with a 16.6-fold increase in the risk for common iliac LNM. Interestingly, tumor size was not an independent risk factor for pelvic LNM in the lower regions, i.e., the obturator, internal iliac and external iliac areas, where LVSI was the most significant predictor for LNM. In addition, parametrial invasion was related to external and internal iliac LNM; deep stromal invasion and age less than 50 years were associated with obturator LNM. Conclusions: The incidence of lymph node metastasis in patients with stage IB1 cervical cancer is low but prognostically relevant. The data offer the opportunity for tailored individual treatment in selected patients with small tumors and obesity.

scholarly journals A Clinical-Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Gallbladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xingyu Liu ◽  
Xiaoyuan Liang ◽  
Lingxiang Ruan ◽  
Sheng Yan
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Gallbladder Cancer ◽  
Prediction Models ◽  
Validation Cohort ◽  
External Validation ◽  
Training Cohort ◽  
Clinical Variables ◽  
Node Metastasis ◽  
Preoperative Prediction

ObjectivesThe aim of the current study was to develop and validate a nomogram based on CT radiomics features and clinical variables for predicting lymph node metastasis (LNM) in gallbladder cancer (GBC).MethodsA total of 353 GBC patients from two hospitals were enrolled in this study. A Radscore was developed using least absolute shrinkage and selection operator (LASSO) logistic model based on the radiomics features extracted from the portal venous-phase computed tomography (CT). Four prediction models were constructed based on the training cohort and were validated using internal and external validation cohorts. The most effective model was then selected to build a nomogram.ResultsThe clinical-radiomics nomogram, which comprised Radscore and three clinical variables, showed the best diagnostic efficiency in the training cohort (AUC = 0.851), internal validation cohort (AUC = 0.819), and external validation cohort (AUC = 0.824). Calibration curves showed good discrimination ability of the nomogram using the validation cohorts. Decision curve analysis (DCA) showed that the nomogram had a high clinical utility.ConclusionThe findings showed that the clinical-radiomics nomogram based on radiomics features and clinical parameters is a promising tool for preoperative prediction of LN status in patients with GBC.

scholarly journals Predictors of lymph node metastasis and residual tumor in early gastric cancer patients after noncurative endoscopic resection: a systematic review and meta-analysis

2020 ◽  
Vol 13 ◽  
pp. 175628482093503
Author(s):  
Bolun Jiang ◽  
Li Zhou ◽  
Jun Lu ◽  
Yizhi Wang ◽  
Junchao Guo
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Early Gastric Cancer ◽  
Tumor Size ◽  
Meta Analysis ◽  
Residual Tumor ◽  
Node Metastasis

Background: It is challenging to identify the prevalence of lymph node metastasis (LNM) and residual tumor in patients with early gastric cancer (EGC) who underwent noncurative endoscopic resection (ER). This present meta-analysis was aimed to establish imperative potential predictive factors in order to select the optimal treatment method. Methods: A systematic literature search of PubMed, Embase, and Cochrane Library databases was performed through 1 February 2019 to identify relevant studies, which investigated risk factors for LNM and residual tumor in patients with EGC who underwent noncurative ER. Eligible data were systematically reviewed through a meta-analysis. Results: Overall, 12 studies investigating the risk factor of LNM were included, totaling 3015 patients, 7 of which also involved cancer residues. After the present meta-analysis, six predictors, including tumor size >30 mm, tumor invasion depth (⩾500 μm from the muscularis mucosae), macroscopic appearance, undifferentiated histopathological type, positive vertical margin, and presence of lymphovascular invasion (including lymphatic invasion and vascular invasion) were significantly associated with LNM, whereas tumor size >30 mm, positive horizontal margin, and positive vertical margin were identified as significant predictors for the risk of residual tumor. No evidence of publication bias was observed. Conclusions: Six and three variables were established as significant risk factors for LNM and residual tumor in patients with EGC who underwent noncurative ER, respectively. Patients with EGC who present these risk factors after noncurative ER are strongly suggested to receive additional surgery, while others might be suitable for strict follow-up. This might shed some new light on the selection of follow-up treatment for noncurative ER.

scholarly journals Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaoxiao Wang ◽  
Cong Li ◽  
Mengjie Fang ◽  
Liwen Zhang ◽  
Lianzhen Zhong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Clinical Risk Factors ◽  
Primary Tumors ◽  
Training Cohort ◽  
Node Metastasis ◽  
Clinical Risk

Abstract Background This study aimed to develope and validate a radiomics nomogram by integrating the quantitative radiomics characteristics of No.3 lymph nodes (LNs) and primary tumors to better predict preoperative lymph node metastasis (LNM) in T1-2 gastric cancer (GC) patients. Methods A total of 159 T1-2 GC patients who had undergone surgery with lymphadenectomy between March 2012 and November 2017 were retrospectively collected and divided into a training cohort (n = 80) and a testing cohort (n = 79). Radiomic features were extracted from both tumor region and No. 3 station LNs based on computed tomography (CT) images per patient. Then, key features were selected using minimum redundancy maximum relevance algorithm and fed into two radiomic signatures, respectively. Meanwhile, the predictive performance of clinical risk factors was studied. Finally, a nomogram was built by merging radiomic signatures and clinical risk factors and evaluated by the area under the receiver operator characteristic curve (AUC) as well as decision curve. Results Two radiomic signatures, reflecting phenotypes of the tumor and LNs respectively, were significantly associated with LN metastasis. A nomogram incorporating two radiomic signatures and CT-reported LN metastasis status showed good discrimination of LN metastasis in both the training cohort (AUC 0.915; 95% confidence interval [CI] 0.832–0.998) and testing cohort (AUC 0.908; 95% CI 0.814–1.000). The decision curve also indicated its potential clinical usefulness. Conclusions The nomogram received favorable predictive accuracy in predicting No.3 LNM in T1-2 GC, and the nomogram showed positive role in predicting LNM in No.4 LNs. The nomogram may be used to predict LNM in T1-2 GC and could assist the choice of therapy.

scholarly journals Risk factors for lymph node metastasis and surgical methods in patients with early-stage peripheral lung adenocarcinoma presenting as ground glass opacity

2020 ◽  
Author(s):  
yongming wang ◽  
lijun jing ◽  
gongchao wang
Keyword(s):  
Risk Factors ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lung Adenocarcinoma ◽  
Early Stage ◽  
Ground Glass Opacity ◽  
Tumor Diameter ◽  
Node Metastasis ◽  
Peripheral Lung ◽  
Stage Ia

Abstract Background: It is difficult to predict lymph node metastasis in patients with early lung cancer. Pure ground glass opacity (GGO) on computed tomography indicates an early-stage adenocarcinoma that can be removed by limited resection or lobectomy without the need for mediastinal lymph node dissection or sampling, and lung adenocarcinoma with GGO therefore has a good prognosis. We examined the incidence and risk factors of lymph node metastasis in patients with clinical stage IA lung adenocarcinoma. Methods: We retrospectively analyzed clinical data for 327 patients with stage IA peripheral lung cancer treated in our hospital from March 2014 to December 2018. The patients were divided into four groups according to computed tomography signs. Lobectomy and systematic lymph node dissection were performed in all patients. Correlations between lymph node metastasis and clinical pathological factors were analyzed by logistic regression.Results: Among the 327 patients, 26 (7.95%) had lymph node metastasis. No patients with pure GGO or GGO-dominant types had lymph node metastasis. Logistic regression identified tumor diameter, solid content, plasma carcinoembryonic antigen (CEA) level, pathological type, lymphovascular invasion, and pleural invasion as factors related to the presence of lymph node metastasis. Conclusions: Tumor diameter, solid component ratio, plasma CEA level, pathological type, vascular tumor thrombus, and pleural invasion are possible independent risk factors for lymph node metastasis in patients with stage IA lung adenocarcinoma. In contrast, lymph node metastasis is rare in patients with pure GGO or GGO-dominant lung adenocarcinoma.

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