647 THE LEFT AND RIGHT RECURRENT LARYNGEAL NERVE LYMPH NODES POSSESS DIFFERENT ANATOMICAL CHARACTERISTICS AND RISK STRATIFICATIONS OF METASTASIS

2021 ◽  
Vol 34 (Supplement_1) ◽  
Author(s):  
Guoqing Zhang ◽  
Shanshan Zhao ◽  
Qiyuan Li ◽  
Lulu Yuan ◽  
Gen Gao ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Laryngeal Nerve ◽  
Short Axis ◽  
Cervical Anastomosis ◽  
Anatomical Characteristics ◽  
Node Metastasis ◽  
Preoperative Ct ◽  
Left And Right

Abstract   The left and right recurrent laryngeal nerve(RLN) are asymmetrical, and the precise differences and metastasis risk stratifications based on preoperative CT scan between the left and right RLN lymph nodes have not yet been analyzed. In this study, we compared the anatomical characteristics and generated prediction models to predict the probability of left and right RLN lymph node metastasis using preoperative clinical data in patients undergoing thoracolaparoscopic esophagectomy with cervical anastomosis to guide clinical treatment. Methods We retrospectively reviewed the clinical data of 1660 consecutive patients with thoracic esophageal cancer who underwent esophagectomy with cervical anastomosis at the Department of Thoracic Surgery at our center between January 2015 and December 2020 and investigated the anatomical characteristics and risks of bilateral RLN lymph nodes according to preoperative CT scan and pathological examination findings. Results A total of 299 and 343 patients who underwent left(right) RLN lymph node dissection were included in the final analysis. By plotting ROC curves, we concluded that the cutoff values of the long and short axis to predict metastasis of the left (right) RLN lymph nodes were 10 (8 mm) and 7.5 (6.5 mm), respectively. The short axis rather than the long axis was significantly associated with left RLN lymph node metastasis. Correspondingly, the long axis was much more important than the short axis in regard to the right RLN lymph nodes. Conclusion There were different anatomical characteristics and precise metastasis risk stratifications between the left and right RLN lymph node metastases.

P152 CLINICAL PREDICTIVE MODEL OF LYMPH NODE METASTASIS AT RIGHT RECURRENT LARYNGEAL NERVE ZONE IN ESOPHAGEAL SQUAMOUS CELL CARCINOMA

2019 ◽  
Vol 32 (Supplement_2) ◽  
Author(s):  
Li Zhigang ◽  
Li Baiwei ◽  
Li Bin ◽  
Yang Yang
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Esophageal Squamous Cell Carcinoma ◽  
Lymph Nodes ◽  
Recurrent Laryngeal Nerve ◽  
Laryngeal Nerve ◽  
Node Metastasis ◽  
Short Diameter

Abstract Aim The aim of this study is to establish a clinical predictive standard for lymph node metastasis at this location by retrospectively comparing the traditional imaging findings of RRLN lymph nodes in esophageal squamous cell carcinoma with postoperative pathology. Background The right recurrent laryngeal nerve (RRLN) is the zone most prone to lymph node metastasis of esophageal squamous cell carcinoma. Although the survival benefit is large after surgical dissection, however, the postoperative mortality rate is significantly increased if the nerve is injured. How to selectively perform lymph node dissection at this location has always been a clinical problem that needs to be addressed. In the past, clinical evaluations mostly used lymph node short diameter ≥1cm as the diagnostic criteria for metastasis, which significantly underestimated the actual clinical situation. Methods 308 patients with thoracic esophageal squamous cell carcinoma who underwent surgical treatment in Shanghai Chest Hospital from Jan 2018 to Dec 2018 were retrospectively analyzed. According to imaging 1mm layer thickness enhanced CT as a tool, the RRLN lymph node short diameter (ctNd) size was measured. All patients were divided into four groups: (A) CT images without RRLN lymph node, (B) CT images with RRLN lymph node was 0<ctNd<5mm, (C) CT images with RRLN lymph node was 5mm≤ctNd<10mm, (D) CT images with RRLN lymph node was ctNd≥10mm. The RRLN lymph node metastasis of each group was analyzed, and the influencing factors were analyzed to establish a predictive model. Results Among all patients, 87.6% of the patients had lymph nodes detected in the RRLN surgical specimens. The sampling rate was 14.5% (121/832), the RRLN lymph node metastasis rate was 19.48%, and the total lymph node metastasis rate was 48.7%. RRLN lymph nodes (57.1%) (A-132, B-43, C-125, D-9) were seen in the preoperative CT scan of 176 patients. The postoperative pathological RRLN lymph node metastasis rate was 9.1%, 18.6%, 27.2% and 66.7%, respectively (P=0.01). Multivariate analysis showed that ctNd, tumor location and N stage were risk factors for RRLN lymph node metastasis (P<0.05). The risk of upper esophageal cancer metastasis was higher than middle segment esophageal cancer (28.2% vs 18.6%, P<0.05). The higher the risk of right laryngeal lymph node metastasis was detected in the later N stage (cN0-13.2%, cN1-21.5%, cN2-46.7%, P<0.05). The 6.5mm short diameter of RRLN lymph nodes on CT scan is the critical value of metastasis at this position (sensitivity 50%, specificity 83.5%), and the higher the risk of metastasis was seen in the larger the short diameter (P<0.05). Conclusion More than 6.5mm short diameter in the CT scan image should be the clinical predictor of lymph node metastasis of the right recurrent laryngeal nerve. The higher risk of metastasis was seen in the greater short diameter. Upper esophageal cancer and multiple lymph node metastasis increase the risk of RRLN lymph node metastasis. Key words esophageal cancer, lymph node metastasis, recurrent laryngeal nerve, computed tomography

Prediction model for cervical lymph node metastasis using CT characteristics in patients with head and neck squamous cell carcinoma.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e17547-e17547
Author(s):  
Young Jun Choi ◽  
Mi Sun Chung ◽  
Jeong Hyun Lee
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Head And Neck ◽  
Cervical Lymph Node ◽  
Cervical Lymph Node Metastasis ◽  
Diameter Ratio ◽  
Short Axis ◽  
Cystic Change ◽  
Node Metastasis

e17547 Background: Lymph node metastasis is a major prognostic determinant for patients with head and neck squamous cell cancer (HNSCC). This study aimed to construct a predictive model using CT characteristics of lymph node and tumor for patients with HNSCC to stratify the risk of lymph node metastasis. Methods: The study population was obtained from historical cohort of 472 cervical lymph nodes from 191 patients with HNSCC in a tertiary referral hospital. We analyzed preoperative CT images of lymph nodes according to diameter, ratio of long-to-short axis diameter, necrosis or cystic change, conglomeration, infiltration to adjacent soft tissue, and laterality (ipsilateral vs contralateral) and analyzed T stage. Reference standard was surgical pathologic results. Multivariate logistic regression analysis was performed to predict whether nodules were diagnosed as metastasis or benign. Results: CT features of lymph nodes, including minimal axial diameter, ratio of long-to-short axis diameter, necrosis or cystic change, and T stage were selected as predictors for lymph nodes metastasis. A 10-point risk scoring system was developed, and the risk of malignancy ranged from 7.3% to 99.8%, which was positively associated with increases in risk scores. The areas under the receiver operating characteristic curve of the development and validation sets were 0.886 and 0.879, respectively. Conclusions: We have devised a simple predictive model using the CT characteristics of lymph nodes and tumor for HNSCC to stratify the risk of cervical lymph node metastasis. [Table: see text]

scholarly journals Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 757
Author(s):  
Sanaz Samiei ◽  
Renée W. Y. Granzier ◽  
Abdalla Ibrahim ◽  
Sergey Primakov ◽  
Marc B. I. Lobbes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Mr Images ◽  
Node Metastasis

Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51–68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41–0.74 and 0.48–0.89 in the training cohorts, respectively, and between 0.30–0.98 and 0.37–0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed.

Peritumoral lymph nodes in pancreatic cancer revisited; is it truly equivalent to lymph node metastasis?

2021 ◽  
Author(s):  
Yoonhyeong Byun ◽  
Kyoung‐Bun Lee ◽  
Jin‐Young Jang ◽  
Youngmin Han ◽  
Yoo Jin Choi ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Node Metastasis

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.

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