lymph node metastasis
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2022 ◽  
Vol 27 ◽  
pp. 300584
Hiroshi Harada ◽  
Takeharu Ono ◽  
Takanori Hirose ◽  
Hirohito Umeno ◽  
Jun Akiba ◽  

2022 ◽  
Vol 36 (1) ◽  
pp. 23-28
Mitsuhiro Tsuboi ◽  
Toshiyuki Hirose ◽  
Hiroyuki Sumitomo ◽  
Ryo Yamada

2022 ◽  
Vol 11 ◽  
Liang Zhao ◽  
Guangyu Bai ◽  
Ying Ji ◽  
Yue Peng ◽  
Ruochuan Zang ◽  

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.

2022 ◽  
Vol 12 (1) ◽  
Na Luo ◽  
Ying Wen ◽  
Qiongyan Zou ◽  
Dengjie Ouyang ◽  
Qitong Chen ◽  

AbstractThe current diagnostic technologies for assessing the axillary lymph node metastasis (ALNM) status accurately in breast cancer (BC) remain unsatisfactory. Here, we developed a diagnostic model for evaluating the ALNM status using a combination of mRNAs and the T stage of the primary tumor as a novel biomarker. We collected relevant information on T1–2 BC from public databases. An ALNM prediction model was developed by logistic regression based on the screened signatures and then internally and externally validated. Calibration curves and the area under the curve (AUC) were employed as performance metrics. The prognostic value and tumor immune infiltration of the model were also determined. An optimal diagnostic model was created using a combination of 11 mRNAs and T stage of the primary tumor and showed high discrimination, with AUCs of 0.828 and 0.746 in the training sets. AUCs of 0.671 and 0.783 were achieved in the internal validation cohorts. The mean external AUC value was 0.686 and ranged between 0.644 and 0.742. Moreover, the new model has good specificity in T1 and hormone receptor-negative/human epidermal growth factor receptor 2- negative (HR−/HER2−) BC and good sensitivity in T2 BC. In addition, the risk of ALNM and 11 mRNAs were correlated with the infiltration of M2 macrophages, as well as the prognosis of BC. This novel prediction model is a useful tool to identify the risk of ALNM in T1–2 BC patients, particularly given that it can be used to adjust surgical options in the future.

2022 ◽  
Vol 12 (1) ◽  
Ying Wei ◽  
Yun Niu ◽  
Zhen-long Zhao ◽  
Xiao-jing Cao ◽  
Li-li Peng ◽  

AbstractCervical lymph node metastasis (CLNM) is common in patients with papillary thyroid carcinoma (PTC), which is responsible for tumor staging and surgical strategy. The accurate preoperative identification of CLNM is essential. In this study, twenty consecutive patients with PTC received a parenchyma injection of Sonazoid followed by contrast enhanced ultrasound (CEUS) to identify CLNM. The specific lymphatic CEUS (LCEUS) signs for diagnosing CLNM were summarized, which were further compared with the resected specimens to get the pathological basis. After the injection of contrast agent, lymphatic vessel and lymph node (LN) could be exclusively displayed as hyperperfusion on LCEUS. The dynamic perfusion process of contrast agent in CLNM over time can be clearly visualized. Perfusion defect and interruption of bright ring were the two characteristic LCEUS signs in diagnosing CLNM. After comparing with pathology, perfusion defect was correlated to the metastatic foci in medulla and interruption of bright ring was correlated to the tumor seeding in marginal sinus (all p values < 0.001). The diagnostic efficacies of these two signs were high (perfusion defect vs. interruption of bright ring: AUC, 0.899, 95% CI 0.752–1.000 vs. 0.904, 0.803–1.000). LCEUS has advantages in identifying CLNM from PTC. The typical LCEUS signs of CLNM correlated with pathology.

Samer A. Naffouje ◽  
Gregory Lauwers ◽  
Jason Klapman ◽  
Aamir Dam ◽  
Luis Pena ◽  

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