AI Article Synopsis

  • The study investigates factors affecting prognosis in lymph node-negative gastric cancer (GC) patients, focusing on tumor size (Ts), lymph node count (LNs), and lymphovascular invasion (LVI).
  • A total of 1,019 patients were analyzed to identify independent prognostic factors and develop a new predictive model called the TsNL staging system.
  • The TsNL system showed improved accuracy in predicting survival outcomes compared to traditional methods, highlighting the importance of combining Ts, LNs, and LVI for better prognostic assessments.

Article Abstract

Background: Various factors may affect the clinical prognosis of lymph node-negative gastric cancer (GC) patients. This study aimed to provide evaluable prognostic information of combination of tumor size (Ts), lymph nodes count (LNs) and lymphovascular invasion (LVI) in lymph node-negative GC patients.

Methods: A total of 1,019 node-negative GC patients were enrolled in this retrospective study from 2000 to 2010. The cutoff points of Ts and LNs were determined using X-tile and patients were randomly categorized into training and validation sets by the sample size ratio 1:1. The clinicopathologic characteristics were analyzed and survival prognostic factors were identified, whereas the survival prediction accuracy was also compared by C-index during the different independent prognostic factors.

Results: The cutoff points for Ts were 3cm and 5cm, while 14 was the cutoff point for LNs. Age, T stage, Ts, LNs and LVI were identified as independent prognostic factors in node-negative GC patients, and a new prognostic predictive model, TsNL staging system which was composed of Ts, LNs and LVI, was proposed in this study. Compared with T staging system, significant improvement of predictive accuracy for TsNL system was found. Furthermore, nomogram based on TsNL was more accurate in prognostic prediction than that based on Ts, LNs and LVI, separately.

Conclusions: Age, T stage, Ts, LNs and LVI were independent prognostic factors in lymph node-negative GC patients. The TsNL staging system, composed of Ts, LNs and LVI, which was closely associated with clinicopathologic features, may improve the prognostic prediction accuracy in node-negative GC patients.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5342163PMC
http://dx.doi.org/10.18632/oncotarget.11035DOI Listing

Publication Analysis

Top Keywords

lns lvi
20
lymph node-negative
16
node-negative patients
16
prognostic factors
12
independent prognostic
12
staging system
12
predictive model
8
tumor size
8
size lymph
8
lymph nodes
8

Similar Publications

Article Synopsis
  • - The study investigates how lymphovascular invasion (LVI) affects overall survival in gastric cancer patients at pN0 stage after they've undergone surgery to remove the tumor.
  • - Out of 497 patients reviewed, 19.9% had LVI, which was linked to significantly poorer survival rates; further analysis identified LVI and the number of examined lymph nodes as independent predictors of survival.
  • - The findings suggest that for better outcomes, it is crucial to dissect more than 15 lymph nodes during surgery for patients with pN0 stage gastric cancer, especially those with LVI.
View Article and Find Full Text PDF

Background: Ki-67 immunostaining is commonly used in neuroendocrine tumors to estimate the proliferative index and for grading. This study investigates its association with the invasiveness of follicular-derived thyroid carcinomas (TCs).

Methods: A retrospective analysis of patients with TC at three McGill University teaching hospitals between January 2018 and November 2023 was conducted.

View Article and Find Full Text PDF

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer.

Sci Rep

July 2024

Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, West Huan-Hu Road, Ti Yuan Bei, Hexi District, Tianjin, 300060, People's Republic of China.

Article Synopsis
  • This study evaluated how well a deep learning model could predict lymphovascular invasion (LVI) status using breast MRI images in patients with invasive breast cancer that did not have axillary lymph node metastasis.
  • Data was analyzed from 280 patients, with various machine learning and deep learning algorithms used to identify key features that predict LVI status.
  • The deep learning model that combined radiomic and clinical features had the best performance, achieving an area under the curve (AUC) of 0.896 for predicting LVI, outperforming other models used in the study.
View Article and Find Full Text PDF

Association of Lymphovascular Invasion with Lymph Node Metastases in Prostate Cancer-Lateralization Concept.

Cancers (Basel)

February 2024

University Center of Excellence in Urology, Department of Minimally Invasive and Robotic Urology, Wroclaw Medical University, Borowska 213, 50-556 Wroclaw, Poland.

Background: Lymphovascular invasion (LVI) is a vital but often overlooked prognostic factor in prostate cancer. As debates on lymphadenectomy's overtreatment emerge, understanding LVI laterality gains importance. This study pioneers the investigation into PCa, aiming to uncover patterns that could influence tailored surgical strategies in the future.

View Article and Find Full Text PDF

Background: For high-risk stageIImismatch repair deficient (dMMR) colon cancers, the benefit of adjuvant chemotherapy remains debatable. The principal aim of this study was to evaluate the prognostic value of high-risk factors and the effect of oxaliplatin-based adjuvant chemotherapy among dMMR stageIIcolon cancers.

Methods: Patients with stage II dMMR colon cancers diagnosed between June 2011 and May 2018 were enrolled in the study.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!