Searching across hundreds of databases

Our searching services are busy right now. Your search will reload in five seconds.

X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

This service exclusively searches for literature that cites resources. Please be aware that the total number of searchable documents is limited to those containing RRIDs and does not include all open-access literature.

Search

Type in a keyword to search

On page 1 showing 1 ~ 4 papers out of 4 papers

A Modified Pathological N Stage Including Status of Tumor Deposits in Colorectal Cancer With Nodal Metastasis.

  • Jun-Peng Pei‎ et al.
  • Frontiers in oncology‎
  • 2020‎

The American Joint Committee on Cancer 8th classification states that colorectal cancer (CRC) is classified as N1c stage when regional lymph nodes (LNs) are negative and tumor deposits (TDs) are positive. However, how to classify TDs when regional LNs are positive remains unclear. The current study aimed to investigate the possibility of combining positive LNs and positive TDs to develop a modified pathological N (mpN) stage for CRC.


Safety and Oncological Outcomes of Laparoscopic NOSE Surgery Compared With Conventional Laparoscopic Surgery for Colorectal Diseases: A Meta-Analysis.

  • Rui-Ji Liu‎ et al.
  • Frontiers in oncology‎
  • 2019‎

Objective: To evaluate the safety and oncological outcomes of laparoscopic colorectal surgery using natural orifice specimen extraction (NOSE) compared with conventional laparoscopic (CL) colorectal surgery in patients with colorectal diseases. Methods: We conducted a systematic search of PubMed, EMBASE, and Cochrane databases for randomized controlled trials (RCTs), prospective non-randomized trials and retrospective trials up to September 1, 2018, and used 5-year disease-free survival (DFS), lymph node harvest, surgical site infection (SSI), anastomotic leakage, and intra-abdominal abscess as the main endpoints. Subgroup analyses were conducted according to the different study types [RCT and NRCT (non-randomized controlled trial)]. A sensitivity analysis was carried out to evaluate the reliability of the outcomes. RevMan5.3 software was used for statistical analysis. Results: Fourteen studies were included (two RCTs, seven retrospective trials and five prospective non-randomized trials) involving a total of 1,435 patients. Compared with CL surgery, the NOSE technique resulted in a shorter hospital stay, shorter time to first flatus, less post-operative pain, and fewer SSIs and total perioperative complications. Anastomotic leakage, blood loss, and intra-abdominal abscess did not differ between the two groups, while operation time was longer in the NOSE group. Oncological outcomes such as proximal margin [weighted mean difference [WMD] = 0.47; 95% confidence interval [CI] -0.49 to 1.42; P = 0.34], distal margin (WMD= -0.11; 95% CI -0.66 to 0.45; P = 0.70), lymph node harvest (WMD = -0.97; 95% CI -1.97 to 0.03; P = 0.06) and 5-year DFS (hazard ratio = 0.84; 95% CI 0.54-1.31; P = 0.45) were not different between the NOSE and CL surgery groups. Conclusions: Compared with CL surgery, NOSE may be a safe procedure, and can achieve similar oncological outcomes. Large multicenter RCTs are needed to provide high-level, evidence-based results in NOSE-treated patients and to determine the risk of local recurrence.


Novel Nomograms Individually Predicting Overall Survival of Non-metastatic Colon Cancer Patients.

  • Jun-Peng Pei‎ et al.
  • Frontiers in oncology‎
  • 2020‎

Background: This study aimed to develop an effective prognostic nomogram for predicting non-metastatic colon cancer. Methods: The Surveillance, Epidemiology, and End Results program was utilized to analyze patients who underwent surgical therapy (25,350 for training, 10,860 for validation). Nomograms were created depending upon multivariate analysis in the training cohort and were compared to current American Joint Committee on Cancer (AJCC) classifications. Areas under the receiver-operating characteristic curves (AUCs), Akaike's information criterions (AICs), and calibration curves were used. The clinical benefit was measured using decision curve analyses (DCAs). The validation cohort was used to validate the results. Results: Nomogram 1 included age, gender, histological grade, T stage, number of retrieved lymph nodes, tumor size, and N stage. Nomogram 2 included age, gender, histological grade, T stage, number of retrieved lymph nodes, tumor size, and number of positive lymph nodes. The prognostic discrimination of nomogram 1 (AUC, 0.729, 95% CI, 0.723-0.736) was better than that of nomogram 2 (AUC, 0.704, 95% CI, 0.698-0.710, p < 0.001) in five-year overall survival in the training cohort. Nomogram 1 (AIC, 137,319) also showed superior model-fitting compared to nomogram 2 (AIC, 137,453). Similarity, nomogram 1 was better than the AJCC 6th and 8th TNM classifications. DCA revealed that nomogram 1 had a superior net benefit than other models. These findings were validated using the validation cohort. Conclusions: The proposed nomogram 1 was a better prognostic prediction model with better discrimination and superior model-fitting for patients with non-metastatic colon cancer, which might prove to be clinically helpful.


Comparison of Different Lymph Node Staging Systems in Patients With Resectable Colorectal Cancer.

  • Jun-Peng Pei‎ et al.
  • Frontiers in oncology‎
  • 2018‎

Background and Objectives: Currently, the United States Joint Commission on Cancer (AJCC) N staging, lymph node positive rate (LNR), and log odds of positive lymph nodes (LODDS) are the main lymph node (LN) staging systems. However, the type of LN staging system that is more accurate in terms of prognostic performance remains controversial. We compared the prognostic accuracy of the three staging systems in patients with CRC and determine the best choice for clinical applications. Methods: From the Surveillance, Epidemiology, and End Results (SEER) database, 56,747 patients were identified who were diagnosed with CRC between 2004 and 2013. Akaike's Information Criterion (AIC) and Harrell's Consistency Index (c-index) were used to assess the relative discriminative abilities of different LN staging systems. Results: In 56,747 patients, when using classification cut-off values for evaluation, the LNR of Rosenberg et al. showed significantly better predictive power, especially when the number of dissected lymph nodes (NDLN) were insufficient. When analyzed as a continuous variable, the LODDS staging system performed the best and was not affected by the NDLN. Conclusions: We suggest that the LNR of Rosenberg et al. should be introduced into the AJCC system as a supplement when the NDLN is insufficient until the optimal LODDS cut-off values are calculated.


  1. SciCrunch.org Resources

    Welcome to the FDI Lab - SciCrunch.org Resources search. From here you can search through a compilation of resources used by FDI Lab - SciCrunch.org and see how data is organized within our community.

  2. Navigation

    You are currently on the Community Resources tab looking through categories and sources that FDI Lab - SciCrunch.org has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.

  3. Logging in and Registering

    If you have an account on FDI Lab - SciCrunch.org then you can log in from here to get additional features in FDI Lab - SciCrunch.org such as Collections, Saved Searches, and managing Resources.

  4. Searching

    Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:

    1. Use quotes around phrases you want to match exactly
    2. You can manually AND and OR terms to change how we search between words
    3. You can add "-" to terms to make sure no results return with that term in them (ex. Cerebellum -CA1)
    4. You can add "+" to terms to require they be in the data
    5. Using autocomplete specifies which branch of our semantics you with to search and can help refine your search
  5. Save Your Search

    You can save any searches you perform for quick access to later from here.

  6. Query Expansion

    We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.

  7. Collections

    If you are logged into FDI Lab - SciCrunch.org you can add data records to your collections to create custom spreadsheets across multiple sources of data.

  8. Facets

    Here are the facets that you can filter your papers by.

  9. Options

    From here we'll present any options for the literature, such as exporting your current results.

  10. Further Questions

    If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.

Publications Per Year

X

Year:

Count: