Deutsch
Nederlands
Predicts 3-year survival after resection in patients with pancreatic cancer based on lymph node ratio. This model includes the number of lymph nodes with metastases in relation to the total number of removed lymph nodes, the lymph node ratio (LNR), as one of the most powerful predictors of survival.
| Condition | Formula |
|---|---|
Additional information
Study population:Between 1992 and 2012, a consecutive series of patients with pancreatic ductal adenocarcinoma (PDAC) who underwent pancreatoduodenectomy at the Academic Medical Centre, Amsterdam, The Netherlands, were included in the present study. A total of 350 patients were eligible for analysis.
Study protocol:
All patients with pancreatic cancer operated on with pancreatoduodenectomy were selected from a database. Clinicopathological characteristics were analysed. Microscopic positive resection margin was defined as the microscopic presence of tumour cells within 1 mm of the margins. A nomogram was created.
Primary endpoints:
The primary endpont of the study was 3-year surival after resection in patients with pancreatic cancer. Mortality was defined as death during the initial admission after the index operation.
Statistical analysis:
A nomogram for 3-year survival rates was created based on the outcomes after multivariable Cox regression analysis, through a stepwise approach, using known predictors of survival from the literature (resection margin, perineural growth and angioinvasion) and factors that were of borderline significance (P <0.200) in a univariable Coxregression analysis. Significant predictive factors for 3-year survival after multivariable Cox regression analysis were then used to create the nomogram
Source:
Tol JA, Brosens LA, van Dieren S, et al. Impact of lymph node ratio on survival in patients with pancreatic and periampullary cancer. Br J Surg. 2015;102(3):237-45.
Study Population
Total population size: 350Continuous characteristics
| Name | Mean | SD | Unit |
|---|---|---|---|
| Age | 64 | 10 | years |
| Body mass index (BMI) | 24 | 6 | kg/m2 |
| Tumor size | 2.8 | 1.5 | cm |
| Name | LL | Q1 | Median | Q3 | UL | Unit |
|---|---|---|---|---|---|---|
| Number of lymph nodes | 5 | 8 | 13 | nodes | ||
| Hospital stay | 10 | 14 | 19 | days | ||
| Lymph node ratio (positive lymph nodes/total number of lymph nodes) | 0 | 0.2 | 0.4 | ratio |
Categorical characteristics
| Name | Subset / Group | Nr. of patients |
|---|---|---|
| American Society of Anesthesiologists (ASA) grade | ASA I | 75 |
| ASA II | 211 | |
| ASA III/IV | 64 | |
| Jaundice | Yes | 288 |
| No | 62 | |
| Preoperative drainage | Yes | 224 |
| No | 126 | |
| Resection procedure | Whipple | 48 |
| PPPD | 302 | |
| Adjuvant therapy | Yes | 123 |
| No | 227 | |
| Overall morbidity | Yes | 184 |
| No | 166 | |
| Surgical morbidity | Yes | 143 |
| No | 207 | |
| In-hospital mortality | Yes | 4 |
| No | 346 | |
| Resection margin | R0 | 141 |
| R1 | 198 | |
| R2 | 11 | |
| TNM classification | T1 | 35 |
| T2 | 84 | |
| T3 | 223 | |
| T4 | 6 | |
| Tx | 2 | |
| N0 | 98 | |
| N1 | 252 | |
| M0 | 342 | |
| M1 | 8 | |
| Perineural growth | Yes | 107 |
| No | 243 | |
| Angioinvasion | Yes | 60 |
| No | 290 | |
| Tumor differentiation | Grade I (well) | 28 |
| Grade II (moderate) | 199 | |
| Grade III (poor) | 123 |
Related files
| Preview | Name | Tags |
|---|---|---|
.png)
|
Kaplan Meier curve (Tol et al, 2015).png 24.54 kB |
Figure (results-page) Kaplan Meier plot |
|
61.32 kB | Institute Logo |
Supporting Publications
| Title or description | Tags |
|---|---|
| Original paper: Tol JA, Brosens LA, van Dieren S, et al. Impact of lymph node ratio on survival in patients with pancreatic and periampullary cancer. Br J Surg. 2015;102(3):237-45. | Internal validation Paper Peer review |
Estimated 3-years survival after pancreatoduodenectomy: ... %
{{ model.survival.PITTitle }}
{{ model.survival.YNETitle }}
Estimated 3-years survival after pancreatoduodenectomy: %
Outcome stratification
Conditional information
Result interpretation
How this model should be used:
This model calculates 3-year survival after resection in patients with pancreatic cancer. The value of the model needs to be confirmed in independent study populations.
Lymph node ratio:
Lymph node ratio (LNR) was identified as a strong predictor of survival in patients with pancreatic cancer. LNR is calculated by dividing the number of positive lymph nodes by the total number of lymph nodes. The optimal cut-off value for LNR was 0.18. In patients with a LNR of 0.18 or less, median survival was 26 months versus 16 months in patients with a LNR greater than 0.18 (P <0⋅001).
Model performance:
Predictive factors for death in patients (n=350) with pancreatic ductal adenocarcinoma included in the nomogram were: R1 resection (hazard ratio (HR) 1.55, 95% CI: 1.07 to 2.25), poor tumour differentiation (HR 2.78, 1.40 to 5.52), LNR above 0.18 (HR 1.75, 1.13 to 2.70) and no adjuvant therapy (HR 1.54, 1.01 to 2.34). The C-statistic was 0.658 (0.632 to 0.698), and calibration was good (Hosmer–Lemeshow χ2 =5.67, P=0.773).
Source:
Tol JA, Brosens LA, van Dieren S, et al. Impact of lymph node ratio on survival in patients with pancreatic and periampullary cancer. Br J Surg. 2015;102(3):237-45.
Calculations alone should never dictate patient care, and are no substitute for professional judgement. See our full disclaimer.
Model feedback
No feedback yet 1 Comment {{ model.comments.length }} CommentsValidation cohort size: 3081
C-Index: Not specified
Validation author: svanroessel
Revision date:
Sign In
Sign Up
With an Evidencio Community account you can:
- Create and publish your own prediction models.
- Share your prediction models with your colleagues, research group, organization or the world.
- Review and provide feedback on models that have been shared with you.
- Validate your models and validate models from other users.
- Find models based on Title, Keyword, Author, Institute, or MeSH classification.
- Use and save prediction models and their data.
- Use patient specific protocols and guidelines based on sequential models and decision trees.
- Stay up-to-date with new models in your field as they are published.
- Create your own lists of favorite models and topics.
Please sign in to enable Evidencio print features
If you don't have an Evidencio Community Account you can create your free personal account at:
https://www.evidencio.com/registration
Printed results - Examples {{ new Date().toLocaleString() }}
Evidencio Community Account Benefits
With an Evidencio Community account you can:
- Create and publish your own prediction models.
- Share your prediction models with your colleagues, research group, organization or the world.
- Review and provide feedback on models that have been shared with you.
- Validate your models and validate models from other users.
- Find models based on Title, Keyword, Author, Institute, or MeSH classification.
- Use and save prediction models and their data.
- Use patient specific protocols and guidelines based on sequential models and decision trees.
- Stay up-to-date with new models in your field as they are published.
- Create your own lists of favorite models and topics.
{{ variable.title }}