ArticleBMC cancer2020
Development and validation of a preoperative nomogram for predicting survival of patients with locally advanced prostate cancer after radical prostatectomy.
Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Development and external validation of a nomogram to predict the prognosis of patients with metastatic prostate cancer who underwent radiotherapy.Gland surgery · 2024Article
- Integrated transcriptomic analysis and machine learning for characterizing diagnostic biomarkers and immune cell infiltration in fetal growth restriction.Frontiers in immunology · 2024Article
- Identification of potential diagnostic and prognostic biomarkers for papillary thyroid microcarcinoma (PTMC) based on TMT-labeled LC-MS/MS and machine learning.Journal of endocrinological investigation · 2023Article
- Fusion Gene Detection in Prostate Cancer Samples Enhances the Prediction of Prostate Cancer Clinical Outcomes from Radical Prostatectomy through Machine Learning in a Multi-Institutional Analysis.The American journal of pathology · 2023Article
- A Clinical Prediction Model of Overall Survival for Patients with Cervical Cancer Aged 25-69 Years.Medicina (Kaunas, Lithuania) · 2023Article
- Prognostic model for the prediction of cancer-specific survival in elderly patients with stage I-III gastric cancer.American journal of translational research · 2023Article
- Nomogram for predicting the overall survival of patients with early-onset prostate cancer: A population-based retrospective study.Cancer medicine · 2022Article
- Pilot Project for a Web-Based Dynamic Nomogram to Predict Survival 1 Year After Hip Fracture Surgery: Retrospective Observational Study.Interactive journal of medical research · 2022Article
- Development and validation of nomograms for predicting overall survival and cancer specific survival in locally advanced breast cancer patients: A SEER population-based study.Frontiers in public health · 2022Article
- Estimating the risk of developing secondary hematologic malignancies in patients with T1/T2 prostate cancer undergoing diverse treatment modalities: A large population-based study.Cancer medicine · 2021Article
- Development and validation of a comprehensive radiomics nomogram for prognostic prediction of primary hepatic sarcomatoid carcinoma after surgical resection.International journal of medical sciences · 2021Article
- The Clinical Characteristics and Prediction Nomograms for Primary Spine Malignancies.Frontiers in oncology · 2021Article
- Calculating the overall survival probability in patients with cervical cancer: a nomogram and decision curve analysis-based study.BMC cancer · 2020Article
- Identification of Prognostic Biomarkers Associated with Cancer Stem Cell Features in Prostate Adenocarcinoma.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFor selected locally advanced prostate cancer (PCa) patients, radical prostatectomy (RP) is one of the first-line treatments. We aimed to develop a preoperative nomogram to identify what kinds of patients can get the most survival benefits after RP.
methodsWe conducted analyses with data from the Surveillance, Epidemiology, and End Results (SEER) database. Covariates used for analyses included age at diagnosis, marital status, race, American Joint Committee on Cancer (AJCC) 7th TNM stage, Prostate specific antigen, Gleason biopsy score (GS), percent of positive cores. We estimated the cumulative incidence function for cause-specific death. The Fine and Gray's proportional subdistribution hazard approach was used to perform multivariable competing risk analyses and reveal prognostic factors. A nomogram was built by these factors (including GS, percent of positive cores and N stage) and validated by concordance index and calibration curves. Risk stratification was established based on the nomogram.
resultsWe studied 14,185 patients. N stage, GS, and percent of positive cores were the independent prognostic factors used to construct the nomogram. For validating, in the training cohort, the C-index was 0.779 (95% CI 0.736-0.822), and in the validation cohort, the C-index was 0.773 (95% CI 0.710-0.836). Calibration curves showed that the predicted survival and actual survival were very close. The nomogram performed better over the AJCC staging system (C-index 0.779 versus 0.764 for training cohort, and 0.773 versus 0.744 for validation cohort). The new stratification of risk groups based on the nomogram also showed better discrimination than the AJCC staging system.
conclusionsThe preoperative nomogram can provide favorable prognosis stratification ability to help clinicians identify patients who are suitable for surgery.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.