ArticleCancer management and research2022
Integrative Analysis of Peripheral Blood Indices for the Renal Sinus Invasion Prediction of T1 Renal Cell Carcinoma: An Ensemble Study Using Machine Learning-Assisted Decision-Support Models.
Article in Cancer management and research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.
- Prognostic value of the prognostic nutritional index for patients with renal cell carcinoma: an update systematic review and meta-analysis.BMC cancer · 2026Pooled it
- Development and validation of intravoxel incoherent motion diffusion weighted imaging-based model for preoperative distinguishing nuclear grade and survival of clear cell renal cell carcinoma complicated with venous tumor thrombus.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- What is hemoglobin, albumin, lymphocyte, platelet (HALP) score? A comprehensive literature review of HALP's prognostic ability in different cancer types.Oncotarget · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeRenal sinus invasion is an attributive factor affecting the prognosis of renal cell carcinoma (RCC). This study aimed to construct a risk prediction model that could stratify patients with RCC and predict renal sinus invasion with the help of a machine learning (ML) algorithm. PATIENTS AND
methodsWe retrospectively recruited 1229 patients diagnosed with T1 stage RCC at the Baotou Cancer Hospital between November 2013 and August 2021. Iterative analysis was used to screen out predictors related to renal sinus invasion, after which ML-based models were developed to predict renal sinus invasion in patients with T1 stage RCC. The receiver operating characteristic curve (ROC), decision curve analysis (DCA), and clinical impact curve (CIC) were performed to evaluate the robustness and clinical practicability of each model.
resultsA total of 21 candidate variables were shortlisted for model building. Iterative analysis screened that neutrophil to albumin ratio (NAR), hemoglobin level * albumin level * lymphocyte count/platelet count ratio (HALP), prognostic nutrition index (PNI), body mass index*serum albumin/neutrophil-lymphocyte ratio (AKI), NAR, and fibrinogen (FIB) concentration (NARFIB), platelet to lymphocyte ratio (PLR), and R.E.N.A.L score was related to renal sinus invasion and contributed significantly to ML-based algorithm. The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, support vector machine (SVM), eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and decision tree (DT) ranged from 0.797 to 0.924. The optimal risk probability of renal sinus invasion predicted was RFC (AUC = 0.924, 95% confidence interval [CI]: 0.414-1.434), which showed robust discrimination for identifying high-risk patients.
conclusionWe successfully develop practical models for renal sinus invasion prediction, particularly the RFC, which could contribute to early detection via integrating systemic inflammatory factors and nutritional parameters.
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.