Evidence map›Paper›PMID 38554186›Full record

ArticleClinical and experimental medicine2024

Development and validation of a novel prognostic nomogram for advanced diffuse large B cell lymphoma.

Mengdi Wan, Wei Zhang, He Huang, Xiaojie Fang, Yungchang Chen, Ying Tian, Yuyi Yao, Huawei Weng, Zegeng Chen, Le Yu and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical and experimental medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Mengdi Wan *Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China.
Wei Zhang *Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China.
He HuangDepartment of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China.
Xiaojie FangDepartment of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China.
Yungchang ChenDepartment of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China.
Ying TianDepartment of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China.
Yuyi YaoDepartment of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China.
Huawei WengDepartment of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China.
Zegeng ChenDepartment of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China.
Le YuDepartment of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China.
Yuke TianDepartment of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China.
Huageng HuangDepartment of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China.
Xudong LiDepartment of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China.
Huangming HongDepartment of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China. honghm3@mail.sysu.edu.cn.
Tongyu LinDepartment of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China. linty@sysucc.org.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advanced diffuse large B cell lymphoma (DLBCL) is a common malignant tumor with aggressive clinical features and poor prognosis. At present, there is lack of effective prognostic tool for patients with advanced (stage III/IV) DLBCL. The aim of this study is to identify prognostic indicators that affect survival and response and establish the first survival prediction nomogram for advanced DLBCL. A total of 402 patients with advanced DLBCL were enrolled in this study. COX multivariate analysis was used to obtain independent prognostic factors. The independent prognostic factors were included in the nomogram, and the nomogram to predict the performance of the model was established by R rms package, C-index (consistency index), AUC curve and calibration curve. The training and validation cohorts included 281 and 121 patients. In the training cohort, multivariate analysis showed that Ki-67 (70% (high expression) vs ≤ 70% (low expression), p < 0.001), LDH (lactate dehydrogenase) (elevated vs normal, p = 0.05), FER (ferritin) (elevated vs normal, p < 0.001), and β2-microglobulin (elevated vs normal, p < 0.001) were independent predictors and the nomogram was constructed. The nomogram showed that there was a significant difference in OS among the low-risk, intermediate-risk and high-risk groups, with 5-year survival rates of 81.6%, 44% and 6%, respectively. The C-index of the nomogram in the training group was 0.76. The internal validation of the training group showed good consistency. In the internal validation cohort of the training group, the AUC was 0.828, and similar results were obtained in the validation group, with a C-index of 0.74 and an AUC of 0.803. The proposed nomogram provided a valuable individualized risk assessment of OS in advanced DLBCL patients.

Indexed as

Lymphoma, Large B-Cell, DiffuseNomogramsHumansMultivariate AnalysisPrognosisDLBCLNHLNomogramPrognosis

Identifiers

PMID38554186
PMCPMC10981611

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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.