Evidence map›Paper›PMID 37264319›Full record

ReviewBMC cancer2023

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer.

Liping Luo, Yubo Tan, Shixuan Zhao, Man Yang, Yurou Che, Kezhen Li, Jieke Liu, Huaichao Luo, Wenjun Jiang, Yongjie Li and 1 more

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.3field-weighted citation impact, top 12% of its field
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

7 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Characterization of blood inflammatory markers in patients with non-small cell lung cancer.International journal of clinical and experimental pathology · 2024
    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

11 authors at 3 institutions in 1 country.

Liping LuoSichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Yubo TanSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Shixuan ZhaoSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Man YangSichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Yurou CheSichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Kezhen LiSchool of Medicine, Southwest Medical University, Luzhou, China.
Jieke LiuSichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Huaichao LuoSichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Wenjun JiangSichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Yongjie LiSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Weidong WangSichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. wwdwyl@uestc.edu.cn.
Sichuan Cancer Hospital · CNUniversity of Electronic Science and Technology of China · CNSouthwest Medical University · CN

Funding

Chengdu Technology Innovation R&D Project 2021YF0501659SNClinical Scientist Program of Sichuan Cancer Hospital YB2022003Key Projects of Sichuan Natural Science Foundation 2022NSFSC0051Sichuan cadre health research project CGY2023-804
6 · The paper itself

Abstract

backgroundNumerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

methodsInitially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

resultsFrom 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

conclusionsThis research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsHematologic TestsHumansNomogramsPrognosisDeepSurv modelHigh-order featureLung cancerPrognosisRoutine blood test

Identifiers

PMID37264319
PMCPMC10233562
OpenAlexW4378982448

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.