Evidence map›Paper›PMID 33718038›Full record

ArticleTranslational lung cancer research2021

Circulating tumor cells can predict the prognosis of patients with non-small cell lung cancer after resection: a retrospective study.

Zhao Li, Ke Xu, Alfredo Tartarone, Mariacarmela Santarpia, Yuming Zhu, Gening Jiang

Open access · diamondAbstract read
In one paragraph

Article in Translational lung cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
3.9field-weighted citation impact, top 4% 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

29 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.

  1. Circulating tumour cells in patients with lung cancer universally indicate poor prognosis.European respiratory review : an official journal of the European Respiratory Society · 2022
    Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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

6 authors at 5 institutions in 2 countries.

Zhao LiDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Ke XuDepartment of Thoracic Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.
Alfredo TartaroneDivision of Medical Oncology, Department of Onco-Hematology, IRCCS-CROB, Referral Cancer Center of Basilicata, Rionero in Vulture (PZ), Italy.
Mariacarmela SantarpiaMedical Oncology Unit, Department of Human Pathology of Adult and Evolutive Age "G. Barresi", University of Messina, Messina, Italy.
Yuming ZhuDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital Tongji University School of Medicine, Shanghai, China.
Gening JiangDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital Tongji University School of Medicine, Shanghai, China.
Tongji University · CNGuangzhou Medical University · CNIstituti di Ricovero e Cura a Carattere Scientifico · ITShanghai Pulmonary Hospital · CNUniversity of Messina · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe development of metastasis is the primary cause of death in patients with non-small cell lung cancer (NSCLC). However, identifying those NSCLC patients who will have loco-regional or distant disease recurrence after surgery is still challenging. Circulating tumor cells (CTCs) can accurately reflect the impact of micro-metastasis of tumor cells in circulating blood on patients' treatment and prognosis. The aim of the present study was to explore the value of preoperative CTC concentration in predicting postoperative metastasis and recurrence risk in patients with NSCLC.

methodsThis study enrolled 347 patients with stage I-IIIA NSCLC. The CTCs were isolated using folate receptor (FR) positivity from peripheral blood samples before surgery, and then enriched and analyzed. Patients were divided into two groups for retrospective survival analysis based on the geometric mean of CTC concentration. The primary study endpoint was recurrence-free survival. Spearman's correlation was used to evaluate the relationship between CTC concentration and clinical characteristics of NSCLC patients. A nomogram based on the multivariate Cox regression model was developed to predict recurrence and metastasis in the NSCLC patients. The performance of the nomogram was evaluated using the concordance index, calibration curve, and Hosmer-Lemeshow test.

resultsThe median follow-up time was 38 months. Preoperative CTC concentration was not significantly related to tumor-node-metastasis staging (P>0.05) and was an independent prognostic factor for NSCLC patients [hazard ratio (HR), 5.489; 95% confidence interval (CI): 2.660-11.326, P<0.001]. The nomogram based on preoperative CTC concentration had a concordance index value of 0.82. Validation revealed that the nomogram possessed excellent predictive ability and calibration.

conclusionsPreoperative CTC concentration is an independent and sensitive biomarker of prognosis in patients with NSCLC. Our nomogram based on preoperative CTC concentration is an effective and non-invasive tool for predicting the recurrence and metastasis of NSCLC.

Indexed as

Circulating tumor cell concentration (CTC concentration)nomogramnon-small cell lung cancer (NSCLC)recurrence and metastasisscreening

Identifiers

PMID33718038
PMCPMC7947419
OpenAlexW3134004960

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.